An integrated thermotherapy heating device and a big data intelligent analysis system for health.

Abstract

This invention relates to the field of thermotherapy data processing technology, specifically to an integrated thermotherapy heating device and a big data intelligent analysis system. For any given thermotherapy period, this invention obtains the beneficial ratio between the changes in local temperature and pressure data for each foot region at all times, as well as the thermotherapy adaptability, based on the changing trends of local temperature and pressure data for each foot region at all times. This yields multiple pressure and temperature segments for each foot region. Furthermore, it analyzes the distribution characteristics of data between different segments to obtain the initiative of pressure changes during each thermotherapy period. Combining the fluctuations in blood pressure data at corresponding times within each pressure segment and the humidity data distribution between pressure segments of different foot regions at corresponding times, it obtains the normality of data for each thermotherapy period. This invention improves the reliability of data analysis by accurately analyzing the normality of data acquired during thermotherapy.

Description

Technical Field

[0001] This invention relates to the field of thermotherapy data processing technology, specifically to an integrated thermotherapy heating device and a big health data intelligent analysis system. Background Technology

[0002] Hyperthermia, as a non-invasive physical therapy, has significant effects on promoting blood circulation, relieving muscle pain, improving sleep quality, and regulating metabolism. Integrated hyperthermia heating devices combine hyperthermia with infrared technology, and simultaneously collect various health-related data from the user's soles using intelligent sensors and upload them to the device terminal for analysis. In existing technologies, health data is processed through data analysis systems to provide real-time health feedback; however, these systems are single-function monitoring tools that only process static, single-dimensional data and cannot analyze data changes during the hyperthermia process. The data integration and real-time performance are poor, and there is a lack of data support for big data analysis, resulting in poor data analysis effects. Summary of the Invention

[0003] To address the technical shortcomings of data analysis that fail to consider dynamic changes during thermotherapy, the present invention aims to provide an integrated thermotherapy heating device and a big data intelligent analysis system. The specific technical solution adopted is as follows:

[0004] This invention proposes a big data intelligent analysis system for integrated thermotherapy heating devices, comprising:

[0005] Thermotherapy heating data acquisition module: Based on the integrated thermotherapy heating device, it acquires the overall temperature data, blood pressure data, humidity data, thermotherapy map, and pressure data of different foot areas of the user's sole at each moment during each thermotherapy period. The thermotherapy map includes local temperature data of different foot areas.

[0006] Data processing module: For any given heat therapy period, based on the distribution of overall temperature and pressure data across different times, the heat therapy adaptability within each heat therapy period is obtained; based on the changing trends of local temperature and pressure data for each plantar region at all times, and the heat therapy adaptability, the beneficial ratio of changes between pressure data and local temperature data for each plantar region is obtained, resulting in multiple pressure and temperature segments for each plantar region; based on the beneficial ratio of changes between pressure data and local temperature data for all plantar regions, and the distribution characteristics of data between different segments, the pressure change initiative for each heat therapy period is obtained.

[0007] Normality Analysis Module: Based on the initiative of pressure changes during each heat therapy period, the fluctuation of blood pressure data at corresponding times within the pressure segments, and the distribution of humidity data at corresponding times between pressure segments in different foot areas, the normality of data for each heat therapy period is obtained.

[0008] Health assessment module: Assess the user's health based on the normality of data during the real-time heat therapy period.

[0009] Furthermore, the method for obtaining the thermotherapy adaptability includes:

[0010] Based on the distribution of overall temperature and pressure data at different times within each heat therapy session, the uniformity of temperature gradient and the frequency of pressure changes for each heat therapy session are obtained.

[0011] A negative correlation mapping is performed on the frequency of pressure changes, and the product of the negative correlation mapping result and the temperature gradient uniformity for each heat therapy period is obtained as the heat therapy adaptability for each heat therapy period.

[0012] Furthermore, the method for obtaining the temperature gradient uniformity includes:

[0013] Obtain the gradient of the overall temperature data between adjacent time points within each hyperthermia period, and use it as the temperature gradient.

[0014] The mean difference in temperature gradient between different groups was obtained as the temperature gradient uniformity for each hyperthermia period.

[0015] Furthermore, the method for obtaining the pressure change frequency includes:

[0016] For any given heat therapy period, the average pressure data of all plantar areas at each moment is obtained as the overall pressure data for that moment.

[0017] Obtain the slope fluctuation characteristics of the overall pressure data between all adjacent time points as the degree of pressure fluctuation; obtain the mean difference between all adjacent time points and perform negative correlation mapping as the rate of change;

[0018] The product of the degree of pressure fluctuation and the rate of change is obtained as the pressure change frequency.

[0019] Furthermore, the method for obtaining the benefit ratio of the change includes:

[0020] Obtain the mean square error of the sequence composed of pressure data and local temperature data of each foot area at all times during each heat therapy period;

[0021] The product of mean square error and thermotherapy adaptability for each thermotherapy period is calculated as the beneficial ratio of the change in pressure data and local temperature data for each plantar area within each thermotherapy period.

[0022] Furthermore, the method for obtaining multiple pressure and temperature segments for each plantar region includes:

[0023] For any data point in the pressure data or local temperature data, obtain the slope of the data between adjacent moments within each heat therapy period; sort the data in ascending order of slope to obtain the first data sequence;

[0024] Select the data segment with the largest difference between adjacent slopes in the first data sequence, and take the middle position of the adjacent slopes in the first data sequence as the split point in the data sequence to obtain two data segments.

[0025] Furthermore, the method for proactively acquiring the pressure change includes:

[0026] For each foot region during any heat therapy period, the range of the mean slope between pressure segments is compared with the range of the mean slope between temperature segments, and this range is used as the first ratio.

[0027] The mean of the first ratio in all plantar regions is obtained as the overall ratio level. The mean of the beneficial ratio corresponding to the changes in different plantar regions during each heat therapy period is negatively correlated and mapped. The product of the negative correlation mapping result and the overall ratio level is calculated as the initiative of pressure change during each heat therapy period.

[0028] Furthermore, the method for obtaining the normality of the data includes:

[0029] For each foot region during any monitoring period, the standard deviation of blood pressure data at all times within each pressure segment is obtained. The blood pressure fluctuation level of each foot region is selected as the one with the largest standard deviation among all pressure segments.

[0030] The difference in average humidity across all time points between pressure segments is used as the degree of humidity variation in each foot region.

[0031] Based on the degree of blood pressure fluctuation, humidity change, and active pressure change in different areas of the foot during each heat therapy session, the normality of the data for each heat therapy session was obtained. The degree of blood pressure fluctuation was negatively correlated with the normality of the data, while the degree of humidity change and the active pressure change were both positively correlated with the normality of the data.

[0032] Furthermore, the negative correlation mapping is performed using an exponential function with the natural constant as the base.

[0033] The present invention also proposes an integrated thermotherapy heating device, the device including a data acquisition unit and a data processor. The data acquisition unit is used to acquire the overall temperature data, blood pressure data, humidity data, thermotherapy map, and pressure data of different areas of the foot at each moment during each thermotherapy period. The thermotherapy map includes local temperature data of different areas of the foot.

[0034] The data processor is used to obtain the thermotherapy adaptability within each thermotherapy period based on the distribution of overall temperature and pressure data between different times for any given monitoring time period; to obtain the beneficial ratio of changes between pressure data and local temperature data for each foot region at all times, and thermotherapy adaptability, based on the changing trends of local temperature and pressure data for each foot region at all times, and to obtain multiple pressure segments and temperature segments for each foot region; and to obtain the pressure change initiative for each thermotherapy period based on the beneficial ratio of changes between pressure data and local temperature data for all foot regions, and the distribution characteristics of data between different segments.

[0035] The normality of the data for each heat therapy period is obtained based on the initiative of pressure changes during each heat therapy period, the fluctuation of blood pressure data at corresponding times within the pressure segments, and the distribution of humidity data at corresponding times between pressure segments in different foot areas.

[0036] Health data are assessed based on the normality of data from real-time hyperthermia periods.

[0037] The present invention has the following beneficial effects:

[0038] This invention, for any given monitoring period, obtains the thermotherapy adaptability within each thermotherapy period based on the distribution of overall temperature and pressure data across different times, reflecting the user's ability to adapt to changes in thermotherapy pressure. Based on the changing trends of local temperature and pressure data for each foot region at all times, and the thermotherapy adaptability, it obtains the beneficial ratio of changes between corresponding pressure and local temperature data for each foot region, resulting in multiple pressure and temperature segments for each foot region, which helps analyze data transition characteristics between segments. Based on the beneficial ratio of changes between corresponding pressure and local temperature data for all foot regions, and the distribution characteristics of data between different segments, it obtains the initiative in pressure changes during each thermotherapy period, assessing the user's ability to actively adjust to pressure. Based on the initiative in pressure changes during each thermotherapy period, the fluctuation of blood pressure data at corresponding times within pressure segments, and the distribution of humidity data at corresponding times between pressure segments in different foot regions, it obtains the normality of data for each thermotherapy period. This invention improves the reliability of data analysis by accurately analyzing the normality of data acquired during thermotherapy. Attached Figure Description

[0039] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0040] Figure 1 A flowchart illustrating a big data intelligent analysis system for an integrated thermotherapy heating device, provided as an embodiment of the present invention;

[0041] Figure 2 A schematic diagram illustrating the distribution of overall temperature data during a single hyperthermia session, provided as an embodiment of the present invention;

[0042] Figure 3 This is a schematic diagram illustrating the distribution of pressure data during a single hyperthermia session, provided in one embodiment of the present invention.

[0043] Figure 4 This is a schematic diagram illustrating the changes in pressure and local temperature data within a certain area of ​​the sole of the foot, provided in an embodiment of the present invention.

[0044] Figure 5 This is a flowchart illustrating a method for obtaining the normality of data according to an embodiment of the present invention. Detailed Implementation

[0045] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of an integrated thermotherapy heating device and a big health data intelligent analysis system proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0046] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0047] The following description, in conjunction with the accompanying drawings, details the specific solution of the integrated thermotherapy heating device and the intelligent analysis system for big health data provided by this invention.

[0048] Please see Figure 1The diagram illustrates a flowchart of a method for a big health data intelligent analysis system applied to an integrated thermotherapy heating device, according to an embodiment of the present invention. Specifically, it includes: a thermotherapy heating data acquisition module 101, a data processing module 102, a normality analysis module 103, and a health assessment module 104.

[0049] Thermotherapy heating data acquisition module 101: Based on the integrated thermotherapy heating device, it acquires the overall temperature data, blood pressure data, humidity data, thermotherapy map, and pressure data of different foot areas at each moment during each thermotherapy period of the user's soles. The thermotherapy map includes local temperature data of different foot areas.

[0050] In embodiments of the present invention, to improve the accuracy of data analysis, data collected during the thermotherapy period from the integrated thermotherapy heating device is processed. The integrated thermotherapy heating device combines thermotherapy and heating functions to provide warmth and health management. The upper platform is adjusted to a suitable position via a lifting mechanism between the upper and lower platforms of the heating device, and the soles of the feet are placed in the foot-shaped area of ​​the lower platform. An infrared electromagnetic heating module is installed in the foot-shaped area to provide infrared magnetic therapy to the user's soles. After the heating device is activated, various health-related data of the user's soles are acquired by a data acquisition device installed on the lower platform of the device. The data is stored and processed by the built-in data analysis unit, which helps to more accurately assess the user's health status and conduct personalized health management.

[0051] The data acquisition unit incorporates data from multiple sensors: a pressure sensor monitors the pressure distribution on the user's soles in real time, recording the stress in different areas; a temperature sensor monitors sole temperature data; a blood pressure sensor monitors the user's systolic and diastolic blood pressure and calculates the average as the corresponding blood pressure data; and a humidity sensor monitors humidity data, reflecting the sweating of the sole skin. The thermotherapy heating device is equipped with thermal imaging technology, enabling real-time monitoring of the sole temperature distribution, i.e., acquiring a thermotherapy map of the user's soles. This thermotherapy map visually shows the heating status of the user's soles and obtains local temperature data for each area. Therefore, based on the integrated thermotherapy heating device, the overall temperature data, blood pressure data, humidity data, thermotherapy map, and pressure data for different sole areas are acquired at each moment during each thermotherapy session. The thermotherapy map includes local temperature data for different sole areas.

[0052] It should be noted that, in the embodiments of the present invention, the heat therapy time period is 10 minutes, that is, every 10 minutes is a heat therapy process. In other embodiments of the present invention, the length of the heat therapy time period can be set according to specific circumstances, and will not be limited or elaborated here.

[0053] It should be noted that, in the embodiments of the present invention, in order to facilitate subsequent data processing and avoid differences in units and numerical magnitudes between data, the data is standardized to eliminate the influence of dimensions in data calculations.

[0054] Data processing module 102: For any given heat therapy period, based on the distribution of overall temperature and pressure data between different times, obtain the heat therapy adaptability within each heat therapy period; based on the changing trends of local temperature and pressure data for each plantar region at all times, and the heat therapy adaptability, obtain the beneficial ratio of changes between pressure data and local temperature data for each plantar region, and obtain multiple pressure segments and temperature segments for each plantar region; based on the beneficial ratio of changes between pressure data and local temperature data for all plantar regions, and the distribution characteristics of data between different segments, obtain the initiative of pressure changes during each heat therapy period.

[0055] Temperature changes reflect the effectiveness and efficiency of thermotherapy, helping to understand the distribution of heat on the soles of the feet; pressure data reflects the pressure analysis of the user's soles, helping to assess the stress on different areas. Combining the analysis of temperature and pressure data quantifies the user's adaptability during thermotherapy. For any given thermotherapy session, the thermotherapy adaptability within each session is obtained based on the distribution of overall temperature and pressure data across different times.

[0056] Preferably, in one embodiment of the present invention, the method for obtaining thermotherapy adaptability includes:

[0057] Based on the distribution of overall temperature and pressure data at different times within each heat therapy session, the uniformity of temperature gradient and the frequency of pressure changes for each heat therapy session are obtained.

[0058] Preferably, in the initial stage of device startup, the rate of temperature change is faster and more inconsistent; subsequently, the temperature changes become more consistent and regular. Figure 2 It illustrates a schematic diagram of the overall temperature data distribution during a single hyperthermia session; analyzing temperature changes helps assess the uniformity of the temperature gradient. In one embodiment of the invention, the method for obtaining the uniformity of the temperature gradient includes:

[0059] Obtain the gradient of the overall temperature data between adjacent time points within each hyperthermia period, and use it as the temperature gradient.

[0060] The average difference in temperature gradient between different groups is obtained as the temperature gradient uniformity for each hyperthermia period. In one embodiment of the present invention, the formula for temperature gradient uniformity is expressed as:

[0061]

[0062] Among them, f e Δy represents the uniformity of the temperature gradient during the e-th heat therapy period; i Δy represents the temperature gradient between adjacent time points in the i-th group; j The m represents the temperature gradient between adjacent time points in the j-th group; e represents the number of differences between temperature gradients within the e-th heat therapy time period; o represents the order of differences between temperature gradients; || represents taking the absolute value; exp() represents an exponential function with the natural constant as the base.

[0063] In the formula for temperature gradient uniformity, the greater the difference in temperature gradient, the more inconsistent the temperature gradients are between adjacent time points in different groups, and the worse the temperature gradient uniformity.

[0064] It should be noted that the gradient reflects the rate of temperature change over time. The larger the gradient, the greater the rate of temperature change. The gradient is calculated as the ratio of the difference between the overall temperature data between adjacent time points to the difference at the corresponding time point. The specific method is a well-known technique to those skilled in the art and will not be elaborated here.

[0065] Preferably, due to adjustments in temperature and posture, the pressure data will exhibit certain fluctuations and changes, such as... Figure 3 It illustrates a schematic diagram of pressure data distribution during a single heat therapy session. The more uneven the distribution of pressure data and the greater the difference in the rate of change, the higher the frequency of pressure changes. In one embodiment of the present invention, the method for obtaining the frequency of pressure changes includes:

[0066] For any given heat therapy period, the average pressure data of all plantar areas at each moment is obtained as the overall pressure data for that moment.

[0067] Obtain the slope fluctuation characteristics of the overall pressure data between all adjacent time points as the degree of pressure fluctuation; obtain the mean difference between all adjacent time points and perform negative correlation mapping as the rate of change;

[0068] The product of the degree of pressure fluctuation and the rate of change is obtained as the pressure change frequency.

[0069] In one embodiment of the present invention, the formula for the frequency of pressure change is expressed as:

[0070]

[0071] Where, d e This indicates the frequency of pressure changes during the e-th heat therapy period; This indicates the slope fluctuation characteristics of the overall pressure data across all adjacent time points; This represents the mean difference between all adjacent time points.

[0072] In the formula for the frequency of pressure change, the greater the slope fluctuation characteristic, the more inconsistent the pressure changes between adjacent moments; the greater the pressure change, the smaller the average difference between adjacent moments; the closer the adjacent moments are, the greater the frequency of change.

[0073] It should be noted that, in one embodiment of the present invention, the slope is obtained by the ratio of the difference between the overall pressure data between adjacent time points to the difference between adjacent time points. In other embodiments of the present invention, the slope can also be obtained by calculating the derivative of the pressure curve composed of the overall pressure data of all time points at each time point. The specific means are well known to those skilled in the art and will not be described in detail here.

[0074] It should be noted that, in one embodiment of the present invention, the fluctuation characteristics are characterized by variance. The larger the variance, the larger the fluctuation characteristics, and the smaller the variance, the smaller the fluctuation characteristics. In other embodiments of the present invention, the fluctuation characteristics can also be represented by standard deviation and range, etc. The specific means are well known to those skilled in the art and will not be described in detail here.

[0075] A negative correlation mapping is performed on the frequency of pressure changes, and the product of the negative correlation mapping result and the temperature gradient uniformity for each heat therapy period is obtained as the heat therapy adaptability for each heat therapy period.

[0076] In one embodiment of the present invention, the formula for thermotherapy adaptability is expressed as:

[0077]

[0078] Among them, F e Indicates the thermotherapy adaptability during the e-th thermotherapy time period; f e Indicates the uniformity of the temperature gradient during the e-th heat therapy period; d e This indicates the frequency of pressure changes during the e-th heat therapy period.

[0079] In the formula for thermotherapy adaptability, d e The addition of 0.01 to +0.01 is to avoid the formula having a denominator of 0, which would render the formula meaningless. The smaller the uniformity of the temperature gradient during the e-th heat therapy period, the greater the temperature change. Irregular changes will affect the user's adaptability to heat therapy. The greater the frequency of pressure changes, the more discomfort caused by temperature changes will be felt during heat therapy, and the lower the adaptability to heat therapy may be.

[0080] During thermotherapy monitoring, the increase in foot temperature typically leads to local vasodilation, increasing blood flow, relaxing muscles and ligaments, and reducing pressure caused by tension. The less pressure applied to the sole of the foot, the greater the difference between temperature and pressure. Figure 4It shows a schematic diagram of the changes in pressure data and local temperature data in a certain plantar area, where T represents temperature data and U represents pressure data; therefore, by analyzing the changing trends of temperature data and pressure data, the beneficial ratio of changes between pressure data and temperature data can be evaluated; based on the changing trends of local temperature data and pressure data of each plantar area at all times, as well as the adaptability of thermotherapy, the beneficial ratio of changes between pressure data and local temperature data corresponding to each plantar area can be obtained.

[0081] Preferably, in one embodiment of the present invention, the method for obtaining the change benefit ratio includes:

[0082] Obtain the mean square error of the sequence composed of pressure data and local temperature data of each foot area at all times during each heat therapy period;

[0083] The product of mean square error and thermotherapy adaptability for each thermotherapy period is calculated as the beneficial ratio of the change in pressure data and local temperature data for each plantar area within each thermotherapy period.

[0084] In one embodiment of the present invention, the formula for varying the benefit ratio is expressed as:

[0085]

[0086] in, This indicates the beneficial ratio between the changes in pressure and local temperature data in the z-th foot region during the e-th heat therapy period; F e This indicates the thermotherapy adaptability during the e-th thermotherapy time period; MSE(U,T) z MSE() represents the mean square error of the sequence U, which consists of pressure data from all moments in the plantar region during each heat therapy period, and the sequence T, which consists of local temperature data; MSE() represents the mean square error function.

[0087] In the formula for the benefit ratio of change, the greater the thermotherapy adaptability during the e-th thermotherapy time period, the better the uniformity of the temperature gradient, and the greater the benefit ratio of change; the greater the mean square error of the sequence formed by the pressure data and local current data in each plantar area, the greater the difference between the local temperature data and pressure data, the more likely it is to be in a state of appropriate pressure reduction, the higher the adaptability of the thermotherapy process, and the greater the benefit ratio of change.

[0088] To analyze the cross-sectional changes between data, multiple pressure and temperature segments were obtained for each plantar region.

[0089] Preferably, in one embodiment of the present invention, the method for obtaining multiple pressure segments and temperature segments for each plantar region includes:

[0090] For any data point in the pressure data or local temperature data, obtain the slope of the data between adjacent moments within each heat therapy period; sort the data in ascending order of slope to obtain the first data sequence;

[0091] Select the data segment with the largest difference between adjacent slopes in the first data sequence, and take the middle position of the adjacent slopes in the first data sequence as the split point in the data sequence to obtain two data segments.

[0092] Considering that users frequently adjust pressure due to uncomfortable sitting posture or poor heat tolerance, resulting in pressure changes, analyzing the beneficial ratio between pressure data and local temperature data, as well as the distribution characteristics of data between different segments, can reflect the adaptability to temperature during thermotherapy and the changes in pressure with temperature, and more accurately assess the degree of pressure change caused by the user's active adjustment; based on the beneficial ratio between pressure data and local temperature data corresponding to all plantar areas, and the distribution characteristics of data between different segments, the initiative of pressure change during each thermotherapy period can be obtained.

[0093] Preferably, in one embodiment of the present invention, the method for actively obtaining pressure changes includes:

[0094] For each foot region during any heat therapy period, the range of the mean slope between pressure segments is compared with the range of the mean slope between temperature segments, and this range is used as the first ratio.

[0095] The mean of the first ratio in all plantar regions is obtained as the overall ratio level. The mean of the beneficial ratio corresponding to the changes in different plantar regions during each heat therapy period is negatively correlated and mapped. The product of the negative correlation mapping result and the overall ratio level is calculated as the initiative of pressure change during each heat therapy period.

[0096] In one embodiment of the present invention, the formula for the initiative of pressure change is expressed as:

[0097]

[0098] Among them, g e This indicates the initiative in pressure changes during the e-th heat therapy period; R represents the mean benefit ratio of changes in all plantar regions during the e-th heat therapy period; R represents the value with the largest mean slope in the pressure segment corresponding to the pressure data in a certain plantar region; L represents the value with the smallest mean slope in the pressure segment corresponding to the pressure data in a certain plantar region; R ′ This represents the value with the largest mean slope among the temperature segments corresponding to a local temperature data point in a specific plantar region; L ′This represents the value with the smallest mean slope among the temperature segments corresponding to a local temperature data point in a certain plantar region.

[0099] In the formula for the initiative of pressure change This means calculating the reciprocal of the mean of the benefit ratio of changes in all plantar areas during the e-th heat therapy period, i.e., performing a negative correlation mapping. The larger the benefit ratio of changes, the more likely it is to be the user's comfortable state, and the less likely the pressure needs to be changed. This represents the ratio of the range of the average slope between pressure segments to the range of the average slope between temperature segments in a certain plantar region. The larger the first ratio, the greater the range of the average slope between pressure segments is compared to the range of the average slope between temperature segments. This indicates a weaker correlation between pressure and temperature changes and a greater degree of initiative in pressure changes.

[0100] Normality Analysis Module 103: Based on the initiative of pressure changes during each heat therapy period, the fluctuation of blood pressure data at corresponding times within the pressure segments, and the distribution of humidity data at corresponding times between pressure segments in different foot areas, the normality of data for each heat therapy period is obtained.

[0101] Analyzing the initiative of plantar pressure changes helps to better understand the effects of heat therapy under different pressure distributions and the user's foot health. In high-temperature environments, there should normally be a strong sweating response to regulate body temperature. If the user sweats too little, it may indicate poor sweat gland function. Excessive sweating, on the other hand, may be related to anxiety or other physiological abnormalities. The distribution of humidity data at corresponding times between different pressure segments of the plantar area can be used to assess whether sweating occurs due to heat. Blood pressure changes with pressure; the greater the change in pressure ratio, the greater the change in blood pressure data. Analyzing blood pressure data fluctuations reflects the stability of blood pressure as a function of pressure. By combining the initiative of pressure changes, blood pressure data fluctuations, and humidity data distribution, the normality of the data can be assessed more accurately and comprehensively.

[0102] Preferably, in one embodiment of the present invention, the method for obtaining the data normality level is described in [reference needed]. Figure 5 It illustrates a flowchart of a method for obtaining data normality, including:

[0103] Step S501: For each foot area during any heat therapy period, obtain the standard deviation of blood pressure data for all times within each pressure segment, and select the one with the largest standard deviation of blood pressure data among all pressure segments as the degree of blood pressure fluctuation in each foot area.

[0104] The standard deviation can be analyzed to reflect the fluctuation of blood pressure data. The larger the standard deviation, the more uneven the distribution of blood pressure data and the greater the degree of fluctuation.

[0105] Step S502: Obtain the difference in average humidity at all times between pressure segments as the degree of humidity change in each foot area.

[0106] In high-temperature environments, there is a strong sweating response to regulate body temperature under normal circumstances. That is, the greater the difference in the average humidity at all times between pressure segments, the more likely there is to be sweating during the temperature rise phase, and the greater the degree of humidity change.

[0107] Step S503: Based on the degree of blood pressure fluctuation, humidity change, and pressure change initiative in different foot areas during each heat therapy period, obtain the data normality for each heat therapy period. The degree of blood pressure fluctuation is negatively correlated with the data normality, while the degree of humidity change and pressure change initiative are both positively correlated with the data normality.

[0108] In one embodiment of the present invention, the formula for the degree of normality of data is expressed as:

[0109]

[0110] Among them, w e Indicates the normality of data within monitoring period e; g e This indicates the initiative in monitoring pressure changes during the e-th monitoring period; σ e ′ Q represents the degree of blood pressure fluctuation in each plantar region during the e-th monitoring period; l Q represents the average humidity value at all times corresponding to the l-th pressure segment within each plantar area; r This represents the average humidity value at all times corresponding to the r-th pressure segment within each plantar region; exp() represents an exponential function with the natural constant as its base.

[0111] In the formula for the normality of the data, an exponential function with the natural constant as the base is used to... Perform negative correlation mapping, and the initiative of pressure change g e The larger the value, the less affected it is by temperature changes, the less it affects heat therapy, and the greater the degree of normality of the data, showing a positive correlation. This indicates that the analysis covers all plantar areas, calculating the average ratio of the differences in blood pressure fluctuations and humidity mean values ​​at all times between pressure segments. The larger the ratio, the greater the blood pressure fluctuation, the more likely fainting or weakness will occur, and the more abnormal the data reflects. The smaller the difference in humidity mean values, the smaller the degree of humidity change, indicating that the user's sweating changes during heat therapy, the more abnormal the data reflects, and the less normal the data may be. That is, blood pressure fluctuations are negatively correlated, while humidity changes are positively correlated.

[0112] Health Assessment Module 104: Assess the user's health based on the normality of data during the real-time hyperthermia period.

[0113] Based on this, the normality of data obtained during the real-time heat therapy period can reflect the user's health status during the heat therapy process. That is, the lower the normality of the data, the less the data obtained when the user is warming up does not conform to the normal physiological state, the more abnormal it is, and the worse the user's health status is. By analyzing the normality of the data, it is helpful to formulate subsequent health plans such as lifestyle adjustments and exercise recommendations, and enhance the continuity and initiative of health management.

[0114] This invention proposes an integrated thermotherapy heating device, which includes a data acquisition unit and a data processor. The data acquisition unit is used to acquire the overall temperature data, blood pressure data, humidity data, thermotherapy map, and pressure data of different areas of the foot at each moment during each thermotherapy period. The thermotherapy map includes local temperature data of different areas of the foot.

[0115] The data processor is used to obtain the thermotherapy adaptability within each thermotherapy period based on the distribution of overall temperature and pressure data between different times for any given thermotherapy period; based on the changing trends of local temperature and pressure data for each plantar region at all times, as well as the thermotherapy adaptability, it obtains the beneficial ratio of changes between pressure data and local temperature data for each plantar region, and obtains multiple pressure and temperature segments for each plantar region; based on the beneficial ratio of changes between pressure data and local temperature data for all plantar regions, and the distribution characteristics of data between different segments, it obtains the pressure change initiative for each thermotherapy period.

[0116] The normality of the data for each heat therapy period is obtained based on the initiative of pressure changes during each heat therapy period, the fluctuation of blood pressure data at corresponding times within the pressure segments, and the distribution of humidity data at corresponding times between pressure segments in different foot areas.

[0117] The user's health is assessed based on the normality of data from the real-time hyperthermia period.

[0118] In summary, this invention, for any given heat therapy period, obtains the beneficial ratio between the changes in local temperature and pressure data for each foot region at all times, as well as the heat therapy adaptability, thus obtaining multiple pressure and temperature segments for each foot region. Furthermore, it analyzes the distribution characteristics of data between different segments to obtain the initiative of pressure changes during each heat therapy period. Combining the fluctuations in blood pressure data at corresponding times within each pressure segment and the distribution of humidity data between pressure segments of different foot regions at corresponding times, it obtains the normality of data for each heat therapy period. This invention improves the reliability of data analysis by accurately analyzing the normality of data acquired during heat therapy.

[0119] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0120] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A big data intelligent analysis system for integrated thermotherapy heating devices, characterized in that, include: Thermotherapy heating data acquisition module: Based on the integrated thermotherapy heating device, it acquires the overall temperature data, blood pressure data, humidity data, thermotherapy map, and pressure data of different foot areas of the user's sole at each moment during each thermotherapy period. The thermotherapy map includes local temperature data of different foot areas. Data processing module: For any given heat therapy period, based on the distribution of overall temperature and pressure data across different times, the heat therapy adaptability within each heat therapy period is obtained; based on the changing trends of local temperature and pressure data for each plantar region at all times, and the heat therapy adaptability, the beneficial ratio of changes between pressure data and local temperature data for each plantar region is obtained, resulting in multiple pressure and temperature segments for each plantar region; based on the beneficial ratio of changes between pressure data and local temperature data for all plantar regions, and the distribution characteristics of data between different segments, the pressure change initiative for each heat therapy period is obtained. The method for obtaining thermotherapy adaptability includes: Based on the distribution of overall temperature and pressure data at different times within each heat therapy session, the uniformity of temperature gradient and the frequency of pressure changes for each heat therapy session are obtained. Negative correlation mapping is performed on the frequency of pressure changes, and the product of the negative correlation mapping result and the temperature gradient uniformity of each heat therapy period is obtained as the heat therapy adaptability of each heat therapy period. The method for obtaining the beneficial ratio of the change includes: Obtain the mean square error of the sequence composed of pressure data and local temperature data of each foot area at all times during each heat therapy period; The product of mean square error and thermotherapy adaptability for each thermotherapy period is calculated as the beneficial ratio of the change in pressure data and local temperature data for each plantar area within each thermotherapy period. The method for obtaining the initiative in pressure change includes: For each foot region during any heat therapy period, the range of the mean slope between pressure segments is compared with the range of the mean slope between temperature segments, and this range is used as the first ratio. The mean of the first ratio in all plantar regions is obtained as the overall ratio level; the mean of the beneficial ratio corresponding to the changes in different plantar regions during each heat therapy period is negatively correlated and mapped, and the product of the negative correlation mapping result and the overall ratio level is calculated as the initiative of pressure change during each heat therapy period; Normality Analysis Module: Based on the initiative of pressure changes during each heat therapy period, the fluctuation of blood pressure data at corresponding times within the pressure segments, and the distribution of humidity data at corresponding times between pressure segments in different foot areas, the normality of data for each heat therapy period is obtained. The methods for obtaining the normality of the data include: For each foot area during any heat therapy period, the standard deviation of blood pressure data at all times within each pressure segment is obtained. The blood pressure fluctuation level of each foot area is selected as the largest standard deviation of blood pressure data among all pressure segments. The difference in average humidity across all time points between pressure segments is used as the degree of humidity variation in each foot region. Based on the degree of blood pressure fluctuation, humidity change, and active pressure change in different foot areas during each heat therapy session, the normality of the data for each heat therapy session was obtained. The degree of blood pressure fluctuation was negatively correlated with the normality of the data, while the degree of humidity change and active pressure change were positively correlated with the normality of the data. Health assessment module: Assess the user's health based on the normality of data during the real-time heat therapy period.

2. The intelligent analysis system for big health data applied to an integrated thermotherapy heating device according to claim 1, characterized in that, The method for obtaining the temperature gradient uniformity includes: Obtain the gradient of the overall temperature data between adjacent time points within each hyperthermia period, and use it as the temperature gradient. The mean difference in temperature gradient between different groups was obtained as the temperature gradient uniformity for each hyperthermia period.

3. The intelligent analysis system for big health data applied to an integrated thermotherapy heating device according to claim 1, characterized in that, The method for obtaining the pressure change frequency includes: For any given heat therapy period, the average pressure data of all plantar areas at each moment is obtained as the overall pressure data for that moment. Obtain the slope fluctuation characteristics of the overall pressure data between all adjacent time points as the degree of pressure fluctuation; obtain the mean difference between all adjacent time points and perform negative correlation mapping as the rate of change; The product of the degree of pressure fluctuation and the rate of change is obtained as the pressure change frequency.

4. The intelligent analysis system for big health data applied to an integrated thermotherapy heating device according to claim 1, characterized in that, The method for obtaining multiple pressure and temperature segments for each plantar region includes: For any data point in the pressure data or local temperature data, obtain the slope of the data between adjacent moments within each heat therapy period; sort the data in ascending order of slope to obtain the first data sequence; Select the data segment with the largest difference between adjacent slopes in the first data sequence, and take the middle position of the adjacent slopes in the first data sequence as the split point in the data sequence to obtain two data segments.

5. The intelligent analysis system for big health data applied to an integrated thermotherapy heating device according to claim 1, characterized in that, The negative correlation mapping is performed using an exponential function with the natural constant as the base.

6. An integrated thermotherapy heating device, the device comprising a data acquisition unit and a data processor, characterized in that, The data acquisition device is used to acquire the overall temperature data, blood pressure data, humidity data, thermogram, and pressure data of different areas of the foot at each moment during each heat therapy session. The thermogram includes local temperature data of different areas of the foot. The data processor is used to obtain the thermotherapy adaptability within each thermotherapy period based on the distribution of overall temperature and pressure data between different times for any given monitoring time period; to obtain the beneficial ratio of changes between pressure data and local temperature data for each foot region at all times, and thermotherapy adaptability, based on the changing trends of local temperature and pressure data for each foot region at all times, and to obtain multiple pressure segments and temperature segments for each foot region; and to obtain the pressure change initiative for each thermotherapy period based on the beneficial ratio of changes between pressure data and local temperature data for all foot regions, and the distribution characteristics of data between different segments. The method for obtaining thermotherapy adaptability includes: Based on the distribution of overall temperature and pressure data at different times within each heat therapy session, the uniformity of temperature gradient and the frequency of pressure changes for each heat therapy session are obtained. Negative correlation mapping is performed on the frequency of pressure changes, and the product of the negative correlation mapping result and the temperature gradient uniformity of each heat therapy period is obtained as the heat therapy adaptability of each heat therapy period. The method for obtaining the beneficial ratio of the change includes: Obtain the mean square error of the sequence composed of pressure data and local temperature data of each foot area at all times during each heat therapy period; The product of mean square error and thermotherapy adaptability for each thermotherapy period is calculated as the beneficial ratio of the change in pressure data and local temperature data for each plantar area within each thermotherapy period. The method for obtaining the initiative in pressure change includes: For each foot region during any heat therapy period, the range of the mean slope between pressure segments is compared with the range of the mean slope between temperature segments, and this range is used as the first ratio. The mean of the first ratio in all plantar regions is obtained as the overall ratio level; the mean of the beneficial ratio corresponding to the changes in different plantar regions during each heat therapy period is negatively correlated and mapped, and the product of the negative correlation mapping result and the overall ratio level is calculated as the initiative of pressure change during each heat therapy period; The normality of the data for each heat therapy period is obtained based on the initiative of pressure changes during each heat therapy period, the fluctuation of blood pressure data at corresponding times within the pressure segments, and the distribution of humidity data at corresponding times between pressure segments in different foot areas. The methods for obtaining the normality of the data include: For each foot area during any heat therapy period, the standard deviation of blood pressure data at all times within each pressure segment is obtained. The blood pressure fluctuation level of each foot area is selected as the largest standard deviation of blood pressure data among all pressure segments. The difference in average humidity across all time points between pressure segments is used as the degree of humidity variation in each foot region. Based on the degree of blood pressure fluctuation, humidity change, and active pressure change in different foot areas during each heat therapy session, the normality of the data for each heat therapy session was obtained. The degree of blood pressure fluctuation was negatively correlated with the normality of the data, while the degree of humidity change and active pressure change were positively correlated with the normality of the data. The user's health is assessed based on the normality of data from the real-time hyperthermia period.

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