Intelligent wearable device time synchronization processing method, device, equipment, medium and system

Abstract

The invention relates to an intelligent wearable device time synchronization processing method, device, equipment, medium and system, and belongs to the technical field of intelligent wearable devices. The intelligent wearable device time synchronization processing method comprises the following steps: acquiring accelerations of a plurality of intelligent wearable devices; after the acceleration of any two intelligent wearable devices exceeds a preset threshold value, recording first data collected by the multiple intelligent wearable devices; the first data comprises acceleration data, angular velocity data and air pressure data; and according to the first data, performing time synchronization processing on clocks of the plurality of intelligent wearable devices. According to the technical scheme, the time synchronization accuracy and reliability can be improved, and hardware does not need to be added.

Description

Technical Field

[0001] The present application relates to the technical field of smart wearable devices, and in particular to a method, device, computing device, computer-readable storage medium, and system for processing time synchronization for smart wearable devices. Background Art

[0002] Smart wearable devices are a general term for wearable devices that utilize wearable technology for everyday wear, medical monitoring, and therapeutic assistance, resulting in intelligent design and development. These devices include glasses, gloves, watches, blood pressure monitors, blood glucose meters, electrocardiogram (ECG) monitors, sleep monitors, and brain-computer interfaces (BCIs). Broadly speaking, these devices include full-featured devices that can function completely or partially independently of a smartphone, such as smartwatches and smart glasses, as well as devices that focus on a specific application and require integration with other devices, such as smartphones. For example, various smart bracelets and smart jewelry for vital sign monitoring are constantly evolving with technological advancements and evolving user needs.

[0003] Due to the portability and intelligence of smart wearable devices, athletes can use them to collect motion data worn on their limbs or other body parts to assess training effectiveness or limb health. However, inconsistent timings across multiple smart wearable devices can lead to inaccurate assessment results. Existing methods for synchronizing the timings of multiple smart wearable devices suffer from inaccuracies or require additional hardware, resulting in high costs. Summary of the Invention

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

[0005] To achieve the above objectives, the technical solution of this application is implemented as follows: In a first aspect, an embodiment of the present application provides a method for synchronizing a time of a smart wearable device, which is applied to a system for synchronizing a time of a smart wearable device. The system includes a server and multiple smart wearable devices. The smart wearable devices include a clock, an accelerometer, a gyroscope, and a barometer. The method includes: Obtaining acceleration of multiple smart wearable devices; After the acceleration of any two of the smart wearable devices exceeds a preset threshold, recording first data collected by the multiple smart wearable devices; the first data includes acceleration data, angular velocity data and air pressure data; Synchronize the clocks of the plurality of smart wearable devices according to the first data.

[0006] Optionally, obtaining the acceleration of the plurality of smart wearable devices includes: In response to a key operation by a user, controlling the smart wearable device to enter a time synchronization mode; After entering the time synchronization mode, the acceleration of the plurality of smart wearable devices is continuously acquired.

[0007] Optionally, the recording of the first data collected by the plurality of smart wearable devices includes: Record the measurement values ​​of the first data and the corresponding time points in the multiple smart wearable devices, and respectively obtain the corresponding time differences in the acceleration data, angular velocity data, and air pressure data of the multiple smart wearable devices.

[0008] Optionally, recording the measurement values ​​of the first data and the corresponding time points in the plurality of smart wearable devices, and respectively obtaining the corresponding time differences in the acceleration data, angular velocity data, and air pressure data of the plurality of smart wearable devices, includes: Record the first time points of the maximum acceleration of the 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 the multiple smart wearable devices.

[0009] Optionally, recording the measurement values ​​of the first data and the corresponding time points in the plurality of smart wearable devices, and respectively obtaining the corresponding time differences in the acceleration data, angular velocity data, and air pressure data of the plurality of smart wearable devices, includes: Record the angular velocity mutation trends of the 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 the multiple smart wearable devices produce the same mutation trend.

[0010] Optionally, recording the measurement values ​​of the first data and the corresponding time points in the plurality of smart wearable devices, and respectively obtaining the corresponding time differences in the acceleration data, angular velocity data, and air pressure data of the plurality of smart wearable devices, includes: Record the air pressure change trends of the 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 the multiple smart wearable devices generate the same air pressure change trend.

[0011] Optionally, synchronizing clocks of the plurality of smart wearable devices according to the first data includes: According to the corresponding time differences in the acceleration data, angular velocity data and air pressure data of the multiple smart wearable devices, combined with the probability principle, the clocks of the multiple smart wearable devices are synchronized.

[0012] Optionally, synchronizing clocks of the plurality of smart wearable devices according to the first data includes: The first data are jointly processed using a weighted fusion algorithm, and the clocks of the plurality of smart wearable devices are synchronized according to the processing results.

[0013] Optionally, synchronizing clocks of the plurality of smart wearable devices according to the first data includes: Obtaining the operating temperatures of the plurality of smart wearable devices; Performing temperature compensation on the first data collected by the smart wearable devices according to the operating temperatures of the multiple smart wearable devices to obtain second data; According to the second data, clocks of the plurality of smart wearable devices are synchronized.

[0014] Optionally, synchronizing clocks of the plurality of smart wearable devices according to the first data includes: Obtaining the air pressure of the working environment of the plurality of smart wearable devices, and correcting the acceleration data collected by the smart wearable devices to obtain third data; According to the third data, clocks of the plurality of smart wearable devices are synchronized.

[0015] Optionally, before controlling the smart wearable device to enter a time synchronization mode in response to a key operation by the user, the method further includes: Calibrate the accelerometers and gyroscopes of the plurality of smart wearable devices.

[0016] Optionally, calibrating the accelerometers and gyroscopes of the plurality of smart wearable devices includes: Obtaining zero drift of at least two axes of the accelerometer in a stationary state and performing calibration accordingly; The zero-point drift of the gyroscope when placed horizontally and stationary is obtained and calibrated accordingly.

[0017] In a second aspect, an embodiment of the present application provides a time synchronization processing device for a smart wearable device, which is applied to a time synchronization processing system for a smart wearable device. The time synchronization processing system for a smart wearable device includes a server and multiple smart wearable devices. The smart wearable devices include: a clock, an accelerometer, a gyroscope, and a barometer. The device includes: An acquisition module, configured to acquire accelerations of the plurality of smart wearable devices; A recording module, configured to record first data collected by the plurality of smart wearable devices after the acceleration of any two of the smart wearable devices exceeds a preset threshold; the first data including acceleration data, angular velocity data, and air pressure data; A processing module is used to synchronize the clocks of the plurality of smart wearable devices according to the first data.

[0018] A third aspect provides a computing device, comprising: a storage component, a communication bus, and a processing component, wherein: The storage component is used to store the time synchronization processing method program of the smart wearable device; The communication bus is used to realize the connection and communication between the storage component and the processing component; The processing component is used to execute the smart wearable device time synchronization processing method program to implement the steps of any one of the methods described above.

[0019] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, on which an executable program is stored. When the executable program is executed by a processor, the steps of any one of the methods described above are implemented.

[0020] In a fifth aspect, an embodiment of the present application provides a time synchronization processing system for a smart wearable device, comprising: The server includes the smart wearable device time synchronization processing device described above; A plurality of smart wearable devices, each comprising a clock, an accelerometer, a gyroscope, and a barometer.

[0021] The embodiments of the present application provide a method, apparatus, computing device, computer-readable storage medium, and system for synchronizing a time of a smart wearable device, comprising: obtaining the acceleration of a plurality of the smart wearable devices; recording first data collected by the plurality of the 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; and synchronizing the clocks of the plurality of the smart wearable devices based on the first data. It can be seen that the method, apparatus, computing device, computer-readable storage medium, and system for synchronizing a time of a smart wearable device provided by the embodiments of the present application record the first data (including acceleration data, angular velocity data, and air pressure data) collected by the plurality of the smart wearable devices after the acceleration of any two of the smart wearable devices exceeds a preset threshold (two-hand clapping or hand-foot clapping), and synchronizing the clocks of the plurality of the smart wearable devices based on the first data. Due to the combination of acceleration data, angular velocity data, and air pressure data, the influence of noise from a single electronic device on time synchronization is reduced, the accuracy and reliability of time synchronization is improved, and no additional hardware is required. Therefore, the smart wearable device time synchronization processing method, device, computing device, computer-readable storage medium and system of the embodiments of the present application can solve the technical problems of inaccurate or high cost time synchronization of smart wearable devices.

[0022] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings: Figure 1 A flowchart of a time synchronization method for a smart wearable device provided in Example 1 of the present application; Figure 2 This is a schematic diagram of the algorithm flow for preprocessing air pressure data in the time synchronization method for a smart wearable device provided in Example 1 of the present application; Figure 3 This is a schematic diagram of the structure of the time synchronization processing device for a smart wearable device provided in Example 2 of the present application; Figure 4 A schematic diagram of the structure of a computing device provided in Example 3 of the present application; Figure 5 This is a structural diagram of the smart wearable device time synchronization processing system provided in Example 5 of the present application.

[0024] Description of reference numerals: 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 DESCRIPTION

[0025] The exemplary embodiments disclosed herein will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present application are shown in the accompanying drawings, it should be understood that the present application can be implemented in various forms and should not be limited by the specific embodiments described herein. Rather, these embodiments are provided to enable a more thorough understanding of the present application and to fully convey the scope of the present application to those skilled in the art.

[0026] In the following description, numerous specific details are provided to provide a more thorough understanding of the present application. However, it will be apparent to those skilled in the art that the present application may be practiced without one or more of these details. In other instances, certain technical features known in the art are not described to avoid confusion with the present application; that is, all features of actual embodiments are not described herein, nor are well-known functions and structures described in detail.

[0027] In order to fully understand the present application, detailed steps and detailed structures will be presented in the following description to illustrate the technical solution of the present application. The preferred embodiments of the present application are described in detail below. However, in addition to these detailed descriptions, the present application may also have other implementation methods.

[0028] During research and development, the applicant of this application discovered that, in existing technologies, time synchronization between 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 means, such as serial ports and serial communication lines.

[0029] However, Bluetooth, WiFi, or LoRa communication methods require the addition of corresponding communication modules, increasing the cost of smart wearable devices. Given the large number of smart wearable device manufacturers, Bluetooth, WiFi, or LoRa communication time synchronization can only be applied to a limited number of products and cannot be widely adopted. Furthermore, even when using Bluetooth, WiFi, or LoRa, there is randomness in data transmission delays, resulting in large time errors and failing to meet the time synchronization requirements for motion data collection.

[0030] Wired time synchronization is cumbersome and infrequent. The microcontroller units (MCUs) included in consumer-grade smart wearable devices experience crystal oscillator frequency drift, which can easily lead to inaccurate time and cannot be saved after a power outage. Therefore, frequent time synchronization is necessary, making wired time synchronization difficult to implement.

[0031] Therefore, after further research and development, the applicant proposed the following technical solution.

[0032] Example 1

[0033] The present invention provides a method for synchronizing time in a smart wearable device. The method can be implemented by a computer, which can be a computing device equipped 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.

[0034] The method is applied to a time synchronization processing system for smart wearable devices, and the time synchronization processing system for smart wearable devices includes a server and multiple smart wearable devices. The server can be a computer for implementing the above-mentioned method, and the smart wearable device includes: a clock, an accelerometer, a gyroscope, and a barometer. Specifically, the server can be a smart mobile terminal, such as a mobile phone, a tablet computer, or a laptop computer, or a cloud server in the cloud. The smart wearable device can be a smart bracelet or a smart anklet. The server and the smart wearable device can be connected to each other through a Bluetooth module, a WiFi module, or a LoRa module. It is understandable that for cost considerations, only one of them is usually set, rather than two or more.

[0035] refer to Figure 1 , the method comprising: Step 101: Obtain acceleration of multiple smart wearable devices; Step 102: After the acceleration of any two smart wearable devices exceeds a preset threshold, record first data collected by multiple smart wearable devices; The first data includes acceleration data, angular velocity data and air pressure data; Step 103: Synchronize the clocks of multiple smart wearable devices according to the first data.

[0036] The time synchronization method of this embodiment includes: colliding two hands wearing bracelets, or colliding hands and feet wearing bracelets and anklets. In this way, the time can be synchronized through the changes in acceleration, angular velocity and air pressure during the collision, and the time corresponding to the changes. For example, in the collision of hands and feet, theoretically, the time when the bracelet generates the maximum acceleration and the time when the anklet generates the maximum acceleration are the same time. If the generation times of the two maximum accelerations obtained by the server are inconsistent, it means that there is a problem of time asynchrony, and the time of any smart wearable device can be adjusted to keep it consistent with the other. The adjustment can be executed by sending instructions through the server and executed by the smart wearable device. It is understandable that, as mentioned above, the time synchronization of consumer-grade smart wearable devices may gradually become invalid after running for a period of time or shutting down, so frequent time synchronization is required.

[0037] In step 101, the acceleration is obtained by reading the accelerometer of the smart wearable device, and the acceleration value is monitored after the acquisition, so that the following steps are executed after the acceleration of any two smart wearable devices exceeds a preset threshold.

[0038] In step 102, it is understood that whether the two hands collide or the hands and feet collide, the acceleration will increase rapidly. Therefore, the recording is set to begin when the acceleration of any two smart wearable devices exceeds a preset threshold. The recorded data includes acceleration data, angular velocity data, and air pressure data, which reduces the impact of noise from a single electronic device on time synchronization and improves the accuracy and reliability of time synchronization. Experimental data shows that the time synchronization error of a single accelerometer is approximately ±50ms. After fusing the three types of data, the error can be reduced to within ±10ms.

[0039] Accelerometers, gyroscopes, and barometers are all electronic components commonly found in common smart wearable devices, eliminating the need for additional hardware costs and making the timing method of this embodiment more widely applicable. The preset threshold can be the minimum acceleration value required to confirm a collision. The preset acceleration threshold is set to 50 m / s² (approximately 5 g). When the acceleration of any two devices exceeds this value simultaneously and persists for 20 ms, a valid collision is determined.

[0040] Specifically, the recorded data includes: after the trigger, three types of data are recorded 500ms before and after the collision, for example: Acceleration data: real-time values ​​of X / Y / Z axes (unit: m / s²); Angular velocity data: roll / pitch / yaw angular rate (unit: ° / s); Air pressure data: absolute air pressure value (unit: hPa) and rate of change.

[0041] In step 103, synchronization can be performed by comprehensively considering acceleration data, angular velocity data, and barometric pressure data to improve accuracy and reliability. Specifically, synchronization can be performed on a pairwise basis. For example, the acceleration data can be used to determine the time difference (i.e., asynchrony) between two smart wearable devices. Similarly, the angular velocity data and barometric pressure data can also be used to determine the time difference between the two smart wearable devices. By comprehensively considering these three differences, the two smart wearable devices are synchronized. After the two smart wearable devices are synchronized, one of the smart wearable devices is used as the reference time, and pairwise synchronization is then performed with the other smart wearable device. This process continues until all smart wearable devices are synchronized.

[0042] The smart wearable device time synchronization method of the embodiment of the present application records the first data (including acceleration data, angular velocity data and air pressure data) collected by multiple smart wearable devices after the acceleration of any two of the smart wearable devices exceeds a preset threshold (clapping of two hands or clapping of hands and feet), and synchronizes the clocks of the multiple smart wearable devices using the first data. Since the acceleration data, angular velocity data and air pressure data are combined, the influence of noise from a single electronic device on time synchronization is reduced, the accuracy and reliability of the synchronization are improved, and no additional hardware is required.

[0043] In some embodiments of the present application, obtaining the acceleration of the plurality of smart wearable devices includes: In response to a key operation by a user, controlling the smart wearable device to enter a time synchronization mode; After entering the time synchronization mode, the acceleration of the plurality of smart wearable devices is continuously acquired.

[0044] That is, the smart wearable device needs to enter the time synchronization mode through user operation. Otherwise, the smart wearable device does not need to monitor the data of related electronic devices and can even go into sleep mode, which is beneficial to saving energy. After the user presses a button, the smart wearable device comes out of sleep and enters the time synchronization mode to start monitoring the data of related electronic devices. After entering the time synchronization mode, the acceleration data of the smart wearable device is first obtained without having to obtain the data of all electronic devices. Only when the acceleration exceeds the preset threshold, the data of multiple electronic devices, such as angular velocity and air pressure, are recorded, which is also beneficial to saving energy. For example, when the smart wearable device is a wristband, the power consumption of the wristband in daily standby mode accounts for about 30%, and the power consumption can be reduced by 5% when the time synchronization mode is not activated.

[0045] Specifically, the key operation may be long pressing a key for more than 2 seconds, which is different from the ordinary short pressing operation.

[0046] In some embodiments of the present application, recording the first data collected by the plurality of smart wearable devices includes: Record the measurement values ​​of the first data and the corresponding time points in the multiple smart wearable devices, and respectively obtain the corresponding time differences in the acceleration data, angular velocity data, and air pressure data of the multiple smart wearable devices.

[0047] As mentioned above, because time synchronization is required, it is necessary to record the measured values ​​and the corresponding time points. For example, time synchronization is performed on a collision basis. Therefore, if the first data of two smart wearable devices have the same value or trend, but the corresponding time points are different, it means that the time is out of sync and time synchronization is required.

[0048] In some embodiments of the present application, recording the measurement values ​​of the first data and the corresponding time points in the plurality of smart wearable devices, and obtaining the corresponding time differences in the acceleration data, angular velocity data, and air pressure data of the plurality of smart wearable devices, respectively, includes: Record the first time points of the maximum acceleration of the 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 the multiple smart wearable devices.

[0049] That is, in a collision, the time at which the two smart wearable devices generate maximum acceleration should coincide. If the times displayed by the smart wearable devices differ, there is a first time difference, which can be used for time synchronization. It is understood that other acceleration data can also be used for time synchronization, such as acceleration trend changes, but this will not be detailed here.

[0050] In some embodiments of the present application, recording the measurement values ​​of the first data and the corresponding time points in the plurality of smart wearable devices, and obtaining the corresponding time differences in the acceleration data, angular velocity data, and air pressure data of the plurality of smart wearable devices, respectively, includes: Record the angular velocity mutation trends of the 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 the multiple smart wearable devices produce the same mutation trend.

[0051] Slightly different from acceleration, in a collision, the maximum angular velocity of two smart wearable devices does not necessarily appear at the same time, but they can produce a mutation trend at the same time, so a second time difference can be obtained.

[0052] In some embodiments of the present application, recording the measurement values ​​of the first data and the corresponding time points in the plurality of smart wearable devices, and obtaining the corresponding time differences in the acceleration data, angular velocity data, and air pressure data of the plurality of smart wearable devices, respectively, includes: Record the air pressure change trends of the 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 the multiple smart wearable devices generate the same air pressure change trend.

[0053] It is understandable that during a collision, the maximum air pressure of the two smart wearable devices may not occur at the same time, but the same time point can be found in the changing trend. Therefore, the third time difference can be obtained.

[0054] In some embodiments of the present application, synchronizing clocks of the plurality of smart wearable devices according to the first data includes: According to the corresponding time differences in the acceleration data, angular velocity data and air pressure data of the multiple smart wearable devices, combined with the probability principle, the clocks of the multiple smart wearable devices are synchronized.

[0055] The probability principle here can be that among the three types of data obtained from the accelerometer, gyroscope and barometer, time synchronization can be performed based on the two closest data. That is, the probability that two electronic devices simultaneously have high noise and cause deviations in the obtained data and affect the time synchronization is much smaller than the probability that one of the electronic devices has high noise and affects the time synchronization.

[0056] For example, if both acceleration and angular velocity data indicate that the time on smart wearable device A is 0.1 seconds faster than the time on smart wearable device B, while the barometric pressure data indicates that the time on smart wearable device A is 0.05 seconds slower than the time on smart wearable device B, then based on probability theory, we can conclude that the time on smart wearable device A is 0.1 seconds faster than the time on smart wearable device B. Note that this data is for example purposes only and does not represent such significant time differences on real smart wearable devices.

[0057] If the judgment results of the three data are consistent, it is more reliable, which improves the accuracy and reliability of timing.

[0058] In some embodiments of the present application, synchronizing clocks of the plurality of smart wearable devices according to the first data includes: The first data are jointly processed using a weighted fusion algorithm, and the clocks of the plurality of smart wearable devices are synchronized according to the processing results.

[0059] Different from the above-mentioned probability algorithm, the weighted fusion algorithm comprehensively processes the data of multiple electronic devices and comprehensively determines the time difference to perform time synchronization.

[0060] For example, this can be processed using a Kalman filter or wavelet transform algorithm. Kalman filtering is a recursive filtering algorithm based on a state-space model. It estimates and updates the system state and fuses the measurement data from multiple electronic devices. For hand and foot tapping detection, 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 moment the hand or foot tapping action occurs.

[0061] Specifically, the algorithm proceeds as follows: First, the system's state equations and measurement equations are established to describe the relationship between the system's dynamic characteristics and the measurements of the electronic devices. Then, based on the initial state estimate and the measurement data, the system's state estimate and covariance matrix are recursively calculated. At each iteration, different weights are assigned 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.

[0062] Wavelet transform is a time-frequency analysis method that decomposes a signal into sub-signals of different frequencies, thereby extracting local features. In detecting hand and foot tapping, wavelet transform can be used to decompose data from accelerometers, gyroscopes, and barometers to extract characteristic frequency components associated with the tapping motion.

[0063] Specifically, the algorithm proceeds as follows: First, appropriate wavelet basis functions are selected to perform wavelet decomposition on the electronic device data, obtaining wavelet coefficients of different scales. Next, weights are assigned to the data of different electronic devices based on the characteristics of the wavelet coefficients (such as energy and amplitude). For example, if a device's wavelet coefficient energy is high within a specific frequency range, it indicates that the device is more responsive to tapping within that frequency range, and a larger weight can be assigned to it. Finally, the weighted wavelet coefficients are reconstructed to obtain a fused signal, thereby determining the moment when the hand or foot tapping occurred.

[0064] It can be understood that the Kalman filter algorithm or the wavelet transform algorithm are both existing weighted fusion algorithms and will not be described in detail here.

[0065] In some embodiments of the present application, synchronizing clocks of the plurality of smart wearable devices according to the first data includes: Obtaining the operating temperatures of the plurality of smart wearable devices; Performing temperature compensation on the first data collected by the smart wearable devices according to the operating temperatures of the multiple smart wearable devices to obtain second data; According to the second data, clocks of the plurality of smart wearable devices are synchronized.

[0066] 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 this influence is related to the temperature value, and temperature compensation can be used to correct the measurement value and improve measurement accuracy.

[0067] As mentioned above, smart wearable devices also include an MCU, which has a built-in temperature sensor. Based on the measurements of the MCU's built-in temperature sensor, temperature compensation can be performed on the measurements of electrical components such as the accelerometer, gyroscope, and barometer. Specifically, temperature compensation can be performed every five minutes.

[0068] Specifically, the calculation of the compensation amount of temperature compensation can refer to expression (1): Δt=k1(T-T0)^2+k2(T-T0) (1) Where Δt is the compensation value, and its dimension is the same as the measured value being compensated. For example, the dimension of the compensated acceleration is the dimension of acceleration. k1 and k2 are calibration coefficients, which must be accurately calibrated through experiments to ensure that the compensation value Δt accurately reflects the temperature drift. T0 is the reference temperature of 25°C, that is, the base temperature. T is the measured temperature of the MCU.

[0069] Here are some examples: When T=40℃, T0=25℃, k1=0.001, k2=0.01, acceleration compensation amount: Δt=0.001×(15)²+0.01×15=0.375m / s², that is, the measured acceleration needs to be subtracted by 0.375m / s².

[0070] In some embodiments of the present application, synchronizing clocks of the plurality of smart wearable devices according to the first data includes: Obtaining the air pressure of the working environment of the plurality of smart wearable devices, and correcting the acceleration data collected by the smart wearable devices to obtain third data; According to the third data, clocks of the plurality of smart wearable devices are synchronized.

[0071] It is understandable that air pressure will affect the measurement of the accelerometer, and the factor affecting air pressure is mainly the altitude. Therefore, this embodiment mainly takes into account that the smart wearable device will be used in plateau areas, so the air pressure needs to be corrected to increase the usage scenarios.

[0072] Specifically, the correction of the acceleration measurement value can refer to expression (2) a correction = a measurement × (1 + k × (ΔP / P0)) (2) Where acorrection is the correction value, and its dimension is the same as the measured value being corrected. For example, the dimension of the corrected acceleration is the dimension of acceleration. k is the accelerometer characteristic coefficient, which needs to be accurately calibrated through experiments to ensure that the correction value acorrection can accurately reflect the influence of air pressure. P0 is the reference air pressure, which is generally standard atmospheric pressure. ΔP is the absolute value of the measured air pressure minus the standard atmospheric pressure.

[0073] Here are some examples: Plateau 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 acorrection = ameasurement × (1 + 0.002 × (313 / 1013)) ≈ ameasurement × 1.006. That is, the measured value needs to be multiplied by 1.006.

[0074] Specifically, when obtaining the air pressure data of the working environment, the air pressure data can be preprocessed. The preprocessing includes: 1. Filtering. The raw data is noisy and requires filtering. For example, smoothing filtering can effectively reduce high-frequency noise.

[0075] 2. Relative change statistics: Statistics on relative changes in air pressure.

[0076] 3. Action validity determination. Quantify and mark valid actions. This allows for effective identification of valid actions and their duration (start and end points).

[0077] Furthermore, the above pressure data preprocessing algorithm includes (refer to Figure 2 ): Step 201: Initialization. Maximum data = minimum data = first data; valid times counter = 0; maximum data max = data0; minimum data min = data0; data0 is the initial value.

[0078] Step 202: New data > old data. That is, data judgment, if yes, proceed to step 203, if not, proceed to step 204.

[0079] Step 203: Determine the maximum data, that is, assign the new data to the maximum data, max=newdata, where newdata is the new data.

[0080] Specifically, it can be implemented using the following code: firstimeUP++; firstimeDN=0; if (firstimeUP==1) {BegindataTmp=newdata;}.

[0081] firstimeUP, firstimeDN, and BegindataTmp are variable names. firstimeUP++ means increasing the value of the variable firstimeUP by 1; firstimeDN=0 means resetting the value of the variable firstimeDN to 0. If (firstimeUP == 1) indicates that if the value of firstimeUP is 1 (i.e., this is the first time this logic is executed), the following operation is executed: BegindataTmp = newdata; the value of the variable newdata is assigned to BegindataTmp, which serves as the baseline 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 becomes the maximum data.

[0082] Step 204: Determine the minimum data, that is, assign the new data to the minimum data, min=newdata.

[0083] Specifically, it can be implemented using the following code: increase=0;decrease=1;EnddataTmp=newdata;if(firstimeDN==1){EnddataTmp=newdata;}.

[0084] Increase, decrease, and EnddataTmp are all variable names. Increase = 0 means "no increase" or "does not trigger the increase state." Decrease = 1 means "decrease" or "trigger the decrease state." 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 and may be used for subsequent comparison, processing, or final assignment.

[0085] if (firstimeDN==1) {EnddataTmp=newdata;}, if firstimeDN is equal to 1 (the flag of "first run"), newdata is assigned to EnddataTmp again.

[0086] Step 205: Determine the difference. delta = max - min. delta is the difference between the maximum data and the minimum data.

[0087] Step 206: Check whether the data is within the set range. If yes, proceed to step 207; if not, return to step 202.

[0088] Step 207: The valid number of times is increased by 1. counter++, where counter is the valid number of times and ++ means increasing by 1 in the program.

[0089] Step 208: Display valid data.

[0090] Specifically, the algorithm further includes: BegindataTmp, EnddataTmp. BegindataTmp temporarily stores the start data, and EnddataTmp temporarily stores the end data.

[0091] In some embodiments of the present application, before controlling the smart wearable device to enter the time synchronization mode in response to a key operation by the user, the method further includes: Calibrate the accelerometers and gyroscopes of the plurality of smart wearable devices.

[0092] Understandably, to account for manufacturing variations, installation deviations, and aging issues that occur during use of electronic components across different smart wearable devices, real-time calibration and error compensation of the raw data from these components are performed during each synchronization process. Accelerometers and gyroscopes, in particular, exhibit significant zero-point drift, making calibration essential to improve the accuracy and reliability of synchronization.

[0093] In some embodiments of the present application, calibrating the accelerometers and gyroscopes of the plurality of smart wearable devices includes: Obtaining zero drift of at least two axes of the accelerometer in a stationary state and performing calibration accordingly; The zero-point drift of the gyroscope when placed horizontally and stationary is obtained and calibrated accordingly.

[0094] As you can understand, the mainstream six-axis calibration method for accelerometer calibration involves measuring the zero drift of the six axes (positive and negative)—X, Y, and Z—and then performing calibration. In this embodiment, for ease of implementation, only the zero drift of at least two axes in a static state is required for calibration, which also achieves good calibration results.

[0095] The gyroscope only needs to be placed horizontally and still for more than 30 seconds, and multiple sets of data are collected and the average value is calculated as the initial zero bias, and then calibration is performed to meet the calibration requirements.

[0096] To help readers better understand the technical solutions of the embodiments of this application, the following examples are given in conjunction with application scenarios: Scenario 1 (Sports): Collaborative time synchronization among multiple devices.

[0097] Marathon runners wear a smart wristband (left hand) and a smart footband (right foot), and clap their hands before starting to synchronize the time.

[0098] Data processing: The maximum acceleration of the wristband occurs at t1=10:00:00.123s, and that of the footband occurs at t2=10:00:00.156s, with Δt1=33ms; The moments of angular velocity mutation are t1'=10:00:00.120s and t2'=10:00:00.150s, Δt2=30ms; Combining Δt1 and Δt2, it is determined that the wristband time is about 31.5ms faster than the footband time. The server sends a command to adjust the footband time to t1+31.5ms.

[0099] Scenario 2 (Daily Life): Cross-device time synchronization.

[0100] Operation: The user wears the bracelet (device A) and the watch (device B) at the same time, and taps the watch dial with the hand wearing the bracelet.

[0101] Compensation process: The ambient temperature T=28℃ is detected, and the acceleration compensation amount Δt=0.001×(3)²+0.01×3=0.039m / s² is calculated; After correction, the acceleration data shows that device A is 20ms faster than device B, and the clock of device B is adjusted synchronously.

[0102] Scenario 3 (extreme environment): Time synchronization in plateau areas.

[0103] Climbers at an altitude of 5,000 meters (air pressure about 540hPa) use two wristbands to compare and check the time.

[0104] Correction steps: Pressure correction coefficient k = 0.003, ΔP = 540-1013 = -473 hPa, a correction = a measurement × (1 + 0.003 × (473 / 1013)) ≈ a measurement × 1.014; Integrating temperature compensation (if T = 10°C, Δt = 0.001×(-15)² + 0.01×(-15) = 0.225m / s²), the final timing error is controlled within ±15ms.

[0105] Please note that the data is for example only and does not represent actual data.

[0106] Example 2

[0107] The embodiment of the present application provides a time synchronization processing device for a smart wearable device (hereinafter referred to as the time synchronization device 30), which is applied to a time synchronization processing system for a smart wearable device. The time synchronization processing system for a smart wearable device includes a server and multiple smart wearable devices. The smart wearable devices include: a clock, an accelerometer, a gyroscope, and a barometer. Figure 3 , the time synchronization device 30 includes: An acquisition module 31 is configured to acquire accelerations of a plurality of the smart wearable devices; a recording module 32 configured to record first data collected by the plurality of smart wearable devices after the acceleration of any two of the smart wearable devices exceeds a preset threshold; the first data including acceleration data, angular velocity data, and air pressure data; The processing module 33 is configured to synchronize the clocks of the plurality of smart wearable devices according to the first data.

[0108] The time synchronization method of this embodiment includes: colliding two hands wearing bracelets, or colliding hands and feet wearing bracelets and anklets. In this way, the time can be synchronized through the changes in acceleration, angular velocity and air pressure during the collision, and the time corresponding to the changes. For example, in the collision of hands and feet, theoretically, the time when the bracelet generates the maximum acceleration and the time when the anklet generates the maximum acceleration are the same time. If the generation times of the two maximum accelerations obtained by the server are inconsistent, it means that there is a problem of time asynchrony, and the time of any smart wearable device can be adjusted to keep it consistent with the other. The adjustment can be executed by sending instructions through the server and executed by the smart wearable device. It is understandable that, as mentioned above, the time synchronization of consumer-grade smart wearable devices may gradually become invalid after running for a period of time or shutting down, so frequent time synchronization is required.

[0109] In the acquisition module 31, the acceleration is acquired by reading the accelerometer of the smart wearable device and the acceleration value is monitored after acquisition, so that the following steps are executed when the acceleration of any two smart wearable devices exceeds a preset threshold.

[0110] In recording module 32, it is understood that whether it is a collision between two hands or a collision between hands and feet, acceleration will increase rapidly. Therefore, recording is set to begin when the acceleration of any two 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 time synchronization. Experimental data shows that the time synchronization error of a single accelerometer is approximately ±50ms. After integrating the three types of data, the error can be reduced to within ±10ms.

[0111] Accelerometers, gyroscopes, and barometers are all electronic components commonly found in common smart wearable devices, eliminating the need for additional hardware costs and making the timing method of this embodiment more widely applicable. The preset threshold can be the minimum acceleration value required to confirm a collision. The preset acceleration threshold is set to 50 m / s² (approximately 5 g). When the acceleration of any two devices exceeds this value simultaneously and persists for 20 ms, a valid collision is determined.

[0112] Specifically, the recorded data includes: after the trigger, three types of data are recorded 500ms before and after the collision, for example: Acceleration data: real-time values ​​of X / Y / Z axes (unit: m / s²); Angular velocity data: roll / pitch / yaw angular rate (unit: ° / s); Air pressure data: absolute air pressure value (unit: hPa) and rate of change.

[0113] In processing module 33, time synchronization can be performed by comprehensively considering acceleration data, angular velocity data, and barometric pressure data to improve accuracy and reliability. Specifically, time synchronization can be performed on a pairwise basis. For example, the acceleration data can be used to determine the time difference (i.e., asynchrony) between two smart wearable devices. Similarly, the angular velocity data and barometric pressure data can also be used to determine the time difference between the two smart wearable devices. By comprehensively considering these three differences, the two smart wearable devices are synchronized. After the two smart wearable devices are synchronized, one smart wearable device is used as the reference time and then synchronized with the other smart wearable device in pairwise synchronization. This process continues until all smart wearable devices are synchronized in pairwise synchronization.

[0114] In some embodiments of the present application, the acquisition module 31 is further configured to: In response to a key operation by a user, controlling the smart wearable device to enter a time synchronization mode; After entering the time synchronization mode, the acceleration of the plurality of smart wearable devices is continuously acquired.

[0115] That is, the smart wearable device needs to enter the time synchronization mode through user operation. Otherwise, the smart wearable device does not need to monitor the data of related electronic devices and can even go into sleep mode, which is beneficial to saving energy. After the user presses a button, the smart wearable device comes out of sleep and enters the time synchronization mode to start monitoring the data of related electronic devices. After entering the time synchronization mode, the acceleration data of the smart wearable device is first obtained without having to obtain the data of all electronic devices. Only when the acceleration exceeds the preset threshold, the data of multiple electronic devices, such as angular velocity and air pressure, are recorded, which is also beneficial to saving energy. For example, when the smart wearable device is a wristband, the power consumption of the wristband in daily standby mode accounts for about 30%, and the power consumption can be reduced by 5% when the time synchronization mode is not activated.

[0116] Specifically, the key operation may be long pressing a key for more than 2 seconds, which is different from the ordinary short pressing operation.

[0117] In some embodiments of the present application, the recording module 32 is further configured to: Record the measurement values ​​of the first data and the corresponding time points in the multiple smart wearable devices, and respectively obtain the corresponding time differences in the acceleration data, angular velocity data, and air pressure data of the multiple smart wearable devices.

[0118] As mentioned above, because time synchronization is required, it is necessary to record the measured values ​​and the corresponding time points. For example, time synchronization is performed on a collision basis. Therefore, if the first data of two smart wearable devices have the same value or trend, but the corresponding time points are different, it means that the time is out of sync and time synchronization is required.

[0119] In some embodiments of the present application, the recording module 32 is further configured to: Record the first time points of the maximum acceleration of the 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 the multiple smart wearable devices.

[0120] That is, in a collision, the time points at which the two smart wearable devices generate maximum acceleration coincide. If the times displayed by the smart wearable devices differ, this provides the first time difference data, which can be used for time synchronization. It is understood that other acceleration data, such as acceleration trend changes, can also be used for time synchronization, but this will not be discussed in detail.

[0121] In some embodiments of the present application, recording the measurement values ​​of the first data and the corresponding time points in the plurality of smart wearable devices, and obtaining the corresponding time differences in the acceleration data, angular velocity data, and air pressure data of the plurality of smart wearable devices, respectively, includes: Record the angular velocity mutation trends of the 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 the multiple smart wearable devices produce the same mutation trend.

[0122] Slightly different from acceleration, in a collision, the maximum angular velocity of two smart wearable devices does not necessarily appear at the same time, but they can produce a mutation trend at the same time, so a second time difference can be obtained.

[0123] In some embodiments of the present application, the recording module 32 is further configured to: Record the air pressure change trends of the 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 the multiple smart wearable devices generate the same air pressure change trend.

[0124] It is understandable that during a collision, the maximum air pressure of the two smart wearable devices may not occur at the same time, but the same time point can be found in the changing trend. Therefore, the third time difference can be obtained.

[0125] In some embodiments of the present application, the recording module 32 is further configured to: According to the corresponding time differences in the acceleration data, angular velocity data and air pressure data of the multiple smart wearable devices, combined with the probability principle, the clocks of the multiple smart wearable devices are synchronized.

[0126] The probability principle here can be that among the three types of data obtained from the accelerometer, gyroscope and barometer, time synchronization can be performed based on the two closest data. That is, the probability that two electronic devices simultaneously have high noise and cause deviations in the obtained data and affect the time synchronization is much smaller than the probability that one of the electronic devices has high noise and affects the time synchronization.

[0127] For example, if both acceleration and angular velocity data indicate that the time on smart wearable device A is 0.1 seconds faster than the time on smart wearable device B, while the barometric pressure data indicates that the time on smart wearable device A is 0.05 seconds slower than the time on smart wearable device B, then based on probability theory, we can conclude that the time on smart wearable device A is 0.1 seconds faster than the time on smart wearable device B. Note that this data is for example purposes only and does not represent such significant time differences on real smart wearable devices.

[0128] If the judgment results of the three data are consistent, it is more reliable, which improves the accuracy and reliability of timing.

[0129] In some embodiments of the present application, the processing module 33 is further configured to: The first data are jointly processed using a weighted fusion algorithm, and the clocks of the plurality of smart wearable devices are synchronized according to the processing results.

[0130] Different from the above-mentioned probability algorithm, the weighted fusion algorithm comprehensively processes the data of multiple electronic devices and comprehensively determines the time difference to perform time synchronization.

[0131] For example, this can be processed using a Kalman filter or wavelet transform algorithm. Kalman filtering is a recursive filtering algorithm based on a state-space model. It estimates and updates the system state and fuses the measurement data from multiple electronic devices. For hand and foot tapping detection, 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 moment the hand or foot tapping action occurs.

[0132] Specifically, the algorithm proceeds as follows: First, the system's state equations and measurement equations are established to describe the relationship between the system's dynamic characteristics and the measurements of the electronic devices. Then, based on the initial state estimate and the measurement data, the system's state estimate and covariance matrix are recursively calculated. At each iteration, different weights are assigned 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.

[0133] Wavelet transform is a time-frequency analysis method that decomposes a signal into sub-signals of different frequencies, thereby extracting local features. In detecting hand and foot tapping, wavelet transform can be used to decompose data from accelerometers, gyroscopes, and barometers to extract characteristic frequency components associated with the tapping motion.

[0134] Specifically, the algorithm proceeds as follows: First, appropriate wavelet basis functions are selected to perform wavelet decomposition on the electronic device data, obtaining wavelet coefficients of different scales. Next, weights are assigned to the data of different electronic devices based on the characteristics of the wavelet coefficients (such as energy and amplitude). For example, if a device's wavelet coefficient energy is high within a specific frequency range, it indicates that the device is more responsive to tapping within that frequency range, and a larger weight can be assigned to it. Finally, the weighted wavelet coefficients are reconstructed to obtain a fused signal, thereby determining the moment when the hand or foot tapping occurred.

[0135] It can be understood that the Kalman filter algorithm or the wavelet transform algorithm are both existing weighted fusion algorithms and will not be described in detail here.

[0136] In some embodiments of the present application, the processing module 33 is further configured to: Obtaining the operating temperatures of the plurality of smart wearable devices; Performing temperature compensation on the first data collected by the smart wearable devices according to the operating temperatures of the multiple smart wearable devices to obtain second data; According to the second data, clocks of the plurality of smart wearable devices are synchronized.

[0137] 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 this influence is related to the temperature value, and temperature compensation can be used to correct the measurement value and improve measurement accuracy.

[0138] As mentioned above, smart wearable devices also include an MCU, which has a built-in temperature sensor. Based on the measurements of the MCU's built-in temperature sensor, temperature compensation can be performed on the measurements of electrical components such as the accelerometer, gyroscope, and barometer. Specifically, temperature compensation can be performed every five minutes.

[0139] Specifically, the calculation of the compensation amount of temperature compensation can refer to the expression (1) in the first embodiment: In some embodiments of the present application, the processing module 33 is further configured to: Obtaining the air pressure of the working environment of the plurality of smart wearable devices, and correcting the acceleration data collected by the smart wearable devices to obtain third data; According to the third data, clocks of the plurality of smart wearable devices are synchronized.

[0140] It is understandable that air pressure will affect the measurement of the accelerometer, and the factor affecting air pressure is mainly the altitude. Therefore, this embodiment mainly takes into account that the smart wearable device will be used in plateau areas, so the air pressure needs to be corrected to increase the usage scenarios.

[0141] Specifically, the correction of the acceleration measurement value can be expressed as (2) in the reference expression of Example 1: In some embodiments of the present application, the acquisition module 31 is further configured to: Calibrate the accelerometers and gyroscopes of the plurality of smart wearable devices.

[0142] Understandably, to account for manufacturing variations, installation deviations, and aging issues that occur during use of electronic components across different smart wearable devices, real-time calibration and error compensation of the raw data from these components are performed during each synchronization process. Accelerometers and gyroscopes, in particular, exhibit significant zero-point drift, making calibration essential to improve the accuracy and reliability of synchronization.

[0143] In some embodiments of the present application, the acquisition module 31 is further configured to: Obtaining zero drift of at least two axes of the accelerometer in a stationary state and performing calibration accordingly; The zero-point drift of the gyroscope when placed horizontally and stationary is obtained and calibrated accordingly.

[0144] As you can understand, the mainstream six-axis calibration method for accelerometer calibration involves measuring the zero drift of all six axes (X, Y, and Z) in both the positive and negative directions, and then performing calibration. In this embodiment, for ease of implementation, only the zero drift of at least two axes in a static state is required for calibration, which can also achieve good calibration results.

[0145] The gyroscope only needs to be placed horizontally and still for more than 30 seconds, and multiple sets of data are collected and the average value is calculated as the initial zero bias, and then calibration is performed to meet the calibration requirements.

[0146] Each module included in this embodiment can be implemented by a processor in a computer; of course, it can also be implemented by a logic circuit in the 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. Among them, the general-purpose processor can be a central processing unit (CPU), a microprocessor (MPU), or any other conventional processor.

[0147] The description of the above device embodiment is similar to the description of the above method embodiment and has similar beneficial effects as the method embodiment. For technical details not disclosed in the device embodiment of this application, please refer to the description of the method embodiment in this application for understanding.

[0148] Example 3

[0149] The present application embodiment provides a computing device 50, referring to Figure 4 The computing device 50 includes a storage component 51, a communication bus 52, and a processing component 53, wherein: The storage component 51 is used to store a program for a time synchronization method for a wearable device; The communication bus 52 is used to realize the connection and communication between the storage component 51 and the processing component 53; The processing component 53 is used to execute the time synchronization processing method program of the smart wearable device to implement the steps of the method described in the first embodiment.

[0150] The type or structure of the storage component 51 can be found in the storage medium below and will not be described in detail here.

[0151] The processing unit 53 may 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 may be a central processing unit (CPU), a microprocessor (MPU), or any other conventional processor.

[0152] In some embodiments, the computing device 50 may further include an input device 54 , an output device 55 , and an external communication interface 56 . These components are interconnected via a bus system and / or other connection mechanisms (not shown).

[0153] In some embodiments, the input device 54 may include, for example, a keyboard, a mouse, a microphone, etc. The output device 55 may output various information to the outside, and may include a display, a speaker, a printer, a projector, a 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, a Universal Serial Bus (USB) interface, or wireless, such as wireless network communication technology (WiFi), Bluetooth, etc.

[0154] The description of the computing device 50 embodiment above is similar to the description of the method embodiment above, and has similar beneficial effects as the method embodiment. For technical details not disclosed in the computing device 50 embodiment of this application, please refer to the description of the method embodiment in this application for understanding.

[0155] Example 4

[0156] An embodiment of the present application provides a computer-readable storage medium having an executable program stored thereon. When the executable program is executed by a processor, the steps of the method described in the first embodiment are implemented.

[0157] Exemplarily, the computer-readable storage medium may adopt any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. A computer-readable storage medium is a tangible device that can hold and store instructions used by an instruction execution device. The readable storage medium may include, for example, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or devices, or any combination of the above. More specific examples of readable storage media (a non-exhaustive list) include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), a flash memory, a portable compact disc read-only memory (CD-ROM), a digital versatile disc (DVD), a memory stick, a floppy disk, a mechanical encoding device, such as a punch card or a raised structure in a groove on which instructions are stored, and any suitable combination of the above. Among them: 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), synchronous link dynamic random access memory (SLDRAM), and direct rambus random access memory (DRRAM).

[0158] The ROM includes: Programmable Read-Only Memory (PROM), Erasable Programmable Read-Only Memory (EPROM), and Electrically Erasable Programmable Read-Only Memory (EEPROM).

[0159] The description of the computer-readable storage medium embodiment above is similar to the description of the method embodiment above, and has similar beneficial effects as the method embodiment. For technical details not disclosed in the computer-readable storage medium of this embodiment, please refer to the description of the method embodiment in this application for understanding.

[0160] Example 5

[0161] The present application embodiment provides a time synchronization processing system for a smart wearable device (hereinafter referred to as the time synchronization system). Figure 5 , the time synchronization system includes: Server 61, including the smart wearable device time synchronization processing device described in Example 2; A plurality of smart wearable devices 62, each of which includes a clock, an accelerometer, a gyroscope, and a barometer.

[0162] The description of the above embodiment of the smart wearable device time synchronization processing system is similar to the description of the above method embodiment, and has similar beneficial effects as the method embodiment. For technical details not disclosed in the embodiment of the smart wearable device time synchronization processing system of this application, please refer to the description of the method embodiment of this application for understanding.

[0163] It should be noted that the various embodiments provided in the embodiments of the present application belong to the same concept; the various technical features in the technical solutions recorded in the various embodiments can be arbitrarily combined to form new embodiments without conflict.

[0164] In the above description, the terms "first\second\..." are only used to distinguish similar objects and do not represent a specific order of the objects. It can be understood that "first\second\third" can be interchanged with a specific order or sequence when permitted.

[0165] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or apparatus comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or apparatus comprising the element.

[0166] In the embodiments of this application, unless otherwise specified and limited, the term "connection" should be understood in a broad sense. For example, it can be an electrical connection, or it can be a connection between two components. It can be a direct connection or an indirect connection through an intermediate medium. For ordinary technicians in this field, the specific meaning of the above terms can be understood according to the specific circumstances.

[0167] The purpose of the terms used herein is only to describe specific embodiments and is not intended to limit the present application. When used herein, the singular forms "a", "an", and "the" are 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, determine the presence of the features, integers, steps, operations, elements and / or parts, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, parts and / or groups. When used herein, the term "and / or" includes any and all combinations of the relevant listed items.

[0168] It should be understood that references to "one embodiment" or "some embodiments" throughout this specification mean that a particular feature, structure, or characteristic associated with the embodiment is included in at least one embodiment of the present application. Therefore, the appearance of "in one embodiment" or "in some embodiments" throughout this specification does not necessarily refer to the same embodiment. Furthermore, these particular features, structures, or characteristics may 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.

[0169] It should be understood that the size of the serial numbers of the above processes does not mean 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 the present application.

[0170] 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 schematic. For example, the division of the modules is merely a logical function division. In actual implementation, there may be other division methods, such as: multiple modules or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or modules can be electrical, mechanical or other forms.

[0171] The modules described above as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical modules; they may be located in one place or distributed on multiple network modules; some or all of the modules may be selected according to actual needs to achieve the purpose of this embodiment.

[0172] In addition, all functional modules in the embodiments of the present 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 above-mentioned integrated modules can be implemented in the form of hardware or in the form of hardware plus software functional modules.

[0173] Those skilled in the art will understand that all or part of the steps of implementing the above method embodiments can be completed 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 executes the steps including the above method embodiments.

[0174] Alternatively, if the above-mentioned integrated modules of the present application are implemented in the form of 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 the present application, or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a number of instructions for enabling an electronic device (which can be a personal computer, server, or network device, etc.) to execute all or part of the methods described in the various embodiments of the present application. Thus, the embodiments of the present application are not limited to any specific combination of hardware and software.

[0175] It should be understood that the above embodiments are exemplary and are not intended to encompass all possible implementations of the technical solutions of the present application. Various modifications and variations may be made based on the above embodiments without departing from the scope disclosed herein. Similarly, the various technical features of the above embodiments may be arbitrarily combined to form additional embodiments of the present application that may not be explicitly described. Therefore, the above embodiments merely illustrate several implementations of the present application and do not limit the scope of protection of the patent application.

Claims

1. A method for synchronizing a smart wearable device, applied to a system for synchronizing a smart wearable device, wherein the system comprises a server and a plurality of smart wearable devices, wherein the smart wearable devices comprise: clock, accelerometer, gyroscope and barometer, characterized in that the method comprises: Obtaining acceleration of multiple smart wearable devices; After the acceleration of any two of the smart wearable devices exceeds a preset threshold, recording first data collected by the multiple smart wearable devices; the first data includes acceleration data, angular velocity data and air pressure data; Synchronize the clocks of the plurality of smart wearable devices according to the first data.

2. The method for synchronizing time of a smart wearable device according to claim 1, wherein: The obtaining of the acceleration of the plurality of smart wearable devices includes: In response to a key operation by a user, controlling the smart wearable device to enter a time synchronization mode; After entering the time synchronization mode, the acceleration of the plurality of smart wearable devices is continuously acquired.

3. The time synchronization method for a smart wearable device according to claim 1, wherein: The recording of the first data collected by the plurality of smart wearable devices includes: Record the measurement values ​​of the first data and the corresponding time points in the multiple smart wearable devices, and respectively obtain the corresponding time differences in the acceleration data, angular velocity data, and air pressure data of the multiple smart wearable devices.

4. The method for synchronizing time of a smart wearable device according to claim 3, wherein: The recording of the measurement values ​​of the first data and the corresponding time points in the plurality of smart wearable devices, and obtaining the corresponding time differences in the acceleration data, the angular velocity data, and the air pressure data of the plurality of smart wearable devices, respectively, includes: Record the first time points of the maximum acceleration of the 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 the multiple smart wearable devices.

5. The method for synchronizing time of a smart wearable device according to claim 3, wherein: The recording of the measurement values ​​of the first data and the corresponding time points in the plurality of smart wearable devices, and obtaining the corresponding time differences in the acceleration data, the angular velocity data, and the air pressure data of the plurality of smart wearable devices, respectively, includes: Record the angular velocity mutation trends of the 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 the multiple smart wearable devices produce the same mutation trend.

6. The method for synchronizing time of a smart wearable device according to claim 3, wherein: The recording of the measurement values ​​of the first data and the corresponding time points in the plurality of smart wearable devices, and obtaining the corresponding time differences in the acceleration data, the angular velocity data, and the air pressure data of the plurality of smart wearable devices, respectively, includes: Record the air pressure change trends of the 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 the multiple smart wearable devices generate the same air pressure change trend.

7. The method for synchronizing time of a smart wearable device according to claim 3, wherein: The step of synchronizing clocks of the plurality of smart wearable devices according to the first data includes: According to the corresponding time differences in the acceleration data, angular velocity data and air pressure data of the multiple smart wearable devices, combined with the probability principle, the clocks of the multiple smart wearable devices are synchronized.

8. The method for synchronizing time of a smart wearable device according to claim 1, wherein: The step of synchronizing clocks of the plurality of smart wearable devices according to the first data includes: The first data are jointly processed using a weighted fusion algorithm, and the clocks of the plurality of smart wearable devices are synchronized according to the processing results.

9. The method for synchronizing time of a smart wearable device according to claim 1, wherein: The step of synchronizing the clocks of the plurality of smart wearable devices according to the first data includes: Obtaining the operating temperatures of the plurality of smart wearable devices; Performing temperature compensation on the first data collected by the smart wearable devices according to the operating temperatures of the multiple smart wearable devices to obtain second data; According to the second data, clocks of the plurality of smart wearable devices are synchronized.

10. The method for synchronizing time of a smart wearable device according to claim 1, wherein: The step of synchronizing the clocks of the plurality of smart wearable devices according to the first data includes: Obtaining the air pressure of the working environment of the plurality of smart wearable devices, and correcting the acceleration data collected by the smart wearable devices to obtain third data; According to the third data, clocks of the plurality of smart wearable devices are synchronized.

11. The method for synchronizing time of a smart wearable device according to claim 2, wherein: Before controlling the smart wearable device to enter the time synchronization mode in response to the user's key operation, the method further includes: Calibrate the accelerometers and gyroscopes of the plurality of smart wearable devices.

12. The method for synchronizing time of a smart wearable device according to claim 11, wherein: The calibrating the accelerometers and gyroscopes of the plurality of smart wearable devices includes: Obtaining zero drift of at least two axes of the accelerometer in a stationary state and performing calibration accordingly; The zero-point drift of the gyroscope when placed horizontally and stationary is obtained and calibrated accordingly.

13. A time synchronization processing device for a smart wearable device, applied to a time synchronization processing system for a smart wearable device, the time synchronization processing system for a smart wearable device comprising a server and a plurality of smart wearable devices, the smart wearable devices comprising: clock, accelerometer, gyroscope and barometer, characterized in that the device comprises: An acquisition module, configured to acquire accelerations of the plurality of smart wearable devices; A recording module, configured to record first data collected by the plurality of smart wearable devices after the acceleration of any two of the smart wearable devices exceeds a preset threshold; the first data including acceleration data, angular velocity data, and air pressure data; A processing module is used to synchronize the clocks of the plurality of smart wearable devices according to the first data.

14. A computing device, characterized in that The computing device comprises: a storage component, a communication bus, and a processing component, wherein: The storage component is used to store the program of the time synchronization method of the smart wearable device; The communication bus is used to realize the connection and communication between the storage component and the processing component; The processing component is used to execute the smart wearable device time synchronization processing method program to implement the steps of any one of the methods described in claims 1 to 12.

15. A computer-readable storage medium, characterized in that An executable program is stored on the computer-readable storage medium, and when the executable program is executed by a processor, the steps of the method according to any one of claims 1 to 12 are implemented.

16. A time synchronization processing system for smart wearable devices, characterized in that: include: The server comprises the smart wearable device time synchronization processing device according to claim 13; A plurality of smart wearable devices, each comprising a clock, an accelerometer, a gyroscope, and a barometer.

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