Intelligent glasses dynamic coding method and intelligent glasses

Abstract

The invention provides a dynamic coding method for intelligent glasses and the intelligent glasses. The method mainly comprises the following steps: collecting energy consumption data of the intelligent glasses in a plurality of historical cycles; obtaining power consumption information of the plurality of interconnected applications obtained based on the energy consumption data in the plurality of historical periods; determining the expected use duration of the intelligent glasses and the current battery remaining amount of the intelligent glasses; and determining a coding mode of the intelligent glasses according to the current battery remaining power of the intelligent glasses, the expected use duration and the power consumption information of each interconnection application, so that the energy consumption of the intelligent glasses in the expected use duration does not exceed the battery remaining power. Therefore, the intelligent glasses can keep the electric quantity not consumed completely before the next charging, and better use experience is provided for users.

Description

Technical Field

[0001] The present disclosure relates to the technical field of smart glasses, and particularly to a dynamic encoding method for smart glasses and a smart glass. Background Art

[0002] With the development of technology, the market of smart glasses is growing day by day and playing an increasingly important role in people's lives. Generally speaking, limited by the market requirements for lightweight and low power consumption of smart glasses, the computing power and battery capacity of smart glasses are usually configured at a relatively low level. Therefore, smart glasses often need to rely on the computing resources of other terminal devices to implement the application services they provide, which requires data transmission between smart glasses and terminal devices.

[0003] However, there are various encoding methods for data transmission between smart glasses and terminal devices. Generally, the smaller the encoding loss, the greater the power consumption, and the greater the encoding loss, the smaller the power consumption. How to balance the power consumption and encoding loss of smart glasses remains to be further studied. Summary of the Invention

[0004] Embodiments of the present disclosure provide a dynamic encoding method for smart glasses and a smart glass to balance the power consumption and encoding loss of smart glasses.

[0005] In a first aspect, the present application provides a dynamic encoding method for smart glasses. The method mainly includes: 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 multiple historical periods, where the power consumption information of each interconnected application includes the power consumption in the active mode and the power consumption in the sleep mode of the interconnected application, and the power consumption in the active mode includes the power consumption under different encoding methods; determining the expected usage duration of the smart glasses and the current battery remaining capacity of the smart glasses; determining the encoding method of the smart glasses according to the current battery remaining capacity, the expected usage duration of the smart glasses, and the power consumption information of each interconnected application, so that the energy consumption of the smart glasses within the expected usage duration does not exceed the battery remaining capacity.

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

[0007] In a possible implementation, determining the encoding mode of the smart glasses according to the current battery level, expected usage duration of the smart glasses, and the power consumption information of each interconnected application includes: calculating the total energy consumption of each interconnected application corresponding to different encoding modes respectively according to the power consumption information of each interconnected application, where the total energy consumption in different encoding modes includes the energy consumption generated by each interconnected application in different encoding modes within the expected usage duration and the energy consumption generated by other interconnected applications in the sleep mode within the expected usage duration; in response to receiving an instruction to activate the first interconnected application in the smart glasses, obtaining the total energy consumption corresponding to the first interconnected application in different encoding modes; determining the encoding mode of the first interconnected application according to the battery level when the activation instruction is received and the total energy consumption corresponding to the first interconnected application in different encoding modes, so that the total energy consumption corresponding to the first interconnected application in the determined encoding mode does not exceed the battery level.

[0008] In a possible implementation, obtaining the expected usage duration of the first interconnected application includes: determining the expected total usage duration of the smart glasses according to the energy consumption data within multiple historical periods; determining the expected usage duration according to the difference between the expected total usage duration and the used duration of the smart glasses within the current period.

[0009] In a possible implementation, determining the expected total usage duration of the smart glasses based on the energy consumption data in multiple historical periods includes: dividing the energy consumption data of multiple historical periods into working-day energy consumption data and rest-day energy consumption data according to the collection time of the energy consumption data of multiple historical periods; calculating the expected total usage duration of working days and the expected total usage duration of rest days respectively; determining the expected usage duration according to the difference between the expected total usage duration and the used duration of the smart glasses in the current period, including: when the current usage period is a working day, taking the difference between the expected total usage duration of the working day and the used duration of the smart glasses in the current period as the expected usage duration; or when the current usage period is a rest day, taking the difference between the expected total usage duration of the rest day and the used duration of the smart glasses in the current period as the expected usage duration.

[0010] In a possible implementation, the energy consumption data in each historical period includes the activity duration data and sleep duration data of each interconnected application installed on the smart glasses, and the power consumption data consumed by the smart glasses in the historical period; the power consumption information of the above-mentioned multiple interconnected applications can be obtained through the following method: calculating the power consumption in the active mode and the power consumption in the sleep mode of each interconnected application in the first coding method according to the activity duration data, sleep duration data of each interconnected application and the power consumption data consumed in multiple historical periods, the first coding method is the coding method with the lowest power consumption among different coding methods, and the power consumption of each interconnected application in the first coding method is the average power consumption of each interconnected application in the active mode in multiple historical periods; calculating the power consumption of each interconnected application in other coding methods respectively according to the preset coefficients between the power consumptions of other coding methods relative to the coding method with the lowest power consumption and the power consumption of each interconnected application in the first coding method.

[0011] In a possible implementation, the energy consumption data in each historical period includes the usage duration and sleep duration data of each interconnected application installed on the smart glasses in different coding methods respectively, and the power consumption data consumed by the smart glasses in the historical period; calculating the power consumption information of each interconnected application in the smart glasses according to the energy consumption data in multiple historical periods may include: calculating the power consumption in different coding methods and the power consumption in the sleep mode of each interconnected application according to the usage duration, sleep duration data of each interconnected application in different coding methods and the power consumption data consumed in multiple historical periods.

[0012] In a possible implementation, the smart glasses include an interconnected application and a local application. The energy consumption data in each historical period includes the power consumption data consumed by the smart glasses in the historical period and the duration data of each interconnected application installed in the smart glasses in the historical period. The duration data includes the sleep duration data of the interconnected application, and the active duration data or the usage duration data under different coding methods. Wherein, the power consumption of the local application is the background power consumption in the active mode and the sleep mode of the interconnected application.

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

[0014] In a second aspect, the present application provides a pair of smart glasses, including a processor and a memory. The memory is used to store program instructions, and the processor is used to run the program instructions so that the smart glasses execute the method according to any one of the above first aspects.

[0015] In a third aspect, the present application provides a non-volatile computer-readable storage medium storing program instructions. When the program instructions are run by the smart glasses, the smart glasses execute the method according to any one of the above first aspects.

[0016] In the above smart glasses dynamic coding method provided by the present application, by statistically analyzing the historical energy consumption data of the smart glasses to estimate the power consumption information of multiple interconnected applications in the smart glasses, it can adapt to the usage habits of different users and the differences in the usage losses of the smart glasses. The statistically analyzed power consumption information is more targeted, and the power consumption information of each interconnected application obtained is also more accurate. The remaining battery capacity can represent the total remaining energy of the smart glasses. When the energy consumption of the smart glasses within the expected usage duration exceeds the total remaining energy currently, the smart glasses are more likely to shut down before the user charges, affecting the user experience. Therefore, the embodiment of the present application determines the coding method of the smart glasses according to the current remaining battery capacity, the expected usage duration and the power consumption information of each interconnected application, so that the energy consumption of the smart glasses within the expected usage duration does not exceed the remaining battery capacity, thereby facilitating the smart glasses to keep the power not completely consumed before the next charging starts, providing a better user experience for the user. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] By describing the exemplary embodiments of the present disclosure in more detail in conjunction with the drawings, the above and other objects, features and advantages of the present disclosure will become more apparent. Among them, in the exemplary embodiments of the present disclosure, the same reference numerals generally represent the same components.

[0018] Figure 1A schematic diagram of smart glasses is shown exemplarily.

[0019] Figure 2 A schematic flow chart of a dynamic encoding method for smart glasses is shown exemplarily.

[0020] Figure 3 A specific flow chart of a dynamic encoding method for smart glasses is shown by way of example.

[0021] Figure 4 A specific flow chart of a dynamic encoding method for smart glasses is shown by way of example.

[0022] Figure 5 A schematic diagram of the structure of smart glasses is shown exemplarily. DETAILED DESCRIPTION

[0023] The preferred embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the preferred embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided to make the present disclosure more thorough and complete, and to fully convey the scope of the present disclosure to those skilled in the art.

[0024] Those skilled in the art should understand that the terms "first", "second", etc. in the present disclosure are used to distinguish similar objects, rather than to describe a specific order or sequence, and have no additional limiting effect.

[0025] Smart glasses are a common type of product in the consumer electronics market and play an increasingly important role in people's daily lives. Figure 1 A schematic diagram of smart glasses is shown as an example. Figure 1 As shown, the smart glasses mainly include a frame and a lens embedded in the frame. The frame includes temples, which can be placed on the user's ears to fix the smart glasses. A main board is arranged in the temples of the smart glasses, and the main board is equipped with various electronic components such as batteries, processors, and sensors. The lens has an optical waveguide display structure, and an optical machine is arranged at the coupling grating of the optical waveguide. The display light emitted by the optical machine can be transmitted to the human eye through the optical waveguide, thereby realizing the display function. The processor can control the display light emitted by the optical machine, so as to change the picture displayed by the smart glasses.

[0026] Most current smart glasses have voice interaction functions, which rely on the microphone in the smart glasses. Figure 1 As shown, the smart glasses may include multiple microphones: mic1, mic2 and mic3. Among them, mic1 is set at the temple position, and mic2 and mic3 are set on both sides of the front of the smart glasses. Figure 1It can be seen that mic2 and mic3 are closer to the wearer's mouth than mic1, so they are 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, so they are often used to collect the non-wearer's voice.

[0027] It is understandable that Figure 1 the mic1, mic2, and mic3 in are only examples. In actual implementation, the smart glasses may have more or fewer microphones, and the positions of the microphones can also be arranged differently. The embodiments of this application do not impose many restrictions on this.

[0028] To implement the voice interaction function, it is necessary to recognize the audio data collected by the microphone. However, since the battery capacity of the smart glasses is much smaller than that of traditional terminal devices (such as smartphones, tablets, car radios, etc.), and frequent charging provides a rather poor experience for users, the smart glasses are particularly sensitive to power consumption. Therefore, power consumption optimization design for smart glasses is necessary. In view of this, currently, smart glasses mostly rely on the computing resources of other terminal devices to implement the application services they provide.

[0029] Taking voice interaction as an example, the smart glasses can send the collected audio data to the interconnected terminal device. The terminal device recognizes the audio data, and after obtaining the recognition result, the terminal device then feeds back the recognition result to the smart glasses, and the smart glasses complete the voice interaction based on the recognition result.

[0030] To reduce the power consumption of voice collection and interconnection transmission, the audio data collected by only one microphone can be encoded, and the encoded audio data can be transmitted to the terminal device. The terminal device can then perform algorithm processing based on the single-channel audio signal, such as speech recognition, voice separation, etc. This encoding method has a better effect in quiet scenarios because the signal-to-noise ratio of the single-channel signal is high in quiet scenarios and does not affect subsequent processes such as speech recognition, voice separation, and intelligent translation. However, in a noisy scenario, since the environmental noise reduces the signal-to-noise ratio of the single-channel signal, it will affect the processing effect of the terminal device on the audio data, thereby affecting the user's voice interaction experience based on the smart glasses.

[0031] If the focus is on improving the voice processing effect, the audio data collected by more microphones can be encoded separately, and the separately encoded audio data can be transmitted to the terminal device. However, this encoding method has a higher power consumption and is not conducive to the battery life of the smart glasses.

[0032] In summary, there are various encoding methods for data transmission between the smart glasses and the terminal device. These encoding methods generally show the rule that the smaller the encoding loss, the greater the power consumption, and the greater the encoding loss, the smaller the power consumption. How to balance the power consumption and encoding loss of the smart glasses remains to be further studied.

[0033] In view of this, an embodiment of the present application provides a method for dynamically encoding an intelligent glasses. This method evaluates the power consumption of each interconnected application in the intelligent glasses by collecting historical power consumption data of the intelligent glasses. Furthermore, when any interconnected application in the intelligent glasses is enabled, the encoding method used by the currently enabled interconnected application can be determined based on the power consumption of each interconnected application and the current battery margin of the intelligent glasses, which is beneficial to balancing the power consumption of the intelligent glasses and the coding loss.

[0034] Figure 2 It is a schematic flowchart of a method for dynamically encoding an intelligent glasses provided by an embodiment of the present application. As Figure 2 shown, the method mainly includes the following steps:

[0035] S201: Collect the power consumption data of the intelligent glasses in multiple historical periods.

[0036] In the embodiment of the present application, the intelligent glasses can collect historical data in cycles. Among them, there are various possibilities for the definition of the cycle. For example, the cycle can be a day, a week, or the interval between each charge, and no further enumeration will be made here.

[0037] In a possible implementation manner, the intelligent glasses can save the power consumption data in a certain number of historical periods, and as time goes by, continuously update the saved power consumption data. For example, the intelligent glasses can save the power consumption data in 10 historical periods. Then, at cycle N, the intelligent glasses can save the power consumption data from cycle N - 10 to cycle N - 1, where cycle N - 10 represents the 10th historical cycle closest to cycle N, and cycle N - 1 represents the 1st historical cycle closest to cycle N. That is to say, what the intelligent glasses save is the power consumption data in multiple historical periods closest to the current time point.

[0038] Exemplarily, the power consumption data can include both the record of the overall power consumption of the intelligent glasses and the record of the power consumption of each application in the intelligent glasses. It can be understood that the applications in the intelligent glasses may be either local applications or interconnected applications with data transmission to the terminal device.

[0039] In the embodiment of the present application, the power consumption data can record the power consumed by all applications (including both interconnected applications and local applications), or only record the power consumed by interconnected applications. It can record the total power consumption of interconnected applications in each cycle, or distinguish and record the power consumed by interconnected applications under different coding methods in each cycle.

[0040] Exemplarily, an application in the smart glasses may be in an active mode, a sleep mode, or may not be started. Among them, the active mode can be understood as the application running in the foreground of the smart glasses. In this mode, the power consumption of the application increases. For an interconnected application, the data transmission between it and the terminal device is more frequent. The sleep mode can be understood as the application running in the background of the smart glasses. In this mode, the power consumption of the application is extremely low. For an interconnected application in the sleep mode, there is no data transmission between it and the terminal device, or there is only extremely low-frequency data transmission. For an application that has not been started, it can be considered that it does not generate energy consumption.

[0041] In the embodiments of the present application, the energy consumption data in each historical period includes the duration data of each interconnected application, and this duration data may include the sleep duration data of the interconnected application in the sleep mode and the active duration data in the active mode. Further, the active duration data of each interconnected application in the active mode may further include the usage duration data of each interconnected application under different coding methods.

[0042] In summary, for different forms of energy consumption data, there may be corresponding different processing methods, and the embodiments of the present application will be exemplarily described in the subsequent content.

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

[0044] In the embodiments of the present application, the smart glasses can directly process the energy consumption data in multiple historical periods locally to obtain the power consumption information of multiple interconnected applications. The smart glasses can also send the energy consumption data in multiple historical periods to the terminal device for processing. The terminal device can process the energy consumption data in multiple historical periods locally to obtain the power consumption information of multiple interconnected applications, or can further send the energy consumption data in multiple historical periods to the cloud server. After the cloud server processes the energy consumption data in multiple historical periods, it feeds back the power consumption information of multiple interconnected applications to the terminal device. After the terminal device obtains the power consumption information of multiple interconnected applications, it feeds back the power consumption information of multiple interconnected applications to the smart glasses.

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

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

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

[0048] This application does not need to calculate the power consumption data of the local application. Compared with the solution for calculating the local application, since the number of applications for which the power consumption needs to be solved is smaller, the amount of energy consumption data for the historical period required for the solution is smaller. For example, there are 3 interconnected applications and 3 local applications installed in the smart glasses. Assuming there are two encoding modes, it is not necessary to solve the power consumption of 3 local applications. Therefore, only 9 historical periods of energy consumption data need to be collected to calculate the power consumption of 3 interconnected applications, and the power consumption of the other 3 local applications will be included in the power consumption of these 3 interconnected applications as background power consumption. In contrast, if the power consumption of these 3 local applications also needs to be calculated, 15 historical periods of energy consumption data need to be collected, and moreover, the energy consumption data within each historical period also needs to include the duration data of each local application.

[0049] Since the power consumption information of the interconnected application is obtained based on the historical energy consumption data in the embodiments of this application, and the user has relatively regular usage habits within a period of time, therefore, including the power consumption of the local application in the sleep power consumption and active power consumption of the interconnected application will not have a great impact on evaluating the power consumption of the smart glasses when selecting the encoding mode. 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, when the energy consumption data includes the duration data of the local application, the power consumption information of the local application can also be obtained. Therefore, the power consumption of the smart glasses can be evaluated more accurately when selecting the encoding mode, which is beneficial to ensuring that the selected encoding mode is more adapted to the power consumption situation of the smart glasses.

[0051] S203: Determine the expected usage duration of the smart glasses and the current battery reserve of the smart glasses.

[0052] It can be understood that the user's usage habits are regular within a period of time. Therefore, the expected usage duration next can be calculated based on historical usage data, that is, the time interval from the current time to the next charging. For example, if charging ends at 8:00 in the morning and starts at 22:00 in the evening, the time period between 8:00 and 22:00 can be regarded as a cycle. In the process of determining the expected usage duration, the smart glasses can determine the expected total usage duration of the smart glasses according to the collection time of the energy consumption data in multiple historical cycles; the smart glasses can then determine the expected usage duration according to the difference between the expected total usage duration and the used duration of the smart glasses in the current cycle.

[0053] Among them, the expected total usage duration can represent the user's habitual charging interval. For example, the smart glasses can determine the duration of each historical cycle from the collection time of the energy consumption data in multiple historical cycles. The smart glasses can use the average duration of multiple historical cycles as the expected total usage duration, or use the duration with the highest occurrence probability as the expected total usage duration, and there is no restriction on this.

[0054] After obtaining the expected total usage duration, the expected usage duration can be further determined based on the difference between the expected total usage duration and the used duration of the smart glasses in the current cycle. Among them, the used duration of the smart glasses in the current cycle can be understood as the time interval between the current time point and the end time point of the most recent charging. For example, if the expected total usage duration is 15 hours, charging ends at 8:00 in the morning, and at 13:00 in the afternoon, the used duration of the smart glasses in the current cycle is 5 hours, and the expected usage duration is 10 hours.

[0055] Furthermore, since the user's usage habits may be different on weekdays and rest days, it can also be processed in different cases. In one possible implementation, determining the expected total usage duration of the smart glasses according to the energy consumption data in multiple historical cycles may include: dividing the energy consumption data of multiple historical cycles into weekday energy consumption data and rest-day energy consumption data according to the collection time of the energy consumption data of multiple historical cycles; calculating the expected total usage duration on weekdays and the expected total usage duration on rest days respectively. Determining the expected usage duration according to the difference between the expected total usage duration and the used duration of the smart glasses in the current cycle may include: when the current usage cycle is a weekday, using the difference between the expected total usage duration on weekdays and the used duration of the smart glasses in the current cycle as the expected usage duration; or, when the current usage cycle is a rest day, using the difference between the expected total usage duration on rest days and the used duration of the smart glasses in the current cycle as the expected usage duration.

[0056] That is to say, it is possible to collect the energy consumption data of multiple historical periods occurring during weekdays, and determine the expected total usage duration of weekdays based on the energy consumption data occurring during weekdays. Similarly, it is possible to collect the energy consumption data of multiple historical periods occurring during rest days, and determine the expected total usage duration of rest days based on the energy consumption data occurring during rest days. Based on this, when the first interconnected application is activated on a weekday, the expected usage duration of the first interconnected application can be determined based on the expected total usage duration of the weekday. When the first interconnected application is activated on a rest day, the expected usage duration of the first interconnected application can be determined based on the expected total usage duration of the rest day.

[0057] S204: Determine the encoding method of the smart glasses according to the current battery reserve, expected usage duration of the smart glasses, and the power consumption information of each interconnected application, so that the energy consumption of the smart glasses within the expected usage duration does not exceed the battery reserve.

[0058] Among them, the battery reserve can represent the total remaining energy of the smart glasses. When the energy consumption of the smart glasses within the expected usage duration exceeds the battery reserve, the smart glasses are more likely to shut down before the user charges, affecting the user experience. Therefore, in the embodiments of the present application, the encoding method of the smart glasses is determined according to the current battery reserve, expected usage duration of the smart glasses, and the power consumption information of each interconnected application, so that the energy consumption of the smart glasses within the expected usage duration does not exceed the battery reserve, which is beneficial to keeping the battery of the smart glasses not completely consumed before the next charging, providing a better user experience for the user.

[0059] In a possible implementation manner, the smart glasses can trigger the process of determining the encoding method in response to the activation of a certain interconnected application. Figure 3 Exemplarily shows a schematic flow diagram of a specific dynamic encoding method for smart glasses provided by the embodiments of the present application, as Figure 3 shown, mainly including the following steps:

[0060] S301: Receive an instruction to activate the first interconnected application in the smart glasses.

[0061] It can be understood that the above-mentioned first interconnected application can be any interconnected application in the smart glasses, that is, the smart glasses need to transmit data with the terminal device / cloud server to implement the services provided by the application. For example, the first interconnected application can be a voice assistant, intelligent translation, intelligent navigation, etc.

[0062] Exemplarily, the smart glasses can activate the first interconnected application based on voice commands or gesture commands input by the user. The smart glasses can also automatically activate the first interconnected application based on the sensing of the usage environment. Activating the first interconnected application can either wake up the first interconnected application in the sleep mode to the active mode or start the first interconnected application that has not been started.

[0063] The smart glasses can dynamically select an encoding method for the first interconnected application when activating the first interconnected application. Specifically, the encoding method of the smart glasses is determined according to the current battery margin, the expected usage duration, and the power consumption information of each interconnected application, including:

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

[0065] S303: Calculate the expected energy consumption of the interconnected applications currently in the sleep mode according to the expected usage duration and the power consumption of the interconnected applications currently in the sleep mode in the sleep mode.

[0066] The interconnected applications currently in the sleep mode also consume energy of the smart glasses. To enable the smart glasses to be used until the end of the expected usage duration, the energy consumption generated by the interconnected applications in the sleep mode within the expected usage duration needs to be taken into account.

[0067] Exemplarily, assume that the expected usage duration is 2 hours. That is to say, according to the user's usage habit, the user will charge the smart glasses after 2 hours. Therefore, the smart glasses only need to ensure that the battery is not depleted within the next 2 hours. Assume that interconnected applications 2, 3, and 4 are currently running in the background. Then, the expected energy consumption of interconnected applications 2, 3, and 4 in the sleep mode within the next 2 hours can be calculated according to the expected usage duration and the power consumption of interconnected applications 2, 3, and 4 in the sleep mode.

[0068] It can be understood that local applications may also be installed in the smart glasses, and the local applications in the sleep mode also consume energy of the smart glasses. However, as mentioned above, in the case where the energy consumption data does not include the duration data of the local applications, the power consumption of the local applications will be included in the power consumption of the interconnected applications. Therefore, it will not have a great impact on the calculation of the expected energy consumption as a whole. In the case where the energy consumption data also includes the duration data of the local applications, the power consumption of the local applications in the sleep mode can be calculated. Furthermore, when calculating the expected energy consumption, the energy consumption of the local applications can be calculated according to the product of the power consumption of the local applications in the sleep mode and the expected usage duration, and this part of the energy consumption can also be included in the expected energy 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 interconnected application can be understood as the maximum energy consumption that can be consumed by the first interconnected application in the active mode. For example, if the current remaining battery capacity is 20% and the expected energy consumption is 5% (both in percentage of the total battery capacity, the same below), then the available energy consumption of the first interconnected application is 15%.

[0071] S305: Determine the encoding method of the first interconnected application based on the expected usage duration, 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 duration does not exceed the available energy consumption.

[0072] As mentioned above, the available energy consumption is the maximum energy consumption that can be provided to the first interconnected application in the active mode. That is to say, if the energy consumption of the first interconnected application within the expected usage time exceeds the available energy consumption, it is very likely that the smart glasses will shut down before charging, affecting the user experience. Therefore, when dynamically selecting the encoding method for the first interconnected application, the energy consumption generated by the first interconnected application within the expected usage duration under different encoding methods should be considered, and the encoding method with the best encoding effect should be selected among the encoding methods whose energy consumption within the expected usage duration does not exceed the available energy consumption.

[0073] For example, assume that the encoding methods of the smart glasses include a single-channel encoding method for encoding audio data from one microphone, and a dual-channel encoding method for encoding audio data from two microphones respectively. Activate the music player, then other applications (voice assistant, translation app, phone, phone translation) are in the sleep mode, and the expected usage duration is 2 hours. Then the total energy consumption required for the music player to use the single-channel encoding method in these 2 hours is expected 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 duration in minutes, a1 represents the power consumption of the music player in the single-channel encoding method, d represents the power consumption of the voice assistant in the sleep mode, f represents the power consumption of the translation app in the sleep mode, h represents the power consumption of the phone in the sleep mode, and j represents the power consumption of the phone translation in the sleep mode.

[0076] The total energy consumption required for the music player to use the dual-channel encoding method in these 2 hours is expected 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 the dual-channel encoding mode.

[0079] Based on the above calculation results, if the current battery remaining capacity is greater than 49.6, the music player can use the dual-channel encoding mode for data transmission; if the battery remaining capacity is not greater than 49.6, the music player can use the single-channel encoding mode for data transmission.

[0080] Adopting the Figure 3 scheme shown can more accurately evaluate the energy consumption of the smart glasses, so that the encoding mode matching the energy consumption can be determined more accurately.

[0081] Similarly but differently, the embodiment of the present application also provides another scheme for determining the encoding mode. Compared with the Figure 3 scheme shown, it can have a faster response speed. Figure 4 Exemplarily shows a schematic flowchart of a specific dynamic encoding method for smart glasses provided by the embodiment of the present application. As Figure 4 shown, it mainly includes the following steps:

[0082] S401: According to the power consumption information of each interconnected application, calculate the total energy consumption of each interconnected application under different encoding modes respectively. The total energy consumption under different encoding modes includes the energy consumption generated by each interconnected application in different encoding modes during the expected usage duration, and the energy consumption generated by other interconnected applications in the sleep mode during the expected usage duration.

[0083] As mentioned above, the smart glasses can support multiple encoding modes with different energy consumptions. In the embodiment of the present application, each interconnected application corresponds to the total energy consumption under different encoding modes. For any interconnected application, the total energy consumption of the interconnected application under a certain encoding mode represents the sum of the energy consumption generated by the interconnected application in this encoding mode and the energy consumption generated by other interconnected applications.

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

[0085] Adopting a similar calculation method, calculate the total energy consumption of each interconnected application under different encoding modes respectively. For example, in the above example, it can be calculated that the total energy consumption of each interconnected application under the single-channel encoding mode can be as shown in Table 1 below:

[0086] Table 1:

[0087] Application Name Duration (minutes) Total Energy Consumption (Percentage of Total Battery) Music Player 120 42.6 Voice Assistant 120 49.9 Translator 120 35.5 Make a Call 120 93.2 Phone Translation 120 168

[0088] The total energy consumption corresponding to each interconnected application under the dual-channel encoding mode can be shown in Table 2 below:

[0089] Table 2:

[0090] Application Name Duration (minutes) Total Energy Consumption (Percentage of Total Battery) Music Player 120 49.6 Voice Assistant 120 58.2 Translator 120 40.9 Make a Call 120 110.3 Phone 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 corresponding to the first interconnected application under different encoding modes.

[0092] Since the smart glasses have previously obtained the total energy consumption corresponding to each interconnected application under different encoding modes before S402, when receiving an instruction to activate the first interconnected application, the total energy consumption corresponding to the first interconnected application under different encoding modes can be directly obtained from the acquired data, omitting the calculation process, and thus the response speed is faster.

[0093] S403: According to the battery remaining amount when receiving the activation instruction and the total energy consumption corresponding to the first interconnected application under different encoding modes, determine the encoding mode of the first interconnected application so that the total energy consumption corresponding to the first interconnected application under the determined encoding mode does not exceed the battery remaining amount.

[0094] Taking Table 1 and Table 2 shown above as examples. Among them, the energy consumption of making a call and phone translation under the dual-channel encoding mode both exceed 100%, so the dual-channel encoding mode is necessarily not applicable. When the smart glasses receive an instruction to activate making a call or phone translation, the single-channel encoding mode can be used by default.

[0095] When the smart glasses receive an instruction to activate music playback, voice assistant, or translator, it can determine whether to use the dual-channel encoding mode according to the battery remaining amount when receiving the instruction. For example, when the battery remaining amount when receiving the instruction is 50% and the total energy consumption corresponding to the translator under the dual-channel encoding mode is 40.9, the dual-channel encoding mode can be used at this time. It can be understood that the smart glasses can recalculate the total energy consumption corresponding to each interconnected application under different encoding modes every once in a while to ensure that the calculated total energy consumption does not have too large an error over time.

[0096] Next, an exemplary description of the calculation method of the power consumption information of each interconnected application will be given.

[0097] Exemplarily, the energy consumption data in each historical period can include the active duration data and sleep duration data of each interconnected application installed in the smart glasses, as well as the power consumption data consumed by the smart glasses in the historical period.

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

[0099] For example, Table 3 below shows the active duration data and sleep duration data included in the energy consumption data statistically obtained within a historical period. Within a historical period, the duration of music playback in the active mode, that is, the active duration data, is 120 minutes, and the duration in the sleep mode, that is, the sleep duration data, is 600 minutes. Other applications follow a similar representation rule, which will not be elaborated here.

[0100] Table 3:

[0101]

[0102]

[0103] As shown in Table 3, within a historical period, the duration of music playback in the active mode, that is, the active duration data, is 120 minutes, and the duration in the sleep mode, that is, the sleep duration data, is 600 minutes. Other applications follow a similar representation rule, which will not be elaborated here.

[0104] As mentioned above, the process of calculating the power consumption information of multiple interconnected applications can be executed by the smart glasses, or by the terminal device or the cloud server. The embodiments of the present application do not impose many restrictions on this. Next, the embodiments of the present application will be described by taking the terminal device as an example. The smart glasses and the cloud server are the same, and will not be listed one by one here.

[0105] Exemplarily, the power consumption information of multiple interconnected applications is obtained through the following method:

[0106] According to the active duration data, sleep duration data, and power consumption data consumed within multiple historical periods of each interconnected application, calculate the power consumption in the first coding method and the power consumption in the sleep mode of each interconnected application. The first coding method is the coding method with the lowest power consumption among different coding methods; according to the preset coefficients between the power consumptions of other coding methods relative to the coding method with the lowest power consumption and the power consumption of the first coding method, calculate the power consumptions in other coding methods respectively.

[0107] In this embodiment, the energy consumption data collected by the smart glasses within multiple historical periods may include the active duration data, sleep duration data, and power consumption data consumed within multiple historical periods of each interconnected application. Among them, the power consumption data consumed within multiple historical periods can be understood as the total power consumed by the smart glasses within the historical period.

[0108] Exemplarily, Table IV shows specific examples of energy consumption data over multiple historical periods:

[0109] Table IV:

[0110]

[0111]

[0112] As shown in Table IV above, in Cycle 0: the active duration data for music playback is 120 minutes, and the sleep duration data is 600 minutes; the active duration data for the voice assistant is 120 minutes, and the sleep duration data is 60 minutes, and the sleep duration data is 600 minutes; the active duration data for the translator is 12 minutes, and the sleep duration data is 660 minutes; the active duration data for making a call is 30 minutes, and the sleep duration data is 0 minutes; the active duration data for phone translation is 10 minutes, and the sleep duration data is 15 minutes; the power consumption within the cycle is 99%. The same applies to other cycles and will not be elaborated here.

[0113] Based on the active duration data, sleep duration data of each interconnected application over multiple historical periods, and the power consumption data over multiple historical periods, the power consumption of each interconnected application in the active mode can be calculated. Exemplarily, the number of historical periods collected by the smart glasses is not less than twice the number of interconnected applications, and the power consumption of each interconnected application in the active mode is calculated by solving a system of multiple equations.

[0114] Taking Table IV above as an example, the following system of equations can be obtained:

[0115] Cycle 0:

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

[0117] Cycle 1:

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

[0119] Cycle 2:

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

[0121] Cycle 3:

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

[0123] Cycle 4:

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

[0125] Cycle 5:

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

[0127] Cycle 6:

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

[0129] Cycle 7:

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

[0131] Cycle 8:

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

[0133] Cycle 9:

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

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

[0136] The above system of equations is a system of linear equations with multiple variables. Solving the above system of equations can obtain the power consumption of 5 applications in the active mode and the sleep mode respectively. There are many methods for solving a system of linear equations with multiple variables, such as the substitution method, the elimination method, the matrix method, etc. These methods are not listed one by one in the embodiments of the present application.

[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 can be understood that it is very difficult to calculate the specific power consumption of interconnected applications. In the embodiments of the present application, the calculated power consumption is the average power consumption of each interconnected application in the active mode and the average power consumption in the sleep mode over multiple historical periods. Among them, the average power consumption in the active mode is the average power consumption calculated by integrating the power consumption of different coding methods over multiple historical periods. For example, in the above example, the power consumption A of music playback in the active mode is actually the average power consumption of music playback in the active mode within cycles 0 - 9. The power consumption A may use multiple coding methods within these 10 cycles, so this power consumption is also the average power consumption of different coding methods.

[0139] In order to further reduce the probability of the smart glasses shutting down before charging, in this embodiment, the average power consumption in the active mode is used as the power consumption of the coding method with the lowest power consumption. For example, in the above example, there are two coding methods for music playback: single-channel coding and dual-channel coding. The power consumption A will actually be higher than the power consumption of single-channel coding, but in this embodiment, the power consumption A is used as the power consumption of single-channel coding, that is, the power consumption of different coding methods will be overestimated to a certain extent, which is beneficial to reducing the probability of the smart glasses shutting down before charging.

[0140] In this embodiment, the terminal device may include preset coefficients corresponding to other coding methods respectively. This preset coefficient is the preset coefficient between the power consumption of other coding methods and the power consumption of the coding method with the lowest power consumption, and the preset coefficient is greater than 1. After calculating the power consumption of each application in the coding method with the lowest power consumption, the power consumption of each interconnected application in other coding methods can be calculated respectively according to the preset coefficient between the power consumption of other coding methods and the power consumption of the coding method with the lowest power consumption, and the power consumption of each interconnected application in the first coding method (the coding method with the lowest power consumption).

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

[0142] Exemplarily, the preset coefficient can be an empirical value, a statistical value, an experimental value, etc., and the embodiments of the present application do not limit this too much.

[0143] In another example, the power consumption of different encoding methods can also be calculated more precisely. Specifically, the energy consumption data in each historical period can include the usage duration and sleep duration data of each interconnected application installed on the smart glasses in different encoding methods, and the power consumption data of the smart glasses in the historical period. Exemplarily, the number of historical periods is not less than (the number of encoding methods + 1) × the number of interconnected applications.

[0144] In this case, calculating the power consumption information of each interconnected application in the smart glasses based on the energy consumption data in multiple historical periods may include: calculating the power consumption of each interconnected application in different encoding methods and the power consumption in the sleep mode according to the usage duration, sleep duration data of each interconnected application in different encoding methods and the power consumption data in multiple historical periods.

[0145] Exemplarily, the power consumption of each interconnected application in different encoding methods and the power consumption in the sleep mode can be obtained by solving a system of linear equations with multiple variables. Taking the multiple encoding methods including single-channel encoding and dual-channel encoding as an example, the power consumption information of each interconnected application in one 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 respectively represent the power consumption of interconnected applications 1 to N in the single-channel encoding mode, t11, t21, ……, tN1 respectively represent the usage duration of interconnected applications 1 to N in the single-channel encoding mode, A12, A22, ……, AN2 respectively represent the power consumption of applications 1 to N in the dual-channel encoding mode, t12, t22, ……, tN2 respectively represent the usage duration of interconnected applications 1 to N in the dual-channel encoding mode, B1, B2, ……, BN respectively represent the power consumption of interconnected applications 1 to N in the sleep mode, T1, T2, ……, TN respectively represent the sleep duration of interconnected applications 1 to N, and Δ represents the power consumed during the historical period.

[0149] Adopting this implementation method can more accurately calculate the power consumption of each interconnected application in different encoding modes, so that when selecting an encoding mode, the encoding effect and the power consumption of the smart glasses can be better balanced.

[0150] It can be understood that there are multiple possibilities for the implementation method of obtaining the power consumption information of multiple interconnected applications based on historical data of multiple periods. For example, in addition to the method of solving equations as described above, it can also be implemented through methods such as large model reasoning. The embodiments of this application will not list them one by one.

[0151] In summary, the embodiments of this application provide a dynamic encoding method for smart glasses, which mainly includes: collecting the energy consumption data of the smart glasses in multiple historical periods; obtaining the power consumption information of multiple interconnected applications based on the energy consumption data in multiple historical periods, where the power consumption information of each interconnected application includes the power consumption in the active mode and the power consumption in the sleep mode, and the power consumption in the active mode includes the power consumption in different encoding modes; determining the expected usage duration of the smart glasses and the current battery remaining capacity of the smart glasses; determining the encoding mode of the smart glasses according to the current battery remaining capacity, the expected usage duration and the power consumption information of each interconnected application, so that the energy consumption of the smart glasses within the expected usage duration does not exceed the battery remaining capacity.

[0152] In the above-mentioned dynamic encoding method for smart glasses provided in this application, by statistically analyzing the historical power consumption data of the smart glasses to estimate the power consumption information of multiple interconnected applications in the smart glasses, it can adapt to the usage habits of different users and the differences in the usage losses of the smart glasses. The statistically obtained power consumption information is more targeted, and the power consumption information of each interconnected application obtained is also more accurate. The remaining battery capacity can represent the total remaining energy of the smart glasses at present. When the power consumption of the smart glasses within the expected usage duration exceeds the total remaining energy at present, there is a high probability that the smart glasses will shut down before the user charges, affecting the user experience. Therefore, the embodiment of this application determines the encoding method of the smart glasses according to the current remaining battery capacity, the expected usage duration, and the power consumption information of each interconnected application of the smart glasses, so that the power consumption of the smart glasses within the expected usage duration does not exceed the remaining battery capacity, thereby facilitating the smart glasses to maintain the battery power not completely consumed before the next charging, providing a better user experience for the user.

[0153] Based on the same technical concept, the embodiment of this application also provides a smart glass, which includes a processor and a memory. Among them, the memory is used to store program instructions, and the processor is used to run the program instructions so that the smart glass executes the method described in any of the above embodiments.

[0154] Exemplarily, with reference to Figure 5 , the structural block diagram of the smart glass that can be used as an embodiment of this application will now be described. It 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 only examples and are not intended to limit the implementation of this application described and / or required herein.

[0155] As Figure 5 shown, the electronic device includes a computing unit 501, which can execute various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 502 or the computer program loaded from the storage unit 508 into the random access memory (RAM) 503. In the RAM 503, various programs and data required for the operation of the electronic device can also be stored. The computing unit 501, the ROM 502, and the RAM 503 are connected to each other through a bus 504. The 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 or character or image information, and generate key signal inputs related to the user settings and / or function controls of the smart glasses. The output unit 507 can be any type of device capable of presenting information, and can include but is not limited to a waveguide display screen, a speaker, a vibrator, etc. The storage unit 508 can include but is not limited to a memory, a hard disk, etc. The communication unit 509 allows the smart glasses to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks, and can include but is not limited to a modem, a network card, an infrared communication device, a wireless communication transceiver, and / or a chipset, such as a Bluetooth device, a WiFi device, a WiMax device, a cellular communication device, 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 a CPU, a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 501 executes the various methods and processes described above. For example, in some embodiments, the method embodiments of the present application can be implemented as a computer program, which is tangibly contained in a machine-readable medium, such as the storage unit 508. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device via the ROM 502 and / or the communication unit 509. In some embodiments, the computing unit 501 can be configured to execute the above-mentioned method by any other suitable means (for example, by means of firmware).

[0158] The computer program for implementing the method of the embodiments of the present application can be written in any combination of one or more programming languages. These computer programs can be provided to the processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing devices, such that when the computer program is executed by the processor or controller, the functions / operations specified in the flowchart and / or block diagram are implemented. The computer program can be executed entirely on the machine, partially on the machine, as an independent software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

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

[0160] Based on the same inventive concept, embodiments of the present application further provide a non-transitory machine-readable medium storing a computer program, wherein the computer program, when executed by a processor of a computer, is configured to cause the computer to execute the method of the embodiments of the present application.

[0161] Based on the same inventive concept, embodiments of the present application further provide a computer program product, including a computer program, wherein the computer program, when executed by a processor of a computer, is configured to cause the computer to execute the method of the embodiments of the present application.

[0162] It should be noted that the term "including" and its variants used in the embodiments of the present application are open-ended, that is, "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"; the term "some embodiments" means "at least some embodiments". The modifications of "one" and "a plurality" mentioned in the embodiments of the present application are illustrative rather than restrictive. Those skilled in the art should understand that, unless clearly stated otherwise in the context, it 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 for analysis, stored data, displayed data, etc.) involved in the embodiments of the present application are all information and data that have been authorized by the user or fully authorized by all parties. Moreover, the collection, use, and processing of the relevant data need to comply with the relevant laws, regulations, and standards of the relevant countries and regions, and corresponding operation entrances are provided for the user to select to authorize or reject.

[0164] In the method embodiments provided by the present application, the steps described can be executed in different orders and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The protection scope of the present application is not limited in this regard.

[0165] The term "embodiment" in this specification means that the specific features, structures or characteristics described in connection with the embodiments may be included in at least one embodiment of the present application. The phrase appears in various positions in the specification and does not necessarily mean the same embodiment, nor does it mean being independent or alternative to other embodiments and mutually exclusive. The various embodiments in this specification are described in a related manner, and the same or similar parts among the embodiments are referred to each other. In particular, for device, equipment, and system embodiments, since they are basically similar to method embodiments, the description is relatively simple, and the relevant parts refer to the partial description of the method embodiments.

[0166] The above-described embodiments merely represent several implementation manners of the present application. The description is relatively specific and detailed, but it should not be construed as a limitation on the scope of patent protection. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the appended claims.

Claims

1. A smart glasses dynamic encoding method, comprising: Collect energy consumption data of smart glasses over multiple historical periods; Acquire power consumption information of multiple interconnected applications based on the energy consumption data in the multiple historical periods, wherein the power consumption information of each interconnected application includes the power consumption of the interconnected application in an active mode and a sleep mode, and the power consumption in the active mode includes the power consumption under different encoding modes; Determining an expected usage time of the smart glasses and a current battery level of the smart glasses; The encoding method of the smart glasses is determined according to the current battery remaining amount of the smart glasses, the expected usage time and the power consumption information of each interconnected application, so that the energy consumption of the smart glasses within the expected usage time does not exceed the battery remaining amount.

2. The method according to claim 1, characterized in that The determining the encoding method of the smart glasses according to the current remaining battery level of the smart glasses, the expected usage time, and the power consumption information of each interconnected application includes: According to the power consumption information of each interconnected application, respectively calculate the total energy consumption under different encoding modes corresponding to each interconnected application, wherein the total energy consumption under different encoding modes includes the energy consumption generated by each interconnected application in different encoding modes within the expected usage time, and the energy consumption generated by other interconnected applications in sleep mode within the expected usage time; In response to receiving an instruction to activate a first interconnected application in the smart glasses, obtaining total energy consumption corresponding to the first interconnected application in different encoding modes; According to the remaining battery level when the instruction to activate the application is received and the total energy consumption of the first interconnected application under different encoding modes, an encoding mode of the first interconnected application is determined so that the total energy consumption of the first interconnected application under the determined encoding mode does not exceed the remaining battery level.

3. The method according to claim 1, characterized in that The determining the expected usage time of the smart glasses includes: Determining an expected total usage time of the smart glasses according to the collection time of the energy consumption data in the multiple historical periods; The expected usage time is determined according to the difference between the expected total usage time and the usage time of the smart glasses in the current cycle.

4. The method according to claim 3, characterized in that The determining the expected total usage time of the smart glasses according to the collection time of the energy consumption data in the multiple historical periods includes: According to the collection time of the energy consumption data of the multiple historical periods, the energy consumption data of the multiple historical periods are divided into working day energy consumption data and rest day energy consumption data; Calculate the expected total usage time of the working day and the expected total usage time of the rest day respectively; The expected usage time is determined according to the difference between the expected total usage time and the usage time of the smart glasses in the current cycle, including: In the case where the current usage cycle is a working day, the difference between the expected total usage time of the working day and the usage time of the smart glasses in the current cycle is used as the expected usage time; or, In the case that the current usage cycle is a rest day, the difference between the expected total usage time of the rest day and the usage time of the smart glasses in the current cycle is used as the expected usage time.

5. The method according to any one of claims 1 to 4, characterized in that The energy consumption data in each historical period includes the activity duration data and sleep duration data of each interconnected application installed by the smart glasses, and the power consumption data of the smart glasses in the historical period; The power consumption information of the plurality of interconnected applications is obtained by the following method: According to the activity duration data, the sleep duration data and the power consumption data in the multiple historical periods of each interconnected application, the power consumption in the first encoding mode and the power consumption in the sleep mode of each interconnected application are calculated, wherein the first encoding mode is the encoding mode with the lowest power consumption among the different encoding modes, and the power consumption of each interconnected application in the first encoding mode is the average power consumption of each interconnected application in the active mode in the multiple historical periods; The power consumption of each interconnected application in other encoding modes is calculated based on preset coefficients between power consumption of other encoding modes relative to the encoding mode with the lowest power consumption and the power consumption of each interconnected application in the first encoding mode.

6. The method according to any one of claims 1 to 4, characterized in that The energy consumption data in each historical period includes usage time data and sleep time data of each interconnected application installed in 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 according to the energy consumption data in the multiple historical periods, including: According to the usage duration data of each interconnected application under the different encoding modes, the sleep duration data and the power consumption data in the multiple historical cycles, the power consumption of each interconnected application under the different encoding modes and the power consumption in the sleep mode are calculated.

7. The method according to any one of claims 1 to 4, characterized in that 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 in the smart glasses in the historical period, and the duration data includes the dormancy duration data of the interconnected application, and the activity duration data or the usage duration data under the different encoding methods; The power consumption of the local application is the background power consumption of the interconnected application in the active mode and the sleep mode.

8. The method according to any one of claims 1 to 4, characterized in that The different encoding modes include at least: a single-channel encoding mode for encoding audio data from one microphone, and a dual-channel encoding mode for encoding audio data from two microphones respectively.

9. A pair of smart glasses, comprising a processor and a memory, wherein the memory is used to store program instructions, and the processor is used to run the program instructions so that the smart glasses execute the method according to any one of claims 1 to 8.

10. A non-volatile computer-readable storage medium storing program instructions, wherein when the program instructions are executed by smart glasses, the smart glasses execute the method according to any one of claims 1 to 8.

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