Heating temperature regulation and control system and method for massage instrument

Abstract

The invention discloses a heating temperature regulation and control system and method for a massage instrument, and relates to the technical field of massage instrument heating temperature control, and the system comprises a user data acquisition module, a massage site temperature regulation and control module, and a temperature curve construction and adaptive adjustment module. The user data acquisition module comprises an array pressure sensor and a skin electric signal sensor, receives pressure of massage positions of the massage instrument through the array pressure sensor, constructs and records pressure distribution diagrams of different massage positions, performs similarity judgment on the pressure distribution diagrams, divides the positions, and transmits the pressure distribution diagrams to the user data acquisition module; a skin electric signal sensor is used for receiving and recording a working surface skin electric signal in the current working process of the massage instrument, and a user database is established for file establishment based on a user id. According to the heating temperature regulation and control system and method for the massage instrument, the massage position can be recognized, the comfortable temperature of a user can be excavated, personalized massage instrument temperature regulation and control can be provided, and the practicability of the massage instrument in the actual use process is enhanced.

Description

Technical Field

[0001] This invention belongs to the field of massage device heating temperature control technology, specifically a heating temperature regulation system and method for massage devices. Background Technology

[0002] With the increasing demand for health-related products, massagers have become a common household health care device. The heating function, as a core auxiliary function of massagers, can effectively promote blood circulation, relieve muscle soreness, and enhance massage comfort through temperature stimulation. Current massagers mostly use fixed temperature settings for heating control, lacking consideration for individual user differences and the specificity of massage locations. Since different users have significantly different skin sensitivities and temperature preferences, and even the same user's tolerance and comfort needs vary for different massage locations on their body, such as the neck, back, and waist, traditional massager control systems do not establish personalized temperature profiles for different users during use. This makes it impossible to accurately match these differentiated needs. Furthermore, massagers struggle to identify the user's actual massage area and real-time comfort level, often failing to meet the user's actual massage requirements. Moreover, current heating control systems lack dynamic adaptive capabilities; during massage, the massager cannot automatically identify the user's massage location and cannot design differentiated heating rates for massage locations with different sensitivity levels, easily causing skin irritation during heating and affecting the user experience. Summary of the Invention

[0003] The present invention aims to at least solve one of the technical problems existing in the prior art. To this end, the present invention proposes a heating temperature control system and method for a massage device to solve the aforementioned technical problem.

[0004] To achieve the above objectives, the present invention adopts the following technical solution: A heating temperature control system and method for a massager includes a user data acquisition module, a massage point temperature control module, and a temperature curve construction and adaptive adjustment module. The user data acquisition module includes an array pressure sensor and a skin conductance signal sensor. The array pressure sensor receives the pressure at the massage position of the massager, constructs and records the pressure distribution map of different massage positions, and performs similarity judgment and position division on the pressure distribution map. The skin conductance signal sensor receives and records the skin conductance signal of the working surface during the current operation of the massager, establishes a user database based on user ID to create profiles, and records the sensor data. The massage point temperature control module, based on the changes in the user's skin electrical signals at the massage point during the temperature change process of the massager collected in the user database, makes personalized adaptive adjustments to the massager heating temperature based on the changing trend of the skin electrical signals within the current user's set fixed temperature level, and records the user's comfortable temperature and adjustment data under the current pressure distribution map in the user database. The temperature curve construction and adaptive adjustment module integrates historical data collected from the user database for the same massage position of the current user, calculates the optimal temperature and sensitivity coefficient for the current massage position, and constructs a personalized temperature curve. It then assigns cluster centers of the pressure distribution map for the user's next massage position and automatically matches the personalized temperature curve of the current position to automatically control the heating temperature of the massager based on the assignment results.

[0005] Furthermore, the user data collection module specifically includes the following steps: The pressure data of the user at the massage area is collected by an array of pressure sensors to obtain the original pressure matrix of the massage area and construct a pressure distribution map. Feature extraction is performed on the pressure distribution map to determine similarity and classify similar pressure distribution maps as the same location. The massager receives and records the skin electrical signals on the working surface of the massager during the current massage area using a skin electrical signal sensor. A user database is established using MySQL to create profiles based on user IDs. Sub-profiles are created in the user ID profiles based on massage locations obtained through clustering. The pressure distribution map of the massage area and the skin electrical signal data are recorded in the corresponding sub-profiles, and the massage location label is also recorded.

[0006] Furthermore, the step of collecting pressure data from the user at the massage area using an array of pressure sensors to obtain an original pressure matrix of the massage area and construct a pressure distribution map, extracting features from the pressure distribution map to determine similarity, and classifying similar pressure distribution maps as the same location, includes the following steps: The pressure values ​​measured by each sensor in the array pressure sensor are arranged according to the sensor's arrangement on the massager surface to obtain a pressure matrix. The original pressure matrix is ​​normalized, where It is the normalized pressure value in the i-th row and j-th column. The original pressure value in row i and column j. , representing the minimum and maximum values ​​in the original pressure matrix respectively, feature extraction is performed on the pressure matrix to construct a feature vector f, where the features include pressure center coordinates, zero-order pressure moment, Hu invariant moment, maximum and minimum pressure, pressure mean, and pressure variance. The feature vector is standardized by z-score standardization. The elbow rule is used to determine the number of clusters K. The K-means clustering algorithm is used to divide the similar pressure distribution map into the same location. For each feature vector, the Euclidean distance between it and the K cluster centers is calculated. The feature vector is assigned to the class containing the nearest cluster center. At the same time, for each cluster k, the average value of all feature vectors in the cluster is calculated as the new cluster center. This process is repeated until the cluster centers converge.

[0007] Furthermore, the massage point temperature control module includes the following steps: The massager is set to a fixed temperature setting with a preset fine-tuning range. Real-time skin electrical signal data of the working surface is collected during the operation of the massager. Based on the real-time skin electrical signal data and the base temperature of the current fixed temperature setting, the direction of the user's adjustment is determined. Based on the adjustment results, the user's comfortable temperature under the current pressure distribution map is determined, and the comfortable temperature and adjustment data are recorded under the corresponding sub-file pressure distribution map.

[0008] Furthermore, the process of presetting a fine-tuning range within a fixed temperature setting on the massager, collecting real-time skin electrical signal data from the working surface during massager operation, determining the user's adjustment probe direction based on the real-time skin electrical signal data and the base temperature of the current fixed temperature setting, and determining the user's comfortable temperature under the current pressure distribution map based on the adjustment probe results, includes the following steps: For a fixed temperature setting G, its base temperature is The fine-tuning range is The collected electrodermal signals were processed using a Butterworth bandpass filter to remove high-frequency noise and low-frequency drift, and the signal was decomposed. ,in The filtered electrodermal signal at time t. These represent the slowly varying skin conductance level, the fast skin conductance response, and the noise residual at time t, respectively. During the period of temperature stability The skin electrophysiological signal features were extracted, among which Represents the start time point. This represents the duration of the stable period, including the average skin electrohydraulic value. Standard deviation of skin conductance Skin conductance level trend slope Skin conductance frequency Average amplitude of skin conductance response ; An discomfort index was constructed using skin conductance characteristics. ,in These represent the transpose of the weight vector and the normalized eigenvector, respectively. The sum of the weight values ​​of different eigenvectors is 1. For the temperature of the massager, the features in the feature vector include the standard deviation of the skin conductance level, the slope of the skin conductance level trend, the frequency of skin conductance response, and the average amplitude of the skin conductance response. For base temperature Abnormal index below Calculations are performed to test subsequent temperature adjustments, among which ,in These represent the weights of skin conductance level and skin conductance response frequency, respectively. , Representing time respectively The skin conductance level and skin conductance response frequency state under the following conditions: ; ; in, These represent the abnormal thresholds for sympathetic nerve excitation and stress response frequency, respectively. A value ≥0.6 is considered an abnormal state. A value less than 0.6 is considered normal. Under abnormal conditions, a cooling-down probing strategy is adopted to probe the temperature, and the cooling sequence is as follows: ; in, Trial step number and trial step size, The temperature probe will terminate and the termination temperature will be taken as the comfort temperature if any of the following conditions are met: , Three consecutive points , Represents the comfort threshold. Represents temperature The discomfort index is as follows: Then the temperature probe will be terminated directly and the comfortable temperature will not be recorded; Under normal conditions, a temperature testing strategy involving temperature increases is adopted, and the cooling sequence is as follows: ; in, Trial step number and trial step size, The temperature trial will terminate if any of the following conditions are met, and the temperature at the previous trial step number will be taken as the comfort temperature: , , ,when Then the temperature probe will be terminated directly and the comfortable temperature will not be recorded.

[0009] Furthermore, the step size during the temperature test needs to be dynamically adjusted based on the anomaly index, specifically including the following steps: when When ≥0.6, use 0.5 times. For the original Replace; When 0.3≤ When <0.6, follow the ratio of 1. For the original Replace; when When <0.3, use 1.2 times For the original Replace it.

[0010] Furthermore, the temperature curve construction and adaptive adjustment module includes the following steps: Collected data from all pressure distribution maps under the same massage location label in the user database, calculate the average optimal temperature and the slope of the skin electrodermal signal as a function of temperature, and generate personalized temperature curves. Under the automatic temperature setting of the massager, the pressure data of the current massage position is collected and features are extracted. The current features are compared with the Euclidean distance of the cluster center. The current massage position is assigned to the class of the nearest cluster center. The personalized temperature curve of the current cluster center is extracted, and the heating rate of the massager is controlled to heat up to the average optimal temperature.

[0011] Furthermore, the step of extracting collected data from all pressure distribution maps under the same massage location tag in the user database, calculating the average optimal temperature and the slope of the skin electrodermal signal change with temperature to generate a personalized temperature curve includes the following steps: Data was collected by extracting all pressure distribution maps under the same massage location tag from the user database, and for each location... Aggregate all historical data and calculate the average optimal temperature using a weighted average method. ,in: ; in, Representative parts The number of times it appears, Represented in the pressure distribution map Targeted parts Find a comfortable temperature. Represents the decay weight. , These represent the attenuation coefficient and the time difference, respectively. For each pressure distribution map under the same massage position label, the temperature-discomfort relationship is fitted by a quadratic polynomial. The slope at the comfortable temperature point is recorded as the local sensitivity coefficient. The local sensitivity coefficients under each pressure distribution map are averaged to obtain the sensitivity coefficient of the current massage position. Based on the sensitivity coefficient, the heating rate is preset to obtain a personalized temperature curve.

[0012] A method for controlling the heating temperature of a massager includes the following steps: S1. Receive the pressure at the massage position of the massager through the array pressure sensor, construct and record the pressure distribution map of different massage positions, and determine the similarity of the pressure distribution map and classify the positions. Receive and record the skin electrical signal of the working surface during the current operation of the massager through the skin electrical signal sensor. Establish a user database based on user ID to create profiles and record the sensor data. S2. Based on the changes in the user's skin electrical signals at the massage points during the temperature change process of the massager collected in the user database, the heating temperature of the massager is adjusted in a personalized and adaptive manner based on the changing trend of the skin electrical signals within the current user's set fixed temperature level, and the user's comfortable temperature and adjustment data under the current pressure distribution map are recorded in the user database. S3. Based on the historical data collected from the user database for the same massage position, calculate the optimal temperature and sensitivity coefficient for the current massage position and construct a personalized temperature curve. Assign cluster centers of the pressure distribution map for the user's next massage position, and automatically control the heating temperature of the massager by matching the personalized temperature curve of the current position based on the assignment results.

[0013] Compared with the prior art, the beneficial effects of the present invention are: In this invention, by collecting pressure data of the massage position during the user's use of the massager, a pressure distribution map is constructed and clustered to obtain different massage position cluster centers. At the same time, during the operation of the massager at a fixed temperature setting, the skin electrical signals of the massage position are collected and the temperature is adjusted. The user's comfortable temperature is found within the acceptable range, so that the user's massage position can be identified and the massage temperature can be automatically adjusted in the subsequent automatic mode, thereby enhancing the functionality. In this invention, by constructing a pressure distribution map and clustering, the unique massage preference area of ​​each user can be accurately identified. Combined with skin electrical signals, temperature regulation is used to discover the comfortable temperature of the user at different massage positions. In automatic mode, the massager can automatically adjust the massage temperature according to the user's specific situation, providing a personalized massage experience for different user IDs. At the same time, different heating rates are adopted for skin with different sensitivity coefficients to avoid irritating the user during the heating process. In this invention, by establishing an independent comfort temperature profile for each massage position of each user, the massager can continuously monitor physiological signals and fine-tune the temperature in real time during the massage process to collect personalized user massage data. Different users have different sensitivities and preferences for temperature in different parts of their bodies. By establishing an independent comfort temperature profile, the needs of each user's different massage positions can be accurately matched, providing a customized massage experience to improve user satisfaction. Attached Figure Description

[0014] Figure 1 This is a block diagram of a heating temperature control system for a massager according to the present invention. Detailed Implementation

[0015] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0016] Example 1: like Figure 1 As shown, a heating temperature control system for a massager includes a user data acquisition module, a massage point temperature control module, and a temperature curve construction and adaptive adjustment module. The user data acquisition module includes an array pressure sensor and a skin conductance signal sensor. The array pressure sensor receives the pressure at the massage position of the massager, constructs and records the pressure distribution map of different massage positions, and performs similarity judgment and position division on the pressure distribution map. The skin conductance signal sensor receives and records the skin conductance signal of the working surface during the current operation of the massager, establishes a user database based on user ID to create profiles, and records the sensor data. The pressure data of the user at the massage area is collected by an array of pressure sensors to obtain the original pressure matrix of the massage area and construct a pressure distribution map. Feature extraction is performed on the pressure distribution map to determine similarity, and similar pressure distribution maps are classified as the same location. The process includes the following steps: The pressure values ​​measured by each sensor in the array pressure sensor are arranged according to the sensor's arrangement on the massager surface to obtain a pressure matrix. The original pressure matrix is ​​normalized, where It is the normalized pressure value in the i-th row and j-th column. The original pressure value in row i and column j. , representing the minimum and maximum values ​​in the original pressure matrix respectively, feature extraction is performed on the pressure matrix to construct a feature vector f, where the features include pressure center coordinates, zero-order pressure moment, Hu invariant moment, maximum and minimum pressure, pressure mean, and pressure variance. The feature vector is standardized by z-score standardization. It should be noted that the pressure center coordinates reflect the centroid position of the pressure distribution, the zero-order pressure moment represents the sum of all elements in the pressure matrix, the Hu invariant moment is used to describe the shape characteristics of the pressure distribution, the maximum and minimum pressure values ​​are obtained directly by traversing the pressure matrix, the pressure mean is the average of all elements in the pressure matrix, and the pressure variance is used to measure the degree of dispersion of the pressure value relative to the mean.

[0017] The elbow rule is used to determine the number of clusters K. The K-means clustering algorithm is used to divide the similar pressure distribution map into the same location. For each feature vector, the Euclidean distance between it and the K cluster centers is calculated. The feature vector is assigned to the class containing the nearest cluster center. At the same time, for each cluster k, the average value of all feature vectors in the cluster is calculated as the new cluster center. This process is repeated until the cluster centers converge.

[0018] It should be noted that the elbow rule is used to determine the number of clusters K. By plotting the sum of squared errors curves corresponding to different K values, the elbow point of the curve is selected as the optimal K value. In the process of determining the convergence of cluster centers, the steps of repeatedly distributing samples and updating cluster centers are repeated until the cluster centers no longer change significantly or the maximum number of iterations is reached. This determines the convergence of cluster centers. The maximum number of iterations needs to be set based on empirical methods according to the actual application.

[0019] The device receives and records the skin electrical signals on the working surface of the massager during the massage of the current massage area using a skin electrical signal sensor. A user database is established using MySQL to create profiles based on user IDs. Sub-profiles are created in the user ID profiles based on massage locations obtained through clustering. The pressure distribution map of the massage area and the skin electrical signal data are recorded in the corresponding sub-profiles, and the massage location label is also recorded. It should be noted that users need to register an ID when using the massager. The system will create a user profile based on this ID. The profile contains basic information about the user registration, including age, gender, and data collected by the sensor during each massage. The skin conductance sensor is integrated with the massager's heating pad / massage head to capture the skin conductance signals on the surface of the contact area in real time during the massager's operation.

[0020] Example 2: The massage point temperature control module, based on the changes in the user's skin electrical signals at the massage points during the temperature change process of the massager collected in the user database, makes personalized adaptive adjustments to the massager's heating temperature based on the changing trend of the skin electrical signals within the current user's set fixed temperature level, and records the user's comfortable temperature and adjustment data under the current pressure distribution map in the user database; A fine-tuning range is preset within a fixed temperature setting on the massager. Real-time skin electrical signal data of the working surface is collected during the operation of the massager. Based on the real-time skin electrical signal data and the base temperature of the current fixed temperature setting, the user's adjustment probe direction is determined. Based on the adjustment probe results, the user's comfortable temperature under the current pressure distribution map is determined. The comfortable temperature and adjustment data are recorded under the corresponding sub-file pressure distribution map, including the following steps: For a fixed temperature setting G, its base temperature is The fine-tuning range is The collected electrodermal signals were processed using a Butterworth bandpass filter to remove high-frequency noise and low-frequency drift, and the signal was decomposed. ,in The filtered electrodermal signal at time t. These represent the slowly varying skin conductance level, the fast skin conductance response, and the noise residual at time t, respectively. It should be noted that the cvxEDA convex optimization method is used to decompose the signal.

[0021] During the period of temperature stability The skin electrophysiological signal features were extracted, among which Represents the start time point. This represents the duration of the stable period, including the average skin electrohydraulic value. Standard deviation of skin conductance Skin conductance level trend slope Skin conductance frequency Average amplitude of skin conductance response ; It should be noted that the average skin electrohydraulic value is among them. Used to measure the average baseline excitation level of a user's sympathetic nervous system during a stable period. The sampling frequency represents the electrodermal signal; the standard deviation of the electrodermal signal level reflects the degree of fluctuation in the electrodermal signal level; and the slope of the electrodermal signal level trend represents the trend. This is used to reveal the changing trend of skin conductance levels; a positive slope indicates a cumulative increase in tension, while a negative slope indicates a decrease during relaxation. These represent the covariance of the time series and the SCL series, respectively, and the variance of the time series. The user's electrodermal keratology (SCR) events are... Continuous range, frequency of skin conductance response Used to measure the frequency of physiological responses, The number of events represents the average amplitude of the skin conductance response. To measure the intensity of a single physiological response, The peak intensity of the waveform representing the k-th SCR event is denoted as . The temperature stabilization period refers to the time period from when the temperature reaches the target value and remains stable during the temperature trial process until the skin electrophysiological response reaches a stable state. It needs to be set based on the actual situation and by consulting experts in the relevant field.

[0022] An discomfort index was constructed using skin conductance characteristics. ,in These represent the transpose of the weight vector and the normalized eigenvector, respectively. The sum of the weight values ​​of different eigenvectors is 1. For the temperature of the massager, the features in the feature vector include the standard deviation of the skin conductance level, the slope of the skin conductance level trend, the frequency of skin conductance response, and the average amplitude of the skin conductance response. It should be noted that, Refers to a specific temperature The discomfort index is a measure of comfort at a given temperature; a lower value indicates greater comfort for the user at that temperature. This is a weighted column vector containing weight coefficients assigned to the four physiological characteristics. The sum of the weight coefficients is 1, and they are typically set to 0.2, 0.2, 0.3, and 0.3. The weight coefficient values ​​can be adjusted as needed to represent the importance of each characteristic in the final discomfort assessment. Each component Feature normalization is performed to obtain the normalized feature vector, where ,in Representing all the temperatures being tested The set, This represents the different temperature points during this temperature test. The original feature values ​​obtained from the above measurement.

[0023] For base temperature Abnormal index below Calculations are performed to test subsequent temperature adjustments, among which ,in These represent the weights of skin conductance level and skin conductance response frequency, respectively. , Representing time respectively The skin conductance level and skin conductance response frequency state under the following conditions: ; ; in, These represent the abnormal thresholds for sympathetic nerve excitation and stress response frequency, respectively. A value ≥0.6 is considered an abnormal state. A value less than 0.6 is considered normal. It should be noted that, These represent the abnormal thresholds for sympathetic nerve excitation and stress response frequency, respectively. This is achieved by collecting the user's SCL (Self-Regulation Lance) data at rest. The mean and standard deviation are used as the threshold, with the sum of the mean and two standard deviations. The value is usually set to 0.4 or 0.6, but can be adjusted according to the actual situation. That is, at the base temperature At a certain point during the stabilization period, user anomalies are determined by assessing the baseline temperature within the current fixed temperature range to ascertain subsequent adjustment and probing strategies.

[0024] Under abnormal conditions, a cooling-down probing strategy is adopted to probe the temperature, and the cooling sequence is as follows: ; in, Trial step number and trial step size, The temperature probe will terminate and the termination temperature will be taken as the comfort temperature if any of the following conditions are met: , Three consecutive points , Represents the comfort threshold. Represents temperature The discomfort index is as follows: Then the temperature probe will be terminated directly and the comfortable temperature will not be recorded; It should be noted that, through To prevent testing from exceeding the preset operating range of the equipment at that gear position, This indicates that the significant physiological stress caused by overheating has largely disappeared. At the current test temperature, the user's thermal discomfort has not improved compared to the previous temperature, for three consecutive points. This means that the user experiences acceptable thermal comfort within a continuous low-temperature range, where... The setup needs to be done by consulting experts in the field based on the actual application situation.

[0025] Under normal conditions, a temperature testing strategy involving temperature increases is adopted, and the cooling sequence is as follows: ; in, Trial step number and trial step size, The temperature trial will terminate if any of the following conditions are met, and the temperature at the previous trial step number will be taken as the comfort temperature: , , ,when Then the temperature probe will be terminated directly and the comfortable temperature will not be recorded.

[0026] It should be noted that, through To prevent testing from exceeding the preset operating range of the equipment at that gear position, This indicates an abnormal state. At the current test temperature, the user's thermal discomfort increased compared to the previous temperature. This indicates that the user's discomfort exceeds the threshold.

[0027] During temperature probing, the probing step size needs to be dynamically adjusted based on the anomaly index, specifically including the following steps: when When ≥0.6, use 0.5 times. For the original Replace; When 0.3≤ When <0.6, follow the ratio of 1. For the original Replace; when When <0.3, use 1.2 times For the original Replace it.

[0028] It should be noted that by making dynamic adjustments based on the anomaly index, smaller step sizes can be used for more precise and safer temperature adjustment probes, while larger step sizes can improve the efficiency of temperature adjustment probes.

[0029] The temperature curve construction and adaptive adjustment module integrates historical data collected from the user database for the same massage position, calculates the optimal temperature and sensitivity coefficient for the current massage position, and constructs a personalized temperature curve. It then assigns cluster centers based on the pressure distribution map for the user's next massage position. Based on the assignment results, it automatically matches the personalized temperature curve for the current position to automatically control the massager's heating temperature, including the following steps: Collected data from all pressure distribution maps under the same massage location tag are extracted from the user database. The average optimal temperature and the slope of the skin electrical signal change with temperature are calculated to generate a personalized temperature profile, including the following steps: Data was collected by extracting all pressure distribution maps under the same massage location tag from the user database, and for each location... Aggregate all historical data and calculate the average optimal temperature using a weighted average method. ,in: ; in, Representative parts The number of times it appears, Represented in the pressure distribution map Targeted parts Find a comfortable temperature. Represents the decay weight. , These represent the attenuation coefficient and the time difference, respectively. It should be noted that the attenuation coefficient is greater than 0, and the specific value needs to be set by consulting experts in the field. By using the attenuation coefficient and the time difference, the weight of the recent pressure distribution map can be increased to reflect the changes in user preferences over time.

[0030] For each pressure distribution map under the same massage position label, the temperature-discomfort relationship is fitted by a quadratic polynomial. The slope at the comfortable temperature point is recorded as the local sensitivity coefficient. The local sensitivity coefficients under each pressure distribution map are averaged to obtain the sensitivity coefficient of the current massage position. Based on the sensitivity coefficient, the heating rate is preset to obtain a personalized temperature curve.

[0031] It should be noted that different heating rates are preset for different sensitivity levels to minimize user discomfort during the massager's heating process. The specific sensitivity level and heating rate need to be determined experimentally by collecting a large amount of skin point signal data at different sensitivity levels and heating rates. The average heating rate below the skin's electrical signal stimulation threshold is selected as the heating rate corresponding to the sensitivity level. The temperature-discomfort relationship is as follows: ; The solution is obtained by least squares, and the slope at the comfort temperature point is used as the local sensitivity coefficient.

[0032] Under the automatic temperature setting of the massager, the pressure data of the current massage position is collected and features are extracted. The current features are compared with the Euclidean distance of the cluster center. The current massage position is assigned to the class of the nearest cluster center. The personalized temperature curve of the current cluster center is extracted, and the heating rate of the massager is controlled to heat up to the average optimal temperature.

[0033] It should be noted that by using the personalized temperature curve of the current cluster center, the sensitivity and average optimal temperature of the current location are obtained. Based on the sensitivity, the heating rate is controlled to gradually increase the temperature to the average optimal temperature of the current massage location. The automatic temperature setting requires the collection of sufficient historical massage data from different user IDs. The specific amount of data needs to be preset based on experience, combined with the appropriate massage position of the current massager. The skin of different massage areas has different sensitivities to temperature changes. By adjusting the heating rate based on the sensitivity, it can better adapt to the massage needs of different positions.

[0034] Example 3: A method for controlling the heating temperature of a massager includes the following steps: S1. Receive the pressure at the massage position of the massager through the array pressure sensor, construct and record the pressure distribution map of different massage positions, and determine the similarity of the pressure distribution map and classify the positions. Receive and record the skin electrical signal of the working surface during the current operation of the massager through the skin electrical signal sensor. Establish a user database based on user ID to create profiles and record the sensor data. S2. Based on the changes in the user's skin electrical signals at the massage points during the temperature change process of the massager collected in the user database, the heating temperature of the massager is adjusted in a personalized and adaptive manner based on the changing trend of the skin electrical signals within the current user's set fixed temperature level, and the user's comfortable temperature and adjustment data under the current pressure distribution map are recorded in the user database. S3. Based on historical data collected from the user database for the same massage position, calculate the optimal temperature and sensitivity coefficient for the current massage position and construct a personalized temperature curve. Assign cluster centers for the pressure distribution map of the user's next massage position, and automatically control the massager's heating temperature based on the personalized temperature curve of the current position. This invention discloses a heating temperature control system and method for a massager. During use, it collects pressure data from massage positions, constructs a pressure distribution map, and clusters the data to obtain cluster centers for different massage positions. Simultaneously, while the massager operates at a fixed temperature setting, it collects and adjusts the skin electrical signals at the massage positions to determine the user's comfortable temperature within an acceptable range. In subsequent automatic modes, it identifies the user's massage positions and automatically adjusts the massage temperature. By establishing an independent comfort temperature profile for each massage position of each user, the massager continuously monitors physiological signals and fine-tunes the temperature in real time during massage to collect personalized user massage data. Different users have significantly different sensitivities and preferences for temperature in different parts of their bodies; independent comfort temperature profiles can accurately match the needs of each user's different massage positions, providing a customized massage experience and improving user satisfaction.

[0035] In the embodiments provided by this invention, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of the method in this embodiment according to actual needs.

[0036] The above embodiments are only used to illustrate the technical methods of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.

Claims

1. A heating temperature control system for a massager, characterized in that: It includes a user data acquisition module, a massage point temperature control module, and a temperature curve construction and adaptive adjustment module; The user data acquisition module includes an array pressure sensor and a skin conductance signal sensor. The array pressure sensor receives the pressure at the massage position of the massager, constructs and records the pressure distribution map of different massage positions, and performs similarity judgment and position division on the pressure distribution map. The skin conductance signal sensor receives and records the skin conductance signal of the working surface during the current operation of the massager, establishes a user database based on user ID to create profiles, and records the sensor data. The massage point temperature control module, based on the changes in the user's skin electrical signals at the massage point during the temperature change process of the massager collected in the user database, makes personalized adaptive adjustments to the massager heating temperature based on the changing trend of the skin electrical signals within the current user's set fixed temperature level, and records the user's comfortable temperature and adjustment data under the current pressure distribution map in the user database. The temperature curve construction and adaptive adjustment module integrates historical data collected from the user database for the same massage position of the current user, calculates the optimal temperature and sensitivity coefficient for the current massage position, and constructs a personalized temperature curve. It then assigns cluster centers of the pressure distribution map for the user's next massage position and automatically matches the personalized temperature curve of the current position to automatically control the heating temperature of the massager based on the assignment results.

2. The heating temperature control system for a massager according to claim 1, characterized in that: The user data collection module specifically includes the following steps: The pressure data of the user at the massage area of ​​the massager is collected by an array of pressure sensors to obtain the original pressure matrix of the massage area and construct a pressure distribution map. Feature extraction is performed on the pressure distribution map to determine similarity and classify similar pressure distribution maps as the same location. The massager receives and records the skin electrical signals on the working surface of the massager during the current massage area using a skin electrical signal sensor. A user database is established using MySQL to create profiles based on user IDs. Sub-profiles are created in the user ID profiles based on massage locations obtained through clustering. The pressure distribution map of the massage area and the skin electrical signal data are recorded in the corresponding sub-profiles, and the massage location label is also recorded.

3. The heating temperature control system for a massager according to claim 2, characterized in that: The process involves collecting pressure data from the user at the massage area using an array of pressure sensors to obtain an original pressure matrix for the massage area, constructing a pressure distribution map, extracting features from the pressure distribution map to determine similarity, and classifying similar pressure distribution maps as belonging to the same location. This includes the following steps: The pressure values ​​measured by each sensor in the array pressure sensor are arranged according to the sensor's arrangement on the massager surface to obtain a pressure matrix. The original pressure matrix is ​​normalized, where It is the normalized pressure value in the i-th row and j-th column. The original pressure value in row i and column j. , representing the minimum and maximum values ​​in the original pressure matrix respectively, feature extraction is performed on the pressure matrix to construct a feature vector f, where the features include pressure center coordinates, zero-order pressure moment, Hu invariant moment, maximum and minimum pressure, pressure mean, and pressure variance. The feature vector is standardized by z-score standardization. The elbow rule is used to determine the number of clusters K. The K-means clustering algorithm is used to divide the similar pressure distribution map into the same location. For each feature vector, the Euclidean distance between it and the K cluster centers is calculated. The feature vector is assigned to the class containing the nearest cluster center. At the same time, for each cluster k, the average value of all feature vectors in the cluster is calculated as the new cluster center. This process is repeated until the cluster centers converge.

4. The heating temperature control system for a massager according to claim 3, characterized in that: The massage point temperature control module includes the following steps: The massager is set to a fixed temperature setting with a preset fine-tuning range. Real-time skin electrical signal data of the working surface is collected during the operation of the massager. Based on the real-time skin electrical signal data and the base temperature of the current fixed temperature setting, the direction of the user's adjustment is determined. Based on the adjustment results, the user's comfortable temperature under the current pressure distribution map is determined, and the comfortable temperature and adjustment data are recorded under the corresponding sub-file pressure distribution map.

5. A heating temperature control system for a massager according to claim 4, characterized in that: The process of pre-setting a fine-tuning range within a fixed temperature setting on the massager, collecting real-time skin electrical signal data from the working surface during massager operation, determining the user's adjustment probe direction based on the real-time skin electrical signal data and the base temperature of the current fixed temperature setting, and determining the user's comfortable temperature under the current pressure distribution map based on the adjustment probe results includes the following steps: For a fixed temperature setting G, its base temperature is The fine-tuning range is The collected electrodermal signals were processed using a Butterworth bandpass filter to remove high-frequency noise and low-frequency drift, and the signal was decomposed. ,in The filtered electrodermal signal at time t. These represent the slowly varying skin conductance level, the fast skin conductance response, and the noise residual at time t, respectively. During the period of temperature stability The skin electrodermal signal features were extracted, among which Represents the start time point. This represents the duration of the stable period, including the average skin electrohydraulic value. Standard deviation of skin conductance Skin conductance level trend slope Skin conductance frequency Average amplitude of skin conductance response ; An discomfort index was constructed using skin conductance characteristics. ,in These represent the transpose of the weight vector and the normalized eigenvector, respectively. The sum of the weight values ​​of different eigenvectors is 1. For the temperature of the massager, the features in the feature vector include the standard deviation of the skin conductance level, the slope of the skin conductance level trend, the frequency of skin conductance response, and the average amplitude of the skin conductance response. For base temperature Abnormal index below Calculations are performed to test subsequent temperature adjustments, among which ,in These represent the weights of skin conductance level and skin conductance response frequency, respectively. , Representing time respectively The skin conductance level and skin conductance response frequency state under the following conditions: ； ； in, These represent the abnormal thresholds for sympathetic nerve excitation and stress response frequency, respectively. A value ≥0.6 is considered an abnormal state. A value less than 0.6 is considered normal. Under abnormal conditions, a cooling-down probing strategy is adopted to probe the temperature, and the cooling sequence is as follows: ； in, Trial step number and trial step size, The temperature probe will terminate and the termination temperature will be taken as the comfort temperature if any of the following conditions are met: , Three consecutive points , Represents the comfort threshold. Represents temperature The discomfort index is as follows: Then the temperature probe will be terminated directly and the comfortable temperature will not be recorded; Under normal conditions, a temperature testing strategy involving temperature increases is adopted, and the cooling sequence is as follows: ； in, Trial step number and trial step size, The temperature trial will terminate if any of the following conditions are met, and the temperature at the previous trial step number will be taken as the comfort temperature: , , ,when Then the temperature probe will be terminated directly and the comfortable temperature will not be recorded.

6. A heating temperature control system for a massager according to claim 5, characterized in that: The temperature testing process requires dynamic adjustment of the testing step size based on the anomaly index, specifically including the following steps: when When ≥0.6, use 0.5 times. For the original Replace; When 0.3≤ When <0.6, follow the ratio of 1. For the original Replace; when When <0.3, use 1.2 times For the original Replace it.

7. A heating temperature control system for a massager according to claim 5, characterized in that: The temperature curve construction and adaptive adjustment module includes the following steps: Collected data from all pressure distribution maps under the same massage location label in the user database, calculate the average optimal temperature and the slope of the skin electrodermal signal as a function of temperature, and generate personalized temperature curves. Under the automatic temperature setting of the massager, the pressure data of the current massage position is collected and features are extracted. The current features are compared with the Euclidean distance of the cluster center. The current massage position is assigned to the class of the nearest cluster center. The personalized temperature curve of the current cluster center is extracted, and the heating rate of the massager is controlled to heat up to the average optimal temperature.

8. A heating temperature control system for a massager according to claim 7, characterized in that: The process of extracting all pressure distribution maps under the same massage location tag from the user database, calculating the average optimal temperature and the slope of the skin electrodermal signal change with temperature to generate a personalized temperature curve includes the following steps: Data was collected by extracting all pressure distribution maps under the same massage location tag from the user database, and for each location... Aggregate all historical data and calculate the average optimal temperature using a weighted average method. ,in: ； in, Representative parts The number of times it appears, Represented in the pressure distribution map Targeted parts Find a comfortable temperature. Represents the decay weight. , These represent the attenuation coefficient and the time difference, respectively. For each pressure distribution map under the same massage position label, the temperature-discomfort relationship is fitted by a quadratic polynomial. The slope at the comfortable temperature point is recorded as the local sensitivity coefficient. The local sensitivity coefficients under each pressure distribution map are averaged to obtain the sensitivity coefficient of the current massage position. Based on the sensitivity coefficient, the heating rate is preset to obtain a personalized temperature curve.

9. A method for controlling the heating temperature of a massager, characterized in that, This method employs a heating temperature control system for a massager as described in any one of claims 1-8, and includes the following steps: S1. Receive the pressure at the massage position of the massager through the array pressure sensor, construct and record the pressure distribution map of different massage positions, and determine the similarity of the pressure distribution map and classify the positions. Receive and record the skin electrical signal of the working surface during the current operation of the massager through the skin electrical signal sensor. Establish a user database based on user ID to create profiles and record the sensor data. S2. Based on the changes in the user's skin electrical signals at the massage points during the temperature change of the massager collected in the user database, the heating temperature of the massager is adjusted in a personalized and adaptive manner based on the changing trend of the skin electrical signals within the current user's set fixed temperature level, and the user's comfortable temperature and adjustment data under the current pressure distribution map are recorded in the user database. S3. Based on the historical data collected from the user database for the same massage position, calculate the optimal temperature and sensitivity coefficient for the current massage position and construct a personalized temperature curve. Assign cluster centers for the pressure distribution map for the user's next massage position, and automatically control the heating temperature of the massager by matching the personalized temperature curve of the current position based on the assignment results.

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