Skin temperature measurement and control method for wearable smart ring

Abstract

The present invention relates to the technical field of temperature measurement and control, and in particular to a skin temperature measurement and control method for a wearable smart ring. The method comprises the following steps: collecting a temperature change signal of the skin surface of a user in real time by means of a skin temperature sensor integrated inside a wearable smart ring to obtain a real-time skin surface temperature change signal; transmitting the real-time skin surface temperature change signal to an internal processing unit of the wearable smart ring, and performing dynamic temperature calibration and compensation on the real-time skin surface temperature change signal by means of the internal processing unit to obtain real-time skin surface temperature calibration data; and using a temperature regulation and control device of the wearable smart ring to perform temperature anomaly control feedback on the real-time skin surface temperature calibration data to generate a skin surface temperature anomaly control feedback response signal, so as to execute a corresponding skin surface temperature anomaly control and regulation operation. The present invention can realize high-precision, real-time and personalized skin temperature monitoring and regulation.

Description

Skin temperature measurement and control method of wearable smart ring TECHNICAL FIELD

[0001] The present application relates to the technical field of temperature measurement control, and particularly relates to a skin temperature measurement and control method of a wearable smart ring. BACKGROUND

[0002] In the field of health monitoring, wearable devices are increasingly widely used. As a portable and comfortable wearable device, the smart ring has attracted attention due to its small design and practicality. In addition, real-time monitoring of skin temperature is of great significance in health management. It can not only be used to monitor body temperature changes, but also assist in assessing user health status and providing personalized health recommendations. Modern technology uses an integrated multi-sensor scheme, such as temperature sensors, humidity sensors, and contact sensors, to collect real-time skin temperature data of the wearer through high-precision sensors, and transmit the collected temperature data to the built-in processing unit. Advanced algorithms are used to analyze and process temperature data to identify temperature change trends and potential health problems. It can also actively adjust the wearer's skin temperature based on the analysis results, such as adjusting the temperature of the smart ring through the built-in heating or cooling device, to optimize comfort and enhance health management. However, traditional skin temperature measurement methods usually rely on a single sensor, which can be affected by environmental temperature and poor skin contact during wear, resulting in insufficient accuracy of the wearer's skin temperature measurement.

[0003] SUMMARY

[0004] Therefore, it is necessary to provide a skin temperature measurement and control method of a wearable smart ring to solve at least one of the above technical problems.

[0005] To achieve the above purpose, a skin temperature measurement and control method of a wearable smart ring includes the following steps:

[0006] Step S1: Real-time collection of temperature change signals of the user's skin surface through the skin temperature sensor integrated inside the wearable smart ring to obtain real-time temperature change signals of the skin surface; anti-interference sampling processing of the real-time temperature change signals of the skin surface through the high-precision change sampling circuit and the signal anti-interference circuit inside the wearable smart ring, and transmission to the internal processing unit of the wearable smart ring to obtain real-time temperature change input signals of the skin surface;

[0007] Step S2: converting the skin surface real-time temperature change input signal into temperature signal fluctuation data by the internal processing unit to obtain skin surface real-time temperature change measurement data; obtaining external environment temperature change data, and based on the external environment temperature change data, dynamically calibrating and compensating the skin surface real-time temperature change measurement data to obtain skin surface real-time temperature calibration data;

[0008] Step S3: uploading the skin surface real-time temperature calibration data to the microcontroller unit of the wearable smart ring, and using the microcontroller unit to mark the skin surface real-time temperature calibration data for temperature abnormal response to obtain skin surface monitoring temperature abnormal data; analyzing the skin surface monitoring temperature abnormal data for temperature control target range to obtain the skin surface temperature abnormal temperature control target range;

[0009] Step S4: using the microcontroller unit to analyze the skin surface temperature abnormal temperature control target range for temperature control demand instruction response to generate skin surface temperature control demand response instruction; responding to the temperature adjustment control device of the wearable smart ring with the skin surface temperature control demand response instruction, and using the temperature adjustment control device to perform temperature abnormality control feedback on the skin surface temperature abnormal temperature control target range to generate skin surface temperature abnormality control feedback response signal to perform corresponding skin surface temperature abnormality control adjustment operation.

[0010] Further, step S1 includes the following steps:

[0011] Step S11: collecting the temperature change signal of the user's skin surface in real time by the skin temperature sensor integrated in the wearable smart ring to obtain the skin surface real-time temperature change signal;

[0012] Step S12: processing the skin surface real-time temperature change signal by the high-precision change circuit in the wearable smart ring to obtain the skin surface real-time temperature high-precision change signal;

[0013] Step S13: performing high-frequency noise optimization filtering on the skin surface real-time temperature high-precision change signal to obtain the skin surface real-time temperature change high-frequency filtered signal;

[0014] Step S14: processing the skin surface real-time temperature change high-frequency filtered signal by the signal anti-interference circuit in the wearable smart ring to obtain the skin surface real-time temperature change anti-interference signal;

[0015] Step S15: transmitting the skin surface real-time temperature change anti-interference signal to the internal processing unit of the wearable smart ring to obtain the skin surface real-time temperature change input signal.

[0016] Further, step S14 includes the following steps:

[0017] Step S141: Circuit multi-level arrangement design optimization is performed on the signal anti-interference circuit in the wearable smart ring to generate a smart ring signal anti-interference multi-level design circuit.

[0018] Step S142: The multi-level signal isolation processing is performed on the high-frequency filtered signal of the real-time temperature change on the skin surface by the smart ring signal anti-interference multi-level design circuit to obtain a multi-level isolated signal of the interference of the real-time temperature change on the skin surface.

[0019] Step S143: The adaptive filtering integration is performed on the multi-level isolated signal of the interference of the real-time temperature change on the skin surface to obtain a filtered signal of the interference of the real-time temperature change on the skin surface.

[0020] Step S144: The signal interference mode recognition analysis is performed on the skin temperature sensor integrated in the wearable smart ring to obtain a smart ring temperature signal acquisition interference mode.

[0021] Step S145: The interference mode dynamic correction is performed on the filtered signal of the interference of the real-time temperature change on the skin surface based on the smart ring temperature signal acquisition interference mode to obtain a real-time temperature change anti-interference signal on the skin surface.

[0022] Further, step S141 includes the following steps:

[0023] The circuit topology structure analysis is performed on the signal anti-interference circuit in the wearable smart ring to obtain a signal anti-interference circuit topology design structure.

[0024] The circuit interference source positioning is performed on the signal anti-interference circuit in the wearable smart ring based on the signal anti-interference circuit topology design structure to obtain a signal anti-interference circuit interference source positioning position.

[0025] The circuit to be isolated planning analysis is performed on the signal anti-interference circuit in the wearable smart ring based on the signal anti-interference circuit interference source positioning position to obtain a signal anti-interference circuit to be isolated planning area.

[0026] The multi-level signal isolator arrangement design optimization is performed on the signal anti-interference circuit in the wearable smart ring based on the signal anti-interference circuit to be isolated planning area to generate a smart ring signal anti-interference multi-level design circuit.

[0027] Further, step S2 includes the following steps:

[0028] Step S21: The change signal frequency spectrum mapping conversion is performed on the real-time temperature change input signal on the skin surface by the internal processing unit to obtain a real-time temperature change signal frequency spectrum on the skin surface.

[0029] Step S22: Perform temperature signal fluctuation amplitude statistical analysis on the skin surface real-time temperature change signal spectrum to obtain the skin surface real-time temperature signal fluctuation amplitude;

[0030] Step S23: Divide the skin surface real-time temperature signal fluctuation amplitude by time sequence point amplitude to obtain the skin surface temperature signal fluctuation amplitude at each time sequence point;

[0031] Step S24: Perform transient response statistical analysis on the skin surface temperature signal fluctuation amplitude at each time sequence point to obtain the skin surface temperature signal fluctuation transient response change value at each time sequence point; and perform temperature signal fluctuation data conversion on the skin surface temperature signal fluctuation transient response change value at each time sequence point to obtain the skin surface real-time temperature change measurement data;

[0032] Step S25: Obtain external environment temperature change data and perform temperature dynamic calibration compensation on the skin surface real-time temperature change measurement data based on the external environment temperature change data to obtain the skin surface real-time temperature calibration data.

[0033] Further, the transient response statistical analysis on the skin surface temperature signal fluctuation amplitude at each time sequence point in step S24 includes the following steps:

[0034] Perform fluctuation amplitude transient response characteristic analysis on the skin surface temperature signal fluctuation amplitude at each time sequence point to obtain the temperature signal fluctuation amplitude transient response characteristic data at each time sequence point;

[0035] Perform fluctuation amplitude change mode analysis on the skin surface temperature signal fluctuation amplitude at each time sequence point to obtain the temperature signal fluctuation amplitude change mode at each time sequence point;

[0036] Perform transient response dynamic correction on the temperature signal fluctuation amplitude transient response characteristic data at each time sequence point based on the temperature signal fluctuation amplitude change mode at each time sequence point to obtain the temperature signal transient response characteristic correction data at each time sequence point;

[0037] Perform transient response change value quantitative calculation on the temperature signal transient response characteristic correction data at each time sequence point to obtain the skin surface temperature signal fluctuation transient response change value at each time sequence point.

[0038] Further, step S25 includes the following steps:

[0039] Step S251: Obtain external environment temperature change data;

[0040] Step S252: Time sequence synchronization processing is performed on the external environment temperature change data and the skin surface real-time temperature change measurement data, to obtain external environment temperature change time sequence synchronization data and skin surface temperature change time sequence synchronization data under the same time sequence dimension;

[0041] Step S253: Time sequence point position alignment processing is performed on the external environment temperature change time sequence synchronization data and the skin surface temperature change time sequence synchronization data under the same time sequence dimension, to obtain external environment temperature data and skin surface real-time temperature data at the same temperature change time sequence point position;

[0042] Step S254: Temperature calibration compensation calculation is performed on the corresponding skin surface real-time temperature data based on the external environment temperature data at the same temperature change time sequence point position, by using a temperature calibration compensation calculation formula, to obtain a skin surface temperature calibration compensation coefficient at each temperature change time sequence point position;

[0043] Step S255: Temperature dynamic calibration compensation is performed on the corresponding skin surface real-time temperature data according to the skin surface temperature calibration compensation coefficient at each temperature change time sequence point position, to obtain skin surface real-time temperature calibration data.

[0044] Further, the temperature calibration compensation calculation formula in step S254 is specifically:

[0045] In the formula, δ(t) is the skin surface temperature calibration compensation coefficient at the temperature change time sequence point position t, t is a time variable parameter of the temperature change time sequence point position, τ is an integral time variable parameter, T s (t) is the skin surface real-time temperature at the temperature change time sequence point position t, T e (t) is the skin surface external environment temperature at the temperature change time sequence point position t, α is a skin surface internal and external temperature difference scaling coefficient, τ c is a time decay rate, β is a skin surface internal and external temperature difference influence coefficient, and η is a correction value of the skin surface temperature calibration compensation coefficient.

[0046] Further, step S3 includes the following steps:

[0047] Step S31: The skin surface real-time temperature calibration data is uploaded to the microcontroller unit of the wearable smart ring, and temperature calibration change curve drawing is performed on the skin surface real-time temperature calibration data by using the microcontroller unit, to generate a skin surface temperature calibration data change curve;

[0048] Step S32: Temperature abnormality response marking is performed on the skin surface temperature calibration data change curve according to a preset skin surface temperature abnormality calibration line, to obtain skin surface monitoring temperature abnormality data;

[0049] Step S33: temperature anomaly distribution analysis is performed on the skin surface monitoring temperature anomaly data to obtain a skin surface monitoring temperature anomaly distribution value;

[0050] Step S34: temperature control target range analysis is performed on the skin surface monitoring temperature anomaly data based on the skin surface monitoring temperature anomaly distribution value to obtain a skin surface temperature anomaly temperature control target range.

[0051] Further, step S4 includes the following steps:

[0052] Step S41: temperature control demand analysis is performed on the skin surface temperature anomaly temperature control target range by the microcontroller unit to obtain a skin surface temperature anomaly temperature control demand;

[0053] Step S42: temperature control demand instruction response analysis is performed on the skin surface temperature anomaly temperature control demand to generate a skin surface temperature control demand response instruction;

[0054] Step S43: the skin surface temperature control demand response instruction is responded to the temperature adjustment control device of the wearable smart ring, and temperature anomaly control feedback is performed on the skin surface temperature anomaly temperature control target range by the temperature adjustment control device to generate a skin surface temperature anomaly control feedback response signal, so as to perform corresponding skin surface temperature anomaly control adjustment work.

[0055] The beneficial effects of the present application are as follows:

[0056] Compared with the prior art, the skin temperature measurement and control method of the wearable smart ring has the beneficial effects that the temperature change signal of the user's skin surface is collected in real time by using the skin temperature sensor integrated in the wearable smart ring, the skin temperature is an important physiological parameter reflecting the health status of the human body, which can provide valuable information about body temperature fluctuations, local inflammatory reactions and metabolism, this step can continuously monitor the body temperature change of the user by collecting the skin temperature change signal in real time, and abnormal conditions such as fever, hypothermia or other health problems are found in time, especially in daily health monitoring management, such real-time data collection can help the user better understand his own health status and quickly respond when health problems occur. At the same time, the real-time temperature change signal of the skin surface is sampled and processed by using the high-precision change circuit in the wearable smart ring, which can capture the tiny temperature change and convert it into high-quality electrical signal, and the signal anti-interference circuit in the wearable smart ring is used for anti-interference processing, which can further enhance the stability and accuracy of the signal, so that it is not affected by external environmental interference, even in complex use environment, the smart ring can still provide stable and reliable temperature data, which means that the wearer can still obtain accurate health monitoring information in various life scenes, for example, during exercise, work or other daily activities, reliable body temperature monitoring data can be continuously provided, thereby ensuring the effectiveness and consistency of the health monitoring system. And the skin surface temperature change signal after anti-interference processing is transmitted to the internal processing unit of the smart ring, which can concentrate the processed and optimized signal to the internal processing unit for further analysis and processing, this step ensures the integrity and accuracy of the signal, and converts it into useful health data, the internal processing unit can analyze these data in depth, such as calculating body temperature trend, detecting abnormal fluctuations, etc., and providing personalized health advice or alarm according to the analysis result, which means that the user can obtain instant and accurate health feedback, thereby helping him better manage his health status. Secondly, the temperature signal fluctuation data is converted by using the internal processing unit to input the real-time temperature change signal of the skin surface, which can convert the transient response change value at the corresponding time point of the real-time temperature change signal of the skin surface into usable temperature signal fluctuation data, the data conversion process simplifies the complex change of transient response into standardized temperature data, the core of this step is to convert the original signal change value obtained by transient response analysis into temperature data consistent with actual measurement, so that the measurement result is more practical and accurate, this conversion can remove or reduce noise and error, so that the measurement data is more reliable, and accurate temperature change measurement results are provided for actual application.The accuracy and reliability of the measurement data can be ensured by acquiring external environmental temperature change data and dynamically calibrating and compensating the real-time temperature change measurement data on the skin surface. Changes in the external environment can interfere with temperature measurements on the skin surface, so this can reduce measurement errors caused by environmental changes and make skin surface temperature measurement data more accurate and consistent. This compensation method can effectively eliminate the influence of environmental factors on measurement results, making the data more accurately reflect the actual temperature state of the skin. Through dynamic calibration and compensation, the reliability and effectiveness of the wearable smart ring can be significantly improved, providing more accurate temperature monitoring and adjustment services for users, thereby improving the measurement accuracy and precision of the wearer's skin surface temperature. Then, by uploading the real-time skin surface temperature calibration data to the microcontroller unit of the wearable smart ring and using the microcontroller unit to mark temperature anomalies in the real-time skin surface temperature calibration data, temperature anomalies can be detected and identified in a timely manner. This step compares the real-time temperature data with the set abnormal calibration line to effectively mark temperature changes that exceed the normal range. This method helps quickly locate abnormal areas and triggers an alarm or takes appropriate measures when an anomaly occurs to prevent potential skin problems or health risks. Through precise anomaly marking, the response speed to temperature changes can be improved, and measures can be taken before the problem becomes serious, thereby improving the safety and reliability of the smart ring temperature monitoring process. By analyzing the temperature control target range of the skin surface monitoring temperature anomaly data, a more accurate temperature control target range can be developed. This step analyzes the abnormal distribution of skin surface monitoring temperature anomaly data to determine the appropriate temperature control range for effective intervention when the skin surface temperature is abnormal. Precise temperature control target range can help develop personalized temperature regulation strategies to ensure that temperature control devices operate within the normal range, thereby improving user comfort and safety. This detailed target range analysis can also optimize the response mechanism of the temperature control device to better adapt to different environments and user needs, thereby improving the overall skin temperature control process experience and effectiveness. Finally, by using the microcontroller unit to analyze the temperature control demand instruction response of the skin surface temperature anomaly temperature control target range, this analysis process involves a detailed interpretation of temperature control demands, including the type, degree, and duration of abnormal temperature. Based on this data, appropriate control strategies and instructions will be developed to guide the operation of the temperature regulation control device. For example, if the skin temperature is too high, the device will start the cooling mechanism, and vice versa. Through this process, the accuracy and effectiveness of temperature regulation operations can be ensured, ensuring that the skin always maintains an ideal temperature control range, thereby improving the comfort and health protection of the wearer.In addition, the skin surface temperature control demand response instruction is responded to the temperature adjustment control device of the wearable smart ring, and the temperature adjustment control device is used to perform temperature anomaly control feedback on the skin surface temperature anomaly temperature control target range. These control devices will adjust their working state according to the corresponding control instruction, such as starting the heating or cooling mechanism, to correct the temperature of the skin surface. During the adjustment process, the temperature adjustment control device will also monitor the temperature change in real time and generate temperature anomaly control feedback response signals. These feedback signals will be transmitted back to the wearable smart ring to ensure the accuracy and immediacy of the control operation. This can achieve active and accurate adjustment of the skin surface temperature, ensure that the temperature always remains within the predetermined comfortable range, and improve the experience and health safety of the wearer. The control strategy can also be continuously adjusted and optimized to ensure stable and reliable long-term temperature control effect. BRIEF DESCRIPTION OF DRAWINGS

[0057] Other features, objects, and advantages of the application will become more apparent from the following detailed description of non-limiting embodiments, read in conjunction with the accompanying drawings:

[0058] Fig. 1 is a schematic diagram of the steps of the skin temperature measurement and control method of the wearable smart ring of the present application;

[0059] Fig. 2 is a schematic diagram of the detailed steps of step S1 in Fig. 1;

[0060] Fig. 3 is a schematic diagram of the detailed steps of step S14 in Fig. 2. DETAILED DESCRIPTION

[0061] The technical method of the present application will be described in detail below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0062] In addition, the accompanying drawings are only schematic illustrations of the present application and are not necessarily drawn to scale. The same reference numerals in the drawings represent the same or similar parts, and thus repeated descriptions thereof will be omitted. Some of the block diagrams shown in the drawings are functional entities, which do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods.

[0063] It should be understood that, although the terms“first,”“second,” etc. can be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, a first element could be termed a second element, and, similarly, a second element could be termed a first element without departing from the scope of the example embodiments. The term“and / or” as used herein includes any and all combinations of one or more of the associated listed items.

[0064] To achieve the above object, please refer to FIG. 1 to FIG. 3, the present application provides a wearable smart ring skin temperature measurement and control method, the method comprises the following steps:

[0065] Step S1: Real-time acquisition of temperature change signals of the user's skin surface by the skin temperature sensor integrated inside the wearable smart ring to obtain real-time temperature change signals of the skin surface; Anti-interference sampling processing of the real-time temperature change signals of the skin surface by the high-precision change sampling circuit and the signal anti-interference circuit inside the wearable smart ring, and transmission to the internal processing unit of the wearable smart ring to obtain real-time temperature change input signals of the skin surface;

[0066] Step S2: Temperature signal fluctuation data conversion of the real-time temperature change input signals of the skin surface by the internal processing unit to obtain real-time temperature change measurement data of the skin surface; obtaining external environmental temperature change data, and temperature dynamic calibration compensation of the real-time temperature change measurement data of the skin surface based on the external environmental temperature change data to obtain real-time temperature calibration data of the skin surface;

[0067] Step S3: Uploading the real-time temperature calibration data of the skin surface to the microcontroller unit of the wearable smart ring, and using the microcontroller unit to mark the real-time temperature calibration data of the skin surface for temperature abnormality response to obtain skin surface monitoring temperature abnormality data; temperature control target range analysis of the skin surface monitoring temperature abnormality data to obtain a skin surface temperature abnormality temperature control target range;

[0068] Step S4: Temperature control demand instruction response analysis of the skin surface temperature abnormality temperature control target range by the microcontroller unit to generate a skin surface temperature control demand response instruction; responding to the temperature adjustment control device of the wearable smart ring with the skin surface temperature control demand response instruction, and using the temperature adjustment control device to perform temperature abnormality control feedback on the skin surface temperature abnormality temperature control target range to generate a skin surface temperature abnormality control feedback response signal to perform corresponding skin surface temperature abnormality control adjustment work.

[0069] In the embodiment of the present application, please refer to the step flow diagram of the skin temperature measurement and control method of the wearable smart ring shown in Figure 1. In this example, the skin temperature measurement and control method of the wearable smart ring includes the following steps:

[0070] Step S1: Real-time acquisition of temperature change signals of the user's skin surface by the skin temperature sensor integrated in the wearable smart ring to obtain the real-time temperature change signals of the skin surface; anti-interference sampling processing of the real-time temperature change signals of the skin surface by the high-precision change sampling circuit and the signal anti-interference circuit in the wearable smart ring, and transmission to the internal processing unit of the wearable smart ring to obtain the real-time temperature change input signals of the skin surface.

[0071] In the embodiment of the present application, the skin temperature sensor integrated in the wearable smart ring is used to real-time acquisition of temperature change signals of the user's skin surface to convert the thermal signals of the skin surface into electrical signals, and through accurate calibration and layout, the sensor can be attached to the skin to reduce environmental interference, and the real-time acquisition of temperature change signals is output in the form of stable voltage change, thereby obtaining the real-time temperature change signals of the skin surface. At the same time, the high-precision change circuit integrated in the wearable smart ring is used to sample and process the previously monitored real-time temperature change signals of the skin surface, which includes a low-noise amplifier and a high-resolution analog-to-digital converter. First, the low-noise amplifier amplifies the weak electrical signals from the skin temperature sensor, and then the high-resolution analog-to-digital converter converts the analog signals into digital signals, ensuring that the acquired temperature change signals have high precision and high resolution. The signal anti-interference circuit integrated in the wearable smart ring is used to optimize the previously filtered high-precision temperature change signals to further process the high-precision filtered temperature change signals and enhance the stability of the signals. The circuit design includes shielding measures, grounding techniques and signal conditioning components, and uses differential amplifiers and noise suppression techniques to reduce the influence of external interference on the signals. The purpose of this circuit processing is to ensure that the temperature change signals are not affected by electromagnetic interference and other factors during transmission. The previously filtered real-time temperature change signals of the skin surface are transmitted to the internal processing unit of the wearable smart ring using wireless transmission technology. After the internal processing unit receives these signals, it further decodes and analyzes the signals, thereby presenting the signals to the user or other systems in a readable format, and ensuring the integrity and accuracy of the data during the transmission of the signals from the skin temperature sensor to the content processing unit. Finally, the real-time temperature change input signals of the skin surface are obtained.

[0072] Step S2: converting the skin surface real-time temperature change input signal into temperature signal fluctuation data by the internal processing unit, obtaining external environment temperature change data, and performing temperature dynamic calibration compensation on the skin surface real-time temperature change measurement data based on the external environment temperature change data to obtain skin surface real-time temperature calibration data;

[0073] In the embodiment of the present application, the signal processing module of the internal processing unit uses the fast Fourier transform (FFT) algorithm to perform frequency spectrum mapping conversion on the previously input skin surface real-time temperature change input signal to convert the input skin surface real-time temperature change input signal into frequency spectrum form, which can reveal the frequency components in the temperature change signal and calculate statistical indicators such as standard deviation and root mean square value of the temperature change signal spectrum to evaluate and analyze the fluctuation amplitude of the temperature change signal. At the same time, the sliding window technique is used to sample and record the data at each time sequence point, and the temperature signal fluctuation amplitude at each time sequence point is accurately captured. By using frequency domain analysis methods such as fast Fourier transform (FFT) or wavelet transform, the fluctuation amplitude of the skin surface temperature signal at each time sequence point obtained by previous analysis is statistically analyzed to convert time series data into frequency spectrum data, and the fluctuation amplitude in the frequency spectrum data is analyzed to identify the characteristics of transient response such as temperature peak value, periodic fluctuation, etc. By using statistical analysis tools such as mean, variance, and dynamic time warping (DTW) algorithm, the fluctuation amplitude of the skin surface temperature signal at each time sequence point is analyzed to identify the change pattern, so as to identify the change trend and pattern of the fluctuation amplitude by comparing the fluctuation changes at different time points. By combining the previously analyzed signal fluctuation amplitude change pattern, the fluctuation amplitude transient response characteristics at each time sequence point are dynamically corrected to apply the change pattern to the transient response characteristic data, adjust the offset or noise in the response characteristic data, and use the following calculation formula to quantitatively calculate the transient response change value of the temperature signal transient response characteristic correction data at each time sequence point after dynamic correction, wherein the calculation formula can be: where ΔU i is the fluctuation transient response change value of the skin surface temperature signal at time sequence point t i , t i is the current time sequence point, and t i-1 is the previous time point, s is the integral time variable parameter of the transient response change value quantization calculation, U(s) is the temperature signal transient response characteristic correction data at time point s, δ1 is the influence coefficient of the first-order derivative of the temperature transient response on the transient change value, δ2 is the influence coefficient of the second-order derivative of the temperature transient response on the transient change value, ∈ is the correction coefficient, to quantify the change of the transient response, and the transient response change value at each time point is converted into standardized temperature measurement data according to the time sequence. The conversion process ensures that the temperature change data accurately reflects the actual temperature change of the skin surface, so as to obtain the skin surface real-time temperature change measurement data. Then, the temperature data of the environment around the wearer of the wearable smart ring is collected through a corresponding environmental temperature sensor (such as a high-precision digital thermometer), so as to obtain the external environment temperature change data. The skin surface real-time temperature change measurement data corresponding to the same time sequence dimension is also dynamically calibrated and compensated for temperature by combining the previously collected external environment temperature change data, so as to compare and adjust the environmental temperature change with the skin surface measurement data, adjust the temperature measurement value at each time point, and eliminate the influence of environmental factors on the measurement data. For example, by using the formula: calibrated temperature = real-time temperature + calibration compensation coefficient, each time point is corrected to ensure that the skin surface temperature data at each time point is closer to the true value after compensation, and finally the skin surface real-time temperature calibration data is obtained.

[0074] Step S3: uploading the skin surface real-time temperature calibration data to the microcontroller unit of the wearable smart ring, and using the microcontroller unit to mark the skin surface real-time temperature calibration data for temperature abnormal response to obtain skin surface monitoring temperature abnormal data; analyzing the skin surface monitoring temperature abnormal data for temperature control target range to obtain the skin surface temperature abnormal temperature control target range;

[0075] In the embodiment of the present application, the skin surface real-time temperature calibration data obtained after previous calibration compensation is uploaded to the microcontroller unit of the wearable smart ring by using wireless transmission technology (such as Bluetooth or wireless local area network), and after the microcontroller unit receives the skin surface real-time temperature calibration data, it visualizes the received skin surface real-time temperature calibration data by using the built-in data visualization algorithm, including arranging the temperature calibration data in chronological order and calculating the temperature change amplitude at each time point, thereby drawing the temperature calibration change curve, and using the pre-set temperature anomaly calibration line as the reference standard to mark the temperature anomaly on the previously drawn temperature calibration change curve, wherein the calibration line represents the upper and lower limits of the normal temperature range, for example, if the normal range is 36.5℃ to 37.5℃, the calibration line will be set in the region of these two temperature values, the microcontroller unit compares the real-time temperature curve with these calibration lines, identifies all temperature points that exceed the calibration line range, and marks these over-standard temperature points as temperature anomalies, thereby obtaining skin surface monitoring temperature anomaly data. Then, by analyzing the previously marked skin surface monitoring temperature anomaly data in detail, the specific operation includes classifying and counting all abnormal data points according to the frequency of occurrence and temperature range, drawing an abnormal data distribution graph, and using statistical methods such as standard deviation and coefficient of variation to evaluate the distribution of abnormal data during the analysis process. In addition, the corresponding skin surface monitoring temperature anomaly data is statistically analyzed to determine the temperature control target range by combining the temperature anomaly distribution values obtained from the previous analysis, so as to calculate the actual deviation range of the skin surface temperature using the temperature anomaly distribution values, for example, by combining the pre-set normal temperature range (such as 36.5℃ to 37.5℃), a suitable temperature control target range is determined (if the corresponding abnormal distribution value is 36℃, the temperature control target range is +0.5℃ to +1.5℃; if the corresponding abnormal distribution value is 38℃, the temperature control target range is -1.5℃ to -0.5℃), which takes into account the degree of temperature anomaly to ensure effective adjustment and control of the skin surface temperature in actual application. Finally, the skin surface temperature anomaly control target range is obtained, which will be used to guide the actual temperature adjustment operation to maintain the temperature of the skin surface within a safe range.

[0076] Step S4: using the microcontroller unit to analyze the temperature control demand instruction response of the skin surface temperature anomaly control target range to generate a skin surface temperature control demand response instruction; responding to the temperature adjustment control device of the wearable smart ring with the skin surface temperature control demand response instruction, and using the temperature adjustment control device to perform temperature anomaly control feedback on the skin surface temperature anomaly control target range to generate a skin surface temperature anomaly control feedback response signal to perform corresponding skin surface temperature anomaly control adjustment operation.

[0077] In the embodiment of the present application, the statistical analysis of the temperature control demand of the previously analyzed skin surface temperature abnormality temperature control target range is performed by using the microcontroller unit, the previously analyzed skin surface temperature abnormality temperature control target range is analyzed and judged by using the embedded control algorithm, and the corresponding temperature control demand result is obtained, that is, if the skin surface temperature abnormality temperature control target range is +0.5°C or -0.5°C, the temperature control demand analysis is determined as heating 0.5°C or cooling 0.5°C, and the corresponding skin surface temperature abnormality temperature control demand is responded by using the preset algorithm (such as PID control algorithm) in the microcontroller unit to generate the corresponding temperature control demand instruction (including heating control response instruction or cooling control response instruction), for example, in the analysis process, the temperature control demand is converted into specific control instructions by considering the accuracy of temperature adjustment, reaction time and system capacity, which includes the amplitude, duration and feedback adjustment mechanism in the adjustment process, so as to respond to generate the skin surface temperature control demand response instruction, which includes detailed operation steps and execution parameters, then the previously analyzed skin surface temperature control demand response instruction is applied to the temperature adjustment control device of the wearable smart ring, and the temperature adjustment control device of the wearable smart ring responds to the temperature control demand response instruction and accurately controls the built-in heating element or refrigeration device according to the temperature control demand response instruction, for example, if the temperature control demand instruction requires to increase the skin surface temperature, the control device will activate the heating element, control the power of the heater by current to gradually increase the temperature, on the contrary, if the temperature needs to be reduced, the refrigeration device is started, and the working state of the cooling system is adjusted, the temperature adjustment control device monitors the temperature change after adjustment in real time and feeds back the temperature adjustment effect to the temperature adjustment control device, based on the feedback information, the temperature adjustment control device responds to generate the skin surface temperature abnormality control feedback response signal to indicate whether the expected temperature control effect is achieved, and then performs necessary fine tuning or maintains the status quo, so as to perform the corresponding skin surface temperature abnormality control adjustment operation.

[0078] Further, step S1 includes the following steps:

[0079] Step S11: The temperature change signal of the user's skin surface is collected in real time by the skin temperature sensor integrated in the wearable smart ring to obtain the real-time temperature change signal of the skin surface;

[0080] Step S12: The real-time temperature change signal of the skin surface is processed by high-precision signal sampling by the high-precision change circuit in the wearable smart ring to obtain the real-time temperature high-precision change signal of the skin surface;

[0081] Step S13: high-frequency noise optimization filtering is performed on the real-time temperature change signal of the skin surface to obtain a high-frequency filtered signal of the real-time temperature change of the skin surface.

[0082] Step S14: the high-frequency filtered signal of the real-time temperature change of the skin surface is subjected to anti-interference processing by a signal anti-interference circuit in the wearable smart ring to obtain an anti-interference signal of the real-time temperature change of the skin surface.

[0083] Step S15: the anti-interference signal of the real-time temperature change of the skin surface is transmitted to an internal processing unit of the wearable smart ring to obtain a real-time temperature change input signal of the skin surface.

[0084] As an embodiment of the present application, referring to FIG. 2, a detailed step flow diagram of step S1 in FIG. 1 is shown. In this embodiment, step S1 includes the following steps:

[0085] Step S11: a skin temperature sensor integrated in the wearable smart ring is used to collect a temperature change signal of the skin surface of a user in real time to obtain a real-time temperature change signal of the skin surface.

[0086] In this embodiment, a skin temperature sensor integrated in the wearable smart ring (the sensor is selected to be a thermocouple type with high sensitivity and fast response) is used to collect a temperature change signal of the skin surface of a user in real time to convert a thermal signal of the skin surface into an electrical signal. Through accurate calibration and layout, the sensor can be ensured to be attached to the skin to reduce environmental interference. The temperature change signal collected in real time is output in the form of stable voltage change, and finally a real-time temperature change signal of the skin surface is obtained.

[0087] Step S12: a high-precision change circuit in the wearable smart ring is used to perform high-precision signal sampling processing on the real-time temperature change signal of the skin surface to obtain a real-time temperature high-precision change signal of the skin surface.

[0088] In this embodiment, a high-precision change circuit integrated in the wearable smart ring is used to perform high-precision signal sampling processing on the real-time temperature change signal of the skin surface obtained in real time. The circuit includes a low-noise amplifier and a high-resolution analog-to-digital converter. First, the low-noise amplifier amplifies the weak electrical signal from the skin temperature sensor. Then, the high-resolution analog-to-digital converter converts the analog signal into a digital signal, ensuring that the obtained temperature change signal has high precision and high resolution. Finally, a real-time temperature high-precision change signal of the skin surface is obtained.

[0089] Step S13: high-frequency noise optimization filtering is performed on the real-time temperature high-precision change signal of the skin surface to obtain a high-frequency filtered signal of the real-time temperature change of the skin surface.

[0090] In the embodiment of the present application, the high-frequency noise in the real-time skin surface temperature change signal obtained after high-precision sampling is optimized and filtered by using a digital filter (such as a FIR or IIR filter), the cutoff frequency and filter coefficients of the filter are set to remove noise components higher than a predetermined frequency and leave the real temperature change signal, the setting of the filter should consider the bandwidth and noise characteristics of the signal to ensure that the high-frequency noise is effectively removed while maintaining the integrity of the signal, and finally a high-frequency filtered real-time skin surface temperature change signal is obtained.

[0091] Step S14: Anti-interference processing of the high-frequency filtered real-time skin surface temperature change signal by the signal anti-interference circuit in the wearable smart ring to obtain an anti-interference real-time skin surface temperature change signal.

[0092] In the embodiment of the present application, the high-frequency filtered real-time skin surface temperature change signal obtained after high-frequency filtering is further processed and the stability of the signal is enhanced by using the signal anti-interference circuit integrated in the wearable smart ring for anti-interference optimization, the circuit design includes shielding measures, grounding techniques and signal conditioning components, differential amplifiers and noise suppression techniques are used to reduce the influence of external interference on the signal, the purpose of this circuit processing is to ensure that the temperature change signal is not affected by electromagnetic interference and other factors during transmission, and finally an anti-interference real-time skin surface temperature change signal is obtained.

[0093] Step S15: Transmission of the anti-interference real-time skin surface temperature change signal to the internal processing unit of the wearable smart ring to obtain a real-time skin surface temperature change input signal.

[0094] In the embodiment of the present application, the anti-interference real-time skin surface temperature change signal obtained after anti-interference is transmitted to the internal processing unit of the wearable smart ring by using wireless transmission technology, and after the internal processing unit receives these signals, the signals are further decoded and analyzed, so that the signals are presented to the user or other systems in a readable format, and the data integrity and accuracy of the signals are ensured during the transmission from the skin temperature sensor to the content processing unit, and finally a real-time skin surface temperature change input signal is obtained.

[0095] Further, step S14 includes the following steps:

[0096] Step S141: Circuit multi-stage arrangement design optimization of the signal anti-interference circuit in the wearable smart ring to generate a smart ring signal anti-interference multi-stage design circuit.

[0097] Step S142: The skin surface real-time temperature change high-frequency filtered signal is subjected to multi-stage signal isolation processing by the smart ring signal anti-interference multi-stage design circuit, to obtain a skin surface real-time temperature change interference multi-stage isolation signal;

[0098] Step S143: The skin surface real-time temperature change interference multi-stage isolation signal is subjected to adaptive filtering integration, to obtain a skin surface real-time temperature change interference filtered signal;

[0099] Step S144: The signal interference mode of the skin temperature sensor integrated in the wearable smart ring is analyzed, to obtain a smart ring temperature signal acquisition interference mode;

[0100] Step S145: The skin surface real-time temperature change interference filtered signal is subjected to interference mode dynamic correction based on the smart ring temperature signal acquisition interference mode, to obtain a skin surface real-time temperature change anti-interference signal.

[0101] As an embodiment of the present application, referring to FIG. 3, which is a detailed step flow diagram of step S14 in FIG. 2, step S14 in the present embodiment includes the following steps:

[0102] Step S141: The signal anti-interference circuit in the wearable smart ring is subjected to multi-stage arrangement design optimization, to generate a smart ring signal anti-interference multi-stage design circuit;

[0103] In the embodiment of the application, by using a circuit design tool (such as Altium Designer or Cadence) to model the signal anti-interference circuit inside the wearable smart ring in detail, the signal path, impedance matching and grounding situation are analyzed according to the circuit diagram, and the circuit structure affecting the signal stability is identified, including evaluating the layout of each circuit module and the connection mode between them, and the positioning of the interference source of the corresponding signal anti-interference circuit inside the wearable smart ring is realized, and by using an electromagnetic field simulation tool (such as ANSYS HFSS or CST Studio) to simulate the electromagnetic interference of the signal anti-interference circuit, the position of the interference source is confirmed, at the same time, by combining the previously determined interference source positioning position, the circuit simulation tool and the design software (such as Mentor Graphics PADS or Siemens Xpedition) are used to analyze the planning of the isolated region of the corresponding signal anti-interference circuit inside the wearable smart ring, to evaluate the interference influence of the interference source and other circuit parts, and to identify the region to be isolated, including the sensitive circuit and signal path near the interference source, to mark the region to be isolated, and by combining the previously planned isolated region, the arrangement design of the multi-stage signal isolator of the corresponding signal anti-interference circuit inside the wearable smart ring is carried out, to design the configuration of the multi-stage signal isolator in the isolated region in the signal anti-interference circuit, including the use of capacitive isolation, magnetic isolator and shielding material, and by using the circuit design tool, the layout of the isolator is designed and optimized according to the requirements of the planning region, the appropriate isolator type and parameters are selected to ensure the best isolation effect, and the simulation test of the design is carried out to verify the effectiveness of the isolator layout, to adjust the design to optimize the signal integrity, and finally the smart ring signal anti-interference multi-stage design circuit is designed.

[0104] Step S142: multi-stage signal isolation processing of the skin surface real-time temperature change high-frequency filtered signal is carried out by the smart ring signal anti-interference multi-stage design circuit to obtain the skin surface real-time temperature change interference multi-stage isolation signal.

[0105] In the embodiment of the application, by using the previously designed smart ring signal anti-interference multi-stage design circuit, the multi-stage signal isolation processing of the corresponding transmitted skin surface real-time temperature change high-frequency filtered signal is carried out, to preliminarily isolate the signal by using the designed anti-interference circuit, which involves frequency separation of the signal by a series of cascaded filters, each filter is optimized for different frequency ranges, in this process, the appropriate filter order and cutoff frequency are selected to ensure that each filter can effectively isolate the interference components in the skin temperature signal, and the signals processed by the multi-stage filter are collected, and finally the skin surface real-time temperature change interference multi-stage isolation signal is obtained.

[0106] Step S143: The skin surface real-time temperature change interference multi-stage isolation signal is adaptively filtered and integrated to obtain a skin surface real-time temperature change interference filtered signal;

[0107] In the embodiment of the present application, the skin surface real-time temperature change interference multi-stage isolation signal after the previous multi-stage signal isolation is adaptively filtered and integrated to adopt an adaptive filtering algorithm such as the LMS (Least Mean Squares) algorithm, a reference signal model is established to predict the interference components, and the weight coefficients of the adaptive filter are adjusted to minimize the prediction error. The real-time processor is used to iteratively calculate the filtering algorithm, and finally the skin surface real-time temperature change interference filtered signal is obtained.

[0108] Step S144: The signal interference mode of the skin temperature sensor integrated in the wearable smart ring is identified and analyzed to obtain a smart ring temperature signal acquisition interference mode.

[0109] In the embodiment of the present application, the signal interference mode of the skin temperature sensor integrated in the wearable smart ring is identified and analyzed to identify the interference mode by analyzing the original temperature signal output by the skin temperature sensor and using data mining techniques and pattern recognition algorithms such as K-means clustering or principal component analysis. First, the signal data of the skin temperature sensor under different environmental conditions is collected, and these data are feature extracted and analyzed to identify specific interference modes such as periodic interference or random noise. Statistical analysis tools such as MATLAB or the SciPy library in Python are used to process the data, and finally the smart ring temperature signal acquisition interference mode is identified.

[0110] Step S145: Based on the smart ring temperature signal acquisition interference mode, the skin surface real-time temperature change interference filtered signal is dynamically corrected for interference mode to obtain a skin surface real-time temperature change anti-interference signal.

[0111] In the embodiment of the present application, the corresponding skin surface real-time temperature change interference filtered signal is dynamically corrected for interference mode by combining the previously analyzed smart ring temperature signal acquisition interference mode to correct the skin surface real-time temperature change signal using the identified interference mode. First, a dynamic filtering algorithm such as the RLS (Recursive Least Squares) algorithm in the adaptive filter is applied to adjust the signal in real time, and the parameters of the filter are adjusted to match the identified interference mode, thereby reducing the influence of the interference signal on the temperature measurement result. This correction process also involves real-time monitoring and adjustment to ensure that the interference is effectively suppressed under different environmental conditions, and finally the skin surface real-time temperature change anti-interference signal is obtained.

[0112] Further, step S141 comprises the following steps:

[0113] The circuit topology of the signal anti-interference circuit in the wearable smart ring is analyzed to obtain a signal anti-interference circuit topology design structure.

[0114] In the embodiment of the present application, the signal anti-interference circuit inside the wearable smart ring is modeled in detail by using a circuit design tool (such as Altium Designer or Cadence), to analyze the signal path, impedance matching and grounding according to the circuit diagram, and identify the circuit structure affecting the signal stability, including evaluating the layout of each circuit module and the connection mode between them, confirming the mutual relationship of the signal line and the power line, and finally obtaining the signal anti-interference circuit topology design structure.

[0115] Preferably, the signal anti-interference circuit inside the wearable smart ring is positioned based on the signal anti-interference circuit topology design structure to obtain a signal anti-interference circuit interference source positioning position.

[0116] In the embodiment of the present application, the interference source of the corresponding signal anti-interference circuit inside the wearable smart ring is positioned by combining the signal anti-interference circuit topology design structure obtained from the previous topology analysis, to perform electromagnetic interference simulation on the signal anti-interference circuit by using an electromagnetic field simulation tool (such as ANSYS HFSS or CST Studio), and identify the position and strength of the interference source from it, while setting sensors or probes at different positions of the circuit to perform actual measurement to verify the simulation results and confirm the position of the interference source, and finally obtain the signal anti-interference circuit interference source positioning position.

[0117] Preferably, the signal anti-interference circuit inside the wearable smart ring is planned for isolation analysis based on the signal anti-interference circuit interference source positioning position to obtain a signal anti-interference circuit isolation planning area.

[0118] In the embodiment of the present application, the signal anti-interference circuit inside the wearable smart ring is planned for isolation analysis by using a circuit simulation tool and design software (such as Mentor Graphics PADS or Siemens Xpedition) in combination with the previously determined signal anti-interference circuit interference source positioning position, to evaluate the interference influence of the interference source and other circuit parts, and identify the area that needs to be isolated from it, including sensitive circuits and signal paths near the interference source, formulate an isolation scheme, mark the area that needs to be isolated, and plan appropriate isolation distance and shielding material, which ensures effective isolation between the interference source and other circuit parts, and finally obtains the signal anti-interference circuit isolation planning area.

[0119] Preferably, the signal anti-interference circuit in the wearable smart ring is arranged and designed with multiple signal isolators based on the signal anti-interference circuit to be isolated planning area, to generate a smart ring signal anti-interference multi-level design circuit.

[0120] In the embodiment of the present application, the corresponding signal anti-interference circuit in the wearable smart ring is arranged and designed with multiple signal isolators by combining the previously planned signal anti-interference circuit to be isolated planning area, to design and configure the multiple signal isolators in the signal anti-interference circuit in the to-be-isolated planning area, including the use of capacitive isolation, magnetic isolator and shielding material, and by using circuit design tools, the layout of the isolator is designed and optimized according to the requirements of the planning area, the appropriate isolator type and parameters are selected to ensure the best isolation effect, and the simulation test of the design is realized to verify the effectiveness of the isolator layout, to adjust the design to optimize the signal integrity, and finally a smart ring signal anti-interference multi-level design circuit is designed.

[0121] Further, step S2 comprises the following steps:

[0122] Step S21: converting the real-time temperature change signal of the skin surface into a frequency spectrum by the internal processing unit;

[0123] In the embodiment of the present application, the signal processing module of the internal processing unit uses the fast Fourier transform (FFT) algorithm to convert the previously input real-time temperature change signal of the skin surface into a frequency spectrum, to convert the input real-time temperature change signal of the skin surface into a frequency spectrum form, which can reveal the frequency components in the temperature change signal, and the internal processing unit applies advanced filters and spectrum analysis algorithms to ensure the accuracy of the spectrum data and exclude the influence of noise, to finally obtain the real-time temperature change signal frequency spectrum of the skin surface.

[0124] Step S22: performing statistical analysis on the temperature signal fluctuation amplitude of the real-time temperature change signal frequency spectrum of the skin surface to obtain the real-time temperature signal fluctuation amplitude of the skin surface;

[0125] In the embodiment of the present application, the statistical analysis tool (such as the statistical toolbox in MATLAB) is used to perform statistical analysis on the signal fluctuation amplitude of the previously converted real-time temperature change signal frequency spectrum of the skin surface, to calculate the standard deviation, root mean square value and other statistical indicators of the temperature change signal frequency spectrum data, to evaluate and analyze the fluctuation amplitude of the temperature change signal, which can identify the main fluctuation trend and abnormal change in the signal, and finally obtain the real-time temperature signal fluctuation amplitude of the skin surface.

[0126] Step S23: dividing the skin surface real-time temperature signal fluctuation amplitude at each time sequence point into a time sequence point amplitude to obtain the skin surface temperature signal fluctuation amplitude at each time sequence point;

[0127] In the embodiment of the present application, the skin surface real-time temperature signal fluctuation amplitude obtained by previous analysis is divided into time sequence points by setting a corresponding time sequence window (for example, 1s), so as to sample and record the data at each time sequence point by using the sliding window technology, and ensure that the temperature signal fluctuation amplitude at each time sequence point is accurately captured, and finally obtain the skin surface temperature signal fluctuation amplitude at each time sequence point.

[0128] Step S24: performing transient response statistical analysis on the skin surface temperature signal fluctuation amplitude at each time sequence point to obtain the skin surface temperature signal fluctuation transient response change value at each time sequence point; and performing temperature signal fluctuation data conversion on the skin surface temperature signal fluctuation transient response change value at each time sequence point to obtain skin surface real-time temperature change measurement data;

[0129] In the embodiment of the present application, by using a frequency domain analysis method, such as fast Fourier transform (FFT) or wavelet transform, the transient response characteristics of the skin surface temperature signal fluctuation amplitude at each time sequence point obtained by previous analysis are statistically analyzed, so as to convert the time series data into frequency spectrum data, and identify the characteristics of the transient response, such as temperature peak value, periodic fluctuation, etc., by analyzing the fluctuation amplitude in the frequency spectrum data, and by using statistical analysis tools such as mean, variance and dynamic time warping (DTW) algorithm to identify and analyze the change mode of the skin surface temperature signal fluctuation amplitude at each time sequence point, so as to identify the change trend and mode of the fluctuation amplitude by comparing the fluctuation changes at different time points, which include the increase and decrease mode of the temperature fluctuation, periodic change and other change modes, and at the same time, the fluctuation amplitude transient response characteristic data at each time sequence point are dynamically corrected by combining the change mode of the signal fluctuation amplitude obtained by previous analysis, so as to apply the change mode to the transient response characteristic data, adjust the offset or noise in the response characteristic data, and by using the following calculation formula to quantitatively calculate the transient response change value of the temperature signal transient response characteristic correction data at each time sequence point obtained by previous dynamic correction, so as to quantify the change amount of the transient response, wherein the calculation formula can be: ΔU i is the skin surface temperature signal fluctuation transient response change value at the time sequence point t i , t i is the current time sequence point, t i-1 is the previous time point, s is the integral time variable parameter of the transient response change value quantization calculation, U(s) is the temperature signal transient response characteristic correction data at the time point s, δ1 is the influence coefficient of the first-order derivative of the temperature transient response on the transient change value, δ2 is the influence coefficient of the second-order derivative of the temperature transient response on the transient change value, and ∈ is a correction coefficient, so as to quantitatively calculate the skin surface temperature signal fluctuation transient response change value at each time point. Then, by using a numerical conversion algorithm (such as interpolation and data smoothing method), the transient response change value at each time point is converted into standardized temperature measurement data according to the time sequence, and the conversion process ensures that the temperature change data accurately reflects the actual temperature change of the skin surface, and finally the skin surface real-time temperature change measurement data is obtained.

[0130] Step S25: Obtain external environment temperature change data, and perform temperature dynamic calibration compensation on the skin surface real-time temperature change measurement data based on the external environment temperature change data to obtain skin surface real-time temperature calibration data.

[0131] In the embodiment of the present application, the temperature data of the environment around the wearer of the wearable smart ring is collected by a corresponding environment temperature sensor (such as a high-precision digital thermometer), thereby obtaining the external environment temperature change data. At the same time, the skin surface real-time temperature change measurement data corresponding to the same time sequence dimension is dynamically calibrated and compensated by combining the previously collected external environment temperature change data, so as to compare and adjust the environmental temperature change with the measurement data of the skin surface, adjust the temperature measurement value at each time point, and eliminate the influence of environmental factors on the measurement data. For example, by using the formula: calibrated temperature = real-time temperature + calibration compensation coefficient, each time point is corrected to ensure that the skin surface temperature data at each time point is closer to the true value after compensation, and finally the skin surface real-time temperature calibration data is obtained.

[0132] Further, the transient response statistical analysis of the skin surface temperature signal fluctuation amplitude at each time point in step S24 includes the following steps:

[0133] The fluctuation amplitude transient response characteristic analysis of the skin surface temperature signal fluctuation amplitude at each time point is performed to obtain the temperature signal fluctuation amplitude transient response characteristic data at each time point.

[0134] In the embodiments of the present application, the fluctuation amplitude of the skin surface temperature signal at each time point is analyzed for transient response characteristics by using a frequency domain analysis method, such as fast Fourier transform (FFT) or wavelet transform, to convert the time series data into frequency spectrum data, and the fluctuation amplitude in the frequency spectrum data is analyzed to identify the characteristics of the transient response, such as temperature peak, periodic fluctuation, etc., and finally the transient response characteristic data of the temperature signal fluctuation amplitude at each time point is obtained.

[0135] Preferably, the fluctuation amplitude of the skin surface temperature signal at each time point is analyzed for fluctuation amplitude change pattern to obtain the temperature signal fluctuation amplitude change pattern at each time point.

[0136] In the embodiments of the present application, the fluctuation amplitude of the skin surface temperature signal at each time point is analyzed for change pattern by using statistical analysis tools such as mean, variance, and dynamic time warping (DTW) algorithm, to identify the change trend and pattern of the fluctuation amplitude by comparing the fluctuation changes at different time points, and the analysis results include the increase / decrease pattern, periodic change, etc. of the temperature fluctuation, and finally the temperature signal fluctuation amplitude change pattern at each time point is obtained, which reflects the change law of the temperature signal over time.

[0137] Preferably, the transient response characteristic data of the temperature signal fluctuation amplitude at each time point is dynamically corrected based on the temperature signal fluctuation amplitude change pattern at each time point to obtain the transient response characteristic correction data of the temperature signal at each time point.

[0138] In the embodiments of the present application, the transient response characteristic data of the temperature signal fluctuation amplitude at each time point is dynamically corrected by combining the temperature signal fluctuation amplitude change pattern at each time point obtained from the previous analysis, to apply the change pattern to the transient response characteristic data, and the deviation in the characteristic data is adjusted by a correction algorithm, for example, the least squares method or Kalman filter can be used to dynamically correct the characteristic data, and in the correction process, the fluctuation amplitude change pattern of the temperature signal is matched with the transient response characteristic data to adjust the offset or noise in the response data, ensuring the accuracy of the data, and finally the transient response characteristic correction data of the temperature signal at each time point is obtained.

[0139] Preferably, the transient response characteristic correction data of the temperature signal at each time point is quantitatively calculated for transient response change value to obtain the skin surface temperature signal fluctuation transient response change value at each time point.

[0140] In the embodiment of the present application, the temperature signal transient response characteristic correction data at each time point after dynamic correction is quantitatively calculated for the transient response change value, which includes differential analysis or integral calculation of the temperature transient response data at each time point to quantify the change of the transient response, for example, using a differential algorithm to calculate the temperature change at adjacent time points, or evaluating the overall response change by integral method, wherein the calculation formula can be: ΔU (s) = U (t) - U (t-1) i is the fluctuation transient response change value of the skin surface temperature signal at the time point t i , t i is the current time point, t i -1 is the previous time point, s is the integral time variable parameter of the transient response change value quantification calculation, U (s) is the temperature signal transient response characteristic correction data at the time point s, δ1 is the influence coefficient of the first order derivative of the temperature transient response on the transient change value, δ2 is the influence coefficient of the second order derivative of the temperature transient response on the transient change value, and ∈ is the correction coefficient. The final quantification calculation obtains the fluctuation transient response change value of the skin surface temperature signal at each time point.

[0141] Further, step S25 includes the following steps:

[0142] Step S251: acquiring external environment temperature change data;

[0143] In the embodiment of the present application, the temperature data of the environment around the wearer of the wearable smart ring is collected by using a corresponding environment temperature sensor (for example, a high-precision digital thermometer), and the temperature data is stored in the smart ring system database in the form of time sequence. The temperature sensor needs to have high sampling rate and high precision to ensure that it can capture subtle temperature changes. These temperature data are arranged according to time stamp and stored in the database, and finally the external environment temperature change data is obtained.

[0144] Step S252: performing time sequence synchronization processing on the external environment temperature change data and the real-time skin surface temperature change measurement data to obtain external environment temperature change time sequence synchronization data and skin surface temperature change time sequence synchronization data in the same time sequence dimension;

[0145] In the embodiment of the present application, the received external environment temperature change data and the skin surface real-time temperature change measurement data are synchronized and aligned in time sequence by using a preset time sequence synchronization algorithm (such as dynamic time warping (DTW) or interpolation algorithm) in the internal processing unit, to ensure that the two sets of data have the same time point, and the temperature change value at the missing time point is determined by calculating the average of the temperatures at the adjacent two time points, and finally the external environment temperature change time sequence synchronization data and the skin surface temperature change time sequence synchronization data in the same time sequence dimension are obtained.

[0146] Step S253: performing time sequence point alignment processing on the external environment temperature change time sequence synchronization data and the skin surface temperature change time sequence synchronization data in the same time sequence dimension, to obtain the external environment temperature data and the skin surface real-time temperature data at the same temperature change time sequence point;

[0147] In the embodiment of the present application, the external environment temperature change time sequence synchronization data and the skin surface temperature change time sequence synchronization data in the same time sequence dimension obtained after the time sequence synchronization are aligned and matched at the same time sequence point by using a timestamp matching method, to find the corresponding data of each temperature change time sequence point by comparing the timestamps of the two data sets, for example, if the timestamps of the external environment temperature data and the skin surface temperature data are recorded once per second, it is ensured that the temperature data at each time point can be correctly matched, thereby forming the temperature data at the same temperature change time sequence point, ensuring that the two sets of temperature data have accurate corresponding relationship at the same time point, and finally obtaining the external environment temperature data and the skin surface real-time temperature data at the same temperature change time sequence point.

[0148] Step S254: performing temperature calibration compensation calculation on the corresponding skin surface real-time temperature data based on the external environment temperature data at the same temperature change time sequence point by using a temperature calibration compensation calculation formula, to obtain the skin surface temperature calibration compensation coefficient at each temperature change time sequence point.

[0149] In the embodiment of the present application, a suitable temperature calibration compensation calculation formula is constructed by combining the time variable parameter of the temperature change time sequence point, the integral time variable parameter, the skin surface real-time temperature, the skin surface external environment temperature, the skin surface internal and external temperature difference scaling coefficient, the time decay speed, the skin surface internal and external temperature difference influence coefficient and related parameters, to quantitatively calculate the temperature calibration compensation coefficient at each temperature change time sequence point by performing temperature calibration compensation calculation on the corresponding skin surface real-time temperature data, to correct the skin surface temperature measurement value, and finally obtain the skin surface temperature calibration compensation coefficient at each temperature change time sequence point.

[0150] Step S255: temperature dynamic calibration compensation is performed on the corresponding real-time skin surface temperature data according to the skin surface temperature calibration compensation coefficient at each temperature change time point, to obtain real-time skin surface temperature calibration data.

[0151] In the embodiment of the present application, the temperature dynamic calibration compensation is performed on the corresponding real-time skin surface temperature data according to the skin surface temperature calibration compensation coefficient at each temperature change time point obtained after the previous temperature calibration compensation. The specific operation includes applying the calibration compensation coefficient to the actual temperature data, adjusting the temperature measurement value at each time point, for example, correcting each measurement point by using the formula: calibrated temperature = real-time temperature + calibration compensation coefficient. This process ensures that the skin surface temperature data at each time point is closer to the true value after compensation, and finally obtains the real-time skin surface temperature calibration data.

[0152] Further, the temperature calibration compensation calculation formula in step S254 is specifically:

[0153] In the formula, δ(t) is the skin surface temperature calibration compensation coefficient at the temperature change time point t, t is the time variable parameter of the temperature change time point, τ is the integral time variable parameter, T s (t) is the real-time skin surface temperature at the temperature change time point t, T e (t) is the skin surface external environment temperature at the temperature change time point t, α is the skin surface internal and external temperature difference scaling coefficient, τ c is the time decay rate, β is the skin surface internal and external temperature difference influence coefficient, and η is the correction value of the skin surface temperature calibration compensation coefficient.

[0154] The present application obtains a temperature calibration compensation calculation formula through the use of a specific mathematical model and verification, which is used for temperature calibration compensation calculation of real-time temperature data of the skin surface at the same temperature change time point. The temperature calibration compensation calculation formula can more accurately reflect the actual temperature state of the skin by calibrating the difference between the skin surface temperature and the environmental temperature. The integral term in the formula takes into account the time decay effect of temperature difference, which can dynamically adjust the calibration compensation coefficient, meaning that not only the current temperature difference is considered, but also the influence of historical temperature difference is included, providing more comprehensive compensation. Secondly, the formula can adapt to different temperature change rates and amplitudes, thereby improving the temperature compensation ability under different environmental conditions. By introducing a correction value, the calibration compensation coefficient can be further adjusted to compensate for systematic errors or incomplete calibration factors in the calculation process. Then, the integral part of the formula provides in-depth analysis of the time series data of temperature change, which helps to identify temperature change trends and patterns, thereby better understanding the dynamic changes of skin temperature. By adopting this compensation method that comprehensively considers the time decay effect, the system performance can be optimized, and the temperature measurement errors caused by environmental factors can be reduced. In summary, the formula fully considers the skin surface temperature calibration compensation coefficient δ(t) at the temperature change time point t, the time variable parameter t of the temperature change time point, the integral time variable parameter τ, the real-time skin surface temperature T s (t) at the temperature change time point t, the external environmental temperature T e (t) of the skin surface at the temperature change time point t, the skin surface internal and external temperature difference scaling coefficient α, the time decay velocity τ c , the skin surface internal and external temperature difference influence coefficient β, the correction value η of the skin surface temperature calibration compensation coefficient, and forms a functional relationship according to the mutual relationship between the skin surface temperature calibration compensation coefficient δ(t) at the temperature change time point t and the above parameters:

[0155] The formula can realize the temperature calibration compensation calculation process of real-time temperature data of the skin surface at the same temperature change time point. At the same time, by introducing the correction value η of the skin surface temperature calibration compensation coefficient, the accuracy and applicability of the temperature calibration compensation calculation formula can be improved according to the error situation in the calculation process.

[0156] Further, step S3 includes the following steps:

[0157] Step S31: uploading the skin surface real-time temperature calibration data to the microcontroller unit of the wearable smart ring, and using the microcontroller unit to draw a skin surface temperature calibration data change curve to generate a skin surface temperature calibration data change curve.

[0158] In the embodiment of the present application, the skin surface real-time temperature calibration data obtained after previous calibration compensation is uploaded to the microcontroller unit of the wearable smart ring by using wireless transmission technology (such as Bluetooth or wireless local area network), and after the microcontroller unit receives the skin surface real-time temperature calibration data, the received skin surface real-time temperature calibration data is visualized by using the built-in data visualization algorithm, the specific operation includes arranging the temperature calibration data in time sequence and calculating the temperature change amplitude at each time point, thereby drawing the temperature calibration change curve, which shows the trend of the skin surface temperature change with time, and finally generating the skin surface temperature calibration data change curve.

[0159] Step S32: Mark the temperature abnormality response of the skin surface temperature calibration data change curve according to the preset skin surface temperature abnormality calibration line to obtain the skin surface monitoring temperature abnormality data.

[0160] In the embodiment of the present application, the preset skin surface temperature abnormality calibration line is used as a reference standard, and the temperature abnormality response is marked on the previously drawn skin surface temperature calibration data change curve, wherein the calibration line represents the upper and lower limits of the normal temperature range, for example, if the normal range is 36.5℃ to 37.5℃, the calibration line will be set in the region of the two temperature values, the microcontroller unit compares the real-time temperature curve with the calibration line, identifies all temperature points that exceed the calibration line range, marks these over-standard temperature points as temperature abnormality, and records the time and specific temperature value of the temperature abnormality, these marked data are sorted into temperature abnormality data, and finally the skin surface monitoring temperature abnormality data is obtained.

[0161] Step S33: Perform temperature abnormality distribution analysis on the skin surface monitoring temperature abnormality data to obtain the skin surface monitoring temperature abnormality distribution value.

[0162] In the embodiment of the present application, the previously marked skin surface monitoring temperature abnormality data is analyzed in detail, the specific operation includes classifying and counting all abnormal data points according to the frequency and temperature range, drawing an abnormal data distribution graph, and using statistical methods such as standard deviation and coefficient of variation to evaluate the distribution of abnormal data during the analysis process, for example, calculating the average value and distribution interval of abnormal temperature data, and finally obtaining the skin surface monitoring temperature abnormality distribution value, which indicates the severity and frequency of temperature abnormality.

[0163] Step S34: Perform temperature control target range analysis on the skin surface monitoring temperature abnormality data based on the skin surface monitoring temperature abnormality distribution value to obtain the skin surface temperature abnormality temperature control target range.

[0164] In the embodiment of the present application, the skin surface temperature abnormality distribution value obtained by the previous analysis is combined with the corresponding skin surface temperature abnormality data to perform statistical analysis on the temperature control target range, so as to calculate the actual deviation range of the skin surface temperature by using the abnormality distribution value. For example, by combining the preset normal temperature range (for example, 36.5-37.5℃), a proper temperature control target range is determined (if the corresponding abnormality distribution value is 36℃, the temperature control target range is +0.5-1.5℃; if the corresponding abnormality distribution value is 38℃, the temperature control target range is -1.5-0.5℃). The target range takes into account the degree of temperature abnormality, so as to effectively adjust and control the skin surface temperature in actual application, and finally obtain the skin surface temperature abnormality temperature control target range, which will be used to guide the actual temperature adjustment operation, so as to maintain the skin surface temperature in the safe range.

[0165] Further, the step S4 comprises the following steps:

[0166] Step S41: performing temperature control demand analysis on the skin surface temperature abnormality temperature control target range by using the microcontroller unit, to obtain the skin surface temperature abnormality temperature control demand;

[0167] In the embodiment of the present application, the skin surface temperature abnormality temperature control target range obtained by the previous analysis is subjected to statistical analysis on the temperature control demand by using the microcontroller unit, so as to analyze and judge the skin surface temperature abnormality temperature control target range obtained by the previous analysis by using the embedded control algorithm, to obtain the corresponding temperature control demand result. That is, if the skin surface temperature abnormality temperature control target range is +0.5℃ or -0.5℃, the temperature control demand analysis is determined as heating 0.5℃ or cooling 0.5℃. These temperature control demands include the temperature amplitude to be adjusted, the adjustment speed and the response time, etc. Finally, the corresponding skin surface temperature abnormality temperature control demand is obtained.

[0168] Step S42: performing temperature control demand instruction response analysis on the skin surface temperature abnormality temperature control demand, to generate the skin surface temperature control demand response instruction;

[0169] In the embodiment of the present application, the corresponding skin surface temperature abnormality control feedback response signal is generated by using the preset algorithm (such as PID control algorithm) in the microcontroller unit to respond to the skin surface temperature abnormality control demand response instruction, so as to generate the corresponding temperature control demand instruction (including heating control response instruction or cooling control response instruction). For example, in the analysis process, the temperature control demand is converted into specific control instructions by considering the accuracy of temperature adjustment, reaction time and system capacity, which include the amplitude, duration of heating or cooling and feedback adjustment mechanism in the adjustment process, and finally the skin surface temperature control demand response instruction is generated, which includes detailed operation steps and execution parameters, and is formatted into a signal format suitable for transmission to the intelligent ring control device.

[0170] Step S43: The skin surface temperature abnormality control feedback response signal is generated by responding to the temperature adjustment control device of the wearable intelligent ring with the skin surface temperature abnormality control demand response instruction, and using the temperature adjustment control device to perform temperature abnormality control feedback on the skin surface temperature abnormality control target range, so as to perform the corresponding skin surface temperature abnormality control adjustment operation.

[0171] In the embodiment of the present application, the skin surface temperature abnormality control feedback response signal is generated by responding to the temperature adjustment control device of the wearable intelligent ring with the skin surface temperature abnormality control demand response instruction, and using the temperature adjustment control device to perform temperature abnormality control feedback on the skin surface temperature abnormality control target range, so as to perform the corresponding skin surface temperature abnormality control adjustment operation.

[0172] Therefore, from any point of view, the embodiments should be regarded as exemplary and non-limiting, the scope of the present application is defined by the appended claims rather than the above description, and therefore all changes falling within the meaning and scope of the equivalent elements of the application file are intended to be included in the present application.

[0173] The foregoing is considered as illustrative only of the principles of the application. Numerous modifications and changes will readily occur to those skilled in the art, and it is intended to embrace all such modifications and changes that fall within the scope of the application. Accordingly, the application is not to be restricted in scope to the specific embodiments disclosed herein but is to be accorded the full scope that the principles and novel features request appropriately granted.

Claims

1. A wearable smart ring skin temperature measurement and control method, characterized in that, Comprising the following steps: Step S1: Real-time acquisition of the temperature change signal of the user's skin surface by the skin temperature sensor integrated inside the wearable smart ring to obtain the real-time temperature change signal of the skin surface; Anti-interference sampling processing of the real-time temperature change signal of the skin surface by the high-precision change sampling circuit and the signal anti-interference circuit inside the wearable smart ring, and transmission to the internal processing unit of the wearable smart ring to obtain the real-time temperature change input signal of the skin surface; Step S2: Temperature signal fluctuation data conversion of the real-time temperature change input signal of the skin surface by the internal processing unit to obtain real-time temperature change measurement data of the skin surface; obtaining external environment temperature change data, and temperature dynamic calibration compensation of the real-time temperature change measurement data of the skin surface based on the external environment temperature change data to obtain real-time temperature calibration data of the skin surface; Step S3: uploading the real-time temperature calibration data of the skin surface to the microcontroller unit of the wearable smart ring, and marking the real-time temperature calibration data of the skin surface for temperature abnormal response by the microcontroller unit to obtain skin surface monitoring temperature abnormal data; temperature control target range analysis of the skin surface monitoring temperature abnormal data to obtain the skin surface temperature abnormal temperature control target range; Step S4: temperature control demand instruction response analysis of the skin surface temperature abnormal temperature control target range by the microcontroller unit to generate skin surface temperature control demand response instruction; responding to the temperature adjustment control device of the wearable smart ring with the skin surface temperature control demand response instruction, and performing temperature abnormal control feedback on the skin surface temperature abnormal temperature control target range by the temperature adjustment control device to generate skin surface temperature abnormal control feedback response signal to perform corresponding skin surface temperature abnormal control adjustment operation. 2.The wearable smart ring skin temperature measurement and control method of claim 1, wherein, Step S1 includes the following steps: Step S11: Real-time acquisition of the temperature change signal of the user's skin surface by the skin temperature sensor integrated inside the wearable smart ring to obtain the real-time temperature change signal of the skin surface; Step S12: high-precision signal sampling processing of the real-time temperature change signal of the skin surface by the high-precision change circuit inside the wearable smart ring to obtain the real-time temperature high-precision change signal of the skin surface; Step S13: high-frequency noise optimization filtering of the real-time temperature high-precision change signal of the skin surface to obtain the real-time temperature change high-frequency filtered signal of the skin surface; Step S14: anti-interference processing of the real-time temperature change high-frequency filtered signal of the skin surface by the signal anti-interference circuit inside the wearable smart ring to obtain the real-time temperature change anti-interference signal of the skin surface; Step S15: transmission of the real-time temperature change anti-interference signal of the skin surface to the internal processing unit of the wearable smart ring to obtain the real-time temperature change input signal of the skin surface. 3.The wearable smart ring skin temperature measurement and control method of claim 2, wherein, Step S14 includes the following steps: Step S141: circuit multi-stage arrangement design optimization of the signal anti-interference circuit inside the wearable smart ring to generate a smart ring signal anti-interference multi-stage design circuit; Step S142: The high-frequency filtered signal of the real-time temperature change of the skin surface is processed by the multi-stage signal isolation of the intelligent ring signal anti-interference multi-stage design circuit to obtain the multi-stage interference isolation signal of the real-time temperature change of the skin surface; Step S143: The multi-stage interference isolation signal of the real-time temperature change of the skin surface is adaptively filtered to obtain the interference filtered signal of the real-time temperature change of the skin surface; Step S144: The signal interference mode recognition analysis is performed on the skin temperature sensor integrated in the wearable intelligent ring to obtain the intelligent ring temperature signal acquisition interference mode; Step S145: The interference mode dynamic correction is performed on the interference filtered signal of the real-time temperature change of the skin surface based on the intelligent ring temperature signal acquisition interference mode to obtain the anti-interference signal of the real-time temperature change of the skin surface. 4.The wearable smart ring skin temperature measurement and control method of claim 3, wherein, Step S141 includes the following steps: The circuit topology structure analysis is performed on the signal anti-interference circuit in the wearable intelligent ring to obtain the signal anti-interference circuit topology design structure; The circuit interference source positioning is performed on the signal anti-interference circuit in the wearable intelligent ring based on the signal anti-interference circuit topology design structure to obtain the signal anti-interference circuit interference source positioning position; The circuit to be isolated planning analysis is performed on the signal anti-interference circuit in the wearable intelligent ring based on the signal anti-interference circuit interference source positioning position to obtain the signal anti-interference circuit to be isolated planning area; The multi-stage signal isolator arrangement design optimization is performed on the signal anti-interference circuit in the wearable intelligent ring based on the signal anti-interference circuit to be isolated planning area to generate the intelligent ring signal anti-interference multi-stage design circuit. 5.The wearable smart ring skin temperature measurement and control method of claim 1, wherein, Step S2 includes the following steps: Step S21: The change signal frequency spectrum mapping conversion is performed on the real-time temperature change input signal of the skin surface by the internal processing unit to obtain the real-time temperature change signal frequency spectrum of the skin surface; Step S22: The temperature signal fluctuation amplitude statistical analysis is performed on the real-time temperature change signal frequency spectrum of the skin surface to obtain the real-time temperature signal fluctuation amplitude of the skin surface; Step S23: The time sequence point amplitude division is performed on the real-time temperature signal fluctuation amplitude of the skin surface to obtain the skin surface temperature signal fluctuation amplitude at each time sequence point; Step S24: The transient response statistical analysis is performed on the skin surface temperature signal fluctuation amplitude at each time sequence point to obtain the skin surface temperature signal fluctuation transient response change value at each time sequence point; The temperature signal fluctuation data conversion is performed on the skin surface temperature signal fluctuation transient response change value at each time sequence point to obtain the real-time temperature change measurement data of the skin surface; Step S25: The external environment temperature change data is obtained, and the temperature dynamic calibration compensation is performed on the real-time temperature change measurement data of the skin surface based on the external environment temperature change data to obtain the real-time temperature calibration data of the skin surface. The transient response statistical analysis of the skin surface temperature signal fluctuation amplitude at each time sequence point in step S24 includes the following steps: 6.The wearable smart ring skin temperature measurement and control method of claim 5, wherein, The fluctuation amplitude transient response characteristic analysis is performed on the skin surface temperature signal fluctuation amplitude at each time sequence point to obtain the temperature signal fluctuation amplitude transient response characteristic data at each time sequence point; ​ The fluctuation amplitude change mode analysis is performed on the skin surface temperature signal fluctuation amplitude at each time point, and the temperature signal fluctuation amplitude change mode at each time point is obtained. The transient response dynamic correction is performed on the temperature signal fluctuation amplitude transient response characteristic data at each time point based on the temperature signal fluctuation amplitude change mode at each time point, and the temperature signal transient response characteristic correction data at each time point is obtained. The transient response change value quantitative calculation is performed on the temperature signal transient response characteristic correction data at each time point, and the skin surface temperature signal fluctuation transient response change value at each time point is obtained. 7.The wearable smart ring skin temperature measurement and control method of claim 5, wherein, Step S25 includes the following steps: Step S251: Obtain external environment temperature change data; Step S252: Perform time sequence synchronization processing on the external environment temperature change data and the skin surface real-time temperature change measurement data to obtain external environment temperature change time sequence synchronization data and skin surface temperature change time sequence synchronization data in the same time sequence dimension; Step S253: Perform time sequence point alignment processing on the external environment temperature change time sequence synchronization data and the skin surface temperature change time sequence synchronization data in the same time sequence dimension to obtain external environment temperature data and skin surface real-time temperature data at the same temperature change time sequence point; Step S254: Perform temperature calibration compensation calculation on the corresponding skin surface real-time temperature data based on the external environment temperature data at the same temperature change time sequence point using a temperature calibration compensation calculation formula to obtain a skin surface temperature calibration compensation coefficient at each temperature change time sequence point; Step S255: Perform temperature dynamic calibration compensation on the corresponding skin surface real-time temperature data according to the skin surface temperature calibration compensation coefficient at each temperature change time sequence point to obtain skin surface real-time temperature calibration data. 8.The wearable smart ring skin temperature measurement and control method of claim 7, wherein, The temperature calibration compensation calculation formula in step S254 is specifically: In the formula, δ(t) is a skin surface temperature calibration compensation coefficient at a time point t when temperature changes, t is a time variable parameter of the time point, τ is an integral time variable parameter, T s (t) is a real-time skin surface temperature at a time point t when temperature changes, T e (t) is an external environment temperature of the skin surface at a time point t when temperature changes, α is a skin surface internal and external temperature difference scaling coefficient, τ c is a time decay rate, β is a skin surface internal and external temperature difference influence coefficient, and η is a correction value of the skin surface temperature calibration compensation coefficient. 9.The wearable smart ring skin temperature measurement and control method of claim 1, wherein, Step S3 includes the following steps: Step S31: Upload the skin surface real-time temperature calibration data to the microcontroller unit of the wearable smart ring, and draw a temperature calibration change curve of the skin surface real-time temperature calibration data using the microcontroller unit to generate a skin surface temperature calibration data change curve; Step S32: Mark the skin surface temperature calibration data change curve according to a preset skin surface temperature abnormality demarcation line to obtain skin surface monitoring temperature abnormality data; Step S33: Perform temperature abnormality distribution analysis on the skin surface monitoring temperature abnormality data to obtain a skin surface monitoring temperature abnormality distribution value; Step S34: Perform temperature control target range analysis on the skin surface monitoring temperature abnormality data based on the skin surface monitoring temperature abnormality distribution value to obtain a skin surface temperature abnormality temperature control target range. 10.The wearable smart ring skin temperature measurement and control method of claim 1, wherein, Step S4 includes the following steps: Step S41: Perform temperature control demand analysis on the skin surface temperature abnormality temperature control target range using the microcontroller unit to obtain a skin surface temperature abnormality temperature control demand; Step S42: Perform temperature control demand instruction response analysis on the skin surface temperature abnormality temperature control demand to generate a skin surface temperature control demand response instruction; Step S43: generate a skin surface temperature anomaly control feedback response signal by responding to the temperature adjustment control device of the wearable smart ring with the skin surface temperature anomaly control target range, and utilizing the temperature adjustment control device to perform corresponding skin surface temperature anomaly control adjustment operations.

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