A music player based on the Five Elements therapy method in Traditional Chinese Medicine

Abstract

This invention discloses a music player based on the Five Elements theory of Traditional Chinese Medicine (TCM), belonging to the field of music player technology. The player includes an audio control system that generates a Basic State Coefficient (BC), an Emotional State Coefficient (ESC), and a Motion State Coefficient (MSC) through a patient basic state analysis module, an emotional state analysis module, and a motion state analysis module. A behavioral state analysis module integrates these coefficients to output a Behavioral State Coefficient (BSC). An environmental analysis module constructs a temperature-brightness adaptation model based on the BSC and outputs a Temperature-Lens Adaptation (TLA). A frequency optimization module combines music playback duration with TLA to generate a target frequency and matches the best music from a music library. This invention innovatively combines TCM Five Elements theory with multi-source physiological-behavioral-environmental data, achieving "differentiated music therapy" through dynamic chain-like control, thus solving the problems of missing TCM logic and insufficient environmental adaptation in existing music therapy devices.

Description

Technical Field

[0001] This invention belongs to the field of media player technology, and in particular relates to a music player based on the Five Elements therapy method in Traditional Chinese Medicine. Background Technology

[0002] Traditional Chinese medicine's Five Elements theory couples human organs, emotions, and musical rhythms, believing that music of specific frequencies can regulate the balance of the internal organs (e.g., the horn tone, belonging to wood, regulates the liver). Current music therapy equipment mostly uses static music libraries, lacking a dynamic response to the patient's real-time physiological state, making it difficult to realize the TCM principle of "treating patients with music based on their individual conditions."

[0003] Current smart music players mainly rely on heart rate or emotion recognition (such as brainwaves) to match music, focusing only on a single physiological or emotional dimension, ignoring the synergistic effects of behavioral activities (such as gait / sitting / lying down) and the environment (temperature / light) as well as the influence of physiological indicators. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a music player based on the Five Elements therapy method in Traditional Chinese Medicine, thus solving the aforementioned problems.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a music player based on the Five Elements therapy method of Traditional Chinese Medicine, comprising an audio control system for dynamically adjusting audio, wherein the audio control system includes:

[0006] The patient baseline status analysis module constructs a baseline status analysis model based on the patient's blood glucose and blood oxygen saturation, and outputs baseline status coefficients.

[0007] The emotion state analysis module constructs an emotion state model based on heart rate and respiratory rate, and outputs emotion state coefficients.

[0008] The motion state analysis module constructs a motion state model based on gait speed, gesture frequency, and the proportion of sitting and lying down time, and outputs motion state coefficients.

[0009] The behavioral state analysis module constructs a behavioral state analysis model based on basic state coefficients, action state coefficients, and emotional state coefficients, and outputs behavioral state coefficients.

[0010] The environmental analysis module constructs a temperature-brightness adaptation model based on the ambient temperature and ambient brightness under the behavioral state coefficients, and outputs the temperature-brightness adaptation degree.

[0011] The frequency optimization module constructs a frequency optimization model based on the continuous playback duration of the music and the temperature-brightness adaptation, and outputs the target frequency of the music.

[0012] Based on the above technical solutions, the present invention also provides the following optional technical solutions:

[0013] Further technical solution: The steps for constructing a frequency optimization model based on music playback duration and temperature-brightness adaptation to output the target frequency of the music are as follows:

[0014] Import music playback duration into the formula The playback duration index is obtained from the data. This indicates that the music is playing continuously. Indicates the half-life of the effect of music intervention;

[0015] The current playback duration index and the current temperature-brightness compatibility are imported into the frequency optimization model to obtain the target frequency. The frequency optimization model is expressed as follows:

[0016]

[0017] in, Indicates the target frequency. Indicates the current music frequency. Indicates frequency adjustment gain. This indicates the current temperature-brightness compatibility. This indicates the temperature-brightness adaptation threshold. This indicates the playback duration index;

[0018] Select the music in the music library that is closest to the target frequency and play it.

[0019] Further technical solution: The steps for constructing a temperature-brightness adaptation model based on ambient temperature and ambient brightness under behavioral state coefficients and outputting the temperature-brightness adaptation degree are as follows:

[0020] Import the current behavior state coefficient into the formula The ideal environmental reference temperature is obtained from the data. Indicates the reference temperature. This indicates the temperature-behavioral state correlation gain. Indicates the coefficient of the current behavior state;

[0021] Import the current behavior state coefficient into the formula Obtain the ideal environmental reference brightness, where, Indicates the reference brightness. This indicates the brightness-behavior state correlation gain. Indicates the coefficient of the current behavior state;

[0022] The current ambient temperature and the current ideal ambient reference temperature are imported into the temperature fit formula to obtain the current temperature fit. The temperature fit formula is expressed as follows:

[0023]

[0024] in, Indicates the current temperature compatibility. Indicates the current ambient temperature. This represents the current ideal ambient reference temperature. Indicates the allowable temperature deviation;

[0025] Import the current ambient brightness and the current ideal ambient reference brightness to obtain the current brightness adaptation score. The brightness adaptation score formula is expressed as:

[0026]

[0027] in, Indicates the current brightness adaptation. Indicates the current ambient brightness. This represents the current ideal ambient light level. Indicates the allowable deviation in brightness. Indicates the brightness suppression coefficient;

[0028] The current temperature adaptation score and the current brightness adaptation score are imported into the temperature-brightness adaptation model to obtain the current temperature-brightness adaptation score. The temperature-brightness adaptation model is expressed as follows:

[0029]

[0030] in, This indicates the current temperature-brightness compatibility. Indicates the current temperature compatibility. Indicates the current brightness adaptation level, the The higher the value, the better the environmental condition.

[0031] A further technical solution: The behavioral state analysis model is represented as follows:

[0032]

[0033] in, Indicates the coefficient of the current behavior state. Indicates the coefficient of the current action state. This represents the current emotional state coefficient. Represents the current basic state coefficients. Indicates the current basic state threshold. Indicates the action state adjustment factor. The basic state sensitivity coefficient, the The larger the value, the more intense the behavior.

[0034] Further technical solution: The steps for constructing a motion state model and outputting motion state coefficients based on gait speed, gesture frequency, and the proportion of sitting and lying down time are as follows:

[0035] Gait speed, gesture frequency, and sitting / lying time percentage are processed by max-min normalization to obtain gait speed index, gesture frequency index, and sitting / lying time percentage index.

[0036] The current gait speed index, current gesture frequency index, and current sitting / lying time percentage index are imported into the action state model to obtain the current action state coefficients. The action state model is represented as follows:

[0037]

[0038] in, Indicates the coefficient of the current action state. Indicates the gait speed index, Indicating the frequency index of gestures, This indicates the percentage of time spent sitting or lying down. Represents the weight coefficient and The The higher the value, the more intense the patient's activity.

[0039] Further technical solution: The steps for constructing an emotion state model based on heart rate and respiratory rate and outputting emotion state coefficients are as follows:

[0040] The heart rate deviation index is obtained by processing the absolute difference between the heart rate and the standard heart rate and then performing maximum-min normalization.

[0041] The respiratory rate index is obtained by processing the absolute difference between the respiratory rate and the standard respiratory rate and then performing maximum-min normalization.

[0042] The current respiratory rate index and respiratory rate deviation index are imported into the emotion state model to output the current emotion state coefficient. The emotion state model is represented as follows:

[0043]

[0044] in, This represents the current emotional state coefficient. This indicates the current heart rate deviation index. This indicates the current respiratory rate deviation index. This represents the sensitivity coefficient to heart rate deviation. This represents the sensitivity coefficient to respiratory rate deviation. Represents the weight coefficient and The Furthermore, the higher the value, the more severe the patient's emotional fluctuations.

[0045] Further technical solution: The steps for constructing a baseline state analysis model based on the patient's blood glucose and blood oxygen saturation and outputting baseline state coefficients are as follows:

[0046] Import patient's blood glucose level into the formula Obtain the blood glucose deviation index, among which Indicates the patient's blood sugar. This represents the optimal blood glucose level. This indicates the permissible deviation from the blood glucose level;

[0047] Import blood oxygen saturation into the formula Obtain the blood oxygen saturation deviation index, among which, Indicates blood oxygen saturation. This indicates optimal blood oxygen saturation. This indicates the permissible deviation of blood oxygen saturation.

[0048] The current blood glucose deviation index and the current blood oxygen saturation deviation index are imported into the baseline state model to output the current baseline state coefficient. The baseline state model is represented as follows:

[0049]

[0050] in, Represents the current basic state coefficients. This indicates the current blood glucose deviation index. This indicates that the current blood oxygen saturation deviates from the index. Represents the weight coefficient and The Furthermore, the higher the value, the better the patient's physiological condition.

[0051] This invention provides a music player based on the Five Elements therapy method in Traditional Chinese Medicine, which has the following advantages compared with the prior art:

[0052] 1. This invention, based on the dynamic mapping of parameters such as blood glucose (belonging to Wood) and heart rate (belonging to Fire) with the Five Elements attributes, achieves precise music intervention of "replenishing water when liver fire is excessive," strengthening the logic of traditional Chinese medicine treatment. It integrates physiological indicators (blood glucose / blood oxygen), behavioral data (gait / gestures), and environmental parameters (temperature / light) through a chain model ( - - - - This enables dynamic optimization of music to enhance therapeutic efficacy. At the same time, the temperature-lightness adaptation model (TLA) can automatically adjust the ideal environmental benchmark based on behavioral status, reducing the impact of external interference on treatment. Attached Figure Description

[0053] Figure 1 This is a schematic diagram of the process of the present invention. Detailed Implementation

[0054] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0055] The specific implementation of the present invention will be described in detail below with reference to specific embodiments.

[0056] Please see Figure 1 A music player based on the Five Elements therapy method in Traditional Chinese Medicine, provided as an embodiment of the present invention, includes:

[0057] An audio control system, used for dynamic adjustment of audio, includes:

[0058] The patient baseline status analysis module constructs a baseline status analysis model based on the patient's blood glucose and blood oxygen saturation, and outputs baseline status coefficients.

[0059] The emotion state analysis module constructs an emotion state model based on heart rate and respiratory rate, and outputs emotion state coefficients.

[0060] The motion state analysis module constructs a motion state model based on gait speed, gesture frequency, and the proportion of sitting and lying down time, and outputs motion state coefficients.

[0061] The behavioral state analysis module constructs a behavioral state analysis model based on basic state coefficients, action state coefficients, and emotional state coefficients, and outputs behavioral state coefficients.

[0062] The environmental analysis module constructs a temperature-brightness adaptation model based on the ambient temperature and ambient brightness under the behavioral state coefficients, and outputs the temperature-brightness adaptation degree.

[0063] The frequency optimization module constructs a frequency optimization model based on the continuous playback duration of the music and the temperature-brightness adaptation, and outputs the target frequency of the music.

[0064] Specifically, the baseline state analysis module continuously monitors blood glucose and blood oxygen saturation. When blood glucose levels deviate from the optimal range, it calculates a blood glucose deviation index using an exponential function. Blood oxygen saturation data undergoes the same processing and is then weighted and summed with the blood glucose deviation index to generate a baseline state coefficient. This coefficient serves as a physiological benchmark input to the behavior state analysis module. When the coefficient falls below a threshold, a protection mechanism is automatically triggered, reducing the intensity of musical stimulation. The emotion state analysis module simultaneously collects heart rate and respiratory rate. When heart rate abnormally increases, it generates a heart rate deviation index through normalization and combines it with the respiratory rate deviation index to calculate an emotion state coefficient. This coefficient reflects the amplitude of emotional fluctuations in real time; when anxiety is detected, it automatically increases the selection weight of soothing music. The action state analysis module acquires gait cycle data through an accelerometer. When rapid walking is detected, it increases the gait speed index and combines it with gesture frequency and sitting / lying data to generate an action state coefficient. This coefficient is input into the behavior state analysis module and nonlinearly fused with the baseline state coefficient and the emotion state coefficient to generate a comprehensive behavior state coefficient. The environmental analysis module dynamically adjusts the ideal environmental parameters based on this coefficient. When the coefficient is high, it automatically increases the baseline brightness value and calculates the temperature-brightness compatibility by comparing it with actual environmental data. The frequency optimization module dynamically adjusts the target frequency parameters based on the compatibility value and the attenuation effect of playback time. When the environmental compatibility decreases, it automatically reduces the amplitude of music frequency fluctuations to avoid overstimulation.

[0065] Preferably, the steps for constructing a baseline state analysis model based on the patient's blood glucose and blood oxygen saturation and outputting baseline state coefficients are as follows:

[0066] Import patient's blood glucose level into the formula Obtain the blood glucose deviation index, among which Indicates the patient's blood sugar. This represents the optimal blood glucose level. This indicates the permissible deviation from the blood glucose level;

[0067] Import blood oxygen saturation into the formula Obtain the blood oxygen saturation deviation index, among which, Indicates blood oxygen saturation. This indicates optimal blood oxygen saturation. This indicates the permissible deviation of blood oxygen saturation.

[0068] The current blood glucose deviation index and the current blood oxygen saturation deviation index are imported into the baseline state model to output the current baseline state coefficient. The baseline state model is represented as follows:

[0069]

[0070] in, Represents the current basic state coefficients. This indicates the current blood glucose deviation index. This indicates that the current blood oxygen saturation deviates from the index. Represents the weight coefficient and The Furthermore, the higher the value, the better the patient's physiological condition.

[0071] Preferably, the steps for constructing an emotion state model based on heart rate and respiratory rate and outputting emotion state coefficients are as follows:

[0072] The heart rate deviation index is obtained by processing the absolute difference between the heart rate and the standard heart rate and then performing maximum-min normalization.

[0073] The respiratory rate index is obtained by processing the absolute difference between the respiratory rate and the standard respiratory rate and then performing maximum-min normalization.

[0074] The current respiratory rate index and respiratory rate deviation index are imported into the emotion state model to output the current emotion state coefficient. The emotion state model is represented as follows:

[0075]

[0076] in, This represents the current emotional state coefficient. This indicates the current heart rate deviation index. This indicates the current respiratory rate deviation index. This represents the sensitivity coefficient to heart rate deviation. This represents the sensitivity coefficient to respiratory rate deviation. Represents the weight coefficient and The Furthermore, the higher the value, the more severe the patient's emotional fluctuations.

[0077] The heart rate deviation index reflects the degree to which the heart rate deviates from the normal range. The respiratory rate deviation index quantifies the degree of abnormality in respiratory rhythm. The exponential function in the emotion state model is used to non-linearly amplify the deviation signal; for example, when the heart rate or respiratory rate deviates from the standard value, the exponential function can enhance its contribution to the emotion state coefficient, thus more sensitively capturing subtle emotional fluctuations. Weighting coefficients are used to adjust the proportion of contribution of heart rate and respiratory rate to emotion assessment; for example, when a patient's emotion is more significantly affected by heart rate, a higher weighting coefficient can be set. value.

[0078] Specifically, the difference between real-time heart rate data and a preset standard heart rate is calculated, and individual differences are eliminated through normalization to obtain a heart rate deviation index. Similarly, respiratory rate data undergoes the same processing to generate a respiratory rate deviation index. After both indices are input into the emotional state model, the physiological deviation is converted into a non-linear response signal through an exponential function. The sensitivity coefficient controls the steepness of the response curve; for example, when… As the heart rate increases, the same deviation will lead to a more significant change in the coefficient. The weighting coefficient dynamically balances the contributions of the two types of physiological indicators. For example, if a patient is in an anxious state, respiratory rate may be more sensitive than heart rate, and this can be adjusted. Higher than The final output emotional state coefficient comprehensively reflects the coordinated changes in heart rate and respiration, providing a dynamic basis for music frequency adjustment.

[0079] Preferably, the steps for constructing a motion state model and outputting motion state coefficients based on gait speed, gesture frequency, and the proportion of sitting / lying time are as follows:

[0080] Gait speed, gesture frequency, and sitting / lying time percentage are processed by max-min normalization to obtain gait speed index, gesture frequency index, and sitting / lying time percentage index.

[0081] The current gait speed index, current gesture frequency index, and current sitting / lying time percentage index are imported into the action state model to obtain the current action state coefficients. The action state model is represented as follows:

[0082]

[0083] in, Indicates the coefficient of the current action state. Indicates the gait speed index, Indicating the frequency index of gestures, This indicates the percentage of time spent sitting or lying down. Represents the weight coefficient and The The higher the value, the more intense the patient's activity.

[0084] The gait speed index is specifically calculated by using an accelerometer to collect walking data and then calculating the displacement per unit time, thus eliminating the impact of individual patient stride differences on data comparability. The gesture frequency index is a quantified value of gesture frequency after maximum-minimum normalization, specifically achieved by using an inertial measurement unit to capture hand movement trajectories and then counting the number of movements. The sitting / lying time ratio index is a quantified value of the proportion of sitting / lying time after maximum-minimum normalization, specifically achieved by using a pressure sensor to detect body position and then calculating the proportion of sitting / lying time to the total monitoring time, used to characterize the patient's static behavioral characteristics. Weighting coefficients are also included. It refers to a pre-defined behavioral characteristic influencing factor, which can be implemented by assigning values ​​based on expert experience or by obtaining parameter combinations through machine learning training. It is used to adjust the contribution of different behavioral dimensions to the assessment of action state.

[0085] Specifically, when constructing the action state model, the sitting / lying time percentage index is inverted to reflect the negative correlation between increased sitting / lying time and decreased activity intensity. A linear weighted model integrates dynamic and static behavioral characteristics, where the gait speed index reflects movement speed, the gesture frequency index reflects limb activity intensity, and the inverted sitting / lying time percentage index reflects the duration of static behavior. Under normalization constraints, the weighting coefficients can be adjusted according to clinical needs; for example, increasing the weight of gait speed in rehabilitation training scenarios and increasing the weight of sitting / lying time in relaxation scenarios.

[0086] Preferably, the behavioral state analysis model is expressed as:

[0087]

[0088] in, Indicates the coefficient of the current behavior state. Indicates the coefficient of the current action state. This represents the current emotional state coefficient. Represents the current basic state coefficients. Indicates the current basic state threshold. Indicates the action state adjustment factor. The basic state sensitivity coefficient, the The larger the value, the more intense the behavior.

[0089] Among them, the action state adjustment factor This refers to the weighting of the action state coefficient in behavioral state assessment. Specifically, it can be achieved using preset empirical values ​​or dynamically adjusted through machine learning, for example, by increasing the weighting when the patient is in a motor state. To enhance the influence of the action state. Base state sensitivity coefficient. This refers to the parameter that controls the intensity of behavioral state inhibition when the baseline state coefficient deviates from a threshold. Specifically, it can be determined using preset empirical values ​​or numerical simulation optimization. For example, when a patient's blood glucose or blood oxygen levels are abnormal, increasing... To quickly reduce the behavioral state coefficient. Base state threshold. It refers to the critical value for judging whether a patient's basic physiological state is stable. It can be obtained through clinical data statistics, such as selecting the average blood glucose and blood oxygen saturation of healthy people as a benchmark.

[0090] Specifically, this model achieves multi-dimensional dynamic assessment by weighting and harmonizing emotional state coefficients and action state coefficients, and introducing a logistic function constraint on the baseline state coefficients. The emotional and action state coefficients, after being adjusted by λ, form a preliminary behavioral state assessment value, which is then non-linearly coupled with the baseline state coefficients. When the baseline state coefficient falls below a threshold, the logistic function output value decreases rapidly, thereby suppressing the final behavioral state coefficient and avoiding high-intensity music intervention when the patient's physiological state is unstable. For example, when a patient's baseline state coefficient decreases due to hypoglycemia, even if their action or emotional state is active, the behavioral state coefficient will still be significantly reduced, ensuring treatment safety.

[0091] Preferably, the step of constructing a temperature-brightness adaptation model based on the ambient temperature and ambient brightness under the behavioral state coefficient and outputting the temperature-brightness adaptation degree is as follows:

[0092] Import the current behavior state coefficient into the formula The ideal environmental reference temperature is obtained from the data. Indicates the reference temperature. This indicates the temperature-behavioral state correlation gain. Indicates the coefficient of the current behavior state;

[0093] Import the current behavior state coefficient into the formula Obtain the ideal environmental reference brightness, where, Indicates the reference brightness. This indicates the brightness-behavior state correlation gain. Indicates the coefficient of the current behavior state;

[0094] The current ambient temperature and the current ideal ambient reference temperature are imported into the temperature fit formula to obtain the current temperature fit. The temperature fit formula is expressed as follows:

[0095]

[0096] in, Indicates the current temperature compatibility. Indicates the current ambient temperature. This represents the current ideal ambient reference temperature. Indicates the allowable temperature deviation;

[0097] Import the current ambient brightness and the current ideal ambient reference brightness to obtain the current brightness adaptation score. The brightness adaptation score formula is expressed as:

[0098]

[0099] in, Indicates the current brightness adaptation. Indicates the current ambient brightness. This represents the current ideal ambient light level. Indicates the allowable deviation in brightness. Indicates the brightness suppression coefficient;

[0100] The current temperature adaptation score and the current brightness adaptation score are imported into the temperature-brightness adaptation model to obtain the current temperature-brightness adaptation score. The temperature-brightness adaptation model is expressed as follows:

[0101]

[0102] in, This indicates the current temperature-brightness compatibility. Indicates the current temperature compatibility. Indicates the current brightness adaptation level, the The higher the value, the better the environmental condition.

[0103] Among them, the behavioral state coefficient is used to characterize the intensity of the patient's current behavioral activity. The temperature-behavioral state correlation gain refers to the adjustment ratio coefficient between ambient temperature and behavioral state, which can be implemented using a positive parameter within a preset range, used to increase or decrease the temperature adjustment amplitude according to the behavioral state. The brightness-behavioral state correlation gain refers to the adjustment ratio coefficient between ambient brightness and behavioral state, which can be implemented using a positive parameter within a preset range, used to dynamically adjust the brightness baseline value according to the behavioral state. The temperature tolerance deviation refers to the allowable difference range between the ambient temperature and the ideal temperature, which can be implemented using the standard deviation parameter of a Gaussian function, used to control the sensitivity of temperature fit to changes in temperature deviation. The brightness suppression coefficient is the intensity parameter for adjusting excessively bright environments that exceed the allowable brightness range, which can be implemented using a value between 0 and 1, used to suppress the negative impact of excessively bright environments on fit.

[0104] Specifically, this technical solution establishes a real-time correlation between environmental parameters and patient behavior by dynamically generating ideal environmental reference temperature and brightness. When the patient's behavior state coefficient changes, the ideal environmental reference temperature and brightness adjust accordingly. For example, when a patient is engaged in strenuous activity, the reference temperature may decrease to accommodate the increased body temperature, and the reference brightness may increase to match the activity's demands. Temperature fit calculation employs an exponential decay function; the greater the deviation between the actual and ideal temperature, the exponentially decreasing the fit, enhancing rapid response to abnormal temperatures. Brightness fit calculation is divided into two stages: within the allowable deviation range, a square root function is used to maintain linear adjustment characteristics; when the brightness exceeds a threshold, a hyperbolic tangent function is used for nonlinear suppression to prevent overly bright environments from stimulating the patient. Finally, a product model is used to fuse temperature and brightness fit to form a comprehensive environmental state evaluation index, providing dynamic parameter inputs from the environmental dimension for music frequency optimization.

[0105] Through the above technical solution, this application can dynamically adjust the ambient temperature and brightness parameters according to the patient's real-time behavioral state, solving the problem of the disconnect between environmental factors and the patient's state in the prior art. For example, when the patient is in a quiet resting state, the system automatically reduces the ambient brightness and maintains a suitable temperature to avoid external environmental interference; when the patient is undergoing rehabilitation training, the system increases the brightness to provide sufficient light and adjusts the temperature to prevent overheating, achieving a precise match between environmental parameters and treatment needs.

[0106] Preferably, the step of constructing a frequency optimization model based on the music playback duration and temperature-brightness adaptation to output the target frequency of the music is as follows:

[0107] Import music playback duration into the formula The playback duration index is obtained from the data. This indicates that the music is playing continuously. Indicates the half-life of the effect of music intervention;

[0108] The current playback duration index and the current temperature-brightness compatibility are imported into the frequency optimization model to obtain the target frequency. The frequency optimization model is expressed as follows:

[0109]

[0110] in, Indicates the target frequency. Indicates the current music frequency. Indicates frequency adjustment gain. This indicates the current temperature-brightness compatibility. This indicates the temperature-brightness adaptation threshold. This indicates the playback duration index;

[0111] Select the music in the music library that is closest to the target frequency and play it.

[0112] Among these, music playback duration refers to the length of time music can be played continuously, which can be calculated using a timer or timestamp difference to quantify the duration of the music intervention effect. Playback duration index is an indicator of the decay rate of the music intervention effect calculated using an exponential decay model, which can be implemented using a natural exponential function combined with a half-life parameter. The half-life parameter controls the rate of decay of the intervention effect over time. Temperature-brightness fit refers to the degree of fit between ambient temperature and brightness relative to an ideal state, reflecting the synergistic effect of environmental factors on the patient's condition. Frequency adjustment gain is a control parameter for the magnitude of music frequency adjustment caused by temperature-brightness fit deviation, which can be implemented using a preset constant or dynamic adjustment coefficient to balance the impact of environmental fit changes on music frequency. Temperature-brightness fit threshold is the critical fit value that triggers music frequency adjustment, which can be determined using empirical calibration or adaptive learning algorithms to determine whether the current environment requires music frequency adjustment.

[0113] Specifically, the duration of music playback is converted into a playback duration exponent using an exponential decay model. This exponent decreases exponentially with increasing playback time, reflecting the time-dependent decay of the music intervention effect. The playback duration exponent is multiplied by the deviation of the temperature-brightness fit, and the adjustment amplitude of the target frequency is controlled by frequency adjustment gain. When the temperature-brightness fit is higher than a threshold, the target frequency is adjusted positively according to the gain coefficient; conversely, it is adjusted negatively. The decay characteristic of the playback duration exponent ensures that the music frequency adjustment amplitude gradually weakens over time, avoiding over-intervention due to prolonged playback. Music files of different frequencies are pre-stored in the music library. By calculating the Euclidean distance between the target frequency and each music frequency, the closest music is selected for switching playback, achieving dynamic music frequency adaptation.

[0114] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus.

[0115] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A music player based on the Five Elements therapy method in Traditional Chinese Medicine, characterized in that, Includes an audio control system for dynamically adjusting audio, the audio control system comprising: The patient baseline status analysis module constructs a baseline status analysis model based on the patient's blood glucose and blood oxygen saturation, and outputs baseline status coefficients. The emotion state analysis module constructs an emotion state model based on heart rate and respiratory rate, and outputs emotion state coefficients. The motion state analysis module constructs a motion state model based on gait speed, gesture frequency, and the proportion of sitting and lying down time, and outputs motion state coefficients. The behavioral state analysis module constructs a behavioral state analysis model based on basic state coefficients, action state coefficients, and emotional state coefficients, and outputs behavioral state coefficients. The environmental analysis module constructs a temperature-brightness adaptation model based on the ambient temperature and brightness under behavioral state coefficients, and outputs the temperature-brightness adaptation degree. The specific steps are as follows: The current ambient temperature and the current ideal ambient reference temperature are imported into the temperature fit formula to obtain the current temperature fit. The temperature fit formula is expressed as follows: in, Indicates the current temperature compatibility. Indicates the current ambient temperature. This represents the current ideal ambient reference temperature. Indicates the allowable temperature deviation; Import the current ambient brightness and the current ideal ambient reference brightness to obtain the current brightness adaptation degree. The formula for the brightness adaptation degree is as follows: in, Indicates the current brightness adaptation. Indicates the current ambient brightness. This represents the current ideal ambient light level. Indicates the allowable deviation in brightness. Indicates the brightness suppression coefficient; The current temperature adaptation score and the current brightness adaptation score are imported into the temperature-brightness adaptation model to obtain the current temperature-brightness adaptation score. The temperature-brightness adaptation model is expressed as follows: in, This indicates the current temperature-brightness compatibility. Indicates the current temperature compatibility. Indicates the current brightness adaptation level, the Furthermore, the higher the value, the better the environmental conditions; The frequency optimization module constructs a frequency optimization model based on the music's continuous playback duration and temperature-brightness adaptation, outputting the music's target frequency. Specific steps include: Import music playback duration into the formula The playback duration index is obtained from the data. This indicates that the music is playing continuously. Indicates the half-life of the effect of music intervention; The current playback duration index and the current temperature-brightness compatibility are imported into the frequency optimization model to obtain the target frequency. The frequency optimization model is expressed as follows: in, Indicates the target frequency. Indicates the current music frequency. Indicates frequency adjustment gain. This indicates the current temperature-brightness compatibility. This indicates the temperature-brightness adaptation threshold. This indicates the playback duration index; Select the music in the music library that is closest to the target frequency and play it.

2. The music player based on the Five Elements therapy method of Traditional Chinese Medicine according to claim 1, characterized in that, The behavioral state analysis model is represented as follows: in, Represents the coefficient of the current behavior state. Indicates the coefficient of the current action state. This represents the current emotional state coefficient. Represents the current basic state coefficients. Indicates the current basic state threshold. Indicates the action state adjustment factor. The basic state sensitivity coefficient, the The larger the value, the more intense the behavior.

3. The music player based on the Five Elements therapy method of Traditional Chinese Medicine according to claim 1 or 2, characterized in that, The steps for constructing a motion state model and outputting motion state coefficients based on gait speed, gesture frequency, and the proportion of sitting / lying time are as follows: Gait speed, gesture frequency, and sitting / lying time percentage are processed by max-min normalization to obtain gait speed index, gesture frequency index, and sitting / lying time percentage index. The current gait speed index, current gesture frequency index, and current sitting / lying time percentage index are imported into the action state model to obtain the current action state coefficients. The action state model is represented as follows: in, Indicates the coefficient of the current action state. Indicates the gait speed index, Indicating the frequency index of gestures, This indicates the percentage of time spent sitting or lying down. Represents the weight coefficient and The The higher the value, the more intense the patient's activity.

4. The music player based on the Five Elements therapy method of Traditional Chinese Medicine according to claim 1 or 2, characterized in that, The steps for constructing an emotion state model based on heart rate and respiratory rate and outputting emotion state coefficients are as follows: The heart rate deviation index is obtained by processing the absolute difference between the heart rate and the standard heart rate and then performing maximum-min normalization. The respiratory rate index is obtained by processing the absolute difference between the respiratory rate and the standard respiratory rate and then performing maximum-min normalization. The current respiratory rate index and respiratory rate deviation index are imported into the emotion state model to output the current emotion state coefficient. The emotion state model is represented as follows: in, This represents the current emotional state coefficient. This indicates the current heart rate deviation index. This indicates the current respiratory rate deviation index. This represents the sensitivity coefficient to heart rate deviation. This represents the sensitivity coefficient to respiratory rate deviation. Represents the weight coefficient and The Furthermore, the higher the value, the more severe the patient's emotional fluctuations.

5. The music player based on the Five Elements therapy method of Traditional Chinese Medicine according to claim 1 or 2, characterized in that, The steps for constructing a baseline state analysis model based on patient blood glucose and blood oxygen saturation and outputting baseline state coefficients are as follows: Import patient's blood glucose level into the formula Obtain the blood glucose deviation index, among which Indicates the patient's blood sugar. This represents the optimal blood glucose level. This indicates the permissible deviation from the blood glucose level; Import blood oxygen saturation into the formula Obtain the blood oxygen saturation deviation index, among which, Indicates blood oxygen saturation. This indicates optimal blood oxygen saturation. This indicates the permissible deviation of blood oxygen saturation. The current blood glucose deviation index and the current blood oxygen saturation deviation index are imported into the baseline state model to output the current baseline state coefficient. The baseline state model is represented as follows: in, Represents the current basic state coefficients. This indicates the current blood glucose deviation index. This indicates that the current blood oxygen saturation deviates from the index. Represents the weight coefficient and The Furthermore, the higher the value, the better the patient's physiological condition.

6. The music player based on the Five Elements therapy method of Traditional Chinese Medicine according to claim 1, characterized in that, The ideal environmental reference temperature is obtained as follows: Import the current behavior state coefficient into the formula The ideal environmental reference temperature is obtained from the data. Indicates the reference temperature. This indicates the temperature-behavioral state correlation gain. This represents the coefficient of the current behavior state.

7. The music player based on the Five Elements therapy method of Traditional Chinese Medicine according to claim 1, characterized in that, The ideal environmental reference brightness is obtained as follows: Import the current behavior state coefficient into the formula Obtain the ideal environmental reference brightness, where, Indicates the reference brightness. This indicates the brightness-behavior state correlation gain. This represents the coefficient of the current behavior state.

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