Intelligent temperature control light wave energy system for health physiotherapy equipment

Abstract

The invention relates to the technical field of light wave energy, and discloses an intelligent temperature control light wave energy system for health physiotherapy equipment, which comprises the following steps: after skin temperature exceeds warning temperature, judging whether the skin temperature needs to be subsequently predicted according to the closeness degree between the skin temperature and the highest skin acceptance temperature; constructing a skin temperature analysis curve, analyzing the type of the curve, predicting the subsequent skin temperature, and judging whether the skin temperature exceeds the highest skin acceptable temperature or not; judging whether the skin temperature needs to be regulated or not by comparing the time point when the skin temperature exceeds the highest skin accepting temperature with the physiotherapy ending time point; determining whether an influence proportion exists between the skin temperature and the light wave energy or not through correlation analysis; and outputting a light wave energy regulation value according to the influence proportion between the skin temperature and the light wave energy in combination with the degree that the skin temperature exceeds the warning temperature. The physical therapy equipment has the advantage of improving the temperature control capability of the physical therapy equipment.

Description

Technical Field

[0001] The present invention relates to the field of light wave energy technology, and in particular to an intelligent temperature-controlled light wave energy system for health therapy equipment. Background Art

[0002] In health therapy equipment, precise adjustment of the temperature control system is crucial to ensure the safety and effectiveness of light wave energy therapy. Precise temperature control can not only optimize the transmission efficiency of light wave energy, allowing it to penetrate the skin and deep tissues more effectively, but also avoid burns caused by excessively high temperatures or reduce the therapeutic effect due to low temperatures. Different individuals have different skin sensitivity, tissue absorption capacity and physiological states. The precise temperature control system can be dynamically adjusted according to real-time monitoring data to meet the needs of different patients and improve the personalization and comfort of treatment. Traditional physical therapy equipment usually relies on fixed temperature settings and does not fully consider individual differences and temperature changes in the skin, which may lead to excessively high or low temperatures, affecting the therapeutic effect and even causing adverse reactions to the skin. Therefore, it is necessary to design an intelligent temperature-controlled light wave energy system for health therapy equipment with improved temperature control capabilities. Summary of the invention

[0003] In view of the shortcomings of the prior art, the present invention provides an intelligent temperature-controlled light wave energy system for health therapy equipment, which has the advantages of helping to improve the temperature control capability of the therapy equipment, making it more accurate, safe and efficient, and solving the problems in the above-mentioned background technology.

[0004] In order to achieve the above-mentioned purpose of helping to improve the temperature control ability of physiotherapy equipment and make it more accurate, safe and efficient, the present invention provides the following technical solutions: an intelligent temperature control light wave energy system for health physiotherapy equipment, comprising:

[0005] Prediction demand analysis module: after the skin temperature exceeds the warning temperature, it is determined whether a subsequent prediction of the skin temperature is needed based on the degree of proximity between the skin temperature and the maximum acceptable temperature of the skin. If so, it enters the prediction module;

[0006] Prediction module: constructs a skin temperature analysis curve, analyzes the curve type, predicts the subsequent skin temperature, and determines whether the skin temperature will exceed the maximum acceptable temperature of the skin. If so, enters the regulation demand analysis module;

[0007] Regulation demand analysis module: By comparing the time point when the skin temperature exceeds the highest acceptable temperature of the skin and the time point when the treatment ends, it is determined whether the skin temperature needs to be regulated. If necessary, the energy analysis module is entered;

[0008] Energy analysis module: through correlation analysis, determine whether there is an influence ratio between skin temperature and light wave energy. If so, send it to the control module;

[0009] Control module: outputs the light wave energy control value based on the influence ratio between skin temperature and light wave energy, and the degree to which the skin temperature exceeds the warning temperature.

[0010] Preferably, the process of determining whether a subsequent prediction of the skin temperature is required is:

[0011] Calculate the deviation between the skin temperature and the maximum acceptable temperature of the skin to obtain the temperature approximation value;

[0012] If the temperature proximity value is less than the temperature proximity threshold, a subsequent prediction of the skin temperature is required.

[0013] Preferably, the process of performing curve type analysis is:

[0014] A connecting line is obtained by connecting the end points of the skin temperature analysis curve;

[0015] Set a number of collection points with the same horizontal coordinates on the skin temperature analysis curve and the connecting line;

[0016] Based on the skin temperature analysis curve and the connecting line, the temperature values ​​corresponding to the skin temperature analysis curve and the connecting line at each collection point are obtained, and are respectively integrated into a first temperature data group and a second temperature data group;

[0017] Calculate the Euclidean distance between the first temperature data group and the second temperature data group to obtain a curve similarity value;

[0018] If the curve similarity value is less than the curve similarity threshold, the curve type is linear, otherwise, it is nonlinear.

[0019] Preferably, the process of predicting the subsequent skin temperature by combining the curve type analysis result and judging whether the skin temperature will exceed the maximum acceptable temperature of the skin includes:

[0020] If the curve type is linear, the skin temperature analysis curve is fitted using the least squares method to obtain the function equation of the fitted straight line. The maximum acceptable skin temperature is used as input to predict the time point when the skin temperature reaches the maximum acceptable skin temperature, that is, the critical time point;

[0021] If the critical time point is earlier than the end time point of physical therapy, it means that the skin temperature will exceed the maximum acceptable temperature of the skin;

[0022] If the critical time point is later than or the same as the end time point of the physical therapy, it means that the skin temperature will not exceed the maximum acceptable temperature of the skin.

[0023] Preferably, the process of predicting the subsequent skin temperature by combining the curve type analysis result and judging whether the skin temperature will exceed the maximum acceptable temperature of the skin also includes:

[0024] If the curve type is nonlinear, a growth analysis is performed on the first temperature data group to determine the fastest growth rate of the skin temperature;

[0025] Calculate the ratio of the temperature approach value to the fastest growth rate of the skin temperature to obtain the time it takes for the skin temperature to reach the highest acceptable temperature of the skin, i.e. the critical time. The end time point of the critical time is the critical time point.

[0026] If the critical time point is earlier than the end time point of physical therapy, it means that the skin temperature will exceed the maximum acceptable temperature of the skin;

[0027] If the critical time point is later than or the same as the end time point of the physical therapy, it means that the skin temperature will not exceed the maximum acceptable temperature of the skin.

[0028] Preferably, the process of analyzing the correlation between skin temperature and light wave energy is as follows:

[0029] Obtain skin temperature under different light wave energies, construct a light wave energy data group and a skin temperature data group, calculate the Pearson correlation coefficient between the light wave energy data group and the skin temperature data group, and take the absolute value to obtain the influence correlation value;

[0030] If the impact correlation value is greater than or equal to the impact correlation threshold, it means that there is an impact ratio between the skin temperature and the light wave energy. Then, the light wave energy data group and the skin temperature data group are averaged and the average ratio is obtained to obtain the impact ratio.

[0031] Preferably, according to the influence ratio between skin temperature and light wave energy, and in combination with the degree to which the skin temperature exceeds the warning temperature, the process of outputting the light wave energy control value includes:

[0032] The temperature deviation between the skin temperature and the warning temperature is calculated, and multiplied and combined with the influence ratio between the skin temperature and the light wave energy to obtain the light wave energy control value.

[0033] Compared with the prior art, the present invention provides an intelligent temperature-controlled light wave energy system for health therapy equipment, which has the following beneficial effects:

[0034] 1. By setting the warning temperature and the maximum acceptable temperature of the skin, the skin temperature is monitored in real time. If the warning temperature is exceeded, the temperature proximity is calculated to evaluate the gap between the skin temperature and the maximum acceptable temperature. Based on this gap, decide whether to enter the subsequent prediction module to further analyze the temperature change trend. Through the construction and type analysis of the skin temperature analysis curve, combined with linear or nonlinear growth analysis, it is predicted whether the skin temperature will exceed the maximum acceptable temperature before the end of physical therapy. If the prediction result shows that the temperature may exceed the safety threshold, it will trigger the regulation demand analysis and provide a decision-making basis for temperature regulation. It not only improves the accuracy and efficiency of skin temperature monitoring, but also effectively reduces the risk of thermal damage caused by excessive temperature, ensures the comfort and safety of patients during physical therapy, and improves the controllability of treatment effects.

[0035] 2. Real-time calculation and dynamic adjustment based on the ratio of skin temperature exceeding the standard and light wave energy impact can ensure that the skin temperature is always kept within a safe range to avoid the negative impact of overheating on the skin. By precisely regulating the light wave energy output, the treatment effect can be maximized while avoiding discomfort or skin damage caused by excessive light wave energy. It can be adjusted individually according to the skin reactions of different individuals, taking into account the sensitivity of different skins to light wave energy, to achieve more sophisticated treatment management. Through real-time monitoring and calculation, the light wave energy output is automatically adjusted without manual intervention, which improves the automation and intelligence level of the treatment process. By calculating the control value of the light wave energy in real time and making feedback adjustments, the skin temperature is effectively avoided from being too high, and the risk of burns or other side effects during treatment is reduced. By precisely adjusting the light wave energy output, not only the treatment effect is improved, but also the comfort during treatment is enhanced to avoid discomfort caused by excessive temperature. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 It is a schematic diagram of the structure of the present invention.

[0037] Figure 2 Schematic diagram of the method of the present invention. DETAILED DESCRIPTION

[0038] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0039] Example 1

[0040] See also Figure 1As shown, an intelligent temperature-controlled light wave energy system for health therapy equipment according to an embodiment of the present invention comprises:

[0041] Prediction demand analysis module: Real-time monitoring of skin temperature. If the skin temperature exceeds the warning temperature, the degree of proximity between the skin temperature and the maximum acceptable temperature of the skin is calculated. Based on the calculation results, it is determined whether subsequent prediction of the skin temperature is needed. If so, the prediction module is entered.

[0042] In the forecast demand analysis module, if the skin temperature exceeds the warning temperature, the process of calculating the proximity between the skin temperature and the maximum acceptable temperature of the skin is as follows:

[0043] Set key temperature parameters, including real-time skin temperature T skin , Warning temperature T alert and the maximum acceptable skin temperature T max , calculate the degree to which the current skin temperature exceeds the warning temperature, the formula is:

[0044] ΔT alert =T skin -T alert

[0045] It should be noted that the warning temperature T alert <The maximum acceptable skin temperature T max , warning temperature T alert Set by the technicians in this area to prevent the real-time skin temperature T skin Exceeds the maximum acceptable skin temperature T max The warning value set;

[0046] If T alert >0, indicating that the skin temperature has exceeded the warning temperature and further assessment is needed to determine how close it is to the maximum acceptable temperature;

[0047] Calculate the temperature difference between the skin temperature and the maximum acceptable temperature, that is, the temperature approach value ΔT max :ΔT max =T max -T skin ;

[0048] If the temperature approach value is less than the temperature approach threshold T thresh , then subsequent prediction of skin temperature is required;

[0049] If the temperature proximity value is greater than or equal to the temperature proximity threshold T thresh , then there is no need to make subsequent predictions of skin temperature and continue monitoring;

[0050] It should be noted that the temperature sensor is used to measure the current temperature of the skin. Based on the experimental data, the maximum temperature that the skin can withstand is determined, and the temperature approximation value is calculated: ΔT = T max -T skin ; Indicates the margin between the current skin temperature and the highest acceptable skin temperature; Set the temperature close to the threshold T thresh ,This threshold is a predefined safety range used to determine whether further prediction of temperature changes is needed;

[0051] It is understandable that the role of determining whether a subsequent prediction of skin temperature is needed is:

[0052] Function 1: By calculating the proximity between the current real-time skin temperature and the maximum acceptable skin temperature, it is determined whether a subsequent prediction of the skin temperature is needed. Specifically, it can prevent the temperature from further rising and causing the skin temperature to overheat, which is conducive to early intervention and adjustment of the physical therapy temperature to ensure the safety of physical therapy.

[0053] Function 2: By judging the proximity between the current real-time skin temperature and the skin's maximum acceptable temperature, the timing of predicting and judging the subsequent skin temperature can be more accurately grasped, thereby improving the efficiency of skin temperature monitoring during physical therapy.

[0054] It is understandable that by setting key temperature parameters and calculating the proximity of skin temperature to the warning temperature and the highest acceptable temperature, it is possible to grasp the timing of subsequent skin temperature prediction and judgment, improve the efficiency of physical therapy skin temperature monitoring, and identify the possibility of skin temperature overheating risk in advance. Prevent skin temperature from exceeding the safe range, thereby effectively reducing the risk of thermal injury and ensuring physical therapy safety and patient comfort.

[0055] Prediction module: Construct a skin temperature analysis curve through the skin temperature data after exceeding the warning temperature, and perform curve type analysis. Combined with the curve type analysis results, predict the subsequent skin temperature to determine whether the skin temperature will exceed the maximum acceptable temperature of the skin. If so, perform the regulation demand analysis module.

[0056] The process of performing curve type analysis in the prediction module is as follows:

[0057] According to the skin temperature after exceeding the warning temperature, a skin temperature analysis curve is constructed with the X-axis being time and the Y-axis being skin temperature;

[0058] A connecting line is obtained by connecting the end points of the skin temperature analysis curve;

[0059] Select several acquisition points with the same horizontal coordinates on the skin temperature analysis curve and the connecting line;

[0060] Acquire skin temperature values ​​at different collection points on the skin temperature analysis curve, and integrate them to obtain a first temperature data group;

[0061] Acquire skin temperature values ​​at different collection points on the connecting line, and integrate them to obtain a second temperature data group;

[0062] Calculate the Euclidean distance between the first temperature data group and the second temperature data group to obtain a curve similarity value;

[0063] Exemplarily, it is assumed that the first temperature data set (temperature on the skin temperature analysis curve) is: T curve ={T curve (x 1 ),T curve (x 2 ),...,T curve (x n )}, the second temperature data set (temperature on the connecting line) is: T line ={T line (x 11 ),T line (x 1 ),...,T line (x n )};

[0064] The Euclidean distance D between the first temperature data group and the second temperature data group is calculated to obtain the curve similarity value. The specific calculation formula is:

[0065]

[0066] Set the curve similarity threshold D thresh :

[0067] If the curve similarity value is less than the threshold D thresh , indicating that the curve type is linear;

[0068] If the curve similarity value is greater than or equal to the threshold D thresh , indicating that the curve type is nonlinear.

[0069] The prediction module predicts the subsequent skin temperature in combination with the curve type analysis result, and the process of judging whether the skin temperature will exceed the maximum acceptable temperature of the skin includes:

[0070] If the curve type is linear, the skin temperature analysis curve is fitted using the least squares method to obtain the function equation of the fitting line, y=k*t+b, where y represents skin temperature, t represents time, k represents the slope of the fitting line, and b represents the intercept;

[0071] Taking the highest acceptable skin temperature as the input of the function equation, predict the time point when the skin temperature reaches the highest acceptable skin temperature, i.e. the critical time point;

[0072] The critical time point was compared with the end of physical therapy time point;

[0073] If the critical time point is earlier than the end time point of physical therapy, it means that the skin temperature will exceed the maximum acceptable temperature of the skin before the end of physical therapy;

[0074] If the critical time point is later than or the same as the end time point of the physical therapy, it means that the skin temperature will not exceed the maximum acceptable skin temperature before the end of the physical therapy.

[0075] The prediction module predicts the subsequent skin temperature in combination with the curve type analysis result, and the process of judging whether the skin temperature will exceed the maximum acceptable temperature of the skin also includes:

[0076] If the curve type is nonlinear, a growth analysis is performed on the first temperature data group to determine the fastest growth rate of the skin temperature; the first temperature data group is the temperature data on the skin temperature analysis curve, which represents the actual skin temperature that changes over time. A growth analysis is performed on this set of data, i.e., the growth rate of the skin temperature is calculated. It is necessary to find out the speed of change of the skin temperature at each moment, especially the fastest growth rate, i.e., the fastest increase in the change of skin temperature. This analysis can be performed by calculating the rate of change of the skin temperature at each moment to determine the fastest growth rate, i.e., the fastest speed at which the skin temperature increases within a certain period of time.

[0077] The ratio of the temperature approach value to the fastest growth rate of skin temperature is calculated to obtain the time required for the skin temperature to reach the highest acceptable temperature of the skin, that is, the critical time. The end time point of the critical time is the critical time point; the temperature approach value is the difference between the skin temperature and the highest acceptable temperature of the skin, indicating the distance of the skin temperature from the highest acceptable temperature. The temperature approach value is then compared with the fastest increase rate to calculate the ratio, which indicates the time required for the skin temperature to reach the highest acceptable temperature of the skin.

[0078] If the critical time point is earlier than the end time point of physical therapy, it means that the skin temperature will exceed the maximum acceptable temperature of the skin;

[0079] If the critical time point is later than or the same as the end time point of the physical therapy, it means that the skin temperature will not exceed the maximum acceptable temperature of the skin.

[0080] Regulation demand analysis module: By comparing the time point when the skin temperature exceeds the maximum acceptable temperature of the skin and the time point when the treatment ends, it is determined whether the skin temperature needs to be regulated. If necessary, the energy analysis module is entered.

[0081] The technical solution of the embodiment of the present invention is: by setting the warning temperature and the maximum acceptable temperature of the skin, the skin temperature is monitored in real time. If the warning temperature is exceeded, the temperature proximity is calculated to evaluate the gap between the skin temperature and the maximum acceptable temperature. Based on this gap, decide whether to enter the subsequent prediction module to further analyze the temperature change trend. Through the construction and type analysis of the skin temperature analysis curve, combined with linear or nonlinear growth analysis, it is predicted whether the skin temperature will exceed the maximum acceptable temperature before the end of physical therapy. If the prediction result shows that the temperature may exceed the safety threshold, it will trigger the regulation demand analysis and provide a decision-making basis for temperature regulation. It not only improves the accuracy and efficiency of skin temperature monitoring, but also effectively reduces the risk of thermal damage caused by excessive temperature, ensures the comfort and safety of patients during physical therapy, and improves the controllability of the treatment effect.

[0082] Example 2

[0083] like Figure 1 As shown, an intelligent temperature control light wave energy system for health therapy equipment also includes the following modules:

[0084] Energy analysis module: performs correlation analysis on skin temperature and light wave energy, and determines whether there is an influence ratio between skin temperature and light wave energy based on the correlation analysis results. If so, it is sent to the control module.

[0085] The process of performing correlation analysis on skin temperature and light wave energy in the energy analysis module is as follows:

[0086] Obtain skin temperature under different light wave energies, construct a light wave energy data group and a skin temperature data group, calculate the Pearson correlation coefficient between the light wave energy data group and the skin temperature data group, and take the absolute value to obtain the influence correlation value;

[0087] For example, assuming that the light wave energy data set measures the energy input of light of different wavelengths to the skin, and the skin temperature data set measures the temperature change of the skin under corresponding lighting conditions, these two data sets should correspond one to one, that is, a set of matching light wave energy and skin temperature values ​​are obtained under the same experimental conditions;

[0088] The Pearson correlation coefficient is used to measure the linear correlation between two variables. The formula is:

[0089]

[0090] In the formula, X i , Y i represent the observed values ​​of light wave energy and skin temperature respectively; Represent the mean values ​​of light wave energy and skin temperature data respectively.

[0091] The value range of Pearson correlation coefficient r is [-1,1];

[0092] If |r| is close to 1, it means that the light wave energy has a strong effect on skin temperature;

[0093] If |r| is close to 1, it means that the light wave energy has a weak effect on skin temperature.

[0094] If the impact correlation value is greater than or equal to the impact correlation threshold, it means that there is an impact ratio between the skin temperature and the light wave energy. Then, the light wave energy data group and the skin temperature data group are averaged and the average ratio is obtained to obtain the impact ratio.

[0095] Control module: outputs the light wave energy control value according to the influence ratio between skin temperature and light wave energy, and the degree to which the skin temperature exceeds the warning temperature.

[0096] The temperature deviation between the skin temperature and the warning temperature is calculated, and multiplied and combined with the influence ratio between the skin temperature and the light wave energy to obtain the light wave energy control value.

[0097] Assume that skin temperature is the current skin temperature, and warning temperature is a set temperature threshold. When the skin temperature approaches or exceeds this temperature, it means that the skin temperature may enter the dangerous range. The temperature deviation is the difference between the skin temperature and the warning temperature. The formula is: ΔT alert =T skin -T alert

[0098] If ΔT alert >0, indicating that the skin temperature exceeds the warning temperature;

[0099] If ΔT alert <0, indicating that the skin temperature is lower than the warning temperature.

[0100] The light wave energy control value is obtained by multiplying the deviation between the skin temperature and the warning temperature by the ratio of the effect of the skin temperature on the light wave energy. The formula is: E control =ΔT alert *P energy

[0101] Where, ΔT alert is the deviation between skin temperature and warning temperature; P energy It is the influence ratio between skin temperature and light wave energy;

[0102] If the skin temperature is high, the light wave energy control value may be adjusted accordingly to reduce the light wave energy output to prevent the skin temperature from continuing to rise; if the skin temperature is low, the light wave energy control value may be increased to improve the light wave energy output.

[0103] According to the calculated light wave energy control value, the light wave energy output is automatically adjusted to ensure that the skin temperature remains within a safe range:

[0104] If E control It is a negative value, indicating that the light wave energy output needs to be reduced;

[0105] If E control A positive value indicates that the light wave energy output needs to be increased.

[0106] According to the real-time calculation and dynamic adjustment of the ratio of skin temperature exceeding the standard and light wave energy impact, it can ensure that the skin temperature is always kept within a safe range to avoid the negative impact of overheating on the skin. By precisely regulating the light wave energy output, the treatment effect is maximized, while avoiding discomfort or skin damage caused by excessive light wave energy. It can be adjusted according to the skin reactions of different individuals, taking into account the sensitivity of different skins to light wave energy, to achieve more sophisticated treatment management. Through real-time monitoring and calculation, the light wave energy output is automatically adjusted without manual intervention, which improves the automation and intelligence level of the treatment process. By calculating the control value of the light wave energy in real time and making feedback adjustments, the skin temperature is effectively avoided from being too high, and the risk of burns or other side effects during treatment is reduced. By precisely adjusting the light wave energy output, not only the treatment effect is improved, but also the comfort during treatment is enhanced to avoid discomfort caused by excessive temperature.

[0107] Example 3

[0108] See also Figure 2 As shown, a smart temperature control light wave energy method for health therapy equipment includes:

[0109] S1: After the skin temperature exceeds the warning temperature, determine whether a subsequent prediction of the skin temperature is required based on the proximity between the skin temperature and the maximum acceptable temperature of the skin. If so, enter the prediction module.

[0110] In the forecast demand analysis module, if the skin temperature exceeds the warning temperature, the process of calculating the proximity between the skin temperature and the maximum acceptable temperature of the skin is as follows:

[0111] Set key temperature parameters, including real-time skin temperature T skin , Warning temperature T alert and the maximum acceptable skin temperature T max , calculate the degree to which the current skin temperature exceeds the warning temperature, the formula is:

[0112] ΔT alert =T skin -T alert

[0113] If T alert>0, indicating that the skin temperature has exceeded the warning temperature and further assessment is needed to determine how close it is to the maximum acceptable temperature;

[0114] Calculate the temperature difference between the skin temperature and the maximum acceptable temperature, that is, the temperature approach value ΔT max :ΔT max =T max -T skin ;

[0115] If the temperature approach value is less than the temperature approach threshold T thres , then subsequent prediction of skin temperature is required;

[0116] If the temperature proximity value is greater than or equal to the temperature proximity threshold T thres , there is no need to make subsequent predictions of skin temperature and continue monitoring.

[0117] S2: Construct a skin temperature analysis curve, analyze the curve type, predict the subsequent skin temperature, and determine whether the skin temperature will exceed the maximum acceptable temperature of the skin. If so, enter the regulation demand analysis module.

[0118] The process of performing curve type analysis in the prediction module is as follows:

[0119] According to the skin temperature after exceeding the warning temperature, a skin temperature analysis curve is constructed with the X-axis being time and the Y-axis being skin temperature;

[0120] A connecting line is obtained by connecting the end points of the skin temperature analysis curve;

[0121] Select several acquisition points with the same horizontal coordinates on the skin temperature analysis curve and the connecting line;

[0122] Acquire skin temperature values ​​at different collection points on the skin temperature analysis curve, and integrate them to obtain a first temperature data group;

[0123] Acquire skin temperature values ​​at different collection points on the connecting line, and integrate them to obtain a second temperature data group;

[0124] Calculate the Euclidean distance between the first temperature data group and the second temperature data group to obtain a curve similarity value;

[0125] Exemplarily, it is assumed that the first temperature data set (temperature on the skin temperature analysis curve) is: T cirve ={T curve (x 1 ),T curve (x 2 ),...,T curve (x n )}, the second temperature data set (temperature on the connecting line) is: T line ={Tline (x 1 ),T line (x 1 ),...,T line (x n )};

[0126] The Euclidean distance D between the first temperature data group and the second temperature data group is calculated to obtain the curve similarity value. The specific calculation formula is:

[0127]

[0128] Set the curve similarity threshold D thresh :

[0129] If the curve similarity value is less than the threshold D thresh , indicating that the curve type is linear;

[0130] If the curve similarity value is greater than or equal to the threshold D thresh , indicating that the curve type is nonlinear.

[0131] The prediction module predicts the subsequent skin temperature in combination with the curve type analysis result, and the process of judging whether the skin temperature will exceed the maximum acceptable temperature of the skin includes:

[0132] If the curve type is linear, the skin temperature analysis curve is fitted using the least squares method to obtain the function equation of the fitting line, y=k*t+b, where y represents skin temperature, t represents time, k represents the slope of the fitting line, and b represents the intercept;

[0133] Taking the highest acceptable skin temperature as the input of the function equation, predict the time point when the skin temperature reaches the highest acceptable skin temperature, i.e. the critical time point;

[0134] The critical time point was compared with the end of physical therapy time point;

[0135] If the critical time point is earlier than the end time point of physical therapy, it means that the skin temperature will exceed the maximum acceptable temperature of the skin before the end of physical therapy;

[0136] If the critical time point is later than or the same as the end time point of the physical therapy, it means that the skin temperature will not exceed the maximum acceptable skin temperature before the end of the physical therapy.

[0137] The prediction module predicts the subsequent skin temperature in combination with the curve type analysis result, and the process of judging whether the skin temperature will exceed the maximum acceptable temperature of the skin also includes:

[0138] If the curve type is nonlinear, a growth analysis is performed on the first temperature data group to determine the fastest growth rate of the skin temperature;

[0139] The ratio of the temperature approach value to the fastest growth rate of the skin temperature is calculated to obtain the time it takes for the skin temperature to reach the highest acceptable temperature of the skin, that is, the critical time.

[0140] If the critical time point is earlier than the end time point of physical therapy, it means that the skin temperature will exceed the maximum acceptable temperature of the skin;

[0141] If the critical time point is later than or the same as the end time point of the physical therapy, it means that the skin temperature will not exceed the maximum acceptable temperature of the skin.

[0142] S3: By comparing the time point when the skin temperature exceeds the maximum acceptable temperature of the skin and the time point when the physical therapy ends, it is determined whether the skin temperature needs to be regulated. If necessary, enter the energy analysis module.

[0143] S4: Determine whether there is an influence ratio between skin temperature and light wave energy through correlation analysis. If so, send it to the control module.

[0144] The process of performing correlation analysis on skin temperature and light wave energy in the energy analysis module is as follows:

[0145] Obtain skin temperature under different light wave energies, construct a light wave energy data group and a skin temperature data group, calculate the Pearson correlation coefficient between the light wave energy data group and the skin temperature data group, and take the absolute value to obtain the influence correlation value;

[0146] The Pearson correlation coefficient is used to measure the linear correlation between two variables. The formula is:

[0147]

[0148] Where, X i , Y i represent the observed values ​​of light wave energy and skin temperature respectively; Represent the mean values ​​of light wave energy and skin temperature data respectively.

[0149] The value range of Pearson correlation coefficient r is [-1,1];

[0150] If |r| is close to 1, it means that the light wave energy has a strong effect on skin temperature;

[0151] If |r| is close to 1, it means that the light wave energy has a weak effect on skin temperature.

[0152] If the impact correlation value is greater than or equal to the impact correlation threshold, it means that there is an impact ratio between the skin temperature and the light wave energy. Then, the light wave energy data group and the skin temperature data group are averaged and the average ratio is obtained to obtain the impact ratio.

[0153] S5: Output the light wave energy control value according to the influence ratio between skin temperature and light wave energy and the degree to which the skin temperature exceeds the warning temperature.

[0154] The temperature deviation between the skin temperature and the warning temperature is calculated, and multiplied and combined with the influence ratio between the skin temperature and the light wave energy to obtain the light wave energy control value.

[0155] Assume that skin temperature is the current skin temperature, and warning temperature is a set temperature threshold. When the skin temperature approaches or exceeds this temperature, it means that the skin temperature may enter the dangerous range. The temperature deviation is the difference between the skin temperature and the warning temperature. The formula is: ΔT alert =T skin -T alert

[0156] If ΔT alert >0, indicating that the skin temperature exceeds the warning temperature;

[0157] If ΔT alert <0, indicating that the skin temperature is lower than the warning temperature.

[0158] The light wave energy control value is obtained by multiplying the deviation between the skin temperature and the warning temperature by the ratio of the effect of the skin temperature on the light wave energy. The formula is: E control =ΔT alert *P energy

[0159] Where, ΔT alert is the deviation between skin temperature and warning temperature; P energy It is the influence ratio between skin temperature and light wave energy;

[0160] If the skin temperature is high, the light wave energy control value may be adjusted accordingly to reduce the light wave energy output to prevent the skin temperature from continuing to rise; if the skin temperature is low, the light wave energy control value may be increased to improve the light wave energy output.

[0161] According to the calculated light wave energy control value, the light wave energy output is automatically adjusted to ensure that the skin temperature remains within a safe range:

[0162] If E control It is a negative value, indicating that the light wave energy output needs to be reduced;

[0163] If E control A positive value indicates that the light wave energy output needs to be increased.

[0164] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0165] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent temperature-controlled light wave energy system for health therapy equipment, characterized in that: include: Prediction demand analysis module: after the skin temperature exceeds the warning temperature, it is determined whether a subsequent prediction of the skin temperature is needed based on the degree of proximity between the skin temperature and the maximum acceptable temperature of the skin. If so, it enters the prediction module; Prediction module: constructs a skin temperature analysis curve, analyzes the curve type, predicts the subsequent skin temperature, and determines whether the skin temperature will exceed the maximum acceptable temperature of the skin. If so, enters the regulation demand analysis module; Regulation demand analysis module: By comparing the time point when the skin temperature exceeds the highest acceptable temperature of the skin and the time point when the treatment ends, it is determined whether the skin temperature needs to be regulated. If necessary, the energy analysis module is entered; Energy analysis module: through correlation analysis, determine whether there is an influence ratio between skin temperature and light wave energy. If so, send it to the control module; Control module: outputs the light wave energy control value based on the influence ratio between skin temperature and light wave energy, and the degree to which the skin temperature exceeds the warning temperature.

2. The intelligent temperature-controlled light wave energy system for health therapy equipment according to claim 1, characterized in that: The process of determining whether a subsequent prediction of the skin temperature is required is as follows: Calculate the deviation between the skin temperature and the maximum acceptable temperature of the skin to obtain the temperature approximation value; If the temperature proximity value is less than the temperature proximity threshold, a subsequent prediction of the skin temperature is required.

3. The intelligent temperature-controlled light wave energy system for health therapy equipment according to claim 2, characterized in that: The process of performing curve type analysis is as follows: A connecting line is obtained by connecting the end points of the skin temperature analysis curve; Set a number of collection points with the same horizontal coordinates on the skin temperature analysis curve and the connecting line; Based on the skin temperature analysis curve and the connecting line, the temperature values ​​corresponding to the skin temperature analysis curve and the connecting line at each collection point are obtained, and are respectively integrated into a first temperature data group and a second temperature data group; Calculate the Euclidean distance between the first temperature data group and the second temperature data group to obtain a curve similarity value; If the curve similarity value is less than the curve similarity threshold, the curve type is linear, otherwise, it is nonlinear.

4. The intelligent temperature-controlled light wave energy system for health therapy equipment according to claim 3, characterized in that: The process of predicting the subsequent skin temperature by combining the curve type analysis result and judging whether the skin temperature will exceed the maximum acceptable temperature of the skin includes: If the curve type is linear, the skin temperature analysis curve is fitted using the least squares method to obtain the function equation of the fitted straight line. The maximum acceptable skin temperature is used as input to predict the time point when the skin temperature reaches the maximum acceptable skin temperature, that is, the critical time point; If the critical time point is earlier than the end time point of physical therapy, it means that the skin temperature will exceed the maximum acceptable temperature of the skin; If the critical time point is later than or the same as the end time point of the physical therapy, it means that the skin temperature will not exceed the maximum acceptable temperature of the skin.

5. The intelligent temperature-controlled light wave energy system for health therapy equipment according to claim 4, characterized in that: The process of predicting the subsequent skin temperature by combining the curve type analysis result and judging whether the skin temperature will exceed the maximum acceptable temperature of the skin also includes: If the curve type is nonlinear, a growth analysis is performed on the first temperature data group to determine the fastest growth rate of the skin temperature; Calculate the ratio of the temperature approach value to the fastest growth rate of the skin temperature to obtain the time it takes for the skin temperature to reach the highest acceptable temperature of the skin, i.e. the critical time. The end time point of the critical time is the critical time point. If the critical time point is earlier than the end time point of physical therapy, it means that the skin temperature will exceed the maximum acceptable temperature of the skin; If the critical time point is later than or the same as the end time point of the physical therapy, it means that the skin temperature will not exceed the maximum acceptable temperature of the skin.

6. The intelligent temperature-controlled light wave energy system for health therapy equipment according to claim 1, characterized in that: The process of correlation analysis between skin temperature and light wave energy is as follows: Obtain skin temperature under different light wave energies, construct a light wave energy data group and a skin temperature data group, calculate the Pearson correlation coefficient between the light wave energy data group and the skin temperature data group, and take the absolute value to obtain the influence correlation value; If the impact correlation value is greater than or equal to the impact correlation threshold, it means that there is an impact ratio between the skin temperature and the light wave energy. Then, the light wave energy data group and the skin temperature data group are averaged and the average ratio is obtained to obtain the impact ratio.

7. The intelligent temperature-controlled light wave energy system for health therapy equipment according to claim 1, characterized in that: According to the influence ratio between skin temperature and light wave energy, and combined with the degree to which the skin temperature exceeds the warning temperature, the process of outputting the light wave energy control value includes: The temperature deviation between the skin temperature and the warning temperature is calculated, and multiplied and combined with the influence ratio between the skin temperature and the light wave energy to obtain the light wave energy control value.

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