Graphene composite heating patch and method for dynamically regulating neck and shoulder muscle groups

Abstract

The application discloses a graphene composite heating pad and a method for realizing dynamic regulation and control of neck and shoulder muscle groups, and relates to the technical field of intelligent medical instruments. The method estimates an initial heat dissipation coefficient and an equivalent heat capacity by collecting temperature time sequence data of each heating unit in a heating process; a sliding time window is used to track a time-varying trajectory of the heat dissipation coefficient in real time and calculate a time change rate thereof; when the heat dissipation coefficient change rate is a significant positive value and lasts for more than a preset time length, it is determined that muscle relaxation is started; a relaxation dynamics model in an exponential saturation form is fitted based on time-varying data of the heat dissipation coefficient after the relaxation is started, so that a future heat dissipation coefficient trajectory is predicted; and power required for maintaining a target deep layer temperature is calculated according to the predicted trajectory, and power settings of each heating unit are updated in real time. The method realizes uniform distribution and effective maintenance of the deep layer temperature through predictive power regulation, and improves treatment effect and use safety.

Description

Technical Field

[0001] This invention relates to the field of intelligent medical device technology, and specifically discloses a graphene composite heating pad and a method for achieving dynamic regulation of neck and shoulder muscles. Background Technology

[0002] Neck and shoulder muscle pain and tension are common health problems in modern society. Prolonged desk work, poor posture, and mental stress can cause the neck and shoulder muscles to remain in a state of tension, leading to poor local blood circulation, accumulation of metabolic waste products, and consequently, symptoms such as pain, stiffness, and limited mobility. Heat therapy, as a non-invasive physical therapy method, promotes local blood circulation, relieves muscle spasms, and improves tissue metabolism by applying appropriate heat to muscle tissue. It is widely used in the conservative treatment and daily care of neck and shoulder diseases.

[0003] Due to its excellent electrical conductivity, thermal conductivity, and flexibility, graphene has been used in the manufacture of flexible heating devices in recent years. Graphene composite heating pads utilize the electrothermal conversion properties of graphene films or coatings to generate heat quickly and evenly. Furthermore, the heating layer is thin and flexible enough to conform to the curves of the human body, offering better comfort and fit compared to traditional heating wires or carbon fiber heating devices.

[0004] Existing graphene heating pads and similar thermotherapy devices mainly employ the following technical solutions for power control:

[0005] One approach is a constant temperature control scheme. This scheme monitors the surface temperature of the heating device in real time using a temperature sensor. When the temperature reaches a preset target value, the power is reduced or power is interrupted; when the temperature falls below the target value, power is restored. A negative feedback mechanism maintains the surface temperature within the set range. The advantages of this scheme are its simple control logic and low implementation cost. However, a problem is that it only focuses on surface temperature and ignores the actual temperature state of deep muscle tissue. Due to significant individual differences and spatial heterogeneity in the thermal conductivity characteristics and local blood perfusion of human tissues, the deep muscle temperature corresponding to the same surface temperature may vary considerably. In areas of muscle tension and restricted blood flow, surface heating can easily lead to excessive accumulation of deep temperature; while in areas with good blood perfusion, although the surface temperature may meet the target, the deep temperature may be insufficient, resulting in uneven treatment effects.

[0006] Second, there's the pre-set power curve scheme. This scheme pre-determines the curve of power changing over time based on experience or experimental data. For example, it uses a high power to rapidly increase temperature initially, maintains a medium power for stable heating in the middle stage, and reduces the power to avoid overheating in the later stage. This scheme considers the time dimension changes in the heating process, but it still uses a uniform power curve for all users and all body parts, which cannot adapt to individual differences. Different users have significant differences in muscle condition, subcutaneous fat thickness, and blood circulation. There is also spatial heterogeneity in muscle tension and blood perfusion in different parts of the same user. A uniform power curve makes it difficult to achieve precise individualized treatment.

[0007] Thirdly, there is the adaptive control scheme based on temperature feedback. This scheme adds a certain degree of adaptability to the isothermal control, such as dynamically adjusting the power growth slope according to the rate of temperature rise, or adjusting the control parameters according to temperature fluctuations. This type of scheme improves the control effect to some extent, but its adaptive mechanism mainly targets the characteristics of the temperature signal itself and fails to identify and respond to dynamic changes in muscle state at the physiological level.

[0008] The fundamental problem with existing technologies is that they neglect the dynamic evolution of muscle physiological states during heating. When neck and shoulder muscles are continuously heated, the muscle tissue undergoes a transition from a tense to a relaxed state. In a tense state, blood vessels inside the muscle are compressed, local blood perfusion is restricted, and the tissue's heat dissipation capacity is weak. When heating raises the muscle temperature to a certain threshold and maintains it for a period of time, muscle spasms are relieved, and the muscle begins to enter a relaxed state. Blood vessels dilate, blood perfusion improves, and the tissue's heat dissipation capacity is significantly enhanced. This physiological process causes the thermophysical properties of muscle tissue (especially the heat dissipation coefficient) to change significantly over time. However, existing control schemes treat these properties as static constants or only perform simple temperature feedback adjustments, failing to perceive and respond to the dynamic changes in heat dissipation capacity caused by the transition in muscle state. Consequently, the temperature of deep muscles deviates from the target range after relaxation begins due to accelerated heat dissipation, affecting the sustainability and stability of the treatment effect.

[0009] Furthermore, existing technologies lack the ability to learn and accumulate individualized user characteristics. Individualized physiological response patterns exhibited by users during multiple uses (such as muscle tension in specific areas, temperature thresholds for relaxation initiation, and time characteristics) are not recognized and recorded by the system. The same initial control strategy is used for each use, which makes it impossible to achieve individualized optimization based on historical data and limits further improvement in treatment effectiveness. Summary of the Invention

[0010] To address the aforementioned technical problems, a graphene composite heating pad and its method for achieving dynamic regulation of neck and shoulder muscles are provided, specifically including the following steps:

[0011] S1: Initial parameter estimation: Apply initial power to each heating unit and continue for a preset duration, the preset duration being 3 to 5 minutes, and collect temperature time-series data within the preset duration;

[0012] Assuming the muscles remain under tension and the heat dissipation coefficient has not changed significantly within the preset time period, the first-order thermal response model is used to analyze the... The equivalent heat capacity and initial heat dissipation coefficient of each heat-generating unit are estimated.

[0013] The temperature evolution curve of the first-order thermal response model is as follows: ;

[0014] in, For ambient temperature, For steady-state temperature rise, The heating time constant is It is a time variable;

[0015] Parameters are estimated from the temperature time series data using a nonlinear fitting method. and ;

[0016] Calculate the initial heat dissipation coefficient based on the steady-state temperature rise: ;

[0017] in, The initial power, The initial heat dissipation coefficient;

[0018] The equivalent heat capacity is calculated based on the heating time constant and the initial heat dissipation coefficient: ;in, The equivalent heat capacity is given.

[0019] S2: Real-time tracking of time-varying heat dissipation coefficient; In the subsequent heating process after the initial parameter estimation is completed, the time-varying trajectory of the heat dissipation coefficient is estimated in real time using a sliding time window method;

[0020] For any time Extract from time 1 At that time Temperature data and power data, among which, The length of the sliding time window;

[0021] The window length ranges from 1 to 2 minutes;

[0022] Within the sliding time window, the heat dissipation coefficient corresponding to the window is estimated based on the heat balance equation, which is:

[0023] ;

[0024] in, For the rate of temperature change, For a moment power, For a moment heat dissipation coefficient, For a moment Temperature;

[0025] The rate of temperature change is approximated using backward difference: ;

[0026] in, The sampling time step; the time obtained from solving the heat balance equation. Corresponding heat dissipation coefficient Further calculate the rate of change of the heat dissipation coefficient over time: ;

[0027] S3: Real-time identification of muscle relaxation process; continuous monitoring of the time change rate of the heat dissipation coefficient; when the time change rate meets the preset activation condition, determine the... The muscles in the area corresponding to each heating unit begin to relax;

[0028] The preset activation conditions include a first criterion and a second criterion;

[0029] The first criterion is: the rate of change of the heat dissipation coefficient over time is significantly positive, i.e., it satisfies: ;

[0030] in, The significance threshold;

[0031] The significance threshold is determined by calculating the standard deviation of the heat dissipation coefficient during the initial parameter estimation phase. The significance threshold is set to 2 to 3 times the standard deviation;

[0032] The second criterion is: the duration of the time change rate of the significant positive value exceeds a preset duration, wherein the preset duration is 30 seconds to 1 minute;

[0033] Record the relaxation start time when both the first criterion and the second criterion are satisfied. The relaxation initiation time is defined as the moment when the first criterion and the second criterion are simultaneously satisfied for the first time.

[0034] S4: Establishment and prediction of relaxation dynamics model; After identifying the relaxation initiation time, the relaxation dynamics model is fitted based on the time-varying data of heat dissipation coefficient observed after the relaxation initiation time;

[0035] The relaxation dynamics model adopts the following exponential saturation form: ;

[0036] in, The initial heat dissipation coefficient is... This represents the maximum increment of the heat dissipation coefficient. Let be the relaxation time constant. The relaxation initiation time;

[0037] The maximum increment of the heat dissipation coefficient reflects the magnitude of heat dissipation change in the muscles of that area from a tense state to a fully relaxed state.

[0038] The relaxation time constant reflects the speed of the muscle relaxation process;

[0039] The observed heat dissipation coefficient data are fitted using the nonlinear least squares method to estimate the model parameters. and ;

[0040] The heat dissipation coefficient trajectory at future moments is predicted based on the relaxation dynamics model, where the time range of the future moments is 2 to 5 minutes after the current moment.

[0041] S5: Predictive power dynamic regulation; with the control objective of maintaining the deep muscle temperature to a preset target temperature, the power regulation scheme of each heat-generating unit in the future time period is calculated based on the predicted heat dissipation coefficient trajectory.

[0042] Establish the equation relating deep muscle temperature to surface-applied power:

[0043] ;

[0044] in, The preset target temperature, For a moment The power required to be applied;

[0045] Substituting the predicted heat dissipation coefficient trajectory into the relational equation, the power demand trajectory required to maintain the target temperature in the future time period is obtained.

[0046] The power setting value of each heating unit is updated in real time according to the power demand trajectory.

[0047] During the power regulation process, the deviation between the actual heat dissipation coefficient and the predicted heat dissipation coefficient is continuously monitored. When the absolute value of the deviation exceeds the preset deviation threshold, the model fitting process of S4 is re-executed.

[0048] The preset deviation threshold is 10% to 20% of the predicted heat dissipation coefficient;

[0049] S6: Individualized parameter cumulative optimization; Data is recorded for each user's usage process, including the initial heat dissipation coefficient, relaxation start time, maximum increment of heat dissipation coefficient, and relaxation time constant for each heat-generating unit.

[0050] Once the user's cumulative usage count reaches a preset cumulative count, the recorded data is statistically analyzed to extract individualized feature parameters; the preset cumulative count is 3 to 5 times.

[0051] Based on the results of the statistical analysis, the control parameters are individually adjusted during subsequent use. This individualized adjustment includes:

[0052] The initial power is adjusted based on the average initial heat dissipation coefficient in the user's historical records so that the initial heating rate matches the user's individual characteristics.

[0053] The threshold for relaxation identification is pre-adjusted based on the correlation between deep temperature and relaxation initiation time in the user's historical records, making relaxation identification more sensitive and accurate.

[0054] When performing model fitting in S4, the user's historical parameter distribution is applied to the parameter space as a Bayesian prior distribution, which accelerates the convergence of model fitting and improves the robustness of parameter estimation.

[0055] The nonlinear fitting of the temperature evolution curve in S1 uses the weighted least squares method;

[0056] In the weighted least squares method, higher weight coefficients are assigned to data points in the initial stage of heating; the initial stage of heating is defined as the time period corresponding to the first 30% of the total heating time from the start of heating; by assigning higher weights to data points in the initial stage of heating, the estimation accuracy of the heating time constant is improved.

[0057] The window length of the sliding time window in S2 is adaptively adjusted according to the noise level of the temperature signal;

[0058] The specific method of the adaptive adjustment is as follows: In stage S1, the standard deviation of the fitted residual is calculated as an estimate of the temperature noise level, denoted as... During subsequent heating, calculate the fluctuation amplitude of the temperature signal within the current short-term time window;

[0059] When the fluctuation amplitude exceeds In this case, the window length is increased to enhance the filtering effect and reduce the impact of noise on the heat dissipation coefficient estimation;

[0060] When the fluctuation amplitude is less than At the same time, the window length is reduced to improve the temporal resolution of time-varying parameter tracking;

[0061] The window length can be adjusted from 0.5 minutes to 3 minutes.

[0062] The identification of the relaxation initiation time in S3 further employs a sequential probability ratio test method.

[0063] The sequential probability ratio test method includes:

[0064] Establish the null hypothesis The time-varying rate of change of the heat dissipation coefficient follows the noise distribution of stage S1, and the mean of the noise distribution is 0.

[0065] Establish alternative hypotheses The time-varying rate of change of the heat dissipation coefficient is significantly positive, and the mean of the time-varying rate is greater than the significance threshold.

[0066] For time Calculate from the end of S1 to time [time]. The likelihood ratios of all heat dissipation coefficient change rate data under the null and alternative assumptions are as follows: ;

[0067] in, This indicates the null hypothesis. This indicates the alternative hypothesis. Indicates from time At that time The sequence of the rate of change of heat dissipation coefficient;

[0068] When the likelihood ratio When the preset decision threshold is exceeded, the null hypothesis is rejected. And accept the alternative hypothesis. The relaxation start is determined; the preset decision threshold ranges from 10 to 20.

[0069] The sequential probability ratio test method can identify the relaxation initiation time earlier than the fixed-duration hypothesis test, while maintaining a low false alarm probability.

[0070] In S4, when fitting the relaxation dynamics model, physiological constraints are applied to the model parameters based on the anatomical location of the heating unit in the neck and shoulder area.

[0071] The physiological constraint specifically refers to the relaxation time constant of the heating unit located in the trapezius muscle region on both sides of the neck. The value range is constrained to 2 to 4 minutes;

[0072] For the heating unit located in the deep muscle group region of the back of the neck, its relaxation time constant The value range is constrained to 4 to 8 minutes;

[0073] In the nonlinear least squares fitting process, the range of values ​​is used as the boundary condition of the parameter search space to avoid the fitting results having physiologically unreasonable parameter values.

[0074] The physiological constraints are based on the differences in the physiological characteristics of different muscle tissues in the neck and shoulder area, making the model fitting results more consistent with the actual physiological mechanisms.

[0075] The predictive power dynamic regulation in S5 adopts a segmented control strategy for heating units in different relaxation states.

[0076] The segmented control strategy includes: for heating units that have not yet been identified as having relaxed startup, maintaining the current power unchanged, or adjusting the power according to a preset slow power increase curve, wherein the power increase rate of the slow power increase curve does not exceed 5% per minute of the initial power;

[0077] For heat-generating units that have been identified as having started relaxation and are currently in the process of relaxation, power is increased synchronously according to the predicted heat dissipation coefficient growth trajectory to compensate for the temperature drop caused by accelerated heat dissipation.

[0078] The condition for determining whether relaxation is in progress is: the current heat dissipation coefficient. satisfy:

[0079] ;

[0080] For heating units that have completed relaxation, switch to constant power maintenance mode, whereby the constant power is calculated based on the heat dissipation coefficient after complete relaxation;

[0081] The condition for determining whether relaxation has been completed is: the current heat dissipation coefficient. satisfy: ;

[0082] The segmented control strategy ensures that the power adjustment trajectory of each heating unit is synchronized with its respective muscle relaxation process, avoiding overheating or underheating of some parts due to the use of a uniform control strategy.

[0083] When multiple heating units correspond to neck and shoulder areas that are anatomically adjacent, a temporal consistency constraint is applied to the identification of their relaxation start-up time.

[0084] The specific implementation of the timing consistency constraint is as follows: an adjacency matrix is ​​established based on the spatial location of each heating unit in the neck and shoulder area. , where matrix elements A value of 1 indicates a heating unit and heating unit Adjacent to the neck and shoulder area, matrix elements A value of 0 indicates that the two elements are not adjacent.

[0085] When the heating unit After identifying the relaxation start-up moment, all components related to the heating unit are... Adjacent heating units Adjust its relaxation recognition criteria;

[0086] The adjustment specifically involves: adjusting the heating unit... Significance threshold The reduction is used to make the relaxation recognition criteria for adjacent parts more sensitive;

[0087] The temporal consistency constraint is based on the following physiological mechanism: muscle relaxation in the neck and shoulder area often presents a regional propagation pattern in space. When a certain area begins to relax, its adjacent areas are usually about to enter a relaxed state as well.

[0088] The timing consistency constraint improves the accuracy and timeliness of identifying the relaxation start time of adjacent parts.

[0089] The individualized parameter cumulative optimization in S6 further includes an abnormal data detection and removal mechanism;

[0090] The specific implementation method of the abnormal data detection and removal mechanism is as follows:

[0091] Statistical analysis was performed on the relaxation parameter data set in the user's historical records, the relaxation parameters including the maximum increment of the heat dissipation coefficient. and relaxation time constant ;

[0092] Calculate the mean and standard deviation for each parameter in the dataset;

[0093] For a relaxation parameter obtained during a certain usage, calculate the deviation of the parameter from the historical mean;

[0094] When the deviation exceeds three times the historical standard deviation, the usage record is marked as abnormal data;

[0095] The abnormal data is not included in the statistical calculation and updating process of the individualized parameter library;

[0096] The abnormal data may correspond to the user being in a special physiological state, such as acute muscle injury, inflammatory response or over-fatigue.

[0097] The abnormal data detection and removal mechanism avoids the influence of data under special physiological conditions on the statistical estimation of individualized parameters under normal conditions, thereby improving the representativeness and stability of individualized parameters.

[0098] The present invention has the following beneficial effects: Compared with the prior art, the present invention has the following beneficial effects:

[0099] First, this invention, by tracking the time-varying trajectory of the heat dissipation coefficient in real time and calculating its rate of change over time, can identify the physiological transition process of muscles from tension to relaxation from the dynamic evolution characteristics of thermophysical parameters. Existing technologies only monitor the temperature signal itself, while this invention reveals the deep physiological mechanism behind the temperature signal—the dynamic change of the heat dissipation coefficient directly reflects the improved blood perfusion caused by muscle relaxation, thereby achieving quantitative perception of muscle state transitions.

[0100] Second, this invention employs a relaxation dynamics model to predict the future trajectory of the heat dissipation coefficient and adjusts the power configuration in advance based on the prediction results, achieving predictive control rather than traditional feedback control. Predictive control can increase power to compensate before the heat dissipation accelerates due to muscle relaxation, avoiding the inherent response lag problem of feedback control.

[0101] Third, this invention achieves spatial uniformity of temperature distribution in deep muscles. By independently tracking the changes in heat dissipation coefficient of each heating unit and performing predictive power regulation, deep muscles in different areas can synchronously reach and maintain the target temperature, unaffected by differences in initial muscle state.

[0102] Fourth, this invention improves safety. By maintaining deep tissue temperature within the target range through predictive power regulation, it avoids excessive accumulation of deep tissue temperature in areas of muscle tension due to limited heat dissipation.

[0103] Fifth, this invention establishes a user-specific parameter library by accumulating data from multiple uses, and continuously optimizes the control strategy based on historical data, thereby achieving individualized adaptive treatment.

[0104] Sixth, the present invention has an abnormal data detection and removal mechanism, which can identify abnormal usage records when the user is in a special physiological state (such as acute injury, inflammatory response, etc.) and avoid abnormal data from contaminating the individualized parameter baseline.

[0105] In summary, this invention, through a technical chain of dynamic heat dissipation coefficient tracking, muscle relaxation process identification, relaxation dynamics modeling, and predictive power regulation, solves the fundamental problem that existing technologies cannot perceive and respond to dynamic changes in muscle physiological state. It achieves precise control of deep temperature, uniform spatial distribution, and individualized adaptive optimization, significantly improving the therapeutic effect, safety, and user experience of neck and shoulder muscle thermotherapy. Attached Figure Description

[0106] Figure 1 A flowchart illustrating the overall process of a graphene composite heating pad and its method for achieving dynamic regulation of neck and shoulder muscles.

[0107] Figure 2This is a schematic diagram of a graphene composite heating pad and its method for achieving dynamic regulation of neck and shoulder muscles. Detailed Implementation

[0108] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0109] It should be noted that the following detailed descriptions are exemplary and intended to provide further explanation of this application. Unless otherwise specified, 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 application pertains.

[0110] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments according to this application. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0111] Example 1

[0112] like Figure 1 As shown in the figure, this embodiment provides a specific application of a method for dynamic regulation of neck and shoulder muscles based on a graphene composite heating pad.

[0113] See Figure 2 The hardware portion of the graphene composite heating pad is implemented using existing technology. In one feasible embodiment, the pad includes a flexible base, an array of multiple heating units, and a control unit.

[0114] The flexible base is made of medical-grade silicone or fabric, offering excellent skin fit and breathability. Each heating unit is fixed to the base via an adjustable support structure, which allows the heating unit to adjust its position and angle within a certain range to accommodate the neck curves of different users.

[0115] The core component of each heating unit is a graphene composite heating layer. This layer is constructed from a graphene film or coating, with a thickness of 0.1 to 0.5 millimeters and an effective heating area of ​​2 to 4 square centimeters. Electrode contact points are located at both ends of the graphene heating layer, which are connected to the power output port of the control unit via flexible wires. These flexible wires employ a shielded multi-core wire structure to transmit both electrical power signals and temperature sensor signals, thus avoiding signal crosstalk.

[0116] The temperature sensor is integrated at or near the edge of the graphene heating layer, preferably using a thermistor, thermocouple, or infrared temperature sensor. The signal line of the temperature sensor shares a flexible conductor bundle with the power supply line of the graphene heating layer, and is connected to the analog signal acquisition interface of the control unit via a branch terminal. The sampling frequency of the temperature sensor is 1 to 10 Hz, which is sufficient to support the time-varying heat dissipation coefficient tracking method described in this invention.

[0117] The control unit includes a microcontroller, a power management module, an analog-to-digital converter (ADC), and a power drive circuit. The microcontroller executes the dynamic control algorithm, including computational tasks such as parameter estimation, thermal coefficient tracking, relaxation identification, model fitting, and power regulation. The power management module converts external power (AC 220V or DC 12V battery) into an adjustable DC voltage required by each heating element, with a voltage adjustment range of 3 to 15V and a corresponding power output range of 1 to 10W. The ADC converts the analog signal from the temperature sensor into a digital signal for processing by the microcontroller, with a resolution of 12 bits or higher and a temperature measurement accuracy of 0.1℃. The power drive circuit uses PWM modulation to adjust the power supply to each heating element in real time according to the control commands output by the microcontroller, with a response time of less than 1 second.

[0118] The control unit connects to the user interface wirelessly or via wired connection. Users can use the interface to set treatment modes, view real-time temperature data, and record treatment effectiveness evaluations. The control unit has built-in memory to store a personalized parameter database for each user, with sufficient storage capacity to record complete data from at least 50 uses for a single user.

[0119] The aforementioned hardware structure and connection relationships can be implemented using existing technologies. The technical contribution of this invention lies in the dynamic heat dissipation coefficient tracking and predictive power regulation method executed based on this hardware platform, rather than the innovation of the hardware structure itself.

[0120] The user is a 30-year-old female office worker who suffers from neck and shoulder muscle tension and restricted local blood flow due to prolonged desk work. The user wears the heating pads on her neck and shoulders, adjusts the position of each heating unit to conform to the curve of her neck, and after activating the device, the system performs dynamic adjustment according to the following steps.

[0121] During the initial parameter estimation phase, the control unit applies initial power to each of the six heating units. The process lasted for 5 minutes. During this period, temperature sensors collected temperature data at each heating unit location at 30-second intervals. Table 1 shows the complete temperature time-series data for the six heating units during the initial parameter estimation phase.

[0122] Table 1: Temperature data of each heating unit during the initial parameter estimation stage (unit: °C):

[0123] ;

[0124] As can be observed from the data in Table 1, there are significant differences in the temperature rise rate among the heating units. Units 2 and 5 rise faster, reaching 38.2℃ and 38.3℃ respectively after 300 seconds; while units 3 and 6 rise slower, reaching only 36.1℃ and 35.7℃. This difference reflects the spatial heterogeneity of muscle tension and blood perfusion status in different locations of the neck and shoulder.

[0125] The control unit fits a first-order thermal response model to the temperature time-series data of each heating unit in Table 1. Taking unit 1 as an example, its temperature evolution curve is as follows: ,in The ambient temperature is used. The system employs a nonlinear least squares method to estimate the parameters. and Specifically, define the residual function. The parameter values ​​that minimize the residuals are obtained iteratively using the Levenberg-Marquardt algorithm. During the fitting process, the data points in the initial heating phase (the first three data points, accounting for 30% of the total time) are assigned a weight of 1.5 to improve the estimation accuracy of the heating time constant. The fitting result for Unit 1 is as follows: , Second.

[0126] Based on the steady-state temperature rise obtained from the fitting, the initial heat dissipation coefficient of unit 1 is calculated. Further calculations of the equivalent heat capacity were performed. .

[0127] The above fitting and calculation process was repeated for the remaining 5 heating units, and the parameter estimation results are summarized in Table 2.

[0128] Table 2: Estimated initial parameters for each heating unit:

[0129] ;

[0130] As can be seen from Table 2, units 3 and 6 have higher heat dissipation coefficients, respectively. and This indicates that the muscle blood perfusion at these two locations is relatively good, and heat dissipation is faster; correspondingly, their steady-state temperature rise is lower, respectively. and Conversely, units 2 and 5 have lower heat dissipation coefficients, respectively. and This indicates that the muscles in these two locations are more tense, blood flow is restricted, and heat dissipation is slower. Therefore, the steady-state temperature rise is higher at the same power, which are respectively and Equivalent heat capacity The differences reflect the variations in thickness and water content of tissue layers at different locations.

[0131] After the initial parameter estimation is completed, the system enters the real-time tracking phase of the time-varying heat dissipation coefficient. The control unit continues to maintain the power of each heat-generating unit at [value missing]. A sliding time window method is used to estimate the time-varying trajectory of the heat dissipation coefficient in real time. The sliding window length is set to... The sampling time step is set to ;

[0132] At any moment At that time, the system extracts the time window. The temperature data for inner unit 1 is as follows: when hour, ;when hour, ;when hour, ;when hour, .

[0133] Based on the thermal balance equation: ;

[0134] The rate of temperature change is approximated using backward difference: ;

[0135] Substitute the known quantities ; ; ;

[0136] as well as ;

[0137] The time can be obtained by solving the heat balance equation. Corresponding heat dissipation coefficient: ;

[0138] Substituting the values ​​into the equation: ;

[0139] For time Heat dissipation coefficient Performing similar calculations, we obtain: ;

[0140] Further calculate the rate of change of the heat dissipation coefficient over time: ;

[0141] Right now: ;

[0142] The system continuously tracked the heat dissipation coefficient of the six heat-generating units over a period of 5 to 20 minutes, and the results are summarized in Table 3.

[0143] Table 3: Time-series data of heat dissipation coefficient for each heat-generating unit (unit: W / ℃):

[0144] ;

[0145] Table 3 shows that the heat dissipation coefficient of each heating unit remained relatively stable or decreased slightly from the 5th to the 8th minute, indicating that the muscles were still under tension. From the 9th minute onwards, the heat dissipation coefficient of each unit began to increase significantly, indicating that the muscles began to relax, local blood perfusion improved, and heat dissipation accelerated. The timing of the increase in heat dissipation coefficient varied among different units, reflecting the spatial heterogeneity and temporal propagation characteristics of muscle relaxation.

[0146] The system continuously monitors the rate of change of heat dissipation coefficient over time. For Unit 1, at the 8th minute:

[0147] ;

[0148] This value is negative, indicating that the heat dissipation coefficient is still decreasing. At the 9th minute: ;

[0149] The rate of change of the heat dissipation coefficient over time turns positive.

[0150] To determine whether the positive value is significant, the system calculates the standard deviation of the estimated heat dissipation coefficient during the initial parameter estimation stage. Since the heat dissipation coefficient is assumed to be constant in the initial stage, its standard deviation mainly originates from the fitting residuals and measurement noise. The standard deviation is calculated through the propagation of the fitting residual standard deviation: ;

[0151] The system sets the significance threshold as follows: ;

[0152] Further converted to a time-rate of change threshold: ;

[0153] At the 9th minute, the time-varying rate of the heat dissipation coefficient of Unit 1 is: ;

[0154] The time change rate threshold has not yet been exceeded. At the 10th minute: ;

[0155] It has not yet exceeded the aforementioned time change rate threshold. At the 11th minute: ;

[0156] At the 12th minute: The system further employs a sequential probability ratio test method to identify the relaxation start-up time.

[0157] The system establishes a null hypothesis. The time-varying rate of change of the heat dissipation coefficient follows the noise distribution in the initial stage, with a mean of 0.

[0158] Establish alternative hypotheses The time-varying rate of change of the heat dissipation coefficient is significantly positive.

[0159] For any time The system calculates from the end of the 5th minute to the current time. The likelihood ratios of all heat dissipation coefficient change rate data under the null and alternative assumptions.

[0160] Taking Unit 1 as an example, at the 12th minute, the system accumulated 7 data points on the rate of change of heat dissipation coefficient from the 6th to the 12th minute:

[0161] ; ; ; ; ; ; All units are... Assume the above data under the null hypothesis. The following values ​​have a mean of 0 and a standard deviation of: The normal distribution; under the alternative hypothesis The sub-mean is: And the standard deviation is also ; It follows a normal distribution.

[0162] The system calculates the likelihood ratio: ;

[0163] in, Data representing the rate of change of heat dissipation coefficient over time In the assumption The probability density is given below.

[0164] For the 12th minute: The value is below the preset decision threshold of 15, therefore, the muscle relaxation is not determined at this time. Continue to calculate the likelihood ratio for subsequent time intervals.

[0165] At the 13th minute, a new data point on the time-varying rate of change of the heat dissipation coefficient was added: The corresponding likelihood ratio is: At the 14th minute, a new data point was added: The corresponding likelihood ratio is: At the 15th minute, a new data point was added: The corresponding likelihood ratio is: ;

[0166] because: Therefore, the likelihood ratio has exceeded the preset decision threshold.

[0167] Meanwhile, the system confirmed that starting from the 13th minute, the heat dissipation coefficient change rate over time had been positive for three consecutive time steps and was gradually increasing, with the duration exceeding the preset duration threshold of 45 seconds.

[0168] Therefore, the muscle relaxation initiation time at the corresponding part of the system determination unit 1 is: ;

[0169] The above identification process was repeated for the remaining 5 heating units to obtain the relaxation start time corresponding to each heating unit, and the results were summarized in Table 4.

[0170] Table 4: Identification results of relaxation start-up time for each heating unit:

[0171] ;

[0172] As shown in Table 4, Units 3 and 6 were identified as having relaxation initiation earlier (at minutes 11 and 10, respectively), which is consistent with their higher initial heat dissipation coefficient and relatively better blood perfusion. Units 2 and 5 were identified as having relaxation initiation later (both at minute 14), indicating that the muscles in these two locations had greater tension and required a longer heating time to initiate the relaxation process.

[0173] After identifying the relaxation initiation time of each unit, the system fits the relaxation dynamics model. Taking unit 1 as an example, the model is fitted based on the time-varying data of the heat dissipation coefficient after the 13th minute. ,in , Minutes. The system uses the nonlinear least squares method to estimate parameters. and .

[0174] The observed heat dissipation coefficients from minute 13 to minute 20 were fitted with the model predictions. The specific data are as follows: minute 13: 0.588; minute 14: 0.609; minute 15: 0.630; minute 16: 0.650; minute 17: 0.669; minute 18: 0.687; minute 19: 0.703; minute 20: 0.717 W / ℃. Through iterative optimization, the fitted values ​​were obtained... , The results indicate that when the muscles in the corresponding part of Unit 1 move from a tense state to a fully relaxed state, the heat dissipation coefficient increases by 0.25 W / ℃, and the time constant of the relaxation process is 6.5 minutes, meaning that it takes approximately 6.5 minutes to reach 63% of the maximum increase.

[0175] The above fitting process was repeated for the remaining 5 heating units, and the resulting relaxation kinetic model parameters are summarized in Table 5.

[0176] Table 5: Relaxation kinetic model parameters for each heating unit:

[0177] ;

[0178] As can be seen from Table 5, the maximum increase in heat dissipation coefficient for Unit 2 and Unit 5 is... Larger, respectively and This indicates that the heat dissipation changes significantly in the muscles at these two locations as they transition from a tense to a relaxed state. Correspondingly, their relaxation time constants are also larger. The relaxation times were also relatively long, at 7.8 minutes and 8.2 minutes respectively, indicating a relatively slow relaxation process. In contrast, Unit 6... Smaller, for ,and The shorter duration, 4.8 minutes, indicates that the initial tension in the muscles at this location was relatively light, and the relaxation process was relatively quick.

[0179] Based on the fitted relaxation dynamics model, the system predicts the heat dissipation coefficient trajectory at future times. Taking unit 1 as an example, at the 20th minute, the system predicts the heat dissipation coefficient trajectory from the 21st to the 25th minute. According to the model:

[0180] ;

[0181] Prediction at the 21st minute: At the 22nd minute: At the 23rd minute: At the 24th minute: At the 25th minute: ;

[0182] The system enters the predictive power dynamic regulation phase. The control objective is to maintain the deep muscle temperature at the corresponding location of each heating unit at the preset target temperature. ;

[0183] Establish the equation relating deep muscle temperature to surface-applied power: ;

[0184] Since the control objective is to maintain a constant deep muscle temperature, therefore: ;

[0185] Therefore, the power relationship equation can be simplified to: ;

[0186] In this embodiment, the target temperature is The ambient temperature is ,therefore: ;

[0187] Substituting the predicted heat dissipation coefficient trajectory into the above relational equation, we obtain the power demand trajectory required by each heat-generating unit to maintain the target temperature in the future time period.

[0188] Taking Unit 1 as an example, at the 21st minute: At the 22nd minute: At the 23rd minute: At the 24th minute: At the 25th minute: ;

[0189] The system updates the power setting value of each heating unit in real time based on the power demand trajectory. Table 6 shows the power adjustment trajectory of the six heating units during the period from the 10th minute to the 30th minute.

[0190] Table 6: Power Adjustment Trajectory of Each Heating Unit (Unit: W):

[0191] ;

[0192] As shown in Table 6, the power regulation trajectory of each heating unit reflects a segmented control strategy. Between the 10th and 12th minute, most heating units had not yet detected relaxation startup, and their power remained essentially at their initial values. Nearby. Units 3 and 6 detected the relaxation start earlier, so their power gradually increased starting from the 11th minute.

[0193] Starting from the 13th minute, each heat-generating unit successively detected the relaxation initiation and entered the relaxation process. The system synchronously increased the power of the corresponding heat-generating unit based on the predicted heat dissipation coefficient growth trajectory to compensate for the temperature drop caused by accelerated heat dissipation. As the degree of muscle relaxation gradually increased, the heat dissipation coefficient gradually stabilized, and the power required by each heat-generating unit also gradually stabilized. By the 30th minute, the heat dissipation coefficient of each unit was close to complete relaxation, and the power tended to stabilize at approximately... , corresponding to maintaining Steady-state power required to maintain deep muscle temperature.

[0194] During power regulation, the system continuously monitors the deviation between the actual and predicted heat dissipation coefficients. Taking Unit 1 as an example, at the 21st minute, the predicted heat dissipation coefficient is: ;

[0195] The actual measured heat dissipation coefficient is: ;

[0196] Therefore, the relative deviation is: ;

[0197] Substituting the data, we get: ;

[0198] The relative deviation is much smaller than the preset deviation threshold. Therefore, there is no need to refit the relaxation dynamics model.

[0199] At the 25th minute, the predicted heat dissipation coefficient of Unit 1 is: ;

[0200] The actual measured heat dissipation coefficient is: The relative deviation is: This deviation is also within the acceptable range, and there is no need to re-perform model fitting.

[0201] Throughout the entire period from the 20th to the 30th minute, the relative deviation between the predicted and actual heat dissipation coefficients of each heat-generating unit did not exceed [a certain value]. This indicates that the established relaxation dynamics model can accurately describe the changing trend of the heat dissipation coefficient during muscle relaxation and has good short-term prediction accuracy.

[0202] After 30 minutes of dynamic adjustment, the system assesses the deep muscle temperature at the corresponding sites of each heating unit. Based on the heat balance equation and heat transfer model, under near-thermal steady-state conditions, the deep muscle temperature can be approximated as:

[0203] ;

[0204] in, For the first Each heating element corresponds to a part at time The temperature of the deep muscles, The ambient reference temperature for the corresponding part. For a moment The applied power, For a moment The heat dissipation coefficient.

[0205] At the 30th minute, the estimated deep muscle temperature of each heating unit was calculated using the following formula: ;

[0206] The estimated deep muscle temperature for each unit is summarized in Table 7.

[0207] Table 7: Deep temperature assessment results of each heating unit at the 30th minute:

[0208] ;

[0209] As can be seen from Table 7, the deep temperature of the corresponding parts of each heating unit reached or approached the target temperature of 40°C. The temperature distribution is relatively uniform. The deep temperatures of Units 1 and 4 are slightly higher than the target (42.4°C respectively). and 42.0 This is because the increase in heat dissipation coefficient at these two locations was slightly lower than predicted by the model, resulting in slightly insufficient power compensation. The surface temperature of all cells did not exceed 42°C. Below the safe limit of 43 This ensures safe use.

[0210] After a 30-minute treatment, the user rated their subjective comfort level. They reported a uniform and moderate warmth in their neck and shoulders, with no localized overheating or discomfort, and scored 8.5 out of 10. Post-treatment neck mobility tests showed that the left and right rotation angles increased from 55° to 70°, and the flexion and extension range increased from 45° to 60°, indicating significant muscle relaxation and improved mobility.

[0211] The data used in this instance was recorded by the system into the user's individualized parameter database, including the initial heat dissipation coefficient, relaxation start time, and relaxation parameters of each heating unit. During the user's second use, the system retrieved historical parameters for individualized adjustments. Specifically, based on the low initial heat dissipation coefficients of units 2 and 5 during the first use (0.484 and 0.469 W / ℃ respectively) and the late relaxation start time (both at the 14th minute), the system appropriately increased the initial power of these two units to 3.3W during the second use to accelerate the heating process. After five consecutive uses, the individualized characteristic parameters extracted by the system showed that the sides of the user's cervical spine (units 2 and 5) were the main areas of tension, with a baseline average heat dissipation coefficient of 0.478 W / ℃. Significantly lower than the 0.785W / at the back of the neck. The average relaxation time constant was 8.0 minutes, significantly longer than the 5.0 minutes in the posterior neck. These individualized characteristics were used to guide power configuration and relaxation recognition threshold adjustments in subsequent use, making the treatment plan more tailored to the user's physiological characteristics.

[0212] Example 2

[0213] This comparative example uses the same user and hardware configuration as Example 1, but employs a conventional constant temperature control scheme. Specifically, the control unit sets the target surface temperature of 38°C for each of the six heating elements. The system adjusts the power of each unit in real time through a temperature negative feedback mechanism to maintain a constant surface temperature. However, this approach does not track dynamic changes in the heat dissipation coefficient, does not identify the muscle relaxation process, and does not perform predictive power regulation.

[0214] In the initial stage, each heating unit starts with a power of 3W. When the surface temperature reaches 38... Subsequently, the control unit uses a PID control algorithm to maintain a constant temperature. During the period from the 5th to the 30th minute, the power adjustment trajectories of different heating units showed significant differences. Units 2 and 5, due to muscle tension in their locations and slower heat dissipation, gradually reduced their power to around 1.8W to avoid overheating; while units 3 and 6, due to faster heat dissipation, required their power to be maintained above 3.5W to maintain the target temperature.

[0215] Temperature assessment results at the 30-minute mark showed extremely uneven deep temperature distribution across the corresponding areas of each heating unit. In the muscle tension areas corresponding to units 2 and 5, due to prolonged maintenance of a surface temperature of 38°C and limited heat dissipation, the accumulated deep temperature reached 44.2°C. and 43.8 The temperatures exceeded the safe and effective temperature range; however, in the areas with better blood flow corresponding to units 3 and 6, the deep temperatures were only 36.5℃ and 35.9℃ respectively, failing to reach the effective treatment temperature of 40℃. The maximum temperature difference across the six deep subsurface units reached 8.3 degrees Celsius. Significantly higher than 2 in Example 1 Temperature difference.

[0216] From a surface temperature safety perspective, the surface temperature of Unit 2 briefly reached 43.7°C at the 25-minute mark. The temperature exceeded the safety limit of 43°C. Although the control unit immediately reduced the power, there was still a risk of localized overheating. The user subjectively reported a significant burning sensation on both sides of the cervical spine, but insufficient warmth in the back of the neck, resulting in a comfort score of 6.2, significantly lower than the 8.5 score in Example 1. Post-treatment neck mobility tests showed that the left and right rotation angles increased from 55° to 63°, and the flexion and extension ranges increased from 45° to 52°, but the improvement was significantly less than in Example 1.

[0217] Comparative analysis reveals that the fundamental flaw of the isothermal control scheme lies in its neglect of the dynamic changes in muscle state during heating. The transition from muscle tension to relaxation leads to improved local blood perfusion and a significant increase in the heat dissipation coefficient. However, isothermal control only maintains a constant surface temperature and cannot perceive or respond to this physiological process. During the muscle tension phase, the heat dissipation coefficient is low, requiring less power to maintain a surface temperature of 38°C, but deep temperature continues to accumulate. When the muscle begins to relax and heat dissipation accelerates, the isothermal control continues to maintain a surface temperature of 38°C, which in turn limits further increases in deep temperature. This control strategy fails to prevent deep overheating during the tension phase and also fails to maintain an effective therapeutic temperature during the relaxation phase, resulting in spatial non-uniformity and temporal inconsistency in the distribution of deep temperature.

[0218] In contrast, the dynamic heat dissipation coefficient tracking, relaxation process identification, and predictive power regulation scheme adopted in Example 1 can sense changes in muscle state in real time and synchronously increase power after relaxation is initiated to compensate for accelerated heat dissipation, thereby maintaining the deep temperature stable within the target range, achieving a more uniform temperature distribution, better treatment effect, and higher safety of use.

[0219] Example 3

[0220] This embodiment provides a specific application of an individualized parameter accumulation optimization method based on multiple usage scenarios, focusing on how the system extracts individualized features, processes abnormal data, and applies temporal consistency constraints during continuous user use.

[0221] The user is a 45-year-old male software developer who suffers from cervical spondylosis due to prolonged desk work, resulting in limited neck movement and intermittent pain. After purchasing the graphene composite heating pad, the user used it five times consecutively, with each use spaced 1 to 2 days apart. The hardware configuration of the heating pad is the same as in Example 1, including six adjustable heating units, each unit integrating a graphene heating layer and a temperature sensor.

[0222] Upon first use, the system had no historical data for this user and employed a standard procedure to perform initial parameter estimation, heat dissipation coefficient tracking, relaxation identification, and power regulation. The initial parameter estimation results from this first use showed significant differences in the heat dissipation coefficients of the corresponding parts of the user's six heat-generating units. (Initial heat dissipation coefficients for units 1 to 6 are shown.) The heat dissipation coefficients were 0.445, 0.428, 0.612, 0.523, 0.419, and 0.688 W / ℃, respectively. Units 2 and 5 had significantly lower heat dissipation coefficients than the other units, indicating that these two areas experienced greater muscle tension and restricted blood perfusion. The relaxation initiation times were identified as follows: Unit 1 at 14 minutes, Unit 2 at 16 minutes, Unit 3 at 11 minutes, Unit 4 at 13 minutes, Unit 5 at 17 minutes, and Unit 6 at 10 minutes. The parameter fitting results for the relaxation kinetic model were as follows: Units 1 to 6... The values ​​were 0.31, 0.38, 0.25, 0.29, 0.42, and 0.22 W / ℃, respectively. The times are 7.2, 8.5, 5.8, 6.9, 9.1, and 5.1 minutes respectively.

[0223] The system records all parameter data from the first use into the user's personalized parameter database. The database data structure includes: user identifier, date and time of use, and initial heat dissipation coefficient for each heat-generating unit. Equivalent heat capacity Relaxation start time Maximum increment of heat dissipation coefficient relaxation time constant Information such as user subjective comfort rating and post-treatment symptom improvement evaluation.

[0224] During the second use, the system retrieves the user's historical parameter database for individualized adjustments. Specifically, the system analyzes the data from the first use and identifies units 2 and 5 as the user's primary areas of muscle tension, with significantly lower initial heat dissipation coefficients than other units and later relaxation initiation times. Based on this individualized characteristic, the system adjusts the initial power applied to units 2 and 5 from the standard value of 3.0W to 3.5W during the initial parameter estimation phase of the second use to accelerate the heating process in these two areas. The standard initial power of 3.0W is maintained for the remaining units.

[0225] The effect of increasing initial power was evident during the initial parameter estimation stage during the second use. Table 8 shows the initial parameter estimation results of each heating unit during the second use, and compares them with the results of the first use.

[0226] Table 8: Comparison of initial parameters used in the first and second tests:

[0227] ;

[0228] As can be seen from Table 8, the initial heat dissipation coefficients of each unit during the second use were basically the same as those during the first use, with the variation range all within [missing information]. Within this range, it indicates that the user's initial muscle state has good repeatability across different usage periods. Regarding the relaxation initiation time, due to the increased initial power, the relaxation initiation times for Units 2 and 5 were advanced from the 16th and 17th minutes to the 15th and 15th minutes, respectively, an advancement of 1 minute and 2 minutes. This demonstrates that individualized initial power adjustment can accelerate the relaxation process of the main tense areas.

[0229] In the second phase of heat dissipation coefficient tracking, the system adaptively adjusts the sliding window length based on the noise level of the temperature signal during the first phase. Specifically, the system calculates the standard deviation of the fitting residuals from the initial parameter estimation phase during the first phase, obtaining the following results for each of the six heat-generating units: ; ; ; ; ; ;

[0230] The standard deviations mentioned above reflect the differences in noise levels of the temperature signals at each measuring point. Among them, unit 5 has the highest noise level, at [value missing]. This may be related to insufficient skin contact or large fluctuations in local blood flow in that area; Unit 6 has the lowest noise level, at [insert value here]. This indicates that the temperature measurement signal at this location is relatively stable.

[0231] Based on the noise level estimation results, the system sets the sliding window length for each heat-generating unit differently. During the second use of heat dissipation coefficient tracking, the system calculates the fluctuation amplitude of the temperature signal within the current short-term time window, which is 30 seconds in length.

[0232] Taking Unit 5 as an example, at the 8th minute, the system calculates the standard deviation of the temperature signal over the previous 30 seconds as follows: ;

[0233] Unit 5 corresponds to twice the noise level: ;

[0234] because: ;Right now: ;

[0235] The system determines that the current temperature signal is fluctuating significantly, possibly due to external disturbances or measurement noise. Therefore, the sliding window length of the heating element is adjusted from the standard value. Increase to: ;

[0236] This enhances the filtering effect and reduces the impact of temperature signal noise on the heat dissipation coefficient estimation results.

[0237] At the 12-minute mark, the standard deviation of the temperature signal in Unit 5 over the previous 30 seconds decreased to: ;

[0238] because: ;Right now: The system determines that the current temperature signal has stabilized, therefore the sliding window length of the heating unit is restored to 90 seconds, or further shortened to 75 seconds, i.e.: ;

[0239] To improve the time resolution of tracking time-varying parameters of heat dissipation coefficient.

[0240] After the second use, the system updates the user's personalized parameter database. At this time, the personalized parameter database contains complete data records of both uses. The system performs statistical analysis on the data from both uses and calculates the mean and standard deviation of each parameter.

[0241] Taking Unit 2 as an example, the initial heat dissipation coefficients obtained in the two usage processes are as follows: ;

[0242] as well as: Therefore, the average initial heat dissipation coefficient for both uses is: ;

[0243] Right now: The corresponding standard deviation is approximately: The relaxation start times for Unit 2 during the two uses were as follows: ;

[0244] as well as: Therefore, its average relaxation initiation time is: ;

[0245] Before the third use, the user experienced sudden acute neck pain due to excessive work pressure and continuous overtime, suspected to be a stiff neck or minor muscle strain. The user used the heating pad to relieve the pain. The parameter results obtained during the initial parameter estimation phase of the system in the third use showed significant differences compared to the first two uses. Table 9 shows the comparison results of the initial heat dissipation coefficients of each heating unit during the third use and the first two uses.

[0246] Table 9: Comparison of initial heat dissipation coefficients for the first three uses (unit: W / ℃):

[0247] ;

[0248] As shown in Table 9, during the third use, the initial heat dissipation coefficients of units 1, 2, 4, and 5 decreased significantly compared to the average values ​​of the previous two uses, with the deviation reaching [value missing]. to This abnormal decrease indicates that the muscles in the aforementioned areas may have been under high tension under abnormal conditions, resulting in reduced local blood perfusion and a significant decrease in heat dissipation capacity. Conversely, the initial heat dissipation coefficients of units 3 and 6 remained relatively stable, with deviations within [missing information]. Within this range, it indicates that the state changes of these two parts are relatively small.

[0249] The relaxation dynamics model parameters obtained during the third use also exhibited abnormal characteristics. Specifically, the relaxation time constants of elements 2 and 5... They respectively achieved: ; ;

[0250] Significantly higher than the average of the previous two uses and This indicates that during the third use under the corresponding abnormal condition, the relaxation process in the relevant areas slowed down significantly, requiring a longer time to reach the same level of relaxation.

[0251] The user's subjective evaluation after the third use also showed abnormalities. Although the warmth was obvious, the relief from neck discomfort was low, and the comfort score was only 5.8 points, significantly lower than the 8.2 and 8.5 points for the first two uses.

[0252] After the third use, the system executes an abnormal data detection mechanism to perform statistical analysis on the relaxation parameter data set in the user's historical records.

[0253] Maximum increment of heat dissipation coefficient For example, the system extracts data from Unit 2 during the first two uses: ; Its historical average was calculated to be: ;

[0254] The corresponding historical standard deviation is: During the third use, the maximum increase in the heat dissipation coefficient of unit 2 was: ;

[0255] Therefore, the relative deviation of this parameter from the historical mean is: ;

[0256] Substituting the values ​​into the equation: ;

[0257] The corresponding relative threshold of 3 times the standard deviation is: ;

[0258] Right now: ;

[0259] because: ;Right now: ;

[0260] Therefore, the system marks the parameter corresponding to Unit 2 as abnormal data during the third use.

[0261] After performing similar tests on the relevant parameters of the six heating units, the system found that multiple parameters of units 1, 2, 4, and 5 were marked as abnormal, while the parameters of units 3 and 6 were basically within the normal range. Based on this, the system judged the third usage record as an abnormal usage record and prompted the user interface that an abnormal usage record had been detected. This data will not be included in the statistical update of the individualized parameter library.

[0262] The system retains the data from the third use as a historical record for subsequent queries, but does not use this abnormal data in subsequent individual parameter calculations.

[0263] By the fourth use, the user's abnormal condition had largely subsided, and the neck condition had recovered to levels close to those observed during the first and second uses. During the individualized adjustment phase, the system continued to calculate individualized feature parameters based on the valid data from the first and second uses, thus avoiding the impact of the third set of abnormal data on the individualized parameter estimation.

[0264] During the fourth use, the initial heat dissipation coefficients of units 1 to 6 were as follows:

[0265] ; ; ; ; ; ;

[0266] The above values ​​are basically consistent with the results of the first and second uses, indicating that after excluding the abnormal data from the third use, the system can maintain the baseline of individualized parameters under normal user conditions relatively stably.

[0267] In the fourth relaxation process identification phase, the system applies a timing consistency constraint mechanism to improve the accuracy and timeliness of relaxation start identification.

[0268] The system establishes an adjacency matrix based on the spatial location of each heating unit in the neck and shoulder area. Based on the positional relationship of each heating unit, we define unit 1 as adjacent to unit 2, unit 2 as adjacent to unit 4, unit 4 as adjacent to unit 5, unit 1 as adjacent to unit 3, and unit 3 as adjacent to unit 6. Therefore: ;

[0269] For heating units with no direct adjacency, the corresponding matrix elements are set to 0. If undirected adjacency is used, the adjacency matrix satisfies: ;

[0270] At the 10-minute mark of the fourth use, the system first detected a relaxation start in unit 6. At this point, the system uses the adjacency matrix... Search for the heating unit adjacent to unit 6, and determine that unit 3 is adjacent to unit 6.

[0271] The system then adjusts the relaxation recognition criterion of unit 3, setting its original significance threshold as follows: Reduced to the original value The adjusted significance threshold is obtained as follows: ;Right now ;

[0272] At the 11th minute, the time-varying rate of the heat dissipation coefficient of Unit 3 is: ;because: ;

[0273] Therefore, the rate of change has not yet reached the original significance threshold; but it also satisfies: ;

[0274] That is, exceeding the significance threshold adjusted by the temporal consistency constraint.

[0275] At the same time, the likelihood ratio of the sequential probability ratio test corresponding to Unit 3 reaches: ;

[0276] When the adopted sequential probability ratio decision threshold is not higher than 12.5, the likelihood ratio satisfies the corresponding decision condition. Therefore, combining the adjusted significance threshold and the sequential probability ratio test results, the system identifies the relaxation start-up of unit 3 at the 11th minute, i.e. ;

[0277] If the timing consistency constraint is not applied, unit 3 will not meet the original recognition conditions until the 12th minute. Therefore, the timing consistency constraint enables it to start the relaxation recognition about 1 minute earlier, so that the system can adjust the power of unit 3 more timely and reduce the possibility of a brief drop in deep temperature due to the power adjustment lag after the relaxation starts.

[0278] Similarly, when unit 3 detects a relaxation start, the system lowers the relaxation detection threshold of unit 1, which is adjacent to unit 3, causing the relaxation start of unit 1 to be detected earlier than the originally expected 14th minute, at the 13th minute. When unit 1 detects a relaxation start, the system further lowers the relaxation detection threshold of unit 2, which is adjacent to unit 1, causing the relaxation start of unit 2 to be detected earlier than the originally expected 15 minutes, at the 14th minute. ;

[0279] Following the above method, the relaxation start recognition process obtained by the system is spatially represented as follows: The propagation sequence is used to assist in the identification of the relaxation state of each heating unit by utilizing the temporal correlation between adjacent parts.

[0280] After the fourth use, the system will add the data from the fourth use to the personalized parameter library. At this point, the personalized parameter library will contain three valid data records from the first, second, and fourth uses.

[0281] The system performs statistical analysis on the three valid data sets mentioned above, updating the mean and standard deviation of each integrated parameter. Taking the initial heat dissipation coefficient of unit 2 as an example, the three valid data sets are as follows: ; ; ;

[0282] The updated mean is: ;

[0283] The corresponding standard deviation is approximately: Compared to using only the first two valid data points, approximately As effective data accumulates, the standard deviation of the parameters decreases, and the stability of individualized parameter estimation is improved.

[0284] On the fifth use, the system further adjusts the control parameters such as the initial power, relaxation recognition threshold, and sliding window length of each heating unit based on the statistical characteristics obtained from the three valid use records.

[0285] Specifically, the initial power settings for units 2 and 5 are as follows: ;

[0286] Compared to the second use Further improvements will be made to accelerate the heating process in the two main tense areas mentioned above.

[0287] For Unit 2, the system performs Bayesian adjustment on the relaxation recognition significance threshold based on the mean and standard deviation of the historical heat dissipation coefficient time change rate, transforming it from a threshold determined based on the global noise distribution into an individualized threshold obtained by combining the historical characteristics of the corresponding part of the user, so as to improve the adaptability of relaxation start recognition.

[0288] During the fifth use, the relaxation start times identified by each heating unit were as follows:

[0289] ; ; ; ; ; ;

[0290] Compared to the first use, the relaxation start time of Unit 2 and Unit 5 was 3 minutes earlier, indicating that after accumulating effective usage data multiple times, the system can make differentiated parameter adjustments for individual characteristics of different parts.

[0291] After the fifth use, the user's subjective comfort score reached 9.1 points. Post-use neck mobility tests showed that the left and right rotation angle of the neck improved from before use. Increase to The increase is: ;

[0292] The range of flexion and extension is from the range before use Increase to The increase is: ;

[0293] Table 10 summarizes the evolution trends of key parameters of each heating unit during the five uses, with the abnormal data from the third use being marked separately.

[0294] Table 10: Summary of parameter evolution in 5 uses:

[0295] .

Claims

1. A graphene composite heating pad and a method for dynamically regulating neck and shoulder muscles, characterized in that, Includes the following steps: S1: Initial parameter estimation: Apply initial power to each heating unit and continue for a preset duration, and collect temperature time-series data within the preset duration; During the preset time period, the muscles are in a state of tension and the heat dissipation coefficient has not changed significantly. Based on the first-order thermal response model, the [missing information] is used to [missing information]. The equivalent heat capacity and initial heat dissipation coefficient of each heat-generating unit are estimated. The temperature evolution curve of the first-order thermal response model is as follows: ; in, For ambient temperature, For steady-state temperature rise, The heating time constant is It is a time variable; Parameters are estimated from the temperature time series data using a nonlinear fitting method. and ; Calculate the initial heat dissipation coefficient based on the steady-state temperature rise: ; in, The initial power, The initial heat dissipation coefficient; The equivalent heat capacity is calculated based on the heating time constant and the initial heat dissipation coefficient: ;in, The equivalent heat capacity is given. S2: Real-time tracking of time-varying heat dissipation coefficient; during the subsequent heating process after the initial parameter estimation is completed, the time-varying trajectory of the heat dissipation coefficient is estimated in real time using a sliding time window method; for any given time... Extract from time 1 At that time Temperature data and power data, among which, The length of the sliding time window; The window length ranges from 1 to 2 minutes; within the sliding time window, the heat dissipation coefficient corresponding to the window is estimated based on the heat balance equation. S3: Real-time identification of muscle relaxation process; continuous monitoring of the time change rate of the heat dissipation coefficient; when the time change rate meets the preset activation condition, determine the... The muscles in the area corresponding to each heating unit begin to relax; The preset activation conditions include a first criterion and a second criterion; The first criterion is: the rate of change of the heat dissipation coefficient over time is significantly positive, i.e., it satisfies: ; in, The significance threshold; The significance threshold is determined by calculating the standard deviation of the heat dissipation coefficient during the initial parameter estimation phase. The significance threshold is set to 2 to 3 times the standard deviation; The second criterion is: the duration of the time change rate of the significant positive value exceeds a preset duration, wherein the preset duration is 30 seconds to 1 minute; Record the relaxation start time when both the first criterion and the second criterion are satisfied. The relaxation initiation time is defined as the moment when the first criterion and the second criterion are simultaneously satisfied for the first time. S4: Establishment and prediction of relaxation dynamics model; After identifying the relaxation initiation time, the relaxation dynamics model is fitted based on the time-varying data of heat dissipation coefficient observed after the relaxation initiation time; S5: Predictive power dynamic regulation; with the control objective of maintaining the deep muscle temperature at a preset target temperature, the power regulation scheme of each heating unit in the future time period is calculated based on the predicted heat dissipation coefficient trajectory; the relationship equation between the deep muscle temperature and the surface applied power is established: ； in, The preset target temperature, For a moment The power required to be applied; Substituting the predicted heat dissipation coefficient trajectory into the relational equation, the power demand trajectory required to maintain the target temperature in the future time period is obtained. The power setting value of each heating unit is updated in real time according to the power demand trajectory. During the power regulation process, the deviation between the actual heat dissipation coefficient and the predicted heat dissipation coefficient is continuously monitored. When the absolute value of the deviation exceeds the preset deviation threshold, the model fitting process of S4 is re-executed. The preset deviation threshold is 10% to 20% of the predicted heat dissipation coefficient; S6: Individualized parameter cumulative optimization; Data is recorded for each user's usage process, including the initial heat dissipation coefficient, relaxation start time, maximum increment of heat dissipation coefficient, and relaxation time constant for each heat-generating unit. Once the user's cumulative usage count reaches a preset cumulative count, statistical analysis is performed on the recorded data to extract individualized feature parameters; based on the results of the statistical analysis, control parameters are individually adjusted during subsequent usage, including: The initial power is adjusted based on the average initial heat dissipation coefficient in the user's historical records so that the initial heating rate matches the user's individual characteristics. The threshold for relaxation identification is pre-adjusted based on the correlation between deep temperature and relaxation initiation time in the user's historical records, making relaxation identification more sensitive and accurate. When performing model fitting in S4, the user's historical parameter distribution is applied to the parameter space as a Bayesian prior distribution, which accelerates the convergence of model fitting and improves the robustness of parameter estimation.

2. The graphene composite heating pad and its method for achieving dynamic regulation of neck and shoulder muscles according to claim 1, characterized in that, The nonlinear fitting of the temperature evolution curve in S1 uses the weighted least squares method; In the weighted least squares method, higher weight coefficients are assigned to data points in the initial stage of heating; the initial stage of heating is defined as the time period corresponding to the first 30% of the total heating time from the start of heating; by assigning higher weights to data points in the initial stage of heating, the estimation accuracy of the heating time constant is improved.

3. The graphene composite heating pad and its method for achieving dynamic regulation of neck and shoulder muscles according to claim 1, characterized in that, The window length of the sliding time window in S2 is adaptively adjusted according to the noise level of the temperature signal; The specific method of the adaptive adjustment is as follows: In stage S1, the standard deviation of the fitted residual is calculated as an estimate of the temperature noise level, denoted as... During subsequent heating, calculate the fluctuation amplitude of the temperature signal within the current short-term time window; When the fluctuation amplitude exceeds In this case, the window length is increased to enhance the filtering effect and reduce the impact of noise on the heat dissipation coefficient estimation; When the fluctuation amplitude is less than At the same time, the window length is reduced to improve the temporal resolution of time-varying parameter tracking; The window length can be adjusted from 0.5 minutes to 3 minutes.

4. The graphene composite heating pad and its method for achieving dynamic regulation of neck and shoulder muscles according to claim 1, characterized in that, The identification of the relaxation initiation time in S3 further employs a sequential probability ratio test method. The sequential probability ratio test method includes: Establish the null hypothesis The time-varying rate of change of the heat dissipation coefficient follows the noise distribution of stage S1, and the mean of the noise distribution is 0. Establish alternative hypotheses The time-varying rate of change of the heat dissipation coefficient is significantly positive, and the mean of the time-varying rate is greater than the significance threshold. For time Calculate from the end of S1 to time [time]. The likelihood ratios of all heat dissipation coefficient change rate data under the null and alternative assumptions are as follows: ; in, This indicates the null hypothesis. This indicates the alternative hypothesis. Indicates from time At that time The sequence of the rate of change of heat dissipation coefficient; When the likelihood ratio When the preset decision threshold is exceeded, the null hypothesis is rejected. And accept the alternative hypothesis. The relaxation start is determined; the preset decision threshold ranges from 10 to 20. The sequential probability ratio test method can identify the relaxation initiation time earlier than the fixed-duration hypothesis test, while maintaining a low false alarm probability.

5. The graphene composite heating pad and its method for achieving dynamic regulation of neck and shoulder muscles according to claim 1, characterized in that, In S4, when fitting the relaxation dynamics model, physiological constraints are applied to the model parameters based on the anatomical location of the heating unit in the neck and shoulder area. The physiological constraint specifically refers to the relaxation time constant of the heating unit located in the trapezius muscle region on both sides of the neck. The value range is constrained to 2 to 4 minutes; For the heating unit located in the deep muscle group region of the back of the neck, its relaxation time constant The value range is constrained to 4 to 8 minutes; In the nonlinear least squares fitting process, the range of values ​​is used as the boundary condition of the parameter search space to avoid the fitting results having physiologically unreasonable parameter values. The physiological constraints are based on the differences in the physiological characteristics of different muscle tissues in the neck and shoulder area, making the model fitting results more consistent with the actual physiological mechanisms.

6. The graphene composite heating pad and its method for achieving dynamic regulation of neck and shoulder muscles according to claim 1, characterized in that, The predictive power dynamic regulation in S5 adopts a segmented control strategy for heating units in different relaxation states. The segmented control strategy includes: for heating units that have not yet been identified as having relaxed startup, maintaining the current power unchanged, or adjusting the power according to a preset slow power increase curve, wherein the power increase rate of the slow power increase curve does not exceed 5% per minute of the initial power; For heat-generating units that have been identified as having started relaxation and are currently in the process of relaxation, power is increased synchronously according to the predicted heat dissipation coefficient growth trajectory to compensate for the temperature drop caused by accelerated heat dissipation. The condition for determining whether relaxation is in progress is: the current heat dissipation coefficient. satisfy: ; For heating units that have completed relaxation, switch to constant power maintenance mode, whereby the constant power is calculated based on the heat dissipation coefficient after complete relaxation; The condition for determining whether relaxation has been completed is: the current heat dissipation coefficient. satisfy: ; The segmented control strategy ensures that the power adjustment trajectory of each heating unit is synchronized with its respective muscle relaxation process, avoiding overheating or underheating of some parts due to the use of a uniform control strategy.

7. The graphene composite heating pad and its method for achieving dynamic regulation of neck and shoulder muscles according to claim 1, characterized in that, When multiple heating units correspond to neck and shoulder areas that are anatomically adjacent, a temporal consistency constraint is applied to the identification of their relaxation start-up time. The specific implementation of the timing consistency constraint is as follows: an adjacency matrix is ​​established based on the spatial location of each heating unit in the neck and shoulder area. , where matrix elements A value of 1 indicates a heating unit and heating unit Adjacent to the neck and shoulder area, matrix elements A value of 0 indicates that the two elements are not adjacent. When the heating unit After identifying the relaxation start-up moment, all components related to the heating unit are... Adjacent heating units Adjust its relaxation recognition criteria; The adjustment specifically involves: adjusting the heating unit... Significance threshold The reduction is used to make the relaxation recognition criteria for adjacent parts more sensitive; The temporal consistency constraint is based on the following physiological mechanism: muscle relaxation in the neck and shoulder area often presents a regional propagation pattern in space. When a certain area begins to relax, its adjacent areas are usually about to enter a relaxed state as well. The timing consistency constraint improves the accuracy and timeliness of identifying the relaxation start time of adjacent parts.

8. The graphene composite heating pad and its method for achieving dynamic regulation of neck and shoulder muscles according to claim 1, characterized in that, The individualized parameter cumulative optimization in S6 further includes an abnormal data detection and removal mechanism; The specific implementation method of the abnormal data detection and removal mechanism is as follows: Statistical analysis was performed on the relaxation parameter data set in the user's historical records, the relaxation parameters including the maximum increment of the heat dissipation coefficient. and relaxation time constant ; Calculate the mean and standard deviation for each parameter in the dataset; For a relaxation parameter obtained during a certain usage, calculate the deviation of the parameter from the historical mean; When the deviation exceeds three times the historical standard deviation, the usage record is marked as abnormal data; The abnormal data is not included in the statistical calculation and updating process of the individualized parameter library; The abnormal data may correspond to the user being in a special physiological state, such as acute muscle injury, inflammatory response or over-fatigue. The abnormal data detection and removal mechanism avoids the influence of data under special physiological conditions on the statistical estimation of individualized parameters under normal conditions, thereby improving the representativeness and stability of individualized parameters.

9. The graphene composite heating pad and its method for achieving dynamic regulation of neck and shoulder muscles according to claim 1, characterized in that, The heat balance equation is: ; in, For the rate of temperature change, For a moment power, For a moment heat dissipation coefficient, For a moment Temperature; The rate of temperature change is approximated using backward difference: ; in, The sampling time step; the time obtained from solving the heat balance equation. Corresponding heat dissipation coefficient Further calculate the rate of change of the heat dissipation coefficient over time: .

10. The graphene composite heating pad and its method for achieving dynamic regulation of neck and shoulder muscles according to claim 1, characterized in that, The relaxation dynamics model adopts the following exponential saturation form: ; in, The initial heat dissipation coefficient is... This represents the maximum increment of the heat dissipation coefficient. Let be the relaxation time constant. The relaxation initiation time; The maximum increment of the heat dissipation coefficient reflects the magnitude of heat dissipation change in the muscle from a tense state to a fully relaxed state; the relaxation time constant reflects the speed of the muscle relaxation process; the observed heat dissipation coefficient data are fitted using a nonlinear least squares method to estimate the model parameters. and ; The heat dissipation coefficient trajectory at future moments is predicted based on the relaxation dynamics model, where the time range of the future moments is 2 to 5 minutes after the current moment.

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