A production-teaching integration platform intelligent knowledge graph construction method, system and device

Abstract

The application relates to the technical field of knowledge graph construction, in particular to a production-teaching integration platform intelligent knowledge graph construction method, system and device. First, the application acquires education resource data, processes the data by using preset classification rules, and obtains knowledge point types with hierarchy; then, the content features of each knowledge point are identified, associated attributes are extracted, and label information is generated; next, based on the knowledge point types and the label information, the quality of the knowledge graph is evaluated by testing the knowledge coverage and the demand matching degree; after the evaluation is completed, the system automatically triggers an update instruction, and dynamically adjusts the knowledge graph according to the optimization instruction; in this way, the integrity of the knowledge system is ensured, the organic combination of theory and practice is realized, and a scientific quality evaluation and optimization mechanism is established, so that the knowledge graph can be continuously optimized and improved according to actual demands.

Description

Technical Field

[0001] This application relates to the technical field of knowledge graph construction, and in particular to a method, system and device for constructing an intelligent knowledge graph for an industry-education integration platform. Background Technology

[0002] With the deepening development of educational informatization and industry-education integration, how to effectively manage and utilize educational resources and organically combine theoretical teaching with practical application has become an important issue in the current education field. Intelligent knowledge graphs, as an important tool for knowledge organization and management, are playing an increasingly important role in educational resource integration and knowledge transfer.

[0003] Existing technologies typically employ rule-based knowledge extraction systems and statistical knowledge association analysis methods to construct knowledge graphs. These methods extract knowledge entities using pre-defined rule templates and utilize statistical algorithms to establish relationships between knowledge points, forming a knowledge network structure to enable the organization and management of educational resources.

[0004] However, the existing knowledge classification is too fixed, and the knowledge association analysis lacks consideration for actual application scenarios, making it difficult to adapt to the dynamic changes and industry-education integration needs of education; this situation needs to be further improved. Summary of the Invention

[0005] To address the problems of existing knowledge classifications being too fixed, knowledge association analysis lacking consideration for practical application scenarios, and failing to adapt to dynamic and industry-education integration educational needs, this application provides a method, system, and device for constructing an intelligent knowledge graph for an industry-education integration platform, employing the following technical solution:

[0006] Firstly, this application provides a method for constructing an intelligent knowledge graph for an industry-education integration platform, comprising the following steps:

[0007] Acquire educational resource data, classify the educational resource data according to preset knowledge classification rules, and obtain the corresponding knowledge point types;

[0008] Obtain the knowledge content corresponding to the knowledge point type, identify the associated attributes of the knowledge content, and obtain tag information representing the characteristics of the knowledge point;

[0009] Based on the knowledge point type and the tag information, test data for testing the quality of the knowledge graph is obtained, including knowledge coverage data and demand matching data.

[0010] Based on the knowledge coverage data and demand matching data, obtain the knowledge graph evaluation result, and trigger the knowledge graph update instruction according to the evaluation result;

[0011] Based on the knowledge graph update instruction, a knowledge optimization instruction is triggered to dynamically adjust the knowledge graph.

[0012] By adopting the above technical solution, this application addresses the problems of rigid knowledge classification, knowledge association detached from practice, and imperfect quality assessment mechanisms in the traditional knowledge graph construction process. First, it acquires educational resource data and processes it using pre-defined classification rules to obtain hierarchical knowledge point types. Then, it identifies the content characteristics of each knowledge point, extracts its difficulty, dependencies, application scenarios, and other related attributes, and generates tag information. Next, based on the knowledge point types and tag information, it evaluates the quality of the knowledge graph by testing knowledge coverage and demand matching. After the evaluation, the system automatically triggers update instructions and dynamically adjusts the knowledge graph according to optimization instructions. This approach ensures the integrity of the knowledge system, achieves an organic combination of theory and practice, and establishes a scientific quality assessment and optimization mechanism, enabling the knowledge graph to be continuously optimized and improved according to actual needs.

[0013] Optionally, educational resource data is acquired, and the data is classified according to preset knowledge classification rules to obtain corresponding knowledge point types. This specifically includes the following steps:

[0014] Acquire historical educational resource data, and based on the historical educational resource data, obtain knowledge point structure information;

[0015] Based on the knowledge point structure information, extract key feature information of knowledge points for knowledge graph construction from the knowledge point structure information;

[0016] According to the preset knowledge classification rules, the key feature information of the knowledge points is classified to obtain the corresponding knowledge point types.

[0017] By adopting the above technical solution, this application first collects and organizes historical education resource data, and extracts the knowledge point structure information through data mining technology, including the hierarchical relationship, degree of correlation and frequency of use between knowledge points; then, it conducts in-depth analysis of this structural information to extract key feature information that is of guiding significance for knowledge graph construction, such as the importance of knowledge points, application scope and learning difficulty; finally, according to the preset classification rules, combined with the extracted key feature information, the knowledge points are scientifically classified.

[0018] Optionally, the knowledge point types include theoretical knowledge and practical skills; obtaining the knowledge content corresponding to the knowledge point type, identifying the association attributes of the knowledge content, and obtaining tag information representing the characteristics of the knowledge point specifically includes the following steps:

[0019] Acquire knowledge content and extract the associated attributes of the knowledge content, wherein the associated attributes include knowledge difficulty, knowledge dependence and application scenario;

[0020] If the knowledge point type is theoretical knowledge, obtain the corresponding theoretical knowledge tag information based on the associated attribute;

[0021] If the knowledge point type is practical skill, obtain the corresponding practical skill tag information based on the associated attribute;

[0022] The tag information includes theoretical knowledge tag information and practical skills tag information.

[0023] By adopting the above technical solution, this application first acquires knowledge content and extracts its associated attributes, including basic features such as knowledge difficulty, knowledge dependence, and application scenarios. Then, it performs segmentation processing according to the type of knowledge point. For theoretical knowledge, it focuses on annotating its conceptual system, derivation process, and application conditions to generate theoretical knowledge tag information. For practical skills, it focuses on annotating its operational points, tool requirements, and implementation environment to generate practical skill tag information. This classification and annotation method not only accurately reflects the characteristics of different types of knowledge points but also establishes a bridge between theory and practice, effectively enhancing the guiding value of knowledge graphs in actual teaching.

[0024] Optionally, based on the knowledge point type and the tag information, test data for testing the quality of the knowledge graph is obtained, specifically including the following steps:

[0025] Based on the knowledge dependency and knowledge difficulty attributes in the theoretical knowledge tag information and the practical skill tag information, knowledge coverage data is generated;

[0026] Based on the application scenario attributes in the theoretical knowledge tag information and the practical skill tag information, demand matching data is generated;

[0027] The knowledge coverage data includes knowledge structure integrity data and knowledge association integrity data; the demand matching data includes theoretical knowledge matching data and practical skills matching data.

[0028] By adopting the above technical solution, this application addresses the shortcomings of existing evaluation methods. Existing methods often focus excessively on the number of knowledge points or the coverage of a single dimension, neglecting the completeness of the knowledge structure and the degree of matching with actual needs. This results in evaluation results that fail to truly reflect the practical value of the knowledge graph. Firstly, based on the tag information of theoretical knowledge and practical skills, this application analyzes knowledge dependencies and difficulty levels to generate coverage data that includes both the completeness of the knowledge structure and the completeness of knowledge associations, ensuring the systematic nature of the knowledge system. Then, based on the application scenario attributes in the tag information, it evaluates the degree of matching between theoretical knowledge and practical skills and actual needs, generating comprehensive demand matching data. Through multi-dimensional data analysis, not only can structural defects in the knowledge system be identified, but the fit between knowledge content and actual application needs can also be accurately assessed, providing a reliable basis for the optimization of the knowledge graph.

[0029] Optionally, the knowledge graph evaluation result is obtained based on the knowledge coverage data and demand matching data. Based on the evaluation result, a knowledge graph update instruction is triggered, specifically including the following steps:

[0030] Based on the knowledge coverage data and the demand matching data, a knowledge graph evaluation instruction is triggered.

[0031] Based on the knowledge graph evaluation instructions, a knowledge graph evaluation result is obtained, which includes the completeness of the knowledge system and the matching degree of knowledge application.

[0032] Based on the evaluation results of the knowledge graph, a knowledge graph update instruction is triggered.

[0033] By adopting the above technical solution, this application first automatically triggers a systematic evaluation instruction based on knowledge coverage data and demand matching data, initiating a comprehensive evaluation process; then, through the execution of the evaluation instruction, it comprehensively analyzes the completeness level of the knowledge system and the matching degree of knowledge application, forming an evaluation result that includes quantitative indicators and qualitative analysis; finally, based on the problems and optimization directions reflected in the evaluation results, it automatically triggers corresponding knowledge graph update instructions; by establishing a linkage mechanism between evaluation data, evaluation results, and update instructions, it achieves automated connection between knowledge graph evaluation and updating.

[0034] Optionally, based on the knowledge graph evaluation results, a knowledge graph update instruction is triggered, specifically including the following steps:

[0035] Based on the completeness of the knowledge system and the matching degree of knowledge application, information on the optimization requirements of the knowledge graph is obtained.

[0036] Based on the knowledge graph optimization requirements and the tag information, a knowledge graph update plan is generated.

[0037] Based on the knowledge graph update plan, a knowledge graph update instruction is triggered.

[0038] By adopting the above technical solution, traditional update methods often directly modify based on evaluation results, lacking systematic demand analysis and planning design. This application first identifies key areas in the knowledge graph that need optimization by analyzing the completeness of the knowledge system and the matching degree of knowledge application, forming specific optimization demand information; then, combined with existing tag information, it fully considers the correlation and application characteristics between knowledge points to formulate an update plan that includes optimization goals, implementation steps, and expected effects; finally, based on this detailed update plan, it triggers corresponding update instructions to guide the optimization work of the knowledge graph, making the knowledge graph update process more orderly and efficient.

[0039] Optionally, based on the knowledge graph update instruction, a knowledge optimization instruction is triggered to dynamically adjust the knowledge graph, specifically including the following steps:

[0040] Based on the knowledge graph update instruction, the knowledge graph update progress is obtained;

[0041] The knowledge graph update progress and the knowledge coverage data are input into a preset knowledge optimization model, and preset knowledge optimization rules are obtained based on the knowledge optimization model.

[0042] Based on the aforementioned knowledge optimization rules, knowledge optimization instructions are triggered to achieve dynamic adjustment of the knowledge graph.

[0043] By adopting the above technical solution, this application first obtains the update progress information of the knowledge graph through a monitoring system to keep track of the execution of optimization work in real time; then, the update progress data and knowledge coverage data are jointly input into a preset knowledge optimization model, and optimization rules that conform to the characteristics of the current optimization stage are generated through model analysis; finally, based on these dynamically generated optimization rules, corresponding optimization instructions are triggered to realize real-time adjustment of the knowledge graph; making the optimization process of the knowledge graph more flexible and accurate, and able to quickly respond to new situations and new needs that arise during the optimization process.

[0044] Secondly, this application provides an intelligent knowledge graph construction system for an industry-education integration platform, comprising:

[0045] The knowledge point type acquisition module is used to acquire educational resource data, classify the educational resource data according to preset knowledge classification rules, and obtain corresponding knowledge point types, wherein the knowledge point types include theoretical knowledge and practical skills;

[0046] The feature tag information acquisition module is used to acquire the knowledge content corresponding to the knowledge point type, identify the association attributes of the knowledge content, and obtain tag information representing the features of the knowledge point.

[0047] The test data acquisition module is used to acquire test data for testing the quality of the knowledge graph based on the knowledge point type and the tag information. The test data includes knowledge coverage data and demand matching data.

[0048] The knowledge graph update instruction triggering module is used to obtain the knowledge graph evaluation result based on the knowledge coverage data and the demand matching data, and trigger the knowledge graph update instruction according to the evaluation result;

[0049] The knowledge graph dynamic adjustment module is used to dynamically adjust the knowledge graph by triggering knowledge optimization instructions based on the knowledge graph update instructions.

[0050] Thirdly, this application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described method for constructing an intelligent knowledge graph for an industry-education integration platform.

[0051] In summary, this application includes at least one of the following beneficial technical effects:

[0052] 1. This application first acquires educational resource data, processes it using preset classification rules, and obtains hierarchical knowledge point types. Then, it identifies the content characteristics of each knowledge point, extracts its difficulty, dependency relationships, application scenarios, and other related attributes, and generates tag information. Next, based on the knowledge point types and tag information, it evaluates the quality of the knowledge graph by testing knowledge coverage and demand matching. After the evaluation is completed, the system automatically triggers update instructions and dynamically adjusts the knowledge graph according to optimization instructions. In this way, the integrity of the knowledge system is ensured, the organic combination of theory and practice is achieved, and a scientific quality evaluation and optimization mechanism is established, enabling the knowledge graph to be continuously optimized and improved according to actual needs.

[0053] 2. This application first acquires the knowledge content and extracts its associated attributes, including basic features such as knowledge difficulty, knowledge dependence, and application scenarios. Then, it performs segmentation processing based on the type of knowledge point. For theoretical knowledge, it focuses on annotating its conceptual system, derivation process, and application conditions to generate theoretical knowledge tag information. For practical skills, it focuses on annotating its operational points, tool requirements, and implementation environment to generate practical skill tag information. This classification and annotation method not only accurately reflects the characteristics of different types of knowledge points but also establishes a bridge between theory and practice, effectively enhancing the guiding value of knowledge graphs in actual teaching.

[0054] 3. This application first automatically triggers a systematic evaluation instruction based on knowledge coverage data and demand matching data, initiating a comprehensive evaluation process; then, through the execution of the evaluation instruction, it comprehensively analyzes the completeness level of the knowledge system and the matching degree of knowledge application, forming an evaluation result that includes quantitative indicators and qualitative analysis; finally, based on the problems and optimization directions reflected in the evaluation results, it automatically triggers corresponding knowledge graph update instructions; by establishing a linkage mechanism between evaluation data, evaluation results, and update instructions, it achieves automated connection between knowledge graph evaluation and updating. Attached Figure Description

[0055] Figure 1 This is a flowchart illustrating a method for constructing an intelligent knowledge graph for an industry-education integration platform according to an embodiment of this application;

[0056] Figure 2 This is a flowchart illustrating step S100 in the intelligent knowledge graph construction method of the industry-education integration platform according to an embodiment of this application.

[0057] Figure 3 This is a flowchart illustrating step S200 in the intelligent knowledge graph construction method of the industry-education integration platform in this embodiment of the application.

[0058] Figure 4 This is a flowchart illustrating step S300 in the intelligent knowledge graph construction method of the industry-education integration platform in this embodiment of the application.

[0059] Figure 5 This is a flowchart illustrating step S400 in the intelligent knowledge graph construction method of the industry-education integration platform in this application embodiment;

[0060] Figure 6 This is a flowchart illustrating step S430 in the intelligent knowledge graph construction method of the industry-education integration platform in this embodiment of the application.

[0061] Figure 7 This is a flowchart illustrating step S500 in the intelligent knowledge graph construction method of the industry-education integration platform in this embodiment of the application.

[0062] Figure 8 This is a schematic diagram of the modules of the intelligent knowledge graph construction system of the industry-education integration platform in this application embodiment;

[0063] Figure 9 This is an internal structural diagram of an electronic device according to an embodiment of this application. Detailed Implementation

[0064] The terminology used in the following embodiments of this application is for the purpose of describing particular embodiments only and is not intended to be limiting of this application. As used in the specification and appended claims of this application, the singular expressions “a,” “an,” “the,” “the,” “the,” and “this” are intended to include the plural expressions as well, unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used in this application refers to any or all possible combinations including one or more of the listed items.

[0065] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as implying or suggesting relative importance or implicitly indicating the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature, and in the description of the embodiments of this application, unless otherwise stated, "multiple" means two or more.

[0066] The embodiments of this application will now be described in further detail with reference to the accompanying drawings.

[0067] Firstly, this application provides a method for constructing an intelligent knowledge graph for an industry-education integration platform, referring to... Figure 1 It includes the following steps:

[0068] S100. Obtain educational resource data, classify the educational resource data according to the preset knowledge classification rules, and obtain the corresponding knowledge point types.

[0069] In this embodiment, educational resource data refers to teaching content derived from school curriculum systems, corporate training materials, industry technical standards, and professional certification requirements. Knowledge classification rules refer to a pre-established classification mapping table, which contains the correspondence between knowledge characteristics and types. Knowledge point types include two main categories: theoretical knowledge and practical skills.

[0070] Specifically, a knowledge feature mapping table is first established, mapping content containing calculation formulas, principle explanations, and concept definitions to theoretical knowledge, and content containing operational steps, tool usage, and experimental procedures to practical skills. Then, text analysis methods are used to extract keywords and semantic features from educational resources, which are matched with the mapping table to determine the type of knowledge point. For example, in a hydraulic transmission course for a mechanical engineering major, "Pascal's Law and its applications" is identified as theoretical knowledge, and "hydraulic system assembly process" is identified as practical skills.

[0071] S200. Obtain the knowledge content corresponding to the knowledge point type, identify the associated attributes of the knowledge content, and obtain the label information representing the characteristics of the knowledge point.

[0072] In this embodiment, knowledge content refers to specific teaching content after classification. The associated attributes include three dimensions: knowledge difficulty, knowledge dependency, and application scenario. Tag information is divided into two categories: theoretical knowledge tag information and practical skills tag information.

[0073] Specifically, a knowledge attribute database is established, where knowledge difficulty is divided into three levels: basic, intermediate, and advanced. Knowledge dependencies are represented by a prior knowledge relationship graph, and application scenarios are defined using a scenario description lexicon. For theoretical knowledge, its conceptual system, derivation process, and application conditions are extracted to generate tags; for practical skills, its operational points, tool requirements, and implementation environment are extracted to generate tags. For example, in a CNC machining course, a knowledge dependency chain containing "machining accuracy requirements - tool selection - machining process" is established, and the application scenario of "workshop training" is labeled.

[0074] S300. Based on the knowledge point type and tag information, obtain test data for testing the quality of the knowledge graph. The test data includes knowledge coverage data and requirement matching data.

[0075] In this embodiment, knowledge coverage data includes knowledge structure completeness data and knowledge association completeness data. Demand matching data includes theoretical knowledge matching data and practical skills matching data. Test data is used to evaluate the quality of the knowledge graph.

[0076] Specifically, a knowledge point assessment index database is established. Coverage data is generated by calculating knowledge point coverage rate and knowledge point relevance, and matching data is generated by analyzing the achievement of teaching objectives and the fit with job skills. A scoring matrix is ​​used to quantify each index, and threshold ranges are set to determine the test results.

[0077] S400: Obtain knowledge graph evaluation results based on knowledge coverage data and demand matching data, and trigger knowledge graph update instructions based on the evaluation results.

[0078] In this embodiment, the knowledge graph evaluation results include two dimensions: knowledge system completeness and knowledge application matching degree. The knowledge graph update command is used to trigger the graph optimization process.

[0079] S500: Based on the knowledge graph update command, trigger the knowledge optimization command to dynamically adjust the knowledge graph.

[0080] In this embodiment, the knowledge optimization instruction includes the optimization objective, optimization scope, and optimization method. Dynamic adjustment of the knowledge graph refers to real-time updates to the knowledge content and structure.

[0081] Specifically, an optimization rule base is established, and appropriate optimization strategies are selected based on the type of update instruction. Optimization parameters are determined through data analysis to achieve dynamic adjustment of the knowledge graph. During the optimization process, an incremental update method is used to maintain the stability of the original knowledge structure.

[0082] In one embodiment, refer to Figure 2 In step S100, educational resource data is acquired, and the data is classified according to preset knowledge classification rules to obtain the corresponding knowledge point types. Specifically, this includes the following steps:

[0083] S110. Obtain historical educational resource data, and based on the historical educational resource data, obtain knowledge point structure information.

[0084] In this embodiment, historical educational resource data refers to structured teaching content from course outlines, textbooks, practical training manuals, and teaching courseware within the past three years. Knowledge point structure information includes three aspects: the hierarchical relationship of knowledge points, the constituent elements of knowledge points, and the connection relationships between knowledge points.

[0085] S120. Based on the knowledge point structure information, extract the key feature information of the knowledge points for knowledge graph construction.

[0086] In this embodiment, the key feature information of a knowledge point includes two types of information: knowledge content features and knowledge application features. Knowledge content features describe the core content and basic attributes of the knowledge point, while knowledge application features describe the practical requirements and application scenarios of the knowledge point.

[0087] Specifically, a feature extraction rule base is established, containing three categories of rules: keyword matching rules, semantic analysis rules, and context association rules. The rule base is used to filter knowledge point structural information and extract key features.

[0088] S130. According to the preset knowledge classification rules, classify the key feature information of knowledge points to obtain the corresponding knowledge point types.

[0089] In this embodiment, the preset knowledge classification rules refer to a classification standard system based on knowledge characteristics. Knowledge point type refers to the attribute of a knowledge point determined according to the classification rules.

[0090] Specifically, a knowledge classification decision tree is constructed, matching the key features of knowledge points with the decision nodes of the tree. The first layer of the decision tree judges theory or skills based on knowledge attributes, and the second layer judges basic knowledge or expertise based on knowledge depth.

[0091] In one embodiment, refer to Figure 3The knowledge point types include theoretical knowledge and practical skills; in step S200, the knowledge content corresponding to the knowledge point type is obtained, the association attributes of the knowledge content are identified, and the label information representing the characteristics of the knowledge point is obtained, which specifically includes the following steps:

[0092] S210. Obtain knowledge content and extract the related attributes of the knowledge content.

[0093] Among them, the associated attributes include knowledge difficulty, knowledge dependence, and application scenario.

[0094] In this embodiment, knowledge content refers to the specific teaching content of each knowledge point, including three parts: definition, core points, and application methods. Among the associated attributes, knowledge difficulty is divided into three levels: beginner, intermediate, and advanced; knowledge dependency refers to the prerequisite knowledge required for this knowledge point; and application scenario refers to the actual application environment of this knowledge point.

[0095] Specifically, an association attribute identification database is established, including a difficulty assessment standard table, a knowledge dependency table, and an application scenario description table. The difficulty assessment standard table divides knowledge points into three levels based on cognitive hierarchy: "understanding - application - innovation"; the knowledge dependency table records the prerequisite relationships between knowledge points; and the application scenario description table lists three scenario characteristics: "classroom teaching, laboratory operation, and enterprise practice." Key information is extracted from the knowledge content using text analysis tools and matched with the identification database to determine various association attributes.

[0096] S220. If the knowledge point type is theoretical knowledge, obtain the corresponding theoretical knowledge tag information based on the association attribute.

[0097] In this embodiment, the theoretical knowledge tagging information includes three categories: concept tags, principle tags, and method tags. Concept tags describe basic definitions, principle tags describe inherent laws, and method tags describe how the theory is applied.

[0098] Specifically, a rule set for generating theoretical knowledge tags is constructed, which includes tag extraction rules and tag combination rules. Tag extraction rules identify key concept words, principle description words, and method description words based on text features; tag combination rules define how multiple tags can be combined. The rule set is used to process associated attribute information to generate tag combinations that conform to the characteristics of theoretical knowledge.

[0099] S230. If the knowledge point type is practical skills, obtain the corresponding practical skills tag information based on the associated attributes.

[0100] In this embodiment, the practical skill tag information includes three categories: operation tags, tool tags, and environment tags. Operation tags describe the specific operation steps, tool tags describe the required equipment and tools, and environment tags describe the operation environment requirements.

[0101] Specifically, a practical skills tag mapping table is established, which includes a set of operational element mappings, a set of tool requirement mappings, and a set of environmental condition mappings. The mapping table is then used to transform associated attributes into standardized skill tags.

[0102] In one embodiment, refer to Figure 4 In step S300, test data for testing the quality of the knowledge graph is obtained based on the knowledge point type and tag information. This specifically includes the following steps:

[0103] S310. Generate knowledge coverage data based on the knowledge dependency and knowledge difficulty attributes in the theoretical knowledge tag information and practical skill tag information.

[0104] Specifically, a knowledge coverage calculation matrix is ​​established, which includes a knowledge point distribution table and a knowledge relationship table. The knowledge point distribution table records the quantity distribution of knowledge points at different difficulty levels; the knowledge relationship table records the strength of dependencies between knowledge points. Coverage data is generated by calculating the coverage indicators in the matrix, which include three aspects: knowledge point coverage, difficulty level coverage, and knowledge dependency coverage.

[0105] S320. Generate demand matching data based on the application scenario attributes in the theoretical knowledge tag information and practical skills tag information.

[0106] Specifically, a requirement matching evaluation table is constructed, which includes a list of scenario features and requirement matching rules. The scenario feature list outlines the key features of different application scenarios; the requirement matching rules define the matching standards between knowledge points and application requirements. Matching degree data is generated through feature matching calculations, which consider three factors: scenario applicability, application frequency, and implementation conditions.

[0107] S330. Knowledge coverage data includes knowledge structure completeness data and knowledge association completeness data; demand matching data includes theoretical knowledge matching data and practical skills matching data.

[0108] Specifically, a test data analysis framework is established, which includes a completeness assessment module and a matching degree assessment module. The completeness assessment module generates completeness data by calculating the proportion of knowledge points, the hierarchical distribution ratio, and the correlation strength coefficient; the matching degree assessment module generates matching degree data by analyzing the applicability of theoretical content and the degree of conformity with skill requirements.

[0109] In one embodiment, refer to Figure 5 In step S400, the knowledge graph evaluation result is obtained based on the knowledge coverage data and the demand matching data. Based on the evaluation result, the knowledge graph update instruction is triggered, which specifically includes the following steps:

[0110] S410. Based on knowledge coverage data and demand matching data, trigger the knowledge graph evaluation instruction.

[0111] In this embodiment, the knowledge graph evaluation command refers to the system command used to initiate the evaluation process. The triggering conditions for the evaluation command include periodic triggering conditions and threshold triggering conditions. Periodic triggering is set to once per semester, and threshold triggering is set to automatically trigger when the coverage is below 85% or the matching degree is below 80%.

[0112] Specifically, an assessment trigger mechanism table is established, containing time-triggered rules and data-triggered rules. Time-triggered rules define a fixed assessment cycle; data-triggered rules set warning thresholds for coverage and matching. The monitoring system periodically checks the trigger conditions, and automatically generates assessment instructions when the conditions are met. For example, after a professional curriculum is updated, the system detects that the knowledge coverage data has dropped to 83%, below the warning threshold, and automatically triggers an assessment instruction for a comprehensive assessment.

[0113] S420. Based on the knowledge graph evaluation instructions, obtain the knowledge graph evaluation results, which include the completeness of the knowledge system and the matching degree of knowledge application.

[0114] In this embodiment, the completeness of the knowledge system refers to the score of the systematicness and completeness of the knowledge structure. The knowledge application matching degree refers to the score of the degree to which the knowledge content matches the actual application needs.

[0115] Specifically, an evaluation scoring standard table is constructed, which includes completeness scoring rules and matching score rules. The completeness scoring rules are based on three dimensions: knowledge point coverage, knowledge level distribution, and knowledge relevance strength. The matching score rules are based on three dimensions: theoretical applicability, practical feasibility, and demand satisfaction. The scores of each dimension are integrated through a weighted calculation method to generate the final evaluation result.

[0116] S430. Based on the knowledge graph evaluation results, trigger the knowledge graph update command.

[0117] Specifically, an update instruction generation rule base is established, which includes evaluation result judgment criteria and an update strategy mapping table. The judgment criteria divide the evaluation score into different intervals, corresponding to different update requirements; the update strategy mapping table determines the update method based on the specific performance of the evaluation results. Specific update instructions are generated through rule matching.

[0118] In one embodiment, refer to Figure 6 In step S430, based on the knowledge graph evaluation results, a knowledge graph update instruction is triggered, which specifically includes the following steps:

[0119] S431. Based on the completeness of the knowledge system and the matching degree of knowledge application, obtain information on the optimization requirements of the knowledge graph.

[0120] In this embodiment, the knowledge graph optimization requirement information includes two parts: optimization goals and optimization content. The optimization goals are divided into completeness improvement goals and matching improvement goals; the optimization content includes three aspects: knowledge points that need to be added, knowledge structures that need to be adjusted, and application scenarios that need to be updated.

[0121] S432. Generate a knowledge graph update plan based on the knowledge graph optimization requirements and tag information.

[0122] In this embodiment, the knowledge graph update plan includes three parts: an update content list, an update priority ranking, and an update execution configuration. The update content list lists the specific update items that need to be executed; the update priority ranking determines the execution order of the update items; and the update execution configuration includes three elements: update method, update time node, and required resources.

[0123] Specifically, an update plan generation matrix is ​​established, containing priority determination rules and execution configuration rules. The priority determination rules categorize updated content into foundational support knowledge and substantive content knowledge: foundational support knowledge includes the knowledge framework structure, knowledge relationships, and knowledge classification system; this type of knowledge has the highest update priority to ensure the integrity of the knowledge graph's basic architecture. Substantive content knowledge includes specific knowledge points, application cases, and practical requirements; this type of knowledge is updated after the basic architecture is stable. For example, when updating the artificial intelligence professional knowledge graph, foundational support knowledge such as the "deep learning theoretical framework" is updated first to establish the algorithm system framework, and then substantive content knowledge such as "convolutional neural network application examples" is updated. The specific execution configuration rules define the update methods for different types of knowledge: foundational support knowledge is updated fully, while substantive content knowledge is updated incrementally. A complete update plan, including the update order and specific configurations, is generated through matrix operations to achieve orderly updates of the knowledge graph.

[0124] S433. Based on the knowledge graph update plan, trigger the knowledge graph update command.

[0125] Specifically, based on the knowledge graph update plan, executable update instructions are generated.

[0126] In one embodiment, refer to Figure 7 In step S500, based on the knowledge graph update instruction, a knowledge optimization instruction is triggered to dynamically adjust the knowledge graph, specifically including the following steps:

[0127] S510: Obtain the knowledge graph update progress based on the knowledge graph update command.

[0128] In this embodiment, the knowledge graph update progress includes three dimensions: update completion rate and update timeliness. Update completion rate represents the execution ratio of update tasks; update timeliness represents the timeliness of update execution.

[0129] S520. Input the knowledge graph update progress and knowledge coverage data into the preset knowledge optimization model, and obtain the preset knowledge optimization rules based on the knowledge optimization model.

[0130] In this embodiment, the knowledge optimization model refers to the analysis model used to determine the update progress and generate optimization rules. The preset knowledge optimization rules are divided into a first optimization rule and a second optimization rule, which are used to guide the optimization process of the knowledge graph under different circumstances.

[0131] Specifically, the acquired knowledge graph update progress data and knowledge coverage data are input into a preset knowledge optimization model. In this model, the update progress is first compared to a preset baseline value. When the update progress falls below the baseline value, it determines which optimization rule is currently being used. If the first optimization rule (e.g., incremental update rule) is being used and the optimization effect is unsatisfactory, it switches to the second optimization rule (e.g., reconstruction update rule). The purpose of changing the optimization rule is to avoid situations where the current optimization rule cannot effectively improve the quality of the knowledge graph, thereby improving the optimization effect by switching optimization strategies. For example, in the optimization process of the computer network course knowledge graph, if it is found that the knowledge coverage still does not meet expectations after using the incremental update method (first optimization rule), the model will automatically switch to the reconstruction update method (second optimization rule) to comprehensively optimize the knowledge system of the course. For example, when using the incremental update rule (first optimization rule) to process the knowledge graph, the original knowledge structure is no longer suitable for new teaching needs; simply adding new knowledge points may lead to confusion in the relationships between knowledge points. Or, an engineering drawing course might be updated using a chapter-by-chapter optimization approach, but in actual teaching, it is necessary to break the traditional chapter structure and reorganize the knowledge points according to engineering project practice. Continuing to use the first optimization rule in this situation will result in investing a lot of resources but failing to achieve the expected results.

[0132] A more specific implementation involves establishing an optimization rule switching matrix, which includes a progress evaluation threshold table and a rule conversion table. The progress evaluation threshold table sets baseline values ​​for key indicators, such as "update completion rate no less than 85%, coverage improvement no less than 10%." The rule conversion table defines the switching conditions and conversion methods between different optimization rules. When it is found that a certain professional module fails to achieve the expected results using the first optimization rule (such as "step-by-chapter optimization"), the system automatically switches to the second optimization rule (such as "optimization by knowledge cluster reorganization") and re-attempts optimization. This dynamic switching mechanism ensures the adaptability and effectiveness of the knowledge graph optimization process.

[0133] S530. Based on the knowledge optimization rules, trigger the knowledge optimization command to achieve dynamic adjustment of the knowledge graph.

[0134] Specifically, based on the first or second optimization rule, a knowledge optimization instruction is triggered to incrementally update or reconstruct the knowledge graph, thereby achieving dynamic adjustment of the knowledge graph.

[0135] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0136] Secondly, this application provides an intelligent knowledge graph construction system for an industry-education integration platform. The intelligent knowledge graph construction system for an industry-education integration platform described below is based on the aforementioned method for constructing an intelligent knowledge graph for an industry-education integration platform.

[0137] Reference Figure 8 A smart knowledge graph construction system for an industry-education integration platform, comprising:

[0138] The knowledge point type acquisition module is used to acquire educational resource data, classify the educational resource data according to preset knowledge classification rules, and obtain the corresponding knowledge point types. Among them, the knowledge point types include theoretical knowledge and practical skills.

[0139] The feature label information acquisition module is used to acquire the knowledge content corresponding to the knowledge point type, identify the associated attributes of the knowledge content, and obtain label information representing the features of the knowledge point.

[0140] The test data acquisition module is used to acquire test data for testing the quality of the knowledge graph based on the knowledge point type and tag information. The test data includes knowledge coverage data and requirement matching data.

[0141] The knowledge graph update instruction triggering module is used to obtain the knowledge graph evaluation results based on knowledge coverage data and demand matching data, and trigger the knowledge graph update instruction according to the evaluation results;

[0142] The knowledge graph dynamic adjustment module is used to dynamically adjust the knowledge graph based on knowledge graph update commands and trigger knowledge optimization commands.

[0143] In one embodiment, this application provides an electronic device, which may be a server, and its internal structure diagram may be as follows: Figure 9As shown, the electronic device includes a processor, memory, and network interface connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage medium. The database stores data. The network interface communicates with external terminals via a network connection. When the computer program is executed by the processor, it implements a method for constructing an intelligent knowledge graph for an industry-education integration platform.

[0144] Those skilled in the art will understand that Figure 9 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the electronic device to which the present application is applied. The specific electronic device may include more or fewer components than shown in the figure, or combine certain components, or have different component arrangements.

[0145] In one embodiment, an electronic device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0146] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical storage, etc. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0147] The above are all preferred embodiments of this application, and are not intended to limit the scope of protection of this application. Therefore, all equivalent changes made in accordance with the structure, shape and principle of this application should be covered within the scope of protection of this application.

Claims

1. A production-teaching integration platform intelligent knowledge graph construction method, characterized in that, The method comprises the following steps: acquiring education resource data, classifying the education resource data according to a preset knowledge classification rule to obtain corresponding knowledge point types; acquiring knowledge content corresponding to the knowledge point types, identifying the association attributes of the knowledge content to obtain label information representing knowledge point characteristics; acquiring test data for testing the quality of the knowledge graph according to the knowledge point types and the label information, the test data comprising knowledge coverage data and demand matching degree data; obtaining a knowledge graph evaluation result based on the knowledge coverage data and the demand matching degree data, and triggering a knowledge graph update instruction according to the evaluation result; triggering a knowledge optimization instruction based on the knowledge graph update instruction to dynamically adjust the knowledge graph; wherein the knowledge point types comprise theoretical knowledge and practical skills, and the label information comprises theoretical knowledge label information and practical skill label information; acquiring knowledge content, extracting the association attributes of the knowledge content, wherein the association attributes comprise knowledge difficulty, knowledge dependency and application scenarios; if the knowledge point type is theoretical knowledge, acquiring corresponding theoretical knowledge label information based on the association attributes; if the knowledge point type is practical skill, acquiring corresponding practical skill label information based on the association attributes; acquiring test data for testing the quality of the knowledge graph according to the knowledge point types and the label information, comprising the following steps: generating knowledge coverage data according to the knowledge dependency and knowledge difficulty attributes in the theoretical knowledge label information and the practical skill label information; generating demand matching degree data according to the application scenario attributes in the theoretical knowledge label information and the practical skill label information; the knowledge coverage data comprises knowledge structure integrity data and knowledge association integrity data; and the demand matching degree data comprises theoretical knowledge matching degree data and practical skill matching degree data. acquiring education resource data, classifying the education resource data according to a preset knowledge classification rule to obtain corresponding knowledge point types, comprising the following steps: 2.The method according to claim 1, characterized in that, acquiring historical education resource data, and acquiring knowledge point structure information according to the historical education resource data; extracting knowledge point key feature information for knowledge graph construction from the knowledge point structure information according to the knowledge point structure information; classifying the knowledge point key feature information according to a preset knowledge classification rule to obtain corresponding knowledge point types. obtaining a knowledge graph evaluation result based on the knowledge coverage data and the demand matching degree data, and triggering a knowledge graph update instruction according to the evaluation result, comprising the following steps: 3.The method according to claim 1, characterized in that, triggering a knowledge graph evaluation instruction based on the knowledge coverage data and the demand matching degree data; obtaining a knowledge graph evaluation result based on the knowledge graph evaluation instruction, the knowledge graph evaluation result comprising knowledge system integrity and knowledge application matching degree; and ​ According to the knowledge graph evaluation result, a knowledge graph update instruction is triggered. 4.The method according to claim 3, characterized in that, According to the knowledge graph evaluation result, a knowledge graph update instruction is triggered, specifically including the following steps: According to the knowledge system integrity and the knowledge application matching degree, knowledge graph optimization requirement information is obtained; According to the knowledge graph optimization requirement information and the label information, a knowledge graph update plan is generated; Based on the knowledge graph update plan, a knowledge graph update instruction is triggered. 5.The method according to claim 1, characterized in that, Based on the knowledge graph update instruction, a knowledge optimization instruction is triggered to dynamically adjust the knowledge graph, specifically including the following steps: Based on the knowledge graph update instruction, a knowledge graph update progress is obtained; The knowledge graph update progress and the knowledge coverage data are input into a preset knowledge optimization model, and a preset knowledge optimization rule is obtained according to the knowledge optimization model; According to the knowledge optimization rule, a knowledge optimization instruction is triggered to realize dynamic adjustment of the knowledge graph.

6. An integrated production and teaching platform intelligent knowledge graph construction system, characterized in that, The application of the production and teaching integration platform intelligent knowledge graph construction method in any one of claims 1-5 comprises: A knowledge point type acquisition module is configured to acquire education resource data, classify the education resource data according to a preset knowledge classification rule, and obtain corresponding knowledge point types, wherein the knowledge point types include theoretical knowledge and practical skills. A feature label information acquisition module is configured to acquire knowledge content corresponding to the knowledge point types, identify associated attributes of the knowledge content, and obtain label information representing knowledge point features. A test data acquisition module is configured to acquire test data for testing the quality of the knowledge graph according to the knowledge point types and the label information, wherein the test data includes knowledge coverage data and requirement matching degree data. A knowledge graph update instruction triggering module is configured to obtain a knowledge graph evaluation result based on the knowledge coverage data and the requirement matching degree data, and trigger a knowledge graph update instruction according to the evaluation result. A knowledge graph dynamic adjustment module is configured to trigger a knowledge optimization instruction based on the knowledge graph update instruction to dynamically adjust the knowledge graph.

7. An electronic device, comprising: A computer program stored in the memory and executable on the processor, when the processor executes the computer program, realizes the steps of the production and teaching integration platform intelligent knowledge graph construction method in any one of claims 1-5.

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