Control method of wearable device, control apparatus thereof, and wearable device

Abstract

This application discloses a control method and control device for a wearable device, a wearable device, and a storage medium. The wearable device includes a main body and a detection component connected to the main body. The main body includes a detection unit. The control method includes the following steps: detecting the operating power consumption of the main body; if the operating power consumption of the main body is not higher than a power consumption threshold, determining the detection unit as the detection subject for acquiring human feature data; and if the operating power consumption of the main body is higher than the power consumption threshold, determining the detection component as the detection subject for acquiring human feature data. In the control method of the wearable device according to the embodiments of this application, different components are determined to acquire human feature data based on the operating power consumption of the main body, which can reduce the operating power consumption of the main body, rationally allocate power resources, and extend the battery life of the wearable device.

Description

Technical Field

[0001] This application relates to the field of electronic technology, and in particular to a control method and control device for a wearable device, the wearable device, and a storage medium. Background Technology

[0002] Wearable devices, such as smart bracelets, smartwatches, and smart glasses, are increasingly impacting people's lives, especially in health management. By detecting various human characteristics, wearable devices allow people to more easily understand their physical condition. However, due to the need to maintain a small size, wearable devices typically have small battery capacities. This limited battery capacity restricts battery life, resulting in poor performance and a less than ideal user experience. Summary of the Invention

[0003] This application provides a control method for a wearable device, a control device for a wearable device, a wearable device, and a storage medium.

[0004] The control method for a wearable device according to embodiments of this application, wherein the wearable device includes a body and a detection component connected to the body, the body including a detection unit, and the control method includes the following steps:

[0005] Detect the operating power consumption of the main body;

[0006] When the operating power consumption is not higher than the power consumption threshold, the detection subject is determined to be the detection unit so as to acquire human feature data through the detection unit; and

[0007] If the operating power consumption is higher than the power consumption threshold, the detection subject is identified as the detection component to obtain the human body feature data through the detection component.

[0008] The control device for a wearable device according to embodiments of this application includes a main body and a detection component connected to the main body. The main body includes a detection unit, and the control device includes:

[0009] A detection module, which is used to detect the operating power consumption of the body;

[0010] A data acquisition module, configured to, when the operating power consumption is not higher than a power consumption threshold, determine the detection subject as the detection unit to acquire human feature data through the detection unit; and

[0011] The data acquisition module is also used to determine the detection subject as the detection component when the operating power consumption is higher than the power consumption threshold, so as to acquire the human body feature data through the detection component.

[0012] The wearable device according to the embodiments of this application includes one or more processors, a memory, and one or more computer programs, wherein the one or more computer programs are stored in the memory, and when the computer programs are executed by the one or more processors, they implement instructions for the control method of the wearable device described in any of the above embodiments.

[0013] The non-volatile computer-readable storage medium of this application embodiment stores a computer program that, when executed by one or more processors, implements instructions for the control method of the wearable device described in any of the above embodiments.

[0014] In the control method, control device, wearable device, and storage medium of the wearable device according to the embodiments of this application, different components are determined to acquire human feature data based on the operating power consumption of the detection body, which can reduce the operating power consumption of the body, rationally allocate power resources, and extend the battery life of the wearable device.

[0015] 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

[0016] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the description of the embodiments taken in conjunction with the following drawings, wherein:

[0017] Figure 1 This is a flowchart illustrating the control method for a wearable device according to an embodiment of this application.

[0018] Figure 2 This is a schematic diagram of the main body and detection component of the wearable device according to an embodiment of this application.

[0019] Figure 3 This is a schematic diagram of the structure of a wearable device according to an embodiment of this application.

[0020] Figure 4 This is a schematic diagram of the control device of a wearable device according to an embodiment of this application.

[0021] Figure 5 This is another schematic flowchart of the control method for a wearable device according to an embodiment of this application.

[0022] Figure 6 This is another schematic flowchart of the control method for a wearable device according to an embodiment of this application.

[0023] Figure 7 This is another schematic flowchart of the control method for a wearable device according to an embodiment of this application.

[0024] Figure 8 This is another schematic flowchart of the control method for a wearable device according to an embodiment of this application.

[0025] Figure 9 This is another schematic flowchart of the control method for a wearable device according to an embodiment of this application.

[0026] Figure 10 This is another schematic flowchart of the control method for a wearable device according to an embodiment of this application.

[0027] Figure 11 This is another schematic flowchart of the control method for a wearable device according to an embodiment of this application.

[0028] Figure 12 This is another schematic flowchart of the control method for a wearable device according to an embodiment of this application. Detailed Implementation

[0029] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain this application, and should not be construed as limiting this application.

[0030] Please see Figure 1 and Figure 2 In the control method of the wearable device according to the embodiments of this application, the wearable device 10 includes a body 12 and a detection component 14 connected to the body 12. The body 12 includes a detection unit 121. The control method includes the following steps:

[0031] S1: Detect the operating power consumption of the main body 12;

[0032] S2: When the operating power consumption of the main body 12 is not higher than the power consumption threshold, the detection unit 121 is determined as the detection subject for acquiring human feature data; and

[0033] S3: When the operating power consumption of the main body 12 is higher than the power consumption threshold, the detection component 14 is determined as the detection subject to obtain human feature data.

[0034] Please see Figure 3This application also provides a wearable device 10. The wearable device 10 includes a processor 102 and a memory 104. The memory 104 stores a computer program 106. When the computer program 106 is executed by the processor 102, it implements the control method of the wearable device 10 of this application. That is, the processor 102 can be used to detect the operating power consumption of the body 12, and to determine the detection unit 121 as the detection subject for acquiring human feature data when the operating power consumption of the body 12 is not higher than the power consumption threshold, and to determine the detection component 14 as the detection subject for acquiring human feature data when the operating power consumption of the body 12 is higher than the power consumption threshold.

[0035] Please see Figure 4 This application also provides a control device 110 for a wearable device 10. The control method of the wearable device 10 in this application embodiment can be implemented by the control device 110. The control device 110 includes a detection module 112 and a data acquisition module 114. S1 can be implemented by the detection module 112, and S2 and S3 can be implemented by the data acquisition module 114. That is, the detection module 112 can be used to detect the operating power consumption of the body 12. The data acquisition module 114 can be used to determine the detection unit 121 as the detection subject for acquiring human feature data when the operating power consumption of the body 12 is not higher than the power consumption threshold, and to determine the detection component 14 as the detection subject for acquiring human feature data when the operating power consumption of the body 12 is higher than the power consumption threshold.

[0036] Specifically, wearable device 10 can be a smart bracelet, smartwatch, smart necklace, smart ring, etc., without any specific limitations. The following explanation uses a smartwatch as an example.

[0037] The wearable device 10 includes a main body 12 and a detection component 14 connected to the main body 12. The main body 12 also includes a detection unit 121. Compared to the detection unit 121, the detection component 14 has higher power consumption, but the data detected by the detection component 14 is more accurate. The detection unit 121 has lower power consumption.

[0038] In this way, by determining which components are used to acquire human feature data based on the operating power consumption of the detection body 12, the operating power consumption of the body 12 can be reduced, power resources can be allocated reasonably, the battery life of the wearable device 10 can be extended, and the user experience can be optimized.

[0039] Furthermore, since the detection accuracy of the detection component 14 is higher than that of the detection unit 121, the accuracy of human feature data can be improved when using the detection component 14 to acquire human feature data.

[0040] Please see Figure 5In some implementations, the control method specifically includes:

[0041] S10: Detect whether the preset function of the wearable device 10 is enabled to determine the operating power consumption of the main body 12;

[0042] S20: Determine that the operating power consumption of the main body 12 does not exceed the power consumption threshold when the preset function is off; and

[0043] S30: When the preset function is enabled, determine that the operating power consumption of the main body 12 is higher than the power consumption threshold.

[0044] Accordingly, for the wearable device 10, the processor 102 can be used to detect whether the preset function of the wearable device 10 is enabled to determine the operating power consumption of the main body 12, to determine that the operating power consumption of the main body 12 is higher than the power consumption threshold when the preset function is enabled, and to determine that the operating power consumption of the main body 12 is not higher than the power consumption threshold when the preset function is disabled.

[0045] For the control device 110 of the wearable device 10, S10 can be implemented by the detection module 112, and S20 and S30 can be implemented by the data acquisition module 114. That is, the detection module 112 can be used to detect whether the preset function of the wearable device 10 is enabled to determine the operating power consumption of the main body 12. The data acquisition module 114 can be used to acquire human feature data through the detection component 14 when the preset function is enabled, and to acquire human feature data through the detection unit 121 to reduce the power consumption of the wearable device 10 when the preset function is disabled.

[0046] The default functions of wearable device 10 may include WiFi, embedded SIM, screen activation, and time display. When these default functions are enabled, the power consumption of the main body 12 may be high. Therefore, when the default functions are disabled, the power consumption of the main body 12 is determined to be no higher than the power consumption threshold. When the default functions are enabled, the power consumption of the main body 12 is determined to be higher than the power consumption threshold.

[0047] Please see Figure 6 In some implementations, the control method includes:

[0048] S40: Control the working status of the detection component 14 according to the number of preset functions activated.

[0049] In some implementations, S40 can be implemented by the data acquisition module 114. That is, the data acquisition module 114 can be used to control the working state of the detection component 14 according to the number of preset functions activated.

[0050] In some implementations, the processor 102 can be used to control the working state of the detection component 14 according to the number of preset functions activated.

[0051] Specifically, when the preset functions are enabled, the power consumption of the main body 12 may be high. Therefore, human feature data is acquired by the detection component 14 outside the main body 12. Furthermore, when the number of preset functions enabled exceeds a preset threshold, the acquisition frequency of human feature data by the detection component 14 can be relatively reduced, and the amount of data acquired can be relatively decreased.

[0052] In some embodiments, a preset threshold of 5 is set. When the number of currently enabled preset functions is 6, the detection component 14 acquires human feature data at a low frequency, resulting in a small amount of data acquired.

[0053] In this way, the working state of the detection component 14 can be adjusted according to the number of preset functions activated, further reducing the operating power consumption of the main body 12, rationally allocating power resources, and extending the battery life of the wearable device 10.

[0054] It should be noted that the preset number threshold can be set according to user settings, the type of wearable device, the usage scenario, processor performance, and other parameters. There is no specific limitation; for example, it can be 3, 5, 6, 10, etc. Multiple number thresholds can also be set to gradually reduce the acquisition frequency and / or the amount of data acquired within the corresponding number range.

[0055] Please see Figure 7 In some implementations, the control method includes:

[0056] S50: Controls the running status of multiple preset functions according to priority.

[0057] In some implementations, S50 can be implemented by the data acquisition module 114. That is, the data acquisition module 114 can be used to control the operating status of multiple preset functions according to priority.

[0058] In some implementations, the processor 102 can be used to control the operating state of multiple preset functions according to priority.

[0059] Specifically, when multiple preset functions are enabled simultaneously, their operation is controlled according to preset priorities. For example, considering that users typically do not need to view the screen or use location information when making calls using wearable device 10, the priority of location and screen-on functions is set lower than that of call functions. When both screen-on and location functions are enabled, if wearable device 10 enables call functions, location and screen-on functions are disabled; or if call functions are enabled, location and screen-on functions are put into a sleep state. Furthermore, since wearable device 10 has limited memory resources, the number of preset functions that can run simultaneously is also limited. Therefore, by setting priorities for preset functions, wearable device 10 can control the operation of multiple preset functions based on memory resources, which also helps ensure the efficient operation of wearable device 10.

[0060] In this way, the operating status of multiple preset functions can be controlled according to priority, reducing the operating power consumption of the main body 12, rationally allocating power resources, and extending the battery life of the wearable device 10.

[0061] Furthermore, when controlling the operation of multiple preset functions according to priority, the frequency and / or amount of human feature data acquired by the detection component 14 can also be controlled accordingly. For example, when the call function is enabled and the location function and screen-on function are in a sleep state, the detection component 14 acquires data at a lower frequency and acquires less data compared to when all three functions are in a sleep state.

[0062] In this way, by controlling the operating status of multiple preset functions according to priority and adjusting the working status of the detection component 14 according to the number of preset functions activated, the operating power consumption of the main body 12 can be further reduced, power resources can be reasonably allocated, and the battery life of the wearable device 10 can be extended.

[0063] It is understandable that for non-preset functions, the wearable device 10 can pre-calculate the power consumption of each function based on the modules activated by different functions and the memory occupied during operation. Then, the power consumption of each activated function is added together to obtain the actual power consumption of the main body 12. Based on whether the power consumption of the main body 12 is higher than the power consumption threshold, different components are used to obtain human feature data, which can reduce the power consumption of the main body 12 and rationally allocate power resources.

[0064] In some embodiments, the detection component 14 is wirelessly connected to the body 12.

[0065] Specifically, the control device 110 of the wearable device 10 includes a wireless communication module 116, that is, the wireless communication module 116 is used to connect the detection component 10 and the main body 12 of the wearable device 10. In this way, the detection component 14 and the main body 12 are connected wirelessly to transmit human feature data and corresponding control commands for controlling the detection component 14 to turn on or off. For example, they can be connected via Bluetooth, cellular network, Wi-Fi, etc., and the specific connection is not limited.

[0066] In some embodiments, the detection component 14 is connected to the main body 12 via Bluetooth. Bluetooth can be Bluetooth Low Energy (BLE), which, compared to classic Bluetooth, further reduces power consumption of both the main body 12 and the detection component 14 while maintaining the same communication range. Furthermore, Bluetooth modules are less expensive, and interconnection via Bluetooth can also reduce production costs.

[0067] In other embodiments, the detection component 14 is connected to the body 12 via a cellular network. Cellular networks offer higher data transmission capacity and greater reliability, ensuring accurate and rapid transmission of human feature data through interconnection.

[0068] Of course, the detection component 14 and the main body 12 are not limited to wireless communication connection. They can also be connected by wired communication depending on the actual situation. No specific limitation is made here.

[0069] In some implementations, the human characteristic data includes at least one or more of blood pressure, blood oxygen, and heart rate.

[0070] Specifically, human characteristic data may include one or more of the following: blood pressure, blood oxygen, heart rate, human activity level, human movement trajectory, and human activity distance.

[0071] Furthermore, the detection component 14 and the detection unit 121 can be equipped with an integrated sensor to uniformly detect human feature data, or multiple sensors can be set, with each sensor corresponding to detect its own human feature data.

[0072] In some embodiments, the detection component 14 and the detection unit 121 are equipped with multiple sensors, including a blood pressure sensor, a blood oxygen sensor, a heart rate sensor, a motion sensor, an accelerometer, and a gyroscope. When the human characteristic data is blood pressure, the sensor used in the detection component 14 or the detection unit 121 is a blood pressure sensor. When the human characteristic data is blood oxygen, the sensor used in the detection component 14 or the detection unit 121 is a blood oxygen sensor. When the human characteristic data is heart rate, the sensor used in the detection component 14 or the detection unit 121 is a heart rate sensor. When the human characteristic data is a human walking trajectory or a human activity distance, the sensor used in the detection component 14 or the detection unit 121 is a motion sensor.

[0073] It should be noted that when only a single human feature data needs to be detected, it can be detected using the corresponding sensor, and other unused sensors can be turned off. When multiple human feature data needs to be detected, it can be detected using sensors corresponding to multiple feature data, and other unused sensors can be turned off.

[0074] In this way, the power consumption of the wearable device 10 can be reduced as much as possible, ensuring the battery life of the wearable device 10 and optimizing the user experience.

[0075] Please see Figure 8 In some embodiments, S1 includes:

[0076] S11, Real-time detection of the operating power consumption of the main body to determine the detection subject;

[0077] Control methods include:

[0078] S4: Determine whether to perform data detection based on user input;

[0079] S5: In the case of data detection, human characteristic data is obtained through a determined detection subject.

[0080] Accordingly, for the wearable device 10, the processor 102 can be used to detect the operating power consumption of the device in real time to determine the detection subject. The processor 102 can also be used to determine whether to perform data detection based on user input, and, if data detection is performed, to acquire human feature data through the determined detection subject.

[0081] For the control device 110 of the wearable device 10, S11 can be implemented by the detection module 112, and S40 and S50 can be implemented by the data acquisition module 114. That is, the detection module 112 can be used to detect the operating power consumption of the device in real time to determine the detection subject. The data acquisition module 114 can be used to determine whether to perform data detection based on user input, and to acquire human feature data through the determined detection subject when data detection is performed.

[0082] It is understandable that users can determine whether to perform data detection to obtain human feature data by inputting control commands. For example, by inputting control commands, users can determine which human feature data to obtain, and then control the wearable device 10 to activate the corresponding sensors to collect human feature data. The wearable device 10 can detect the operating power consumption of the main body 12 in real time to determine which component should be used as the detection subject to obtain human feature data in the current operating state, that is, to determine the detection subject in real time. After determining the detection subject, if user input is received, human feature data can be directly obtained through the determined detection subject. That is, when the operating power consumption of the main body 12 is not higher than the power consumption threshold, human feature data is obtained through the detection unit 121; and when the operating power consumption of the main body 12 is higher than the power consumption threshold, human feature data is obtained through the detection component 14.

[0083] Please see Figure 9 In some embodiments, S1 includes:

[0084] S12: Determine whether to perform data detection based on user input;

[0085] S13: When performing data detection, the operating power consumption of the detection body 12 is used to determine the detection body;

[0086] Control methods include:

[0087] S6: Obtain human characteristic data through a defined detection subject.

[0088] Accordingly, for the wearable device 10, the processor 102 can be used to determine whether to perform data detection based on user input, and, if data detection is performed, to detect the operating power consumption of the body 121 to determine the detection subject. The processor 102 can also be used to acquire human feature data through the determined detection subject.

[0089] For the control device 110 of the wearable device 10, steps S12 and S13 can be implemented by the detection module 112, and step S6 can be implemented by the data acquisition module 114. That is, the detection module 112 can be used to determine whether to perform data detection based on user input, and when data detection is performed, to detect the operating power consumption of the body 121 to determine the detection subject. The data acquisition module 114 can be used to acquire human feature data through the determined detection subject.

[0090] It is understandable that the wearable device 10 does not need to detect the operating power consumption of the main body 12 in real time. Instead, it detects the operating power consumption of the main body 12 after confirming the data detection based on user input to determine the detection subject. Then, after determining the detection subject, it acquires human feature data based on the determined detection subject. That is, when the operating power consumption of the main body 12 is not higher than the power consumption threshold, human feature data is acquired through the detection unit 121; and when the operating power consumption of the main body 12 is higher than the power consumption threshold, human feature data is acquired through the detection component 14. In this way, the frequency of power consumption detection can be reduced.

[0091] Please see Figure 10 In some implementations, the control method includes:

[0092] S7: If the power consumption of the main body 12 is higher than the power consumption threshold when human feature data is obtained through the detection unit 121, the detection main body is switched to the detection component 14 and human feature data is obtained through the detection component 14.

[0093] S8: If the power consumption of the main body 12 is not higher than the threshold when human feature data is acquired through the detection component 14, the detection main body is switched to the detection unit 121 and human feature data is acquired through the detection unit 121.

[0094] Accordingly, for the wearable device 10, the processor 102 can be used to switch the detection subject to the detection component 14 and acquire human feature data through the detection component 14 if the operating power consumption of the body 12 is higher than the power consumption threshold when human feature data is acquired through the detection unit 121, and to switch the detection subject to the detection unit 121 and acquire human feature data through the detection unit 121 if the operating power consumption of the body 12 is not higher than the threshold when human feature data is acquired through the detection component 14.

[0095] For the control device 110 of the wearable device 10, S7 and S8 can be implemented by the data acquisition module 114. That is to say, the data acquisition module 114 can be used to switch the detection subject to the detection component 14 and acquire human feature data through the detection component 14 when the operating power consumption of the body 12 is higher than the power consumption threshold when the human feature data is acquired through the detection unit 121, and to switch the detection subject to the detection unit 121 and acquire human feature data through the detection unit 121 when the operating power consumption of the body 12 is not higher than the threshold when the human feature data is acquired through the detection component 14.

[0096] In this way, during the process of acquiring human feature data, the wearable device 10 can detect the operating power consumption of the main body 12 in real time, and determine whether to switch the detection subject based on the operating power consumption of the main body 12. This helps to reduce the operating power consumption of the main body 12, rationally allocate power resources, extend the battery life of the wearable device 10, and optimize the user experience.

[0097] Please see Figure 11 In some implementations, the control method includes:

[0098] S9A: If the operating power consumption is detected to be higher than the power consumption threshold when human feature data is acquired through the detection unit, or if the operating power consumption is detected to be lower than the threshold when human feature data is acquired through the detection component, then a main body switching option is issued.

[0099] S9B: Receives user input based on subject switching options to determine whether to switch the detection subject.

[0100] In some implementations, S9A and S9B can be implemented by the detection module 112. That is, the detection module 112 can be used to issue a subject switching option when the operating power consumption is higher than the power consumption threshold when human feature data is obtained through the detection unit or when the operating power consumption is not higher than the threshold when human feature data is obtained through the detection component, and to receive input from the user based on the subject switching option to determine whether to switch the detection subject.

[0101] In some implementations, the processor 102 may issue a subject switching option if it detects that the operating power consumption is higher than a power consumption threshold when human feature data is acquired through the detection unit or if it detects that the operating power consumption is not higher than the threshold when human feature data is acquired through the detection component, and may also receive input from the user based on the subject switching option to determine whether to switch the detection subject.

[0102] It is understandable that the wearable device 10 can acquire a specific human feature data only after the user inputs confirmation to perform data detection. Since the detection accuracy of the detection component 14 and the detection unit 121 may differ, the human feature data acquired by the detection component 14 and the detection unit 121 as the detection subjects during the data detection process cannot be continuously used in the same analysis process. That is, when the human feature data collected by the wearable device 10 is used for analysis, it should be continuous data collected by the same detection subject. Therefore, during the human data acquisition process, a subject switching option can be issued, allowing the user to select whether to switch the detection subject based on the input of the subject switching option and corresponding control commands. This avoids the situation where directly switching the detection subject requires re-detecting the human feature data, thus improving the user experience.

[0103] Please see Figure 12 In some implementations, S2 includes:

[0104] S22: When the operating power consumption of the main body 12 is higher than the power consumption threshold, human body feature data is acquired through the detection component 14, and the acquisition frequency and / or acquisition amount of human body feature data are determined according to the current power of the detection component 14.

[0105] In some implementations, S22 can be implemented by the data acquisition module 114. That is, the data acquisition module 114 can be used to acquire human feature data through the detection component 14 when the operating power consumption of the body 12 is higher than the power consumption threshold, and determine the acquisition frequency and / or the amount of data to be acquired based on the current power consumption of the detection component 14.

[0106] In some implementations, the processor 102 can be used to acquire human feature data through the detection component 14 when the operating power consumption of the body 12 is higher than the power consumption threshold, and determine the acquisition frequency and / or the amount of data to be acquired based on the current power consumption of the detection component 14.

[0107] Specifically, both the detection component 14 and the main body 12 are equipped with independent batteries. When the operating power consumption of the main body 12 is higher than the power consumption threshold, after acquiring human feature data through the detection component 14, the acquisition frequency and / or the amount of data acquired by the detection component 14 can be determined based on the current battery level of the detection component 14. When the current battery level of the detection component 14 is lower than the first battery level threshold, the detection component 14 detects human feature data at a lower frequency and acquires less data.

[0108] In this way, the working state of the detection component 14 can be adjusted according to the power usage, further reducing the operating power consumption of the main body 12, rationally allocating power resources, and extending the battery life of the wearable device 10.

[0109] It should be noted that the first battery threshold can be set according to user settings, the type of wearable device, the usage scenario, processor performance, and other parameters. There is no specific limitation; for example, it could be 30%, 50%, 60%, etc. Multiple battery thresholds can also be set to gradually reduce the acquisition frequency and / or the amount of data acquired within the corresponding battery range.

[0110] In some embodiments, a first battery threshold is set to 40%. With the preset function enabled, human feature data is acquired by the detection component 14. If the current battery level of the detection component 14 is 30%, then it is determined that the acquisition frequency of the detection component 14 decreases by 20%.

[0111] In this way, the working state of the detection component 14 can be adjusted according to the power usage, further reducing the operating power consumption of the main body 12, rationally allocating power resources, and extending the battery life of the wearable device 10.

[0112] Please refer to it again. Figure 12 In some implementations, S3 includes:

[0113] S32: When the operating power consumption of the main body 12 is not higher than the power consumption threshold, human body feature data is acquired through the detection unit 121, and the acquisition frequency and / or acquisition amount of human body feature data are determined according to the current power of the main body 12.

[0114] In some implementations, S32 can be implemented by the data acquisition module 114. That is, the data acquisition module 114 can be used to acquire human feature data through the detection unit 121 when the operating power consumption of the body 12 is not higher than the power consumption threshold, and determine the acquisition frequency and / or the amount of data to be acquired based on the current power consumption of the body 12.

[0115] In some implementations, the processor 102 can be used to acquire human feature data through the detection unit 121 when the operating power consumption of the body 12 is not higher than the power consumption threshold, and determine the acquisition frequency and / or the amount of data to be acquired based on the current power consumption of the body 12.

[0116] Specifically, similar to the aforementioned implementation method, when the operating power consumption of the main body 12 is not higher than the power consumption threshold, human body feature data is acquired through the detection unit 121. The acquisition frequency and / or acquisition amount of human body feature data can be determined based on the current power consumption of the main body 12, which will not be elaborated here.

[0117] In this way, the working state of the detection unit 121 can be adjusted according to the power usage, further reducing the operating power consumption of the main body 12, rationally allocating power resources, and extending the battery life of the wearable device 10.

[0118] In some implementations, S3 includes:

[0119] S34: When the power consumption of the main body 12 is not higher than the power consumption threshold, human body feature data is acquired through the detection unit 121, and the acquisition frequency and / or the amount of data acquired are determined according to the working mode of the wearable device 10.

[0120] In some implementations, S34 can be implemented by the data acquisition module 114. That is, the data acquisition module 114 can be used to acquire human feature data through the detection unit 121 when the operating power consumption of the body 12 is not higher than the power consumption threshold, and determine the acquisition frequency and / or the amount of data to be acquired according to the working mode of the wearable device 10.

[0121] In some implementations, the processor 102 can be used to acquire human feature data through the detection unit 121 when the operating power consumption of the body 12 is not higher than the power consumption threshold, and determine the acquisition frequency and / or the amount of data to be acquired according to the working mode of the wearable device 10.

[0122] Specifically, when the power consumption of the main body 12 does not exceed the power consumption threshold, after acquiring human feature data through the detection unit 121, the acquisition frequency and / or the amount of data acquired can be determined according to the working mode of the wearable device 10. The working mode of the wearable device 10 can include multiple modes, such as a first mode, a second mode, a third mode, etc., without specific limitations. The following explanation uses the first mode and the second mode as examples.

[0123] Furthermore, the wearable device 10 can be a dual-system architecture, that is, a hardware architecture based on two processor chips, where each processor runs an independent operating system, such as a big-core system and a little-core system. The two operating systems interact with each other to realize the functions of the wearable device 10. The operating modes of the wearable device 10 can include a first mode and a second mode. In the first mode, the little-core system can be run, and the power consumption of the device 12 is low when the little-core system is running. In the second mode, the big-core system can be run, and the power consumption of the device 12 is higher when the big-core system is running.

[0124] When the power consumption of the main body 12 is not higher than the power consumption threshold, human feature data is acquired through the detection unit 121, and the acquisition frequency and / or data acquisition amount are determined according to the working mode of the wearable device 10. Specifically, when the wearable device 10 is running in the first mode, the power consumption of the main body 12 is low, so the acquisition frequency and / or data acquisition amount can be appropriately increased. When the wearable device 10 is running in the second mode, the power consumption of the main body 12 is high, so the acquisition frequency and / or data acquisition amount can be appropriately decreased.

[0125] In some embodiments, the wearable device 10 is running in a first mode when the power consumption of the main body 12 is not higher than a power consumption threshold. Since the power consumption of the main body 12 is lower when running in the first mode, the detection unit 121 detects human feature data at a higher frequency than when running in the second mode, and acquires a larger amount of data.

[0126] In this way, the working state of the detection unit 121 can be adjusted according to the module operation status of the wearable device 10, further reducing the operating power consumption of the main body 12, rationally allocating power resources, and extending the battery life of the wearable device 10.

[0127] In some embodiments, the contact area between the detection component 14 and the human body is greater than the contact area between the body 12 and the human body.

[0128] Specifically, please refer to [the relevant document] again. Figure 2 The wearable device 10 can be a smartwatch. The main body 12 of the wearable device 10 can be set inside the watch body 12. The detection component 14 is similar in shape to the watch strap and can be set inside the watch strap. The contact area between the detection component 14 and the human body is larger than the contact area between the main body 12 and the human body. Therefore, the human feature data detected by the detection component 14 is relatively more accurate.

[0129] This application also provides a non-volatile computer-readable storage medium storing a computer program. When the computer program is executed by one or more processors, it implements the control method for the wearable device described in any of the above embodiments.

[0130] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "illustrative embodiment," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with an embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0131] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0132] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0133] The storage media mentioned above can be read-only memory, disk, or optical disk, etc.

[0134] Although embodiments of this application have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of this application, the scope of which is defined by the claims and their equivalents.

Claims

1. A control method for a wearable device, characterized in that, The wearable device includes a main body and a detection component connected to the main body. The main body includes a detection unit, and the detection component is disposed inside the strap of the wearable device. The control method includes the following steps: The device detects whether a preset function of the wearable device is enabled in order to detect the operating power consumption of the device itself; the preset function includes at least one of the following: WiFi function, embedded SIM function, screen on function, and time display function; With the preset function disabled, the operating power consumption is determined to be no higher than the power consumption threshold; and When the preset function is enabled, it is determined that the operating power consumption is higher than the power consumption threshold. When the operating power consumption is not higher than the power consumption threshold, the detection unit is determined as the detection subject for acquiring human feature data, and the acquisition frequency and / or data acquisition amount are determined according to the working mode of the wearable device; wherein, the working mode includes a first mode and a second mode, and the processors corresponding to the first mode and the second mode are different; and If the operating power consumption is higher than the power consumption threshold, the detection component is determined as the detection subject for acquiring the human feature data; wherein the operating power consumption of the detection component is higher than the operating power consumption of the detection unit.

2. The control method according to claim 1, characterized in that, The control method includes: The operation status of multiple preset functions is controlled according to priority.

3. The control method according to claim 1, characterized in that, The detection of the operating power consumption of the body includes: The operating power consumption of the main body is detected in real time to determine the detection subject; The control method includes: Determine whether to perform data detection based on user input; In the case of data detection, the human feature data is obtained through the identified detection subject.

4. The control method according to claim 1, characterized in that, The detection of the operating power consumption of the body includes: Determine whether to perform data detection based on user input; In the case of data detection, the operating power consumption of the main body is detected to determine the detection subject; The control method includes: The human characteristic data is obtained by identifying the detection subject.

5. The control method according to any one of claims 1-4, characterized in that, The control method includes: If the power consumption is detected to be higher than the power consumption threshold when the human body feature data is acquired through the detection unit, the detection subject is switched to the detection component and the human body feature data is acquired through the detection component. If the power consumption is detected to be no higher than the power consumption threshold when the human body feature data is acquired through the detection component, then the detection subject is switched to the detection unit and the human body feature data is acquired through the detection unit.

6. The control method according to claim 5, characterized in that, The control method includes: If the operating power consumption is detected to be higher than the power consumption threshold when the human body feature data is acquired through the detection unit, or if the operating power consumption is detected to be lower than the power consumption threshold when the human body feature data is acquired through the detection component, then a main body switching option is issued; The system receives input from the user based on the subject switching option to determine whether to switch the detection subject.

7. A control device for a wearable device, characterized in that, The wearable device includes a main body and a detection component connected to the main body. The main body includes a detection unit, and the detection component is disposed inside the strap of the wearable device. The control device includes: The detection module is used to detect whether a preset function of the wearable device is enabled, so as to detect the operating power consumption of the device; the preset function includes at least one of the following: WiFi function, embedded SIM function, screen on function, and time display function; when the preset function is disabled, the operating power consumption is determined to be no higher than a power consumption threshold; and when the preset function is enabled, the operating power consumption is determined to be higher than the power consumption threshold. A data acquisition module is configured to, when the operating power consumption is not higher than the power consumption threshold, determine the detection subject as the detection unit to acquire human feature data through the detection unit, and determine the acquisition frequency and / or the amount of data to be acquired according to the working mode of the wearable device; wherein the working mode includes a first mode and a second mode, and the processors corresponding to the first mode and the second mode are different; and The data acquisition module is further configured to determine the detection subject as the detection component when the operating power consumption is higher than the power consumption threshold, so as to acquire the human feature data through the detection component; wherein the operating power consumption of the detection component is higher than the operating power consumption of the detection unit.

8. The control device according to claim 7, characterized in that, The control device further includes: A wireless communication module is provided for connecting the detection component to the main body.

9. A wearable device, characterized in that, It includes one or more processors, a memory, and one or more computer programs, wherein the one or more computer programs are stored in the memory, and when executed by the one or more processors, the computer programs implement instructions for the control method of the wearable device according to any one of claims 1-6.

10. A non-volatile computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by one or more processors, it implements the instructions for the control method of the wearable device according to any one of claims 1-6.

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