Coordinated feedback integrated drum teaching method

Abstract

This application provides a coordinated feedback-integrated drum music teaching method, which includes: calculating the user's human biological index, human psychological parameters, and physiological stress index; constructing a user feature profile based on the human biological index, human psychological parameters, and physiological stress index; matching the user feature profile with a constructed drum music repertoire library to select drum music repertoire matching the user; and generating a drum music teaching plan for the user based on the selected drum music repertoire. This solution achieves comprehensive quantification of the user's state through multi-dimensional index calculation, systematic integration of environmental factors based on dynamic profile construction, and targeted adaptation of teaching objectives through a precise matching mechanism. It fundamentally solves the problems of insufficient matching accuracy, poor environmental adaptability, and vague teaching objectives in existing technologies, providing an innovative technical path for drum music teaching that integrates biomedical indicators, psychological characteristics, and environmental stress theory.

Description

Technical Field

[0001] This invention relates to the field of artificial intelligence, and specifically to a coordinated feedback integrated drumming teaching method. Background Technology

[0002] In the field of music education and personalized recommendations, drumming, as a highly rhythmic art form, requires repertoire recommendations that take into account multiple factors such as the user's physiological state, psychological characteristics, and environmental adaptability. Currently, drumming repertoire recommendation systems are mostly based on matching users' historical performance data or preference tags. For example, they generate recommendation lists by analyzing users' selection habits of drum tempo and timbre, or implement progressive recommendations based on the difficulty level of the repertoire to achieve drumming instruction.

[0003] Existing technologies and traditional drumming instruction suffer from the following shortcomings: Firstly, they rely solely on user behavior data, lacking quantitative analysis of users' physiological indicators (such as heart rate variability and blood pressure fluctuations) and psychological states (such as anxiety levels and emotional tendencies), making it difficult to accurately capture the impact of users' real-time states on drumming adaptability. Secondly, they fail to consider the effects of seasons and environments on the user's playing experience, making it impossible to recommend repertoire that matches the human body's biorhythms. Furthermore, traditional solutions lack targeted adaptation to drumming instruction goals, making it difficult to balance the dual needs of skill training and mental well-being during the recommendation process.

[0004] The aforementioned technical deficiencies result in insufficient matching accuracy and poor adaptability in existing drumming instruction methods. This is particularly problematic in scenarios requiring personalized teaching that incorporates physiological and psychological characteristics, making it difficult to meet users' demands for scientific rigor and professionalism. Therefore, there is an urgent need for a drumming instruction method that comprehensively considers human biological indicators, psychological parameters, and environmental stress factors. Summary of the Invention

[0005] To address the aforementioned technical problems, this application provides a coordinated feedback integrated drumming teaching method to at least solve or alleviate the problems existing in the prior art.

[0006] To achieve the above objectives, according to one aspect of this application, a coordinated feedback integrated drumming teaching method is provided, comprising:

[0007] Calculate the user's human biological index, human psychological parameters, and physiological stress index; Based on human biological index, human psychological parameters, and physiological stress index, user profiles are constructed. User profiles are used to match the built drum music library to filter out drum music that matches the user. Based on the selected drum music tracks, a drum music teaching plan is generated for the user.

[0008] The solution provided in this application has at least the following technical advantages: 1. Quantification of multi-dimensional physiological and psychological states By calculating human biological indices (integrating physiological indicators such as heart rate variability and blood pressure), human psychological parameters (combining psychological characteristics such as anxiety tendency and emotional stability), and physiological stress indices (correlated with seasonal adaptation data), a comprehensive characterization of the user's state is achieved. Compared to traditional solutions that rely solely on behavioral data, this method can accurately capture real-time physiological and psychological fluctuations in users (such as Zhang's mild anxiety and autumn stress response in the example), providing a scientific quantitative basis for recommendations. It solves the problem of "lack of quantitative analysis of physiological and psychological indicators" in existing technologies, expanding the basis for teaching from a single behavioral dimension to a multi-dimensional biological-psychological-environmental dimension.

[0009] 2. Construction of Dynamic User Feature Profiles Based on three major indices, a user profile is constructed that includes physiological feature vectors, psychological feature vectors, and seasonal adaptation feature vectors. This profile can dynamically reflect the user's characteristics in terms of rhythm adaptability, emotional tendencies, and environmental tolerance (e.g., labels such as "low-frequency rhythm preference coefficient 0.65" and "low tolerance to first-function mode coding (or lung function mode coding)" in Zhang's profile). This profile construction method overcomes the shortcomings of traditional solutions that "do not consider the impact of environmental factors on the performance experience," transforming environmental stress factors such as seasons into calculable feature dimensions. This enables teaching methods to match the human body's biorhythms, such as adjusting the timbre attributes of the repertoire based on the user's autumn phase shift.

[0010] 3. Precise track matching mechanism By matching user profiles within a drum music repertoire database, personalized selection based on physiological and psychological characteristics can be achieved. For example, a medium-tempo track can be recommended based on Zhang's cardiovascular function data, a soothing melody can be matched to his anxiety state, and timbre frequency compensation can be adjusted based on stress index. This mechanism solves the problem of "insufficient fit of teaching objectives" in traditional recommendations. By linking biological indicators, psychological parameters, and repertoire characteristics (such as rhythmic complexity, melodic mode, and timbre attributes), it takes into account both the needs of skill training (such as recommendations for basic syncopation exercises) and mental and physical regulation (such as soothing tracks for second-function mode coding (or liver function mode coding)), thereby improving the scientific and practical nature of teaching. Attached Figure Description

[0011] Figure 1 This is a flowchart illustrating a coordinated feedback integrated drum teaching method according to an embodiment of this application. Figure 2 This is a schematic diagram of the structure of a coordinated feedback integrated drum teaching device according to an embodiment of this application; Figure 3This is a schematic diagram of the structure of an electronic device according to an embodiment of this application; Figure 4 This is a schematic diagram of the structure of a coordinated feedback integrated drum teaching system according to an embodiment of this application. Detailed Implementation

[0012] Figure 1 This is a flowchart illustrating a coordinated feedback integrated drumming teaching method according to an embodiment of this application. Figure 1 As shown, it includes: Calculate the user's human biological index, human psychological parameters, and physiological stress index; Based on human biological index, human psychological parameters, and physiological stress index, user profiles are constructed. User profiles are used to match the built drum music library to filter out drum music that matches the user. Based on the selected drum music tracks, a drum music teaching plan is generated for the user.

[0013] Optionally, the user's biometrics are calculated, including: Obtain the user's biological description; The user's human biological description is analyzed to obtain standardized basic biological data; Feature extraction and weight assignment are performed on standardized biological baseline data to calculate the human biological index.

[0014] Preferably, in a specific implementation, the specific processing for calculating the user's human biometric index is implemented as follows: Wearable device synchronization: Connect to devices such as smart bracelets and heart rate belts via Bluetooth / WiFi to collect real-time data such as heart rate, blood oxygen, steps, and sleep quality.

[0015] User input: Design a structured questionnaire that covers static information such as age, gender, height, weight, and medical history, as well as behavioral data such as dietary preferences (e.g., "Do you often eat spicy food?") and exercise habits (e.g., "Weekly exercise frequency").

[0016] Healthcare system integration: By connecting to the hospital's electronic medical record system via the HL7 protocol, biochemical indicators (such as blood sugar and cholesterol) and genetic information (such as family medical history) from physical examination reports can be obtained.

[0017] Outlier detection: The isolated forest algorithm is used to identify outliers (such as sudden changes in heart rate) in the data collected by the device, and then corrected by sliding window mid-value filtering.

[0018] Privacy protection: Sensitive data (such as medical records) is homomorphically encrypted to ensure that only authorized modules can decrypt it during transmission and storage.

[0019] In the above scheme, the priority of the questionnaire can be dynamically adjusted according to the user's recent behavior (such as "exercise volume in the past 7 days"), and questions related to high-frequency behaviors can be placed first to improve the timeliness of data.

[0020] In addition, a unified data lake (DataLake) can be built, and Apache NiFi can be used to realize the real-time ingestion and standardized storage of multi-source heterogeneous data (device time-series data, questionnaire structured data, medical text data).

[0021] In this application, the BERT-Chinese model is used to perform semantic parsing on open-ended text entered by users (such as "often stay up late to work overtime"), extract keywords ("stay up late", "work overtime") and map them to predefined biometric labels (such as "sleep quality" and "stress level").

[0022] Rule engine correction: The rule base is built based on medical knowledge graphs (such as UMLS) to correct NLP parsing ambiguities (such as associating "fast heartbeat" with both "heart rate" and "anxiety").

[0023] Dimensional normalization: Numerical data (such as height and weight) are normalized to the [0,1] interval using Min-Max; categorical data (such as "whether or not one smokes") are One-Hot encoded.

[0024] Time-series data segmentation: Time-series data such as sleep and exercise are segmented into 24-hour segments according to physiological cycles (such as circadian rhythms) and statistical features (mean, variance, peak frequency) are extracted.

[0025] In this application, the NLP parsing weights can be dynamically adjusted by combining user geolocation (such as "whether they are in a gym") and device sensor data (such as accelerometer data) to improve the accuracy of text feature extraction.

[0026] In addition, the normalization method is automatically selected based on the data distribution (such as using Box-Cox transformation for skewed data) to avoid information loss in traditional methods.

[0027] In this application, when calculating the biological index through feature extraction and weight assignment, features strongly correlated with drum music preferences are selected based on the importance ranking of features in a random forest (such as heart rate variability (HRV) being correlated with rhythm sensitivity, ρ=0.78). User constitution types (balanced constitution, qi deficiency constitution, etc.) are converted into numerical features and integrated with Western medicine indicators.

[0028] An improved SHapleyAdditiveexPlanations (SHAP) algorithm is employed, combined with users' historical listening behavior data (such as whether they skipped a particular drum track) to train a weight model, enabling dynamic adjustment of feature weights based on user preferences. A Pareto frontier optimization model is constructed to balance the objectivity of physiological indicators with users' subjective preferences, for example, to find the optimal weight combination between "health risk" and "music preference".

[0029] Exponent Calculation: Weighted Summation Model: , where wi is the dynamic weight and xi is the standardized feature value. The exponential result is mapped to the [0,1] interval by applying the Sigmoid function to enhance the interpretability of the result.

[0030] In this application, a cross-modal attention mechanism network can be constructed to automatically learn the correlation weights between TCM constitution characteristics and Western medicine physiological indicators, thereby improving the index's ability to represent complex constitutions.

[0031] In this application, the weight model is updated in real time through incremental learning algorithms (such as online gradient descent) to adapt to changes in the user's physiological state (such as increased heart rate variability after exercise).

[0032] Optionally, calculating the user's psychological parameters includes: Obtain the user's psychological profile; The user's psychological descriptions are structured and classified to obtain standardized basic psychological data; Standardized psychological baseline data are fused from multiple dimensions to calculate human psychological parameters.

[0033] Preferably, when acquiring the user's psychological description, multi-source psychological data collection is performed: Active input: Design an interactive questionnaire based on psychological scales, supporting voice input and slider rating, reducing the barrier for users to fill in the questionnaire.

[0034] Passive perception: Social media text mining: Scrape text from users' social media platforms through API interfaces and extract sentiment trends using sentiment analysis models (such as VADER).

[0035] Voice emotion recognition: Collect daily voice clips of users (such as call recordings), extract features such as tone, speech rate, and pauses, and input them into a pre-trained Wav2Vec2-Emotion model to identify emotional states.

[0036] Biosignal monitoring: Collects skin conductance (GSR) and electroencephalogram (EEG) signals through wearable devices to mark psychological states such as stress and relaxation in real time.

[0037] Preferably, when acquiring a user's psychological description, data quality control includes: Noise filtering: Spectral subtraction is used to denoise the speech signal, and TF-IDF is used to filter meaningless words (such as "um" and "ah") from the text data.

[0038] Consistency check: Compare the sentiment of questionnaire answers with that of social media text (e.g., questionnaires select "happy" but the text contains many negative words), and mark contradictory samples for secondary confirmation.

[0039] Preferably, when generating standardized psychological baseline data based on structured classification, a multi-dimensional labeling system is constructed: Psychological theory mapping: Based on the DSM-5 diagnostic criteria for mental disorders and the Big Five personality model, a hierarchical labeling system is defined (such as "emotional stability" → "anxiety tendency" → "social anxiety").

[0040] Dynamic tag expansion: Discover emerging psychological tags (such as "digital nomad loneliness") through user community clustering analysis (such as LDA topic model).

[0041] Preferably, when generating standardized psychological baseline data based on structured classification, structured classification processing is performed: Text classification: The BERT-BiLSTM-CRF model is used to perform sequence labeling on user text and extract psychological feature entities (such as "I have been having trouble sleeping lately" → label "sleep disorder").

[0042] Speech classification: Input speech features into the TCN (Temporal Convolutional Network) model to classify emotional states (such as happiness, sadness, anger).

[0043] Biosignal classification: Wavelet packet decomposition of EEG signals was performed to extract the α / β wave power ratio, and an SVM classifier was used to identify focused / relaxed states.

[0044] Preferably, when generating standardized psychological baseline data based on structured classification, data fusion is performed: Conflict resolution: When labels from different data sources contradict each other (e.g., the text shows "happy" but the voice analysis shows "sad"), the DS evidence theory is used to fuse the decision and output the label with the highest confidence.

[0045] Preferably, when calculating human psychological parameters through multi-dimensional fusion, a feature fusion model is constructed: Temporal feature encoding: For temporal data such as emotional states, the Transformer encoder is used to extract context-dependent features.

[0046] Static feature embedding: Static data such as personality traits are mapped into dense vectors using Word2Vec.

[0047] Cross-modal attention fusion: Design a multi-head attention mechanism to dynamically allocate weights for text, speech, and biosignal features (e.g., increase the weight of speech features when emotionally agitated).

[0048] Preferably, when performing multi-dimensional fusion calculations of human psychological parameters, the psychological parameter calculation is performed: Weighted summation model: ,in For dynamic weights, These are the standardized psychological characteristic values; Nonlinear correction: The parameters are mapped to the [0,1] interval by the Sigmoid function, where 0 represents "extremely negative" and 1 represents "extremely positive".

[0049] Preferably, when calculating human psychological parameters through multi-dimensional fusion, dynamic weight adjustment is performed: Context-aware weights: Adjust feature weights based on the user's current context (e.g., "work" or "leisure"). For example, increase the weight of "stress"-related features in a work context.

[0050] Online learning mechanism: The weights are updated in real time through the FTRL (Follow-the-Regularized-Leader) algorithm to adapt to changes in the user's psychological state (such as the weights of users with long-term anxiety shifting slowly).

[0051] Optionally, the user's physiological stress index is calculated, including: Acquire raw data on users' perceptions of natural climate / seasonal phenomena; Stress feature identification processing is performed on users' raw data on natural climate / seasonal perception to generate basic stress data; The stress index is transformed by combining the basic stress data with the constructed TCM feature database to calculate the physiological stress index.

[0052] Preferably, when acquiring raw data on users' natural climate / seasonal perceptions, multimodal data acquisition is performed: Active input: Design seasonal health questionnaires that include open-ended questions (such as "Are you easily irritable in the summer?") and scale ratings (such as a 1-5 scale to assess "the degree of cold intolerance in winter"), and support voice-to-text input to lower the barrier to completion.

[0053] Passive sensing: Environmental sensors: Collect data such as temperature, humidity, light intensity, and air pressure of the user's environment through IoT devices (such as temperature and humidity sensors).

[0054] Physiological signal monitoring: Use smart bracelets / chest straps to collect time-series data such as HRV (heart rate variability), GSR (skin conductance), and body temperature, and label them with seasonal attributes (e.g., "2023-07-15_Summer").

[0055] Traditional Chinese Medicine Constitution Identification: Collect user constitution type (such as Qi deficiency constitution, Yang deficiency constitution) through questionnaires or pulse diagnosis instruments.

[0056] Preferably, when acquiring raw data on users' natural climate / seasonal perceptions, data preprocessing is performed: Spatiotemporal alignment: Synchronize environmental data with physiological signals according to timestamps to construct a four-dimensional data matrix of user-season-environment-physiology.

[0057] Missing value imputation: KNN interpolation is used for intermittent data (such as GSR signals), and historical average values ​​are used to fill in long-term missing data (such as data that was not worn during a certain season).

[0058] Preferably, when acquiring raw data on users' natural climate / seasonal perceptions, a closed-loop data chain of "environmental stress-physiological response" can be constructed. For example, the impact delay of environmental temperature and humidity changes on HRV can be predicted using an LSTM model (usually 30-60 minutes).

[0059] Preferably, when acquiring the user's raw data on natural climate / seasonal perception, the constitution type can be quantified according to traditional Chinese medicine: the constitution type is converted into a numerical vector (e.g., Qi deficiency constitution is encoded as [0.2,0.8,0...]), and compressed into a low-dimensional embedding space by AutoEncoder, which is convenient for integration with Western medicine indicators.

[0060] Preferably, when acquiring raw data on a user's natural climate / seasonal perception, stress feature identification processing includes: Structured feature extraction: Text features: The BERT-Chinese model is used to perform semantic parsing on users' seasonal descriptions (such as "joint pain during the rainy season"), extract keywords ("dampness", "pain") and map them to preset stress labels (such as "dampness invasion").

[0061] Temporal characteristics: Wavelet packet decomposition is performed on physiological signals (such as HRV) to extract seasonal fluctuation characteristics (such as the mean HRV in summer being 15% lower than in winter).

[0062] Statistical characteristics: Calculate the variance and peak frequency of users' physiological indicators in different seasons (e.g., the fluctuation range of GSR is significantly higher in winter than in summer).

[0063] Seasonal pattern discovery: Periodic analysis: The seasonal cycle of users' physiological indicators is detected by Lomb-Scargle periodic graph (e.g., some users show 6-month periodic stress fluctuations).

[0064] Anomaly detection: The isolated forest algorithm is used to identify seasonal stress anomalies (such as a user’s GSR suddenly increases in spring but environmental data does not change significantly).

[0065] Feature fusion: Cross-modal attention: Design a Transformer encoder to dynamically assign weights to text, temporal, and statistical features (e.g., the weight of the keyword "humid" increases by 30% during the rainy season).

[0066] Preferably, when acquiring raw data on a user's natural climate / seasonal sensitivity, the stress characteristic recognition threshold can be dynamically adjusted based on the user's constitution (e.g., a Yang-deficient constitution is more sensitive to cold) to avoid a "one-size-fits-all" standard.

[0067] Preferably, when acquiring raw data on users' natural climate / seasonal perceptions, causal reasoning can be enhanced: the causal relationship between environmental factors and physiological responses can be verified using Pearl causal models (e.g., the confidence level of "low temperature → decreased HRV" is ≥0.9).

[0068] Preferably, when combining the stress index with the traditional Chinese medicine feature database, the construction of the traditional Chinese medicine feature database is performed: Knowledge Graph: Constructing a network of relationships between "seasons, pathogenic factors, internal organs, and symptoms" (e.g., "summer → excessive heart fire → insomnia → red tongue tip").

[0069] Weighting: Based on expert experience and bibliometrics, each relationship was assigned an initial weight (e.g., "excessive heart fire" contributes 0.7 to summer stress).

[0070] Preferably, when converting the stress index using the TCM feature database, the stress index calculation model is executed: Feature mapping: Stress features (such as "reduced HRV") are mapped to the TCM concept space (such as "insufficient heart qi") through knowledge graph embedding (such as the TransE algorithm).

[0071] Weighted fusion: ,in For dynamic weights, The coefficient representing the integration of traditional Chinese and Western medicine.

[0072] Nonlinear correction: The exponent is mapped to the [0,1] interval by the Sigmoid function, where 0 represents "full adaptation" and 1 represents "extreme stress".

[0073] In this application, a two-stream neural network (SiameseNetwork) can be constructed to process Western medicine time-series data and traditional Chinese medicine knowledge graphs separately, and the feature spaces can be aligned by comparing loss functions. In addition, interpretability can be enhanced by generating attribution reports of stress indices (such as "the index of 0.85 is caused by excessive heart fire in summer (contributing 60%) and reduced HRV (contributing 40%)"), supporting clinical validation in traditional Chinese medicine.

[0074] Optionally, user profiles can be constructed based on human biological indices, human psychological parameters, and physiological stress indices, including: Human biological indices are mapped to a constructed standardized physiological feature space to generate physiological feature vectors; Human psychological parameters are input into the constructed emotion fluctuation assessment model for evaluation to generate psychological feature vectors; The physiological stress index is mapped to a multi-feature coupled dynamic equilibrium space to generate seasonal adaptation feature vectors; User profiles are constructed based on physiological feature vectors, psychological feature vectors, and seasonal adaptation feature vectors.

[0075] Optionally, user feature profiles are constructed based on physiological feature vectors, psychological feature vectors, and seasonal adaptation feature vectors, including: Calculate the probability distribution entropy value of the physiological feature vector; Calculate the joint dimensional entropy value of the psychological feature vectors; Calculate the dynamic transition entropy value of the seasonal adaptation feature vector; A feature fusion weight matrix is ​​generated based on the probability distribution entropy, joint dimension entropy, and dynamic transition entropy. Based on the feature fusion weight matrix, the physiological feature vector, psychological feature vector, and seasonal adaptation feature vector are weighted tensor fusion to generate a multi-dimensional fused feature vector. The multi-dimensional fused feature vectors are transformed into the constructed teaching demand mapping space to generate user feature profiles.

[0076] Optionally, the probability distribution entropy value of the physiological feature vector is calculated, including: The physiological feature vectors are binned into equal-frequency bins, and the sample probability of each bin interval is calculated. Based on the sample probabilities of each bin interval, the probability distribution entropy value of the physiological feature vector is calculated.

[0077] Optionally, the joint dimensional entropy value of the psychological feature vectors is calculated, including: The psychological feature vector is dimensionally segmented, and the marginal probability of each dimension is calculated. Based on the marginal probabilities of each dimension, the joint dimensional entropy value of the psychological feature vectors is calculated.

[0078] Optionally, the dynamic transition entropy value of the seasonal adaptation feature vector is calculated, including: Periodic phase extraction is performed on the seasonal adaptation feature vector; Based on the extracted periodic phase, the dynamic transition entropy value of the seasonal adaptation feature vector is calculated.

[0079] Optionally, a feature fusion weight matrix is ​​generated based on the probability distribution entropy, joint dimension entropy, and dynamic transition entropy, including: Based on the probability distribution entropy, joint dimension entropy, and dynamic transfer entropy, calculate the information gain rate of physiological feature vector, psychological feature vector, and seasonal adaptation feature vector; Based on the constructed ternary dynamic coupling model, the feature coupling coefficient matrix is ​​calculated according to the information gain ratio of physiological feature vector, psychological feature vector, and seasonal adaptation feature vector. Based on the constructed Lyapunov stability constraints, the feature coupling coefficient matrix is ​​iteratively updated to generate the feature fusion weight matrix.

[0080] Preferably, a dynamic calculation framework for the entropy value of the probability distribution of physiological characteristics is constructed, and an intelligent binning strategy is used to process physiological data: first, the initial number of bins is automatically determined based on the data distribution characteristics, and then the boundaries are optimized and adjusted to ensure that each interval contains approximately equal sample sizes. For abnormally high-value data (such as heart rate exceeding 120 beats / minute), they are separately divided into special intervals to avoid distorting the overall distribution. After calculating the frequency of samples in each interval, possible zero-probability cases are smoothed to ensure the stability of the calculation. Finally, the entropy values ​​are unified to the same metric through mathematical transformation, facilitating comparisons between different features.

[0081] Preferably, a tensor calculation system for the joint dimensional entropy of psychological features is constructed, dividing psychological features into multiple independent dimensions, and verifying the independence of these dimensions through statistical methods. Probability estimation is performed on the features of each dimension; continuous features are processed using smoothing techniques, while discrete features are directly statistically analyzed for frequency. Then, the correlation entropy between features is calculated through multi-dimensional joint analysis. To address the computational complexity issue caused by high-dimensional data, an approximation algorithm is introduced for efficient computation, significantly improving processing speed while maintaining computational accuracy.

[0082] Preferably, a quantum transfer entropy calculation model for seasonal adaptation characteristics is constructed. Time-frequency analysis is performed on seasonally related feature sequences to extract periodic variation features, and dynamic time warping technology is used to eliminate time offsets in data from different years. When constructing the state transition model, density estimation technology is used instead of traditional methods to capture complex nonlinear transition patterns. By quantitatively analyzing the information changes during the characteristic state transition process, the transfer entropy value is calculated, and the weights are automatically adjusted according to the severity of seasonal changes, enabling the model to better adapt to the characteristic change patterns of different seasons.

[0083] Preferably, a quantum field theory optimization system for feature fusion weights is constructed. The importance of each feature is evaluated through quantitative analysis, and dynamic differential equations are constructed to describe the interaction and evolution of different types of feature weights. High-precision numerical calculation methods are used to solve the equations, and stability constraints are introduced to ensure the convergence and reliability of the weight calculation process. Through iterative optimization and adjustment of the model parameters, a reasonable feature fusion weight matrix is ​​finally generated.

[0084] Preferably, a quantum tensor fusion architecture for multi-dimensional features is constructed, extending physiological, psychological, and seasonal features into a five-dimensional space that includes time and teaching scenarios. A high-order tensor is constructed using quantum state encoding, and feature fusion is achieved through quantum Tucker decomposition and QSVD. At the same time, QSVT is used for dimensionality reduction while maintaining quantum coherence. Finally, under the quantum-classical hybrid architecture, the efficient fusion and compression of multi-dimensional features are achieved by leveraging the characteristics of quantum entanglement and parallel computing, thereby improving feature representation ability and computational efficiency and providing support for user profile construction.

[0085] Preferably, a knowledge graph of teaching needs based on quantum theory is constructed to formally represent teaching experience and characteristic information. A quantum attention mechanism is designed to enhance the focus on key teaching needs, learning the mapping relationship between characteristics and teaching needs through quantum state evolution. The mapping effect is evaluated using quantum metrics, and a quantum incremental learning mechanism is used to dynamically update user profiles, enabling them to reflect users' learning progress and changes in needs in a timely manner.

[0086] Optionally, user profiles can be used to match within a built drum repertoire library to filter out drum repertoire that matches the user, including: The user profile is mapped to music dimensions to generate personalized music parameter vectors. Multidimensional feature extraction is performed on the drum music repertoire library to generate a standardized repertoire feature matrix; A cyclic feature coupling matching process is performed on the personalized music parameter vector and the track feature matrix to generate an initial matching degree matrix; The initial matching degree matrix is ​​dynamically weighted to generate an adaptively adjusted matching score vector. The adaptive matching score vector is sorted specifically for drum music teaching to generate a personalized list of recommended tracks.

[0087] Preferably, when mapping user feature profiles to a music dimension to generate personalized music parameter vectors, firstly, physiological music parameter mapping rules are established based on drumming teaching theory (e.g., matching high-frequency rhythmic patterns to those with high heart rate variability), and the physiological feature vectors are converted into rhythm intensity parameters through tensor product operations; secondly, the BERT model is used to analyze the emotional tendency of psychological feature texts and map it to a melody emotional index (e.g., matching an anxious state to a soothing melody in E minor); finally, the seasonal attributes of timbre are dynamically adjusted based on the phase entropy value of seasonal adaptation features (e.g., the crisp drumbeats of the second functional mode code (or liver function mode code) correspond to spring). An adaptive mapping matrix generation algorithm is developed, which iteratively optimizes the mapping parameters through users' historical performance data to achieve a non-linear transformation from physiological and psychological seasonal features to a 12-dimensional music parameter vector.

[0088] Preferably, when performing multi-dimensional feature extraction processing on the drum music repertoire to generate a standardized repertoire feature matrix, a dedicated drum music feature extraction engine is constructed: the audio feature module generates a rhythm spectrogram through short-time Fourier transform and captures timbre details by combining Mel frequency cepstral coefficients (MFCC); the performance parameter module parses the drum beat dynamics curve in the MIDI data and extracts compound rhythmic patterns (such as the "triplets + syncopation" combination of African drums); the semantic parsing module uses graph attention network (GAT) to understand the repertoire description text and generate attribute tags based on timbre physical characteristics, including "first functional mode coding (or lung function mode coding) features, second functional mode coding (or liver function mode coding) features, third functional mode coding (or kidney function mode coding) features, fourth functional mode coding (or heart function mode coding) features, and fifth functional mode coding (or spleen function mode coding) features." The semantic parsing module utilizes a graph attention network (GAT) to understand the music description text and generate timbre attribute labels: "First functional mode code (or lung function mode code), Second functional mode code (or liver function mode code), Third functional mode code (or kidney function mode code), Fourth functional mode code (or heart function mode code), and Fifth functional mode code (or spleen function mode code)." The three types of features are then concatenated according to a structure of 50 dimensions for audio, 15 dimensions for performance, and 768 dimensions for semantics, and dimensionality is reduced to an 800-dimensional standardized feature matrix using principal component analysis (PCA).

[0089] Preferably, when performing cyclic feature coupling matching on the personalized music parameter vector and the track feature matrix to generate the initial matching degree matrix, a cyclic matching algorithm based on multi-dimensional feature space transformation maps the music parameter vector and the track feature matrix to a five-dimensional feature space (first dimension, second dimension, third dimension, fourth dimension, and fifth dimension). Feature transformation operators are designed to implement cyclic mapping of the feature space (e.g., the transformation from the first dimension to the second dimension corresponds to the transformation from the timbre of the first functional mode code (or also known as the lung function mode code) to a low-frequency rhythm). Feature conflicts are corrected through feature constraint matrices (e.g., excessively high-pitched melodies automatically reduce rhythmic complexity weight). A cyclic matching algorithm based on the organ function mode coding theory maps the music parameter vector and the track feature matrix to a five-dimensional feature space (first functional mode code (or also known as lung function mode code), second functional mode code (or also known as liver function mode code), third functional mode code (or also known as kidney function mode code), fourth functional mode code (or also known as heart function mode code), and fifth functional mode code (or also known as spleen function mode code)). The design incorporates a feature coupling transformation operator to implement cyclic mapping of the feature space (e.g., the conversion from the timbre of the first functional mode code (or pulmonary function mode code) to a low-frequency rhythm by coupling the third functional mode code (or renal function mode code) with the first functional mode code (or pulmonary function mode code)). Feature conflicts are corrected using a feature suppression constraint matrix (e.g., automatically reducing the rhythmic complexity weight for excessively high-pitched melodies). Tensor dot product operations are used to calculate feature similarity, generating a 1×N dimensional initial matching degree matrix (N being the size of the music library). The matrix elements are normalized to matching scores between 0 and 1 using the softmax function.

[0090] Preferably, when performing dynamic weight correction on the initial matching degree matrix to generate an adaptively adjusted matching score vector, a three-tiered dynamic correction system is constructed: feature entropy weight correction dynamically adjusts the matching dimension weights based on the information entropy values ​​of physiological, psychological, and seasonal characteristics (the smaller the entropy value, the higher the weight); real-time physiological feedback correction adjusts the tempo matching weights based on the user's current heart rate variability (HRV) (automatically reducing the weight of fast-tempo tracks when HRV is low); and seasonal phase correction enhances the matching score of tracks with corresponding seasonal attributes based on the phase difference of the user's seasonal adaptation characteristics. An adaptive matching score vector is generated through weighted matrix multiplication, with the weight coefficients iteratively optimized by the LSTM network based on historical matching results.

[0091] Preferably, the adaptive matching score vector is subjected to drum music teaching-specific sorting processing to generate a personalized recommended repertoire list. Repertoire exceeding the user's playing ability is filtered based on the user's physiological characteristics (e.g., high-intensity drumbeat repertoire is excluded for those with insufficient wrist strength). Secondly, a therapeutic weight model is constructed based on the theory of mind-body regulation to improve the ranking of repertoire with mind-body regulation effects (e.g., soothing repertoire using the second functional mode coding (or liver function mode coding) is prioritized for those with excessive liver fire). Finally, a reinforcement learning algorithm is used to plan a progressive learning path, ensuring that the difficulty of the recommended repertoire increases exponentially (difficulty coefficient = 0.8^n, where n is the recommended sequence position). The sorting result includes the repertoire ID, matching score, and teaching suitability description (e.g., "suitable for beginner repertoire to improve rhythm").

[0092] Optionally, based on the selected drum music pieces, a drum music teaching plan is generated for the user, including the following steps: The selected drum music tracks are subjected to multi-dimensional feature analysis and structured processing to generate structured track data; Multimodal acquisition and quantitative modeling of user performance data are performed to generate user capability profiles; Structured music data is matched and broken down with user ability profiles to generate personalized teaching plans.

[0093] Preferably, the selected drum music tracks are subjected to multi-dimensional feature analysis and structured processing to generate structured music data, including the following steps: for the selected drum music track audio, the tracks of each instrument in the drum kit (bass drum, snare drum, hi-hat, etc.) are separated, and environmental noise is removed by combining spectral subtraction / Wiener filtering to achieve multi-track separation and signal cleaning; CRNN is used to detect the position and type of drum beats. STFT is used to analyze the temporal / frequency domain features of the rhythm pattern (such as periodicity and entropy). DTW is then used to align the beat grid to complete the rhythm feature extraction and standardization. A three-dimensional difficulty model is constructed based on physical complexity, technical requirements, and dynamic range, and a hierarchical label system of "basic-intermediate-advanced" is established.

[0094] Structured track data is generated based on separated tracks, standardized rhythm sequences, and three-dimensional difficulty labels.

[0095] Preferably, the user's performance data is collected and quantitatively modeled in a multimodal manner to generate a user capability profile, including the following steps: Acquire actual performance data from the user (audio, MIDI hit points, camera posture data); Based on the user's actual performance data, the DTW distance, BPM standard deviation (time dimension), body movement time difference and strike point deviation (spatial dimension), dynamic range and variance of force (force dimension) are calculated to obtain a three-dimensional ability index for quantitative evaluation. We use Bayesian knowledge tracing (BKT) to update the skill mastery probability, combine it with Kalman filtering to predict the progress trend, and construct a dynamic ability model to calculate the skill mastery probability matrix. User capability profiles are generated based on three-dimensional capability indicators and a skill mastery probability matrix.

[0096] Preferably, the structured music data is matched and decomposed with user ability profiles to generate personalized teaching plans, including: Based on structured track data, the track difficulty is broken down into an atomic skill tree of "basic rhythm pattern → transitional embellishment → advanced techniques" to establish skill dependencies (such as double jump → compound jump). Based on user ability profiles, a learning path is generated by matching user ability and skill requirements through multi-objective optimization (difficulty suitability + learning interest). Personalized teaching plans are generated based on skill dependencies and learning paths.

[0097] Therefore, the technical advantages of the above solution are as follows: 1. Standardization and increased granularity in the analysis of drum music repertoire By using models such as Wave-U-Net to separate drum tracks, the problem of "difficulty in accurately disassembling mixed audio" in traditional teaching is solved, enabling independent analysis of instrument tracks such as bass drum and snare drum (e.g., separate extraction of bass drum rhythm patterns), providing possibilities for refined teaching. By combining CRNN with STFT to detect drum beat positions and analyze rhythmic features (such as the periodic quantization of Euclidean rhythms), abstract musical rhythms are transformed into standardized sequences that can be computed by machines (such as timestamps with an accuracy of 10ms), thus avoiding the shortcomings of "vague rhythm descriptions" in traditional teaching.

[0098] 2. Objectivity and multi-dimensional coverage of user capability assessment Multimodal data acquisition (audio + MIDI + vision) covers three-dimensional capabilities: time (rhythmic accuracy), space (limb coordination), and dynamics (control ability). Compared with traditional teaching that relies solely on auditory feedback, it can capture more comprehensive performance details (such as triggering visual warnings when the strike point deviation is >5mm). Quantitative indicators such as DTW distance and BPM standard deviation replace subjective evaluations (such as "unstable rhythm"), keeping the error of ability assessment within ±5% and providing a scientific basis for personalized teaching.

[0099] 3. Construct a dual-domain knowledge system of "track-user". The three-dimensional model, which considers physical complexity (such as the rhythm of limb crossing), technical requirements (such as the frequency of rolls), and dynamic range (the amplitude of dynamic changes), breaks away from the traditional single standard of "grading by speed alone." For example, a piece of music may be of moderate speed, but because it contains high-frequency double-jump techniques, it is still marked as "advanced," to prevent users from feeling frustrated due to a mismatch in skill levels.

[0100] Bayesian Knowledge Tracking (BKT) updates the probability of skill mastery in real time (e.g., the probability of mastering "double jump" increases from 60% to 85%), and Kalman filtering predicts the progress trend (e.g., it is expected that the compound jump can be mastered within 2 weeks), forming a composite profile of "current level + potential prediction", which solves the problem that "static assessment cannot adapt to the learning progress" in traditional teaching.

[0101] 4. Dynamically Adaptive Intelligence: Generating Personalized Teaching Paths for Each User The pieces are broken down into an atomic skill tree of "basic rhythm patterns → transitional embellishments → advanced techniques" (e.g., "Autumn River Moonlit Night" is broken down into 8 basic rhythm patterns and 3 embellishment techniques), and skill dependencies are established (e.g., one must master double jumps to learn compound jumps). This avoids the problem of "skill jumps leading to learning gaps" in traditional teaching and makes the teaching path conform to cognitive laws.

[0102] The multi-objective optimization algorithm (difficulty fit + interest) prioritizes recommending skills that match the user's ability (e.g., recommending skills with a difficulty of 0.6-0.7 when the rhythm accuracy is 0.6), and combines it with DQN reinforcement learning to dynamically adjust the path (e.g., reducing the recommendation weight if a skill is skipped 3 times in a row), avoiding "too difficult and damaging confidence" or "too easy and wasting time", thus improving learning efficiency by more than 30%.

[0103] Specific implementation examples of the above-mentioned coordinated feedback integrated drumming teaching method: I. Examples of User Data Collection and Index Calculation Scenarios Taking Mr. Zhang, a 25-year-old male user, as an example, the system collected his physiological data through a smart bracelet, including a resting heart rate of 75 beats per minute and blood pressure of 120 / 80 mmHg. Meanwhile, a questionnaire survey revealed that his recent self-rating anxiety score was 58, indicating mild anxiety. He also mentioned that he tends to experience chest tightness and a slowed sense of rhythm every autumn.

[0104] 1. Calculation process of human biological index The front-end device synchronously acquired heart rate variability (HRV) signals (sampling frequency 250Hz) and basic physiological indicators such as gender and age. The HRV signals were bandpass filtered from 0.5 to 40Hz to eliminate motion artifacts, and then Kalman filtering was used to correct blood pressure fluctuations. Next, the SDNN (standard deviation of adjacent RR intervals) of HRV was calculated to be 50ms, and the RMSSD (root mean square difference of adjacent RR intervals) was calculated to be 25ms. Combined with a BMI of 23.5, a 1×5 physiological feature vector V_bio=[SDNN, RMSSD, BMI, age, gender] was generated. The importance of each feature was assessed based on information entropy, and a 5×5 feature weight matrix W_bio was constructed. HRV-related features (SDNN and RMSSD) received matrix element weights of 0.35 and 0.3 respectively due to their lower entropy values. Finally, the bioactivity index was calculated using matrix multiplication, resulting in a score of 72 (out of 100), indicating good cardiovascular function but mild fatigue.

[0105] 2. Calculation process of human psychological parameters His statement that "he feels his rhythm is disrupted when under pressure" was collected through voice interaction, and the questionnaire showed that he scored high on the neuroticism dimension. The BERT model was used to parse the text, outputting a 768-dimensional emotional feature vector. This was then combined with the 5-dimensional psychological dimension vector generated by the Big Five Personality Test to construct a 773×773 psychological feature covariance matrix. The fundamental frequency fluctuation feature of the voice (the fundamental frequency standard deviation increased by 15% when anxious) was fused with the questionnaire score using tensors. Introducing emotion regulation theory, this was mapped to a 5×5 imbalance state matrix M_emotion, representing the "fourth function modulation coding (or also known as cardiac function modulation coding) and the fifth function modulation coding (or also known as spleen function modulation coding)". Finally, through matrix eigenvalue decomposition, the emotion stability index was obtained as 62, which is relatively low.

[0106] 3. Calculation process of physiological stress index User feedback indicated a "delay in rhythm perception during the first week of autumn, requiring 3 days to adapt." The system extracted the annual phase of body temperature using continuous wavelet transform, generating a 365×1 phase distribution vector. It was found that the autumn phase shift reached 15 days (normal range ±7 days). A seasonal correlation matrix R_season (5×4 dimensions, corresponding to the correlation weights of five physiological features with the four seasons) was constructed using seasonal physiological response data. The weight of the matrix element between the respiratory system adaptation index and autumn rhythm adaptation was set to 0.78. A seasonal correlation matrix R_tradition (5×4 dimensions, corresponding to the correlation weights of visceral modulation codes with the four seasons) was constructed using visceral modulation coding theory. The weight of the matrix element between the first functional modulation code (or lung function modulation code) feature and autumn rhythm adaptation was set to 0.78. The physiological stress index was calculated to be 68 using matrix multiplication; a higher value indicates poorer adaptation.

[0107] II. Examples of User Profile Construction Techniques Based on the above indices, the system constructs user feature profiles according to the following steps: 1. Feature Space Mapping The 72-point biological index is input into the rhythm adaptability mapping matrix T_rhythm (10×10 dimensions) to generate a 1×10-dimensional low-frequency rhythm preference vector P_rhythm=[0.65,0.3,0.05,0,0,0,0,0,0,0,0]; the 62-point emotional stability index is input into the 8×8 melody mode matrix T_melody associated with the LSTM emotion model to obtain a 1×8-dimensional E minor melody tendency vector P_melody=[0,0.82,0.1,0,0,0,0.08,0]; and the stress feature mapping matrix T_stress (5×5 dimensions) is used to generate a low-frequency rhythm preference vector P_rhythm=[0,0.82,0.1,0,0,0,0.08,0]; The stress index of 68 points is converted into a 1×5 dimensional timbre adaptation vector P_tone=[0,0,0.43,0,0.2], indicating that he has a low tolerance for the first functional mode coding (or pulmonary function mode coding). Through the visceral mode coding mapping matrix T_five (5×5 dimensions), the stress index of 68 points is converted into a 1×5 dimensional timbre adaptation vector P_tone=[0,0,0.43,0,0.2] for the "first functional mode coding (or pulmonary function mode coding)," indicating that he has a low tolerance for the first functional mode coding (or pulmonary function mode coding).

[0108] 2. Entropy Calculation and Weight Matrix Generation The HRV data was divided into 10 equal-frequency bins, and a 10×10 probability distribution entropy matrix H_bio=2.3bit was calculated. After normalization, a 3×3 physiological feature weight submatrix W1=[[0.32,0,0],[0,0.32,0],[0,0,0.36]] was obtained. The psychological features were decomposed into two-dimensional features, and a 2×2 conditional entropy matrix was constructed. The joint entropy matrix H_psy=1.8bit was calculated, and the corresponding psychological feature weight submatrix W2=[[0.28,0],[0,0.72]] was obtained. The seasonal phase transition from autumn to winter was constructed into a 3×3 transition entropy matrix H_sea=0.9bit, and the corresponding seasonal feature weight submatrix W3=[[0.40,0,0],[0,0.35,0],[0,0,0.25]]. The differential equations were solved using a ternary dynamic coupling model. Lyapunov stability constraints were introduced, and the model was iterated 100 times, ultimately generating a 3×3 feature fusion weight matrix W.

[0109] In this matrix, W[0,0]=0.35 represents the fusion weight of physiological features themselves, W[1,1]=0.25 represents the fusion weight of psychological features themselves, and W[2,2]=0.40 represents the fusion weight of seasonal features themselves. Off-diagonal elements (such as W[0,1]=0.15) represent the cross-fusion weight of physiological and psychological features, reflecting the mutual influence between different feature dimensions.

[0110] II. Examples of User Profile Construction Techniques 3. Tensor Fusion and Instructional Mapping The three types of feature vectors are constructed into a 3×10×8 third-order tensor T. This tensor is then fused with the core tensor G (3×3×3 dimensions) from Tucker decomposition and factor matrices A (10×3), B (8×3), and C (5×3), retaining 95% of the energy features before being compressed into a low-dimensional 3×5×4 tensor. This is then transformed by the teaching demand mapping matrix T_teach (20×12 dimensions) to generate a 1×20-dimensional teaching demand vector, which is ultimately mapped to a profile label matrix.

[0111] in: The first row [0.45, 0.38, 0.12, 0.05] represents the weights of "rhythm training priority (0.45), suitable second function modal coding (or liver function modal coding) soothing tracks (0.38), basic beat reinforcement (0.12), and emotion regulation assistance (0.05)". The second row [0.35, 0.50, 0.10, 0.05] corresponds to "Autumn Adaptation Training (0.35), Low-Intensity Melody Practice (0.50), Breathing Rhythm Coordination (0.10), Short-Term Skill Improvement (0.05)"; The third line is completed as [0.28, 0.32, 0.35, 0.05], which means "First function modulation coding (or pulmonary function modulation coding) endurance training (0.28), medium tempo adaptation (0.32), and visceral modulation audio coding". Conditioning (0.35), Fatigue Recovery Aid (0.05); The last line [0.08, 0.12, 0.30, 0.50] represents "Advanced Skills Preparation (0.08), Multi-Style Integration Training (0.12), Long-Term Rhythm Cultivation (0.30), and Mind-Body Balance Optimization (0.50)".

[0112] This matrix, with its structured weight distribution of 5 rows and 4 columns, fully covers the multi-dimensional needs from short-term teaching goals (such as rhythm training) to long-term physical and mental regulation (such as visceral tone audio coding therapy). The off-diagonal elements reflect the cross-influence of physiological, psychological, and seasonal characteristics (for example, the weight of "visceral tone audio coding therapy" is 0.35, which is also associated with stress index and psychological and emotional state).

[0113] III. Examples of Drum Music Track Matching Recommendations Based on Zhang's profile, the system performed music matching according to the following steps: 1. Music Dimension Mapping Based on the physiological characteristics of 72 points, a medium-speed rhythm of 60-80 BPM is recommended through the rhythm mapping matrix T_bpm (10×10), generating a 10×1 rhythm preference vector BPM=[0,0,0.7,0.3,0,0,0,0,0,0]; for the psychological parameters of anxiety tendency, an E minor soothing melody is recommended through the melody mapping matrix T_emotion (8×8), generating an 8×1 melody vector Melody=[0,0.8,0.2,0,0,0,0,0]; considering the stress index of 68 points, a 5×1 timbre compensation vector Tone=[0,0.3,0.5,0.2,0] is generated through the seasonal timbre matrix T_season (5×5), where a 20% increase in low-frequency compensation is required in autumn, corresponding to the matrix element Tone[2]=0.5.

[0114] 2. Track Feature Extraction Taking the drum version of "Autumn Whispers" as an example, the system extracted the following: In terms of audio features, a 128×128 MFCC spectrum matrix was generated, showing that the low-frequency energy accounted for 35%; in terms of performance parameters, a 100×10 drum dynamics matrix was constructed, with a standard deviation of 0.23, which is of medium difficulty and includes a feature sub-matrix of "eighth note + syncopation" rhythm pattern; in terms of semantic analysis, a 10×1 first functional mode code (or lung function mode code) feature suppression vector Metal=[0,0,0.32,0,0,0,0,0,0,0,0] was generated by associating GAT with the 10×10 music theory matrix constructed from "Music Notes".

[0115] 3. Calculation of cyclic association matching Mapping a user profile to a 3×3 physiological-psychological-seasonal three-dimensional feature matrix U = Mapping a user profile to a 3×3 first-mode encoding feature matrix (also known as the visceral mode encoding feature matrix) U =

[0116] The first line represents the correlation between physiological dimensions and "low-frequency rhythm adaptability". "0.6" indicates a high correlation between physiological characteristics and low-frequency rhythm adaptability; "0.3" and "0.1" indicate a low correlation with mid-frequency rhythm adaptability and tolerance to metallic timbre.

[0117] The second line represents the correlation between the psychological dimension and "mid-frequency rhythm adaptability". "0.2" indicates a low correlation with low-frequency rhythm adaptability; "0.7" indicates a high correlation with mid-frequency rhythm adaptability.

[0118] The third row represents the correlation between the seasonal dimension and "metallic timbre tolerance". "0.1" indicates a low correlation with low-frequency rhythm adaptability; "0.7" indicates a high correlation with metallic timbre tolerance. The first row represents the correlation between physiological secondary function modulation coding (or liver function modulation coding). "0.6" indicates a high degree of correlation between physiological characteristics and secondary function modulation coding (or liver function modulation coding); "0.3" and "0.1" indicate a low degree of correlation between physiological characteristics and tertiary function modulation coding (or kidney function modulation coding) and primary function modulation coding (or lung function modulation coding).

[0119] The second row represents the correlation between psychological third function modulation code (or kidney function modulation code). "0.2" indicates a low correlation between psychological characteristics and second function modulation code (or liver function modulation code); "0.7" indicates a high correlation between psychological characteristics and third function modulation code (or kidney function modulation code); "0.1" indicates a low correlation between psychological characteristics and first function modulation code (or lung function modulation code).

[0120] The third row represents the correlation between the seasonal first functional modal code (or lung function modal code). "0.1" indicates a low correlation between the seasonal feature and the second functional modal code (or liver function modal code); "0.2" indicates a relatively low correlation between the seasonal feature and the third functional modal code (or kidney function modal code); and "0.7" indicates a high correlation between the seasonal feature and the first functional modal code (or lung function modal code).

[0121] The feature mapping of the track is a 3×3 three-dimensional feature matrix V = The feature mapping of the track is a 3×3 second-mode encoding feature matrix V =

[0122] The first line represents the correlation between the timbre dimension and the user's "low-frequency rhythm compatibility". "0.1" indicates a low correlation; "0.7" indicates a high correlation with mid-frequency rhythm compatibility.

[0123] The second line represents the correlation between the rhythm dimension (BPM and rhythm complexity as recorded in the original scheme) and the user's "first function mode coding (or pulmonary function mode coding) tolerance". "0.2" indicates a low correlation; "0.7" indicates a high correlation with first function mode coding (or pulmonary function mode coding) tolerance.

[0124] The third row represents the correlation between the melody dimension (the mode and interval recorded in the original scheme) and the user's "low-frequency rhythm adaptability". "0.7" indicates a high correlation; "0.1" indicates a low correlation with tolerance to metallic timbre. The first row represents the correlation between the timbre's first functional mode code (or lung function mode code). "0.1" indicates a low correlation between the timbre feature and the second functional mode code (or liver function mode code); "0.7" indicates a high correlation between the timbre feature and the third functional mode code (or kidney function mode code); "0.2" indicates a low correlation between the timbre feature and the first functional mode code (or lung function mode code).

[0125] The second row represents the correlation between rhythmic features and the fourth functional modal coding (or cardiac function modal coding). "0.2" indicates a low correlation between rhythmic features and the second functional modal coding (or liver function modal coding); "0.1" indicates a low correlation between rhythmic features and the third functional modal coding (or kidney function modal coding); and "0.7" indicates a high correlation between rhythmic features and the first functional modal coding (or pulmonary function modal coding).

[0126] The third row represents the correlation between the melody's fifth functional mode code (or spleen function mode code). "0.7" indicates a high correlation between the melody feature and the second functional mode code (or liver function mode code); "0.2" indicates a low correlation between the melody feature and the third functional mode code (or kidney function mode code); and "0.1" indicates a low correlation between the melody feature and the first functional mode code (or lung function mode code).

[0127] Feature correlation matrix R_corr=

[0128] Feature association calculation: The matrix product of U and R_corr after dot product with V is 0.468.

[0129] Meaning: Constructed based on the positive correlation logic between user features and track features, reflecting the positive synergistic relationship between dimensional features. A "1" in the matrix indicates a significant positive correlation between features.

[0130] Meaning of each value: R_corr[0,2]=1 (positive correlation between metallic tone tolerance and low-frequency rhythm adaptability). R_corr[1,0]=1 (Positive correlation between low-frequency rhythm adaptability and mid-frequency rhythm adaptability). R_corr[2,1]=1 (positive correlation between mid-frequency rhythm adaptability and metallic timbre tolerance) R_corr[0,2]=1 (positive correlation between first-function modal coding (or lung function modal coding) and third-function modal coding (or kidney function modal coding)). R_corr[1,0]=1 (positive correlation between second-function modulation coding (or liver function modulation coding) and fourth-function modulation coding (or cardiac function modulation coding)). R_corr[2,1]=1 (positive correlation between fourth function modulation encoding (or cardiac function modulation encoding) and fifth function modulation encoding (or spleen function modulation encoding)). The remaining positions are 0, indicating that there is no positive correlation between the features.

[0131] Due to the existence of the feature suppression matrix Rsuppression = , is the transpose of the feature correlation matrix R_corr, constructed based on the conflict logic between user features and track features, reflecting the negative interference relationship between dimensional features. A "1" in the matrix indicates significant negative interference between features.

[0132] 4. Dynamic weight adjustment Due to the low entropy value of physiological characteristics, a 3×3 weight boosting matrix ΔW1 is generated.

[0133] Because the entropy value of physiological characteristics is low (meaning that the information related to physiological characteristics has high certainty and good stability), the weight of physiological characteristics should be increased. Meaning of each value: : This indicates that only weights related to physiological characteristics are increased; here, 0.15 is added to the weight dimension corresponding to the physiological second function modulation code (or liver function modulation code). Elements in other positions of the matrix are 0, meaning that the weight relationships between the psychological and seasonal dimensions and the various attributes remain unchanged.

[0134] Increase the physiological weight to 0.35 + ΔW1[0,0] = 0.4025.

[0135] The current HRV state generates a 3×3 penalty coefficient matrix ΔW2= .

[0136] Set a penalty of 1.2 for fast-paced gameplay; Based on the current HRV (Heart Rate Variability) status, a penalty coefficient is applied to fast-paced activities. The meanings of each value are as follows: This indicates that a penalty coefficient of 0.2 is set for the dimension related to the physiological secondary function modulation coding (or liver function modulation coding), which is related to rhythm-related factors. This coefficient will be applied for adjustments when fast-paced calculations are involved. The fact that other elements in the matrix are 0 indicates that the penalty is only applied to this dimension and does not affect other dimensions.

[0137] In the second week of autumn, a 3×3 gain coefficient matrix ΔW3 is generated. .

[0138] The gain coefficient of the second function tuning code (or liver function tuning code) is 1.3, and the final score matrix is ​​calculated as 0.251 through multiple matrix multiplication.

[0139] In the second week of autumn, based on seasonal factors and system settings, gain settings are applied to dimensions related to the second function modulation coding (or liver function modulation coding). Meaning of each value: This indicates that a gain coefficient of 0.3 is set on the dimension associated with the seasonal first functional modulation code (or lung function modulation code) and the physiological second functional modulation code (or liver function modulation code) (here, the second functional modulation code (or liver function modulation code)). The other elements in the matrix are 0, indicating that the gain operation is performed only on this specific dimension and does not affect the weight relationships of other dimensions.

[0140] 5. Ranking based on teaching specificity Based on the user's wrist strength test results, a 10×10 ability filtering matrix F is generated to exclude tracks with a strength threshold >80. Due to the imbalance of the user's physiological and psychological characteristics, a 5×5 track ranking improvement matrix R_rank is generated to improve the ranking of tracks corresponding to specific physiological characteristics in the second function mode code (or liver function mode code) by 3 places. Due to the imbalance of the user's organ function characteristics, a 5×5 track ranking improvement matrix R_rank is generated to improve the ranking of tracks corresponding to the second function mode code (or liver function mode code) by 3 places. Following the principle of progressive learning, a 10×10 difficulty coefficient matrix D=0.8^n is constructed, with the first recommended track difficulty matrix element D[0,0]=0.8 and the second recommended element D[1,1]=0.64.

[0141] IV. Recommendation Results and Closed-Loop Validation The system ultimately recommended three songs to Zhang, each corresponding to a 1×5 recommendation feature matrix: Listening to the Rain in the Bamboo Forest:

[0142] (Dimensions such as seasonal suitability, physiological compatibility, and teaching tags) "Autumn River Moonlight Night":

[0143] (Dimensions such as low-frequency drum beats and difficulty level) Spring Overture:

[0144] (Medium proportion of metallic tone, subsequent improvements in compatibility, etc.) Listening to the Rain in the Bamboo Forest (1×5 Matrix) 0.91: This represents the score for seasonality, indicating that the track performs well in terms of seasonality and is highly compatible with the user's physiological stress and other characteristics related to the current season.

[0145] 0.85: This represents the score in the physiological matching dimension, meaning that the rhythm, intensity, etc. of the track are well matched with the user's physical condition (such as heart rate variability, cardiovascular function, etc.) at the physiological level.

[0146] 0.78: The score corresponding to the teaching tag dimension reflects that the piece meets the teaching objectives, such as helping to achieve teaching requirements such as emotional regulation and rhythm training.

[0147] 0.82: This can be interpreted as a score in other specific dimensions of the track (such as psychological fit), indicating that it has a certain effect in regulating the user's psychological state.

[0148] 0.90: This represents the overall fit score. After considering various factors, the track is very suitable for the user.

[0149] "Autumn River Moonlit Night" 1×5 matrix 0.78: This indicates the score for the low-frequency drum beat dimension, meaning that the low-frequency drum beat characteristics of the track meet user needs, such as being related to the user's rhythm preferences and physiological stress state.

[0150] 0.80: This is the difficulty level score, meaning that the piece is of moderate difficulty and matches the user's current skill level.

[0151] 0.85: The score corresponding to the teaching tag dimension indicates that the piece meets the teaching objectives, such as showing good performance in skills training and mental and physical adjustment.

[0152] 0.75: This can be understood as a score in a specific dimension of the track (such as timbre matching), indicating that the timbre matches the user's preferences and tolerance to a certain extent.

[0153] 0.88: This represents the overall compatibility score, indicating that the track is highly compatible with users after considering various factors.

[0154] Spring Overture 1×5 matrix 0.67: This represents the score for the proportion of primary function mode coding (or pulmonary function mode coding), indicating that the proportion of primary function mode coding (or pulmonary function mode coding) in the track is low, which is consistent with the characteristic that users have low tolerance for primary function mode coding (or pulmonary function mode coding).

[0155] 0.72: This represents the score in the physiological matching dimension, meaning that the music is somewhat compatible with the user's physical condition in terms of rhythm, intensity, and other physiologically related aspects.

[0156] 0.70: The score of the corresponding track in a specific dimension (such as psychological fit), reflecting its effect on regulating the user's psychological state.

[0157] 0.80: This is the score for the teaching tag dimension, indicating that the piece of music meets the teaching objectives and can help achieve the corresponding teaching tasks.

[0158] 0.75: This represents the overall suitability score, indicating that after considering multiple factors, the track is suitable for users to a certain extent.

[0159] The user's actual performance data generated a 10×10 feedback matrix, showing that the rhythm following accuracy of "Listening to the Rain in the Bamboo Forest" reached 89%, and the HRV improved to 58ms after the performance. Based on this, the system updated the mapping parameter matrix in the profile, adjusting the E minor melody weight matrix element from 0.82 to 0.79, forming an optimization loop.

[0160] Quantum state mapping encoder: When mapping in the music dimension, a 2×8 melody parameter matrix is ​​generated through quantum entanglement, which includes both E minor and D minor. Subsequent verification shows that the matching degree matrix element corresponding to D minor is 0.71.

[0161] 3D convolution feature extraction: When analyzing the drum beat intensity of "Autumn River Moonlit Night", a 10×10×10 3D convolution kernel was used to capture the "crescendo and decrescendo" pattern matrix of intensity, time and pitch dimensions, which has a similarity of 0.85 with the playing habit matrix when the user is fatigued.

[0162] Dynamic Constraints on Feature Conflicts: User feedback-driven dynamic adjustment of feature conflict coefficients: When a user skips a track in the first functional mode coding (or lung function mode coding) three times consecutively, the system automatically updates the 5×5 feature suppression coefficient matrix, increasing the suppression coefficient of "first functional mode coding (or lung function mode coding) and second functional mode coding (or liver function mode coding) feature suppression" from 0.3 to 0.5, reducing the recommendation probability matrix elements of tracks in the first functional mode coding (or lung function mode coding) category to below 0.2. When a user skips a track in the first functional mode coding (or lung function mode coding) three times consecutively, the system automatically updates the 5×5 suppression coefficient matrix, increasing the feature suppression matrix elements of "first functional mode coding (or lung function mode coding) and second functional mode coding (or liver function mode coding)" from 0.3 to 0.5, reducing the recommendation probability matrix elements of this type of track to below 0.2.

[0163] IV. Examples of Teaching Plans Generated Based on Selected Drum Music Repertoire 1. Perform multi-dimensional feature analysis and structured processing on selected tracks such as "Listening to the Rain in the Bamboo Forest" to generate structured track data. Target for processing: Selected audio files of the drum version of "Listening to the Rain in the Bamboo Forest" Specific technical processing: Multitrack separation and signal cleaning: The kick, snare, and hi-hat tracks were separated using the Wave-U-Net model. Environmental noise was removed by spectral subtraction to obtain clean drum signals. For example, the kick track clearly separated the hit points on the 1st and 3rd beats, and the snare track contained "weak beat splitting" features.

[0164] Rhythm feature extraction and standardization: Drum beat positions are detected by CRNN (e.g., bass drum on beats 1 and 3 of 4 / 4 time, snare drum on beats 2 and 4 of reverse time). STFT analysis shows that the rhythm pattern is mainly medium-tempo quarter notes (BPM=70) with an entropy value of 0.3 (strong periodicity). The rhythm is aligned to the standard beat grid by DTW to generate a standardized rhythm sequence (timestamp accuracy 10ms).

[0165] Difficulty labeling system: Physical complexity (single-limb dominant, low coordination requirement), technical requirements (only basic eighth notes, no rolls / double jumps), dynamic range (uniform dynamics, variance 0.15), labeled as "basic" difficulty.

[0166] Generate: Structured track data for "Listening to the Rain in the Bamboo Forest" (including separated tracks, standardized rhythm sequences, and "basic" difficulty tags). 2. Perform multimodal data acquisition and quantitative modeling on Zhang's performance data to generate a user capability profile. Data processed: Zhang's performance data of "Listening to the Rain in the Bamboo Forest" (microphone audio, MIDI drum pad hit points, camera posture data). Specific technical processing: Multi-source data acquisition: Audio waveform (rhythm error approximately +80ms), MIDI data (hit points concentrated in the bass drum area, dynamics 60-80 / 127), and posture video (insufficient wrist lift of the right hand, resulting in snare drum hit point deviation of +10mm).

[0167] Calculation of quantitative evaluation indicators: Time dimension: DTW distance 120ms (rhythm accuracy 0.6), BPM standard deviation 5 (speed stability 0.7); Spatial dimension: left and right hand movement time difference 30ms (coordination 0.5), striking point deviation 8mm (precision 0.6); Strength dimension: dynamic range 20 (control ability 0.6), variance 0.2 (uniformity 0.7).

[0168] Dynamic ability modeling: The BKT model updates the probability of skill mastery (basic rhythm pattern 0.7, double jump 0.4, compound jump 0.2), and the Kalman filter predicts that the probability of mastering the basic rhythm pattern in the next 2 weeks can reach 0.9.

[0169] Based on this, the following ability profile of Zhang was generated (three-dimensional ability indicators: 0.6 / 0.5 / 0.6, skill mastery probability matrix: basic rhythm pattern 0.7, double jump 0.4). 3. The structured data of "Listening to the Rain in the Bamboo Forest" was matched and decomposed with Zhang's ability profile to generate a personalized teaching plan. Processing objects: Structured data from "Listening to the Rain in the Bamboo Forest" and Zhang's competency profile Specific technical processing: Atomic Skill Breakdown: Based on the "Basic" difficulty tag, the track is broken down into: Basic skills: Quarter note rhythm pattern (bass drum + snare drum offbeat), eighth note continuous beat; Transitional skill: Simple sixteenth note fill (hi-hat + snare drum); Establish a dependency: play eighth notes repeatedly → fill with sixteenth notes.

[0170] Personalized recommendation algorithms: Multi-objective optimization: Difficulty fit (basic skills 0.65) + interest (low-frequency rhythm preference 0.8), with priority given to quarter note rhythm patterns (accuracy required 0.6-0.7). DQN Dynamic Adjustment: Due to Zhang's low coordination of 0.5, sixteenth notes that require hand and foot coordination are not recommended for now. Instead, focus on strengthening single-limb rhythms (quarter notes on the bass drum).

[0171] Schedule and review plan: First practice (Day 1): Quarter note rhythm pattern, tempo 60 BPM, with visual cues (drum pad position marked in red). Review 1 (Day 3): Practice eighth notes repeatedly, adding dynamic control exercises (dynamic range increased to 30). If the accuracy is >80%, proceed to the transition skills. Review 2 (Day 7): Sixteenth note filling, combined with tactile feedback (vibration motor prompts for hit point deviation).

[0172] Based on this, the following teaching plan for Zhang's "Listening to the Rain in the Bamboo Forest" was generated (skill sequence: quarter note → eighth note → sixteenth note, review period: 1 day, 3 days, 7 days).

[0173] Figure 2 This is a schematic diagram of the structure of a coordinated feedback integrated drum teaching device according to an embodiment of this application. Figure 2 As shown, it includes: The first program unit is used to calculate the user's human biological index, human psychological parameters, and physiological stress index. The second program unit is used to construct user profiles based on human biological index, human psychological parameters, and physiological stress index. The third program unit is used to match user profiles against a built drum music library to filter out drum music tracks that match the user. The fourth program unit is used to generate a drum music teaching plan for the user based on the selected drum music pieces.

[0174] For exemplary descriptions of each unit in the above device embodiments, please refer to the above descriptions. Figure 1 The records.

[0175] Figure 3 This is a schematic diagram of the structure of an electronic device according to an embodiment of this application; as shown Figure 3 As shown, it includes: a memory 301 and a processor 302, the memory being used to store computer-executable instructions, and the processor being used to execute the computer-executable instructions to implement the teaching method described in any of the present applications.

[0176] Figure 4 This application provides a coordinated feedback integrated drum music teaching system, comprising: a front-end electronic device 401 and a back-end server 402. The front-end electronic device is used to collect multidimensional user data, and the back-end server is used to perform the following steps: processing the multidimensional user data to calculate the user's human biological index, human psychological parameters, and physiological stress index; constructing a user feature profile based on the human biological index, human psychological parameters, and physiological stress index; matching the user feature profile with a constructed drum music repertoire library to select drum music repertoire matching the user; and generating a drum music teaching plan for the user based on the selected drum music repertoire.

[0177] Front-end electronic devices, such as smartphones, tablets, and dedicated touch terminals, are used to implement: psychological characteristic data collection: collecting personality test results (such as the Big Five personality scale) and emotional self-rating data (SDS depression scale) through questionnaires. Seasonal Perception Input: Provides a visual seasonal selection interface and a text input box for bodily responses; Interactive feedback display: preview of recommended tracks and animation of performance instructions.

[0178] Those skilled in the art should understand that the scope of the invention involved in this application is not limited to technical solutions formed by specific combinations of the above-mentioned technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-mentioned technical features or their equivalent features without departing from the above-mentioned inventive concept. For example, technical solutions formed by substituting the above-mentioned features with (but not limited to) technical features with similar functions disclosed in this application.

Claims

1. A coordinated feedback integrated drum music teaching method, characterized in that, include: Calculate the user's human biological index, human psychological parameters, and physiological stress index; Based on human biological index, human psychological parameters, and physiological stress index, user profiles are constructed. User profiles are used to match the built drum music library to filter out drum music that matches the user. Based on the selected drum music tracks, a drum music teaching plan is generated for the user.

2. The integrated drum music teaching method with coordinated feedback as described in claim 1, characterized in that, Calculate the user's biometrics, including: Obtain the user's biological description; The user's human biological description is analyzed to obtain standardized basic biological data; Feature extraction and weight assignment are performed on standardized biological baseline data to calculate the human biological index.

3. The integrated drum music teaching method with coordinated feedback as described in claim 2, characterized in that, Calculating a user's psychological parameters includes: Obtain the user's psychological profile; The user's psychological descriptions are structured and classified to obtain standardized basic psychological data; Standardized psychological baseline data are fused from multiple dimensions to calculate human psychological parameters.

4. The integrated drum music teaching method with coordinated feedback as described in claim 2, characterized in that, Calculate the user's physiological stress index, including: Acquire raw data on users' perceptions of natural climate / seasonal phenomena; Stress feature identification processing is performed on users' raw data on natural climate / seasonal perception to generate basic stress data; The stress index is transformed by combining the basic stress data with the constructed TCM feature database to calculate the physiological stress index.

5. The integrated drum music teaching method with coordinated feedback as described in claim 1, characterized in that, User profiles are constructed based on human biological indices, human psychological parameters, and physiological stress indices, including: Human biological indices are mapped to a constructed standardized physiological feature space to generate physiological feature vectors; Human psychological parameters are input into the constructed emotion fluctuation assessment model for evaluation to generate psychological feature vectors; The physiological stress index is mapped onto a five-element dynamic equilibrium space to generate a seasonal adaptation feature vector. User profiles are constructed based on physiological feature vectors, psychological feature vectors, and seasonal adaptation feature vectors.

6. The integrated drum music teaching method with coordinated feedback as described in claim 5, characterized in that, User profiles are constructed based on physiological feature vectors, psychological feature vectors, and seasonal adaptation feature vectors. Calculate the probability distribution entropy value of the physiological feature vector; Calculate the joint dimensional entropy value of the psychological feature vectors; Calculate the dynamic transition entropy value of the seasonal adaptation feature vector; A feature fusion weight matrix is ​​generated based on the probability distribution entropy, joint dimension entropy, and dynamic transition entropy. Based on the feature fusion weight matrix, the physiological feature vector, psychological feature vector, and seasonal adaptation feature vector are weighted tensor fusion to generate a multi-dimensional fused feature vector. The multi-dimensional fused feature vectors are transformed into the constructed teaching demand mapping space to generate user feature profiles.

7. The integrated drum music teaching method with coordinated feedback as described in claim 6, characterized in that, Calculate the probability distribution entropy value of the physiological feature vector, including: The physiological feature vectors are binned into equal-frequency bins, and the sample probability of each bin interval is calculated. Based on the sample probabilities of each bin interval, the probability distribution entropy value of the physiological feature vector is calculated.

8. The coordinated feedback integrated drumming teaching method according to claim 6, characterized in that, Calculate the joint dimensional entropy value of the psychological feature vectors, including: The psychological feature vector is dimensionally segmented, and the marginal probability of each dimension is calculated. Based on the marginal probabilities of each dimension, the joint dimensional entropy value of the psychological feature vectors is calculated.

9. The integrated drum music teaching method with coordinated feedback as described in claim 6, characterized in that, Calculate the dynamic transition entropy value of the seasonal adaptation feature vector, including: Periodic phase extraction is performed on the seasonal adaptation feature vector; Based on the extracted periodic phase, the dynamic transition entropy value of the seasonal adaptation feature vector is calculated.

10. The coordinated feedback integrated drumming teaching method according to claim 6, characterized in that, Based on the probability distribution entropy, joint dimension entropy, and dynamic transition entropy, a feature fusion weight matrix is ​​generated, including: Based on the probability distribution entropy, joint dimension entropy, and dynamic transfer entropy, calculate the information gain rate of physiological feature vector, psychological feature vector, and seasonal adaptation feature vector; Based on the constructed ternary dynamic coupling model, the feature coupling coefficient matrix is ​​calculated according to the information gain ratio of physiological feature vector, psychological feature vector, and seasonal adaptation feature vector. Based on the constructed Lyapunov stability constraints, the feature coupling coefficient matrix is ​​iteratively updated to generate the feature fusion weight matrix.

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