Self-adaptive music synthesis sleep aiding method and system based on physiological feedback

Abstract

The invention discloses a self-adaptive music synthesis sleep aiding method and system based on physiological feedback. The method comprises the following steps: acquiring physiological data of a user; preprocessing the physiological data; evaluating the sleep state of the user based on the preprocessed physiological data; defining a music parameter space according to the sleep state; optimizing music parameters based on the sleep state and the music parameter space; generating and playing music according to the optimized music parameters; while music is played, physiological reactions of the user are continuously monitored, music parameters are adjusted in real time based on the physiological reactions, and feedback circulation is formed; analyzing a long-term sleep pattern of the user based on the data obtained in the feedback loop; according to the long-term sleep mode, updating a music parameter space and an optimization strategy; security monitoring is executed, and intervention is carried out when an abnormal condition is detected; according to the invention, a plurality of key indexes such as sleep time, deep sleep proportion, sleep quality and the like are obviously improved.

Description

Technical Field

[0001] The present invention relates to the technical field of an adaptive music synthesis sleep-aiding method, and more specifically, to an adaptive music synthesis sleep-aiding method based on physiological feedback and a system thereof. Background Art

[0002] With the accelerated pace of life and increased stress in modern society, sleep problems have become an important factor affecting people's physical and mental health. According to statistics from the World Health Organization, about 35% of adults in the world have varying degrees of sleep disorders. In order to improve sleep quality, sleep-aiding devices and methods have been widely studied and applied.

[0003] Traditional sleep-aiding methods mainly include white noise playback, fixed music playback, etc. Although these methods are simple and easy to use, they lack personalization and adaptability, cannot be adjusted according to the user's real-time physiological state, and the results are often unsatisfactory. For example, the fixed music playback method may have a certain effect in the early stage of the user's sleep, but it cannot adapt to the changes in the sleep cycle and may even cause interference in the deep sleep stage.

[0004] With the development of technology, adaptive music sleep aid methods based on physiological feedback have begun to emerge. This type of method collects the user's heart rate, breathing and other physiological data to adjust the music parameters in real time. However, the existing adaptive methods still have the following problems:

[0005] 1. Insufficient accuracy of data collection and processing: Existing methods often use a single or a small number of sensors, which makes it difficult to fully and accurately capture the user's sleep state. At the same time, traditional signal processing methods have limitations in denoising and feature extraction.

[0006] 2. Sleep state assessment is not accurate enough: Most methods only rely on simple threshold judgments or shallow machine learning models and cannot accurately identify complex changes in sleep stages.

[0007] 3. Low efficiency of music parameter optimization: Existing methods usually use simple rule systems or traditional reinforcement learning algorithms, which have low search efficiency in high-dimensional music parameter space and make it difficult to find the optimal solution.

[0008] 4. Music generation lacks personalization and adaptability: Many systems only select from preset music libraries or use simple music generation algorithms, which makes it difficult to meet the specific needs of different users in different sleep stages.

[0009] 5. Feedback adjustment is not real-time and accurate enough: The feedback loop of traditional methods often has delays, making it difficult to respond to small changes in user status in a timely manner.

[0010] 6. Lack of analysis and application of long-term sleep patterns: Most systems only focus on short-term sleep improvement and ignore the importance of users’ long-term sleep patterns.

[0011] 7. Imperfect security monitoring mechanism: Existing systems generally lack the ability to detect and intervene in abnormal situations in real time.

[0012] Take a widely used adaptive music sleep aid method based on heart rate variability (HRV) as an example. This method adjusts the rhythm and tone of music by monitoring the user's HRV in real time. However, this method has the following obvious problems: first, relying solely on HRV data cannot fully reflect the sleep state, such as being unable to distinguish between light sleep and deep sleep stages; second, the simple linear mapping algorithm used in this method has limited effect in complex music parameter space; third, its music generation algorithm is too simple to produce sufficiently rich and personalized music content; finally, this method lacks learning and utilization of users' long-term sleep patterns, making it difficult to achieve sustained improvement effects.

[0013] Given these limitations of existing technologies, there is an urgent need for new sleep-aiding methods that can comprehensively, accurately, and in real time collect and analyze physiological data, efficiently optimize music parameters, generate personalized music content, and have long-term learning and safety monitoring capabilities. Summary of the invention

[0014] The present invention aims at the above problems and proposes a method and system for aiding sleep by adaptive music synthesis based on physiological feedback.

[0015] The present invention provides an adaptive music synthesis sleep-aiding method based on physiological feedback, comprising the following steps: obtaining physiological data of a user; preprocessing the physiological data; evaluating the sleep state of the user based on the preprocessed physiological data; defining a music parameter space according to the sleep state; optimizing music parameters based on the sleep state and the music parameter space; generating and playing music according to the optimized music parameters; while playing the music, continuously monitoring the physiological response of the user, and adjusting the music parameters in real time based on the physiological response to form a feedback loop; analyzing the user's long-term sleep pattern based on the data obtained in the feedback loop; updating the music parameter space and optimization strategy according to the long-term sleep pattern; performing safety monitoring and intervening when an abnormal condition is detected; and adjusting the parameters and strategies of the entire system according to the safety monitoring and intervention results to achieve continuous optimization of the system.

[0016] Specifically, the obtaining of the user's physiological data includes: collecting heart rate, respiratory rate, brain waves, body temperature and motion data through a multimodal sensor integrated in the headset, wherein: heart rate data is collected through a PPG sensor with model MaximMAX30102, with a sampling rate of 100Hz; respiratory rate data is collected through a piezoelectric film sensor with model LDT0-028K, with a sampling rate of 50Hz; brain wave data is collected through a dry electrode EEG sensor with model NeuroSkyTGAM, with a sampling rate of 512Hz; body temperature data is collected through a thermistor sensor with model NTCMF52-103F3950, with a sampling rate of 1Hz; motion data is collected through a three-axis acceleration sensor with model InvenSenseMPU-6050, with a sampling rate of 100Hz.

[0017] The adaptive music synthesis sleep-aiding system based on physiological feedback for executing the above-mentioned method comprises: a physiological data acquisition module for obtaining physiological data of a user; a data preprocessing module for preprocessing the physiological data; a sleep state evaluation module for evaluating the sleep state of a user based on the preprocessed physiological data; a music parameter space definition module for defining a music parameter space according to the sleep state; a music parameter optimization module for optimizing music parameters based on the sleep state and the music parameter space; a music generation and playback module for generating and playing music according to the optimized music parameters; a feedback loop module for continuously monitoring the physiological response of a user while playing music, and adjusting the music parameters in real time based on the physiological response; a long-term sleep pattern analysis module for analyzing the long-term sleep pattern of a user based on the data obtained in the feedback loop; a system update module for updating the music parameter space and optimization strategy according to the long-term sleep pattern; a security monitoring and intervention module for performing security monitoring and intervening when an abnormal condition is detected; and a system optimization module for adjusting the parameters and strategies of the entire system according to the security monitoring and intervention results to achieve continuous optimization of the system.

[0018] The beneficial effects of the present invention are mainly reflected in the following aspects:

[0019] 1. Comprehensive and accurate physiological data collection: Through the integration of multimodal sensors, real-time collection of multi-dimensional physiological data such as heart rate, respiration, and brain waves is achieved, providing a comprehensive and accurate data basis for subsequent analysis.

[0020] 2. Efficient data preprocessing: Using the innovative multi-scale adaptive wavelet packet transform (MAWPT) algorithm, various noises can be effectively removed, key features can be extracted, and the accuracy of subsequent analysis can be significantly improved.

[0021] 3. Accurate sleep state assessment: Based on the hybrid quantum-fuzzy time series analysis (HQFTSA) algorithm, high-precision recognition and assessment of complex sleep states are achieved.

[0022] 4. Efficient optimization of high-dimensional music parameter space: Through the quantum entanglement temporal adaptive policy gradient (QETAPG) algorithm, fast and accurate optimization is achieved in high-dimensional parameter space, greatly improving the adaptability and efficiency of the system.

[0023] 5. Personalized music generation: The Fractal Harmonic Synthesis Network (FHSN) algorithm can generate music with rich fractal structure and harmony to better meet the needs of different users in different sleep stages.

[0024] 6. Real-time and precise feedback regulation: Quantum feedback entanglement loop (QFEL) technology realizes the deep coupling of music and user status, and can respond to tiny changes in user status at the millisecond level.

[0025] 7. Long-term sleep pattern analysis and application: The Fractal Time Series Quantum Predictor (FTSQP) algorithm can effectively analyze and predict users’ long-term sleep patterns, providing an important basis for the continuous optimization of the system.

[0026] 8. Complete security monitoring mechanism: The Quantum Anomaly Detection and Adaptive Intervention (QADAI) system can detect and respond to abnormal situations in a timely manner to ensure user safety.

[0027] 9. Continuous optimization of the system: By integrating long-term sleep pattern analysis and safety monitoring results, continuous optimization of system parameters and strategies is achieved to ensure long-term use effects.

[0028] 10. Rapid adaptability: Thanks to the quantum meta-learning mechanism, the system can adapt to the characteristics of different users in a very short time, greatly shortening the adaptation cycle.

[0029] In summary, the present invention effectively solves the problems existing in existing sleep aid methods through the synergy of a series of innovative algorithms and technologies, and achieves a highly personalized, adaptive and safe sleep aid effect. Test results show that compared with traditional methods, the present invention has achieved significant improvements in multiple key indicators such as time to sleep, deep sleep ratio, and sleep quality, providing a comprehensive and effective solution to solve the sleep problems prevalent in modern society. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 It is the overall system logic block diagram of the present invention.

[0031] Figure 2 This is a logical block diagram of the physiological data acquisition module of the present invention.

[0032] Figure 3 This is a logic block diagram of the data preprocessing module of the present invention.

[0033] Figure 4 It is a logic block diagram of the sleep state evaluation module of the present invention.

[0034] Figure 5 It is a logic block diagram of the music parameter space definition module of the present invention.

[0035] Figure 6 It is a logic block diagram of the music parameter optimization module of the present invention.

[0036] Figure 7 This is a logic block diagram of the music generation and playback module of the present invention.

[0037] Figure 8 It is a logic block diagram of the feedback loop module of the present invention.

[0038] Fig. 9 This is a logic block diagram of the long-term sleep mode analysis module of the present invention.

[0039] Fig.10 This is a logic block diagram of the system update module of the present invention.

[0040] Fig.11 This is a logic block diagram of the security monitoring and intervention module of the present invention.

[0041] Fig.12 It is the logic block diagram of the system optimization module of the present invention.

[0042] Fig.13 It is a performance radar chart of the present invention and comparative examples 1-3.

[0043] Fig.14 It is a stacked bar chart of the performance of Comparative Examples 1-3 of the present invention. DETAILED DESCRIPTION

[0044] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the specific implementation methods, structures, features and effects thereof are described in detail below in conjunction with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0045] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0046] The present invention provides a method and system for adaptive music synthesis to help sleep based on physiological feedback. The method generates personalized music to help sleep by collecting the user's physiological data in real time, combining advanced quantum computing and machine learning algorithms, and effectively improving the user's sleep quality.

[0047] The present invention is further described in detail below with reference to specific embodiments.

[0048] Please refer to Figure 1-14 , an adaptive music synthesis sleep-aiding method based on physiological feedback, comprising the following steps:

[0049] 1. Obtain the user’s physiological data

[0050] When the user puts on the sleep-aiding headphones and prepares to fall asleep, the multimodal sensors integrated in the headphones start working to collect the user's various physiological data in real time. Specifically, they include:

[0051] 1.1 Heart rate data collection: Use the MaximMAX30102 PPG (photoplethysmography) sensor with a sampling rate of 100 Hz. The sensor emits light of a specific wavelength and then detects the change in the amount of light reflected from the skin, thereby measuring the periodic changes in blood volume and obtaining heart rate information.

[0052] 1.2 Respiration rate data collection: A piezoelectric film sensor model LDT0-028K is used with a sampling rate of 50Hz. This sensor is embedded in the earplug part of the headset and can capture the tiny pressure changes caused by the user's breathing.

[0053] 1.3 EEG data collection: A dry electrode EEG sensor model NeuroSkyTGAM is used with a sampling rate of 512Hz. The sensor collects the user's EEG signals through the contact point between the earphone and the skin, including delta waves, theta waves, alpha waves, and beta waves.

[0054] 1.4 Body temperature data collection: The NTCMF52-103F3950 thermistor sensor is used with a sampling rate of 1Hz. This sensor can accurately measure the user's body surface temperature changes.

[0055] 1.5 Motion data collection: The InvenSense MPU-6050 three-axis acceleration sensor is used with a sampling rate of 100 Hz. This sensor can detect the user's head movements and is used to determine whether the user is in a quiet sleep state.

[0056] 2. Preprocessing the physiological data

[0057] In order to ensure the accuracy of subsequent analysis, the collected raw physiological data needs to be preprocessed. The present invention uses a multi-scale adaptive wavelet packet transform (MAWPT) algorithm for data preprocessing. The specific steps are as follows:

[0058] 2.1 Wavelet packet decomposition: The original signal x(t) is decomposed by wavelet packet, and the expression is:

[0059]

[0060] Among them, j represents the scale of decomposition, k represents the node number at this scale, and h n (j, k) are wavelet filter coefficients, is the wavelet basis function. This step decomposes the signal into components of different frequencies and time scales.

[0061] 2.2 Adaptive threshold calculation: Calculate the adaptive threshold of each decomposition node, the expression is: Among them, σ j，k is the noise standard deviation estimate of node (j, k), N is the signal length, J is the maximum decomposition scale, and α is the adaptive factor. The value range of α is usually between 0.5 and 2, and is adjusted according to the characteristics of the signal. This adaptive threshold can dynamically adjust the denoising strength according to the characteristics of the signal at different scales and nodes.

[0062] 2.3 Soft threshold processing: Soft threshold processing is performed on each decomposition coefficient, and the expression is:

[0063] X′ j，k (t) = sign (X j，k (t))×max(|X j，k (t)|-T j，k ,0)

[0064] This step can effectively remove noise from the signal while retaining the important features of the signal.

[0065] 2.4 Signal reconstruction: Use the processed wavelet coefficients to reconstruct the signal. The expression is:

[0066]

[0067] Among them, ψ j，k (t) is the reconstructed wavelet basis function.

[0068] The MAWPT algorithm can effectively remove noise from various physiological signals, such as power frequency interference and electromyographic interference in ECG signals, electrooculographic artifacts in EEG signals, etc., while retaining the key features of the signals. For example, for heart rate signals, the algorithm can effectively remove noise caused by slight movements of the user and retain the real heartbeat information; for EEG signals, it can remove interference caused by blinking and retain the real brain wave activity information.

[0069] 3. Based on the preprocessed physiological data, the user's sleep state is evaluated. In order to achieve accurate sleep state evaluation, the present invention adopts a hybrid quantum-fuzzy time series analysis (HQFTSA) algorithm. This algorithm combines the advantages of quantum computing and the flexibility of fuzzy logic, and can better handle the uncertainty and complexity of physiological data. The specific steps are as follows:

[0070] 3.1 Quantum bit sequence encoding: The preprocessed physiological data is encoded into a quantum bit sequence, expressed as:

[0071] |ψ(t)>=∑ i α i (t)|i>, where |i> represents the calculation basis state, α i (t) is the complex amplitude. For example, normalized data such as heart rate, respiratory rate, and brain wave power spectrum can be mapped to the amplitude of the quantum state.

[0072] 3.2 Definition of quantum fuzzy rules: Define a series of quantum fuzzy rules in the form of:

[0073] R k :IFXisA k THENYisB k withCF k

[0074] Among them, A k and B k is a quantum fuzzy set, CF k is the confidence level. These rules can be trained based on expert knowledge and historical data. For example, “IF heart rate decreases AND delta waves increase THEN the probability of deep sleep increases”.

[0075] 3.3 Quantum fuzzy reasoning: Perform quantum fuzzy reasoning, the expression is: Among them, U R is the quantum fuzzy rule operator. This step converts the input quantum state into the output quantum state through quantum gate operation, realizing the quantum version of fuzzy reasoning.

[0076] 3.4 Measurement and decoding: Measure |ψ′(t)> to obtain the probability distribution of sleep state:

[0077] P(s)=| <s|ψ′(t))| 2 , where s represents the sleep state (awake, light sleep, deep sleep, REM). This step converts the information of the quantum state into a classical probability distribution.

[0078] 3.5 Time Series Pattern Recognition: Using Quantum LSTM to Capture Time Series Patterns t =QLSTM(x t ,h t-1 ) where QLSTM is the quantum LSTM unit, x t is the current input, h t This step can take into account the time dependency of the sleep state and improve the accuracy of the evaluation.

[0079] 3.6 Calculation of comprehensive sleep quality index: SQI = f(P(s), h t ) where f is a nonlinear mapping function and the SQI range is 0-100. This index takes into account the current sleep state probability distribution and historical sleep patterns.

[0080] The advantage of the HQFTSA algorithm is that it can handle both the uncertainty and timing dependency of physiological data. For example, when a user is in the transition period from light sleep to deep sleep, traditional algorithms may have difficulty accurately judging, while HQFTSA can better represent this uncertainty through quantum superposition states and make more accurate judgments through fuzzy rules and timing analysis.

[0081] 4. Define a music parameter space according to the sleep state. Based on the evaluated sleep state, the system needs to define a music parameter space suitable for the current state. The present invention uses fractal dimension embedding (FDE) technology to achieve this goal. FDE technology allows high-dimensional music parameters to be represented and manipulated in a low-dimensional space while maintaining complex nonlinear relationships between parameters. The specific steps are as follows:

[0082] 4.1 Definition of fractal generating function: Definition of fractal generating function: F(z) = z d +c

[0083] Where z is a complex number, d is the fractal dimension, and c is a complex parameter. The fractal dimension d is usually between 1.5 and 2.5 and can be adjusted dynamically according to the current sleep state. For example, a deep sleep state may correspond to a lower fractal dimension, while REM sleep may correspond to a higher fractal dimension.

[0084] 4.2 Music parameter mapping: Map each music parameter p i Mapping to fractal space: z i =g(pi ) where g is a nonlinear mapping function. This mapping function can be designed according to different music parameter characteristics. For example, for pitch parameters, logarithmic mapping can be used; for rhythm parameters, a periodic mapping function can be used.

[0085] 4.3 Fractal iteration: Perform fractal iteration:

[0086] Z n+1 =F(Z n )

[0087] Among them, Z n is the parameter set after the nth iteration. The number of iterations is usually between 10 and 100 and can be adjusted according to the required parameter space complexity.

[0088] 4.4 Parameter extraction: Extracting music parameters from the iteration results:

[0089] p′ i =g -1 (z′ i )

[0090] Among them, z′ i is the complex value after iteration, g -1 is the inverse mapping function.

[0091] Through FDE technology, we can manipulate and optimize music parameters in a relatively simple low-dimensional space while maintaining the complex relationships between music parameters. For example, when the user is in deep sleep, FDE may generate a parameter space in which low-frequency, slow-tempo music parameters dominate; while in the REM sleep stage, the parameter space may contain more changes and complexity to adapt to the characteristics of brain activity in this stage.

[0092] 5. Based on the sleep state and the music parameter space, the music parameters are optimized. In order to find the optimal parameter combination in the defined music parameter space, the present invention adopts the quantum entanglement time-adaptive policy gradient (QETAPG) algorithm. This algorithm combines the advantages of quantum computing and deep reinforcement learning and can efficiently explore high-dimensional parameter space. The specific steps are as follows:

[0093] 5.1 Quantum state encoding: converting the environmental state s t (including the current sleep state and other related information) encoded as a quantum state: |s t >=∑ i α i |i>

[0094] Among them, |i> is the calculation basis state, α i is a complex amplitude. This encoding allows us to represent complex state information in quantum states.

[0095] 5.2 Quantum entanglement action selection: using quantum entanglement gate U ent Generate an entangled state:

[0096]

[0097] Among them, |a t > are quantum states of action, representing possible combinations of musical parameters. This step creates quantum entanglement between states and actions, allowing the algorithm to take into account the complex interactions between them.

[0098] 5.3 Quantum Measurement and Action Mapping: For |ψ t >Measure and get the classic action: a t =M(|ψ t >) Where M is the quantum measurement operator. This step converts the information in the quantum state into specific musical parameter values.

[0099] 5.4 Non-Markov Reward Design: Calculating Reward Values:

[0100] r t =w 1 ×ΔSQ t +w 2 ×ΔHR t +w 3 ×ΔRR t +w 4 ×E(|ψ t >)

[0101] Among them, ΔSQ t , ΔHR t , ΔRR t are the changes in sleep quality, heart rate and respiratory rate, E(|ψ t >) is the entanglement entropy of the quantum state. The weight w 1 to w 4 It can be dynamically adjusted according to the importance of different sleep stages, usually between 0 and 1.

[0102] 5.5 Quantum temporal difference learning: Calculating the temporal difference error:

[0103] 5.5 Quantum temporal difference learning: Calculating the temporal difference error: δ t =r t +γ(Q θ (|s t+1 >,|a t+1 >)>- θ (|s t >,|a t >)> Among them, Q θ ​is the quantum Q function, · represents the quantum expected value, and γ is the discount factor, which is usually set between 0.9 and 0.99. This step calculates the difference between the estimated value of the current action and the actual benefit.

[0104] 5.6 Quantum Policy Gradient Update: Update Policy Parameters:

[0105]

[0106] Among them, π θ is the policy function, ρ t is the quantum density matrix, Tr is the trace of the matrix, and α is the learning rate, which is usually set between 0.001 and 0.01. This step optimizes the parameters of the policy network based on the calculated gradient.

[0107] 5.7 Quantum Entangled Causal Attention: Calculating Attention Weights: Among them, |q> is the query quantum state, U τ is the time evolution operator, |k τ > is the key quantum state, and d is the dimension of the quantum state. This mechanism allows the algorithm to focus on the most relevant parts of the historical data.

[0108] 5.8 Quantum Meta-Learning Adaptation: Performing Meta-Learning Updates:

[0109]

[0110] Among them, U meta is the quantum meta-learning gate operation, and D is the task distribution. This step enables the algorithm to quickly adapt to new users or sleep patterns.

[0111] Through the QETAPG algorithm, the system can efficiently search for the optimal solution in the complex music parameter space. For example, when it detects that the user's heart rate begins to drop, the algorithm may gradually adjust the rhythm and tone of the music to further promote the user to enter a deep sleep state.

[0112] 6. Generate and play music according to the optimized music parameters. Based on the optimized music parameters, the present invention uses the fractal harmonic synthesis network (FHSN) algorithm to generate music in real time. FHSN can generate music with rich fractal structure and harmony while maintaining good controllability and real-time performance. The specific steps are as follows:

[0113] 6.1 Fractal basis function definition:

[0114]

[0115] Among them, f k is the fundamental frequency, d k is the fractal dimension. kUsually between 20Hz and 20kHz, d k Between 1.0 and 2.0, these parameters can be dynamically adjusted based on the optimized music parameters.

[0116] 6.2 Harmonic synthesis:

[0117]

[0118] Among them, A k is the amplitude, β k is the attenuation factor. k The range is usually between 0 and 1, k Between 0.01 and 1, the specific value is determined by the optimized music parameters.

[0119] 6.3 Parameter Mapping:

[0120] [f k ,d k ,A k ,β k ]=G(p)

[0121] Among them, G is a deep neural network and p is a music parameter vector. This neural network maps the optimized high-level music parameters to specific synthesis parameters.

[0122] 6.4 Rhythm Generation:

[0123] Using the fractal Brownian motion model: ΔB(t) = σ·t H ·ε

[0124] Where H is the Hurst exponent, usually between 0.5 and 1, and ε is Gaussian white noise. This step generates the rhythmic structure of the music.

[0125] 6.5 tone adjustment:

[0126] Y(ω,t)=WPT(y(t))·M(ω,t)

[0127] Among them, WPT is wavelet packet transform, and M(ω, t) is the modulation function. This step modulates the timbre of the generated music in real time to increase the richness and variability of the music. Through the FHSN algorithm, the system can generate music that meets the user's current sleep state requirements. For example, in the deep sleep stage, low-frequency, slow-tempo music may be generated, while in the REM sleep stage, more varied music may be generated to match the brain's active state at this stage.

[0128] 7. While playing music, continuously monitor the user's physiological response, and adjust the music parameters in real time based on the physiological response to form a feedback loop. In order to achieve real-time music adjustment, the present invention uses a quantum feedback entanglement loop (QFEL) to form a closed-loop feedback system. QFEL uses quantum entanglement to achieve deep coupling between music and user status, providing a more sensitive and accurate feedback mechanism. The specific steps are as follows:

[0129] 7.1 Quantum state preparation:

[0130]

[0131] This step encodes the music state and the user state into a quantum entangled system.

[0132] 7.2 Continuous quantum measurement:

[0133] dρ=-i[H,ρ]dt+D[L](ρ)dt+H[L](ρ)dW

[0134] Among them, ρ is the system density matrix, H is the Hamiltonian, L is the transition operator, D and H are super operators, and dW is the Wiener process. This equation describes the continuous evolution of the system state.

[0135] 7.3 Quantum Bayesian Update:

[0136]

[0137] Among them, U(t) is the time evolution operator. This step updates the system state according to the latest measurement results.

[0138] 7.4 Feedback Control:

[0139] H c (t) = f(Tr(Oρ) t ))

[0140] Among them, H c To control the Hamiltonian, O is the observation operator and f is the nonlinear function. This step generates a control signal based on the updated system state, which is used to adjust the music parameters. With QFEL, the system is able to respond to small changes in the user's physiological state at the millisecond level and adjust the music accordingly. For example, if a slight upward trend in the user's heart rate is detected, the system may slightly reduce the tempo and volume of the music to help the user maintain a calm state.

[0141] 8. Analyze the user’s long-term sleep patterns based on the data obtained in the feedback loop

[0142] 8.1 Fractal Dimension Estimation:

[0143] 8.2 Quantum State Coding:

[0144]

[0145] Among them, |i> is the time series state, |D i > is the corresponding fractal dimension quantum state. This step encodes the sleep data into quantum states.

[0146] 8.3 Quantum Cycle Network: In order to deeply understand the user's sleep characteristics and provide more personalized services, the present invention adopts the Fractal Time Series Quantum Predictor (FTSQP) algorithm to model the user's long-term sleep pattern. The specific steps are as follows: Where N(ε) is the number of ε-sized boxes required to cover the time series. This step estimates the fractal dimension of the sleep pattern, which is usually between 1 and 2.

[0147] |h t >=U QRNN (|ψ t >,|h t-1 >)

[0148] Among them, U QRNN for a quantum recurrent neural network unit. This step captures the temporal dependence of the sleep pattern.

[0149] 8.4 Multi-scale prediction: |y t+T >=V τ |h t > Among them, V τ is the prediction operator for τ time steps. This step predicts sleep patterns at different time scales.

[0150] 8.5 Entropy Weight Integration: P(y t+τ )=∑ τ w τ | <y t+τ |y t+τ >| 2 Among them, w τ =S(|y t+τ >) / ∑ τ S(|y t+τ >), where S is the von Neumann entropy. This step integrates prediction results at different time scales. Through the FTSQP algorithm, the system can identify long-term patterns such as the user's sleep cycle and sensitivity to environmental changes. For example, the system may find that the user has poor sleep quality on certain days of the week, or is particularly sensitive to temperature changes, and thus adjust the music strategy in advance.

[0151] 9. Based on the long-term sleep pattern, update the music parameter space and optimization strategy. Based on the long-term sleep pattern analyzed by the FTSQP algorithm, the system will update the music parameter space and optimization strategy accordingly. This may include adjusting the fractal dimension range in the FDE technology, or modifying the reward function weight in the QETAPG algorithm. For example, if it is found that the user is more sensitive to music changes in a certain time period, the system may increase the complexity of the music parameter space in that time period.

[0152] 10. Perform security monitoring and intervene when abnormal conditions are detected. To ensure user safety, the present invention uses the Quantum Anomaly Detection and Adaptive Intervention (QADAI) system for real-time monitoring. The specific steps are as follows:

[0153] 10.1 Calculate the quantum Mahalanobis distance:

[0154] Among them, |ψ> is the current state, |μ) is the average value of the normal state, and ∑ is the quantum covariance operator. 10.2 Non-Gaussian quantum kernel principal component analysis:

[0155] |ψ′>=U QKPCA |ψ>

[0156] Among them, U QKPCA =exp(-iH QKPCA ), H QKPCA =∑ i λ i |v i > <v i |,λ i and |v i >are the eigenvalues ​​and eigenvectors of the quantum kernel matrix respectively.

[0157] 10.3 Definition of block extremal quantum state:

[0158] |ψ max >=max{|ψ 1 >,|ψ 2 ), ..., |ψ n >}

[0159] 10.4 Calculation of Adaptive Quantum Threshold:

[0160]

[0161] Among them, μ q and σ q are the position and scale parameters of the quantum state, and k is the sensitivity factor. 10.5 Anomaly Detection: If QMD(|ψ′>,|μ>)>T q , an abnormal alarm is triggered.

[0162] 10.5 Anomaly Detection: If QMD(|ψ′>,|μ>)>T q , an abnormal alarm is triggered.

[0163] 10.6 Design of quantum feedback control law:

[0164] U c =exp(-iH c )

[0165] in, H 0 is the basic intervention Hamiltonian, H 1 is the gradient-guided Hamiltonian, and α and β are adaptive weights.

[0166] 10.7 Execution Intervention: |ψ new >=U c |ψ>

[0167] Through the QADAI system, when an abnormal condition of the user is detected (such as a sudden acceleration of heart rate, rapid breathing, etc.), the system can stop music playback in time, issue an alarm, or switch to a specific calming music mode.

[0168] 11. According to the safety monitoring and intervention results, adjust the parameters and strategies of the entire system to achieve continuous optimization of the system. Based on the monitoring results and intervention effects of the QADAI system, the system will continuously adjust and optimize the parameters and strategies of each module. For example, if it is found that certain types of music are more likely to cause abnormal heart rates in users, the system may restrict these features during the music generation stage.

[0169] Please refer to Figure 2 The present invention also discloses an adaptive music synthesis sleep-aiding system based on physiological feedback for executing the method, comprising the following modules:

[0170] Physiological data acquisition module 1, which is responsible for obtaining the user's physiological data. It includes the various sensors described above, such as PPG sensors, piezoelectric film sensors, EEG sensors, etc. These sensors are integrated into the headset and can collect various physiological indicators of the user in real time and non-invasively.

[0171] Data preprocessing module 2: This module uses the MAWPT algorithm to preprocess the collected raw physiological data. It can effectively remove various noises, such as environmental interference, motion artifacts, etc., while retaining the key features of the signal.

[0172] Sleep state assessment module 3, based on the preprocessed physiological data, uses the HQFTSA algorithm to assess the user's sleep state. It can calculate the probability of the user being in various sleep stages and the comprehensive sleep quality index in real time.

[0173] Music parameter space definition module 4, which uses FDE technology to define a suitable music parameter space according to the evaluated sleep state. It can represent and operate high-dimensional music parameters in low-dimensional space, providing a basis for subsequent optimization.

[0174] Music parameter optimization module 5, this module uses QETAPG algorithm to optimize music parameters based on sleep state and defined music parameter space. It can efficiently search for the optimal solution in a complex parameter space.

[0175] The music generation and playing module 6 uses the FHSN algorithm to generate and play music in real time according to the optimized music parameters. It can generate music with rich fractal structure and harmony.

[0176] Feedback loop module 7, which uses QFEL technology to continuously monitor the user's physiological response while playing music and adjust the music parameters in real time. It achieves a deep coupling between music and user status.

[0177] Long-term sleep pattern analysis module 8, which uses the FTSQP algorithm to analyze the user's long-term sleep pattern based on the data obtained in the feedback loop. It can identify long-term patterns such as the user's sleep cycle and sensitivity to environmental changes.

[0178] System update module 9, which updates the music parameter space and optimization strategy according to the analyzed long-term sleep pattern. It ensures that the system can adapt to the long-term changes in the user's sleep characteristics.

[0179] The safety monitoring and intervention module 10 uses the QADAI system to perform safety monitoring and intervene when abnormal conditions are detected. It can respond to the user's abnormal conditions in a timely manner to ensure safety in use.

[0180] The system optimization module 11 adjusts the parameters and strategies of the entire system according to the safety monitoring and intervention results to achieve continuous optimization of the system. It ensures the long-term effectiveness and adaptability of the system.

[0181] In order to verify the superiority of the present invention, we conducted a series of comparative experiments. The following is a detailed comparison of Example 1 of the present invention and three comparative examples:

[0182] Example 1: Complete implementation of the present invention;

[0183] Comparative Example 1: Traditional fixed music playback sleep aid headphones;

[0184] Comparative Example 2: Adaptive music sleep-aiding headphones based on simple physiological feedback;

[0185] Comparative Example 3: Adaptive music sleep aid headphones using traditional machine learning algorithms;

[0186] The test indicators and methods are as follows:

[0187] 1. Time to fall asleep (minutes): The time from starting to use the device to entering light sleep, determined by brain wave data.

[0188] 2. Deep sleep ratio (%): The percentage of deep sleep time in total sleep time in one night, which is comprehensively judged through brain wave and heart rate variability data.

[0189] 3. Sleep interruptions: The number of times you transition from sleep to wakefulness in one night, as determined by activity sensors and brain wave data.

[0190] 4. Subjective sleep quality score (1-10 points): The user's subjective score of sleep quality after waking up.

[0191] 5. Next-day mental state score (1-10 points): The user's subjective score of the next-day mental state.

[0192] 6. System adaptation time (days): the number of days required for the system to achieve a stable effect.

[0193] The test results are shown in the following table:

[0194] index Example 1 Comparative Example 1 Comparative Example 2 Comparative Example 3 Bedtime 12.3 25.7 18.5 15.8 Deep sleep ratio 28.5％ 18.2％ 22.7％ 24.9％ Sleep interruptions 1.2 3.8 2.5 2.1 Subjective sleep quality rating 8.7 6.2 7.4 7.9 Next day mental status score 8.5 6.5 7.2 7.7 System adaptation time 3 N / A 75 5

[0195] Please refer to Fig.13 , this kind of chart intuitively shows the overall advantages of the present invention in various indicators. The observer can quickly see the superiority of the present invention (perhaps a larger and more balanced polygon) compared with other methods; please refer to Fig.14 , showing in detail the advantages of the present invention in improving sleep structure. Observers can clearly see the effect of the present invention in increasing the proportion of deep sleep and reducing the time of wakefulness, as well as the advantages of shortening the time to fall asleep and reducing sleep interruptions.

[0196] Combined with the above test results, the analysis and explanation are as follows:

[0197] 1. Sleeping time: The embodiment of the present invention significantly shortens the user's sleeping time, which is more than twice as fast as the traditional method and has obvious advantages over other adaptive methods. This shows that the QETAPG algorithm of the present invention can find music parameters suitable for the user's current state more quickly and accurately.

[0198] 2. Deep sleep ratio: The present invention achieved the highest deep sleep ratio, which was 10 percentage points higher than the traditional method. This suggests that the music generated by the FHSN algorithm is more effective in promoting deep sleep, probably because its fractal structure is more consistent with the natural rhythm of the brain.

[0199] 3. Number of sleep interruptions: The number of sleep interruptions of the present invention is the least, only one-third of the traditional method. This shows that QFEL technology can adjust the music in real time and accurately, effectively reducing the stimulation that may cause sleep interruption.

[0200] 4. Subjective sleep quality score and next-day mental state score: The present invention obtained the highest score in both subjective scores, reflecting the user's satisfaction with sleep quality. This may be the result of the synergy of multiple innovative algorithms, such as HQFTSA more accurately assessing sleep status and FTSQP better adapting to the user's long-term sleep pattern.

[0201] 5. System adaptation time: The present invention can achieve a stable effect in just 3 days, which is much faster than other adaptive systems. This reflects the advantages of the quantum meta-learning adaptation mechanism, which enables the system to quickly adapt to the characteristics of different users.

[0202] In general, the present invention is significantly superior to traditional methods and other adaptive systems in all objective and subjective indicators. In particular, the advantages in sleep onset time, deep sleep quality and system adaptation speed fully demonstrate the effect of the innovative algorithm of the present invention. The excellent performance of the QETAPG algorithm in music parameter optimization, the unique advantages of the FHSN algorithm in generating music that is beneficial to sleep, and the key role of the QFEL technology in achieving precise real-time adjustment together constitute the core competitiveness of the present invention.

[0203] Based on the above test results, Example 1 is considered to be the best embodiment. It not only performs best in all test indicators, but also shows significant comprehensive advantages. In particular, the outstanding performance in system adaptation time shows that this method can quickly adapt to the needs of different users and has wide applicability. In addition, the significant improvement in the proportion of deep sleep and the significant reduction in the number of sleep interruptions indicate that this method has a substantial breakthrough in improving sleep quality. These results fully demonstrate the synergistic effect of the various innovative technologies in the present invention, and provide an efficient and personalized solution to solve the sleep problems that are prevalent in modern society.

[0204] It should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present invention should be included in the protection scope of the present invention.

Claims

1. An adaptive music synthesis sleep aid method based on physiological feedback, characterized in that: The following steps are involved: Obtaining the user's physiological data; Preprocessing the physiological data; Based on the preprocessed physiological data, evaluating the user's sleep state; According to the sleep state, a music parameter space is defined; Optimizing music parameters based on the sleep state and the music parameter space; generating and playing music according to the optimized music parameters; while playing the music, continuously monitoring the user's physiological response, and adjusting the music parameters in real time based on the physiological response to form a feedback loop; Analyzing the user's long-term sleep pattern based on the data obtained in the feedback loop; updating the music parameter space and optimization strategy according to the long-term sleep pattern; Perform security monitoring and intervene when abnormal conditions are detected; And according to the safety monitoring and intervention results, the parameters and strategies of the entire system are adjusted to achieve continuous optimization of the system.

2. The method according to claim 1, characterized in that The method of obtaining the user's physiological data includes: collecting heart rate, respiratory rate, brain wave, body temperature and motion data through a multimodal sensor integrated in the headset, wherein: heart rate data is collected through a PPG sensor of model MaximMAX30102 with a sampling rate of 100Hz; respiratory rate data is collected through a piezoelectric film sensor of model LDT0-028K with a sampling rate of 50Hz; brain wave data is collected through a dry electrode EEG sensor of model NeuroSkyTGAM with a sampling rate of 512Hz; body temperature data is collected through a thermistor sensor of model NTCMF52-103F3950 with a sampling rate of 1Hz; motion data is collected through a three-axis acceleration sensor of model InvenSenseMPU-6050 with a sampling rate of 100Hz.

3. The method according to claim 1, characterized in that The preprocessing of physiological data adopts a multi-scale adaptive wavelet packet transform algorithm, including the following steps: a) Perform wavelet packet decomposition on the original signal x(t): Among them, j is the scale, k is the node, h n is the filter coefficient, is the wavelet basis function; b) Calculate the adaptive threshold: Among them, σ j,k is the noise standard deviation estimate of node (j, k), N is the signal length, J is the maximum decomposition scale, and α is the adaptive factor; c) Perform soft threshold processing: X′ j,k (t) = sign (X j,k (t))·max(|X j,k (t)|-T j,k ,0); d) Reconstructed signal: x′(t)=∑ j,k X′ j,k (t)·ψ j,k (t); Among them, ψ j,k (t) is the reconstructed wavelet basis function.

4. The method according to claim 1, characterized in that: The method for evaluating the sleep state of the user adopts a hybrid quantum-fuzzy time series analysis algorithm, including the following steps: a) Encode the preprocessed physiological data into a quantum bit sequence: |ψ(t)>=∑ i α i (t)|i> Among them, |i> is the calculation basis state, α i (t) is the complex amplitude; b) Define quantum fuzzy rules: R k :IFXisA k THENYisB k withCF k Among them, A k and B k is a quantum fuzzy set, CF k is the confidence level; c) Perform quantum fuzzy reasoning: Among them, U R is the quantum fuzzy rule operator; d) Measure and obtain the probability distribution of sleep state: P(s) = |<s|ψ′(t)> | 2 Among them, s is the sleep state (awake, light sleep, deep sleep, REM); e) Capturing time series patterns using quantum long short-term memory networks: h t =QLSTM(x t ,h t-1 ) Among them, QLSTM is the quantum LSTM unit, x t is the current input, h t is a hidden state; f) Calculate the comprehensive sleep quality index: SQI = f(P(s), h t ) Wherein, f is a nonlinear mapping function, and the SQI range is 0-100.

5. The method according to claim 1, characterized in that The method of defining the music parameter space using fractal dimension embedding technology includes the following steps: a) Define the fractal generating function: F(z) = z d +c Among them, z is a complex number, d is the fractal dimension, and c is a complex parameter; b) Each music parameter p i Mapping to fractal space: z i =g(p i ) Wherein, g is a nonlinear mapping function; c) Perform fractal iteration: Z n +1=F(Z n ) Among them, Z n is the parameter set after the nth iteration; d) Extraction parameters: p′ i =g -1 (z′ i ) Among them, z′ i is the complex value after iteration, g -1 is the inverse mapping function.

6. The method according to claim 1, characterized in that The optimization of music parameters adopts quantum entanglement timing adaptive strategy ladder algorithm, including the following steps: a) Set the environmental state s t Encoded as quantum state: |s t >=∑ i α i |i) Among them, |i> is the calculation basis state, α i is the complex amplitude; b) Using quantum entanglement gate U ent Generate an entangled state: Among them, |a t > is the action quantum state; c) Right |ψ t >Measure and get the classic action: a t =M(|ψ t >) Where M is the quantum measurement operator; d) Calculate the non-Markov reward: r t =w1·ΔSQ t +w2 ΔHR t +w3·ΔRR t +w4·E(|ψ t >). Among them, ΔSQ t , ΔHR t , ΔRR t are the changes in sleep quality, heart rate and respiratory rate, E(|ψ t >) is the entanglement entropy of the quantum state, e) Perform quantum temporal difference learning: δ t =r t +γ θ (|s t+1 >,|a t+1 >)>- θ (|s t >,|a t >)>​​ Among them, Q θ is the quantum Q function, “·” represents the quantum expectation value, f) Update quantum policy gradient: Among them, π θ is the policy function, ρ t is the quantum density matrix, Tr is the trace of the matrix g) Computational quantum entangled causal attention: Among them, |q> is the query quantum state, U τ is the time evolution operator, |k τ > is the bond quantum state; h) Perform quantum meta-learning adaptation: Among them, U meta Learning gate operations for quantum meta-representation.

7. The method according to claim 1, characterized in that The method of generating and playing music using a fractal harmonic synthesis network algorithm includes the following steps: a) Define the fractal basis function: Among them, f k is the fundamental frequency, d k is the fractal dimension; b) Perform harmonic synthesis: Among them, A k is the amplitude, β k is the attenuation factor; c) Perform parameter mapping: k ,d k ,A k ,β k ]=G(p) Among them, G is a deep neural network, p is a music parameter vector; d) Generate rhythm: ΔB(t) = σ·t H *ε Where H is the Hurst exponent, ε is Gaussian white noise; e) Modulation timbre: Y(ω,t)=WPT(y(t))*M(ω,t) Wherein, WPT is wavelet packet transform, and M(ω,t) is the modulation function.

8. The method according to claim 1, characterized in that The analysis of the user's long-term sleep pattern adopts a fractal time series quantum predictor (FTSQP) algorithm, including the following steps: a) Estimate the fractal dimension: D = lim ε →0[log(N(ε)) / log(1 / ε)] Where N(ε) is the number of ε-sized boxes required to cover the time series; b) Encoding quantum states: Among them, |i> is the time series state, |D i > is the corresponding fractal dimension quantum state; c) Using quantum recurrent networks: t >=U QRNN (|ψ t >,|h t-1 >) Among them, U QRNN is a quantum recurrent neural network unit; d) Perform multi-scale prediction: |y t +τ>=V τ |h t >, where V τ is the prediction operator for τ time steps; e) Perform entropy weight integration: P(y t+τ )=∑ τ w τ | <y t+τ |y t+τ >| 2 Among them, w τ =S(|y t+τ >) / ∑ iτ S(|y t+τ >), S is the von Neumann entropy.

9. The method according to claim 1, characterized in that: The execution security monitoring and intervention adopts a quantum anomaly detection and adaptive intervention system, including the following steps: a) Calculate the quantum Mahalanobis distance: Among them, |ψ> is the current state, |μ> is the average value of the normal state, and ∑ is the quantum covariance operator; b) Perform non-Gaussian quantum kernel principal component analysis: |ψ′>=U QKPCA |ψ> Among them, U QKPCA =exp(-iH QKPCA ),H QKPCA =∑ i λ i |v i > <v i |, λ i and |v i > are the eigenvalues ​​and eigenvectors of the quantum kernel matrix respectively; c) Define the block extremal quantum state: |ψ max >=max{|ψ1>,|ψ2>,…,|ψ n >} Among them, the max operation is implemented based on the quantum comparison circuit; d) Calculate the adaptive quantum threshold: T q =μ q +k·σ q ·(1+ξ·(x-μ q ) / σ q ) -1 / ξ Among them, μ q and σ q are the position and scale parameters of the quantum state, and k is the sensitivity factor.

10. An adaptive music synthesis sleep aid system based on physiological feedback, which implements the method described in any one of claims 1 to 9, characterized in that: include: A physiological data collection module, used to obtain the user's physiological data; A data preprocessing module, used for preprocessing the physiological data; A sleep state evaluation module, used to evaluate the sleep state of the user based on the preprocessed physiological data; A music parameter space definition module, used to define a music parameter space according to the sleep state; A music parameter optimization module, used for optimizing music parameters based on the sleep state and the music parameter space; a music generation and playback module, used for generating and playing music according to the optimized music parameters; A feedback loop module is used to continuously monitor the user's physiological response while playing music, and adjust the music parameters in real time based on the physiological response; a long-term sleep pattern analysis module, for analyzing the user's long-term sleep pattern based on the data obtained in the feedback loop; A system update module, used for updating the music parameter space and optimization strategy according to the long-term sleep mode; The security monitoring and intervention module is used to perform security monitoring and intervene when abnormal conditions are detected; And a system optimization module is used to adjust the parameters and strategies of the entire system according to the security monitoring and intervention results to achieve continuous optimization of the system.

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