Blood oxygen monitoring reminding method of smart bracelet, smart bracelet, medium and product

Abstract

The invention discloses a blood oxygen monitoring reminding method of a smart bracelet, the smart bracelet, a medium and a product. By monitoring the noise intensity in real time and analyzing the change period of the noise intensity, the blood oxygen detection triggering time is adjusted to the lowest noise period, meanwhile, the reminding threshold value is dynamically increased according to the influence of the highest noise value on the oxygen conveying efficiency of the human body, and when it is monitored that the blood oxygen value is abnormal, a user is reminded; the method not only solves the problem that detection by using a fixed period is susceptible to noise interference, but also solves the problem that a fixed threshold value cannot adapt to a noise environment and affects physiological index monitoring, so that the technical effects of remarkably improving blood oxygen detection accuracy and reminding effectiveness in high-noise environments such as the industry and the like are achieved, and the method is suitable for popularization and application. And more reliable health risk early warning is provided for the user.

Description

Technical Field

[0001] The present application relates to the field of smart bracelets, and in particular to a blood oxygen monitoring reminder method, a smart bracelet, a medium, and a product of a smart bracelet. Background Art

[0002] With the development of health monitoring technology, smart wristbands, as portable health monitoring devices, have become widely used in daily life. Smart wristbands are often equipped with multiple health monitoring functions, among which blood oxygen monitoring is a key indicator for assessing a user's health, especially for those at risk of cardiopulmonary disease. In specific environments, accurate and timely blood oxygen monitoring can effectively prevent health risks caused by hypoxia.

[0003] In related technologies, smart wristband blood oxygen monitoring technology typically uses a fixed cycle for detection, automatically triggering the blood oxygen detection function at fixed intervals. When the detected blood oxygen value falls below a preset fixed threshold, the smart wristband will issue a health risk alert. This fixed cycle and fixed threshold monitoring method works well in environments with low electromagnetic interference, such as homes and offices.

[0004] However, in specific environments such as factories and industrial areas, various electronic and mechanical devices generate electromagnetic noise of varying intensities, often with periodic variations. Existing technologies use a fixed-period and fixed-threshold monitoring method. If a smart wristband performs blood oxygen measurement during a period when electromagnetic noise intensity reaches its peak, electromagnetic interference can directly affect the sensor's photoelectric conversion process, causing interference and distortion in the signal received by the sensor, resulting in inaccurate results. Summary of the Invention

[0005] This application provides a blood oxygen monitoring reminder method for a smart bracelet, a smart bracelet, a medium, and a product, which are used to improve the accuracy of blood oxygen monitoring and the effectiveness of reminders of the smart bracelet in a specific area environment with electromagnetic noise interference.

[0006] In the first aspect, the present application provides a blood oxygen monitoring reminder method for a smart bracelet, including: when the user is in a first preset area, real-time monitoring of the electromagnetic noise intensity around the user by the smart bracelet; when the electromagnetic noise intensity is greater than a preset noise intensity threshold, determining the change period of the electromagnetic noise intensity in the first preset area based on the change of the electromagnetic noise intensity monitored for a previously preset period of time; adjusting the periodic trigger time of the blood oxygen detection function of the smart bracelet to the time point when the electromagnetic noise intensity is lowest in the change period; according to the actual efficiency of oxygen delivery in the human body corresponding to the highest value of the electromagnetic noise intensity in the change period, increasing the blood oxygen detection reminder threshold to obtain the current blood oxygen detection reminder threshold; when the current blood oxygen value is lower than the current blood oxygen detection reminder threshold, the smart bracelet issues a health risk reminder, and the current blood oxygen value is measured according to the adjusted periodic trigger time.

[0007] In the above embodiment, by real-time monitoring of noise intensity and analyzing its changing cycle, the trigger time of blood oxygen detection is adjusted to the period with the lowest noise. At the same time, the reminder threshold is dynamically increased according to the impact of the highest noise value on the oxygen delivery efficiency of the human body. This effectively solves the technical problems in the existing technology that fixed-period detection is susceptible to noise interference, resulting in signal distortion, and the fixed threshold cannot adapt to the actual impact of the noise environment on physiological indicator monitoring. This achieves the technical effect of significantly improving the accuracy of blood oxygen detection and the effectiveness of reminders in high-noise environments such as industry, providing users with more reliable health risk warnings.

[0008] In combination with some embodiments of the first aspect, in some embodiments, when the user is within a first preset area, the electromagnetic noise intensity around the user is monitored in real time through a smart bracelet, specifically including: when the user is within the first preset area, the smart bracelet initially monitors the electromagnetic noise intensity data at a low frequency, and calculates the electromagnetic noise intensity standard deviation based on the monitoring data; compares the electromagnetic noise intensity standard deviation with a preset electromagnetic noise volatility threshold to determine whether the standard deviation exceeds the threshold range; if the electromagnetic noise intensity standard deviation does not exceed the preset electromagnetic noise volatility threshold, the smart bracelet maintains low-frequency monitoring of the electromagnetic noise intensity data; if the electromagnetic noise intensity standard deviation exceeds the preset electromagnetic noise volatility threshold, the smart bracelet switches to high-frequency real-time monitoring of the electromagnetic noise intensity data.

[0009] In the above embodiment, due to the adoption of a technology for dynamic switching of electromagnetic noise monitoring frequency based on noise volatility, the volatility is judged by calculating the standard deviation of noise intensity, and low-frequency monitoring is maintained when the volatility is low, and high-frequency monitoring is switched when the volatility is high. This effectively solves the technical problem in the prior art that fixed-frequency monitoring causes increased energy consumption due to excessively high frequency, or that noise characteristics cannot be captured in a timely manner due to excessively low frequency. This achieves the technical effect of reducing equipment power consumption and extending the battery life of the smart bracelet while ensuring the accuracy of noise monitoring, and balances the monitoring needs and energy efficiency management in complex environments.

[0010] In combination with some embodiments of the first aspect, in some embodiments, when the electromagnetic noise intensity is greater than a preset noise intensity threshold, based on the change of the electromagnetic noise intensity monitored for a previously preset period of time, determining the change period of the electromagnetic noise intensity in the first preset area, specifically including: when the electromagnetic noise intensity is greater than the preset electromagnetic noise intensity threshold, based on the electromagnetic noise intensity data within the preset period of time, obtaining the periodic characteristics of the electromagnetic noise intensity through electromagnetic spectrum analysis, the periodic characteristics being periodic fluctuation characteristics; extracting the confidence value of the periodic characteristics calculated from the periodic characteristics, and judging whether the confidence value of the periodic characteristics reaches the preset confidence threshold; if the confidence value of the periodic characteristics reaches the preset confidence threshold, then based on the change of the electromagnetic noise intensity monitored for the previously preset period of time, According to the change situation, the variation period of the electromagnetic noise intensity in the first preset area is determined; if the periodic feature does not reach the preset confidence threshold, the next environmental position that the user is about to enter is predicted according to the user's geographical location and movement trajectory, and the next environmental position is still in the first preset area; according to the historical electromagnetic noise intensity spectrum in the noise monitoring system in the first preset area, the noise period feature of the next environmental position is extracted, and the historical electromagnetic noise intensity spectrum includes the periodic features of each position; the historical electromagnetic noise database in the noise monitoring system in the first preset area is retrieved, and according to the noise period feature of the next environmental position, the typical noise period pattern of the next environmental position is matched to obtain the variation period of the electromagnetic noise intensity of the next environmental position, and the historical electromagnetic noise database includes typical periodic patterns of different positions.

[0011] In the above embodiment, the confidence level of noise periodicity is extracted through electromagnetic spectrum analysis. When the confidence level is insufficient, the user's location, motion trajectory, and historical noise database are used to match typical periodic patterns. This effectively solves the technical problems of existing technologies that rely solely on real-time data, resulting in unclear noise periodicity or failure of detection strategies when the environment changes rapidly. This enables the smart bracelet to adapt to dynamic environmental changes and accurately obtain noise periodicity characteristics in complex scenarios such as factories, where noise characteristics vary across multiple areas. This ensures the continuity and accuracy of blood oxygen monitoring strategies during user movement.

[0012] In combination with some embodiments of the first aspect, in some embodiments, the blood oxygen detection reminder threshold is increased according to the actual efficiency of oxygen delivery in the human body corresponding to the maximum value of the electromagnetic noise intensity in the change cycle, and the current blood oxygen detection reminder threshold is obtained, specifically including: the smart bracelet calculates the average value of the maximum electromagnetic noise intensity in the change cycle based on the maximum value of the electromagnetic noise intensity in the change cycle; the average value of the maximum electromagnetic noise intensity in the change cycle is input into the pre-trained association model of oxygen delivery efficiency and noise to obtain the oxygen delivery efficiency impact value; according to the oxygen delivery efficiency impact value, the smart bracelet obtains the current blood oxygen detection reminder threshold by calculation.

[0013] In the above embodiment, the oxygen delivery efficiency impact value is obtained by calculating the average of the highest noise values and inputting it into a pre-trained model, thereby adjusting the blood oxygen alert threshold. This effectively solves the technical problem that fixed thresholds in the existing technology do not consider the actual effects of noise on human physiological functions and rely solely on the absolute value of the sensor signal, which can lead to misjudgments or missed judgments. This achieves the technical effect of ensuring that the blood oxygen alert threshold can both avoid noise interference on signal acquisition and adapt to the actual changes in the human body's oxygen delivery efficiency in noisy environments, thereby improving the physiological relevance and scientific accuracy of health alerts.

[0014] In combination with some embodiments of the first aspect, in some embodiments, the smart bracelet calculates the average value of the maximum electromagnetic noise intensity in the change cycle based on the maximum value of the electromagnetic noise intensity in the change cycle, specifically including: based on whether the change amplitude of the magnetic field noise intensity within the preset time length reaches the average value of the electromagnetic noise intensity, determining the number of cycles from which the maximum electromagnetic noise value needs to be extracted within the preset time length, and the number of cycles within the preset time length is greater than 1; if the change amplitude reaches the average value of the electromagnetic noise intensity, the smart bracelet extracts the maximum electromagnetic noise value in each cycle; if the change amplitude does not reach the average value of the electromagnetic noise intensity, the smart bracelet extracts the maximum electromagnetic noise value in one cycle; based on the extracted maximum electromagnetic noise value, the average value of the maximum electromagnetic noise value is calculated.

[0015] In the above embodiment, the maximum value of a single cycle or multiple cycles is extracted and the average is calculated based on the amplitude of the noise intensity change within a preset time period. This effectively solves the problem of data redundancy or feature loss that is prone to occur in scenarios with different noise fluctuations in the existing fixed-cycle data extraction method. It further achieves the technical effect of the smart bracelet quickly and accurately obtaining key noise intensity characteristics in complex noise environments, provides reliable input data for subsequent threshold adjustment, and enhances the algorithm's adaptability to noisy environments.

[0016] In combination with some embodiments of the first aspect, in some embodiments, when the current blood oxygen value is lower than the current blood oxygen detection reminder threshold, the smart bracelet issues a health risk reminder, and the current blood oxygen value is measured according to the adjusted periodic trigger time. After the step, the method also includes: the smart bracelet detects whether the user clicks the screen confirmation control, clicking indicates that the reminder has been responded to, and not clicking indicates that the reminder has been ignored; if the smart bracelet detects that the user has ignored it three times in a row, the reminder interval is extended in the corresponding time period of each day.

[0017] In the above embodiment, the use of user-responsive behavior-based reminder strategy adaptation technology—that is, by detecting whether a user confirms or ignores a reminder, and extending the reminder interval for the same time period after three consecutive ignoring attempts—effectively addresses the existing problem of fixed-frequency reminders causing user fatigue or disabling features due to frequent interruptions. This enables personalized adaptation of the health reminder mechanism in smart wristbands, ensuring the delivery of important risk information while reducing unnecessary interruptions, thereby improving user acceptance and long-term user stickiness.

[0018] In combination with some embodiments of the first aspect, in some embodiments, when the current blood oxygen value is lower than the current blood oxygen detection reminder threshold, the smart bracelet issues a health risk reminder, and the current blood oxygen value is measured according to the adjusted periodic trigger time, specifically including: when the current blood oxygen value is lower than the current blood oxygen detection reminder threshold, based on whether the decibel value of the surrounding environment of the smart bracelet exceeds the preset decibel value, the health risk reminder method is judged; if the decibel value of the surrounding environment does not exceed the preset decibel value, the smart bracelet vibrates and displays the risk reminder content on the screen; if the decibel value of the surrounding environment exceeds the preset decibel value, the smart bracelet emits a beep and displays the risk reminder content on the screen.

[0019] In the above embodiment, the multimodal reminder technology based on the decibel level of ambient noise is adopted, that is, the reminder mode is dynamically switched according to the decibel level of the surrounding environment. This effectively solves the problem of excessive or ineffective reminders that often occurs in different noise scenarios in the existing technology, thereby achieving the universality and reliability of the health risk reminder of the smart bracelet in multiple scenarios. In particular, in high-noise environments such as industrial environments, the effective reach of the reminder is improved by adapting to the ambient noise level, maximizing the health protection function of the device.

[0020] In a second aspect, an embodiment of the present application provides a smart bracelet, comprising: one or more processors and a memory; the memory is coupled to the one or more processors, the memory being used to store computer program code, the computer program code comprising computer instructions, the one or more processors calling the computer instructions to cause the smart bracelet to execute the method described in the first aspect and any possible implementation manner of the first aspect.

[0021] In a third aspect, an embodiment of the present application provides a computer-readable storage medium comprising instructions. When the instructions are executed on a smart bracelet, the smart bracelet executes the method described in the first aspect and any possible implementation of the first aspect.

[0022] In a fourth aspect, an embodiment of the present application provides a computer program product. When the above-mentioned computer program product is run on a smart bracelet, the smart bracelet executes the method described in the first aspect and any possible implementation manner of the first aspect.

[0023] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages: 1. By adopting the technology of dynamic monitoring of electromagnetic noise intensity and dual adaptive adjustment of detection cycle and reminder threshold, the system adjusts the blood oxygen detection trigger time to the period with the lowest noise by real-time monitoring of noise intensity and analyzing its changing cycle. At the same time, the reminder threshold is dynamically increased according to the impact of the highest noise value on the human body's oxygen delivery efficiency. This effectively solves the technical problems in the existing technology that fixed-cycle detection is susceptible to noise interference, resulting in signal distortion, and fixed thresholds cannot adapt to the actual impact of the noise environment on physiological indicator monitoring. This achieves the technical effect of significantly improving the accuracy of blood oxygen detection and the effectiveness of reminders in high-noise environments such as industry, providing users with more reliable health risk warnings.

[0024] 2. By combining real-time spectrum analysis with historical noise data prediction, the system first determines the clarity of the noise period through real-time data. When data is insufficient, it then uses historical noise maps and location information to pre-match the noise period characteristics of the next environment. This effectively solves the technical problems in existing technologies that rely solely on real-time data, resulting in unclear noise periodicity or failure of detection strategies when the environment changes rapidly. This enables the smart bracelet to adapt to dynamic environmental changes and accurately obtain noise period characteristics in complex scenarios with different noise characteristics in multiple areas such as factories, ensuring the continuity and accuracy of the blood oxygen monitoring strategy of the sports bracelet during scene changes.

[0025] 3. By adopting the dynamic threshold calibration technology that models the correlation between noise intensity and human oxygen delivery efficiency, the potential impact of noise on blood oxygen metabolism is quantified first, and then the threshold is dynamically corrected based on the impact value to reflect the actual risk of hypoxia. This effectively solves the technical problem that the fixed threshold in the existing technology does not consider the actual effect of noise on human physiological functions and only relies on the absolute value of the sensor signal, which may lead to misjudgment or missed judgment. In turn, the blood oxygen reminder threshold can avoid the interference of noise on signal acquisition and adapt to the changes in the actual oxygen delivery efficiency of the human body in a noisy environment. This improves the physiological relevance and scientific accuracy of health reminders. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 This is a schematic diagram of an exemplary scenario of a blood oxygen monitoring reminder in a specific area environment with electromagnetic noise interference; Figure 2 This is a flow chart of the blood oxygen monitoring reminder method in an embodiment of the present application; Figure 3 This is another flowchart of the blood oxygen monitoring reminder method in an embodiment of the present application; Figure 4 This is a hardware architecture diagram of the smart bracelet in the embodiment of the present application. DETAILED DESCRIPTION

[0027] In order to make the objectives, technical solutions and advantages of this application clearer, the embodiments of the present invention will be described in further detail below with reference to the accompanying drawings.

[0028] The terms used in the following examples of the present application are for the purpose of describing specific embodiments only and are not intended to limit the present application. As used in the specification and appended claims of the present application, the singular expressions "a," "an," "said," "above," "the," and "this" are intended to include plural expressions as well, unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used in the present application refers to and encompasses any or all possible combinations of one or more of the listed items.

[0029] In the following, the terms "first" and "second" are used for descriptive purposes only and should not be understood to imply or suggest relative importance or implicitly indicate the number of the technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the embodiments of this application, unless otherwise specified, "plurality" means two or more.

[0030] Before introducing the embodiments of the present application, some terms involved in the embodiments of the present application are first defined and explained.

[0031] Blood oxygen: refers to the oxygen in the blood. The normal blood oxygen saturation of the human body is above 95%.

[0032] Electromagnetic noise: Electromagnetic wave signals cause certain chaotic signals due to the electromagnetic field.

[0033] Analog-to-digital converter: Usually refers to an electronic component that converts an analog signal into a digital signal. A typical analog-to-digital converter converts an input voltage signal into an output digital signal.

[0034] Robustness: describes the ability of a system to maintain normal function in non-ideal environments (such as noise, missing data, parameter fluctuations, and external interference).

[0035] This application aims to solve the problem that in a specific area environment with electromagnetic noise interference, the sensor receiving signal of the smart bracelet is interfered with and distorted, thereby producing inaccurate results.

[0036] Figure 1 This is a schematic diagram of an exemplary scenario in which a smart bracelet provides a blood oxygen monitoring reminder to a user in a specific area environment with electromagnetic noise interference.

[0037] See also Figure 1 In specific environments such as factories or industrial areas, various electronic and mechanical equipment will generate electromagnetic noise of varying intensities, which will interfere with the smart bracelet's monitoring of blood oxygen levels.

[0038] Existing technology uses a fixed cycle and fixed threshold to monitor the user's blood oxygen level. If the smart bracelet performs blood oxygen detection during the period when the electromagnetic noise intensity reaches its peak, the electromagnetic interference will directly affect the sensor's photoelectric conversion process, causing interference and distortion of the signal received by the sensor, thereby producing inaccurate results.

[0039] By adopting the blood oxygen monitoring reminder method in the embodiment of the present application, the noise intensity is monitored in real time and its change cycle is analyzed, and the blood oxygen detection trigger time is adjusted to the period of lowest electromagnetic noise. At the same time, the reminder threshold is dynamically increased according to the impact of the maximum electromagnetic noise value on the human body's oxygen delivery efficiency. The user is reminded when an abnormal blood oxygen value is monitored, which significantly improves the accuracy of blood oxygen detection and the effectiveness of reminders in high magnetic field interference environments such as industrial environments.

[0040] The following combines the above Figure 1 The figure shows an exemplary scenario diagram of the blood oxygen monitoring reminder method, which describes the blood oxygen monitoring reminder method in an embodiment of the present application: in a specific environment such as a factory or industrial area, the smart bracelet monitors the electromagnetic noise intensity in real time, analyzes its change cycle, performs blood oxygen detection when the noise interference is lowest, and optimizes the blood oxygen detection trigger time and reminder threshold to accurately issue health risk reminders.

[0041] See also Figure 2 , which is a flow chart of the blood oxygen monitoring reminder method in an embodiment of the present application.

[0042] 201. When the user is within a first preset area, the electromagnetic noise intensity around the user is monitored in real time by the smart bracelet; The smart bracelet determines that the user has entered the first preset area through the built-in GPS; The smart bracelet collects electromagnetic noise signals in the first preset area in real time through a built-in electromagnetic sensor, and then converts the electromagnetic noise signals into digital signals through an analog-to-digital converter. After passing through a signal processing circuit, the digital signals are filtered, amplified, and processed to finally obtain electromagnetic noise intensity data. The filtering process is to remove mixed noise or useless components in the digital signal through a preset algorithm (such as FIR, IIR filter) and retain valid information. The amplification process is to adjust the amplitude of the digital signal through a multiplication operation (such as multiplication by a coefficient) so that it adapts to the input requirements of subsequent circuits (such as processors, memories).

[0043] 202. When the electromagnetic noise intensity is greater than a preset noise intensity threshold, determine a change period of the electromagnetic noise intensity in the first preset area based on changes in the electromagnetic noise intensity monitored for a previously preset period of time; When the electromagnetic noise intensity is greater than a preset noise intensity threshold (e.g., 50 μT), a periodic analysis process of the electromagnetic noise intensity is triggered; The extracted electromagnetic noise intensity data is converted into the frequency domain by fast Fourier transform (FFT), and then the main peak frequency is identified and the period is calculated. FFT can effectively separate different frequency components, filter out non-periodic noise, accurately capture the periodic fluctuations of electromagnetic noise (such as power frequency interference of industrial equipment), and avoid the problem that direct analysis in the time domain is susceptible to burr interference. Frequency domain analysis based on Fourier theory can ensure the reliability of the period calculation results and provide an accurate time reference for subsequent detection time adjustment. The calculation process for calculating the main period value is: First, calculate the main frequency list, which contains the information of all frequency components. The frequency component information represents the distribution of the signal at all frequency points: Where X[k] is the main frequency list; the e -j2πkn / N is a complex exponential function that represents the phase rotation of the frequency component. By introducing j, the time domain signal is mapped to the frequency domain, and the amplitude and phase information of the signal are separated. The x[n] is the original electromagnetic signal value of the nth sampling point; N represents the number of sampling points per second; and k is the corresponding actual frequency. In the above formula, e -j2πkn / N It is used to project the time domain signal onto the kth frequency in the frequency domain, indicating the intensity of the signal at the kth frequency point, and converting the time information of the signal into frequency information. Each item X[n]·e -j2πkn / N Represents the contribution of x[n] at the kth frequency, and the complete frequency component X[k] is obtained by adding. X[k] is an array of length N, which provides the strength information of the signal at each frequency point; Then calculate the main frequency f max, the dominant frequency is the frequency corresponding to the frequency point with the maximum energy in the signal. It is extracted from the dominant frequency list X[k] and is determined by finding the index corresponding to the maximum value of |X[k]|: f max =argmax|X[k]|·f s / N; where f max is the frequency component with the largest amplitude, i.e. the main frequency; f s is the sampling frequency, which indicates the number of times the signal is sampled per second; the argmax() function indicates the value of the variable when the formula in the brackets reaches the maximum value; The above calculation process is used to calculate the frequency corresponding to the largest frequency component. The argmax() function is used to find the index corresponding to the largest frequency point in |X[k]|. Based on this index and the sampling frequency, the main frequency f is calculated. max ; Finally, calculate the main period T, that is, the main frequency f max The reciprocal of: T=1 / f max ; Main frequency f max It is the repetition frequency of the signal, which describes the number of times the signal is repeated per second. The period T is the time required for the signal to complete a complete repetition. The main frequency and period are inversely proportional to each other.

[0044] 203. Adjust the periodic triggering time of the blood oxygen detection function of the smart bracelet to the time point when the electromagnetic noise intensity is lowest in the change period; During the identified period, the sliding window noise intensity mean is calculated by sliding the time window. The sliding window mechanism can suppress instantaneous impulse noise (such as spark interference) and output a stable noise intensity estimate. Compared with single-point sampling, the statistical average of the data in the window can reduce the impact of occasional noise and improve the robustness of noise intensity monitoring. The formula for calculating the sliding window noise intensity mean is as follows: in is the mean noise intensity of the sliding window; W is the width of the sliding window (e.g., 10 minutes); x[n] is the original electromagnetic signal value of the nth sampling point; μ is the mean of the sampling points; N=10 represents the number of sampling points per second; t is the current time point; The noise signal will fluctuate over time. There may be moments with lower noise in some local time periods. In order to find the period with the lowest noise, the sliding window method is used to calculate the mean of the noise intensity. The sliding window takes data in the time range of tW / 2 to t+W / 2. Calculate the root mean square of each sampling point to measure the fluctuation intensity of the signal, average the local mean within the sliding window, and obtain the mean noise intensity within the window According to the mean value of the sliding window noise intensity, the midpoint of three consecutive windows with the lowest mean value is selected as the detection trigger time.

[0045] 204. According to the actual efficiency of oxygen transport in the human body corresponding to the highest value of the electromagnetic noise intensity in the change period, increase the blood oxygen detection reminder threshold to obtain a current blood oxygen detection reminder threshold; Obtain the initial blood oxygen detection reminder threshold T0 of the smart bracelet and the maximum value N of the electromagnetic noise intensity in the change cycle max and the actual efficiency of oxygen delivery within the current user; Combined with the maximum value of electromagnetic noise N max The oxygen transport efficiency E of the human body oxy Calculate the current blood oxygen detection reminder threshold. This calculation method allows the threshold to be adjusted as the noise environment changes, solving the problem that the traditional fixed threshold cannot adapt to noise interference (such as signal distortion or physiological efficiency reduction caused by noise). The formula for calculating the current blood oxygen detection reminder threshold is as follows: Where T1 is the current blood oxygen detection reminder threshold; k is used to adjust the influence of noise and oxygen efficiency on the reminder threshold; E oxy The oxygen transport efficiency in the human body; N max is the maximum value of the electromagnetic noise intensity during the change period; In the above formula It is the normalized expression of the maximum value of noise intensity, indicating the degree of influence of noise on the signal. The difference between represents the combined effect of noise and physiological status. The positive or negative value of this difference determines the direction of the alert threshold adjustment. The importance of noise interference and physiological status may vary in different scenarios, so k is needed to balance the weights of the two and determine the value to be adjusted. Finally, this value is added to the initial blood oxygen detection alert threshold, T0, to obtain the current blood oxygen detection alert threshold, T1.

[0046] 205. When the current blood oxygen value is lower than the current blood oxygen detection reminder threshold, the smart bracelet issues a health risk reminder, and the current blood oxygen value is measured according to the adjusted periodic trigger time.

[0047] The smart bracelet detects that the user's blood oxygen level is lower than the current blood oxygen detection reminder threshold; The smart bracelet issues a health risk reminder and feeds back the situation to the user.

[0048] The blood oxygen monitoring reminder method in the embodiments of this application not only effectively solves the problem of fixed-period detection being susceptible to noise interference and causing signal distortion, but also overcomes the limitation of fixed thresholds that cannot adapt to the actual impact of noisy environments on physiological indicator monitoring. This method significantly improves the accuracy of blood oxygen detection and the effectiveness of reminders in high-noise environments such as industrial environments, thereby providing users with more reliable health risk warnings.

[0049] The following combines the above Figure 1 The scenario diagram shown describes the blood oxygen monitoring reminder method in an embodiment of the present application: this method mainly determines the noise period based on the real-time spectrum of electromagnetic noise and historical data, adjusts the trigger time of blood oxygen detection, extracts the highest value according to the noise fluctuation, and calibrates the blood oxygen reminder threshold through the pre-trained model.

[0050] See also Figure 3 , is another flow chart of the blood oxygen monitoring reminder method in an embodiment of the present application.

[0051] 301. When the user is within a first preset area, the electromagnetic noise intensity around the user is monitored in real time by the smart bracelet; When the smart bracelet is within the first preset area, the smart bracelet initially monitors electromagnetic noise intensity data at a low frequency and calculates the standard deviation of the electromagnetic noise intensity based on the monitoring data; The standard deviation of electromagnetic noise intensity represents the final result of noise signal volatility. The standard deviation can accurately describe the discrete degree of noise intensity, provide an objective basis for monitoring frequency switching, and balance equipment power consumption and monitoring accuracy. The formula for calculating the standard deviation of electromagnetic noise intensity is as follows: Where σ is the standard deviation of electromagnetic noise intensity; N is the number of collected data points; is the mean of all sampling points; in the above formula, The difference between the noise signal value and the mean value of each sampling point reflects the degree of deviation between the noise intensity at a certain moment and the overall level. The squared deviation of each sample point from the mean eliminates the effects of positive and negative signs, making all deviations positive. The squared deviation reflects the degree of fluctuation of the sample point relative to the mean. N-1 is an adjustment factor in the unbiased estimate to prevent underestimation of sample fluctuations. It measures the average fluctuation of all sampling points. The standard deviation σ is the square root of the mean of the squared deviation, indicating the fluctuation amplitude of the noise and reflecting the instability of the signal. Comparing the electromagnetic noise intensity standard deviation with a preset electromagnetic noise volatility threshold (e.g., 15 μT) to determine whether the standard deviation exceeds the threshold range; If the electromagnetic noise intensity standard deviation does not exceed the preset electromagnetic noise volatility threshold, the smart bracelet maintains a low frequency (e.g., 10 Hz) to monitor the electromagnetic noise intensity data; If the standard deviation of the electromagnetic noise intensity exceeds the preset electromagnetic noise volatility threshold, the smart bracelet switches to a high frequency (eg, 100 Hz) to monitor the electromagnetic noise intensity data in real time.

[0052] 302. When the electromagnetic noise intensity is greater than a preset electromagnetic noise intensity threshold, obtain a periodic characteristic of the electromagnetic noise intensity through electromagnetic spectrum analysis based on the electromagnetic noise intensity data within the preset time period, where the periodic characteristic is a periodic fluctuation characteristic; When the electromagnetic noise intensity is greater than the preset electromagnetic noise intensity threshold, based on the electromagnetic noise intensity data within the preset time period, the main frequency list X[k] of the electromagnetic noise intensity period is obtained by electromagnetic spectrum analysis and calculation, and the main frequency f in the main frequency list is max It is the frequency component with the largest amplitude (the calculation process has been explained in the previous article and will not be repeated here), corresponding to the main periodic characteristics of the signal.

[0053] 303. Extracting a confidence value of the periodic feature calculated from the periodic feature, and determining whether the confidence value of the periodic feature reaches a preset confidence threshold; The confidence value of the periodic feature is calculated based on the periodic feature to determine whether the noise signal has clear periodicity. The confidence value can avoid misjudging non-periodic signals (such as random noise) as periodic signals, ensuring that only periodic features with high confidence (such as 0.8) are subsequently processed (such as detection time adjustment), reducing the risk of misjudgment. In an industrial environment, when noise is generated by a mixture of multi-source equipment (such as the coexistence of industrial frequency and variable frequency equipment), this indicator can distinguish the dominant periodic components and improve the accuracy of period identification in complex scenarios. The confidence value calculation formula for the periodic feature is: Where C is the confidence value of the periodic characteristics of electromagnetic noise; X[k] is the main frequency list of electromagnetic noise, that is, the main period value; X[k peak ] refers to the complex amplitude of the corresponding main frequency; N represents the number of sampling points per second; k is the corresponding actual frequency; In the above formula, |X[k peak ]| 2 is the energy of the main frequency component, It is the total energy of the signal, the proportion of the energy of the main frequency component in the total energy of the signal, indicating the significance of the periodicity; Determining whether the confidence value of the periodic feature reaches a preset confidence threshold (e.g., 0.7) based on the calculation result; If yes, then executing step 304, determining a change period of the electromagnetic noise intensity in the first preset area based on the change of the electromagnetic noise intensity monitored for the preset time period; If not, step 305 is executed to predict the next environment location that the user will enter based on the user's geographical location and movement trajectory, and the next environment location is still in the first preset area.

[0054] 304. Determine a change period T of the electromagnetic noise intensity in the first preset area based on changes in the electromagnetic noise intensity monitored for a previously preset period of time (the calculation process has been described in 202 and will not be repeated here); 305. Predicting the next environment location that the user will enter based on the user's geographic location and movement trajectory, where the next environment location is still within the first preset area; Obtain the user's geographic location through the built-in GPS of the smart bracelet; The user's motion trajectory is obtained through the built-in accelerometer and gyroscope of the smart bracelet; Predict the next environment location the user is about to enter based on the user's geographic location and movement trajectory.

[0055] 306. Extracting noise periodic characteristics of the next environmental location based on a historical electromagnetic noise intensity spectrum in the noise monitoring system within the first preset area, wherein the historical electromagnetic noise intensity spectrum includes periodic characteristics of each location; The smart bracelet is connected to the noise monitoring system in the first preset area through wireless communication to obtain a historical electromagnetic noise intensity spectrum in the first preset area, wherein the historical electromagnetic noise intensity spectrum includes periodic characteristics of each position in the first preset area; According to the next environmental position, searching for a nearest neighbor position in the historical electromagnetic noise intensity map; The periodic parameters of the nearest neighbor position are extracted to obtain the noise periodic characteristics of the next environmental position.

[0056] 307. Adjust the periodic triggering time of the blood oxygen detection function of the smart bracelet to the time point when the electromagnetic noise intensity is lowest in the change period; In the identified period, the sliding window noise intensity mean is calculated by sliding the time window (The calculation process has been described in 203 and will not be repeated here); According to the mean value of the sliding window noise intensity, the midpoint of three consecutive windows with the lowest mean value is selected as the detection trigger time.

[0057] 308. Based on whether the amplitude of the change in the magnetic field noise intensity within the preset time period reaches the average electromagnetic noise intensity, determine the number of cycles within the preset time period from which the maximum electromagnetic noise value needs to be extracted, and the number of cycles within the preset time period is greater than 1. Based on the magnetic field noise intensity monitored during the preset time period, the variation range of the magnetic field noise intensity is calculated, and the variation range is obtained by subtracting the minimum value of the magnetic field noise intensity during the preset time period from the maximum value. The formula for calculating the variation range of the magnetic field noise intensity is: A=I max -I min ; In the above formula, A is the amplitude of change of magnetic field noise intensity; max is the maximum value of the magnetic field noise intensity within the preset time period; min The minimum value of the magnetic field noise intensity within the preset time period; Based on the calculation result, it is determined whether the change amplitude of the magnetic field noise intensity within the preset time period reaches the average electromagnetic noise intensity; if so, step 309 is executed, and the smart bracelet extracts the maximum electromagnetic noise value in each cycle; If not, step 310 is executed, and the smart bracelet extracts the maximum value of electromagnetic noise within a cycle.

[0058] 309. The smart bracelet extracts the maximum value of electromagnetic noise in each cycle; Extract the highest value I of each cycle within the preset time length peak,i =max{I1, I2, ..., In}, wherein i is the maximum value of the electromagnetic noise intensity extracted in the i-th cycle, and n is the number of extracted cycles.

[0059] 310. The smart bracelet extracts the maximum value of electromagnetic noise within a cycle; Extract the highest value I of a cycle within the preset time length peak,i .

[0060] 311. Based on the extracted maximum electromagnetic noise value, calculate the average value of the maximum electromagnetic noise value; For the periodic noise generated by industrial equipment, the statistical averaging of multiple cycles can more accurately capture the steady-state characteristics of the noise. If a certain cycle has an extremely high value (such as 150μT) due to sudden interference, the arithmetic mean will be pulled down by other normal cycle values (such as 85μT, 90μT, 92μT), avoiding excessive threshold adjustment caused by a single abnormal value (such as incorrectly raising the warning threshold to cover up the actual hypoxemia risk). Calculate the average value of the highest value of electromagnetic noise The formula is: In the above formula, It represents the average value of the maximum electromagnetic noise value obtained by adding the maximum value of each cycle in n cycles and dividing the total value by the number of cycles n.

[0061] 312. Inputting the average value of the highest electromagnetic noise intensity in the change period into a pre-trained correlation model of oxygen delivery efficiency and noise to obtain an oxygen delivery efficiency impact value; The oxygen delivery efficiency and noise correlation model is trained and constructed. The training and construction process of the oxygen delivery efficiency and noise correlation model is as follows: Collect electromagnetic noise, user blood oxygen levels, and oxygen delivery efficiency data from laboratory and real-world scenarios to obtain the original training dataset; Clean outliers from the original training dataset, normalize features, and split the dataset into training and test sets; Select a machine learning model based on the segmented dataset and establish a correlation between noise and oxygen delivery efficiency; Using a loss function and optimization algorithm, the correlation model between oxygen delivery efficiency and noise is trained through multiple rounds of iterations. The model is updated with training set data in each round to minimize the error. The test set was used to evaluate the performance of the model correlating oxygen delivery efficiency with noise, and the parameters were optimized to improve the prediction accuracy. The trained oxygen delivery efficiency and noise association model f noise Embedded in smart bracelets to achieve real-time calculation; The average value of the highest electromagnetic noise intensity in the change period Input the pre-trained oxygen delivery efficiency and noise association model f noise , calculate the basic impact value of oxygen delivery efficiency, the formula for calculating the basic impact value of oxygen delivery efficiency is: where ΔE O2,base is the basic impact value of oxygen delivery efficiency corresponding to the average value of the highest value of electromagnetic noise intensity in the change period; based on the user's historical blood oxygen value, the basic oxygen delivery efficiency impact value is corrected. It should be determined in combination with the user's historical blood oxygen level to adapt to the individual user characteristics. The formula for calculating the corrected basic oxygen delivery efficiency impact value is: ΔE O2 =ΔE O2,base (1+k); where ΔE O2 is the corrected oxygen delivery efficiency impact value; the ΔE O2,baseis the basic impact value of oxygen delivery efficiency corresponding to the average of the highest electromagnetic noise intensity values during the variation period; k is the sensitivity coefficient, or correction factor, ranging from k∈[0.1, 0.3] and determined based on the user's historical blood oxygen level. Multiplying the two together yields the corrected basic impact value of oxygen delivery efficiency through linear scaling.

[0062] 313. Based on the oxygen delivery efficiency impact value, the smart bracelet calculates the current blood oxygen detection reminder threshold; when calculating the oxygen delivery efficiency adjustment value, in addition to considering the user's historical blood oxygen level, the impact of environmental noise on the oxygen delivery efficiency must also be considered. The formula for calculating the oxygen delivery efficiency adjustment value is: ΔT=а·ΔE O2 ; Where ΔT is the adjustment value of oxygen delivery efficiency; O2 is the corrected oxygen delivery efficiency impact value; а is the sensitivity factor, ranging from а∈[0.1,0.5], determined according to the ambient noise. The multiplication of the two achieves dynamic correction of the efficiency value through linear proportional adjustment; Get the default basic blood oxygen reminder threshold T in the system of the smart bracelet base , combined with the adjustment value ΔT of the oxygen delivery efficiency, the current blood oxygen detection reminder threshold is obtained. The formula of the current blood oxygen detection reminder threshold is: T new =T base +ΔT; Where T new is the current blood oxygen detection reminder threshold; base It is the default basic blood oxygen reminder threshold in the system of the smart bracelet.

[0063] 314. When the current blood oxygen value is lower than the current blood oxygen detection reminder threshold, the smart bracelet issues a health risk reminder, and the current blood oxygen value is measured according to the adjusted periodic trigger time; When the current blood oxygen value is lower than the current blood oxygen detection reminder threshold, determining the health risk reminder mode based on whether the decibel value of the surrounding environment of the smart bracelet exceeds the preset decibel value; If the decibel value of the surrounding environment does not exceed the preset decibel value, the smart bracelet vibrates and displays the risk reminder content on the screen; if the decibel value of the surrounding environment exceeds the preset decibel value, the smart bracelet emits a beep and displays the risk reminder content on the screen.

[0064] 315. The smart bracelet detects whether the user responds to the health risk reminder. Clicking the screen indicates that the user has responded to the reminder, and not clicking the screen indicates that the user has ignored the reminder. The smart bracelet detects whether the user clicks on the screen to confirm the control; If so, the smart bracelet records the user behavior as confirmed; If not, the smart bracelet records the user behavior as ignored.

[0065] 316. If the smart bracelet detects that the user has ignored the reminder three times in a row, the reminder interval is extended during the corresponding time period each day.

[0066] If the smart bracelet records that the user behavior has been ignored for three consecutive times, the reminder interval will be extended during the corresponding time period every day.

[0067] In the embodiments of the present application, real-time spectrum analysis is combined with a historical noise database to pre-match noise periodicity characteristics, and the blood oxygen detection trigger time is adjusted to the valley period with the lowest noise intensity. This allows accurate alignment of detection timing to avoid noise peak interference even when the noise periodicity is not obvious or when the user moves around the environment. This solves the problem that fixed-period detection is susceptible to noise distortion and improves the anti-interference capability and detection accuracy of blood oxygen signal acquisition. In addition, the maximum value is dynamically extracted based on the noise fluctuation amplitude, and the impact of noise on human oxygen delivery efficiency is quantified through a pre-trained model to calibrate the warning threshold. This allows the warning threshold to adapt to the actual hypoxia risk even in the presence of dual interference from electromagnetic noise on the sensor signal and the human physiological mechanism. This solves the problem that traditional fixed thresholds are prone to misjudgment or missed judgment, and improves the scientific nature and clinical reference value of health reminders.

[0068] The above describes the embodiment of the smart bracelet blood sample detection reminder method of the present application. The following describes the hardware architecture of the present application: See also Figure 4 , is a schematic diagram of the hardware architecture of the smart bracelet in an embodiment of the present application.

[0069] As a portable health monitoring terminal, the smart bracelet integrates multi-source sensors and adaptive algorithm modules, including a processor 401, a sensor 402, a storage 403, an input device 404 and an output device 405, to achieve closed-loop control of the blood oxygen detection strategy in a dynamically changing electromagnetic noise environment.

[0070] Processor 401 executes the blood oxygen threshold dynamic compensation algorithm and noise period prediction to complete real-time signal processing and decision control.

[0071] Sensor 402 includes an electromagnetic noise sensor, a blood oxygen photoelectric sensor, and an accelerometer.

[0072] The memory 403 stores the environmental noise spectrum and the dynamic threshold calculation model parameters.

[0073] The input device 404 includes a touch screen and physical buttons.

[0074] The output device 405 includes a vibration motor, a buzzer and a display screen.

[0075] Sensor 402 collects the environmental electromagnetic noise intensity, user's blood oxygen signal and movement status in real time. Processor 401 dynamically calculates the electromagnetic noise period and optimizes the detection time window based on the noise spectrum database and pre-trained model stored in memory 403. It receives user touch instructions through input device 404 to update the threshold parameters. Finally, output device 405 triggers a graded reminder according to the environmental decibel value, and writes the adjusted detection strategy back to memory 403 to complete closed-loop control.

[0076] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

[0077] As used in the above embodiments, the term “when…” may be interpreted to mean “if…” or “after…” or “in response to determining…” or “in response to detecting…”, depending on the context. Similarly, the phrases “upon determining…” or “if (stated condition or event) is detected” may be interpreted to mean “if determining…” or “in response to determining…” or “upon detecting (stated condition or event)” or “in response to detecting (stated condition or event)”, depending on the context.

[0078] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center via a wired (e.g., coaxial cable, optical fiber, digital subscriber line) or wireless (e.g., infrared, wireless, microwave, etc.) method. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrations. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state hard disk).

[0079] Those skilled in the art will appreciate that all or part of the process steps in the above-described method embodiments can be implemented by a computer program instructing the relevant hardware. The program can be stored in a computer-readable storage medium, and when executed, the program can include the process steps in the above-described method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM or random access memory (RAM), magnetic disks, or optical disks.

Claims

1. A blood oxygen monitoring reminder method for a smart bracelet, characterized in that: include: When the user is in the first preset area, the smart bracelet monitors the electromagnetic noise intensity around the user in real time; When the electromagnetic noise intensity is greater than a preset noise intensity threshold, determining a change period of the electromagnetic noise intensity in the first preset area based on changes in the electromagnetic noise intensity monitored over a previously preset period of time; Adjusting the periodic triggering time of the blood oxygen detection function of the smart bracelet to the time point when the electromagnetic noise intensity is lowest in the change period; According to the actual efficiency of oxygen delivery in the human body corresponding to the highest value of the electromagnetic noise intensity in the change period, the blood oxygen detection reminder threshold is increased to obtain the current blood oxygen detection reminder threshold; When the current blood oxygen value is lower than the current blood oxygen detection reminder threshold, the smart bracelet issues a health risk reminder, and the current blood oxygen value is measured according to the adjusted periodic trigger time.

2. The method according to claim 1, characterized in that When the user is in the first preset area, the smart bracelet is used to monitor the electromagnetic noise intensity around the user in real time, specifically including: When the smart bracelet is within the first preset area, the smart bracelet initially monitors electromagnetic noise intensity data at a low frequency and calculates the standard deviation of the electromagnetic noise intensity based on the monitoring data; Comparing the electromagnetic noise intensity standard deviation with a preset electromagnetic noise volatility threshold to determine whether the standard deviation exceeds the threshold range; If the electromagnetic noise intensity standard deviation does not exceed the preset electromagnetic noise volatility threshold, the smart bracelet maintains low-frequency monitoring of electromagnetic noise intensity data; If the standard deviation of the electromagnetic noise intensity exceeds the preset electromagnetic noise volatility threshold, the smart bracelet switches to high-frequency real-time monitoring of electromagnetic noise intensity data.

3. The method according to claim 1, characterized in that When the electromagnetic noise intensity is greater than a preset noise intensity threshold, determining a change period of the electromagnetic noise intensity in the first preset area based on changes in the electromagnetic noise intensity monitored for a previously preset period of time specifically includes: When the electromagnetic noise intensity is greater than a preset electromagnetic noise intensity threshold, a periodic characteristic of the electromagnetic noise intensity is obtained by electromagnetic spectrum analysis based on the electromagnetic noise intensity data within the preset time period, where the periodic characteristic is a periodic fluctuation characteristic; Extracting a confidence value of the periodic feature calculated from the periodic feature, and determining whether the confidence value of the periodic feature reaches a preset confidence threshold; If the confidence value of the periodic feature reaches a preset confidence threshold, determining the change period of the electromagnetic noise intensity in the first preset area based on the change of the electromagnetic noise intensity monitored for a previously preset period of time; If the periodic feature does not reach the preset confidence threshold, predicting the next environment location that the user will enter based on the user's geographic location and movement trajectory, and the next environment location is still in the first preset area; The noise periodic characteristics of the next environmental position are extracted based on the historical electromagnetic noise intensity spectrum of the noise monitoring system in the first preset area, wherein the historical electromagnetic noise intensity spectrum includes the periodic characteristics of each position.

4. The method according to claim 1, wherein According to the actual efficiency of oxygen delivery in the human body corresponding to the highest value of the electromagnetic noise intensity in the change period, the blood oxygen detection reminder threshold is increased to obtain the current blood oxygen detection reminder threshold, specifically including: The smart bracelet calculates an average value of the maximum electromagnetic noise intensity in the change period based on the maximum electromagnetic noise intensity in the change period; Inputting the average value of the highest electromagnetic noise intensity in the change period into a pre-trained correlation model of oxygen delivery efficiency and noise to obtain an oxygen delivery efficiency impact value; According to the oxygen delivery efficiency impact value, the smart bracelet obtains the current blood oxygen detection reminder threshold by calculation.

5. The method according to claim 4, characterized in that The smart bracelet calculates an average value of the maximum electromagnetic noise intensity in the change period based on the maximum electromagnetic noise intensity in the change period, specifically including: Based on whether the amplitude of the change in the magnetic field noise intensity within the preset time period reaches the average electromagnetic noise intensity, determining the number of cycles within the preset time period from which the maximum electromagnetic noise value needs to be extracted, wherein the number of cycles within the preset time period is greater than 1; If the variation reaches the average value of the electromagnetic noise intensity, the smart bracelet extracts the maximum value of the electromagnetic noise in each cycle; If the variation amplitude does not reach the average value of the electromagnetic noise intensity, the smart bracelet extracts the maximum value of the electromagnetic noise within a cycle; Based on the extracted maximum electromagnetic noise values, an average value of the maximum electromagnetic noise values is calculated.

6. The method according to claim 1, wherein When the current blood oxygen value is lower than the current blood oxygen detection reminder threshold, the smart bracelet issues a health risk reminder, and the current blood oxygen value is measured according to the adjusted periodic trigger time. After the step, the method further includes: The smart bracelet detects whether the user responds to the health risk reminder, and clicking the screen indicates that the reminder has been responded to, and not clicking indicates that the reminder has been ignored; If the smart bracelet detects that the user has ignored the reminder three times in a row, the reminder interval is extended during the corresponding time period each day.

7. The method according to claim 1, characterized in that When the current blood oxygen value is lower than the current blood oxygen detection reminder threshold, the smart bracelet issues a health risk reminder, and the current blood oxygen value is measured according to the adjusted periodic trigger time, specifically including: When the current blood oxygen value is lower than the current blood oxygen detection reminder threshold, determining the health risk reminder mode based on whether the decibel value of the surrounding environment of the smart bracelet exceeds the preset decibel value; If the ambient decibel value does not exceed the preset decibel value, the smart bracelet vibrates and displays risk reminder content on the screen; If the decibel value of the surrounding environment exceeds the preset decibel value, the smart bracelet will emit a beep and display risk reminder content on the screen.

8. A smart bracelet, characterized in that: include: one or more processors and memory; The memory is coupled to the one or more processors, and the memory is used to store computer program code, where the computer program code includes computer instructions. The one or more processors call the computer instructions to enable the smart bracelet to execute the method according to any one of claims 1 to 7.

9. A computer-readable storage medium comprising instructions, characterized in that: When the instruction is executed on the smart bracelet, the smart bracelet executes the method according to any one of claims 1 to 7.

10. A computer program product, characterized in that When the computer program product runs on the smart bracelet, the smart bracelet executes the method according to any one of claims 1 to 7.

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