Time processing methods, devices, equipment, media and systems for smart wearable devices

Abstract

This application relates to a time synchronization method, apparatus, device, medium, and system for smart wearable devices, belonging to the technical field of smart wearable devices. The time synchronization method for smart wearable devices includes: acquiring the acceleration of multiple smart wearable devices; recording first data collected by the multiple smart wearable devices after the acceleration of any two smart wearable devices exceeds a preset threshold; the first data includes acceleration data, angular velocity data, and air pressure data; and performing time synchronization processing on the clocks of the multiple smart wearable devices based on the first data. The technical solution of this application can improve the accuracy and reliability of time synchronization without requiring additional hardware.

Description

Technical Field

[0001] This application relates to the field of smart wearable device technology, and in particular to a smart wearable device time synchronization processing method, apparatus, computing device, computer-readable storage medium and system. Background Technology

[0002] Smart wearable devices are a general term for intelligent devices designed and developed using wearable technology for daily wear, medical monitoring, and assisted treatment. Examples include glasses, gloves, watches, blood pressure monitors, blood glucose meters, electrocardiogram monitors, sleep monitors, and brain-computer interfaces. In a broader sense, smart wearable devices include fully functional devices that can achieve complete or partial functionality without relying on a smartphone, such as smartwatches or smart glasses, as well as devices that focus on a specific application function and require cooperation with other devices such as smartphones, such as various smart bracelets and smart jewelry for vital sign monitoring. With technological advancements and changing user needs, the form and application trends of smart wearable devices are constantly evolving.

[0003] Because of their portability and intelligence, smart wearable devices allow athletes to collect exercise data by wearing them on their limbs or other body parts, thereby assessing training effectiveness or limb health. However, inconsistencies in the time displayed by multiple smart wearable devices can lead to inaccurate assessment results. Existing technologies include methods for synchronizing the time of multiple smart wearable devices, but these methods suffer from inaccuracies or require additional hardware, resulting in high costs. Summary of the Invention

[0004] In view of the above, this application provides a method, apparatus, computing device, computer-readable storage medium and system for time processing of smart wearable devices in order to solve at least one problem existing in the background art.

[0005] To achieve the above objectives, the technical solution of this application is implemented as follows:

[0006] In a first aspect, embodiments of this application provide a time synchronization method for smart wearable devices, applied to a smart wearable device time synchronization system. The smart wearable device time synchronization system includes a server and multiple smart wearable devices, each smart wearable device including a clock, accelerometer, gyroscope, and barometer. The method includes:

[0007] Obtain the acceleration of multiple smart wearable devices;

[0008] After the acceleration of any two of the smart wearable devices exceeds a preset threshold, the first data collected by the multiple smart wearable devices is recorded; the first data includes acceleration data, angular velocity data, and air pressure data.

[0009] Based on the first data, the clocks of the multiple smart wearable devices are synchronized.

[0010] Optionally, obtaining the acceleration of the plurality of smart wearable devices includes:

[0011] In response to the user's key press operation, the smart wearable device is controlled to enter the time synchronization mode;

[0012] After entering the time synchronization mode, the acceleration of multiple smart wearable devices is continuously acquired.

[0013] Optionally, the recording of the first data collected by the multiple smart wearable devices includes:

[0014] Record the measured values ​​of the first data and the corresponding time points in multiple smart wearable devices, and obtain the time differences in acceleration data, angular velocity data and air pressure data of multiple smart wearable devices respectively.

[0015] Optionally, the step of recording the measured values ​​and corresponding time points of the first data from multiple smart wearable devices, and obtaining the time differences corresponding to the acceleration data, angular velocity data, and air pressure data of the multiple smart wearable devices, includes:

[0016] Record the first time point of maximum acceleration of multiple smart wearable devices and obtain first time difference data; the first time difference data is the difference data between the first time points of multiple smart wearable devices.

[0017] Optionally, the step of recording the measured values ​​and corresponding time points of the first data from multiple smart wearable devices, and obtaining the time differences corresponding to the acceleration data, angular velocity data, and air pressure data of the multiple smart wearable devices, includes:

[0018] Record the angular velocity mutation trends of multiple smart wearable devices and obtain second time difference data; the second time difference data is the difference data between the second time points when multiple smart wearable devices produce the same mutation trend.

[0019] Optionally, the step of recording the measured values ​​and corresponding time points of the first data from multiple smart wearable devices, and obtaining the time differences corresponding to the acceleration data, angular velocity data, and air pressure data of the multiple smart wearable devices, includes:

[0020] Record the air pressure change trends of multiple smart wearable devices and obtain third time difference data; the third time difference data is the difference data between the third time points when multiple smart wearable devices generate the same air pressure change trend.

[0021] Optionally, the step of performing time synchronization processing on the clocks of the plurality of smart wearable devices based on the first data includes:

[0022] Based on the time differences in acceleration data, angular velocity data, and air pressure data obtained from multiple smart wearable devices, and combined with probability principles, the clocks of the multiple smart wearable devices are synchronized.

[0023] Optionally, the step of performing time synchronization processing on the clocks of the plurality of smart wearable devices based on the first data includes:

[0024] The first data is jointly processed using a weighted fusion algorithm, and the clocks of the multiple smart wearable devices are synchronized based on the processing results.

[0025] Optionally, the step of performing time synchronization processing on the clocks of the plurality of smart wearable devices based on the first data includes:

[0026] Obtain the operating temperature of multiple smart wearable devices;

[0027] Based on the operating temperatures of multiple smart wearable devices, temperature compensation is applied to the first data collected by the smart wearable devices to obtain the second data.

[0028] Based on the second data, the clocks of the multiple smart wearable devices are synchronized.

[0029] Optionally, the step of performing time synchronization processing on the clocks of the plurality of smart wearable devices based on the first data includes:

[0030] The air pressure of the working environment of multiple smart wearable devices is obtained, and the acceleration data collected by the smart wearable devices is corrected to obtain the third data.

[0031] Based on the third data, the clocks of the multiple smart wearable devices are synchronized.

[0032] Optionally, before controlling the smart wearable device to enter time synchronization mode in response to a user's key press, the method further includes:

[0033] The accelerometers and gyroscopes of the aforementioned smart wearable devices are calibrated.

[0034] Optionally, the calibration of the accelerometers and gyroscopes of the plurality of smart wearable devices includes:

[0035] The zero-point drift of at least two axes of the accelerometer in a stationary state is obtained and calibrated accordingly;

[0036] The zero-point drift of the gyroscope when it is placed horizontally and stationary is obtained and calibrated accordingly.

[0037] Secondly, embodiments of this application provide a time synchronization processing device for smart wearable devices, applied to a time synchronization processing system for smart wearable devices. The time synchronization processing system includes a server and multiple smart wearable devices, each smart wearable device including a clock, accelerometer, gyroscope, and barometer. The device includes:

[0038] The acquisition module is used to acquire the acceleration of multiple smart wearable devices;

[0039] The recording module is used to record first data collected by multiple smart wearable devices after the acceleration of any two of the smart wearable devices exceeds a preset threshold; the first data includes acceleration data, angular velocity data, and air pressure data.

[0040] The processing module is used to perform time synchronization processing on the clocks of the multiple smart wearable devices based on the first data.

[0041] A third aspect is a computing device, the computing device comprising: a storage unit, a communication bus, and a processing unit, wherein:

[0042] The storage component is used to store the time synchronization processing method program for smart wearable devices;

[0043] The communication bus is used to enable communication between the storage component and the processing component;

[0044] The processing component is used to execute the smart wearable device time synchronization method program to implement the steps of any of the methods described above.

[0045] Fourthly, embodiments of this application provide a computer-readable storage medium storing an executable program, which, when executed by a processor, implements the steps of any of the methods described above.

[0046] Fifthly, embodiments of this application provide a time synchronization system for a smart wearable device, comprising:

[0047] The server includes the smart wearable device time processing device described above;

[0048] Multiple smart wearable devices, including: a clock, an accelerometer, a gyroscope, and a barometer.

[0049] The smart wearable device time synchronization processing method, apparatus, computing device, computer-readable storage medium, and system provided in this application include: acquiring the acceleration of multiple smart wearable devices; recording first data collected by the multiple smart wearable devices after the acceleration of any two smart wearable devices exceeds a preset threshold; the first data includes acceleration data, angular velocity data, and air pressure data; and performing time synchronization processing on the clocks of the multiple smart wearable devices based on the first data. It can be seen that the smart wearable device time synchronization processing method, apparatus, computing device, computer-readable storage medium, and system of this application, after the acceleration of any two smart wearable devices exceeds a preset threshold (by clapping hands or feet), records the first data (including acceleration data, angular velocity data, and air pressure data) collected by the multiple smart wearable devices, and performs time synchronization processing on the clocks of the multiple smart wearable devices using the first data. Because it combines acceleration data, angular velocity data, and air pressure data, it reduces the impact of noise from a single electronic device on time synchronization, improves the accuracy and reliability of time synchronization, and requires no additional hardware. Therefore, the smart wearable device time synchronization processing method, apparatus, computing device, computer-readable storage medium and system of the present application embodiments can solve the technical problems of inaccurate time synchronization or high cost of smart wearable devices.

[0050] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0051] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0052] Figure 1 This is a flowchart illustrating the time synchronization method for a smart wearable device provided in Embodiment 1 of this application.

[0053] Figure 2 This is a schematic diagram of the algorithm flow for preprocessing barometric pressure data in the time synchronization method of the smart wearable device provided in Embodiment 1 of this application.

[0054] Figure 3 This is a schematic diagram of the time synchronization processing device for a smart wearable device provided in Embodiment 2 of this application;

[0055] Figure 4 This is a schematic diagram of the structure of the computing device provided in Embodiment 3 of this application;

[0056] Figure 5 This is a schematic diagram of the time synchronization system for a smart wearable device provided in Embodiment 5 of this application.

[0057] Explanation of reference numerals in the attached figures:

[0058] 30. Time synchronization device; 31. Acquisition module; 32. Recording module; 33. Processing module; 50. Computing device; 51. Storage component; 52. Communication bus; 53. Processing component; 54. Input device; 55. Output device; 56. External communication interface; 61. Server; 62. Smart wearable device. Detailed Implementation

[0059] Exemplary embodiments of the present application will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present application are shown in the drawings, it should be understood that the present application may be implemented in various forms and should not be limited to the specific embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present application and to fully convey the scope of the disclosure of the present application to those skilled in the art.

[0060] The following description provides numerous specific details to offer a more thorough understanding of this application. However, it will be apparent to those skilled in the art that this application can be practiced without one or more of these details. In other instances, to avoid confusion with this application, some technical features well-known in the art have not been described; that is, not all features of actual embodiments are described herein, nor are well-known functions and structures described in detail.

[0061] To fully understand this application, detailed steps and structures will be presented in the following description to illustrate the technical solution of this application. Preferred embodiments of this application are described in detail below; however, in addition to these detailed descriptions, this application may have other implementation methods.

[0062] The applicant of this application discovered during the research and development that, in the prior art, time synchronization of multiple smart wearable devices can be achieved through Bluetooth, Wireless Fidelity (WiFi), or LoRa (Long Range Communication). Time synchronization can also be achieved through wired methods, such as through a serial port and a serial communication cable.

[0063] However, Bluetooth, WiFi, or LoRa communication methods require additional communication modules, increasing the cost of smart wearable devices. Given the large number of smart wearable device manufacturers, time synchronization via Bluetooth, WiFi, or LoRa can only be applied to a limited number of products, hindering widespread adoption. Furthermore, even with Bluetooth, WiFi, or LoRa, the randomness of data transmission delays leads to significant time errors, failing to meet the time synchronization requirements for collecting motion data.

[0064] Wired time synchronization is cumbersome and cannot be performed frequently. Furthermore, the microcontroller units (MCUs) in consumer-grade smart wearable devices suffer from crystal frequency drift, which can easily lead to inaccurate time readings, and the data cannot be saved after power loss. Therefore, frequent time synchronization is necessary, making wired time synchronization difficult to promote.

[0065] Therefore, based on further research and development by the applicant, the following technical solution was proposed.

[0066] Example 1

[0067] This application provides a time synchronization method for a smart wearable device. The method can be implemented by a computer, which can be a computing device configured with a processor. The processor can be a general-purpose processor, a digital signal processor (DSP), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0068] The method is applied to a time synchronization system for smart wearable devices, which includes a server and multiple smart wearable devices. The server can be a computer implementing the method described above, and the smart wearable devices include a clock, accelerometer, gyroscope, and barometer. Specifically, the server can be a smart mobile terminal, such as a mobile phone, tablet, or laptop, or a cloud server. The smart wearable devices can be smart bracelets or smart ankle bracelets. The server and the smart wearable devices can communicate via Bluetooth, WiFi, or LoRa modules. Understandably, for cost reasons, usually only one of these methods is used, rather than two or more.

[0069] refer to Figure 1 The method includes:

[0070] Step 101: Obtain the acceleration of multiple smart wearable devices;

[0071] Step 102: After the acceleration of any two smart wearable devices exceeds a preset threshold, record the first data collected by multiple smart wearable devices;

[0072] The first set of data includes acceleration data, angular velocity data, and air pressure data;

[0073] Step 103: Based on the first data, synchronize the clocks of multiple smart wearable devices.

[0074] The time synchronization method in this embodiment includes colliding two hands wearing wristbands together, or colliding a hand and foot wearing both wristbands and ankle bracelets together. This allows time synchronization based on changes in acceleration, angular velocity, and air pressure during the collision, and the corresponding time intervals. For example, in a hand-foot collision, theoretically, the time when the wristband generates maximum acceleration and the time when the ankle bracelet generates maximum acceleration should be the same. If the server obtains inconsistent times for the two maximum accelerations, it indicates a time synchronization problem. The time of either smart wearable device can be adjusted to match the other. This adjustment can be performed by the smart wearable device following a command sent from the server. Understandably, as mentioned earlier, the time synchronization of consumer-grade smart wearable devices may gradually become invalid after a period of operation or after being turned off, thus requiring frequent time synchronization.

[0075] In step 101, the acceleration is obtained by reading the accelerometer of the smart wearable device, and the acceleration value is monitored after acquisition. Subsequent steps are executed only after the acceleration of any two smart wearable devices exceeds a preset threshold.

[0076] In step 102, it is understood that whether it's a collision between two hands or between hands and feet, the acceleration will increase rapidly. Therefore, recording begins once the acceleration of any two of the smart wearable devices exceeds a preset threshold. The recorded data includes acceleration data, angular velocity data, and air pressure data, reducing the impact of noise from a single electronic device on time synchronization and improving the accuracy and reliability of timekeeping. Experimental data shows that the timekeeping error of a single accelerometer is approximately ±50ms, and the error can be reduced to within ±10ms after fusing the three types of data.

[0077] Accelerometers, gyroscopes, and barometers are all electronic components found in common smart wearable devices, requiring no additional hardware costs and making the time synchronization method in this embodiment easier to implement universally. The preset threshold can be the lowest acceleration value required to confirm a collision. The preset acceleration threshold is set to 50 m / s² (approximately 5g). When the acceleration of any two devices simultaneously exceeds this value and remains so for 20 ms, it is considered a valid collision.

[0078] Specifically, the recorded data includes three types of data: data recorded 500ms before and after the collision following the trigger, for example:

[0079] Acceleration data: Real-time values ​​of X / Y / Z axes (unit: m / s²);

[0080] Angular velocity data: roll / pitch / yaw rate (unit: ° / s);

[0081] Air pressure data: absolute air pressure value (unit: hPa) and rate of change.

[0082] In step 103, time synchronization can be performed by comprehensively considering acceleration data, angular velocity data, and air pressure data to improve the accuracy and reliability of time synchronization. Specifically, time synchronization can be performed pairwise. For example, acceleration data can reveal the time difference (i.e., asynchrony) between two smart wearable devices. Similarly, angular velocity data and air pressure data can also reveal the time difference between two smart wearable devices. By comprehensively considering these three differences, the two smart wearable devices are synchronized. After the two smart wearable devices have completed time synchronization, one of them is used as the reference time to synchronize with the other smart wearable device pairwise, until all smart wearable devices have completed pairwise time synchronization.

[0083] The time synchronization method for smart wearable devices in this application embodiment records first data (including acceleration data, angular velocity data, and air pressure data) collected by multiple smart wearable devices after the acceleration of any two smart wearable devices exceeds a preset threshold (by clapping with both hands or clapping with hands and feet). The clocks of the multiple smart wearable devices are then synchronized using the first data. By combining acceleration data, angular velocity data, and air pressure data, the impact of noise from a single electronic device on time synchronization is reduced, improving the accuracy and reliability of time synchronization, and no additional hardware is required.

[0084] In some embodiments of this application, obtaining the acceleration of the plurality of smart wearable devices includes:

[0085] In response to the user's key press operation, the smart wearable device is controlled to enter the time synchronization mode;

[0086] After entering the time synchronization mode, the acceleration of multiple smart wearable devices is continuously acquired.

[0087] In short, user interaction is required for the smart wearable device to enter time synchronization mode. Otherwise, the device doesn't need to monitor data from its electronic components and can even go into sleep mode, saving energy. After the user presses a button, the smart wearable device exits sleep mode and enters time synchronization mode, beginning to monitor data from its electronic components. In time synchronization mode, it first acquires the device's acceleration data, without needing to collect data from all electronic components. Only when the acceleration exceeds a preset threshold does it begin recording data from multiple electronic components, such as angular velocity and air pressure, again contributing to energy savings. For example, when the smart wearable device is a fitness tracker, its daily standby power consumption accounts for approximately 30%, and power consumption can be reduced by 5% when time synchronization mode is not activated.

[0088] Specifically, button operations can be different from ordinary short press operations, such as pressing and holding a button for more than 2 seconds.

[0089] In some embodiments of this application, recording the first data collected by the plurality of smart wearable devices includes:

[0090] Record the measured values ​​of the first data and the corresponding time points in multiple smart wearable devices, and obtain the time differences in acceleration data, angular velocity data and air pressure data of multiple smart wearable devices respectively.

[0091] As mentioned earlier, because time synchronization is required, it is necessary to record the measured values ​​and their corresponding time points. For example, time synchronization involves collision detection. Therefore, if the initial data from two smart wearable devices have the same value or trend, but the corresponding time points are different, it indicates that the time is out of sync and time synchronization is required.

[0092] In some embodiments of this application, the step of recording the measured values ​​and corresponding time points of the first data from multiple smart wearable devices, and obtaining the time differences corresponding to the acceleration data, angular velocity data, and air pressure data of the multiple smart wearable devices, includes:

[0093] Record the first time point of maximum acceleration of multiple smart wearable devices and obtain first time difference data; the first time difference data is the difference data between the first time points of multiple smart wearable devices.

[0094] In a collision, the two smart wearable devices should reach their maximum acceleration at the same time. If the times displayed on the smart wearable devices are different, there is a first time difference data, which can be used for time synchronization. This is understandable; other acceleration data, such as acceleration trend changes, can also be used for time synchronization, but these will not be detailed here.

[0095] In some embodiments of this application, the step of recording the measured values ​​and corresponding time points of the first data from multiple smart wearable devices, and obtaining the time differences corresponding to the acceleration data, angular velocity data, and air pressure data of the multiple smart wearable devices, includes:

[0096] Record the angular velocity mutation trends of multiple smart wearable devices and obtain second time difference data; the second time difference data is the difference data between the second time points when multiple smart wearable devices produce the same mutation trend.

[0097] Slightly different from acceleration, in a collision, the maximum angular velocities of the two smart wearable devices do not necessarily occur at the same time, but they can produce abrupt changes at the same time, thus allowing for the acquisition of a second time difference.

[0098] In some embodiments of this application, the step of recording the measured values ​​and corresponding time points of the first data from multiple smart wearable devices, and obtaining the time differences corresponding to the acceleration data, angular velocity data, and air pressure data of the multiple smart wearable devices, includes:

[0099] Record the air pressure change trends of multiple smart wearable devices and obtain third time difference data; the third time difference data is the difference data between the third time points when multiple smart wearable devices generate the same air pressure change trend.

[0100] Understandably, during a collision, the maximum air pressure values ​​of the two smart wearable devices may not occur at the same time, but they can be found at the same point in time within the changing trends. Therefore, a third time difference can be obtained.

[0101] In some embodiments of this application, the step of performing time synchronization processing on the clocks of the plurality of smart wearable devices based on the first data includes:

[0102] Based on the time differences in acceleration data, angular velocity data, and air pressure data obtained from multiple smart wearable devices, and combined with probability principles, the clocks of the multiple smart wearable devices are synchronized.

[0103] The probability principle here can be that among the three types of data obtained from accelerometers, gyroscopes, and barometers, time synchronization can be performed based on two of the more closely related data. That is, the probability that two electronic devices simultaneously have high noise, causing the acquired data to deviate and affecting the time synchronization, is much lower than the probability that one electronic device has high noise and affecting the time synchronization.

[0104] For example, based on acceleration and angular velocity data, it shows that smart wearable device A's time is 0.1 seconds faster than smart wearable device B's time. However, based on barometric pressure data, it shows that smart wearable device A's time is 0.05 seconds slower than smart wearable device B's time. Therefore, according to probability theory, we can determine that smart wearable device A's time is 0.1 seconds faster than smart wearable device B's time. Note that the data is only an example and does not represent such a large time difference in real-world smart wearable devices.

[0105] If the judgment results of the three data are consistent, then it is more reliable, that is, it improves the accuracy and reliability of time synchronization.

[0106] In some embodiments of this application, the step of performing time synchronization processing on the clocks of the plurality of smart wearable devices based on the first data includes:

[0107] The first data is jointly processed using a weighted fusion algorithm, and the clocks of the multiple smart wearable devices are synchronized based on the processing results.

[0108] Unlike the probabilistic algorithms mentioned above, the weighted fusion algorithm integrates data from multiple electronic devices to comprehensively determine time differences for time synchronization.

[0109] For example, it can be processed using the Kalman filter algorithm or the wavelet transform algorithm. The Kalman filter is a recursive filtering algorithm based on a state-space model. It estimates and updates the system state and fuses measurement data from multiple electronic devices. In the detection of hand and foot slapping actions, data such as acceleration, angular velocity, and air pressure can be used as system measurements, and the Kalman filter algorithm can be used to estimate the time of the hand and foot slapping action.

[0110] Specifically, the algorithm process can be as follows: First, establish the system's state equation and measurement equation to describe the relationship between the system's dynamic characteristics and the electronic device measurements. Then, based on the initial state estimate and measurement data, recursively calculate the system's state estimate and covariance matrix. In each iteration, assign different weights to the data from different electronic devices based on the reliability of the measurement data (i.e., the variance of the measurement noise), thereby achieving weighted data fusion.

[0111] Wavelet transform is a time-frequency analysis method that decomposes a signal into sub-signals of different frequencies, thereby extracting local features of the signal. In the detection of hand and foot slapping actions, wavelet transform can be used to decompose data from accelerometers, gyroscopes, and barometers to extract characteristic frequency components related to the slapping action.

[0112] Specifically, the algorithm process can be as follows: First, select appropriate wavelet basis functions to perform wavelet decomposition on the electronic device data to obtain wavelet coefficients at different scales. Then, assign weights to the data of different electronic devices based on the characteristics of the wavelet coefficients (such as energy, amplitude, etc.). For example, if the wavelet coefficients of a certain electronic device have high energy in a specific frequency range, it indicates that the electronic device has a strong response to the slapping action in that frequency range, and can be assigned a larger weight. Finally, reconstruct the weighted wavelet coefficients to obtain the fused signal, thereby determining the time when the hand and foot slapping action occurs.

[0113] Understandably, both the Kalman filter algorithm and the wavelet transform algorithm are existing weighted fusion algorithms, and will not be described in detail here.

[0114] In some embodiments of this application, the step of performing time synchronization processing on the clocks of the plurality of smart wearable devices based on the first data includes:

[0115] Obtain the operating temperature of multiple smart wearable devices;

[0116] Based on the operating temperatures of multiple smart wearable devices, temperature compensation is applied to the first data collected by the smart wearable devices to obtain the second data.

[0117] Based on the second data, the clocks of the multiple smart wearable devices are synchronized.

[0118] In other words, the measurement results of electrical components such as accelerometers, gyroscopes, and barometers in smart wearable devices can be affected by temperature, resulting in deviations. However, the degree of influence is related to the temperature value, and temperature compensation can be performed accordingly to correct the measured values ​​and improve measurement accuracy.

[0119] As mentioned earlier, smart wearable devices also include an MCU (Microcontroller Unit), which has a built-in temperature sensor. This sensor can be used to compensate for the temperature readings of electrical components such as accelerometers, gyroscopes, and barometers. Specifically, temperature compensation can be performed every 5 minutes.

[0120] Specifically, the calculation of the temperature compensation amount can be referenced in expression (1):

[0121] Δt=k1(T-T0)^2+k2(T-T0) (1)

[0122] Wherein, Δt is the compensation amount, and its dimension is the same as the measured value being compensated. For example, the dimension of the compensation acceleration is the same as that of the acceleration. k1 and k2 are calibration coefficients, which need to be accurately calibrated through experiments to ensure that the compensation amount Δt can accurately reflect the temperature drift. T0 is the reference temperature of 25℃, i.e., the base temperature. T is the actual measured temperature of the MCU.

[0123] The following example illustrates this:

[0124] When T=40℃, T0=25℃, k1=0.001, k2=0.01, the acceleration compensation is:

[0125] Δt = 0.001 × (15)² + 0.01 × 15 = 0.375 m / s², that is, the measured acceleration needs to be reduced by 0.375 m / s².

[0126] In some embodiments of this application, the step of performing time synchronization processing on the clocks of the plurality of smart wearable devices based on the first data includes:

[0127] The air pressure of the working environment of multiple smart wearable devices is obtained, and the acceleration data collected by the smart wearable devices is corrected to obtain the third data.

[0128] Based on the third data, the clocks of the multiple smart wearable devices are synchronized.

[0129] Understandably, air pressure affects accelerometer measurements, and the main factor affecting air pressure is altitude. Therefore, this embodiment mainly considers that smart wearable devices will be used in high-altitude areas, so air pressure correction is needed to expand the usage scenarios.

[0130] Specifically, the correction for the acceleration measurement can be referred to expression (2).

[0131] a correction = a measurement × (1 + k × (ΔP / P0)) (2)

[0132] Where, 'a' is the correction amount, and its dimension is the same as the measured value being corrected. For example, the dimension of the correction acceleration is the same as that of acceleration. 'k' is the accelerometer characteristic coefficient, which needs to be accurately calibrated through experiments to ensure that the correction amount 'a' can accurately reflect the influence of air pressure. 'P0' is the reference air pressure, which is generally the standard atmospheric pressure. 'ΔP' is the absolute value of the measured air pressure minus the standard atmospheric pressure.

[0133] The following example illustrates this:

[0134] High-altitude scenario: At an altitude of 3000 meters (air pressure approximately 700 hPa), ΔP = 700 - 1013 = -313 hPa. If k = 0.002, the acceleration correction value a_correction = a_measured × (1 + 0.002 × (313 / 1013)) ≈ a_measured × 1.006, meaning the measured value needs to be multiplied by 1.006.

[0135] Specifically, in acquiring atmospheric pressure data of the working environment, the atmospheric pressure data can be preprocessed. Preprocessing includes:

[0136] 1. Filtering. The raw data contains a lot of noise and needs to be filtered. For example, smoothing filtering can effectively reduce high-frequency noise.

[0137] 2. Relative Change Statistics. Statistical analysis of the relative changes in air pressure.

[0138] 3. Action validity assessment. Quantification and valid action labeling. This allows for the effective identification of valid actions and their duration (start and end points).

[0139] Furthermore, the algorithms for preprocessing the above air pressure data include (see reference). Figure 2 ):

[0140] Step 201: Initialization. Maximum data = Minimum data = First data; Valid count counter = 0; Maximum data max = data0; Minimum data min = data0; data0 is the initial value.

[0141] Step 202: New data > Old data. That is, data judgment: if yes, proceed to step 203; otherwise, proceed to step 204.

[0142] Step 203: Determine the maximum data. This involves assigning the new data to the maximum data, where max = newdata, and newdata is the new data.

[0143] Specifically, this can be achieved using the following code: firstimeUP++; firstimeDN=0; if (firstimeUP==1){BegindataTmp=newdata;}.

[0144] firstimeUP, firstimeDN, and BegindataTmp are variable names. firstimeUP++ means incrementing the value of the variable firstimeUP by 1; firstimeDN=0 means resetting the value of the variable firstimeDN to 0.

[0145] If `firstimeUP == 1`, it means that if the value of `firstimeUP` is equal to 1 at this point (i.e., this is the first time this logic has been executed), then the following operations are performed; `BegindataTmp = newdata`; the value of the variable `newdata` is assigned to `BegindataTmp`, thus serving as the base data for subsequent processing. In this way, as the value of `firstimeUP` increases by 1, new data is continuously assigned to the maximum data until it decreases (`firstimeDN`), at which point the assignment stops, and the data at this point is the maximum data.

[0146] Step 204: Determine the minimum data. This involves assigning the new data to the minimum data, min = newdata.

[0147] Specifically, this can be achieved using the following code: increase=0; decrease=1; EnddataTmp=newdata; if (firstimeDN==1){EnddataTmp=newdata;}.

[0148] Increase, decrease, and EnddataTmp are all variable names. increase=0 means "no increase" or "no increase state triggered". decrease=1 means "decrease" or "decrease state triggered". EnddataTmp=newdata assigns the new data newdata to the temporary variable EnddataTmp, indicating that the current new data is temporarily stored in a temporary variable, which may be used for subsequent comparison, processing, or final assignment.

[0149] If (firstimeDN == 1) {EnddataTmp = newdata;}, if firstimeDN equals 1 (the "first run" flag), then newdata is assigned to EnddataTmp again.

[0150] Step 205: Determine the difference. delta = max - min. delta is the difference between the maximum and minimum data points.

[0151] Step 206: Is the data within the set range? If yes, proceed to step 207; otherwise, return to step 202.

[0152] Step 207: Increment the number of valid counts by 1. `counter++`, where `counter` represents the number of valid counts, and `++` in the program means incrementing by 1.

[0153] Step 208: Valid data is displayed.

[0154] Specifically, this algorithm also includes: BegindataTmp and EnddataTmp. BegindataTmp temporarily holds the starting data, and EnddataTmp temporarily holds the ending data.

[0155] In some embodiments of this application, before controlling the smart wearable device to enter time synchronization mode in response to a user's key press, the method further includes:

[0156] The accelerometers and gyroscopes of the aforementioned smart wearable devices are calibrated.

[0157] Understandably, given the manufacturing differences, installation deviations, and aging issues of electronic components among different smart wearable devices, real-time calibration and error compensation are performed on the raw data of electronic components during each time synchronization process. Accelerometers and gyroscopes, in particular, exhibit significant zero-point drift, making calibration necessary to improve the accuracy and reliability of time synchronization.

[0158] In some embodiments of this application, the calibration of the accelerometers and gyroscopes of the plurality of smart wearable devices includes:

[0159] The zero-point drift of at least two axes of the accelerometer in a stationary state is obtained and calibrated accordingly;

[0160] The zero-point drift of the gyroscope when it is placed horizontally and stationary is obtained and calibrated accordingly.

[0161] Understandably, the mainstream calibration method for accelerometers is the six-axis calibration method, which involves measuring the zero-point drift of the six axes (X, Y, and Z, both positive and negative) and then performing calibration. In this embodiment, for ease of implementation, it is only necessary to obtain the zero-point drift of at least two axes in a stationary state and calibrate accordingly, which can also achieve a good calibration effect.

[0162] The gyroscope only needs to be placed horizontally and stationary for more than 30 seconds to collect multiple sets of data and calculate the average value as the initial zero bias for calibration, which can meet the calibration requirements.

[0163] To help readers better understand the technical solutions of the embodiments of this application, the following examples illustrate the solutions in conjunction with application scenarios:

[0164] Scenario 1 (Motion): Multi-device collaborative time synchronization.

[0165] Marathon runners wear a smart wristband (left wrist) and a smart ankle bracelet (right foot), and clap their hands together before the start to activate the time synchronization.

[0166] Data processing:

[0167] The maximum acceleration of the wristband occurs at t1=10:00:00.123s, and that of the ankle bracelet occurs at t2=10:00:00.156s, with Δt1=33ms;

[0168] The abrupt changes in angular velocity occur at t1'=10:00:00.120s and t2'=10:00:00.150s, respectively, with Δt2=30ms;

[0169] Based on Δt1 and Δt2, it is determined that the wristband time is about 31.5ms faster than the ankle bracelet time. The server then sends a command to adjust the ankle bracelet time to t1+31.5ms.

[0170] Scenario 2 (Daily Life): Time synchronization across devices.

[0171] Operation: The user wears both a wristband (device A) and a watch (device B) at the same time, and gently taps the watch face with the hand wearing the wristband.

[0172] Compensation process:

[0173] The ambient temperature was detected as T=28℃. The acceleration compensation was calculated as Δt=0.001×(3)²+0.01×3=0.039m / s².

[0174] The corrected acceleration data shows that device A is 20ms ahead of device B, so the clock of device B is adjusted synchronously.

[0175] Scenario 3 (Extreme Environment): Timekeeping in High-Altitude Areas.

[0176] At an altitude of 5,000 meters (air pressure approximately 540 hPa), climbers timed their time by colliding their two wristbands together.

[0177] Correction steps:

[0178] The pressure correction factor k = 0.003, ΔP = 540 - 1013 = -473 hPa, a correction = a measurement × (1 + 0.003 × (473 / 1013)) ≈ a measurement × 1.014;

[0179] With temperature compensation (if T=10℃, Δt=0.001×(-15)²+0.01×(-15)=0.225m / s²), the final time synchronization error is controlled within ±15ms.

[0180] Note that the data is for illustrative purposes only and does not represent actual data.

[0181] Example 2

[0182] This application provides a time synchronization device for smart wearable devices (hereinafter referred to as time synchronization device 30), applied to a smart wearable device time synchronization system. The smart wearable device time synchronization system includes a server and multiple smart wearable devices, each of which includes a clock, accelerometer, gyroscope, and barometer. (Reference) Figure 3 The time synchronization device 30 includes:

[0183] Acquisition module 31 is used to acquire the acceleration of multiple smart wearable devices;

[0184] The recording module 32 is used to record first data collected by multiple smart wearable devices after the acceleration of any two of the smart wearable devices exceeds a preset threshold; the first data includes acceleration data, angular velocity data and air pressure data;

[0185] The processing module 33 is used to perform time synchronization processing on the clocks of the multiple smart wearable devices based on the first data.

[0186] The time synchronization method in this embodiment includes colliding two hands wearing wristbands together, or colliding a hand and foot wearing both wristbands and ankle bracelets together. This allows time synchronization based on changes in acceleration, angular velocity, and air pressure during the collision, and the corresponding time intervals. For example, in a hand-foot collision, theoretically, the time when the wristband generates maximum acceleration and the time when the ankle bracelet generates maximum acceleration should be the same. If the server obtains inconsistent times for the two maximum accelerations, it indicates a time synchronization problem. The time of either smart wearable device can be adjusted to match the other. This adjustment can be performed by the smart wearable device following a command sent from the server. Understandably, as mentioned earlier, the time synchronization of consumer-grade smart wearable devices may gradually become invalid after a period of operation or after being turned off, thus requiring frequent time synchronization.

[0187] In module 31, acceleration is acquired by reading the accelerometer of the smart wearable device, and the acceleration value is monitored after acquisition. Subsequent steps are executed only after the acceleration of any two smart wearable devices exceeds a preset threshold.

[0188] In recording module 32, it is understood that whether it's a collision between two hands or between hands and feet, the acceleration will increase rapidly. Therefore, recording begins once the acceleration of any two of the smart wearable devices exceeds a preset threshold. The recorded data includes acceleration data, angular velocity data, and air pressure data, reducing the impact of noise from a single electronic device on time synchronization and improving the accuracy and reliability of timekeeping. Experimental data shows that the timekeeping error of a single accelerometer is approximately ±50ms, while the error can be reduced to within ±10ms after fusing the three types of data.

[0189] Accelerometers, gyroscopes, and barometers are all electronic components found in common smart wearable devices, requiring no additional hardware costs and making the time synchronization method in this embodiment easier to implement universally. The preset threshold can be the lowest acceleration value required to confirm a collision. The preset acceleration threshold is set to 50 m / s² (approximately 5g). When the acceleration of any two devices simultaneously exceeds this value and remains so for 20 ms, it is considered a valid collision.

[0190] Specifically, the recorded data includes three types of data: data recorded 500ms before and after the collision following the trigger, for example:

[0191] Acceleration data: Real-time values ​​of X / Y / Z axes (unit: m / s²);

[0192] Angular velocity data: roll / pitch / yaw rate (unit: ° / s);

[0193] Air pressure data: absolute air pressure value (unit: hPa) and rate of change.

[0194] In processing module 33, time synchronization can comprehensively consider acceleration data, angular velocity data, and air pressure data to improve the accuracy and reliability of time synchronization. Specifically, time synchronization can be performed pairwise. For example, acceleration data can reveal the time difference (i.e., asynchrony) between two smart wearable devices. Similarly, angular velocity data and air pressure data can also reveal the time difference between two smart wearable devices. By comprehensively considering these three differences, the two smart wearable devices are synchronized. After the two smart wearable devices have completed time synchronization, one of them is used as the reference time to synchronize with the other smart wearable device pairwise, until all smart wearable devices have completed pairwise time synchronization.

[0195] In some embodiments of this application, the acquisition module 31 is further configured to:

[0196] In response to the user's key press operation, the smart wearable device is controlled to enter the time synchronization mode;

[0197] After entering the time synchronization mode, the acceleration of multiple smart wearable devices is continuously acquired.

[0198] In short, user interaction is required for the smart wearable device to enter time synchronization mode. Otherwise, the device doesn't need to monitor data from its electronic components and can even go into sleep mode, saving energy. After the user presses a button, the smart wearable device exits sleep mode and enters time synchronization mode, beginning to monitor data from its electronic components. In time synchronization mode, it first acquires the device's acceleration data, without needing to collect data from all electronic components. Only when the acceleration exceeds a preset threshold does it begin recording data from multiple electronic components, such as angular velocity and air pressure, again contributing to energy savings. For example, when the smart wearable device is a fitness tracker, its daily standby power consumption accounts for approximately 30%, and power consumption can be reduced by 5% when time synchronization mode is not activated.

[0199] Specifically, button operations can be different from ordinary short press operations, such as pressing and holding a button for more than 2 seconds.

[0200] In some embodiments of this application, the recording module 32 is further configured to:

[0201] Record the measured values ​​of the first data and the corresponding time points in multiple smart wearable devices, and obtain the time differences in acceleration data, angular velocity data and air pressure data of multiple smart wearable devices respectively.

[0202] As mentioned earlier, because time synchronization is required, it is necessary to record the measured values ​​and their corresponding time points. For example, time synchronization involves collision detection. Therefore, if the initial data from two smart wearable devices have the same value or trend, but the corresponding time points are different, it indicates that the time is out of sync and time synchronization is required.

[0203] In some embodiments of this application, the recording module 32 is further configured to:

[0204] Record the first time point of maximum acceleration of multiple smart wearable devices and obtain first time difference data; the first time difference data is the difference data between the first time points of multiple smart wearable devices.

[0205] In a collision, the two smart wearable devices reach their maximum acceleration at the same point in time. If the times displayed on the smart wearable devices are different, there is a first time difference data, which can be used for time synchronization. This is understandable; time synchronization can also be performed using other acceleration data, such as acceleration trend changes, but these will not be elaborated upon.

[0206] In some embodiments of this application, the step of recording the measured values ​​and corresponding time points of the first data from multiple smart wearable devices, and obtaining the time differences corresponding to the acceleration data, angular velocity data, and air pressure data of the multiple smart wearable devices, includes:

[0207] Record the angular velocity mutation trends of multiple smart wearable devices and obtain second time difference data; the second time difference data is the difference data between the second time points when multiple smart wearable devices produce the same mutation trend.

[0208] Slightly different from acceleration, in a collision, the maximum angular velocities of the two smart wearable devices do not necessarily occur at the same time, but they can produce abrupt changes at the same time, thus allowing for the acquisition of a second time difference.

[0209] In some embodiments of this application, the recording module 32 is further configured to:

[0210] Record the air pressure change trends of multiple smart wearable devices and obtain third time difference data; the third time difference data is the difference data between the third time points when multiple smart wearable devices generate the same air pressure change trend.

[0211] Understandably, during a collision, the maximum air pressure values ​​of the two smart wearable devices may not occur at the same time, but they can be found at the same point in time within the changing trends. Therefore, a third time difference can be obtained.

[0212] In some embodiments of this application, the recording module 32 is further configured to:

[0213] Based on the time differences in acceleration data, angular velocity data, and air pressure data obtained from multiple smart wearable devices, and combined with probability principles, the clocks of the multiple smart wearable devices are synchronized.

[0214] The probability principle here can be that among the three types of data obtained from accelerometers, gyroscopes, and barometers, time synchronization can be performed based on two of the more closely related data. That is, the probability that two electronic devices simultaneously have high noise, causing the acquired data to deviate and affecting the time synchronization, is much lower than the probability that one electronic device has high noise and affecting the time synchronization.

[0215] For example, based on acceleration and angular velocity data, it shows that smart wearable device A's time is 0.1 seconds faster than smart wearable device B's time. However, based on barometric pressure data, it shows that smart wearable device A's time is 0.05 seconds slower than smart wearable device B's time. Therefore, according to probability theory, we can determine that smart wearable device A's time is 0.1 seconds faster than smart wearable device B's time. Note that the data is only an example and does not represent such a large time difference in real-world smart wearable devices.

[0216] If the judgment results of the three data are consistent, then it is more reliable, that is, it improves the accuracy and reliability of time synchronization.

[0217] In some embodiments of this application, the processing module 33 is further configured to:

[0218] The first data is jointly processed using a weighted fusion algorithm, and the clocks of the multiple smart wearable devices are synchronized based on the processing results.

[0219] Unlike the probabilistic algorithms mentioned above, the weighted fusion algorithm integrates data from multiple electronic devices to comprehensively determine time differences for time synchronization.

[0220] For example, it can be processed using the Kalman filter algorithm or the wavelet transform algorithm. The Kalman filter is a recursive filtering algorithm based on a state-space model. It estimates and updates the system state and fuses measurement data from multiple electronic devices. In the detection of hand and foot slapping actions, data such as acceleration, angular velocity, and air pressure can be used as system measurements, and the Kalman filter algorithm can be used to estimate the time of the hand and foot slapping action.

[0221] Specifically, the algorithm process can be as follows: First, establish the system's state equation and measurement equation to describe the relationship between the system's dynamic characteristics and the electronic device measurements. Then, based on the initial state estimate and measurement data, recursively calculate the system's state estimate and covariance matrix. In each iteration, assign different weights to the data from different electronic devices based on the reliability of the measurement data (i.e., the variance of the measurement noise), thereby achieving weighted data fusion.

[0222] Wavelet transform is a time-frequency analysis method that decomposes a signal into sub-signals of different frequencies, thereby extracting local features of the signal. In the detection of hand and foot slapping actions, wavelet transform can be used to decompose data from accelerometers, gyroscopes, and barometers to extract characteristic frequency components related to the slapping action.

[0223] Specifically, the algorithm process can be as follows: First, select appropriate wavelet basis functions to perform wavelet decomposition on the electronic device data to obtain wavelet coefficients at different scales. Then, assign weights to the data of different electronic devices based on the characteristics of the wavelet coefficients (such as energy, amplitude, etc.). For example, if the wavelet coefficients of a certain electronic device have high energy in a specific frequency range, it indicates that the electronic device has a strong response to the slapping action in that frequency range, and can be assigned a larger weight. Finally, reconstruct the weighted wavelet coefficients to obtain the fused signal, thereby determining the time when the hand and foot slapping action occurs.

[0224] Understandably, both the Kalman filter algorithm and the wavelet transform algorithm are existing weighted fusion algorithms, and will not be described in detail here.

[0225] In some embodiments of this application, the processing module 33 is further configured to:

[0226] Obtain the operating temperature of multiple smart wearable devices;

[0227] Based on the operating temperatures of multiple smart wearable devices, temperature compensation is applied to the first data collected by the smart wearable devices to obtain the second data.

[0228] Based on the second data, the clocks of the multiple smart wearable devices are synchronized.

[0229] In other words, the measurement results of electrical components such as accelerometers, gyroscopes, and barometers in smart wearable devices can be affected by temperature, resulting in deviations. However, the degree of influence is related to the temperature value, and temperature compensation can be performed accordingly to correct the measured values ​​and improve measurement accuracy.

[0230] As mentioned earlier, smart wearable devices also include an MCU (Microcontroller Unit), which has a built-in temperature sensor. This sensor can be used to compensate for the temperature readings of electrical components such as accelerometers, gyroscopes, and barometers. Specifically, temperature compensation can be performed every 5 minutes.

[0231] Specifically, the calculation of the temperature compensation amount can be referred to expression (1) in Example 1:

[0232] In some embodiments of this application, the processing module 33 is further configured to:

[0233] The air pressure of the working environment of multiple smart wearable devices is obtained, and the acceleration data collected by the smart wearable devices is corrected to obtain the third data.

[0234] Based on the third data, the clocks of the multiple smart wearable devices are synchronized.

[0235] Understandably, air pressure affects accelerometer measurements, and the main factor affecting air pressure is altitude. Therefore, this embodiment mainly considers that smart wearable devices will be used in high-altitude areas, so air pressure correction is needed to expand the usage scenarios.

[0236] Specifically, the correction of the acceleration measurement value can be made using the reference expression (2) in Example 1.

[0237] In some embodiments of this application, the acquisition module 31 is further configured to:

[0238] The accelerometers and gyroscopes of the aforementioned smart wearable devices are calibrated.

[0239] Understandably, given the manufacturing differences, installation deviations, and aging issues of electronic components among different smart wearable devices, real-time calibration and error compensation are performed on the raw data of electronic components during each time synchronization process. Accelerometers and gyroscopes, in particular, exhibit significant zero-point drift, making calibration necessary to improve the accuracy and reliability of time synchronization.

[0240] In some embodiments of this application, the acquisition module 31 is further configured to:

[0241] The zero-point drift of at least two axes of the accelerometer in a stationary state is obtained and calibrated accordingly;

[0242] The zero-point drift of the gyroscope when it is placed horizontally and stationary is obtained and calibrated accordingly.

[0243] Understandably, the mainstream calibration method for accelerometers is the six-axis calibration method, which involves measuring the zero-point drift of six axes (X, Y, and Z) in both positive and negative directions, and then performing calibration. In this embodiment, for ease of implementation, it is only necessary to obtain the zero-point drift of at least two axes in a stationary state and calibrate accordingly, which can also achieve a good calibration effect.

[0244] The gyroscope only needs to be placed horizontally and stationary for more than 30 seconds to collect multiple sets of data and calculate the average value as the initial zero bias for calibration, which can meet the calibration requirements.

[0245] The modules included in this embodiment can be implemented using a processor in a computer; alternatively, they can be implemented using logic circuits in a computer. The processor can be a general-purpose processor, a digital signal processor (DSP), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a central processing unit (CPU), a microprocessor (MPU), or any other conventional processor.

[0246] The descriptions of the apparatus embodiments above are similar to those of the method embodiments above, and have similar beneficial effects. For technical details not disclosed in the apparatus embodiments of this application, please refer to the descriptions of the method embodiments in this application for understanding.

[0247] Example 3

[0248] This application provides a computing device 50, with reference to... Figure 4 The computing device 50 includes: a storage unit 51, a communication bus 52, and a processing unit 53, wherein:

[0249] The storage component 51 is used to store the time synchronization processing method program for wearable devices;

[0250] The communication bus 52 is used to realize the connection and communication between the storage component 51 and the processing component 53.

[0251] The processing unit 53 is used to execute the smart wearable device time synchronization processing method program to implement the steps of the method described in Embodiment 1.

[0252] The type or structure of the storage component 51 can be found in the storage medium section below, and will not be repeated here.

[0253] The processing unit 53 can be a general-purpose processor, a digital signal processor (DSP), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among them, the general-purpose processor can be a central processing unit (CPU), a microprocessor (MPU), or any other conventional processor.

[0254] In some embodiments, the computing device 50 may further include an input device 54, an output device 55, and an external communication interface 56, which are interconnected via a bus system and / or other forms of connection mechanisms (not shown).

[0255] In some embodiments, the input device 54 may include, for example, a keyboard, mouse, microphone, etc. The output device 55 may output various information to the outside, including a display, speaker, printer, projector, and communication network and its connected remote output devices, etc. The external communication interface 56 may be wired, such as a standard serial port (RS232), a General-Purpose Interface Bus (GPIB) interface, an Ethernet interface, or a Universal Serial Bus (USB) interface, or it may be wireless, such as wireless network communication technology (WiFi), Bluetooth, etc.

[0256] The description of the above-described embodiments of the computing device 50 is similar to that of the above-described method embodiments, and has similar beneficial effects. For technical details not disclosed in the embodiments of the computing device 50 of this application, please refer to the description of the method embodiments in this application for understanding.

[0257] Example 4

[0258] This application provides a computer-readable storage medium storing an executable program, which, when executed by a processor, implements the steps of the method described in Embodiment 1.

[0259] Exemplary examples show that a computer-readable storage medium may be any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A computer-readable storage medium is a tangible device capable of holding and storing instructions for use by an instruction execution device. A readable storage medium may, for example, include, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatuses, or devices, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), flash memory, compact disc read-only memory (CD-ROM), digital versatile discs (DVDs), memory sticks, floppy disks, mechanical encoding devices, such as punch cards or recessed protrusions storing instructions thereon, and any suitable combinations thereof.

[0260] The RAM includes: Static Random Access Memory (SRAM), Synchronous Static Random Access Memory (SSRAM), Dynamic Random Access Memory (DRAM), Synchronous Dynamic Random Access Memory (SDRAM), Double Data Rate Synchronous Dynamic Random Access Memory (DDRSDRAM), Enhanced Synchronous Dynamic Random Access Memory (ESDRAM), SyncLink Dynamic Random Access Memory (SLDRAM), and Direct Rambus Random Access Memory (DRRAM).

[0261] The ROM includes: Programmable Read-Only Memory (PROM), Erasable Programmable Read-Only Memory (EPROM), and Electrically Erasable Programmable Read-Only Memory (EEPROM).

[0262] The description of the above computer-readable storage medium embodiments is similar to the description of the above method embodiments, and has similar beneficial effects. For technical details not disclosed in the computer-readable storage medium of this embodiment, please refer to the description of the method embodiments in this application for understanding.

[0263] Example 5

[0264] This application provides a time synchronization system for a smart wearable device (hereinafter referred to as a time synchronization system), referencing... Figure 5 The time synchronization system includes:

[0265] Server 61 includes the smart wearable device time synchronization processing device described in Embodiment 2;

[0266] Multiple smart wearable devices 62, the smart wearable devices 62 including: a clock, an accelerometer, a gyroscope and a barometer.

[0267] The description of the above embodiments of the smart wearable device time synchronization system is similar to the description of the above method embodiments, and has similar beneficial effects. For technical details not disclosed in the embodiments of the smart wearable device time synchronization system of this application, please refer to the description of the method embodiments in this application for understanding.

[0268] It should be noted that the various embodiments provided in this application belong to the same concept; the technical features in the technical solutions described in each embodiment can be arbitrarily combined to form new embodiments without conflict.

[0269] In the above description, the terms "first, second, ..." are used only to distinguish similar objects and do not represent a specific order of objects. Understandably, "first, second, third" can be interchanged in a specific order or sequence where permitted.

[0270] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0271] In the embodiments described in this application, unless otherwise stated and limited, the term "connection" should be interpreted broadly. For example, it can be an electrical connection or a connection between two internal components. It can be a direct connection or an indirect connection through an intermediate medium. Those skilled in the art can understand the specific meaning of the above term according to the specific circumstances.

[0272] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of this application. When used herein, the singular forms “a,” “an,” and “the” are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the terms “comprising” and / or “including,” when used in this specification, identify the presence of the stated features, integers, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups. When used herein, the term “and / or” includes any and all combinations of the associated listed items.

[0273] It should be understood that the phrases "an embodiment" or "some embodiments" throughout the specification mean that a specific feature, structure, or characteristic related to an embodiment is included in at least one embodiment of this application. Therefore, "in an embodiment" or "in some embodiments" appearing throughout the specification do not necessarily refer to the same embodiment. Furthermore, these specific features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. It should be understood that in the various embodiments of this application, the embodiment numbers are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0274] It should be understood that the sequence number of each process does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0275] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods, such as: multiple modules or components can be combined, or integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the various components shown or discussed can be through some interfaces, and the indirect coupling or communication connection between devices or modules can be electrical, mechanical, or other forms.

[0276] The modules described above as separate components may or may not be physically separate. The components shown as modules may or may not be physical modules. They may be located in one place or distributed across multiple network modules. Some or all of the modules may be selected to achieve the purpose of this embodiment according to actual needs.

[0277] In addition, each functional module in the various embodiments of this application can be integrated into one processing module, or each functional module can be a separate module, or two or more functional modules can be integrated into one module; the integrated module can be implemented in hardware or in the form of hardware plus software functional modules.

[0278] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments.

[0279] Alternatively, if the integrated modules described above are implemented as software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of this application, or the parts that contribute to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause an electronic device (which may be a personal computer, server, or network device, etc.) to execute all or part of the methods described in the various embodiments of this application. Thus, the embodiments of this application are not limited to any specific hardware and software combination.

[0280] It should be understood that the above embodiments are exemplary and are not intended to encompass all possible implementations of the technical solutions contained in this application. Various modifications and changes can be made to the above embodiments without departing from the scope of this application. Similarly, the various technical features of the above embodiments can be arbitrarily combined to form other embodiments of this application that may not be explicitly described. Therefore, the above embodiments merely illustrate several implementations of this application and do not limit the scope of protection of this patent application.

Claims

1. A time synchronization method for smart wearable devices, applied to a time synchronization system for smart wearable devices, the system comprising a server and multiple smart wearable devices, the smart wearable devices comprising: Clocks, accelerometers, gyroscopes, and barometers, characterized in that the method comprises: Acquire the acceleration of multiple smart wearable devices; After the acceleration of any two smart wearable devices exceeds a preset threshold, the measured values ​​of the first data and the corresponding time points in multiple smart wearable devices are recorded, and the time differences of the multiple smart wearable devices in the first data are obtained respectively; the first data includes acceleration data, angular velocity data and air pressure data, so as to reduce the impact of noise from a single electronic device on time synchronization; The air pressure data includes absolute air pressure values, rate of change, and air pressure change trends; The process of recording the measurement values ​​and corresponding time points of the first data from multiple smart wearable devices, and obtaining the time differences corresponding to the acceleration data, angular velocity data, and air pressure data of the multiple smart wearable devices, includes: Record the air pressure change trends of multiple smart wearable devices and obtain third time difference data; the third time difference data is the difference data between the third time points when multiple smart wearable devices generate the same air pressure change trend; Based on the first data, time synchronization processing is performed on the clocks of multiple smart wearable devices, including: The first data is jointly processed using a weighted fusion algorithm, and the clocks of multiple smart wearable devices are synchronized based on the processing results; or, the clocks of multiple smart wearable devices are synchronized based on the time differences between the multiple smart wearable devices in acceleration data, angular velocity data and air pressure data, combined with probability principles. The step of performing time synchronization processing on the clocks of multiple smart wearable devices based on the first data further includes: The air pressure of the working environment of multiple smart wearable devices is obtained to correct the acceleration data collected by the smart wearable devices and obtain third data; based on the third data, the clocks of the multiple smart wearable devices are synchronized.

2. The time synchronization method for smart wearable devices according to claim 1, characterized in that, The acquisition of the acceleration of the multiple smart wearable devices includes: In response to the user's key press operation, the smart wearable device is controlled to enter the time synchronization mode; After entering the time synchronization mode, the acceleration of multiple smart wearable devices is continuously acquired.

3. The time synchronization method for smart wearable devices according to claim 1, characterized in that, The process of recording the measurement values ​​and corresponding time points of the first data from multiple smart wearable devices, and obtaining the time differences corresponding to the acceleration data, angular velocity data, and air pressure data of the multiple smart wearable devices, further includes: Record the first time point of maximum acceleration of multiple smart wearable devices and obtain first time difference data; the first time difference data is the difference data between the first time points of multiple smart wearable devices.

4. The time synchronization method for smart wearable devices according to claim 1, characterized in that, The process of recording the measurement values ​​and corresponding time points of the first data from multiple smart wearable devices, and obtaining the time differences corresponding to the acceleration data, angular velocity data, and air pressure data of the multiple smart wearable devices, further includes: Record the angular velocity mutation trends of multiple smart wearable devices and obtain second time difference data; the second time difference data is the difference data between the second time points when multiple smart wearable devices produce the same mutation trend.

5. The time synchronization method for smart wearable devices according to claim 1, characterized in that, The step of performing time synchronization processing on the clocks of the multiple smart wearable devices based on the first data includes: Obtain the operating temperature of multiple smart wearable devices; Based on the operating temperatures of multiple smart wearable devices, temperature compensation is applied to the first data collected by the smart wearable devices to obtain the second data. Based on the second data, the clocks of the multiple smart wearable devices are synchronized.

6. The time synchronization method for smart wearable devices according to claim 2, characterized in that, Before controlling the smart wearable device to enter time synchronization mode in response to a user's key press, the method further includes: The accelerometers and gyroscopes of the aforementioned smart wearable devices are calibrated.

7. The time synchronization method for smart wearable devices according to claim 6, characterized in that, The calibration of the accelerometers and gyroscopes of the multiple smart wearable devices includes: The zero-point drift of at least two axes of the accelerometer in a stationary state is obtained and calibrated accordingly; The zero-point drift of the gyroscope when it is placed horizontally and stationary is obtained and calibrated accordingly.

8. A time synchronization processing device for smart wearable devices, applied to a time synchronization processing system for smart wearable devices, the time synchronization processing system for smart wearable devices comprising a server and multiple smart wearable devices, the smart wearable devices comprising: A clock, accelerometer, gyroscope, and barometer, characterized in that the device comprises: The acquisition module is used to acquire the acceleration of multiple smart wearable devices; The recording module is used to record the measured values ​​of first data and corresponding time points of multiple smart wearable devices after the acceleration of any two of the smart wearable devices exceeds a preset threshold, and to obtain the time differences of the multiple smart wearable devices in the first data; the first data includes acceleration data, angular velocity data, and air pressure data; the air pressure data includes absolute air pressure value, rate of change, and air pressure change trend; it is also used to record the air pressure change trend of multiple smart wearable devices and obtain third time difference data; the third time difference data is the difference data between the third time points when multiple smart wearable devices produce the same air pressure change trend; The processing module is configured to perform time synchronization processing on the clocks of multiple smart wearable devices based on the first data, including: performing joint processing on the first data using a weighted fusion algorithm, and performing time synchronization processing on the clocks of multiple smart wearable devices based on the processing result; or, performing time synchronization processing on the clocks of multiple smart wearable devices based on the time differences corresponding to the acceleration data, angular velocity data, and air pressure data of the multiple smart wearable devices, combined with probability principles. It is also used to: acquire the air pressure of the working environment of multiple smart wearable devices, correct the acceleration data collected by the smart wearable devices, and obtain third data; and perform time synchronization processing on the clocks of the multiple smart wearable devices based on the third data.

9. A computing device, characterized in that, The computing device includes: a storage component, a communication bus, and a processing component, wherein: The storage component is used to store the time synchronization processing method program for smart wearable devices; The communication bus is used to enable communication between the storage component and the processing component; The processing component is used to execute a smart wearable device time synchronization method program to implement the steps of the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores an executable program that, when executed by a processor, implements the steps of the method as described in any one of claims 1 to 7.

11. A time synchronization system for a smart wearable device, characterized in that, include: The server includes the smart wearable device time processing device as described in claim 8; Multiple smart wearable devices, including: a clock, an accelerometer, a gyroscope, and a barometer.

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