Intelligent glasses dynamic coding method and intelligent glasses

Abstract

The application provides a smart glasses dynamic coding method and smart glasses. The method mainly comprises the following steps: collecting energy consumption data of the smart glasses in multiple historical periods; obtaining power consumption information of multiple interconnected applications based on the energy consumption data in the multiple historical periods; determining an expected use duration of the smart glasses and a current battery capacity of the smart glasses; determining a coding mode of the smart glasses according to the current battery capacity of the smart glasses, the expected use duration and the power consumption information of each interconnected application, so that the energy consumption of the smart glasses in the expected use duration does not exceed the battery capacity. The smart glasses can be kept from running out of power before the next charging, and better use experience is provided for users.

Description

Technical Field

[0001] This disclosure relates to the field of smart glasses technology, and in particular to a dynamic coding method for smart glasses and smart glasses. Background Technology

[0002] With technological advancements, the market for smart glasses is growing rapidly, playing an increasingly important role in people's lives. Generally, due to market demands for lightweight and low-power smart glasses, their computing power and battery capacity are typically relatively low. Therefore, smart glasses often rely on the computing resources of other terminal devices to provide their application services, necessitating data transmission between the smart glasses and the terminal devices.

[0003] However, data transmission between smart glasses and terminal devices involves various encoding methods. Generally, these methods exhibit a pattern: lower encoding loss results in higher power consumption, while higher encoding loss results in lower power consumption. How to balance power consumption and encoding loss in smart glasses requires further research. Summary of the Invention

[0004] This disclosure provides a dynamic encoding method for smart glasses and smart glasses, which balances the power consumption and encoding loss of smart glasses.

[0005] Firstly, this application provides a dynamic encoding method for smart glasses. This method mainly includes: collecting energy consumption data of the smart glasses over multiple historical periods; acquiring power consumption information of multiple interconnected applications based on the energy consumption data over multiple historical periods, wherein the power consumption information of each interconnected application includes the power consumption of the interconnected application in active mode and in sleep mode, and the power consumption in active mode includes the power consumption under different encoding methods; determining the expected usage time of the smart glasses and the current remaining battery capacity of the smart glasses; and determining the encoding method of the smart glasses based on the current remaining battery capacity, the expected usage time, and the power consumption information of each interconnected application, so that the energy consumption of the smart glasses during the expected usage time does not exceed the remaining battery capacity.

[0006] In one possible implementation, before determining the expected usage time of the smart glasses, the method further includes: receiving an instruction to activate a first interconnected application in the smart glasses; determining the encoding method of the smart glasses based on the current battery level of the smart glasses, the expected usage time, and the power consumption information of each interconnected application, including: obtaining the power consumption of the first interconnected application in active mode and the interconnected applications currently in sleep mode on the smart glasses from the power consumption information of multiple interconnected applications; calculating the expected energy consumption of the interconnected applications currently in sleep mode based on the expected usage time and the power consumption of the interconnected applications currently in sleep mode; calculating the available energy consumption of the first interconnected application based on the difference between the battery level and the expected energy consumption; and determining the encoding method of the first interconnected application based on the expected usage time, the power consumption of the first interconnected application under different encoding methods, and the available energy consumption, so that the energy consumption of the first interconnected application within the expected usage time does not exceed the available energy consumption.

[0007] In one possible implementation, the encoding method of the smart glasses is determined based on the current battery level, expected usage time, and power consumption information of each connected application. This includes: calculating the total energy consumption of each connected application under different encoding methods based on the power consumption information of each connected application, where the total energy consumption under different encoding methods includes the energy consumption generated by each connected application under different encoding methods during the expected usage time, as well as the energy consumption generated by other connected applications in sleep mode during the expected usage time; in response to receiving an instruction to activate the first connected application in the smart glasses, obtaining the total energy consumption of the first connected application under different encoding methods; and determining the encoding method of the first connected application based on the battery level at the time of receiving the activation instruction and the total energy consumption of the first connected application under different encoding methods, so that the total energy consumption of the first connected application under the determined encoding method does not exceed the battery level.

[0008] In one possible implementation, obtaining the expected usage time of the first interconnected application includes: determining the total expected usage time of the smart glasses based on energy consumption data over multiple historical periods; and determining the expected usage time based on the difference between the total expected usage time and the usage time of the smart glasses in the current period.

[0009] In one possible implementation, the expected total usage time of the smart glasses is determined based on energy consumption data from multiple historical periods, including: dividing the energy consumption data from multiple historical periods into weekday energy consumption data and rest day energy consumption data based on the collection time of the energy consumption data from multiple historical periods; calculating the expected total usage time for weekdays and the expected total usage time for rest days respectively; and determining the expected usage time based on the difference between the expected total usage time and the actual usage time of the smart glasses in the current period, including: if the current usage period is a weekday, using the difference between the expected total usage time for weekdays and the actual usage time of the smart glasses in the current period as the expected usage time; or, if the current usage period is a rest day, using the difference between the expected total usage time for rest days and the actual usage time of the smart glasses in the current period as the expected usage time.

[0010] In one possible implementation, the energy consumption data for each historical period includes the activity duration and sleep duration data of each connected application installed on the smart glasses, as well as the power consumption data of the smart glasses within the historical period. The power consumption information of the aforementioned multiple connected applications can be obtained by the following method: based on the activity duration data, sleep duration data, and power consumption data of each connected application within multiple historical periods, the power consumption of each connected application under a first encoding method and the power consumption under sleep mode are calculated. The first encoding method is the encoding method with the lowest power consumption among different encoding methods, and the power consumption of each connected application under the first encoding method is the average power consumption of each connected application under the activity mode within multiple historical periods. Based on the preset coefficients between the power consumption of other encoding methods and the power consumption of each connected application under the first encoding method, the power consumption of each connected application under other encoding methods is calculated.

[0011] In one possible implementation, the energy consumption data for each historical period includes the usage time and sleep time data of each connected application installed on the smart glasses under different encoding methods, as well as the power consumption data of the smart glasses within the historical period; the power consumption information of each connected application in the smart glasses is calculated based on the energy consumption data of multiple historical periods, which may include: calculating the power consumption of each connected application under different encoding methods and the power consumption in sleep mode based on the usage time and sleep time data of each connected application under different encoding methods and the power consumption data of multiple historical periods.

[0012] In one possible implementation, the smart glasses include interconnected applications and local applications. The energy consumption data for each historical period includes the power consumption data of the smart glasses during the historical period and the duration data of each interconnected application installed on the smart glasses during the historical period. The duration data includes the sleep duration data of the interconnected applications, as well as the activity duration data or the usage duration data under different encoding methods. The power consumption of the local application is the background power consumption of the interconnected application in both active mode and sleep mode.

[0013] In one possible implementation, the above-mentioned different encoding methods include at least: a single-channel encoding method that encodes audio data originating from one microphone, and a dual-channel encoding method that encodes audio data originating from two microphones respectively.

[0014] In a second aspect, this application provides a smart glasses system including a processor and a memory, wherein the memory is used to store program instructions and the processor is used to execute the program instructions to cause the smart glasses to perform any of the methods described in the first aspect above.

[0015] Thirdly, this application provides a non-volatile computer-readable storage medium storing program instructions, which, when executed by smart glasses, cause the smart glasses to perform any of the methods described in the first aspect above.

[0016] In the dynamic encoding method for smart glasses provided in this application, the power consumption information of multiple interconnected applications in the smart glasses is estimated by statistically analyzing the historical energy consumption data of the smart glasses. This method can adapt to different users' usage habits and the differences in wear and tear of the smart glasses, making the statistically analyzed power consumption information more targeted and the power consumption information of each interconnected application more accurate. Battery capacity represents the total remaining energy of the smart glasses. When the energy consumption of the smart glasses within the expected usage time exceeds the current remaining total energy, the smart glasses are highly likely to shut down before the user can charge, affecting the user experience. Therefore, this application's embodiments determine the encoding method of the smart glasses based on the current battery capacity, expected usage time, and power consumption information of each interconnected application, ensuring that the energy consumption of the smart glasses within the expected usage time does not exceed the battery capacity. This helps to ensure that the smart glasses retain sufficient power before the next charging, providing a better user experience. Attached Figure Description

[0017] The above and other objects, features and advantages of this disclosure will become more apparent from the more detailed description of exemplary embodiments thereof taken in conjunction with the accompanying drawings, in which the same reference numerals generally represent the same components.

[0018] Figure 1An exemplary schematic diagram of smart glasses is shown.

[0019] Figure 2 An exemplary schematic diagram of a dynamic encoding method for smart glasses is shown.

[0020] Figure 3 An exemplary schematic diagram of a specific dynamic coding method for smart glasses is shown.

[0021] Figure 4 An exemplary schematic diagram of a specific dynamic coding method for smart glasses is shown.

[0022] Figure 5 An exemplary schematic diagram of a smart glasses structure is shown. Detailed Implementation

[0023] Preferred embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While preferred embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that the present disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0024] Those skilled in the art will understand that the terms "first," "second," etc., in this disclosure are used to distinguish similar objects, rather than to describe a specific order or sequence, and do not have any additional limiting effect.

[0025] Smart glasses are a common type of product in the consumer electronics market and are playing an increasingly important role in people's daily lives. Figure 1 An exemplary schematic diagram of smart glasses is shown, such as Figure 1 As shown, smart glasses mainly consist of a frame and lenses embedded in the frame. The frame includes temples that rest on the user's ears to secure the smart glasses. A mainboard is housed within the temples, containing various electronic components such as a battery, processor, and sensors. The lenses have a waveguide display structure, with an optical engine located at the coupling grating of the waveguide. The display light emitted by the optical engine is transmitted to the user's eye through the waveguide, thus enabling the display function. The processor controls the display light emitted by the optical engine, thereby changing the image displayed by the smart glasses.

[0026] Most current smart glasses feature voice interaction capabilities, which rely on microphones within the glasses. For example... Figure 1 As shown, the smart glasses may include multiple microphones: mic1, mic2, and mic3. Microphone 1 is located at the temple, while mic2 and mic3 are located on either side of the front of the smart glasses. Figure 1As can be seen, mic1 is closer to the wearer's mouth than mic2 and mic3, and is therefore often used to collect the wearer's voice. mic2 and mic3 are closer to the non-wearer directly in front of the wearer than mic1, and are therefore often used to collect the non-wearer's voice.

[0027] Understandable, Figure 1 The mic1, mic2, and mic3 in the examples are merely examples. In actual implementations, smart glasses may have more or fewer microphones, and the microphone positions may also be arranged differently. This application does not impose any restrictions on this.

[0028] To enable voice interaction, the audio data collected by the microphone needs to be recognized. However, because the battery capacity of smart glasses is much smaller than that of traditional terminal devices (such as smartphones, tablets, and in-vehicle systems), and frequent charging is a poor user experience, smart glasses are particularly sensitive to power consumption. Therefore, power-efficient design for smart glasses is essential. Consequently, most smart glasses currently rely on the computing resources of other terminal devices to provide their application services.

[0029] Taking voice interaction as an example, smart glasses can send the collected audio data to the connected terminal device, which will then recognize the audio data and send the recognition result back to the smart glasses. The smart glasses will then complete the voice interaction based on the recognition result.

[0030] To reduce power consumption during voice collection and interconnection transmission, audio data collected by a single microphone can be encoded and transmitted to the terminal device. The terminal device can then perform algorithmic processing based on the single audio signal, such as speech recognition and speech separation. This encoding method works well in quiet environments because the signal-to-noise ratio of a single signal is high, which does not affect subsequent processing such as speech recognition, speech separation, and intelligent translation. However, in noisy environments, environmental noise reduces the signal-to-noise ratio of the single signal, affecting the terminal device's processing performance of the audio data and thus impacting the user's voice interaction experience based on smart glasses.

[0031] If the focus is on improving voice processing performance, audio data collected from multiple microphones can be encoded separately, and the encoded audio data can then be transmitted to the terminal device. However, this encoding method consumes a lot of power, which is detrimental to the battery life of smart glasses.

[0032] In summary, data transmission between smart glasses and terminal devices employs various encoding methods. Generally, these methods exhibit a pattern: lower encoding loss results in higher power consumption, while higher encoding loss leads to lower power consumption. How to balance power consumption and encoding loss in smart glasses requires further research.

[0033] In view of this, embodiments of this application provide a dynamic encoding method for smart glasses. This method evaluates the power consumption of each interconnected application in the smart glasses by collecting historical power consumption data. Therefore, when any interconnected application in the smart glasses is activated, the encoding method used by the currently activated application can be determined based on the power consumption of each interconnected application and the current battery level of the smart glasses, thereby helping to balance the power consumption and encoding loss of the smart glasses.

[0034] Figure 2 This is a schematic flowchart of a dynamic encoding method for smart glasses provided in an embodiment of this application, as shown below. Figure 2 As shown, the method mainly includes the following steps:

[0035] S201: Collects energy consumption data of smart glasses over multiple historical periods.

[0036] In this embodiment, the smart glasses can collect historical data periodically. The definition of this period can vary, including days, weeks, or the interval between charging sessions; these will not be listed exhaustively.

[0037] In one possible implementation, the smart glasses can store a certain amount of energy consumption data over historical periods and continuously update the stored data over time. For example, if the smart glasses can store energy consumption data over 10 historical periods, then at period N, the smart glasses can store energy consumption data from period N-10 to period N-1, where period N-10 represents the 10th historical period closest to period N, and period N-1 represents the 1st historical period closest to period N. In other words, the smart glasses store energy consumption data from the most recent historical periods.

[0038] For example, energy consumption data can include records of the overall power usage of the smart glasses, or records of the power consumption of individual applications within the smart glasses. It is understood that the applications within the smart glasses can be either local applications or interconnected applications that transmit data with the terminal device.

[0039] In this embodiment, the energy consumption data can record the power consumption of all applications (including both connected and local applications), or it can record only the power consumption of connected applications. It can record the total power consumption of connected applications in each cycle, or it can distinguish and record the power consumption of connected applications under different encoding methods in each cycle.

[0040] For example, applications in smart glasses may be in active mode, sleep mode, or not started at all. Active mode can be understood as the application running in the foreground of the smart glasses. In this mode, the application's power consumption increases, and for connected applications, data transmission with the terminal device is more frequent. Sleep mode can be understood as the application running in the background of the smart glasses. In this mode, the application's power consumption is extremely low. For connected applications in sleep mode, there is no data transmission with the terminal device, or only very infrequent data transmission. Applications that are not started can be considered to consume no energy.

[0041] In this embodiment, the energy consumption data for each historical cycle includes the duration data of each interconnected application. This duration data may include the sleep duration data of the interconnected application in sleep mode and the activity duration data in active mode. Furthermore, the activity duration data of each interconnected application in active mode may further include the usage duration data of each interconnected application under different encoding methods.

[0042] In summary, different processing methods can be used for different forms of energy consumption data, and the embodiments of this application will be illustrated in the following content.

[0043] S202: Obtain power consumption information of multiple interconnected applications based on energy consumption data from multiple historical periods. The power consumption information of each interconnected application includes the power consumption of the interconnected application in active mode and in sleep mode. The power consumption in active mode includes the power consumption under different encoding methods.

[0044] In this embodiment, the smart glasses can process energy consumption data from multiple historical periods locally to obtain power consumption information for multiple interconnected applications. Alternatively, the smart glasses can send the energy consumption data from multiple historical periods to a terminal device for processing. The terminal device can process the energy consumption data from multiple historical periods locally to obtain power consumption information for multiple interconnected applications, or it can further send the energy consumption data from multiple historical periods to a cloud server. After processing the energy consumption data from multiple historical periods, the cloud server feeds back the power consumption information for multiple interconnected applications to the terminal device. Once the terminal device obtains the power consumption information for multiple interconnected applications, it feeds back the power consumption information for multiple interconnected applications to the smart glasses.

[0045] In this embodiment of the application, power consumption information of multiple interconnected applications can be obtained. The power consumption information of each interconnected application may include the power consumption of the interconnected application in active mode and in sleep mode. The power consumption in active mode further includes the power consumption under different encoding methods.

[0046] For example, the power consumption information of interconnected application 1 may include the power consumption of interconnected application 1 in active mode and the power consumption of interconnected application 1 in sleep mode. Among them, the power consumption of interconnected application 1 in active mode may further include the power consumption of interconnected application 1 under encoding method 1, encoding method 2, ..., encoding method n (n is the number of encoding methods).

[0047] In one possible implementation, if the energy consumption data does not include the duration data of local applications, only the power consumption information of multiple interconnected applications in the smart glasses can be obtained. The power consumption of local applications will be included as background power consumption in the sleep and activity power consumption of interconnected applications. That is, the sleep and activity power consumption of interconnected applications obtained based on this energy consumption data is higher than the actual sleep and activity power consumption of interconnected applications. The duration data of local applications describes the time a local application spends in active mode and sleep mode within a historical period.

[0048] This application eliminates the need to calculate the power consumption data of local applications. Compared to schemes that calculate the power consumption of local applications, since fewer applications require power consumption calculation, the amount of historical energy consumption data needed for the calculation is significantly reduced. For example, if a smart glasses device has three interconnected applications and three local applications installed, assuming two encoding methods exist, there is no need to calculate the power consumption of the three local applications. Therefore, only nine historical periods of energy consumption data are needed to calculate the power consumption of the three interconnected applications. The power consumption of the other three local applications is included as background power consumption in the calculation of the power consumption of these three interconnected applications. In contrast, if the power consumption of these three local applications also needs to be calculated, 15 historical periods of energy consumption data would be required, and the energy consumption data in each historical period would also need to include the duration of each local application.

[0049] Since the power consumption information of interconnected applications in this embodiment is obtained based on historical energy consumption data, and users have relatively regular usage habits over a period of time, including the power consumption of local applications in the sleep and active power consumption of interconnected applications will not have a significant impact on the evaluation of smart glasses power consumption when selecting the encoding method. Moreover, this form of energy consumption data is more concise, and the calculation process for the power consumption information of multiple interconnected applications is also simpler.

[0050] In another possible implementation, if the energy consumption data includes the duration of local applications, the power consumption information of the local applications can also be obtained. Therefore, when selecting an encoding method, the power consumption of smart glasses can be assessed more accurately, which helps to ensure that the selected encoding method is more suitable for the power consumption of smart glasses.

[0051] S203: Determine the expected usage time of the smart glasses and the current remaining battery level of the smart glasses.

[0052] It's understandable that users' usage habits tend to follow patterns over a period of time. Therefore, the expected usage time can be calculated based on historical usage data, which is essentially the time interval between the current time and the next charge. For example, if charging ends at 8:00 AM and starts at 10:00 PM, the period between 8:00 AM and 10:00 PM can be considered a cycle. In determining the expected usage time, the smart glasses can determine the total expected usage time based on the collection time of energy consumption data over multiple historical cycles. The smart glasses can then determine the actual expected usage time based on the difference between the total expected usage time and the actual usage time within the current cycle.

[0053] The expected total usage time can characterize the user's habitual charging intervals. For example, smart glasses can determine the duration of each historical period from the collection time of energy consumption data over multiple historical periods. The smart glasses can use the average duration of multiple historical periods as the expected total usage time, or they can use the duration with the highest probability as the expected total usage time, without any restrictions.

[0054] After obtaining the expected total usage time, the actual usage time can be further determined based on the difference between the expected total usage time and the actual usage time of the smart glasses within the current period. The actual usage time of the smart glasses within the current period can be understood as the time interval between the current point in time and the last charging end time. For example, if the expected total usage time is 15 hours, charging ends at 8:00 AM, and at 1:00 PM, the actual usage time of the smart glasses within the current period is 5 hours, then the expected usage time is 10 hours.

[0055] Furthermore, since users' usage habits may differ on weekdays and weekends, the approach can be tailored to different scenarios. In one possible implementation, determining the expected total usage time of the smart glasses based on energy consumption data from multiple historical periods can include: dividing the energy consumption data from multiple historical periods into weekday energy consumption data and weekend energy consumption data based on the collection time of the data; and calculating the expected total usage time for weekdays and weekends separately. Determining the expected usage time based on the difference between the expected total usage time and the actual usage time of the smart glasses in the current period can include: if the current usage period is a weekday, using the difference between the expected total usage time on weekdays and the actual usage time of the smart glasses in the current period as the expected usage time; or, if the current usage period is a weekend, using the difference between the expected total usage time on weekends and the actual usage time of the smart glasses in the current period as the expected usage time.

[0056] In other words, energy consumption data from multiple historical periods occurring during weekdays can be collected, and the expected total usage time for weekdays can be determined based on this data. Similarly, energy consumption data from multiple historical periods occurring during rest days can be collected, and the expected total usage time for rest days can be determined based on this data. Therefore, if the first connected application is activated on a weekday, its expected usage time can be determined based on the expected total usage time for the weekday. Conversely, if the first connected application is activated on a rest day, its expected usage time can be determined based on the expected total usage time for the rest day.

[0057] S204: Determine the encoding method of the smart glasses based on the current battery level, expected usage time, and power consumption information of each connected application, so that the power consumption of the smart glasses during the expected usage time does not exceed the remaining battery level.

[0058] The remaining battery capacity represents the total energy remaining in the smart glasses. When the energy consumption of the smart glasses exceeds the remaining battery capacity within the expected usage time, there is a high probability that the smart glasses will shut down before the user can recharge, affecting the user experience. Therefore, this application embodiment determines the encoding method of the smart glasses based on the current remaining battery capacity, the expected usage time, and the power consumption information of each connected application, so that the energy consumption of the smart glasses within the expected usage time does not exceed the remaining battery capacity. This helps to ensure that the smart glasses are not completely depleted before the next charging, providing a better user experience.

[0059] In one possible implementation, smart glasses can respond to the activation of an interconnected application by triggering a process to determine the encoding method. Figure 3 An exemplary schematic diagram illustrates a specific dynamic encoding method flowchart for smart glasses provided in an embodiment of this application, such as... Figure 3 As shown, the main steps include:

[0060] S301: Received instruction to activate the first connected application in the smart glasses.

[0061] It is understandable that the aforementioned first interconnected application can be any interconnected application within the smart glasses; that is, the smart glasses need to transmit data with the terminal device / cloud server to provide the services offered by the application. For example, the first interconnected application could be a voice assistant, intelligent translation, intelligent navigation, etc.

[0062] For example, smart glasses can activate the first connected application based on user-input voice commands or gesture commands. Smart glasses can also automatically activate the first connected application based on sensing the usage environment. Activating the first connected application can either wake up a dormant application from sleep mode to active mode, or it can start an application that has not yet been launched.

[0063] The smart glasses can dynamically select an encoding method for the first connected application upon activation. Specifically, the encoding method is determined based on the smart glasses' current battery level, expected usage time, and power consumption information for each connected application, including:

[0064] S302: Obtain the power consumption of the first interconnected application in active mode and the power consumption of the interconnected application currently in sleep mode on the smart glasses from the power consumption information of multiple interconnected applications.

[0065] S303: Calculate the expected power consumption of the currently sleeping application based on the expected usage duration and the power consumption of the currently sleeping application in sleep mode.

[0066] Connected applications currently in sleep mode also consume energy for smart glasses. In order for smart glasses to be used until the expected usage time ends, the energy consumption generated by connected applications in sleep mode during the expected usage time needs to be taken into account.

[0067] For example, assuming the expected usage time is 2 hours, meaning the user will charge the smart glasses after 2 hours based on their usage habits, the smart glasses only need to ensure the battery is not completely depleted within the next 2 hours. Assuming that connected applications 2, 3, and 4 are currently running in the background, the expected energy consumption of connected applications 2, 3, and 4 in sleep mode over the next 2 hours can be calculated based on the expected usage time and the power consumption of connected applications 2, 3, and 4 in sleep mode.

[0068] It's understandable that smart glasses may also have local applications installed, and these applications, even in sleep mode, will consume power. However, as mentioned earlier, if the power consumption data doesn't include the duration of use of local applications, their power consumption will be included in the power consumption of connected applications, thus not significantly affecting the overall calculation of expected power consumption. If the power consumption data does include the duration of use of local applications, then the power consumption of these applications in sleep mode can be calculated. Therefore, when calculating expected power consumption, the power consumption of the local applications can be calculated by multiplying their power consumption in sleep mode by their expected usage time, and this portion of the power consumption can also be included in the expected power consumption.

[0069] S304: Calculate the available energy consumption of the first interconnected application based on the difference between the remaining battery capacity and the expected energy consumption.

[0070] The available energy consumption of the first connected application can be understood as the maximum energy consumption that the first connected application can consume in active mode. For example, if the current battery level is 20% and the expected energy consumption is 5% (as a percentage of the total battery capacity, the same below), then the available energy consumption of the first connected application is 15%.

[0071] S305: Based on the expected usage time, the power consumption and available energy consumption of the first interconnection application under different encoding methods, determine the encoding method of the first interconnection application so that the energy consumption of the first interconnection application within the expected usage time does not exceed the available energy consumption.

[0072] As mentioned earlier, available energy consumption is the maximum energy that can be provided to the first connected application in active mode. In other words, if the energy consumption of the first connected application exceeds the available energy consumption during the expected usage time, it will likely cause the smart glasses to shut down before charging, affecting the user experience. Therefore, when dynamically selecting an encoding method for the first connected application, the energy consumption generated by the application under different encoding methods during the expected usage time should be considered, and the encoding method with the best encoding effect should be selected from those whose energy consumption does not exceed the available energy consumption during the expected usage time.

[0073] For example, suppose the smart glasses use either a single-channel encoding method (encoding audio data from one microphone) or a dual-channel encoding method (encoding audio data from two microphones). If the music player is activated, and other applications (voice assistant, translator, phone, phone translator) are in sleep mode, with an expected usage time of 2 hours, then the total energy consumption of the music player using the single-channel encoding method for these 2 hours is estimated to be:

[0074] 120×a1+120×d+120×f+120×h+120×j=42.6 (1)

[0075] In the above formula (1), 120 represents the expected usage time in minutes, a1 represents the power consumption of the music player in single-channel encoding mode, d represents the power consumption of the voice assistant in sleep mode, f represents the power consumption of the translator in sleep mode, h represents the power consumption of the phone in sleep mode, and j represents the power consumption of the phone translator in sleep mode.

[0076] The total energy consumption for these 2 hours when the music player uses dual-channel encoding is estimated to be:

[0077] 120×a² + 120×d + 120×f + 120×h + 120×j = 49.6 (2)

[0078] In the above formula (2), a2 represents the power consumption of the music player in dual-channel encoding mode.

[0079] Based on the above calculations, if the current battery level is greater than 49.6%, the music player can use dual-encoding mode for data transmission; if the battery level is not greater than 49.6%, the music player can use single-encoding mode for data transmission.

[0080] Adopting such Figure 3 The proposed solution can more accurately assess the energy consumption of smart glasses, thereby enabling a more precise determination of the coding method that matches the energy consumption.

[0081] Similar to, but different from, this application also provides another scheme for determining the encoding method, compared to Figure 3 The proposed solution can achieve a faster response time. Figure 4 An exemplary schematic diagram illustrates a specific dynamic encoding method flowchart for smart glasses provided in an embodiment of this application, such as... Figure 4 As shown, the main steps include:

[0082] S401: Based on the power consumption information of each interconnected application, calculate the total energy consumption of each interconnected application under different encoding methods. The total energy consumption under different encoding methods includes the energy consumption generated by each interconnected application under different encoding methods during the expected usage period, as well as the energy consumption generated by other interconnected applications in sleep mode during the expected usage period.

[0083] As mentioned above, smart glasses can support a variety of different energy consumption encoding methods. In the embodiments of this application, each interconnected application corresponds to a total energy consumption under different encoding methods. For any interconnected application, the total energy consumption under a certain encoding method represents the sum of the energy consumption generated by the interconnected application under that encoding method and the energy consumption generated by other interconnected applications.

[0084] For example, smart glasses support single-channel encoding and dual-channel encoding. The total energy consumption of the music player under the single-channel encoding method can be shown in the above formula (1), and the total energy consumption under the dual-channel encoding method can be shown in the above formula (1).

[0085] Using a similar calculation method, the total energy consumption of each interconnect application under different coding schemes is calculated. For example, in the example above, the total energy consumption of each interconnect application under a single coding scheme can be calculated as shown in Table 1 below:

[0086] Table 1:

[0087] Music Player 120 42.6 voice assistant 120 49.9 Translator 120 35.5 Call up 120 93.2 Telephone translation 120 168

[0088] The total energy consumption of each interconnected application under dual-channel encoding is shown in Table 2 below:

[0089] Table 2:

[0090] Music Player 120 49.6 voice assistant 120 58.2 Translator 120 40.9 Call up 120 110.3 Telephone translation 120 184

[0091] S402: In response to receiving an instruction to activate the first interconnected application in the smart glasses, obtain the total energy consumption of the first interconnected application under different encoding methods.

[0092] Since the smart glasses have already obtained the total energy consumption of each connected application under different encoding methods before S402, when they receive the instruction to activate the first connected application, they can directly obtain the total energy consumption of the first connected application under different encoding methods from the obtained data, saving the calculation process and thus making the response speed faster.

[0093] S403: Based on the remaining battery level when the activation command is received and the total energy consumption of the first interconnected application under different encoding methods, determine the encoding method of the first interconnected application so that the total energy consumption of the first interconnected application under the determined encoding method does not exceed the remaining battery level.

[0094] Taking Tables 1 and 2 as examples, it's clear that both making a phone call and translating a phone call consume over 100% of their power in the dual-encoding mode, making it unsuitable. When smart glasses receive an instruction to activate a phone call or phone translation, they can default to using the single-encoding mode.

[0095] When smart glasses receive commands to activate music playback, a voice assistant, or a translator, they can determine whether to use dual-encoding mode based on the remaining battery level at the time of the command. For example, if the battery level is 50% when the command is received, and the total energy consumption for the translator in dual-encoding mode is 40.9 kilowatts, then dual-encoding mode can be used. Understandably, the smart glasses can recalculate the total energy consumption of each connected application under different encoding modes periodically to ensure that the calculated total energy consumption does not become excessively erroneous over time.

[0096] Next, we will provide an example of how to calculate the power consumption information for each interconnected application.

[0097] For example, the energy consumption data for each historical period may include activity and sleep duration data for each connected application installed on the smart glasses, as well as the power consumption data of the smart glasses during the historical period.

[0098] Among them, the activity duration data of an interconnected application can characterize the duration of the interconnected application in the active mode, and the sleep duration data can characterize the duration of the interconnected application in the sleep mode.

[0099] For example, Table 3 below shows the activity duration and sleep duration data included in the energy consumption statistics for a historical period. In one historical period, the duration of music playback in active mode (activity duration) was 120 minutes, and the duration in sleep mode (sleep duration) was 600 minutes. Other applications use similar representation patterns, which will not be elaborated further.

[0100] Table 3:

[0101]

[0102]

[0103] As shown in Table 3, within a historical period, the duration of music playback in active mode (activity duration) is 120 minutes, and the duration in sleep mode (sleep duration) is 600 minutes. Other applications use similar representation patterns, which will not be elaborated further.

[0104] As mentioned earlier, the process of calculating the power consumption information of multiple interconnected applications can be performed by smart glasses, or by a terminal device or a cloud server. This application embodiment does not impose further limitations on this. The following description uses a terminal device as an example; the same applies to smart glasses and cloud servers, and will not be listed individually.

[0105] For example, power consumption information for multiple interconnected applications is obtained through the following methods:

[0106] Based on the activity duration data, sleep duration data, and power consumption data of each interconnected application within multiple historical periods, the power consumption of each interconnected application under the first encoding method and the power consumption under sleep mode are calculated. The first encoding method is the encoding method with the lowest power consumption among different encoding methods. Based on the preset coefficients between the power consumption of other encoding methods and the power consumption of the first encoding method, the power consumption under other encoding methods is calculated respectively.

[0107] In this embodiment, the energy consumption data collected by the smart glasses over multiple historical periods may include activity duration data, sleep duration data, and power consumption data for each connected application over multiple historical periods. The power consumption data over multiple historical periods can be understood as the total power consumed by the smart glasses over those historical periods.

[0108] For example, Table 4 shows specific examples of energy consumption data over multiple historical periods:

[0109] Table 4:

[0110]

[0111]

[0112] As shown in Table 4 above, within cycle 0: the activity duration of music playback was 120 minutes, and the sleep duration was 600 minutes; the activity duration of the voice assistant was 120 minutes, and the sleep duration was 60 minutes, with a total sleep time of 600 minutes; the activity duration of the translator was 12 minutes, and the sleep time was 660 minutes; the activity duration of phone calls was 30 minutes, and the sleep time was 0 minutes; the activity duration of phone translation was 10 minutes, and the sleep time was 15 minutes; the power consumption within this cycle was 99%. Other cycles follow the same pattern and will not be elaborated further.

[0113] By analyzing the activity duration data, sleep duration data, and power consumption data of each connected application across multiple historical periods, the power consumption of each connected application in active mode can be calculated. For example, the number of historical periods collected by the smart glasses is no less than twice the number of connected applications, and the power consumption of each connected application in active mode is calculated by solving a system of multivariate equations.

[0114] Taking Table 4 above as an example, we can obtain the following system of equations:

[0115] Period 0:

[0116] 120*A+600*B+60*C+600*D+12*E+660*F+30*G+0*H+10*I+15*J=99

[0117] Period 1:

[0118] 110*A+550*B+55*C+650*D+10*E+600*F+5*G+20*H+30*I+15*J=89

[0119] Period 2:

[0120] 100*A+550*B+40*C+660*D+5*E+660*F+10*G+20*H+5*I+15*J=67

[0121] Period 3:

[0122] 90*A+610*B+20*C+600*D+20*E+660*F+30*G+20*H+30*I+15*J=92

[0123] Period 4:

[0124] 80*A+610*B+30*C+630*D+30*E+500*F+30*G+10*H+15*I+15*J=84

[0125] Period 5:

[0126] 125*A+550*B+60*C+600*D+8*E+660*F+30*G+10*H+8*I+20*J=98

[0127] Period 6:

[0128] 130*A+400*B+50*C+640*D+15*E+600*F+29*G+5*H+14*I+20*J=99

[0129] Period 7:

[0130] 135*A+500*B+10*C+610*D+10*E+600*F+30*G+10*H+30*I+20*J=98

[0131] Period 8:

[0132] 125*A+400*B+20*C+620*D+5*E+600*F+25*G+10*H+40*I+10*J=99

[0133] Period 9:

[0134] 10*A+630*B+30*C+600*D+20*E+660*F+30*G+5*H+30*I+10*J=72

[0135] Where A represents the power consumption of music playback in active mode, B represents the power consumption of music playback in sleep mode; C represents the power consumption of the voice assistant in active mode, D represents the power consumption of the voice assistant in sleep mode; E represents the power consumption of the translator in active mode, F represents the power consumption of the translator in sleep mode; G represents the power consumption of making a phone call in active mode, H represents the power consumption of making a phone call in sleep mode; I represents the power consumption of phone translation in active mode, J represents the power consumption of phone translation in sleep mode.

[0136] The above system of equations is a system of linear equations with multiple variables. Solving this system yields the power consumption of five applications in active and sleep modes. There are many methods for solving systems of linear equations with multiple variables, such as substitution, elimination, and matrix methods, which will not be listed in detail in this embodiment.

[0137] By solving the above system of equations, we can obtain A = 0.29083868, B = 0.01113791, C = 0.34465106, D = 0.003531, E = 0.2258319, F = 0.00480547, G = 0.71364756, H = 0.01227589, I = 0.66738255 and J = 0.04360714.

[0138] It is understood that the specific power consumption of interconnected applications is difficult to calculate. The embodiments of this application calculate the average power consumption of each interconnected application in active mode and average power consumption in sleep mode over multiple historical periods. Among them, the average power consumption in active mode is the average power consumption over multiple historical periods calculated by combining the power consumption of different encoding methods. For example, in the example above, the power consumption A of music playback in active mode is actually the average power consumption of music playback in active mode within period 0-9. Power consumption A may have used multiple encoding methods in these 10 periods, so this power consumption is also the average power consumption of different encoding methods.

[0139] To further reduce the probability of the smart glasses shutting down before charging, this embodiment uses the power consumption in the average activity mode as the power consumption of the encoding method with the lowest power consumption. For example, in the example above, music playback has two encoding methods: single-channel encoding and dual-channel encoding. The power consumption A is actually higher than that of single-channel encoding. However, in this embodiment, power consumption A is used as the power consumption of single-channel encoding, which means that the power consumption of different encoding methods is overestimated to some extent, thereby helping to reduce the probability of the smart glasses shutting down before charging.

[0140] In this embodiment, the terminal device may include preset coefficients corresponding to other encoding methods. These preset coefficients are preset coefficients between the power consumption of other encoding methods and the power consumption of the encoding method with the lowest power consumption, and the preset coefficients are greater than 1. After calculating the power consumption of each application under the encoding method with the lowest power consumption, the power consumption of each interconnected application under other encoding methods can be calculated based on the preset coefficients between the power consumption of other encoding methods and the power consumption of each interconnected application under the first encoding method (the encoding method with the lowest power consumption).

[0141] For example, the preset coefficient between the power consumption of dual-encoding mode and single-encoding mode is 1.2, that is, the power consumption of dual-encoding mode / the power consumption of single-encoding mode = 1.2. Then, after calculating the power consumption of music playback in single-encoding mode as 0.29083868 in the example above, we can further calculate the power consumption of music playback in dual-encoding mode as 0.29083868 × 1.2 = 0.349006416.

[0142] For example, the preset coefficient can be an empirical value, a statistical value, an experimental value, etc., and this application embodiment does not impose any restrictions on it.

[0143] In another example, the power consumption of different encoding methods can be calculated more precisely. Specifically, the energy consumption data for each historical period can include the usage time and sleep time data of each connected application installed on the smart glasses under different encoding methods, as well as the power consumption data of the smart glasses during the historical period. For example, the number of historical periods is no less than (number of encoding methods + 1) × number of connected applications.

[0144] In this case, the power consumption information of each connected application in the smart glasses can be calculated based on the energy consumption data over multiple historical periods. This can include: calculating the power consumption of each connected application under different encoding methods and the power consumption in sleep mode based on the usage time and sleep time data of each connected application under different encoding methods and the power consumption data over multiple historical periods.

[0145] For example, the power consumption of each interconnect application under different coding methods and in sleep mode can be obtained by solving a system of multiple linear equations. Taking multiple coding methods, including single-path coding and dual-path coding, as an example, the power consumption information of each interconnect application in a historical period satisfies the following formula (3):

[0146] A11·t11+A12·t12+B1·T1+A21·t21+A22·t22+B2·T2…+

[0147] AN1·tN1+AN2·tN2+BN·TN=Δ) (3)

[0148] In the above formula (3), A11, A21, ..., AN1 represent the power consumption of interconnected applications 1 to N in single-path coding mode, t11, t21, ..., tN1 represent the usage time of interconnected applications 1 to N in single-path coding mode, A12, A22, ..., AN2 represent the power consumption of applications 1 to N in dual-path coding mode, t12, t22, ..., tN2 represent the usage time of interconnected applications 1 to N in dual-path coding mode, B1, B2, ..., BN represent the power consumption of interconnected applications 1 to N in sleep mode, T1, T2, ..., TN represent the sleep duration of interconnected applications 1 to N, and Δ represents the power consumed in the historical period.

[0149] This approach allows for more accurate calculation of the power consumption of each interconnected application under different encoding methods, thus enabling a better balance between encoding performance and the power consumption of smart glasses when selecting an encoding method.

[0150] It is understandable that there are many possible ways to obtain power consumption information of multiple interconnected applications based on historical data from multiple periods. For example, in addition to the method of solving a system of equations mentioned above, it can also be achieved through methods such as large model inference, which will not be listed in detail in the embodiments of this application.

[0151] In summary, this application provides a dynamic encoding method for smart glasses. The method mainly includes: collecting energy consumption data of the smart glasses over multiple historical periods; obtaining power consumption information of multiple interconnected applications based on the energy consumption data over multiple historical periods, wherein the power consumption information of each interconnected application includes the power consumption in active mode and the power consumption in sleep mode, and the power consumption in active mode includes the power consumption under different encoding methods; determining the expected usage time of the smart glasses and the current battery level of the smart glasses; and determining the encoding method of the smart glasses based on the current battery level, the expected usage time, and the power consumption information of each interconnected application, so that the energy consumption of the smart glasses during the expected usage time does not exceed the remaining battery level.

[0152] In the dynamic encoding method for smart glasses provided in this application, the power consumption information of multiple interconnected applications in the smart glasses is estimated by statistically analyzing the historical energy consumption data of the smart glasses. This method can adapt to different users' usage habits and the differences in wear and tear of the smart glasses, making the statistically analyzed power consumption information more targeted and the power consumption information of each interconnected application more accurate. Battery capacity represents the total remaining energy of the smart glasses. When the energy consumption of the smart glasses within the expected usage time exceeds the current remaining total energy, the smart glasses are highly likely to shut down before the user can charge, affecting the user experience. Therefore, this application's embodiments determine the encoding method of the smart glasses based on the current battery capacity, expected usage time, and power consumption information of each interconnected application, ensuring that the energy consumption of the smart glasses within the expected usage time does not exceed the battery capacity. This helps to ensure that the smart glasses retain sufficient power before the next charging, providing a better user experience.

[0153] Based on the same technical concept, embodiments of this application also provide smart glasses, which include a processor and a memory. The memory stores program instructions, and the processor executes the program instructions to cause the smart glasses to perform the methods described in any of the above embodiments.

[0154] For example, refer to Figure 5 The following is a structural block diagram of a smart glasses that can serve as an embodiment of this application, which is an example of a hardware device that can be applied to various aspects of this application. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of this application described and / or claimed herein.

[0155] like Figure 5 As shown, the electronic device includes a computing unit 501, which can perform various appropriate actions and processes based on a computer program stored in a read-only memory (ROM) 502 or a computer program loaded from a storage unit 508 into a random access memory (RAM) 503. The RAM 503 may also store various programs and data required for the operation of the electronic device. The computing unit 501, ROM 502, and RAM 503 are interconnected via a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.

[0156] Multiple components in the electronic device are connected to the I / O interface 505, including: an input unit 506, an output unit 507, a storage unit 508, and a communication unit 509. The input unit 506 can be any type of device capable of inputting information into the electronic device. The input unit 506 can receive input digital, character, or image information, and generate key signal inputs related to user settings and / or function control of the smart glasses. The output unit 507 can be any type of device capable of presenting information, and may include, but is not limited to, an optical waveguide display screen, a speaker, a vibrator, etc. The storage unit 508 may include, but is not limited to, memory, a hard drive, etc. The communication unit 509 allows the smart glasses to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks, and may include, but is not limited to, modems, network cards, infrared communication devices, wireless communication transceivers and / or chipsets, such as Bluetooth devices, WiFi devices, WiMax devices, cellular communication devices and / or the like.

[0157] The computing unit 501 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 501 include, but are not limited to, CPUs, graphics processing units (GPUs), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any suitable processor, controller, microcontroller, etc. The computing unit 501 performs the various methods and processes described above. For example, in some embodiments, the method embodiments of this application can be implemented as computer programs tangibly contained in a machine-readable medium, such as storage unit 508. In some embodiments, part or all of the computer program can be loaded and / or installed on an electronic device via ROM 502 and / or communication unit 509. In some embodiments, the computing unit 501 can be configured to perform the methods described above by any other suitable means (e.g., by means of firmware).

[0158] Computer programs used to implement the methods of the embodiments of this application may be written in any combination of one or more programming languages. These computer programs may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0159] In the context of embodiments of this application, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable signal medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, or infrared systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0160] Based on the same technical concept, embodiments of this application also provide a non-transitory machine-readable medium storing a computer program, wherein the computer program, when executed by a computer's processor, is used to cause the computer to perform the method of embodiments of this application.

[0161] Based on the same technical concept, embodiments of this application also provide a computer program product, including a computer program, wherein the computer program, when executed by a computer's processor, is used to cause the computer to perform the methods of embodiments of this application.

[0162] It should be noted that the term "comprising" and its variations used in the embodiments of this application are open-ended, meaning "including but not limited to". The term "based on" means "at least partially based on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; and the term "some embodiments" means "at least some embodiments". The modifications of "one" and "multiple" mentioned in the embodiments of this application are illustrative and not restrictive. Those skilled in the art should understand that, unless explicitly indicated otherwise in the context, they should be understood as "one or more".

[0163] The user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in the embodiments of this application are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, use and processing of related data must comply with the relevant laws, regulations and standards of the relevant countries and regions, and corresponding operation entry points are provided for users to choose to authorize or refuse.

[0164] The steps described in the method embodiments provided in this application can be performed in different orders and / or in parallel. Furthermore, the method embodiments may include additional steps and / or omit the steps shown. The scope of protection of this application is not limited in this respect.

[0165] The term "embodiment" in this specification refers to a specific feature, structure, or characteristic described in connection with an embodiment that may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily imply the same embodiment, nor does it imply independence from or alternative to other embodiments. The various embodiments in this specification are described in a related manner, with reference to each other for similar or identical parts. In particular, for apparatus, device, and system embodiments, since they are substantially similar to method embodiments, the description is relatively simple, and relevant details are referred to in the description of the method embodiments.

[0166] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of patent protection. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the appended claims.

Claims

1. A dynamic encoding method for smart glasses, comprising: Collect energy consumption data for smart glasses over multiple historical periods; The power consumption information of multiple interconnected applications is obtained based on the energy consumption data within the multiple historical periods. The power consumption information of each interconnected application includes the power consumption of the interconnected application in active mode and the power consumption in sleep mode. The power consumption in active mode includes the power consumption under different encoding methods. Determine the expected usage time of the smart glasses and the current remaining battery level of the smart glasses; The encoding method of the smart glasses is determined based on the current battery level of the smart glasses, the expected usage time, and the power consumption information of each connected application, so that the energy consumption of the smart glasses during the expected usage time does not exceed the battery level. The encoding method of the smart glasses is determined based on the current battery level, the expected usage time, and the power consumption information of each connected application, including: Based on the power consumption information of each interconnected application, the total energy consumption under different encoding methods for each interconnected application is calculated. The total energy consumption under different encoding methods includes the energy consumption generated by each interconnected application under different encoding methods during the expected usage period, as well as the energy consumption generated by other interconnected applications in sleep mode during the expected usage period. In response to receiving an instruction to activate the first interconnected application in the smart glasses, the total energy consumption of the first interconnected application under different encoding methods is obtained; Based on the remaining battery level when the activation instruction is received and the total energy consumption of the first interconnected application under different encoding methods, the encoding method of the first interconnected application is determined so that the total energy consumption of the first interconnected application under the determined encoding method does not exceed the remaining battery level. The energy consumption data for each historical period includes the activity duration and sleep duration data of each connected application installed on the smart glasses, as well as the power consumption data of the smart glasses during the historical period. The power consumption information of the multiple interconnected applications was obtained through the following method: Based on the activity duration data of each interconnected application, the sleep duration data, and the power consumption data within the multiple historical periods, the power consumption of each interconnected application under the first encoding method and the power consumption under the sleep mode are calculated. The first encoding method is the encoding method with the lowest power consumption among the different encoding methods. The power consumption of each interconnected application under the first encoding method is the average power consumption of each interconnected application under the activity mode within the multiple historical periods. Based on the preset coefficients between the power consumption of other encoding methods and the power consumption of each interconnect application under the first encoding method, the power consumption of each interconnect application under other encoding methods is calculated.

2. The method according to claim 1, wherein, Determining the expected usage time of the smart glasses includes: The expected total usage time of the smart glasses is determined based on the collection time of energy consumption data within the multiple historical periods. The expected usage time is determined based on the difference between the expected total usage time and the usage time of the smart glasses in the current period.

3. The method according to claim 2, wherein, The expected total usage time of the smart glasses is determined based on the collection time of energy consumption data within the multiple historical periods, including: Based on the collection time of the energy consumption data for the multiple historical periods, the energy consumption data for the multiple historical periods are divided into weekday energy consumption data and rest day energy consumption data. Calculate the expected total usage time for the workday and the expected total usage time for the rest day, respectively; The determination of the expected usage time, based on the difference between the expected total usage time and the actual usage time of the smart glasses in the current period, includes: If the current usage cycle is a weekday, the expected total usage time for the weekdays is taken as the difference between the expected total usage time and the actual usage time of the smart glasses within the current cycle; or, When the current usage cycle is a rest day, the difference between the expected total usage time on the rest day and the usage time of the smart glasses in the current cycle is taken as the expected usage time.

4. The method according to any one of claims 1 to 3, wherein the energy consumption data in each historical period includes usage duration data and sleep duration data of each interconnected application installed on the smart glasses under the different encoding methods, and power consumption data of the smart glasses in the historical period; The power consumption information of each interconnected application in the smart glasses is calculated based on the energy consumption data from the multiple historical periods, including: Based on the usage duration data of each interconnected application under different encoding methods, the sleep duration data, and the power consumption data within the multiple historical periods, the power consumption of each interconnected application under different encoding methods and the power consumption in sleep mode are calculated.

5. The method according to any one of claims 1 to 3, wherein the smart glasses include interconnected applications and local applications, and the energy consumption data in each historical period includes the power consumption data of the smart glasses in the historical period and the duration data of each interconnected application installed on the smart glasses in the historical period, wherein the duration data includes the sleep duration data of the interconnected applications, and the activity duration data or the usage duration data under the different encoding methods; in, The power consumption of the local application is the background power consumption of the interconnected application in active mode and sleep mode.

6. The method according to any one of claims 1 to 3, wherein the different encoding methods include at least: A single-channel encoding method that encodes audio data from one microphone, and a dual-channel encoding method that encodes audio data from two microphones respectively.

7. A smart glasses comprising a processor and a memory, the memory for storing program instructions, and the processor for executing the program instructions to cause the smart glasses to perform the method as described in any one of claims 1 to 6.

8. A non-volatile computer-readable storage medium storing program instructions, wherein when the program instructions are executed by smart glasses, the smart glasses perform the method as described in any one of claims 1 to 6.

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