Intelligent temperature control eye massage system and method based on acupoint stimulation
By analyzing the temperature and massage head pressure data of the eye acupoint area and dynamically adjusting the smoothing coefficient, the accuracy problem of traditional algorithms in the prediction of eye acupoint temperature is solved, and accurate constant temperature control and massage effect are achieved.
Patent Information
- Application Number
- CN202510976006.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-07-16
- Publication Date
- 2025-08-15
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
Traditional exponential smoothing algorithms are difficult to adapt to rapid changes in the temperature prediction of eye acupoints, resulting in low temperature prediction accuracy and affecting the constant temperature control effect.
By obtaining the temperature and massage head pressing pressure data of the eye acupoint area, the local smoothness and trend deviation are calculated, the local correlation at significant moments are obtained, the optimal smoothing coefficient is selected, and dynamic temperature adjustment is performed using the exponential smoothing algorithm and the PID control algorithm.
It improves the accuracy of temperature prediction, achieves accurate constant temperature control of the eye acupoint area, and improves the massage and stimulation effect of around the eye acupoints.
Smart Images
Figure CN120478141A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the field of massager control technology, and in particular to an intelligent temperature-controlled eye massage system and method based on acupoint stimulation. Background Art
[0002] Meibomian glands are glands hidden beneath the skin of the upper and lower eyelids. Their primary function is to secrete meibum, a key component of the tear film. Long-term contact lens wear, heavy eye makeup, excessive eye strain, and prolonged use of electronic devices can lead to blockage of the meibomian glands, which in turn can cause insufficient meibomian gland lipid secretion and induce dry eye syndrome. In such cases, massaging and stimulating the eye acupoints with an eye massager can promote blood circulation around the eyes and relax the eye muscles, thereby alleviating dry eyes and blurred vision to a certain extent.
[0003] When massaging and stimulating the eye acupoints, appropriate temperature can effectively promote blood circulation. In the process of predicting and controlling the skin temperature at the eye acupoints, traditional exponential smoothing algorithms generally select a fixed smoothing coefficient. However, since the changes in skin temperature in the eye acupoint area are easily affected by the pressure of the massage head, the algorithm is difficult to adapt to the rapid changes in skin temperature in the eye acupoint area, resulting in low temperature prediction accuracy and deviations in the constant temperature control, which affects the massage stimulation intervention effect of the acupoints around the eyes. Summary of the Invention
[0004] In order to solve the above technical problems, an intelligent temperature-controlled eye massage system and method based on acupoint stimulation are provided to solve the existing problems.
[0005] The solution to the technical problem of this application is to provide an intelligent temperature-controlled eye massage system and method based on acupoint stimulation, comprising the following steps: In a first aspect, an embodiment of the present application provides an intelligent temperature-controlled eye massage method based on acupoint stimulation, the method comprising the following steps: When the eye massager is working, the temperature of the eye acupoint area at each moment before the current moment and the pressing pressure of the massage head on the eye at each moment are obtained; multiple moments in the neighborhood of each moment are recorded as a local period of each moment; Analyzing the temperature deviation at different moments in the local time period and the steepness of the temperature distribution, and calculating the local smoothness at each moment; By calculating the difference in temperature change trends between each moment and the adjacent moments, the trend deviation of each moment is calculated, and each significant moment is obtained; Obtaining a local correlation degree for each significant moment based on correlations between temperature and pressing pressure at different moments within the local period of each significant moment; By selecting multiple preset smoothing coefficients, the exponential smoothing algorithm is used to predict the temperature, and the difference between the predicted value and the actual temperature is analyzed to obtain the optimal smoothing coefficient; Based on local smoothness and local correlation, the optimal smoothing coefficient is corrected to obtain the corrected smoothing coefficient corresponding to each moment, and the exponential smoothing algorithm is used to predict the temperature after the current moment. The PID control algorithm controls the temperature of the eye acupoint area according to the prediction results.
[0006] Preferably, the calculating of the local smoothness at each moment includes: Calculating the kurtosis of the temperature at all moments in the local time period; recording the difference between the kurtosis and a preset first value as a difference amount; Calculate the average of the temperatures at all times in the local period, and record it as the average temperature; and record the average of the differences between the temperatures at all times in the local period and the average temperature, and record it as the relative deviation; The local smoothness is a result of fusing the difference and the relative deviation.
[0007] Preferably, the specific process of the fusion is: taking the ratio of the difference amount to the relative deviation as the local smoothness at each moment.
[0008] Preferably, the calculating of the trend deviation at each moment includes: Perform curve fitting on the temperature at all moments before the current moment, and calculate the slope of the tangent line at each moment on the fitting curve; The trend deviation is the difference between the slope of the tangent line at each moment and the slope at the previous moment.
[0009] Preferably, the process of acquiring the significant moments is: recording the moments when the trend deviation is greater than a preset threshold as significant moments, and vice versa, recording them as stable moments.
[0010] Preferably, obtaining the local correlation degree of each significant moment includes: The degree of correlation between the temperature and the pressing pressure at all moments within the local period of each significant moment is recorded as the local correlation degree of each significant moment, wherein the local correlation degree of each stable moment is a preset second value.
[0011] Preferably, the temperature is predicted using an exponential smoothing algorithm, comprising: Divide the temperatures of all moments before the current moment into a prediction set and a test set; A plurality of preset smoothing coefficients are selected and recorded as candidate smoothing coefficients; based on the candidate smoothing coefficients, the prediction set is predicted by the exponential smoothing algorithm to obtain the prediction values of multiple moments after the last moment in the prediction set.
[0012] Preferably, the process of obtaining the optimal smoothing coefficient is: calculating the mean square error between the predicted value and the temperature in the test set, recording it as the prediction error corresponding to each candidate smoothing coefficient; and recording the candidate smoothing coefficient with the smallest prediction error as the optimal smoothing coefficient.
[0013] Preferably, The corrected smoothing coefficient corresponding to the moment The calculation formula is: ,in, is the optimal smoothing coefficient, For the The local smoothness of the moment, For the Local correlation at a moment.
[0014] In the second aspect, an embodiment of the present application also provides an intelligent temperature-controlled eye massage system based on acupoint stimulation, comprising a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of any one of the above-mentioned intelligent temperature-controlled eye massage methods based on acupoint stimulation.
[0015] This application has at least the following beneficial effects: The present application calculates the local smoothness of each moment by analyzing the temperature offset and the concentration of temperature distribution in a local time period. The beneficial effect is that it takes into account the stable temperature change in the eye acupuncture point area to reflect the volatility of temperature change in the local time period; calculates the trend difference through the trend change difference of temperature at adjacent moments, and obtains each significant moment. The beneficial effect is that the moment when the temperature changes significantly, so as to subsequently evaluate the correlation between the sudden change of temperature at the significant moment and the pressing pressure; and then obtains the local correlation of each significant moment. The beneficial effect is that it takes into account the correlation between the drastic change of temperature and the change of the pressing pressure of massage and rubbing; by selecting multiple preset smoothing coefficients, using the exponential smoothing algorithm to predict the temperature, analyzing the difference between the predicted value and the actual temperature, and obtaining the optimal smoothing coefficient. The beneficial effect is that Through multiple preset smoothing coefficients, the exponential smoothing algorithm is used for prediction, and then the preset smoothing coefficient with the smaller error between the predicted value and the actual temperature is selected. The beneficial effect is that the smoothing coefficient when the exponential smoothing algorithm has a better prediction effect is analyzed without considering the influence of the temperature fluctuation on the pressing pressure; based on the local smoothness and local correlation, the optimal smoothing coefficient is corrected to obtain the corrected smoothing coefficient corresponding to each moment, and the exponential smoothing algorithm is used to predict the temperature after the current moment. The PID control algorithm controls the temperature of the eye acupoint area according to the prediction results. The beneficial effect is that by dynamically adjusting the optimal smoothing coefficient, when the temperature change is affected by the pressing pressure of friction, the temperature can be quickly responded to, thereby improving the accuracy of temperature prediction, thereby more accurately controlling the temperature of the eye acupoint area and improving the massage stimulation intervention effect on the acupoints around the eyes. BRIEF DESCRIPTION OF THE DRAWINGS
[0016] The intelligent temperature-controlled eye massage method based on acupoint stimulation of the present application is further described in detail below with reference to the accompanying drawings.
[0017] Figure 1 A flowchart of the steps of the intelligent temperature-controlled eye massage method based on acupoint stimulation provided in an embodiment of the present application; Figure 2 A flowchart of the steps of the method for obtaining the local correlation degree of each significant moment provided in an embodiment of the present application. DETAILED DESCRIPTION
[0018] In order to make the purpose, technical solutions and advantages of this application more clearly understood, the following, in conjunction with the accompanying drawings and implementation examples, further describes in detail the intelligent temperature-controlled eye massage system and method based on acupoint stimulation proposed in this application. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.
[0019] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
[0020] See also Figure 1 , which shows a flowchart of the steps of an intelligent temperature-controlled eye massage method based on acupoint stimulation provided by one embodiment of the present application, the method comprising the following steps: Step 1: When the eye massager is working, obtain the temperature of the eye acupoint area at each moment before the current moment, and the pressing pressure of the massage head on the eye at each moment.
[0021] When the eye acupoint area is rubbed and physically massaged by an eye massager, the temperature of the area around the eye will rise due to friction. Compared with healthy people, when the eye acupoint area of users with dry eye syndrome is rubbed and physically massaged by an eye massager, the tear film secretion of users with dry eye syndrome is reduced. Excessive temperature will cause further evaporation of the tear film of users with dry eye syndrome, aggravating the symptoms of dry eye. In the absence of tear film protection, the cornea is repeatedly rubbed by the pressure weight of the massager, which will cause damage to the cornea during the friction process.
[0022] NTC (Negative Temperature Coefficient Sensor) thermistor is a highly sensitive temperature sensor whose resistance value decreases as the temperature rises. Therefore, the NTC thermistor installed in the eye massager can monitor the temperature of the eye acupuncture points at any time in real time. When the eye massager starts to rub and physically massage the eye acupuncture point area, the pressure applied by the eye massager's massage contact head is obtained in real time to obtain the pressing pressure of the massage head on the eye at each moment; In this embodiment, the data collection time interval is 1 second, and the eye massager works for 20 minutes. As other implementation methods, the implementer can set it according to the actual situation.
[0023] And fill missing values for all collected data. In this embodiment, the Lagrange interpolation algorithm is used to fill missing values. The Lagrange interpolation algorithm is a well-known technology and will not be described here. As other implementation methods, the implementer can adopt other methods of the existing technology, such as the cubic spline interpolation method, etc. This embodiment does not impose any special restrictions on this.
[0024] At this point, when the eye massager is working, the temperature of the eye acupuncture point area at each moment and the pressing pressure of the massage head on the eye at each moment are obtained.
[0025] Step 2: Analyze the temperature deviation at different moments in the local time period and the steepness of the temperature distribution, and calculate the local smoothness at each moment.
[0026] When the eye massager is massaging the eye acupuncture points, it will continuously apply friction pressure to the skin area in contact with the massager. At this time, the skin temperature in the area in contact with the massager will increase. Therefore, the local smoothness is calculated based on the temperature fluctuations in the local time period at each moment. Specifically, Taking each moment as the center, multiple moments in its neighborhood are recorded as local time periods; In this embodiment, the number of all moments in the local period is 60. As for other implementations, the implementer can set it according to actual conditions.
[0027] Calculating the kurtosis of the temperature at all moments in the local time period; recording the difference between the kurtosis and a preset first value as a difference amount; In this embodiment, the preset first value is 3. Since the kurtosis of the normal distribution is 3, the closer the temperature change in the local time period is to the normal distribution, the more concentrated the temperature distribution is. Therefore, the absolute value of the difference between the kurtosis and the preset value is recorded as the difference amount.
[0028] It should be noted that the calculation process of kurtosis is a well-known technology and will not be described in detail here.
[0029] Calculate the average temperature at all times within the local period, and record it as the average temperature; The average of the differences between the temperature at all moments in the local period and the average temperature is recorded as the relative deviation; In this embodiment, the average of the absolute values of the differences between the temperatures at all moments in the local time period and the average temperature is recorded as the relative deviation.
[0030] Normalizing the ratio of the difference to the relative deviation as the local smoothness at each moment; In this embodiment, the sigmoid function is used for normalization processing, wherein the sigmoid function is a well-known technology and will not be described in detail here. As other implementation methods, the implementer can adopt other methods of the existing technology, such as the softmax function, etc. This embodiment does not impose any special restrictions on this.
[0031] It should be noted that the larger the difference, the more concentrated the temperature distribution in the local time period; the smaller the relative deviation, the more stable the temperature fluctuation in the local time period; the greater the local smoothness, the more smooth the temperature change in the local time period at that moment, and the relatively low temperature volatility.
[0032] At this point, the local smoothness at each moment is obtained.
[0033] Step 3: Calculate the trend deviation of each moment by the difference in temperature change trends between each moment and the adjacent moments, and obtain each significant moment; based on the correlation between the temperature and the pressing pressure at different moments in the local period of each significant moment, obtain the local correlation of each significant moment.
[0034] Secondly, when the eye massager massages the eye acupuncture point area, the temperature of the contact position between the massage head and the skin will change with the pressure of the massage head. When the pressure increases, the temperature tends to rise. Therefore, by analyzing the correlation between the temperature and the pressure when the temperature changes drastically in the local time period, the local correlation degree is calculated. The flowchart of the steps of the method for obtaining the local correlation degree at each significant moment provided in the embodiment of the present application is as follows: Figure 2 As shown, specifically including: Perform curve fitting on the temperature at all moments before the current moment, and calculate the slope of the tangent line at each moment on the fitting curve; In this embodiment, the least square method is used for curve fitting. The least square method and the calculation of the tangent slope are both well-known technologies and will not be described in detail here.
[0035] Calculate the difference in the slope of the tangent line at each moment and the previous moment, and record it as the trend deviation; In this embodiment, the absolute value of the difference between the tangent slope at each moment and the previous moment is calculated and recorded as the trend deviation.
[0036] The moments when the trend deviation is greater than a preset threshold are recorded as significant moments, and vice versa as stable moments; In this embodiment, the preset threshold value is 0.5. As for other implementations, the implementer can set it according to actual conditions.
[0037] It should be noted that when the trend deviation is greater than the preset threshold, it indicates that the temperature change at that moment is more significant, which may be related to the change in pressing pressure. Otherwise, it indicates that the temperature change at that moment is relatively stable.
[0038] Recording the degree of correlation between the temperature and the pressing pressure at all moments within the local period of each significant moment as the local correlation degree of each significant moment, wherein the local correlation degree of each stable moment is a preset second value; In this embodiment, the degree of correlation is measured by calculating the Pearson correlation coefficient between the temperature and the pressing pressure at all moments in the local time period of each significant moment, wherein the calculation of the Pearson correlation coefficient is a well-known technology and will not be repeated here. As other implementation methods, the implementer may adopt other methods of the existing technology, such as cosine similarity, Spearman correlation coefficient, etc., and this embodiment does not impose any special restrictions on this; secondly, the preset second numerical value is 0.1. As other implementation methods, the implementer may set it according to actual conditions.
[0039] It should be noted that the greater the trend deviation, the greater the difference in the temperature change trend between the two moments, and the drastic temperature change. The local correlation reflects that when the temperature fluctuates drastically, the temperature change is affected by the change in pressing pressure, that is, the temperature rise in the eye acupoint area may be caused by the pressure of massage and rubbing. The greater the local correlation, the more significant the correlation between the temperature change in the eye acupoint area at this moment and the pressing pressure of the massage head on the eye.
[0040] At this point, the local correlation at each moment is obtained.
[0041] Step 4: By selecting multiple preset smoothing coefficients, the exponential smoothing algorithm is used to predict the temperature, and the difference between the predicted value and the actual temperature is analyzed to obtain the optimal smoothing coefficient; based on the local smoothness and local correlation, the optimal smoothing coefficient is corrected to obtain the corrected smoothing coefficient corresponding to each moment, and the exponential smoothing algorithm is used to predict the temperature after the current moment. The PID control algorithm controls the temperature of the eye acupoint area according to the prediction results.
[0042] Since the skin in the eye acupoint area is relatively thin and users with eye fatigue are more sensitive to temperature changes, in order to more accurately regulate the temperature of the eye acupoint area, the temperature of the eye acupoint area is predicted and the temperature of the eye massager is constantly controlled based on the prediction results.
[0043] In order to improve the accuracy of temperature prediction, it is necessary to smooth the temperature through the exponential smoothing algorithm to eliminate the interference of noise. Among them, the unreasonable selection of the smoothing coefficient in the exponential smoothing algorithm will affect the accuracy of the prediction results. If the smoothing coefficient is set too large, it will lead to excessive dependence on recent temperatures. The prediction results are very sensitive to recent temperature changes and easily affected by noise interference. Conversely, if the smoothing coefficient is set too small, it will lead to excessive dependence on temperature change trends in historical periods and a slow response to changes in recent data.
[0044] Based on the above analysis, the smoothing coefficient of the exponential smoothing algorithm is modified based on the local smoothness and the local correlation, specifically: Selecting multiple preset smoothing coefficients, recorded as candidate smoothing coefficients; In this embodiment, the value range is A plurality of preset smoothing coefficients are selected within the range, and the candidate smoothing coefficients are 0.1, 0.3, 0.5, 0.7, and 0.9 respectively. As other implementation methods, the implementer can set them according to the actual situation.
[0045] Divide the temperatures of all moments before the current moment into a prediction set and a test set; In this embodiment, the ratio is 7:3. As other implementation methods, the implementer can set it according to the actual situation.
[0046] Using each candidate smoothing coefficient, the prediction set is predicted using an exponential smoothing algorithm to obtain the predicted values for multiple moments after the last moment in the prediction set; the mean square error between the predicted value and the temperature in the test set is recorded as the prediction error corresponding to each candidate smoothing coefficient; It should be noted that 70% of the temperature data at all moments are predicted in turn using each candidate smoothing coefficient to obtain the predicted values at different moments after the prediction set. By comparing the error between the predicted value and the actual temperature in the test set, the candidate smoothing coefficient with the smaller error is selected. The smaller the error, the higher the prediction accuracy of the candidate smoothing coefficient. The exponential smoothing algorithm is a well-known technology and will not be described in detail here.
[0047] The candidate smoothing coefficient with the smallest prediction error is recorded as the optimal smoothing coefficient; The calculation formula of the corrected smoothing coefficient corresponding to each moment is: in, For the The moment corresponds to the corrected smoothing coefficient, is the optimal smoothing coefficient, For the The local smoothness of the moment, For the Local correlation of moments; The corrected smoothing coefficient is used to predict the temperature after the current moment using an exponential smoothing algorithm. The predicted result is used as the input of a PID (proportional-integral-derivative control) control algorithm to perform feedforward predictive control on the temperature of the eye acupuncture point area, and the heating or cooling mechanism of the massager is adjusted in advance to ensure that the temperature of the eye acupuncture point area remains constant.
[0048] It should be noted that the exponential smoothing algorithm and the PID control algorithm are well-known technologies and will not be described in detail here.
[0049] It should be noted that if the temperature changes in the local range at each moment are relatively smooth, the greater the local smoothness, the closer the corrected smoothness coefficient is to the optimal smoothness coefficient. On the contrary, it means that the massager's kneading and friction causes the temperature of the eye acupoint area to rise. At this time, the optimal smoothness coefficient is corrected by the local correlation.
[0050] Based on the same inventive concept as the above method, an embodiment of the present application also provides an intelligent temperature-controlled eye massage system based on acupoint stimulation, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the steps of any one of the above-mentioned intelligent temperature-controlled eye massage methods based on acupoint stimulation are implemented.
[0051] It should be understood that although Figure 1 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. In addition, Figure 1 At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.
[0052] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.
[0053] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the present application. It should be noted that a person skilled in the art can make various modifications and improvements without departing from the spirit of the present application. Therefore, any simple modifications, equivalent variations, and modifications to the above embodiments made in accordance with the technical essence of the present application without departing from the content of the present application's technical solution fall within the scope of protection of the present application's technical solution.
Claims
1. An intelligent temperature-controlled eye massage method based on acupoint stimulation, characterized in that: The method comprises the following steps: When the eye massager is working, the temperature of the eye acupoint area at each moment before the current moment and the pressing pressure of the massage head on the eye at each moment are obtained; multiple moments in the neighborhood of each moment are recorded as a local period of each moment; Analyzing the temperature deviation at different moments in the local time period and the steepness of the temperature distribution, and calculating the local smoothness at each moment; By calculating the difference in temperature change trends between each moment and the adjacent moments, the trend deviation of each moment is calculated, and each significant moment is obtained; Obtaining a local correlation degree for each significant moment based on correlations between temperature and pressing pressure at different moments within the local period of each significant moment; By selecting multiple preset smoothing coefficients, the exponential smoothing algorithm is used to predict the temperature, and the difference between the predicted value and the actual temperature is analyzed to obtain the optimal smoothing coefficient; Based on local smoothness and local correlation, the optimal smoothing coefficient is corrected to obtain the corrected smoothing coefficient corresponding to each moment, and the exponential smoothing algorithm is used to predict the temperature after the current moment. The PID control algorithm controls the temperature of the eye acupoint area according to the prediction results.
2. The intelligent temperature-controlled eye massage method based on acupoint stimulation according to claim 1, characterized in that: The calculation of the local smoothness at each moment includes: Calculating the kurtosis of the temperature at all moments in the local time period; recording the difference between the kurtosis and a preset first value as a difference amount; Calculate the average of the temperatures at all times in the local period, and record it as the average temperature; and record the average of the differences between the temperatures at all times in the local period and the average temperature, and record it as the relative deviation; The local smoothness is a result of fusing the difference and the relative deviation.
3. The intelligent temperature-controlled eye massage method based on acupoint stimulation according to claim 2, characterized in that: The specific process of the fusion is: taking the ratio of the difference amount to the relative deviation as the local smoothness at each moment.
4. The intelligent temperature-controlled eye massage method based on acupoint stimulation according to claim 1, characterized in that: The calculation of the trend deviation at each moment includes: Perform curve fitting on the temperature at all moments before the current moment, and calculate the slope of the tangent line at each moment on the fitting curve; The trend deviation is the difference between the slope of the tangent line at each moment and the slope at the previous moment.
5. The intelligent temperature-controlled eye massage method based on acupoint stimulation according to claim 1, characterized in that: The acquisition process of each significant moment is: the moment when the trend deviation is greater than a preset threshold is recorded as a significant moment, otherwise it is recorded as a stable moment.
6. The intelligent temperature-controlled eye massage method based on acupoint stimulation according to claim 5, characterized in that: Obtaining the local correlation of each significant moment includes: The degree of correlation between the temperature and the pressing pressure at all moments within the local period of each significant moment is recorded as the local correlation degree of each significant moment, wherein the local correlation degree of each stable moment is a preset second value.
7. The intelligent temperature-controlled eye massage method based on acupoint stimulation according to claim 1, characterized in that: The method of using an exponential smoothing algorithm to predict temperature includes: Divide the temperatures of all moments before the current moment into a prediction set and a test set; A plurality of preset smoothing coefficients are selected and recorded as candidate smoothing coefficients; based on the candidate smoothing coefficients, the prediction set is predicted by the exponential smoothing algorithm to obtain the prediction values of multiple moments after the last moment in the prediction set.
8. The intelligent temperature-controlled eye massage method based on acupoint stimulation according to claim 7, characterized in that: The process of obtaining the optimal smoothing coefficient is as follows: calculating the mean square error between the predicted value and the temperature in the test set, recording it as the prediction error corresponding to each candidate smoothing coefficient; and recording the candidate smoothing coefficient with the smallest prediction error as the optimal smoothing coefficient.
9. The intelligent temperature-controlled eye massage method based on acupoint stimulation according to claim 1, characterized in that: No. The corrected smoothing coefficient corresponding to the moment The calculation formula is: ,in, is the optimal smoothing coefficient, For the The local smoothness of the moment, For the Local correlation at a moment.
10. An intelligent temperature-controlled eye massage system based on acupoint stimulation, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the intelligent temperature-controlled eye massage method based on acupoint stimulation as described in any one of claims 1 to 9 are implemented.