Integrated thermal therapy heating device and large health data intelligent analysis system

Abstract

The invention relates to the technical field of thermal therapy data processing, in particular to an integrated thermal therapy heating device and a large health data intelligent analysis system. For any thermal therapy time period, according to the change trend and thermal therapy adaptability of local temperature data and pressure data of each foot sole area at all moments, the change beneficial ratio between the pressure data and the local temperature data corresponding to each foot sole area is obtained, and a plurality of pressure segments and temperature segments of each foot sole area are obtained; analyzing the distribution characteristics of data among different segments, and obtaining the pressure change initiative of each thermal therapy time period; and the data normal degree of each thermal therapy time period is obtained by combining the blood pressure data fluctuation at the corresponding moment in the pressure segments and the humidity data distribution at the corresponding moment between the pressure segments of different sole areas. According to the invention, the reliability of data analysis is improved by accurately analyzing the normal degree of data obtained in the thermal therapy process.

Description

Technical Field

[0001] The present invention relates to the technical field of hyperthermia data processing, and particularly relates to an integrated hyperthermia heating device and a big health data intelligent analysis system. Background Art

[0002] As a non-invasive physical therapy, hyperthermia has significant effects in promoting blood circulation, relieving muscle pain, improving sleep quality, and regulating metabolism; the integrated hyperthermia heating device can combine hyperthermia with infrared technology, and at the same time collect various health-related data of the user's sole through intelligent sensors and upload them to the device terminal for analysis. In the prior art, through the data analysis system, the health data is processed to provide health feedback in real time; however, the system is a single-function monitoring tool that only processes static and single-dimensional data, and cannot analyze in combination with the data changes during the hyperthermia process. The integrity and real-time nature of the data are poor, lacking certain data support for the analysis of big data, and the data analysis effect is poor. Summary of the Invention

[0003] In order to solve the technical problem that the data dynamic changes during hyperthermia are not considered and the data analysis is poor, the purpose of the present invention is to provide an integrated hyperthermia heating device and a big health data intelligent analysis system, and the specific technical solutions adopted are as follows:

[0004] The present invention proposes a big health data intelligent analysis system applied to an integrated hyperthermia heating device, including:

[0005] Hyperthermia heating data acquisition module: Based on the integrated hyperthermia heating device, obtain the overall temperature data, blood pressure data, humidity data, hyperthermia map, and pressure data of different sole regions at each moment during each hyperthermia time period of the user's sole, and the hyperthermia map includes the local temperature data of different sole regions;

[0006] Data processing module: For any hyperthermia time period, obtain the hyperthermia adaptability during each hyperthermia time period according to the distribution of the overall temperature data and pressure data between different moments; according to the change trends of the local temperature data and pressure data of each sole region at all moments, and the hyperthermia adaptability, obtain the beneficial change ratio between the corresponding pressure data and local temperature data of each sole region, and obtain multiple pressure segments and temperature segments of each sole region; according to the beneficial change ratio between the corresponding pressure data and local temperature data of all sole regions, and the distribution characteristics of the data between different segments, obtain the initiative of pressure change during each hyperthermia time period;

[0007] Normal degree analysis module: Obtain the data normal degree of each hyperthermia time period according to the initiative of pressure change in each hyperthermia time period, the blood pressure data fluctuation at the corresponding moment within the pressure segments, and the humidity data distribution at the corresponding moment between the pressure segments in different plantar regions;

[0008] Health assessment module: Assess the user's health according to the data normal degree of the real-time hyperthermia time period.

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

[0010] Obtain the temperature gradient uniformity and the pressure change frequency of each hyperthermia time period according to the distribution of the overall temperature data and the pressure data between different moments within each hyperthermia time period;

[0011] Perform a negative correlation mapping on the pressure change frequency, and obtain the product of the negative correlation mapping result and the temperature gradient uniformity of each hyperthermia time period as the hyperthermia adaptability of each hyperthermia time period.

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

[0013] Obtain the gradient of the overall temperature data between each group of adjacent moments within each hyperthermia time period as the temperature gradient;

[0014] Obtain the difference mean value between the temperature gradients of different groups as the temperature gradient uniformity of each hyperthermia time period.

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

[0016] For any hyperthermia time period, obtain the mean value of the pressure data at all plantar regions at each moment as the overall pressure data at each moment;

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

[0018] Obtain the product of the pressure fluctuation degree and the change rate as the pressure change frequency.

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

[0020] Obtain the mean square error of the sequence formed by the pressure data and the local temperature data at all moments in each plantar region within each hyperthermia time period;

[0021] Calculate the product of the mean square error and the hyperthermia adaptability of each hyperthermia time period as the beneficial change ratio between the pressure data and the local temperature data in each plantar region within each hyperthermia time period.

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

[0023] For either the pressure data or the local temperature data, obtain the slope between adjacent moments within each hyperthermia time period; sort the data in ascending order of the slope to obtain a first data sequence;

[0024] Select the one with the largest difference value between the corresponding adjacent slopes in the first data sequence, and use the middle position corresponding to the adjacent slopes on the first data sequence as the segmentation point on the data sequence to obtain two data segments.

[0025] Further, the method for obtaining the initiative of pressure change includes:

[0026] For each plantar region in any hyperthermia time period, obtain the range ratio of the mean slope between pressure segments to the mean slope between temperature segments as a first ratio;

[0027] Obtain the mean value of the first ratios in all plantar regions as the overall ratio level; perform a negative correlation mapping on the mean value of the change beneficial ratios corresponding to different plantar regions in each hyperthermia time period, and calculate the product of the negative correlation mapping result and the overall ratio level as the initiative of pressure change in each hyperthermia time period.

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

[0029] For each plantar region in any monitoring time period, obtain the standard deviation of the blood pressure data corresponding to all moments within each pressure segment, and select the one with the largest standard deviation value of the blood pressure data among all pressure segments as the blood pressure fluctuation degree of each plantar region;

[0030] Obtain the difference in the mean humidity corresponding to all moments between pressure segments as the humidity change degree of each plantar region;

[0031] According to the blood pressure fluctuation degree, humidity change degree, and initiative of pressure change of different plantar regions within each hyperthermia time period, obtain the data normality of each hyperthermia time period. The blood pressure fluctuation degree is negatively correlated with the data normality, and both the humidity change degree and the initiative of pressure change are positively correlated with the data normality.

[0032] Further, use an exponential function with the natural constant as the base for the negative correlation mapping.

[0033] The present invention also provides an integrated heat therapy warming device, which includes a data collector and a data processor. The data collector is used to obtain the overall temperature data, blood pressure data, humidity data, heat therapy map, and pressure data of different plantar regions of the user's foot at each moment during each heat therapy period. The heat therapy map includes local temperature data of different plantar regions.

[0034] For any monitoring period, the data processor is used to obtain the heat therapy adaptability during each heat therapy period according to the distribution of the overall temperature data and pressure data between different moments; obtain the beneficial change ratio between the corresponding pressure data and local temperature data of each plantar region according to the change trends of the local temperature data and pressure data of each plantar region at all moments and the heat therapy adaptability, and obtain multiple pressure segments and temperature segments for each plantar region; obtain the pressure change initiative of each heat therapy period according to the beneficial change ratio between the corresponding pressure data and local temperature data of all plantar regions and the distribution characteristics of the data between different segments.

[0035] Obtain the data normality of each heat therapy period according to the pressure change initiative of each heat therapy period, the blood pressure data fluctuation at the corresponding moments within the pressure segments, and the humidity data distribution at the corresponding moments between the pressure segments of different plantar regions.

[0036] Evaluate the health data according to the data normality of the real-time heat therapy period.

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

[0038] For any monitoring period, the present invention obtains the heat therapy adaptability during each heat therapy period according to the distribution of the overall temperature data and pressure data between different moments, which can reflect the user's adaptability to the change of heat therapy pressure; obtains the beneficial change ratio between the corresponding pressure data and local temperature data of each plantar region according to the change trends of the local temperature data and pressure data of each plantar region at all moments and the heat therapy adaptability, and obtains multiple pressure segments and temperature segments for each plantar region, which helps to analyze the data transition characteristics between segments; obtains the pressure change initiative of each heat therapy period according to the beneficial change ratio between the corresponding pressure data and local temperature data of all plantar regions and the distribution characteristics of the data between different segments, and evaluates the user's ability to actively adjust to the pressure; obtains the data normality of each heat therapy period according to the pressure change initiative of each heat therapy period, the blood pressure data fluctuation at the corresponding moments within the pressure segments, and the humidity data distribution at the corresponding moments between the pressure segments of different plantar regions. The present invention improves the reliability of data analysis by accurately analyzing the normality of the data obtained during the heat therapy process. Description of the Drawings

[0039] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0040] Figure 1 Flowchart of a big health data intelligent analysis system applied to an integrated heat therapy heating device provided by an embodiment of the present invention;

[0041] Figure 2 Schematic diagram of the distribution of overall temperature data during a single heat therapy period provided by an embodiment of the present invention;

[0042] Figure 3 Schematic diagram of the distribution of pressure data during a single heat therapy period provided by an embodiment of the present invention;

[0043] Figure 4 Schematic diagram of the changes in pressure data and local temperature data in a certain plantar area provided by an embodiment of the present invention;

[0044] Figure 5 Flowchart of a method for obtaining the normality of data provided by an embodiment of the present invention. Detailed implementation manners

[0045] To further elaborate on the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following will, in conjunction with the drawings and preferred embodiments, detail the specific implementation manners, structures, features, and effects of an integrated heat therapy 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. In addition, the 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 those of ordinary skill in the technical field to which the present invention belongs.

[0047] The following will specifically describe the specific solutions of an integrated heat therapy heating device and a big health data intelligent analysis system provided by the present invention with reference to the drawings.

[0048] Please refer to Figure 1, which shows a method flowchart of a big health data intelligent analysis system applied to an integrated heat therapy and heating device provided by an embodiment of the present invention, specifically including: a heat therapy and heating data acquisition module 101, a data processing module 102, a normality analysis module 103, and a health assessment module 104:

[0049] Heat therapy and heating data acquisition module 101: Based on the integrated heat therapy and heating device, obtain the overall temperature data, blood pressure data, humidity data, heat therapy map, and pressure data of different plantar regions of the user's sole at each moment during each heat therapy period. The heat therapy map includes local temperature data of different plantar regions.

[0050] In an embodiment of the present invention, in order to improve the accuracy of data analysis, the data in the integrated heat therapy and heating device collected during the heat therapy period is processed; the integrated heat therapy and heating device is a device that combines the functions of heat therapy and heating, used to provide warmth and health management; the upper table surface is adjusted to a suitable position through the lifting mechanism between the upper and lower table surfaces of the heating device, and the sole is placed at the foot-shaped area of the lower table surface. An infrared electromagnetic heating module is provided at the foot-shaped area for infrared magnetic therapy of the user's sole. After the heating device is started, various health-related data of the user's sole are obtained through the data collector installed on the lower table surface of the device, and the data is stored and processed through the built-in data analysis unit, which helps to more accurately evaluate the user's health status and perform personalized health management in the future.

[0051] Among them, the data collector contains data of various sensors: the pressure sensor monitors the pressure distribution of the user's sole in real time, records the force conditions of different regions, the temperature sensor monitors the sole temperature data, the blood pressure sensor monitors the user's systolic and diastolic blood pressures, and calculates the average value as the corresponding blood pressure data, and the humidity sensor monitors the humidity data, reflecting the sweating condition of the sole skin; the integrated heat therapy and heating device is equipped with thermal imaging technology, which can monitor the temperature distribution of the sole in real time, that is, collect the plantar heat therapy map of the user; the local temperature data of each plantar region can be intuitively obtained from the plantar heat therapy map; therefore, based on the integrated heat therapy and heating device, obtain the overall temperature data, blood pressure data, humidity data, heat therapy map, and pressure data of different plantar regions of the user's sole at each moment during each heat therapy period. The heat therapy map includes local temperature data of different plantar regions;

[0052] It should be noted that in an embodiment of the present invention, the heat therapy period is 10 minutes, that is, each heat therapy process is every 10 minutes. In other embodiments of the present invention, the size of the heat therapy period can be specifically set according to specific circumstances, and no limitation and elaboration are made here.

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

[0054] Data processing module 102: For any thermal therapy time period, the thermal therapy adaptability within each thermal therapy time period is obtained based on the distribution of the overall temperature data and pressure data between different moments; based on the change trend of the local temperature data and pressure data of each plantar area at all moments, as well as the thermal therapy adaptability, the change benefit ratio between the corresponding pressure data and local temperature data of each plantar area is obtained, and multiple pressure segments and temperature segments of each plantar area are obtained; based on the change benefit ratio between the corresponding pressure data and local temperature data of all plantar areas, as well as the distribution characteristics of the data between different segments, the pressure change initiative of each thermal therapy time period is obtained.

[0055] Temperature changes reflect the effectiveness and efficiency of heat therapy and help understand heat distribution across the soles of the feet. Pressure data provides pressure analysis on the user's soles, helping to assess the stress levels in different areas. Combining temperature and pressure data allows us to quantify the user's adaptability during heat therapy. For any given heat therapy session, the distribution of overall temperature and pressure data at different moments in time allows us to determine the adaptability of the therapy within that session.

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

[0057] According to the distribution of the overall temperature data and pressure data at different moments in each hyperthermia treatment period, the temperature gradient uniformity and the pressure change frequency of each hyperthermia treatment period are obtained;

[0058] Preferably, in the early stage of device startup, the temperature change rate is faster and the change is more inconsistent, and the subsequent temperature changes are more consistent and more regular, such as Figure 2 , which shows a schematic diagram of the distribution of overall temperature data during a hyperthermia treatment period; analyzing temperature changes helps to evaluate the uniformity of the temperature gradient. In one embodiment of the present invention, a method for obtaining the uniformity of the temperature gradient includes:

[0059] Obtaining the gradient of the overall temperature data between each group of adjacent moments in each hyperthermia treatment period as the temperature gradient;

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

[0061]

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

[0063] In the formula for temperature gradient uniformity, the greater the temperature gradient difference, the more inconsistent the temperature gradients between adjacent moments of different groups, and the worse the temperature gradient uniformity.

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

[0065] Preferably, due to the adjustment of temperature and different postures, the pressure data will have certain fluctuations and changes, such as Figure 3 , which shows a schematic diagram of the distribution of pressure data during a thermal treatment period. The more uneven the distribution of pressure data is, the greater the difference in the change rate is, and the greater the pressure change frequency is. In one embodiment of the present invention, a method for obtaining the pressure change frequency includes:

[0066] For any thermal therapy period, the mean pressure data of all plantar areas at each moment is obtained as the overall pressure data at each moment;

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

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

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

[0070]

[0071] Among them, d e represents the pressure change frequency of the e-th thermal treatment period; Represents the slope fluctuation characteristics of the overall pressure data between all adjacent moments; represents the mean of the differences between all adjacent moments.

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

[0073] It should be noted that in an embodiment of the present invention, the method for obtaining the slope is the ratio of the difference in the overall pressure data between adjacent moments to the difference between adjacent moments. In other embodiments of the present invention, the derivative of the pressure curve formed by the overall pressure data at each moment can also be calculated as the corresponding slope; the specific means are well-known technical means to those skilled in the art and will not be elaborated here.

[0074] It should be noted that in an embodiment of the present invention, the fluctuation characteristic is characterized by variance. The greater the variance, the greater the fluctuation characteristic, and the smaller the variance, the smaller the fluctuation characteristic; in other embodiments of the present invention, the fluctuation characteristic can also be represented by standard deviation, range, etc. The specific means are well-known technical means to those skilled in the art and will not be elaborated here.

[0075] Perform a negative correlation mapping on the pressure change frequency, and obtain the product of the negative correlation mapping result and the temperature gradient uniformity of each hyperthermia time period as the hyperthermia adaptability of each hyperthermia time period.

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

[0077]

[0078] Where F e represents the hyperthermia adaptability of the e-th hyperthermia time period; f e represents the temperature gradient uniformity of the e-th hyperthermia time period; d e represents the pressure change frequency of the e-th hyperthermia time period.

[0079] In the formula for hyperthermia adaptability, adding 0.01 to d e +0.01 is to avoid the denominator of the formula being 0 and the formula being meaningless; the smaller the temperature gradient uniformity of the e-th hyperthermia time period, the greater the temperature change, the more irregular the change, and the more it will affect the user's hyperthermia adaptability. The greater the pressure change frequency, the more discomfort caused by temperature change will be felt during hyperthermia, and the smaller the hyperthermia adaptability may be.

[0080] During the hyperthermia monitoring process, as the plantar temperature rises, it usually causes local blood vessel dilation, increases blood flow, relaxes muscles and ligaments, and reduces the pressure caused by tension. The smaller the pressure exerted on the sole, the greater the difference between temperature and pressure, as Figure 4, which shows a schematic diagram of the changes in pressure data and local temperature data within a certain plantar region, where T represents temperature data and U represents pressure data; therefore, by analyzing the change trends of the temperature data and the pressure data, the beneficial change ratio between the pressure data and the temperature data is evaluated; according to the change trends of the local temperature data and the pressure data at all times in each plantar region, and the thermotherapy adaptability, the beneficial change ratio between the corresponding pressure data and the local temperature data in each plantar region is obtained.

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

[0082] Obtain the mean square error of the sequences formed by the pressure data and the local temperature data at all times in each plantar region during each thermotherapy time period;

[0083] Calculate the product of the mean square error and the thermotherapy adaptability of each thermotherapy time period as the beneficial change ratio between the pressure data and the local temperature data in each plantar region during each thermotherapy time period.

[0084] In an embodiment of the present invention, the formula for the beneficial change ratio is expressed as:

[0085]

[0086] Wherein, represents the beneficial change ratio between the pressure data and the local temperature data in the z-th plantar region during the e-th thermotherapy time period; F e represents the thermotherapy adaptability of the e-th thermotherapy time period; MSE(U, T) z represents the mean square error of the sequences U formed by the pressure data at all times in the z-th plantar region during each thermotherapy time period and the sequences T formed by the local temperature data; MSE() represents the mean square error function.

[0087] In the formula for the beneficial change ratio, the greater the thermotherapy adaptability of the e-th thermotherapy time period, the better the temperature gradient uniformity, and the greater the beneficial change ratio; the greater the mean square error of the sequences formed between the pressure data and the local current data under each plantar region, the greater the difference between the local temperature data and the 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 beneficial change ratio.

[0088] In order to analyze the cross-sectional changes between data, multiple pressure segments and temperature segments of each plantar region are obtained.

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

[0090] For any one of the pressure data or local temperature data, obtain the slope of the data between adjacent moments during each hyperthermia time period; sort the data in ascending order of the slope to obtain a first data sequence;

[0091] Select the one with the largest difference value between the corresponding adjacent slopes in the first data sequence, and use the middle position corresponding to the adjacent slopes on the first data sequence as the segmentation point on the data sequence to obtain two data segments.

[0092] Considering that the user may frequently adjust the pressure due to discomfort in sitting posture or poor heat tolerance, resulting in pressure changes. By analyzing the beneficial change ratio between the pressure data and the local temperature data and the distribution characteristics of the data between different segments, it can reflect the adaptability to temperature and the change of pressure with temperature during hyperthermia, and more accurately evaluate the degree of pressure change caused by the user's active adjustment; according to the beneficial change ratio between the pressure data and the local temperature data corresponding to all plantar regions, and the distribution characteristics of the data between different segments, obtain the pressure change initiative for each hyperthermia time period.

[0093] Preferably, in an embodiment of the present invention, the method for obtaining the pressure change initiative includes:

[0094] For each plantar region in any hyperthermia time period, obtain the ratio of the range of the mean slope between pressure segments to the range of the mean slope between temperature segments as a first ratio;

[0095] Obtain the mean value of the first ratio among all plantar regions as the overall ratio level; perform a negative correlation mapping on the mean value of the beneficial change ratio corresponding to different plantar regions during each hyperthermia time period, and calculate the product of the negative correlation mapping result and the overall ratio level as the pressure change initiative for each hyperthermia time period.

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

[0097]

[0098] where g e represents the pressure change initiative during the e-th hyperthermia period; represents the mean value of the beneficial change ratio corresponding to all plantar regions during the e-th hyperthermia period; R represents the maximum value of the mean slope corresponding to the pressure segments of the pressure data in a certain plantar region; L represents the minimum value of the mean slope corresponding to the pressure segments of the pressure data in a certain plantar region; R ′ represents the maximum value of the mean slope corresponding to the temperature segments of the local temperature data in a certain plantar region; L ′Represents the value with the smallest mean slope corresponding to the temperature segment in the local temperature data in a certain plantar region.

[0099] In the formula for the initiative of pressure change, Represents taking the reciprocal of the mean of the beneficial change ratios corresponding to all plantar regions within the e-th hyperthermia period segment, that is, performing a negative correlation mapping. The larger the beneficial change ratio, the more likely it is the user's comfortable state, and the less likely the pressure needs to change; Represents, in a certain plantar region, the range of the mean slopes between pressure segments divided by the range of the mean slopes between temperature segments, that is, the first ratio. The larger the first ratio, the greater the range of the mean slopes between pressure segments is greater than the range of the mean slopes between temperature segments, and the smaller the correlation between the pressure change and the temperature change, and the greater the initiative of pressure change.

[0100] Normal degree analysis module 103: Obtain the data normal degree of each hyperthermia period according to the initiative of pressure change during each hyperthermia period, the blood pressure data fluctuation at the corresponding moments within the pressure segments, and the humidity data distribution at the corresponding moments between the pressure segments of different plantar regions.

[0101] By analyzing the initiative of plantar pressure change, it helps to better understand the effect of hyperthermia under different pressure distributions and the plantar health status of the user; in a high-temperature environment, there should be a strong sweating response under normal circumstances to regulate body temperature. If the user sweats too little, it may be due to poor sweat gland function; excessive sweating may be related to anxiety or other physiological abnormalities. Evaluate the sweating situation during heating through the humidity data distribution at the corresponding moments between the pressure segments of different plantar regions; blood pressure changes positively with pressure. The greater the pressure ratio change, the greater the resulting blood pressure data change. Reflect the stability of blood pressure change with pressure by analyzing the blood pressure data fluctuation; comprehensively consider the initiative of pressure change, blood pressure data fluctuation, and humidity data distribution to more accurately and comprehensively evaluate the data normal degree.

[0102] Preferably, in an embodiment of the present invention, for the method of obtaining the data normal degree, please refer to Figure 5 , which shows a flowchart of a method for obtaining the data normal degree, including:

[0103] Step S501: For each plantar region in any hyperthermia period, obtain the standard deviation of the blood pressure data at all corresponding moments within each pressure segment, and select the one with the largest standard deviation value of the blood pressure data among all pressure segments as the blood pressure fluctuation degree of each plantar region.

[0104] By analyzing the standard deviation, the fluctuation state of the blood pressure data can be reflected. The larger the standard deviation, the more uneven the distribution of the blood pressure data and the greater the fluctuation degree of the blood pressure data.

[0105] Step S502: Obtain the difference in the humidity means corresponding to all moments between pressure segments as the degree of humidity change for each plantar region.

[0106] In a high-temperature environment, under normal circumstances, there is a strong sweating response to regulate body temperature. That is, the greater the difference in the humidity means corresponding to all moments between pressure segments, the more likely it is that sweating occurs during the temperature increase stage, and the greater the degree of humidity change.

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

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

[0109]

[0110] where w e represents the normality degree of the data during the e-th monitoring period; g e represents the initiative of pressure change during the e-th monitoring period; σ e ′ represents the degree of blood pressure fluctuation for each plantar region during the e-th monitoring period; Q l represents the humidity mean corresponding to all moments for the l-th pressure segment in each plantar region; Q r represents the humidity mean corresponding to all moments for the r-th pressure segment in each plantar region; exp() represents the exponential function with the natural constant as the base.

[0111] In the formula for the normality degree of the data, through the exponential function with the natural constant as the base, a negative correlation mapping is performed. The greater the initiative of pressure change g e , the less affected it is by temperature changes, the less impact it has on hyperthermia, and the greater the normality degree of the data, showing a positive correlation; represents the analysis of all plantar regions, calculating the ratio mean of the degree of blood pressure fluctuation and the difference in the humidity means corresponding to all moments between pressure segments. The larger the ratio, the greater the degree of blood pressure fluctuation, the more likely it is to have syncope or weakness, and the more abnormal the data reflects. The smaller the difference in humidity means, the smaller the degree of humidity change, indicating that the change in sweating of the user during hyperthermia is smaller, and the more abnormal the data reflects, and the normality degree of the data may be smaller. That is, the degree of blood pressure fluctuation is negatively correlated, and the degree of humidity change is positively correlated.

[0112] Health assessment module 104: Assess the user's health based on the normality degree of the data in the real-time hyperthermia time period.

[0113] Based on this, obtaining the data normality during the real-time hyperthermia period can reflect the user's health status during hyperthermia, that is, the smaller the data normality, the more the data obtained by the user during warming 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 data normality, it is helpful to formulate subsequent health plans such as lifestyle adjustment and exercise recommendation, and enhance the sustainability and enthusiasm of health management.

[0114] The present invention proposes an integrated hyperthermia warming device, which includes a data collector and a data processor. The data collector is used to obtain the overall temperature data, blood pressure data, humidity data, hyperthermia map, and pressure data of different plantar regions of the user's sole at each moment during each hyperthermia period. The hyperthermia map includes local temperature data of different plantar regions;

[0115] For any hyperthermia period, the data processor is used to obtain the hyperthermia adaptability during each hyperthermia period according to the distribution of the overall temperature data and pressure data between different moments; according to the change trends of the local temperature data and pressure data of each plantar region at all moments, and the hyperthermia adaptability, obtain the beneficial change ratio between the corresponding pressure data and local temperature data of each plantar region, and obtain multiple pressure segments and temperature segments of each plantar region; according to the beneficial change ratio between the corresponding pressure data and local temperature data of all plantar regions, and the distribution characteristics of the data between different segments, obtain the pressure change initiative of each hyperthermia period;

[0116] According to the pressure change initiative of each hyperthermia period, the blood pressure data fluctuation at the corresponding moment within the pressure segment, and the humidity data distribution at the corresponding moment between the pressure segments of different plantar regions, obtain the data normality of each hyperthermia period;

[0117] Evaluate the user's health according to the data normality of the real-time hyperthermia period.

[0118] In summary, for any hyperthermia period, the present invention obtains the beneficial change ratio between the corresponding pressure data and local temperature data of each plantar region according to the change trends of the local temperature data and pressure data of each plantar region at all moments and the hyperthermia adaptability, and obtains multiple pressure segments and temperature segments of each plantar region; furthermore, analyze the distribution characteristics of the data between different segments to obtain the pressure change initiative of each hyperthermia period; combine the blood pressure data fluctuation at the corresponding moment within the pressure segment and the humidity data distribution at the corresponding moment between the pressure segments of different plantar regions to obtain the data normality of each hyperthermia period. The present invention improves the reliability of data analysis by accurately analyzing the normality of the data obtained during hyperthermia.

[0119] It should be noted that the above-mentioned sequence of embodiments of the present invention is only for description and does not represent the superiority or inferiority of the embodiments. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0120] Each embodiment in this specification is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other. The key point of each embodiment is to illustrate the differences from other embodiments.

Claims

1. A big health data intelligent analysis system applied to an integrated heat therapy heating device, characterized in that, Including: Thermotherapy heating data acquisition module: Based on the integrated thermotherapy heating device, obtain the overall temperature data, blood pressure data, humidity data, thermotherapy map, and pressure data of different plantar regions of the user's sole at each moment during each thermotherapy period. The thermotherapy map includes local temperature data of different plantar regions. Data processing module: For any thermotherapy period, obtain the thermotherapy adaptability during each thermotherapy period according to the distribution of the overall temperature data and pressure data between different moments; according to the change trends of the local temperature data and pressure data of each plantar region at all moments, and the thermotherapy adaptability, obtain the beneficial change ratio between the corresponding pressure data and local temperature data of each plantar region, and obtain multiple pressure segments and temperature segments for each plantar region; according to the beneficial change ratio between the corresponding pressure data and local temperature data of all plantar regions, and the distribution characteristics of the data between different segments, obtain the pressure change initiative during each thermotherapy period. Normal degree analysis module: Obtain the data normal degree of each thermotherapy period according to the pressure change initiative during each thermotherapy period, the blood pressure data fluctuation at the corresponding moments within the pressure segment, and the humidity data distribution at the corresponding moments between the pressure segments of different plantar regions. Health assessment module: Assess the user's health according to the data normal degree of the real-time thermotherapy period.

2. The intelligent big health data analysis system applied to the integrated heat therapy heating device according to claim 1, characterized in that, The method for obtaining the thermotherapy adaptability includes: According to the distribution of the overall temperature data and pressure data between different moments during each thermotherapy period, obtain the temperature gradient uniformity and the pressure change frequency during each thermotherapy period. Perform a negative correlation mapping on the pressure change frequency, and obtain the product of the negative correlation mapping result and the temperature gradient uniformity of each thermotherapy period as the thermotherapy adaptability of each thermotherapy period.

3. The intelligent big health data analysis system applied to the integrated hyperthermia heating device according to claim 2, wherein The method for obtaining the temperature gradient uniformity includes: Obtain the gradient of the overall temperature data between each group of adjacent moments during each thermotherapy period as the temperature gradient. Obtain the average difference between the temperature gradients of different groups as the temperature gradient uniformity of each thermotherapy period.

4. The intelligent big health data analysis system applied to the integrated heat therapy heating device according to claim 2, wherein, The method for obtaining the pressure change frequency includes: For any thermotherapy period, obtain the average pressure data of all plantar regions at each moment as the overall pressure data at each moment. Obtain the slope fluctuation characteristics of the overall pressure data between all adjacent moments as the pressure fluctuation degree; obtain the average difference between all adjacent moments and perform a negative correlation mapping as the change rate. Obtain the product of the pressure fluctuation degree and the change rate as the pressure change frequency.

5. The intelligent big health data analysis system applied to the integrated heat therapy heating device according to claim 1, characterized in that, The method for obtaining the beneficial change ratio includes: Obtain the mean square error of the sequence formed by the pressure data and local temperature data of each plantar region at all moments during each thermotherapy period. Calculate the product of the mean square error and the thermotherapy adaptability of each thermotherapy period as the beneficial change ratio between the pressure data and local temperature data of each plantar region during each thermotherapy period.

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

7. An intelligent big health data analysis system applied to an integrated heat therapy heating device according to claim 1, characterized in that The method for obtaining the initiative of pressure change includes: For each plantar region in any hyperthermia time period, obtain the ratio of the range of the mean slope between pressure segments to the range of the mean slope between temperature segments as the first ratio; Obtain the mean value of the first ratios in all plantar regions as the overall ratio level; perform a negative correlation mapping on the mean value of the change beneficial ratio corresponding to different plantar regions in each hyperthermia time period, and calculate the product of the negative correlation mapping result and the overall ratio level as the initiative of pressure change in each hyperthermia time period.

8. An intelligent big health data analysis system applied to an integrated heat therapy heating device according to claim 1, characterized in that The method for obtaining the data normality includes: For each plantar region in any hyperthermia time period, obtain the standard deviation of the blood pressure data corresponding to all moments within each pressure segment, and select the one with the largest standard deviation value of the blood pressure data among all pressure segments as the blood pressure fluctuation degree of each plantar region; Obtain the difference in the mean humidity corresponding to all moments between pressure segments as the humidity change degree of each plantar region; According to the blood pressure fluctuation degree, humidity change degree, and initiative of pressure change in different plantar regions within each hyperthermia time period, obtain the data normality of each hyperthermia time period. The blood pressure fluctuation degree is negatively correlated with the data normality, and both the humidity change degree and the initiative of pressure change are positively correlated with the data normality.

9. The intelligent big health data analysis system applied to the integrated hyperthermia heating device according to claim 2, characterized in that, Use the exponential function with the natural constant as the base for the negative correlation mapping.

10. An integrated thermotherapy heating device, the device comprising a data collector and a data processor, characterized in that, The data collector is used to obtain the overall temperature data, blood pressure data, humidity data, hyperthermia map, and pressure data of different plantar regions at each moment of the user's plantar during each hyperthermia time period. The hyperthermia map includes local temperature data of different plantar regions; The data processor is used to, for any monitoring time period, obtain the hyperthermia adaptability within each hyperthermia time period according to the distribution of the overall temperature data and pressure data between different moments; obtain the change beneficial ratio between the corresponding pressure data and local temperature data of each plantar region according to the change trend of the local temperature data and pressure data of each plantar region at all moments and the hyperthermia adaptability, and obtain multiple pressure segments and temperature segments of each plantar region; obtain the initiative of pressure change in each hyperthermia time period according to the change beneficial ratio between the corresponding pressure data and local temperature data of all plantar regions and the distribution characteristics of the data between different segments; Obtain the data normality of each hyperthermia time period according to the initiative of pressure change in each hyperthermia time period, the blood pressure data fluctuation within the pressure segment corresponding to the moment, and the humidity data distribution corresponding to the moment between the pressure segments of different plantar regions; Evaluate the user's health according to the data normality of the real-time hyperthermia time period.

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