Music education training management method and system

Abstract

The invention relates to the technical field of information, and discloses a music education training management method and system.According to the scheme, objective and comprehensive learning data are obtained through a multi-dimensional data acquisition system S1, quantification and standardization of ability evaluation are achieved through acoustic feature analysis and deep learning modeling S2, and the ability evaluation efficiency is improved. A logic structure of course content is computable through S3 course knowledge graph modeling, individuation of course recommendation is realized through S4 multi-dimensional difference calculation and a self-adaptive matching strategy, closed-loop optimization of a teaching process is realized through S5 real-time monitoring and a dynamic correction mechanism, a qualitative teaching decision is converted into a quantifiable and optimizable mathematical model, and the teaching process is optimized. And the self-adaptive system converts static course allocation into dynamic response to student ability change.

Description

Technical Field

[0001] This invention relates to the field of information technology, specifically to a method and system for music education and training management. Background Technology

[0002] With the deep integration of information technology and education, music education and training are gradually incorporating digital management methods, including online course platforms, practice record systems, and electronic assessment tools. In music education and training management, student ability assessment, course content organization, and learning path planning are core components. Current methods primarily rely on teacher experience-based judgment combined with simple grade records for management.

[0003] Existing music education and training management methods have many shortcomings. Student ability assessment relies on teachers' subjective judgment, with inconsistent assessment standards and limited data dimensions, making it difficult to comprehensively reflect students' true ability status and development trends. Curriculum content organization lacks systematic modeling; knowledge point dependencies and difficulty levels are mostly implicit information, resulting in a lack of scientific basis for curriculum arrangement. Learning path planning uses fixed sequences, failing to consider individual student differences and ability changes, and lacking dynamic adjustment mechanisms and closed-loop feedback systems.

[0004] Therefore, we propose a music education and training management method to address the aforementioned issues. Summary of the Invention

[0005] The purpose of this invention is to provide a music education and training management method to solve the problems mentioned in the background art, such as the reliance on teachers' subjective judgment for student ability assessment, inconsistent assessment standards, and single data dimensions, which make it difficult to fully reflect the students' true ability status and development trend.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a music education and training management method, the specific steps of which are as follows: S1. Learning Data Collection: Collect multi-dimensional learning data of students, including technical feature data of performance audio, practice behavior data, historical learning record data and system interaction behavior data, and transmit them to the teaching management platform in real time through terminal devices; S2. Ability Status Modeling: Based on the collected multidimensional data, a student ability model is constructed. The student's abilities in dimensions such as pitch, rhythm, skill, and expressiveness are structurally modeled. Acoustic feature analysis, learning behavior analysis, and deep learning algorithms are used to generate a dynamic student model that reflects the student's current ability status and ability change trend. S3. Course Content Modeling: Perform structured semantic modeling of music course content, decompose the knowledge points, skill elements, skill dependencies, difficulty levels and teaching objectives of the course and establish a course knowledge graph; S4. Dynamic Matching Calculation: Executes a dynamic matching algorithm between the student model and the course knowledge graph. Based on the difference between the student's ability vector and the course skill requirement vector, the learning path dependency analysis, and the adaptive matching strategy based on reinforcement learning or graph reasoning, it generates course recommendation results and personalized learning paths that best match the student's current ability. S5. Feedback and Path Adjustment: Real-time monitoring of students' performance data and learning behavior data during subsequent learning processes, and dynamic adjustment of learning paths based on changes in students' ability models.

[0007] Preferably, in step S1, the specific steps for learning data acquisition are as follows: S1.1 The terminal device records the student's performance audio through the audio acquisition module, obtains pitch, rhythm and timbre characteristic data, and synchronously records practice duration, number of repetitions, interruption frequency and time of error operation to form practice behavior data. It also extracts historical course completion records, assessment scores, skill mastery annotations and learning curve trends to form historical learning data, and captures platform access path, dwell time, click sequence and login time to form interactive behavior data. S1.2 The terminal device performs format conversion on the performance audio, removes noise through digital filtering algorithms, extracts pitch deviation values, rhythm stability and timbre spectrum distribution, converts them into structured feature vectors, aligns multi-source data according to timestamps, establishes associated indexes based on student identifiers, completes structured integration, and transmits standardized data to the cloud storage center in real time through encryption protocols, providing a data foundation for subsequent capability modeling.

[0008] Preferably, in step S2, the specific steps for capability state modeling are as follows: S2.1 Perform acoustic analysis on the performance audio, calculate pitch deviation value through pitch detection algorithm and quantify it into pitch index, extract beat stability and speed accuracy through rhythm analysis algorithm and quantify it into rhythm index, identify timbre changes and dynamic control through spectrum analysis and quantify them into technical index, evaluate emotional expression and performance integrity through melody analysis and quantify them into expressiveness index, statistically analyze practice frequency, duration and error correction speed to form learning behavior index, extract historical progress curve and performance growth rate to form growth index, and summarize to form a multi-dimensional ability feature vector group; S2.2 Input the ability feature vector group into the deep neural network, and use multi-layer mapping to weight and fuse each ability dimension to generate an ability distribution matrix that includes score value, confidence interval and dimensional correlation strength. Use a long short-term memory network to predict the trend of ability change, use a clustering algorithm to identify low-scoring dimensions and mark weak links, integrate ability snapshots, diagnostic information and growth path to build a dynamic student model, extract feature vectors after new data is collected and compare them with historical data, adjust network parameters to update the model, and pass the generated model to the course matching module.

[0009] Preferably, in step S3, the specific steps for course content modeling are as follows: S3.1 Obtain the teaching documents of the music course, and break them down layer by layer into the smallest teaching unit according to the course framework, unit music education and training management methods, knowledge modules, and skill training items. Extract the core knowledge points, skill elements and skill types of each unit, identify the prerequisite skills and parallel auxiliary content through the dependency analysis algorithm, establish a skill dependency chain, and quantify and map the scores according to the skill complexity, knowledge depth and performance difficulty to the difficulty level standard. Transform the teaching objectives into quantitative indicators such as pitch error range, rhythm stability threshold and skill completion percentage, and form a structured course element dataset. S3.2. Teaching granular units are mapped to knowledge graph nodes. Each node stores attributes such as a unique identifier, knowledge point text, skill category, difficulty level value, teaching objective indicators, and estimated learning time. Directed edges are created between nodes based on skill dependency chains, and dependency types are labeled to form a single-course skill association network. Cross-course node pairs are identified and cross-course edges are established through knowledge point text matching and skill tag comparison. Association strength coefficients and knowledge transfer directions are labeled, and nodes are assigned multi-dimensional attributes such as difficulty weight, duration weight, and skill coverage vector. The knowledge graph data is stored in a graph structure format and an attribute index is established.

[0010] Preferably, step S3.1 specifically includes the following steps: S3.11. Obtain teaching documents and decompose them to obtain hierarchical elements such as course framework, unit music education and training management methods, knowledge modules, and skill training items. Extract the knowledge points and skill elements of each teaching unit. Through the skill evolution tracing algorithm, recursively analyze the source path of the basic ability of the unit's skills upward and recursively predict the development direction of higher-order abilities downward. Construct a vertical skill evolution spectrum. Cross-compare the skill elements of different units at the same difficulty level to identify unit groups that share the same underlying skill foundation. Mine the implicit skill dependency relationships that are not explicitly labeled, calculate the dependency strength coefficient, and form a multi-level skill dependency network that includes explicit dependency edges, implicit dependency edges, and dependency strength levels. S3.12. Introduce a cognitive load threshold model to decompose the learning process into three dimensions: information input load, working memory load, and skill integration load. Calculate the weighted sum of parameters such as the number of notes to be received per unit time. When the load value exceeds the preset threshold, perform a secondary decomposition of the unit and insert a skill consolidation buffer unit. Recalculate the comprehensive difficulty score and perform difficulty stratification based on the cognitive load distribution curve. Establish an efficiency function model of goal achievement and cognitive load input. Adaptively calibrate the teaching objectives according to the contribution weight to form a dynamic target indicator system and integrate it into an enhanced curriculum element dataset.

[0011] Preferably, step S3.2 specifically includes the following steps: S3.21. Map the teaching granular units to basic nodes and set fields such as identification code, knowledge point text, skill classification, and difficulty level value to construct an abstract node layer. Use an aggregation algorithm to classify basic nodes from the same skill evolution lineage into skill group nodes and record the evolution and inheritance strength. Use a word embedding algorithm to convert knowledge point text into semantic vectors. Fuse the semantic vectors with the multi-label encoding of skill classification to generate a deep semantic representation vector. Calculate the cosine similarity of semantic vectors between nodes. When the similarity exceeds the threshold, establish semantic association edges to form an implicit association subgraph. Based on the cognitive load distribution and target calibration results, encode numerical attributes such as difficulty value and duration into dynamic embedding vectors containing current state, change trend, elastic range, and path distribution, and store them. S3.22. Based on the skill dependency network, create directed edges between basic nodes and label the dependency type and dependency strength coefficient to form a single-course skill association subgraph. Calculate the semantic vector similarity, skill label intersection ratio, and evolutionary lineage overlap ratio across course nodes to obtain the comprehensive matching degree. Introduce a skill transfer strength model and calculate the transfer strength coefficient and transfer direction based on difficulty span, compatibility, cognitive load difference, and duration ratio. When the transfer strength exceeds the threshold, establish cross-course edges. Calculate node in-degree and out-degree, identify critical path bottleneck nodes and insert buffer nodes, identify redundant edges and perform pruning, calculate node importance weight, reachability weight, coverage vector, and load balancing weight, store the graph data in a graph structure format, and establish a multi-dimensional index.

[0012] Preferably, in step S4, the specific steps of dynamic matching calculation are as follows: S4.1 Calculate the difference between the student's ability vector and the node's skill requirement vector to identify the ability gap. Set the compensation priority coefficient at the level of the evolution chain according to the gap. Calculate the node's compensation contribution and multiply it by the priority coefficient to obtain the weighted compensation total score. Select the top 30% of nodes, check for cognitive load overload and remove risk nodes. Check the integrity of the prerequisite dependencies. Complete nodes enter the candidate set. Calculate the supplementary path cost for nodes that do not meet the dependencies. Nodes below the threshold are used as expansion candidates. S4.2 Perform multi-path reasoning on candidate nodes. When the out-degree is greater than the threshold and the standard deviation of the intensity coefficient is less than the similar threshold, expand in parallel. Test constraints such as difficulty gradient, cognitive load, and skill coverage. Calculate smoothness, synergistic gain, compensation integrity, and duration fit as immediate rewards. Calculate the contribution of the endpoint distance as a delayed reward. Accumulate discounts to obtain the overall reward value. Adjust the path granularity according to practice preferences. Calculate the cost-benefit ratio of expanding candidate nodes and generate hierarchical recommendation results.

[0013] Preferably, step S4.1 specifically includes the following steps: S4.11. Calculate the difference between the student's ability vector and the node's skill requirement vector to identify the gap dimension. Based on the gap, set the compensation priority coefficient at the evolution chain level. Calculate the transmission influence weight of the gap at the basic layer and add it to the priority coefficient. Calculate the node coverage overlap, fit coefficient, and evolutionary inheritance strength, and sum them up to obtain the compensation contribution. Multiply it with the priority coefficient and add it to obtain the weighted compensation total score. Identify the gap's dependency relationship and reduce the effectiveness weight of subsequent gaps. Select the top 30% of nodes to enter the candidate set. S4.12 Extract the cognitive load of nodes and sum them up. Calculate the student's capacity margin. Adjust the margin according to the error rate trend. Remove nodes when the margin is exceeded. Check the integrity of the prerequisite dependencies. Recursively calculate the dependency chain depth. Remove nodes when the depth exceeds the threshold. Calculate the cost of the supplementary path. Build extended candidate options when the depth is below the threshold. Nodes with complete dependencies enter the second stage candidate set.

[0014] Preferably, step S4.2 specifically includes the following steps: S4.21. Read candidate nodes and perform multi-path reasoning. When the out-degree exceeds the threshold and the standard deviation of the intensity coefficient is less than the nearest threshold, dynamically adjust the number of parallel paths according to the importance weight of the nodes. Select the corresponding edge according to the intensity coefficient to create a path and iterate. Introduce multi-level constraint verification. Difficulty gradient verification corrects the safety threshold according to the actual ability improvement rate. Cognitive load verification introduces a time decay model to calculate the cumulative load. Skill coverage verification identifies complementary skill pairs to calculate the coverage matching degree. When the constraint is violated, the path priority weight is reduced. Paths higher than the median are selected to enter the evaluation stage. S4.22 Calculate path evaluation metrics: smoothness is the inverse of gradient variance minus the negative penalty; collaborative gain is the mean of inter-node transmission strength; compensation integrity is the weighted sum of coverage and compensation depth; duration fit is the inverse of occupancy ratio multiplied by elasticity gain; immediate reward is the weighted sum of the four metrics; delayed reward is the inverse of distance multiplied by path diversity; total reward is the sum of immediate reward and delayed reward multiplied by a discount factor; adjust path granularity according to practice preferences; calculate the cost-benefit ratio of expanded candidates; generate hierarchical recommendation results with added extended information; and output the results in a weighted order based on priority and reward.

[0015] Compared with the prior art, the beneficial effects of the present invention are: 1. This solution acquires objective and comprehensive learning data through the S1 multi-dimensional data acquisition system; quantifies and standardizes ability assessment through S2 acoustic feature analysis and deep learning modeling; makes the logical structure of course content computable through S3 course knowledge graph modeling; personalizes course recommendations through S4 multi-dimensional difference calculation and adaptive matching strategies; and achieves closed-loop optimization of the teaching process through S5 real-time monitoring and dynamic correction mechanisms. It transforms qualitative teaching decisions into quantifiable and optimizable mathematical models, and static course allocation into an adaptive system that dynamically responds to changes in student abilities. This solution enhances the personalization and teaching efficiency of music education, effectively solving core problems in traditional teaching such as insufficient individualized instruction, rigid learning paths, and fluctuating teaching quality. It reduces reliance on high-level teachers, improves the efficiency of teaching resource utilization, shortens the time students take to achieve learning goals, and enhances the learning experience and teaching effectiveness.

[0016] 2. Step S3.1, through the construction of a multi-level skill dependency network (S3.11) and adaptive optimization of cognitive load (S3.12), achieves a deep analytical process from coarse-grained curriculum decomposition to refined cognitive adaptation. In traditional music teaching, curriculum design mainly relies on teacher experience for content organization and difficulty classification. Skill dependencies are only marked with explicit prerequisite course requirements, while a large number of implicit skill relationships are hidden in teaching practice and difficult to systematically extract. The difficulty classification criteria are highly subjective and lack quantitative consideration of students' cognitive load, leading to a significant discrepancy between curriculum design and students' actual learning abilities. This step constructs a vertical skill spectrum through a skill evolution tracing algorithm, revealing the evolutionary path of skills from basic to advanced levels. Through cross-comparison and implicit dependency mining, it identifies cross-unit skill relationships that are difficult to discover using traditional methods. By quantifying dependency strength, it distinguishes between strong and weak dependencies, providing a priority basis for path planning.

[0017] This application also provides a music education and training management system; please refer to [link / reference]. Figure 2 It includes a learning data acquisition module, a capability status modeling module, a course content modeling module, a dynamic matching calculation module, and a feedback and path adjustment module; The learning data acquisition module is used to collect students' performance audio technical characteristics, practice behavior, historical learning records, and system interaction data. The ability status modeling module is used to quantify and model students’ abilities in pitch, rhythm, skill and expressiveness and generate dynamic student models. The course content modeling module is used to decompose the knowledge points, skill dependencies, and difficulty levels of the course and construct a course knowledge graph. The dynamic matching calculation module is used to calculate the difference between the student's ability vector and the course skill requirement vector and generate a personalized learning path. The feedback and path adjustment module is used to monitor student learning data and dynamically adjust the learning path according to changes in the ability model. Attached Figure Description

[0018] Figure 1 This is a diagram illustrating the method steps of the present invention.

[0019] Figure 2 This is a system flowchart of the present invention. Detailed Implementation

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] Example 1: Please refer to Figure 1 A music education and training management method, the specific steps of which are as follows: S1. Learning Data Collection: Collect multi-dimensional learning data of students, including technical feature data of performance audio, practice behavior data, historical learning record data and system interaction behavior data, and transmit them to the teaching management platform in real time through terminal devices; S2. Ability Status Modeling: Based on the collected multidimensional data, a student ability model is constructed. The student's abilities in dimensions such as pitch, rhythm, skill, and expressiveness are structurally modeled. Acoustic feature analysis, learning behavior analysis, and deep learning algorithms are used to generate a dynamic student model that reflects the student's current ability status and ability change trend. S3. Course Content Modeling: Perform structured semantic modeling of music course content, decompose the knowledge points, skill elements, skill dependencies, difficulty levels and teaching objectives of the course and establish a course knowledge graph; S4. Dynamic Matching Calculation: Executes a dynamic matching algorithm between the student model and the course knowledge graph. Based on the difference between the student's ability vector and the course skill requirement vector, the learning path dependency analysis, and the adaptive matching strategy based on reinforcement learning or graph reasoning, it generates course recommendation results and personalized learning paths that best match the student's current ability. S5. Feedback and Path Adjustment: Real-time monitoring of students' performance data and learning behavior data during subsequent learning processes, and dynamic adjustment of learning paths based on changes in students' ability models.

[0022] In this embodiment: S1 establishes a multi-dimensional data acquisition system covering performance audio technical characteristics, practice behavior patterns, historical learning trajectories, and system interactive operations. This data is transmitted in real-time to the teaching management platform via terminal devices, solving the problems of data bias and lag caused by relying solely on teacher classroom observation in traditional music teaching. The systematic acquisition of multi-source data provides objective quantitative evidence for subsequent ability assessments, avoiding assessment biases caused by data gaps. Simultaneously, the real-time transmission mechanism ensures data timeliness, enabling teaching decisions to be adjusted based on the students' latest status, thus improving the scientific nature and responsiveness of teaching management.

[0023] S2 quantifies students' objective performance in technical dimensions such as pitch and rhythm through acoustic feature analysis. It combines learning behavior data mining to explore learning engagement and cognitive pattern characteristics, and utilizes deep learning algorithms to integrate multi-dimensional data to construct a dynamic student model. This overcomes the limitations of traditional music education where ability assessment relies on subjective teacher judgment and lacks standardized criteria. The model not only accurately depicts the distribution of students' current ability status but also predicts ability development trends and identifies potential learning obstacles, achieving a shift from static assessment to dynamic tracking. This provides a quantitative basis for implementing differentiated teaching strategies and effectively solves the problem of insufficient targeting of teaching plans caused by the crude assessment in traditional teaching.

[0024] S3 employs deep, structured modeling of music course content. Through a course knowledge graph, it clarifies the prerequisite dependencies, difficulty levels, and connections between learning objectives among knowledge points, making the previously implicit course logic explicit and computable. This overcomes the shortcomings of fragmented knowledge and ambiguous logical relationships in traditional course organization. This modeling approach provides a clear knowledge topology foundation for subsequent intelligent matching, ensuring accurate alignment between recommended courses and students' abilities. It avoids the polarization of learning difficulties or resource waste caused by inappropriate course selection, thus improving the effective utilization of course resources.

[0025] S4 accurately identifies students' skill gaps and strengths by calculating the multi-dimensional differences between student ability vectors and course skill requirement vectors. Combined with learning path dependency analysis, it ensures that recommended courses align with cognitive development patterns. An adaptive matching strategy dynamically optimizes the recommendation results, overcoming the limitations of traditional teaching methods that rely on empirical rules for course allocation. The generated learning paths guarantee both the systematic and coherent nature of knowledge while specifically addressing students' weaknesses. This represents a leap from coarse-grained grouping to fine-grained personalized recommendations, improving learning efficiency and goal achievement rates. It effectively solves the core problems of rigid and poorly adaptable learning paths in traditional teaching.

[0026] S5 continuously monitors changes in students' performance and behavioral data during the learning process, updates the ability model in real time, and dynamically corrects the learning path. This achieves closed-loop management of the teaching process, overcoming the rigidity of traditional teaching methods where path planning is difficult to adjust once determined. When a student's ability fluctuates or new learning obstacles arise, the system can promptly adjust the course difficulty, rearrange the learning order, or supplement specialized training resources. This ensures that the learning path always maintains an optimal match with the student's current ability level, enhancing the flexibility and robustness of the teaching plan. It effectively prevents teaching mismatch caused by ability model drift and guarantees the continuity and stability of personalized teaching effects.

[0027] This invention proposes a music education and training management method that achieves end-to-end technological innovation, encompassing data collection, model building, content organization, intelligent matching, and dynamic adjustment. Traditional music teaching primarily relies on teachers' subjective experience for ability assessment and curriculum arrangement, resulting in systemic flaws such as inconsistent assessment standards, poor adaptability of teaching plans, and an inability to dynamically respond to changes in student abilities. This leads to teaching quality being heavily dependent on individual teacher performance and difficult to replicate on a large scale. This solution acquires objective and comprehensive learning data through the S1 multi-dimensional data acquisition system; quantifies and standardizes ability assessment through S2 acoustic feature analysis and deep learning modeling; makes the logical structure of course content computable through S3 course knowledge graph modeling; personalizes course recommendations through S4 multi-dimensional difference calculation and adaptive matching strategies; and achieves closed-loop optimization of the teaching process through S5 real-time monitoring and dynamic correction mechanisms. It transforms qualitative teaching decisions into quantifiable and optimizable mathematical models and static curriculum allocation into an adaptive system that dynamically responds to changes in student abilities. This solution enhances the personalization and teaching efficiency of music education, effectively addresses core issues in traditional teaching such as insufficient individualized instruction, rigid learning paths, and fluctuating teaching quality. It reduces reliance on high-level teachers, improves the efficiency of teaching resource utilization, shortens the time it takes for students to achieve learning goals, and enhances the learning experience and teaching effectiveness.

[0028] Example 2: Please refer to Figure 1 In step S1, the specific steps for learning data collection are as follows: S1.1 The terminal device records the student's performance audio through the audio acquisition module, obtains pitch, rhythm and timbre characteristic data, and synchronously records practice duration, number of repetitions, interruption frequency and time of error operation to form practice behavior data. It also extracts historical course completion records, assessment scores, skill mastery annotations and learning curve trends to form historical learning data, and captures platform access path, dwell time, click sequence and login time to form interactive behavior data. S1.2 The terminal device performs format conversion on the performance audio, removes noise through digital filtering algorithms, extracts pitch deviation values, rhythm stability and timbre spectrum distribution, converts them into structured feature vectors, aligns multi-source data according to timestamps, establishes associated indexes based on student identifiers, completes structured integration, and transmits standardized data to the cloud storage center in real time through encryption protocols, providing a data foundation for subsequent capability modeling.

[0029] In this embodiment: S1.1 The audio acquisition module acquires data on the pitch, rhythm, and timbre characteristics of the student's performance, and simultaneously records the practice duration, number of repetitions, interruption frequency, and error operation times to form practice behavior data. Historical course completion records, assessment scores, skill mastery annotations, and learning curve trends are extracted to form historical learning data. Platform access paths, dwell time, click sequences, and login times are captured to form interactive behavior data. This solves the problem of incomplete information caused by the single data source and reliance on teacher classroom observation in traditional music teaching. It provides a comprehensive data foundation covering technical performance, learning input, development trajectory, and behavioral preferences for subsequent ability modeling, ensuring the objectivity and accuracy of ability assessment.

[0030] S1.2 performs format conversion on the performance audio and removes noise using a digital filtering algorithm. It extracts pitch deviation values, rhythm stability, and timbre spectrum distribution, converting them into structured feature vectors. It performs time-series alignment of multi-source data by timestamp and establishes an associated index based on student identifiers to complete structured integration. It transmits standardized data to the cloud storage center in real time through an encryption protocol, solving problems such as heterogeneous original data formats, time sequence misalignment, and insufficient security. This ensures the consistency and comparability of data from different sources, providing standardized input data for subsequent modeling algorithms. At the same time, the real-time transmission mechanism ensures the timeliness and security of the data.

[0031] Step S1 establishes a multi-dimensional data acquisition system through S1.1 and completes data preprocessing and transmission through S1.2, realizing a complete process from raw data acquisition to standardized data storage. Traditional music teaching data acquisition mainly relies on teacher classroom observation and simple grade records, which suffers from drawbacks such as single data dimensions, strong subjectivity, and poor timeliness, making it difficult to comprehensively reflect students' true learning status. This step acquires objective performance technique data through the audio acquisition module, obtains complete behavioral and historical data through system recording, and acquires implicit learning preference data through interactive monitoring, constructing a multi-dimensional data system covering technical performance, learning engagement, development trajectory, and behavioral preferences, thus overcoming the limitations of traditional data acquisition methods.

[0032] Example 3: Please refer to Figure 1 In step S2, the specific steps for capability state modeling are as follows: S2.1 Perform acoustic analysis on the performance audio, calculate pitch deviation value through pitch detection algorithm and quantify it into pitch index, extract beat stability and speed accuracy through rhythm analysis algorithm and quantify it into rhythm index, identify timbre changes and dynamic control through spectrum analysis and quantify them into technical index, evaluate emotional expression and performance integrity through melody analysis and quantify them into expressiveness index, statistically analyze practice frequency, duration and error correction speed to form learning behavior index, extract historical progress curve and performance growth rate to form growth index, and summarize to form a multi-dimensional ability feature vector group; S2.2 Input the ability feature vector group into the deep neural network, and use multi-layer mapping to weight and fuse each ability dimension to generate an ability distribution matrix that includes score value, confidence interval and dimensional correlation strength. Use a long short-term memory network to predict the trend of ability change, use a clustering algorithm to identify low-scoring dimensions and mark weak links, integrate ability snapshots, diagnostic information and growth path to build a dynamic student model, extract feature vectors after new data is collected and compare them with historical data, adjust network parameters to update the model, and pass the generated model to the course matching module.

[0033] In this embodiment: S2.1 uses a pitch detection algorithm to calculate pitch deviation values ​​and quantifies them into pitch indicators; uses a rhythm analysis algorithm to extract beat stability and speed accuracy and quantifies them into rhythm indicators; uses spectrum analysis to identify timbre changes and dynamic control and quantifies them into skill indicators; uses melody analysis to evaluate emotional expression and performance completeness and quantifies them into expressiveness indicators; combines practice frequency, duration, and error correction speed to form learning behavior indicators; and extracts historical progress curves and performance growth rates to form growth indicators. This solves the problems of traditional music teaching ability assessment relying solely on teachers' subjective scoring, having a single assessment dimension, and inconsistent standards. It realizes the transformation from subjective evaluation to objective quantification and provides a comprehensive ability characteristic data foundation for subsequent modeling.

[0034] S2.2 inputs the ability feature vector group into a deep neural network and generates an ability distribution matrix containing score values, confidence intervals, and dimensional correlation strength through multi-layer mapping and weighted fusion. A long short-term memory network is used to predict the trend of ability changes. A clustering algorithm is used to identify low-scoring dimensions and mark weak links. A dynamic student model is constructed by integrating ability snapshots, diagnostic information, and growth paths. When new data is collected, the network parameters are adjusted by feature comparison to update the model. This solves the problems of static ability assessment, inability to predict development trends, and difficulty in accurately locating weak links in traditional teaching. It realizes the transformation from static assessment to dynamic tracking and provides accurate ability status basis for subsequent course matching and path planning.

[0035] Step S2, through S2.1 multidimensional ability feature extraction and S2.2 dynamic student model construction, achieves a complete transformation from objective data to an ability model. Traditional music teaching assessment primarily relies on teachers' subjective judgments based on classroom performance and exam scores, which suffers from inconsistent assessment standards, one-sided assessment dimensions, and static assessment results, making it difficult to accurately reflect students' true ability status and development potential. This step, through acoustic analysis technology, transforms performance audio into multiple objective quantitative indicators such as pitch, rhythm, technique, and expressiveness. Combined with learning behavior data and historical growth data, it constructs a multidimensional feature system covering technical ability, learning engagement, and development potential, overcoming the subjectivity and one-sidedness limitations of traditional assessments.

[0036] Example 4: Please refer to Figure 1 In step S3, the specific steps for modeling the course content are as follows: S3.1 Obtain the teaching documents of the music course, and break them down layer by layer into the smallest teaching unit according to the course framework, unit music education and training management methods, knowledge modules, and skill training items. Extract the core knowledge points, skill elements and skill types of each unit, identify the prerequisite skills and parallel auxiliary content through the dependency analysis algorithm, establish a skill dependency chain, and quantify and map the scores according to the skill complexity, knowledge depth and performance difficulty to the difficulty level standard. Transform the teaching objectives into quantitative indicators such as pitch error range, rhythm stability threshold and skill completion percentage, and form a structured course element dataset. S3.2. Teaching granular units are mapped to knowledge graph nodes. Each node stores attributes such as a unique identifier, knowledge point text, skill category, difficulty level value, teaching objective indicators, and estimated learning time. Directed edges are created between nodes based on skill dependency chains, and dependency types are labeled to form a single-course skill association network. Cross-course node pairs are identified and cross-course edges are established through knowledge point text matching and skill tag comparison. Association strength coefficients and knowledge transfer directions are labeled, and nodes are assigned multi-dimensional attributes such as difficulty weight, duration weight, and skill coverage vector. The knowledge graph data is stored in a graph structure format and an attribute index is established.

[0037] In this embodiment: S3.1 The teaching documents of the music course are broken down layer by layer into the smallest teaching unit according to the course framework, units, knowledge modules, and skill training items. The core knowledge points, skill elements and skill types of each unit are extracted. The prerequisite skills and parallel auxiliary content are identified by the dependency analysis algorithm to establish a skill dependency chain. The skill complexity, knowledge depth and performance difficulty are quantitatively scored and mapped to the difficulty level standard. The teaching objectives are transformed into quantitative indicators such as pitch error range, rhythm stability threshold and skill completion percentage. This solves the problems of knowledge fragmentation, implicit dependency relationship and fuzzy difficulty standard in traditional course organization. It makes the logical structure of the course content explicit and computable, and provides a standardized course element data foundation for subsequent intelligent matching.

[0038] S3.2 maps teaching granular units to knowledge graph nodes and stores attributes such as unique identifiers, knowledge point texts, skill categories, difficulty level values, teaching objective indicators, and estimated learning time. It creates directed edges between nodes based on skill dependency chains and labels the dependency types to form a single-course skill association network. Through knowledge point text matching and skill tag comparison, it identifies cross-course node pairs and establishes cross-course edges, labeling the association strength coefficient and knowledge transfer direction. It assigns nodes multi-dimensional attributes such as difficulty weight, duration weight, and skill coverage vector. This solves the problems of missing inter-course relationships, difficulty in knowledge reuse, and uncomputable learning paths in traditional course organization. It structures the networked relationships of course content and supports graph algorithm processing, providing a computable knowledge topology foundation for subsequent path planning and dynamic matching.

[0039] Step S3, through S3.1 structured analysis of course elements and S3.2 construction of a course knowledge graph, achieves a complete transformation from unstructured teaching documents to a computable knowledge graph. In traditional music teaching, course content is mainly organized in text-based syllabi or textbook chapters. Information such as dependencies between knowledge points, difficulty levels, and teaching objectives are implicit in the textual descriptions, lacking unified organizational standards and quantitative expression. This leads to course selection relying primarily on teachers' experience and judgment, making precise personalized matching difficult. This step breaks down the course into the smallest teaching units layer by layer, explicitly reveals the pre-dependencies and parallel connections between knowledge points through dependency analysis algorithms, transforms vague difficulty descriptions into comparable numerical standards through quantitative scoring, and converts abstract teaching requirements into measurable skill indicators through objective quantification, thus overcoming the unstructured limitations of traditional course organization.

[0040] Example 5: Please refer to Figure 1 The specific steps of step S3.1 are as follows: S3.11. Obtain teaching documents and decompose them to obtain hierarchical elements such as course framework, unit music education and training management methods, knowledge modules, and skill training items. Extract the knowledge points and skill elements of each teaching unit. Through the skill evolution tracing algorithm, recursively analyze the source path of the basic ability of the unit's skills upward and recursively predict the development direction of higher-order abilities downward. Construct a vertical skill evolution spectrum. Cross-compare the skill elements of different units at the same difficulty level to identify unit groups that share the same underlying skill foundation. Mine the implicit skill dependency relationships that are not explicitly labeled, calculate the dependency strength coefficient, and form a multi-level skill dependency network that includes explicit dependency edges, implicit dependency edges, and dependency strength levels. S3.12. Introduce a cognitive load threshold model to decompose the learning process into three dimensions: information input load, working memory load, and skill integration load. Calculate the weighted sum of parameters such as the number of notes to be received per unit time. When the load value exceeds the preset threshold, perform a secondary decomposition of the unit and insert a skill consolidation buffer unit. Recalculate the comprehensive difficulty score and perform difficulty stratification based on the cognitive load distribution curve. Establish an efficiency function model of goal achievement and cognitive load input. Adaptively calibrate the teaching objectives according to the contribution weight to form a dynamic target indicator system and integrate it into an enhanced curriculum element dataset.

[0041] In this embodiment: S3.11 decomposes the teaching document into hierarchical elements such as course framework, units, knowledge modules, and skill training items. Through the skill evolution tracing algorithm, it recursively analyzes the source path of basic abilities of unit skills upward and recursively predicts the development direction of higher-order abilities downward to construct a vertical skill evolution spectrum. It cross-compares the skill elements of different units at the same difficulty level to identify unit groups that share the same underlying skill foundation, mines implicit skill dependencies that are not explicitly labeled, and calculates the dependency strength coefficient. This solves the problems in traditional course organization, such as only labeling explicit prerequisite courses, missing implicit skill associations, and unclear skill evolution paths. It forms a multi-level skill dependency network containing explicit dependency edges, implicit dependency edges, and dependency strength levels, providing a complete skill association foundation for subsequent path planning.

[0042] S3.12 introduces a cognitive load threshold model, decomposing the learning process into three dimensions: information input load, working memory load, and skill integration load. It calculates the weighted sum of parameters such as the number of notes to be received per unit of time. When the load value exceeds a preset threshold, the unit is further decomposed and a skill consolidation buffer unit is inserted. The overall difficulty score is recalculated, and difficulty stratification is performed based on the cognitive load distribution curve. An efficiency function model of goal achievement and cognitive load input is established, and the teaching objectives are adaptively calibrated based on contribution weights. This solves the problems in traditional curriculum design where difficulty division relies solely on subjective judgment, fails to consider students' cognitive load capacity, and results in a mismatch between teaching objectives and actual input. It ensures that the difficulty design of course units conforms to students' cognitive patterns and improves learning efficiency.

[0043] Step S3.1, through the construction of a multi-level skill dependency network (S3.11) and adaptive optimization of cognitive load (S3.12), achieves a deep analytical process from coarse-grained curriculum decomposition to refined cognitive adaptation. In traditional music teaching, curriculum design primarily relies on teacher experience for content organization and difficulty classification. Skill dependencies are only marked with explicit prerequisite requirements, while numerous implicit skill relationships remain hidden in teaching practice and are difficult to systematically extract. Difficulty classification standards are highly subjective and lack quantitative consideration of students' cognitive load, leading to significant discrepancies between curriculum design and students' actual learning abilities. This step constructs a vertical skill spectrum using a skill evolution tracing algorithm, revealing the evolutionary path of skills from basic to advanced levels. Through cross-comparison and implicit dependency mining, it identifies cross-unit skill relationships that are difficult to discover using traditional methods. Quantifying dependency strength distinguishes between strong and weak dependencies, providing a priority basis for path planning.

[0044] Example 6: Please refer to Figure 1 The specific steps of step S3.2 are as follows: S3.21. Map the teaching granular units to basic nodes and set fields such as identification code, knowledge point text, skill classification, and difficulty level value to construct an abstract node layer. Use an aggregation algorithm to classify basic nodes from the same skill evolution lineage into skill group nodes and record the evolution and inheritance strength. Use a word embedding algorithm to convert knowledge point text into semantic vectors. Fuse the semantic vectors with the multi-label encoding of skill classification to generate a deep semantic representation vector. Calculate the cosine similarity of semantic vectors between nodes. When the similarity exceeds the threshold, establish semantic association edges to form an implicit association subgraph. Based on the cognitive load distribution and target calibration results, encode numerical attributes such as difficulty value and duration into dynamic embedding vectors containing current state, change trend, elastic range, and path distribution, and store them. S3.22. Based on the skill dependency network, create directed edges between basic nodes and label the dependency type and dependency strength coefficient to form a single-course skill association subgraph. Calculate the semantic vector similarity, skill label intersection ratio, and evolutionary lineage overlap ratio across course nodes to obtain the comprehensive matching degree. Introduce a skill transfer strength model and calculate the transfer strength coefficient and transfer direction based on difficulty span, compatibility, cognitive load difference, and duration ratio. When the transfer strength exceeds the threshold, establish cross-course edges. Calculate node in-degree and out-degree, identify critical path bottleneck nodes and insert buffer nodes, identify redundant edges and perform pruning, calculate node importance weight, reachability weight, coverage vector, and load balancing weight, store the graph data in a graph structure format, and establish a multi-dimensional index.

[0045] In this embodiment: S3.21, teaching granular units are mapped to basic nodes and fields such as identifier code, knowledge point text, skill classification, and difficulty level value are set. An aggregation algorithm is used to group basic nodes from the same skill evolution lineage into skill group nodes and record the intensity of evolutionary inheritance, constructing an abstract node layer from fine-grained to coarse-grained. This mechanism solves the problems of single node representation, lack of hierarchical structure, and difficulty in grasping the skill evolution trajectory from a macro perspective in traditional course organization. It enables the knowledge graph to support both fine-grained precise matching and coarse-grained global analysis, improving the graph's expressive power and application flexibility.

[0046] A word embedding algorithm is used to convert knowledge point text into semantic vectors and fuse them with multi-label encoding for skill classification to generate deep semantic representation vectors. Semantic association edges are established by calculating the cosine similarity between semantic vectors of nodes, forming an implicit association subgraph, thus uncovering cross-unit knowledge associations that are difficult to discover using traditional methods. Based on cognitive load distribution and target calibration results, numerical attributes such as difficulty and duration are encoded into dynamic embedding vectors that include current state, trend, and elasticity range. This overcomes the limitations of traditional static attribute representation, enabling node attributes to reflect both current features and dynamic evolution patterns, providing a rich semantic and dynamic feature foundation for subsequent graph-based intelligent reasoning.

[0047] S3.22 creates directed edges between basic nodes based on the skill dependency network and labels the dependency type and dependency strength coefficient, forming a single-course skill association subgraph. This clarifies the skill evolution path and dependency strength within the course, providing a clear logical foundation for path planning. A comprehensive matching degree is obtained by calculating the semantic vector similarity, skill tag intersection ratio, and evolutionary lineage overlap ratio across course nodes. A skill transfer strength model is introduced to calculate the transfer strength coefficient and transfer direction based on difficulty span, compatibility, cognitive load difference, and duration ratio. When the transfer strength exceeds a threshold, cross-course edges are established, breaking the isolation between courses in traditional course organization and enabling cross-course knowledge flow and reuse, providing students with richer learning path choices.

[0048] By identifying bottleneck nodes on critical paths and inserting buffer nodes through computational node in-degree and out-degree calculations, the coherence and feasibility of learning paths are optimized. Redundant edges are identified and pruned, improving the computational and storage efficiency of the knowledge graph. Multi-dimensional attributes such as node importance weights, reachability weights, coverage vectors, and load balancing weights provide rich evaluation dimensions for subsequent path recommendations. Storing the knowledge graph data in a graph structure format and establishing a multi-dimensional index ensures efficient retrieval and rapid computation, significantly improving the practicality and computational performance of the knowledge graph.

[0049] Step S3.2 achieves deep integration from discrete course units to a unified knowledge network through multi-level node representation, semantic association mining, cross-course network construction, and graph structure optimization. In traditional music teaching, course content is organized as independent course units, with single node representation methods and static attributes. There is a lack of systematic association mechanisms between different courses, making it difficult to discover and utilize implicit semantic associations between knowledge points. The unoptimized graph structure leads to low computational efficiency. These problems severely restrict the cross-course reuse of course resources and the generation of personalized learning paths. This step achieves multi-level representation from fine-grained to coarse-grained by constructing an abstract node layer. Deep semantic vectors are generated through word embedding and multi-label encoding fusion to capture the semantic information and skill features of knowledge points. Implicit association edges are established through semantic similarity calculation to mine cross-unit knowledge associations. Dynamic embedding vector encoding enables node attributes to reflect both the current state and the trend of change. Cross-course edges are established through a skill transfer strength model to break down the silos between courses. The graph structure is optimized by identifying bottleneck nodes and redundant edges. The computational efficiency and application value of the graph are improved by calculating multi-dimensional weights and establishing multi-dimensional indexes. This step integrates scattered course resources into a unified knowledge network, transforms static course attributes into dynamic embedded representations, and converts implicit knowledge associations into computable graph structures. It effectively solves the problems of isolated resources, missing associations, simple expressions, and inefficient structures in traditional course organization, and provides a high-quality knowledge topology foundation for subsequent intelligent matching and dynamic path planning based on graph algorithms, thereby improving the organization efficiency and utilization value of course resources.

[0050] Example 7: Please refer to Figure 1 In step S4, the specific steps for dynamic matching calculation are as follows: S4.1 Calculate the difference between the student's ability vector and the node's skill requirement vector to identify the ability gap. Set the compensation priority coefficient at the level of the evolution chain according to the gap. Calculate the node's compensation contribution and multiply it by the priority coefficient to obtain the weighted compensation total score. Select the top 30% of nodes, check for cognitive load overload and remove risk nodes. Check the integrity of the prerequisite dependencies. Complete nodes enter the candidate set. Calculate the supplementary path cost for nodes that do not meet the dependencies. Nodes below the threshold are used as expansion candidates. S4.2 Perform multi-path reasoning on candidate nodes. When the out-degree is greater than the threshold and the standard deviation of the intensity coefficient is less than the similar threshold, expand in parallel. Test constraints such as difficulty gradient, cognitive load, and skill coverage. Calculate smoothness, synergistic gain, compensation integrity, and duration fit as immediate rewards. Calculate the contribution of the endpoint distance as a delayed reward. Accumulate discounts to obtain the overall reward value. Adjust the path granularity according to practice preferences. Calculate the cost-benefit ratio of expanding candidate nodes and generate hierarchical recommendation results.

[0051] In this embodiment: S4.1 identifies ability gaps by calculating the difference between the student's ability vector and the node's skill requirement vector. Based on the gap, a compensation priority coefficient is set at the level of the evolutionary chain. The node's compensation contribution is multiplied by the priority coefficient to obtain a weighted compensation total score, and the top 30% of nodes are selected. Cognitive overload is checked and risk nodes are removed. The integrity of prerequisite dependencies is checked and complete nodes are included in the candidate set. For nodes that do not meet the dependencies, the supplementary path cost is calculated, and those below the threshold are used as extended candidates. This solves the problems of traditional course recommendations that rely only on historical grades or simple rule matching, cannot accurately locate ability gaps, do not consider students' cognitive load capacity, and ignore knowledge dependencies. It achieves accurate screening based on ability differences and ensures that the recommended nodes are both in line with the student's ability status and meet the rationality of the learning path.

[0052] S4.2 performs multi-path reasoning on candidate nodes. When the out-degree is greater than the threshold and the standard deviation of the intensity coefficient is less than the nearest threshold, it expands in parallel. It examines constraints such as difficulty gradient, cognitive load, and skill coverage. It calculates smoothness, synergistic gain, compensation integrity, and duration fit as immediate rewards, and calculates the endpoint distance contribution as a delayed reward and discounts and accumulates it to obtain the overall reward value. It adjusts the path granularity according to practice preferences, calculates the cost-benefit ratio of expanded candidate options, and generates hierarchical recommendation results. It solves the problems of single learning paths, lack of diversity of choices, failure to comprehensively consider short-term effects and long-term goals, and lack of personalization in path recommendations in traditional teaching. It realizes intelligent path generation based on multi-dimensional constraints and dual reward mechanism, provides a hierarchical scheme of main recommended path and alternative path, and improves the accuracy and adaptability of recommendation results.

[0053] Step S4, through S4.1 ability gap identification and candidate node screening and S4.2 multi-path reasoning and hierarchical recommendation generation, achieves an intelligent matching process from student ability status to personalized learning paths. Traditional music teaching relies primarily on teachers' subjective judgment based on students' historical grades and classroom performance, or on simple rule-based matching methods for course allocation. This suffers from problems such as crude ability assessment, inaccurate gap identification, singular recommendation results, and a lack of systematic consideration of cognitive load and knowledge dependencies, leading to significant discrepancies between recommended courses and students' actual needs. This step accurately identifies students' gaps in various skill dimensions by calculating ability vector differences, ensures priority compensation for basic ability deficiencies by setting priority coefficients at the evolutionary chain level, comprehensively evaluates the relevance of nodes through weighted compensation total scores, ensures that recommended nodes are both within students' capacity and meet learning logic through cognitive load testing and pre-dependency integrity checks, and achieves intelligent compensation for missing dependencies through supplementary path cost calculation.

[0054] Example 8: Please refer to Figure 1 The specific steps of step S4.1 are as follows: S4.11. Calculate the difference between the student's ability vector and the node's skill requirement vector to identify the gap dimension. Based on the gap, set the compensation priority coefficient at the evolution chain level. Calculate the transmission influence weight of the gap at the basic layer and add it to the priority coefficient. Calculate the node coverage overlap, fit coefficient, and evolutionary inheritance strength, and sum them up to obtain the compensation contribution. Multiply it with the priority coefficient and add it to obtain the weighted compensation total score. Identify the gap's dependency relationship and reduce the effectiveness weight of subsequent gaps. Select the top 30% of nodes to enter the candidate set. S4.12 Extract the cognitive load of nodes and sum them up. Calculate the student's capacity margin. Adjust the margin according to the error rate trend. Remove nodes when the margin is exceeded. Check the integrity of the prerequisite dependencies. Recursively calculate the dependency chain depth. Remove nodes when the depth exceeds the threshold. Calculate the cost of the supplementary path. Build extended candidate options when the depth is below the threshold. Nodes with complete dependencies enter the second stage candidate set.

[0055] In this embodiment: S4.11 identifies the gap dimension by calculating the difference between the student's ability vector and the node's skill requirement vector. Based on the gap, a compensation priority coefficient is set at the evolutionary chain level. The transmission influence weight of the basic layer gap is calculated and accumulated to the priority coefficient. The compensation contribution is obtained by weighted summation of node coverage overlap, fit coefficient, and evolutionary inheritance strength. This is multiplied by the priority coefficient and accumulated to obtain the weighted compensation total score. The gap-to-dependency relationship is identified and the effectiveness weight of subsequent gaps is reduced. The top 30% of nodes are selected to enter the candidate set. This solves the problems of traditional course recommendation, which only identifies surface ability defects, does not consider the transmission influence of basic abilities on higher-order abilities, and lacks priority ranking for gap compensation. It achieves accurate positioning from surface gaps to deep roots and scientific screening from single scoring to multi-dimensional weighting, ensuring that recommended nodes can prioritize solving key ability defects.

[0056] S4.12 calculates the student's capacity margin by summing the cognitive load of nodes, adjusts the margin according to the error rate trend and removes nodes exceeding the margin, checks the integrity of prerequisite dependencies and recursively calculates the dependency chain depth, removes nodes when the depth exceeds a threshold, calculates the cost of supplementing the path and builds expanded candidate options when the cost is below the threshold, and includes nodes with complete dependencies in the second-stage candidate set. This solves the problems in traditional course recommendations, such as learning overload due to not considering the student's cognitive load capacity, learning difficulties due to ignoring prerequisite dependencies, and incomplete paths due to the lack of dependency supplementation mechanisms. It achieves safe screening based on dynamic adjustment of cognitive load and path integrity assurance based on recursive verification of dependencies, ensuring that the recommended nodes are both in line with the student's current capacity and meet the rationality of the learning logic.

[0057] Step S4.1, through S4.11 precise location of ability gaps and priority calculation of compensation, and S4.12 cognitive load constraints and dependency verification, realizes a two-layer filtering mechanism from coarse-grained ability matching to fine-grained safety screening. Traditional music teaching primarily relies on simple matching based on students' historical grades or current assessment results. This only identifies superficial deficiencies in a particular skill dimension, failing to trace the deeper root causes of these deficiencies. It does not consider the transmission impact of basic ability deficiencies on the development of higher-order abilities, lacks a priority ranking mechanism for ability gap compensation, and ignores students' cognitive load capacity and the completeness of knowledge dependencies. This results in insufficient targeted course recommendations, unreasonable learning paths, and students easily experiencing learning overload or inability to understand due to a lack of prior knowledge. This step accurately identifies gaps in various dimensions by calculating the difference in capability vectors, traces the root causes of gaps through evolutionary chain hierarchical analysis and sets priority coefficients, ensures priority is given to addressing fundamental capability deficiencies by calculating the transmission impact weight of gaps in the basic layer, comprehensively evaluates the relevance of nodes by calculating compensation contribution through multi-dimensional weighted calculation, and dynamically adjusts the weights of subsequent gaps by identifying gap dependencies to avoid duplicate compensation, thus achieving scientific priority ranking and accurate node selection.

[0058] Example 9: Please refer to Figure 1 The specific steps of step S4.2 are as follows: S4.21. Read candidate nodes and perform multi-path reasoning. When the out-degree exceeds the threshold and the standard deviation of the intensity coefficient is less than the nearest threshold, dynamically adjust the number of parallel paths according to the importance weight of the nodes. Select the corresponding edge according to the intensity coefficient to create a path and iterate. Introduce multi-level constraint verification. Difficulty gradient verification corrects the safety threshold according to the actual ability improvement rate. Cognitive load verification introduces a time decay model to calculate the cumulative load. Skill coverage verification identifies complementary skill pairs to calculate the coverage matching degree. When the constraint is violated, the path priority weight is reduced. Paths higher than the median are selected to enter the evaluation stage. S4.22 Calculate path evaluation metrics: smoothness is the inverse of gradient variance minus the negative penalty; collaborative gain is the mean of inter-node transmission strength; compensation integrity is the weighted sum of coverage and compensation depth; duration fit is the inverse of occupancy ratio multiplied by elasticity gain; immediate reward is the weighted sum of the four metrics; delayed reward is the inverse of distance multiplied by path diversity; total reward is the sum of immediate reward and delayed reward multiplied by a discount factor; adjust path granularity according to practice preferences; calculate the cost-benefit ratio of expanded candidates; generate hierarchical recommendation results with added extended information; and output the results in a weighted order based on priority and reward.

[0059] In this embodiment: S4.21 Read candidate nodes and perform multi-path reasoning. When the out-degree exceeds the threshold and the standard deviation of the intensity coefficient is less than the similar threshold, the number of parallel paths is dynamically adjusted according to the importance weight of the nodes. The corresponding edges are selected according to the intensity coefficient to create paths and iteratively advance. This solves the problems of single learning paths, lack of diversity of choices, and inability to adapt to different learning styles in traditional teaching. Through the parallel expansion mechanism, multiple feasible paths are explored in the knowledge graph, providing students with a richer learning choice space and improving the diversity and flexibility of the recommendation results.

[0060] A multi-level constraint verification mechanism is introduced. Difficulty gradient verification adjusts the safety threshold based on the actual rate of ability improvement to avoid an excessively steep or gentle learning curve. Cognitive load verification uses a time decay model to calculate cumulative load and prevent learning overload. Skill coverage verification identifies complementary skill pairs and calculates the matching degree of coverage to ensure the comprehensiveness of the path. When constraints are violated, the path priority weight is reduced, and paths above the median are selected for the evaluation phase. This mechanism overcomes the limitations of traditional path planning, such as single constraints, static verification methods, and lack of dynamic adjustment capabilities. Through multi-level verification, it ensures the rationality of each path in terms of difficulty progression, cognitive load, and skill coverage, improving the scientific validity and feasibility of the recommended paths.

[0061] S4.22 calculates the path evaluation indicators. Smoothness is calculated by subtracting the negative value penalty from the inverse of the gradient variance to ensure a smooth learning curve. Synergistic gain is calculated by the mean of the transmission strength between nodes to reflect the tightness of knowledge association. Compensation integrity is calculated by the weighted sum of coverage and compensation depth to measure the degree of capacity gap filling. Duration adaptability is calculated by multiplying the inverse of the occupancy ratio by the elastic gain to ensure feasibility under time constraints. Immediate reward is calculated by the weighted sum of the four indicators to evaluate the short-term effect of the path. Delayed reward is calculated by multiplying the inverse of the distance by the path diversity to evaluate the contribution to the long-term goal. The total reward is calculated by multiplying the immediate reward and the delayed reward by the discount factor to balance short-term benefits and long-term value. This solves the problems of single-dimensional path evaluation, lack of comprehensive consideration of short-term effects and long-term goals, and lack of quantitative evaluation mechanism in traditional teaching.

[0062] This mechanism achieves personalized adaptation by adjusting the granularity of the learning path based on practice preferences, optimizes resource allocation by calculating the cost-effectiveness ratio of expanded candidate options, generates hierarchical recommendation results, and provides primary recommended paths and alternative solutions with supplementary information. The results are then output in a priority and reward-weighted ranking to ensure scientific rigor. This mechanism overcomes the limitations of traditional path recommendation methods, such as coarse evaluation, lack of hierarchy, and failure to consider student preferences. Through a dual reward mechanism and multi-dimensional evaluation indicators, it achieves comprehensive quantification of path quality, provides students with scientific learning plans through hierarchical recommendation and priority ranking, and improves the accuracy and practicality of the recommendation results.

[0063] Step S4.2 achieves an intelligent generation process from candidate nodes to personalized learning paths through multi-path parallel expansion, multi-level constraint verification, dual-reward evaluation, and hierarchical recommendation output. Traditional music teaching relies primarily on teachers arranging fixed course sequences for students based on the syllabus and experience. This results in problems such as single paths, lack of diversity, one-sided evaluation dimensions, and failure to comprehensively consider students' abilities and individual needs, leading to poor adaptability and unsatisfactory learning outcomes. This step explores multiple feasible paths in the knowledge graph through a parallel expansion mechanism. It dynamically adjusts the number of parallel paths based on node importance weights to avoid combinatorial explosion, selects high-quality edges for expansion based on intensity coefficients to improve path quality, dynamically adapts by adjusting the safety threshold based on the rate of ability improvement through difficulty gradient verification, introduces a time decay model through cognitive load verification to prevent learning overload, identifies complementary skill pairs through skill coverage verification to ensure path comprehensiveness, and automatically filters and optimizes paths by reducing priority weights when constraints are violated. The evaluation phase calculates the short-term effects of the path using immediate rewards across four dimensions: smoothness, synergistic gain, compensation completeness, and duration adaptability. It assesses the long-term contribution of delayed rewards by calculating the inverse distance and path diversity. A discount factor balances short-term gains with long-term value to achieve optimal path selection. Personalized adaptation is achieved by adjusting path granularity based on practice preferences. The cost-benefit ratio of expanded candidate options is calculated to optimize resource allocation. Finally, tiered recommendation results are generated, providing a primary recommended path and alternative solutions to meet diverse needs. This step transforms experience-driven path planning into data-driven intelligent generation, single-path recommendation into multi-path tiered recommendation, and static evaluation into dynamic constraint verification and dual-reward evaluation. This effectively addresses the problems of single-path approach, coarse evaluation, and poor adaptability in traditional teaching.

[0064] This application also provides a music education and training management system; please refer to [link / reference]. Figure 2 It includes a learning data acquisition module, a capability status modeling module, a course content modeling module, a dynamic matching calculation module, and a feedback and path adjustment module; The learning data acquisition module is used to collect students' performance audio technical characteristics, practice behavior, historical learning records, and system interaction data; The ability status modeling module is used to quantify and model students’ abilities in the dimensions of pitch, rhythm, skill and expressiveness and generate dynamic student models. The course content modeling module is used to decompose the knowledge points, skill dependencies, and difficulty levels of the course and construct a course knowledge graph. The dynamic matching calculation module is used to calculate the difference between the student's ability vector and the course skill requirement vector and generate a personalized learning path; The feedback and path adjustment module is used to monitor student learning data and dynamically adjust the learning path based on changes in the ability model.

[0065] The contents not described in detail in this specification are existing technologies known to those skilled in the art.

[0066] Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method of managing music education training, characterized by: The specific steps are as follows: S1, learning data acquisition: collecting multi-dimensional learning data of students, including technical feature data of performance audio, practice behavior data, historical learning record data and system interaction behavior data, and transmitting to the teaching management platform in real time through the terminal device; S2, ability state modeling: based on the collected multi-dimensional data, a student ability model is constructed, the student's ability in pitch, rhythm, skill, expression and other dimensions is structured modeled, and acoustic feature analysis, learning behavior analysis and deep learning algorithm are used to generate a dynamic student model reflecting the student's current ability state and ability change trend; S3, course content modeling: the music course content is structured and semantically modeled, the knowledge points, skill elements, skill dependency relationships, difficulty levels and teaching objectives of the course are decomposed and a course knowledge graph is established; S4, dynamic matching calculation: the dynamic matching algorithm of student model and course knowledge graph is executed, the difference between student ability vector and course skill demand vector is calculated, learning path dependency relationship analysis and adaptive matching strategy based on reinforcement learning or graph reasoning are carried out, and the course recommendation result and individualized learning path most matched with the student's current ability are generated; S5, feedback and path adjustment: the performance data and learning behavior data of students in the subsequent learning process are monitored in real time, and the learning path is dynamically corrected according to the change of student ability model.

2. The method of claim 1, wherein: In step S1, the specific steps of learning data acquisition are as follows: S1.1, the terminal device records the student's performance audio through the audio acquisition module, obtains the pitch, rhythm and timbre characteristic data, synchronously records the practice time, repetition frequency, interruption frequency and error operation time, forms the practice behavior data, extracts the historical course completion record, evaluation score, skill mastery annotation and learning curve trend, forms the historical learning data, captures the platform access path, stay time, click sequence and login period, and forms the interaction behavior data; S1.2, the terminal device performs format conversion on the performance audio, removes noise through digital filtering algorithm, extracts pitch deviation value, rhythm stability and timbre spectrum distribution, converts into structured feature vector, aligns the multi-source data in time sequence according to the time stamp, establishes association index according to the student identifier, completes structured integration, and transmits the standardized data to the cloud storage center in real time through encryption protocol, providing data basis for subsequent ability modeling.

3. The method of claim 2, wherein: In step S2, the specific steps of ability state modeling are as follows: S2.1, acoustic analysis is performed on the performance audio, pitch deviation value is calculated through pitch detection algorithm and quantified as pitch index, rhythm stability and speed accuracy are extracted through rhythm analysis algorithm and quantified as rhythm index, timbre change and dynamics control are identified through spectrum analysis and quantified as skill index, emotional expression and performance completeness are evaluated through melody analysis and quantified as expression index, learning behavior index is formed by counting practice frequency, time and error correction speed, growth index is formed by extracting historical progress curve and score growth rate, and multi-dimensional ability feature vector group is formed by summarizing. S2.2, input the ability feature vector group into the deep neural network, weight and integrate each ability dimension through multi-layer mapping, generate an ability distribution matrix containing score value, confidence interval and dimension correlation strength, predict the ability change trend by using long short-term memory network, identify low-score dimensions and label weak links by clustering algorithm, integrate ability snapshot, diagnosis information and growth path to build a dynamic student model, compare the feature vector extracted after new data collection with historical data, adjust network parameters to realize model updating, and the generated model is transmitted to the course matching module.

4. The method of claim 3, wherein: In the step S3, the course content modeling specifically comprises the following steps: S3.1, obtain the teaching document of the music course, decompose it into the smallest teaching granule unit layer by layer according to the course framework, unit music education training management method, knowledge module and skill training item, extract the core knowledge points, skill elements and skill types of each unit, identify the pre-required skills and parallel auxiliary content through the dependency analysis algorithm, establish a skill dependency chain, quantitatively score and map to the difficulty classification standard according to the skill complexity, knowledge depth and performance difficulty, convert the teaching objectives into quantitative indicators such as pitch error range, rhythm stability threshold and skill completion percentage, and form a structured course element data set; S3.2, map the teaching granule unit to the knowledge graph node, store the unique identification code, knowledge point text, skill classification, difficulty level value, teaching target index and estimated learning time in the node, create a directed edge between the nodes according to the skill dependency chain and label the dependency type, form a single course skill association network, identify cross-course node pairs through knowledge point text matching and skill label comparison and establish cross-course edges, label the correlation strength coefficient and knowledge transfer direction, assign the node with multi-dimensional attributes such as difficulty weight, time weight and skill coverage vector, store the knowledge graph data in the form of graph structure and establish attribute index.

5. The method of claim 4, wherein: The step S3.1 specifically comprises the following steps: S3.11, obtain the teaching document and decompose it into course framework, unit music education training management method, knowledge module, skill training item and other hierarchical elements, extract the knowledge points and skill elements of each teaching granule unit, recursively analyze the source path of the basic ability of the unit skill upwards through the skill evolution tracing algorithm, recursively predict the development direction of high-order ability downwards, construct a vertical skill evolution pedigree, cross-compare the skill elements of different units at the same difficulty level, identify unit groups that share the same underlying skill base, mine implicit skill dependency relationships that are not explicitly labeled, calculate the dependency strength coefficient, and form a multi-level skill dependency network containing explicit dependency edges, implicit dependency edges and dependency strength classification; S3.12, introduce a cognitive load threshold model, decompose the learning process into information input load, working memory occupation load, and skill integration load, calculate the weighted sum of parameters such as the number of notes required per unit time, and when the load value exceeds the preset threshold, perform secondary decomposition on the unit and insert a skill consolidation buffer unit, recalculate the comprehensive difficulty score and combine the cognitive load distribution curve to stratify the difficulty, establish an efficiency function model of target achievement and cognitive load input, adaptively calibrate the teaching objectives according to the contribution weight, form a dynamic target index system and integrate it into an enhanced course element dataset.

6. The method of claim 5, wherein: The specific steps of the step S3.2 are as follows: S3.21, map the teaching granule unit to the basic node and set the identification code, knowledge point text, skill classification, difficulty level value and other fields, build an abstract node layer, aggregate the basic nodes of the same skill evolution pedigree into skill population nodes and record the evolution inheritance strength, use word embedding algorithm to convert the knowledge point text into semantic vector, fuse the semantic vector with the multi-label encoding of skill classification to generate deep semantic representation vector, calculate the cosine similarity of semantic vectors between nodes, when the similarity exceeds the threshold, establish semantic association edges to form an implicit association subgraph, encode numerical attributes such as difficulty value and time length into dynamic embedding vectors containing current state, change trend, flexibility range and path distribution according to cognitive load distribution and target calibration results, and store them; S3.22, create directed edges between basic nodes according to skill dependency network and label dependency type and dependency strength coefficient, form a single course skill association subgraph, calculate the semantic vector similarity, skill label intersection ratio and evolution pedigree overlap ratio of cross-course nodes to get the comprehensive matching degree, introduce a skill transfer strength model, calculate the transfer strength coefficient and transfer direction according to the difficulty span, compatibility, cognitive load difference and time length ratio, when the transfer strength exceeds the threshold, establish cross-course edges, calculate the node in-degree and out-degree to identify key path bottleneck nodes and insert buffer nodes, identify redundant edges and perform pruning, calculate node importance weight, reachability weight, coverage vector and load balancing weight, store the graph data in graph structure format and establish multi-dimensional index.

7. The method of claim 6, wherein: In the step S4, the specific steps of dynamic matching calculation are as follows: S4.1, calculate the difference between the student ability vector and the node skill demand vector to identify the ability gap, set a compensation priority coefficient according to the gap in the evolution chain, calculate the weighted compensation total score by multiplying the node compensation contribution degree and the priority coefficient, select the top thirty nodes, check cognitive load overload and remove risk nodes, check the completeness of the pre-requisite, complete nodes enter the candidate set, nodes that do not meet the dependency calculate the path cost, and nodes below the threshold are considered as expansion candidates; S4.2, perform multi-path reasoning on candidate nodes, when out-degree is greater than threshold and intensity coefficient standard deviation is less than proximity threshold, expand in parallel, verify difficulty gradient, cognitive load, skill coverage, etc. constraints, calculate smoothness, synergy gain, compensation integrity, time length adaptation degree as immediate reward, calculate terminal distance contribution degree as delayed reward, discount cumulative total reward value, adjust path granularity according to exercise preference, calculate the cost-benefit ratio of expansion candidates, generate hierarchical recommendation results.

8. The method of claim 7, wherein: The specific steps of the step S4.1 are as follows: S4.11, calculate the difference between the student ability vector and the node skill demand vector to identify the gap dimension, set the compensation priority coefficient according to the gap in the evolution chain level, calculate the conduction influence weight of the basic layer gap and add it to the priority coefficient, calculate the weighted sum of node coverage overlap, adaptation degree coefficient and evolution inheritance strength to get the compensation contribution degree, multiply the priority coefficient to get the weighted compensation total score, identify the gap dependence and reduce the effectiveness weight of the subsequent gap, select the top 30 nodes into the candidate set; S4.12, extract the sum of node cognitive load, calculate the student's bearing capacity margin, adjust the margin according to the error rate trend, remove the node when the margin is exceeded, check the preposition dependence integrity, recursively calculate the dependence chain depth, remove the node when the threshold is exceeded, calculate the path cost, build expansion candidates when the threshold is below, and the dependent complete node enters the second stage candidate set.

9. The method of claim 8, wherein: The specific steps of the step S4.2 are as follows: S4.21, read the candidate nodes to perform multi-path reasoning, when the out-degree is greater than the threshold and the intensity coefficient standard deviation is less than the proximity threshold, dynamically adjust the number of parallel paths according to the node importance weight, select the corresponding edge to create paths and iterate, introduce multi-level constraint verification, difficulty gradient verification modifies the safety threshold according to the actual ability improvement rate, cognitive load verification introduces a time decay model to calculate the cumulative load, skill coverage verification identifies complementary skills to calculate the coverage matching degree, and the path priority weight is reduced when the constraint is violated, and the path above the median is selected into the evaluation stage; S4.22, calculate the path evaluation index, smoothness is the inverse of gradient variance minus negative value penalty, synergy gain is the average of the transmission strength between nodes, compensation integrity is the weighted sum of coverage rate and compensation depth, time length adaptation degree is the inverse of occupation proportion multiplied by elastic gain, immediate reward is the weighted sum of four indexes, delay reward is the distance reciprocal multiplied by path diversity, total reward is the immediate reward multiplied by the discount factor, adjust the path granularity according to the exercise preference, calculate the cost-benefit ratio of expansion candidates, generate hierarchical recommendation results and add expansion information, and output according to the priority and reward weighted sorting.

10. A music education training management system characterized by: The music education training management system is used to execute the music education training management method of any one of claims 1-9, and the system comprises a learning data acquisition module, an ability state modeling module, a course content modeling module, a dynamic matching calculation module, and a feedback and path adjustment module; The learning data acquisition module is used to acquire the student's performance audio technical features, exercise behavior, historical learning records and system interaction data; The ability state modeling module is configured to quantitatively model the students' abilities in the dimensions of intonation, rhythm, skill and expressiveness and generate dynamic student models; The course content modeling module is configured to decompose the knowledge points, skill dependency relationships and difficulty levels of a course and construct a course knowledge graph; The dynamic matching calculation module is configured to calculate the difference between the student ability vector and the course skill requirement vector and generate an individualized learning path; The feedback and path adjustment module is configured to monitor student learning data and dynamically correct the learning path according to the changes in the ability model.

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