A high school course knowledge atomic value intelligent analysis system and method

Abstract

This invention discloses an intelligent analysis system and method for the value of knowledge atoms in university courses. The system includes: a knowledge atom library management module for digitizing and managing the entire lifecycle of knowledge atoms; a data acquisition and fusion module for automatically or semi-automatically collecting raw data from various data sources, transforming and associating it to form a standardized indicator dataset; a configurable value calculation module for calculating the comprehensive value of knowledge atoms in conjunction with the indicator dataset; and an intelligent diagnosis and optimization module for visually diagnosing and analyzing the curriculum system based on the comprehensive value calculation results of knowledge atoms and automatically generating optimization suggestions. This invention, by constructing a technical platform integrating data acquisition, model calculation, intelligent analysis, and visual recommendation, transforms curriculum system construction from experience-driven to data-driven, ultimately achieving a scientific reshaping of the professional curriculum system, forming a logically coherent, focused curriculum system that supports core professional competencies.

Description

Technical Field

[0001] This invention relates to the field of educational information technology and curriculum system engineering technology, and in particular to an intelligent analysis system and method for the atomic value of knowledge in higher education courses. Background Technology

[0002] Currently, in the construction of professional curriculum systems in universities, the selection and optimization of knowledge points mainly rely on manual judgment based on teaching experience, lacking systematic technical support. Existing curriculum design methods have the following technical shortcomings: First, the assessment of the relevance of knowledge points is vague, making it difficult to determine the structural position of individual knowledge points within the curriculum system, resulting in a loose curriculum structure. Second, the value measurement of knowledge points is based on a single dimension, failing to fully consider key factors such as teaching input, importance level, frequency of reuse, and contribution to core competencies, thus failing to accurately identify core knowledge points. Third, curriculum system iteration lacks data support; the addition, deletion, and adjustment of knowledge points are highly subjective, making it difficult to form a logically rigorous, systematic layout that supports core competencies, and thus unable to meet the needs of reshaping professional curriculum systems.

[0003] Therefore, there is an urgent need for a technical solution that integrates course data assetization, evaluation model algorithmization, decision-making process visualization, and system iteration automation to overcome the above-mentioned technical deficiencies. Summary of the Invention

[0004] This invention addresses the problems of existing curriculum design methods, such as vague assessment of the correlation between knowledge points, single dimension of measuring the value of knowledge points, and lack of data support for curriculum system iteration. It proposes an intelligent analysis system and method for the atomic value of knowledge in college courses. By constructing a technical platform that integrates data collection, model calculation, intelligent analysis, and visualization recommendation, the invention transforms curriculum system construction from experience-driven to data-driven, ultimately achieving a scientific reshaping of the professional curriculum system and forming a logically coherent, key-focused curriculum system that supports core professional competencies.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] The first aspect of this invention proposes an intelligent analysis system for the atomic value of knowledge in university courses, comprising:

[0007] The Knowledge Atom Repository Management Module is used to realize the digitization and full lifecycle management of knowledge atoms;

[0008] The data acquisition and fusion module is used to automatically or semi-automatically collect raw data from various data sources, and transform and correlate it to form a standard indicator dataset;

[0009] A configurable value calculation module is used to perform comprehensive value calculation of knowledge atoms by combining indicator datasets;

[0010] The intelligent diagnosis and optimization module is used to perform visual diagnostic analysis of the curriculum system and automatically generate optimization suggestions based on the calculation results of the comprehensive value of knowledge atoms.

[0011] Furthermore, the knowledge atom library management module includes an atomization parsing unit and a graph-based storage unit;

[0012] The atomized analysis unit is used to analyze knowledge atoms from the relevant knowledge of the curriculum system. The knowledge atom is the smallest indivisible knowledge unit in the curriculum system.

[0013] The graph-based storage unit is used to store knowledge atoms and their relationships using a graph database or a relational database.

[0014] Furthermore, the data acquisition and fusion module includes a teaching time sequence acquisition unit, a topology relationship construction unit, an attribute annotation unit, and a capability-knowledge chain mapping unit;

[0015] The teaching time sequence acquisition unit is used to collect the duration of each knowledge atom in actual teaching;

[0016] The topology relationship construction unit is used to establish predecessor-successor dependencies between knowledge atoms, automatically record and calculate the number of predecessor knowledge atoms and the number of successor knowledge atoms for each atom, and determine the structure type and structure association quantification value according to preset rules.

[0017] The attribute annotation unit is used to batch annotate the importance level of knowledge atoms;

[0018] The capability-knowledge chain mapping unit is used to construct and store capability-knowledge chains, which are ordered sequences of knowledge atoms that indicate the knowledge paths that support a certain core capability.

[0019] Furthermore, the configurable value calculation module includes a reuse rate calculation unit, a core capability contribution calculation unit, a weight dynamic configuration unit, and a comprehensive value calculation unit;

[0020] The reuse rate calculation unit is used to automatically count the number of times knowledge atoms appear in different courses to obtain the reuse rate.

[0021] The core capability contribution calculation unit is used to calculate the core capability contribution based on the capability-knowledge chain.

[0022] The weight dynamic configuration unit is used to dynamically adjust the weight coefficients of each indicator;

[0023] The comprehensive value calculation unit is used to automatically calculate the comprehensive value of each knowledge atom.

[0024] Furthermore, the core capability contribution calculation unit calculates the core capability contribution in the following manner:

[0025] If the capability-knowledge chain contains y knowledge atoms, the core capability corresponds to the y-th knowledge atom, and the contribution of the core capability of the z-th knowledge atom is z / y, where 1≤z≤y;

[0026] If a knowledge atom belongs to n capability-knowledge chains, the contribution values ​​of each chain are z1 / y1, z2 / y2, ..., z. n / y n The total contribution of core capabilities is C= .

[0027] Furthermore, in the comprehensive value calculation unit, the comprehensive value of each knowledge atom is calculated in the following manner:

[0028]

[0029] Where V represents the comprehensive value of a knowledge atom; W a W k W l W f W c These represent the weighting coefficients corresponding to indicators A, K, L, F, and C, respectively; A represents the duration of knowledge atoms in actual teaching; K is the quantitative value of structural association, derived from structural type and association strength; L is the importance level; F is the reuse rate; and C is the contribution of core competencies.

[0030] Furthermore, the intelligent diagnosis and optimization module includes a multi-view visualization unit, an optimization generation unit, and a reconstruction and deduction unit;

[0031] The multi-perspective visualization unit is used to perform multi-dimensional visual diagnostic analysis of knowledge atoms in the curriculum system based on the comprehensive value and various indicators.

[0032] The optimization generation unit is used to automatically identify high-value and low-value knowledge atoms and generate targeted optimization suggestions based on the built-in optimization strategy library;

[0033] The reconstruction simulation unit provides an interactive simulation environment for curriculum system reconstruction, supports users in performing operations such as adding, deleting, merging, and adjusting the order of knowledge atoms, and calculates the impact of changes on the value of related knowledge atoms in real time, thus assisting decision-making in a visual manner.

[0034] The second aspect of this invention proposes an intelligent analysis method for the atomic value of knowledge in university courses, comprising:

[0035] The knowledge related to the target curriculum system is broken down into multiple knowledge atoms, and a knowledge atom library is constructed.

[0036] Collect the duration of each knowledge atom in actual teaching; establish the predecessor-successor dependency relationship between knowledge atoms, automatically record and calculate the number of predecessor knowledge atoms and successor knowledge atoms for each atom, and determine the structure type and structure association quantification value according to preset rules; batch label the importance level of knowledge atoms, and build and store capability-knowledge chains;

[0037] The frequency of knowledge atoms appearing in different courses is counted to obtain the reuse rate; the core capability contribution of each knowledge atom is calculated based on the capability-knowledge chain, and then the comprehensive value of each knowledge atom is calculated.

[0038] Based on the comprehensive value and various quantitative indicators, a multi-dimensional visual diagnostic analysis is performed on the knowledge atoms in the target curriculum system to automatically identify high-value and low-value knowledge atoms and generate targeted optimization suggestions based on the built-in optimization strategy library.

[0039] Furthermore, it also includes:

[0040] The curriculum system was optimized and its logical structure restructured based on the optimization suggestions.

[0041] The curriculum structure is dynamically updated based on feedback.

[0042] A third aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the functions of any one of the intelligent analysis systems for the atomic value of knowledge in higher education courses.

[0043] Compared with the prior art, the present invention has the following beneficial effects:

[0044] 1) It has achieved comprehensive digitization of course elements, transforming vague teaching experiences into structured data assets, and solving the fundamental problems of data gaps and inconsistencies in course analysis;

[0045] 2) It provides an automated analysis tool for knowledge value based on a clear algorithm model, which greatly improves the efficiency and objectivity of course evaluation and makes it possible to conduct regular analysis and diagnosis of large-scale curriculum systems.

[0046] 3) Through configurable weight optimization, the system is transformed into a highly adaptable decision support platform that can flexibly respond to the personalized needs of different majors, different training stages, and different reform orientations.

[0047] 4) Supports dynamic weight adjustment, which can adapt to the needs of curriculum system reshaping for different majors and training objectives, and is highly flexible;

[0048] 5) A complete technical closed loop of "data collection → model calculation → intelligent recommendation → feedback update" has been constructed, which can promote the dynamic optimization of the professional curriculum system, ensure that the curriculum system is accurately matched with the professional core competency training objectives, and realize the scientific reshaping of the curriculum system. Attached Figure Description

[0049] Figure 1 This is a schematic diagram of the architecture of an intelligent analysis system for the atomic value of knowledge in university courses, according to an embodiment of the present invention.

[0050] Figure 2 This is one of the flowcharts of an intelligent analysis method for the atomic value of knowledge in university courses according to an embodiment of the present invention;

[0051] Figure 3 This is the second flowchart of an intelligent analysis method for the atomic value of knowledge in university courses according to an embodiment of the present invention. Detailed Implementation

[0052] The present invention will be further explained below with reference to the accompanying drawings and specific embodiments:

[0053] like Figure 1 As shown, the present invention provides an intelligent analysis system for the atomic value of knowledge in university courses, comprising:

[0054] The Knowledge Atom Repository Management Module is used to realize the digitization and full lifecycle management of knowledge atoms;

[0055] The data acquisition and fusion module is used to automatically or semi-automatically collect raw data from various data sources, and transform and correlate it to form a standard indicator dataset;

[0056] A configurable value calculation module is used to perform comprehensive value calculation of knowledge atoms by combining indicator datasets;

[0057] The intelligent diagnosis and optimization module is used to perform visual diagnostic analysis of the curriculum system and automatically generate optimization suggestions based on the calculation results of the comprehensive value of knowledge atoms.

[0058] Furthermore, the system specifically includes:

[0059] (I) Knowledge Atom Base Management Module

[0060] This module serves as the system's data foundation, enabling the digitization and full lifecycle management of knowledge atoms.

[0061] 1. Atomized analytical unit

[0062] It provides standard interfaces and templates to support the parsing of knowledge atoms from teaching outlines, textbook catalogs, and courseware. Each knowledge atom serves as the smallest teaching unit, and is the smallest indivisible knowledge unit in the curriculum system, possessing independent teaching objectives, content boundaries, and application scenarios.

[0063] 2. Graphical storage unit

[0064] Graph databases or relational databases are used to store knowledge atoms and their relationships, providing underlying data support for constructing course knowledge graphs.

[0065] (II) Data Acquisition and Fusion Module

[0066] This module is responsible for automatically or semi-automatically collecting raw data from various data sources, and transforming and correlating it to form a standard indicator dataset.

[0067] 1. Teaching sequence data collection unit

[0068] It connects with smart classroom systems, online teaching platforms, or manual input interfaces to collect the teaching, discussion, and practice time (Indicator A) of each knowledge atom in actual teaching, and measures the teaching resource input of the knowledge atom.

[0069] 2. Topology Relationship Building Unit

[0070] The system provides visual editing tools, allowing course designers to establish predecessor-successor dependencies between knowledge atoms. It assesses the structural positioning of knowledge atoms within the professional curriculum system, automatically recording and calculating the number of predecessors (knowledge atoms) (z) and successors (knowledge atoms) (r) for each atom. Based on rules (no association = single point S, only predecessors or only successors = multiple points M, both = association R), it determines the structural type and corresponding quantitative value of structural association (index K). Specifically, for the case of no association, the structural type is single point S, K=0; for the case of only predecessors or only successors, the structural type is multiple points M, K = z or r; for the case of both predecessors and successors, the structural type is association R, K = z+r.

[0071] 3. Attribute annotation unit

[0072] It supports batch labeling of the importance level (L) of knowledge atoms according to the teaching syllabus, such as levels 1 to 3, corresponding to the three levels of understanding, familiarity, and mastery. The higher the level, the greater the quantitative value.

[0073] 4. Capability-Knowledge Chain Mapping Unit

[0074] Based on the core competencies defined in professional certification standards or training programs, an interface is provided for teaching experts to construct competency-knowledge chains. Each chain is an ordered sequence of knowledge atoms, indicating the knowledge path supporting a specific core competency. The system stores this mapping relationship in a structured manner.

[0075] (iii) Configurable value calculation module

[0076] This module is the intelligent core of the system, with a built-in algorithm model that performs comprehensive calculations on the fused indicator data.

[0077] 1. Reuse Rate Calculation Unit

[0078] By querying the course database, the system automatically counts the number of times knowledge atoms appear in different courses, obtains the reuse rate (F), measures the frequency of cross-course application of knowledge atoms in the professional curriculum system, and supports the cross-course collaborative reconstruction of the curriculum system.

[0079] 2. Core Competency Contribution Calculation Unit

[0080] This sub-unit executes a specific algorithm: for a given knowledge atom, it queries all the capability-knowledge chains to which it belongs. It calculates the supporting role of the knowledge atom in the development of core professional competencies, and calculates the contribution based on the capability-knowledge chains, ensuring a precise match between the curriculum system and the core competency development objectives.

[0081] a) Single-chain contribution: If the capability-knowledge chain contains y nodes (knowledge atoms), the core capability corresponds to the y-th knowledge atom, and the contribution of the z-th knowledge atom is z / y, where 1≤z≤y;

[0082] b) Total Contribution of Multiple Chains: If a knowledge atom belongs to n (n>1) capability-knowledge chains, the contribution of each chain is z1 / y1, z2 / y2,...,z n / y n The total contribution is denoted as C= .

[0083] 3. Weighted Dynamic Configuration Unit

[0084] Provides an administrator interface that allows dynamic adjustment of the weighting coefficients (W) of the five major indicators (A, K, L, F, C) based on professional characteristics, course stage, or reform goals. a W k W l W f W c The weights are all 1 by default, but can be adjusted as needed, such as increasing the weight W for the contribution of core capabilities. c .

[0085] 4. Comprehensive Value Calculation Unit

[0086] The system automatically calculates the comprehensive value V of each knowledge atom using a pre-defined weighted summation algorithm. The calculation formula is as follows:

[0087]

[0088] Wherein, K is the quantitative value of structural association, and the specific calculation method varies depending on the structural type (single-point S, multi-point M, or associated R); F is the reuse rate, and C is the contribution of core capabilities.

[0089] (iv) Intelligent Diagnosis and Optimization Module

[0090] This module transforms the calculation results into actionable insights and recommendations.

[0091] 1. Multi-view visualization unit

[0092] It provides knowledge graph views (node ​​size and color represent V value), value distribution histograms, indicator contribution radar charts, and other multi-dimensional presentations of analysis results.

[0093] 2. Optimize the generation unit

[0094] The system includes a built-in configurable optimization library (e.g., suggestions to simplify or merge V values ​​below threshold T1, and suggestions to strengthen V values ​​above threshold T2). The system automatically identifies high-value and low-value knowledge atoms and generates descriptive textual suggestions (e.g., image template operations are highly valuable, suggesting increased practical lessons; histogram matching has lower value, suggesting integration with the histogram balancing module).

[0095] 3. Reconstruction and Deduction Unit

[0096] The system provides a "sandbox" simulation environment for the curriculum system. Users can try deleting, merging, or adjusting the order of knowledge atoms based on suggestions. The system will recalculate the value of the affected atoms in real time based on structural correlation indicators and call the configurable value calculation module, visually displaying the consequences of the operations to assist in the final decision. After the decision is confirmed, the changes will be officially submitted to the knowledge atom library management module for storage, forming a new version of the curriculum system.

[0097] like Figure 2 As shown, the present invention provides an intelligent analysis method for the atomic value of knowledge in university courses, comprising:

[0098] The knowledge related to the target curriculum system is broken down into multiple knowledge atoms, and a knowledge atom library is constructed.

[0099] Collect the duration of each knowledge atom in actual teaching; establish the predecessor-successor dependency relationship between knowledge atoms, automatically record and calculate the number of predecessor knowledge atoms and successor knowledge atoms for each atom, and determine the structure type and structure association quantification value according to preset rules; batch label the importance level of knowledge atoms, and build and store capability-knowledge chains;

[0100] The frequency of knowledge atoms appearing in different courses is counted to obtain the reuse rate; the core capability contribution of each knowledge atom is calculated based on the capability-knowledge chain, and then the comprehensive value of each knowledge atom is calculated.

[0101] Based on the comprehensive value and various quantitative indicators, a multi-dimensional visual diagnostic analysis is performed on the knowledge atoms in the target curriculum system to automatically identify high-value and low-value knowledge atoms and generate targeted optimization suggestions based on the built-in optimization strategy library.

[0102] Furthermore, such as Figure 3 As shown, the method also includes:

[0103] The curriculum system was optimized and its logical structure restructured based on the optimization suggestions.

[0104] The curriculum structure is dynamically updated based on feedback.

[0105] As one possible implementation method, this embodiment takes the optimization of the "Image Intelligent Processing and Recognition" course system in the artificial intelligence major of a certain university as an example to describe the workflow of the present invention in detail.

[0106] Step 1: System initialization and knowledge atom library construction.

[0107] The course coordinator logs into the system and, in the knowledge atom library management module, uses file import (such as a syllabus Word document) and manual supplementation to initially break down the "Image Intelligent Processing and Recognition" course into 84 knowledge atoms, such as "Overview of Contrast Transformation", "Histogram Equalization", "Image Template Operation", and "Fundamentals of Convolutional Neural Networks", and enters their basic descriptions.

[0108] Step 2: Multi-dimensional data collection and correlation.

[0109] The teaching assistant used the teaching time sequence collection unit interface to supplement or verify the actual teaching time of each knowledge unit. For example, "Image Template Operation" was recorded as 25 minutes.

[0110] The course team experts used a graphical interface to construct units based on topological relationships, establishing connections between knowledge atoms. The system recorded that the predecessor of "image template operation" was "convolution operation" (1 unit), and the successors were "image smoothing filtering" and "first-order differential operator edge detection" (2 units), and automatically classified them as "associative structure R", with a quantization value K=1+2=3.

[0111] The system automatically assigns importance levels (L) to knowledge atoms based on preset course syllabus tags. "Image template operation" is marked as "Mastery" level, L=3.

[0112] The system searched the entire professional course database and found that "image template operation" did not appear explicitly in other courses, so the reuse rate F=0.

[0113] In the competency-knowledge chain mapping unit, the teaching experts dragged the "Image Template Calculation" node into the "Image Intelligent Fast Processing Capability" chain (which has 10 nodes and is the 7th node) and the "Dynamic Small Target Image Intelligent Recognition Capability" chain (which has 9 nodes and is the 6th node). The system automatically established and stored these mapping relationships.

[0114] Step 3: Value Calculation and Intelligent Diagnosis.

[0115] Once all data is ready, the administrator initiates value calculation using the default weights (all 1). The algorithm is invoked to calculate the core capability contribution C of "image template operation": contribution to the first chain 7 / 10 = 0.7, contribution to the second chain 6 / 9 ≈ 0.667, and the total C = 1.367.

[0116] Substitute into the comprehensive value formula: .

[0117] In the intelligent diagnosis and optimization module, the system displays the V-value ranking of all 84 knowledge atoms in a dashboard format. A comparison reveals that the V-value of the "Histogram Matching" atom is only 10.3. Based on the optimization module (V<15 and not a critical predecessor), an automatic suggestion is generated: the knowledge atom "Histogram Matching" (V=10.3) has low overall value, and its necessity should be assessed, considering: 1. Reducing the explanation time; 2. Integrating it with the "Histogram Balancing" content.

[0118] Step 4: Decision-making on curriculum system reshaping.

[0119] Core knowledge atoms are retained and strengthened: "Image template operation" has high comprehensive value (V=32.47) and is a core supporting knowledge point in the curriculum system. It will be retained in the curriculum system reshaping and its proportion of practical teaching will be increased.

[0120] Non-core knowledge atomic optimization: Compared with "histogram matching" (8 minutes of explanation, quantification value of structural correlation 1, importance level 1, reuse rate 0, core competency contribution 0.3, V=8+1+1+0+0.3=10.3), its value is low. In the reshaping of the curriculum system, the explanation time can be reduced (from 8 minutes to 5 minutes) or integrated into the "histogram balance" knowledge point module;

[0121] System logic reconstruction: Based on the structural correlation indicators of each knowledge atom, the knowledge network of the course is reconstructed, and "convolution operation" → "image template operation" → "image smoothing filter" and "differential filter edge detection - first-order differential operator" are constructed into the core knowledge module of "image enhancement and feature extraction", which strengthens the logical coherence of the course system.

[0122] Step 5: Dynamic iteration.

[0123] Each semester, we collect teaching feedback and industry technology development data. If "image template operation" is followed by a new knowledge point, "deep learning image feature extraction," in practical applications, we update the structure association quantization value (the number of successors r=3, and the quantization value is adjusted to 4), recalculate the value, and optimize the curriculum layout.

[0124] The present invention also proposes a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the function of the intelligent analysis system for the atomic value of knowledge in higher education courses.

[0125] In summary, this invention achieves comprehensive digitization of course elements, transforming vague teaching experiences into structured data assets and solving the fundamental problems of data gaps and inconsistencies in course analysis. It provides an automated analysis tool for knowledge value based on a clear algorithmic model, greatly improving the efficiency and objectivity of course evaluation and enabling regular analysis and diagnosis of large-scale course systems. Through configurable weight optimization, the system is transformed into a highly adaptable decision support platform, capable of flexibly responding to the personalized needs of different majors, different training stages, and different reform orientations. It supports dynamic weight adjustment, adapting to the needs of curriculum system reshaping for different majors and different training objectives, offering high flexibility. This invention constructs a complete technical closed loop of "data collection → model calculation → intelligent recommendation → feedback update," which can promote the dynamic optimization of professional curriculum systems, ensuring precise matching between the curriculum system and the professional core competency training objectives, and achieving the scientific reshaping of the curriculum system.

[0126] The above description is only a preferred embodiment of the present invention. It should be noted that those skilled in the art can make several improvements and modifications without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A smart analysis system for the atomic value of knowledge in university courses, characterized in that, include: The Knowledge Atom Repository Management Module is used to realize the digitization and full lifecycle management of knowledge atoms; The data acquisition and fusion module is used to automatically or semi-automatically collect raw data from various data sources, and transform and correlate it to form a standard indicator dataset; A configurable value calculation module is used to perform comprehensive value calculation of knowledge atoms by combining indicator datasets; The intelligent diagnosis and optimization module is used to perform visual diagnostic analysis of the curriculum system and automatically generate optimization suggestions based on the calculation results of the comprehensive value of knowledge atoms.

2. The intelligent analysis system for the atomic value of knowledge in university courses according to claim 1, characterized in that, The knowledge atom library management module includes an atomized parsing unit and a graph-based storage unit; The atomized analysis unit is used to analyze knowledge atoms from the relevant knowledge of the curriculum system. The knowledge atom is the smallest indivisible knowledge unit in the curriculum system. The graph-based storage unit is used to store knowledge atoms and their relationships using a graph database or a relational database.

3. The intelligent analysis system for the atomic value of knowledge in university courses according to claim 1, characterized in that, The data acquisition and fusion module includes a teaching time sequence acquisition unit, a topology relationship construction unit, an attribute annotation unit, and a capability-knowledge chain mapping unit; The teaching time sequence acquisition unit is used to collect the duration of each knowledge atom in actual teaching; The topology relationship construction unit is used to establish predecessor-successor dependencies between knowledge atoms, automatically record and calculate the number of predecessor knowledge atoms and the number of successor knowledge atoms for each atom, and determine the structure type and structure association quantification value according to preset rules. The attribute annotation unit is used to batch annotate the importance level of knowledge atoms; The capability-knowledge chain mapping unit is used to construct and store capability-knowledge chains, which are ordered sequences of knowledge atoms that indicate the knowledge paths that support a certain core capability.

4. The intelligent analysis system for the atomic value of knowledge in university courses according to claim 3, characterized in that, The configurable value calculation module includes a reuse rate calculation unit, a core capability contribution calculation unit, a weight dynamic configuration unit, and a comprehensive value calculation unit. The reuse rate calculation unit is used to automatically count the number of times knowledge atoms appear in different courses to obtain the reuse rate. The core capability contribution calculation unit is used to calculate the core capability contribution based on the capability-knowledge chain. The weight dynamic configuration unit is used to dynamically adjust the weight coefficients of each indicator; The comprehensive value calculation unit is used to automatically calculate the comprehensive value of each knowledge atom.

5. The intelligent analysis system for the atomic value of knowledge in university courses according to claim 4, characterized in that, The core capability contribution calculation unit calculates the core capability contribution in the following manner: If the capability-knowledge chain contains y knowledge atoms, the core capability corresponds to the y-th knowledge atom, and the contribution of the core capability of the z-th knowledge atom is z / y, where 1≤z≤y; If a knowledge atom belongs to n capability-knowledge chains, the contribution values ​​of each chain are z1 / y1, z2 / y2, ..., z. n / y n The total contribution of core capabilities is C= .

6. The intelligent analysis system for the atomic value of knowledge in university courses according to claim 5, characterized in that, In the comprehensive value calculation unit, the comprehensive value of each knowledge atom is calculated in the following manner: Where V represents the comprehensive value of a knowledge atom; W a W k W l W f W c These represent the weighting coefficients corresponding to indicators A, K, L, F, and C, respectively; A represents the duration of knowledge atoms in actual teaching; K is the quantitative value of structural association, derived from structural type and association strength; L is the importance level; F is the reuse rate; and C is the contribution of core competencies.

7. The intelligent analysis system for the atomic value of knowledge in university courses according to claim 6, characterized in that, The intelligent diagnosis and optimization module includes a multi-view visualization unit, an optimization generation unit, and a reconstruction and deduction unit. The multi-perspective visualization unit is used to perform multi-dimensional visual diagnostic analysis of knowledge atoms in the curriculum system based on the comprehensive value and various indicators. The optimization generation unit is used to automatically identify high-value and low-value knowledge atoms and generate targeted optimization suggestions based on the built-in optimization strategy library; The reconstruction simulation unit provides an interactive simulation environment for curriculum system reconstruction, supports users in performing operations such as adding, deleting, merging, and adjusting the order of knowledge atoms, and calculates the impact of changes on the value of related knowledge atoms in real time, thus assisting decision-making in a visual manner.

8. A method for intelligent analysis of the atomic value of knowledge in university courses, characterized in that, include: The knowledge related to the target curriculum system is broken down into multiple knowledge atoms, and a knowledge atom library is constructed. The duration of each knowledge element in actual teaching is collected; Establish predecessor-successor dependencies between knowledge atoms, automatically record and calculate the number of predecessor knowledge atoms and the number of successor knowledge atoms for each atom, and determine the structure type and structure association quantification value according to preset rules; Batch label the importance level of knowledge atoms, and build and store the capability - knowledge chain; The frequency of occurrence of knowledge atoms in different courses is used to obtain the reuse rate; The core capability contribution of each knowledge atom is calculated based on the capability-knowledge chain, and then the comprehensive value of each knowledge atom is calculated. Based on the comprehensive value and various quantitative indicators, a multi-dimensional visual diagnostic analysis is performed on the knowledge atoms in the target curriculum system to automatically identify high-value and low-value knowledge atoms and generate targeted optimization suggestions based on the built-in optimization strategy library.

9. The intelligent analysis method for the atomic value of knowledge in university courses according to claim 8, characterized in that, Also includes: The curriculum system was optimized and its logical structure restructured based on the optimization suggestions. The curriculum structure is dynamically updated based on feedback.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the functions of the intelligent analysis system for atomic value of knowledge in college courses as described in any one of claims 1 to 7.

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