System control method and device, wearable equipment and storage medium

Abstract

The invention relates to a system control method and device, wearable equipment and a storage medium, the system control method and device are applied to the wearable equipment, the wearable equipment comprises a first processor and a second processor, the first processor is used for operating a first system, the second processor is used for operating a second system, and the power consumption of the second processor is lower than that of the first processor. The wearable device obtains the sensor data through the first system, and if any one of the recognition results corresponding to the sensor data meets a preset condition, the wearable device enters a cooperative working mode of the first system and the second system. By adopting the method, the triggering modes of the wearable device for system control can be enriched, and the system control efficiency is improved.

Description

Technical Field

[0001] The present application relates to the field of terminal technology, and in particular to a system control method, apparatus, wearable device and storage medium. Background Art

[0002] With the development of communication technology, wearable devices have attracted more attention from consumers.

[0003] In the traditional method, the user can trigger the switching system control on the display interface of the current system. After the system responds to the user's triggering operation, it can control the device to restart and switch to the new system.

[0004] However, the switching mode of the above system control method is single and cannot achieve seamless switching between the two systems in the wearable device. Summary of the invention

[0005] Embodiments of the present application provide a system control method, apparatus, wearable device, and storage medium.

[0006] A system control method is applied to a wearable device, wherein the wearable device includes a first processor and a second processor, wherein the first processor is used to run a first system, and the second processor is used to run a second system, and the power consumption of the second processor is lower than the power consumption of the first processor. The method includes:

[0007] acquiring sensor data based on the first system;

[0008] If any of the recognition results corresponding to the sensor data meets a preset condition, the collaborative working mode of the first system and the second system is entered.

[0009] A system control device is applied to a wearable device, wherein the wearable device includes a first processor and a second processor, wherein the first processor is used to run a first system, and the second processor is used to run a second system, and the power consumption of the second processor is lower than that of the first processor, and the above includes:

[0010] An acquisition module, used for acquiring sensor data based on the first system;

[0011] The collaborative working module is used to enter the collaborative working mode of the first system and the second system when any recognition result among the recognition results corresponding to the sensor data meets a preset condition.

[0012] A wearable device includes a memory and a first processor and a second processor, wherein the first processor is used to run a first system, the second processor is used to run a second system, the power consumption of the second processor is lower than the power consumption of the first processor, and a computer program is stored in the memory. When the computer program is executed by the first processor, the first processor executes the steps of the above-mentioned system control method.

[0013] A computer-readable storage medium stores a computer program, which implements the steps of the above-mentioned system control method when executed by a processor.

[0014] The above-mentioned system control method, device, wearable device and storage medium are applied to a wearable device, and the wearable device includes a first processor and a second processor, wherein the first processor is used to run the first system, the second processor is used to run the second system, and the power consumption of the second processor is lower than that of the first processor. The wearable device obtains sensor data through the first system, and if any of the recognition results corresponding to the sensor data meets the preset conditions, the first system and the second system enter the collaborative working mode. Since the wearable device obtains the sensor data, it can identify according to the sensor and obtain the recognition result corresponding to the sensor data, so as to determine whether to enter the collaborative working mode according to the recognition result, thereby enriching the triggering mode of the system control; further, since the wearable device matches the recognition result with the preset condition; when any of the recognition results meets the preset condition, the wearable device can enter the collaborative working mode of the first system and the second system, thereby improving the efficiency of the system control. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0016] FIG1 is an application environment diagram of a system control method in one embodiment;

[0017] Figure 2 is a flow chart of a system control method in one embodiment;

[0018] Figure 3 is a flow chart of a system control method in another embodiment;

[0019] Figure 4 is a flow chart of a system control method in another embodiment;

[0020] Figure 5 is a flow chart of a system control method in another embodiment;

[0021] Figure 6 is a flow chart of a system control method in another embodiment;

[0022] Figure 7 is a flow chart of a system control method in another embodiment;

[0023] Figure 8 is a structural block diagram of a system control device in one embodiment;

[0024] Fig. 9 is a structural block diagram of a system control device in another embodiment;

[0025] Fig.10 is a structural block diagram of a system control device in another embodiment;

[0026] Fig.11 is a structural block diagram of a system control device in another embodiment;

[0027] Fig.12 is a structural block diagram of a system control device in another embodiment;

[0028] Fig.13 is a structural block diagram of a system control device in another embodiment;

[0029] Fig.14 is a structural block diagram of a wearable device in one embodiment. DETAILED DESCRIPTION

[0030] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0031] FIG1 is a schematic diagram of an application environment of a system control method in an embodiment. As shown in FIG1 , the application environment includes a wearable device 100, and the wearable device may be, but is not limited to, a smart bracelet, a smart watch, smart glasses, a smart head-mounted device, and the like. The wearable device 100 may include a first processor and a second processor, wherein the first processor is used to run a first system, and the second processor is used to run a second system, and the power consumption of the second processor is lower than the power consumption of the first processor. The first system and the second system may be switched, and the second system may be a system running on a central processing unit (CPU). The first system may be a system running on a microcontroller unit (MCU), or a system running on a graphics processing unit (GPU), which is not limited here.

[0032] Figure 2 FIG. 1 is a flow chart of a system control method in an embodiment. The system control method in this embodiment is described by taking the system control method running on the wearable device in FIG. 1 as an example. Figure 2 As shown, the system control method includes:

[0033] S101. Acquire sensor data based on a first system.

[0034] The method can be applied to wearable devices. The wearable device can be a portable device integrated into the user, which can exchange data with the cloud or other terminal devices. The wearable device can be a wrist wearable device, such as a smart bracelet, a smart watch, etc.; the wearable device can also be a head-mounted device, such as a smart helmet, smart glasses, etc. The wearable device can also be various types of devices such as smart shoes and smart gloves.

[0035] Multiple operating systems can be run in the wearable device to reduce the power consumption of the device. The operating system may include a first system and a second system. The first system and the second system may run on different control modules to realize different functions. The two operating systems may respectively realize functions such as sensor data acquisition, and user interaction functions. For example, the first system in the wearable device may be an operating system running on the MCU, and the user's heart rate data, number of steps, etc. may be measured in the first system; the second system may be an operating system running on the main control board, and intelligent interaction with the user may be performed through the second system, such as a chat function. It should be noted that the first system and the second system are only used to distinguish between the two systems. The second system may also be a system that realizes the sensor data acquisition function, and the first system may also be a system that realizes the user interaction function. The wearable device may switch from the first system to the second system, or from the second system to the first system, and the first system and the second system may also work together.

[0036] The wearable device may be provided with a variety of sensors, including motion sensors, biosensors, and environmental sensors. For example, the motion sensor may be an acceleration sensor, a gyroscope, a geomagnetic sensor, an atmospheric pressure sensor, and the like, and the motion sensor may be used to implement functions such as motion detection, navigation, and human-computer interaction. The biosensor may include a temperature sensor, an electroencephalogram sensor, and the like; the environmental sensor may include a temperature sensor, an ambient light sensor, an air pressure sensor, an audio sensor, and a camera, and the like.

[0037] When the wearable device runs the first system, sensor data can be obtained based on the first system. The above sensor data can be data collected by a sensor in the wearable device, or data collected by multiple sensors in the wearable device. The above multiple sensors can be multiple sensors of fixed types, or multiple sensors selected by the user according to needs. For example, the user can select image sensors, audio sensors, etc. on the first system for system control. Different types of sensors correspond to different types of sensor data. The above sensor data can include image data collected by a camera, distance data collected by a distance sensor, temperature data collected by a temperature sensor, etc.

[0038] The wearable device can receive the sensor data sent by the sensor in real time, or can receive the sensor data sent by the sensor at a collection time corresponding to a preset timing; the method for obtaining the above sensor data is not limited here.

[0039] S102: If any of the recognition results corresponding to the sensor data meets a preset condition, a collaborative working mode of the first system and the second system is entered.

[0040] On the basis of obtaining the sensor data, the wearable device can perform recognition processing on the sensor data to obtain a recognition result corresponding to the sensor data.

[0041] For different types of sensor data, the wearable device may perform different recognition processing methods. If the sensor data is image data, the wearable device may perform image recognition processing on the image data; if the sensor data is temperature data, the wearable device may perform analog-to-digital conversion on the temperature data; the recognition processing method is not limited here.

[0042] After the wearable device recognizes and processes different types of sensor data, it can obtain different types of recognition results. The wearable device can recognize image data to obtain objects in the image, or recognize the scene type in the image, and can also recognize video data to obtain user actions, such as blinking, waving, etc.; the wearable device can convert temperature data to obtain temperature values; the types of the above recognition results are not limited here.

[0043] The wearable device can identify and process various types of sensor data at the same time, or it can identify and process the sensor data in sequence according to a preset sensor priority order, which is not limited here.

[0044] Among them, the above-mentioned preset conditions may include preset conditions corresponding to multiple sensors. For the preset conditions corresponding to each sensor, it may include multiple target recognition results corresponding to the sensor, for example, when the above-mentioned sensor data is image data, the above-mentioned target recognition result may be multiple target image objects; it may also include conditions that the recognition result corresponding to the sensor needs to meet, for example, if the above-mentioned sensor is a distance sensor, the recognition result corresponding to the above-mentioned sensor is the distance between the wearable device and the user, and the preset condition corresponding to the above-mentioned sensor may be that the distance between the wearable device and the user is greater than a preset threshold; the form of the above-mentioned preset conditions is not limited here.

[0045] In addition, the above-mentioned preset conditions may also include conditions that are commonly satisfied by the sensor data. For example, when the distance between the wearable device and the user is greater than a preset threshold and the object identified by the image data is a target object, it can be considered that the sensor data satisfies the preset conditions.

[0046] The wearable device can simultaneously match the recognition results corresponding to each type of sensor data with the corresponding preset conditions; or it can match each recognition result with the corresponding preset conditions in sequence according to the preset priority order; this is not limited here. When any one of the recognition results corresponding to all sensor data meets the preset conditions, the wearable device can enter the collaborative working mode of the first system and the second system.

[0047] When the wearable device enters the collaborative working mode of the first system and the second system, the system in the first system can exchange data with the second system. After the wearable device enters the collaborative working mode, the first system and the second system can work simultaneously, and the first system can transmit the collected sensor data to the second system through the data transmission channel between the first processor and the second processor, and the second system performs further processing and display. After the wearable device enters the collaborative working mode, it can also control the first system or the second system to enter the sleep mode according to the requirements of the current business processing capability, and complete the switching between the first system and the second system. For example, when the processing capability required for the current business processing is high, it is switched to the first system for processing, and the second system is in a dormant state, or the first system performs the main processing work, and the second system performs auxiliary processing work such as data transmission. When the processing capability required for the current business processing is low, it can be processed by the second system. At this time, the first system enters a dormant state. Since the power consumption of the first processor is greater than that of the second processor, in the case of collaborative work, some businesses can be handed over to the second system for processing, thereby reducing power consumption and extending the battery life of the wearable device.

[0048] The above-mentioned system control method, device, wearable device and storage medium are applied to a wearable device, and the wearable device includes a first processor and a second processor, wherein the first processor is used to run the first system, the second processor is used to run the second system, and the power consumption of the second processor is lower than that of the first processor. The wearable device obtains sensor data through the first system, and if any of the recognition results corresponding to the sensor data meets the preset conditions, the wearable device enters the collaborative working mode of the first system and the second system. Since the wearable device obtains the sensor data, it can identify according to the sensor and obtain the recognition result corresponding to the sensor data, so as to determine whether to enter the collaborative working mode according to the recognition result, thereby enriching the triggering mode of the system control; further, since the wearable device matches the recognition result with the preset condition; when any of the recognition results meets the preset condition, the wearable device can enter the collaborative working mode of the first system and the second system, thereby improving the efficiency of the system control.

[0049] In one embodiment, based on the above embodiment, the sensor data obtained by the wearable device includes motion posture data collected by the motion sensor and audio data collected by the audio sensor.

[0050] Among them, the above-mentioned motion sensor can be an acceleration sensor and a gravity sensor A+G sensor, and the wearable device can obtain the motion posture data of the wearable device collected by the above-mentioned A+G sensor, and the above-mentioned motion posture data can include the acceleration information and gravity information of the wearable device. The above-mentioned audio sensor can be a microphone mounted on the first system of the wearable device, and the above-mentioned audio sensor can pick up sound from the surrounding environment and collect audio data in the environment where the wearable device is located. The above-mentioned audio data can include the user's voice data, and can also include sound data, interference sound, etc. generated in the surrounding environment.

[0051] The wearable device can simultaneously collect data collected by the motion sensor and the audio sensor, and then recognize and process the motion posture data and the audio data respectively. The wearable device can recognize and process the motion posture data and the audio data through a recognition model, and the recognition model can be a deep learning model or other types of algorithm models, which are not limited here.

[0052] The wearable device can call the recognition model matching the sensor data to perform recognition processing on the sensor data to obtain a set of recognition results; wherein the set of recognition results includes the action recognition results corresponding to the action posture data and the speech recognition results corresponding to the audio data. The recognition model corresponding to the action posture data can be a motion recognition model; and the action recognition model corresponding to the audio data can be a speech recognition model.

[0053] In addition, the above sensor data also includes image data collected by the camera. The wearable device can call the image recognition model to identify and process the image data, and then determine the scene type output by the image recognition model as the recognition result corresponding to the image data.

[0054] In the above system control method, the sensor data obtained by the wearable device includes motion posture data collected by the motion sensor and audio data collected by the audio sensor, so that the user can choose either motion or voice to control the system, enriching the triggering method for the system to enter the collaborative working mode; further, the wearable device can simultaneously recognize and process the motion posture data and audio data separately to obtain a collection of recognition results, thereby improving the recognition efficiency of the sensor data and thus improving the efficiency of the system control.

[0055] Figure 3 FIG. 1 is a flowchart of a system control method in another embodiment. This embodiment relates to a method for a wearable device to identify and process sensor data. Based on the above embodiment, when the sensor data is action posture data, such as Figure 3 As shown, the above S102 includes:

[0056] S201. Calculate acceleration information and posture angle information according to motion posture data.

[0057] Wearable devices can perform analog-to-digital conversion on the motion posture data collected by motion sensors to obtain acceleration information and posture angle information of the wearable devices.

[0058] S202: Using the acceleration information and the posture angle information as inputs of a motion recognition model to obtain a motion recognition result.

[0059] Furthermore, the wearable device can use the acceleration information and posture angle information as inputs to the motion recognition model to obtain motion recognition results. The above-mentioned motion recognition model can analyze the acceleration information and posture angle information input by the wearable device, determine how the user performs the action, and obtain the acceleration change and posture angle change of the wearable device to generate the above-mentioned acceleration information and posture angle information. The above-mentioned motion recognition results may include the action type of the user holding the wearable device, and may also include the user's motion trajectory, which is not limited here. The above-mentioned action types may include waving, climbing stairs, shaking hands, running, etc.; the above-mentioned motion trajectory may include leftward motion, rightward motion, circular motion, etc.

[0060] In the above-mentioned system control method, the wearable device recognizes and processes the motion posture data through the motion recognition model, and can accurately obtain the motion recognition result corresponding to the motion posture data, so that the accurate motion recognition result can be matched with the corresponding preset conditions to determine whether to enter the collaborative working mode, thereby improving the accuracy of system control; in addition, the wearable device is triggered to enter the collaborative working mode through the motion recognition result, avoiding various touch operations of the user on the mobile phone screen, so that the user can more conveniently realize system control, thereby improving the user experience.

[0061] Figure 4 is a flowchart of a system control method in another embodiment. This embodiment relates to a method for a wearable device to identify and process sensor data. Based on the above embodiment, when the sensor data is audio data, such as Figure 4 As shown, the above S102 includes:

[0062] S301: Use the audio data as input of a speech recognition model to extract speech information from the audio data.

[0063] For the audio data collected by the wearable device, the wearable device can use the audio data as the input of the speech recognition model, and use the speech recognition model to identify whether the audio data contains the data collected when the user speaks, and then convert the data collected when the user speaks into speech information containing the user's instructions. Furthermore, the wearable device can also perform noise reduction processing on the speech information to better recognize the user's voice instructions.

[0064] Wearable devices can recognize the voice of the user bound to the wearable device based on the user's voice features. For example, when a user starts using a wearable device, the wearable device can collect the user's voice information and extract the user's voice features; the wearable device can only recognize the user's voice commands in the audio data, avoiding the system switching of the wearable device by mistake when the audio data contains the voices of other users.

[0065] If the wearable device does not extract voice information from the audio data, the wearable device can identify through other sensor data to determine whether the user needs to enter the collaborative working mode.

[0066] S302: Convert the voice information into text to obtain a voice recognition result.

[0067] When the wearable device extracts voice information, the voice information can be converted into text to obtain text information corresponding to the user's voice, and the above text information is used as the user's voice recognition result.

[0068] In the above system control method, the wearable device recognizes and processes the audio data through the voice recognition model, and can accurately obtain the voice recognition results corresponding to the audio data, so that the voice recognition results can be matched with the corresponding preset conditions to determine whether to enter the collaborative working mode, thereby improving the accuracy of system control; in addition, using the above method, the user can trigger the wearable device to enter the collaborative working mode through voice commands, avoiding various touch operations on the mobile phone screen, so that the user can more conveniently realize system control, thereby improving the user experience; voice input can be more in line with the user's input habits, further improving the user experience.

[0069] In one embodiment, the wearable device matches the recognition result with the preset condition. After the wearable device obtains the recognition result set, each recognition result can be matched with the corresponding preset condition to determine whether the recognition result meets the preset condition.

[0070] When the sensor data obtained by the wearable device includes motion posture data and audio data, the preset conditions may include motion trigger conditions corresponding to the motion sensor and voice trigger conditions corresponding to the audio sensor; wherein the above-mentioned action trigger conditions include multiple target action types that can trigger the system to enter the collaborative working mode, and the voice trigger conditions include multiple target keywords that can trigger the system to enter the collaborative working mode.

[0071] The target action type in the above action trigger condition can be an action identifier or an action name, which is not limited here. When the action recognition result obtained by the wearable device through the action recognition model is an action type, if the action type corresponding to the above action recognition result matches the preset target action type, then it can be determined that the action recognition result meets the preset condition.

[0072] The above-mentioned voice triggering conditions include multiple target keywords that can trigger the system to enter the collaborative working mode, and may also include a combination relationship of target keywords, which is not limited here. The wearable device can perform a keyword extraction operation on the voice recognition result; if the keyword carried in the voice recognition result matches the preset target keyword, it is determined that the voice recognition result meets the preset condition. The wearable device may consider that when the voice recognition result contains a preset target keyword, the voice recognition result matches the preset target keyword; or, the wearable device may consider that when the number of target keywords contained in the voice recognition result is greater than a preset threshold, the keyword carried in the voice recognition result matches the target keyword; or, the wearable device may consider that when the appearance order of the target keywords contained in the voice recognition result is the same as the preset keyword combination method, the keyword carried in the voice recognition result matches the target keyword. The above-mentioned matching method is not limited here.

[0073] For example, the keywords in the above-mentioned voice trigger conditions may include "collaborative work", "big core system", "sleep", "switch", etc. When "big core system" and "collaborative work" are extracted from the voice recognition results, the wearable device may consider that the voice recognition result meets the preset conditions; or, when "switch" and "big core system" are extracted in sequence from the voice recognition results, the wearable device may consider that the voice recognition result meets the preset conditions.

[0074] When the sensor data includes image data, the preset conditions also include at least one target scene type corresponding to the second system. The wearable device can match the scene type output by the image recognition model with the preset conditions. When the scene type is the target scene type, the wearable device can consider that the recognition result corresponding to the image data meets the preset conditions and can enter the collaborative working mode. For example, the scene types output by the image recognition model can include sports scenes, sleep scenes and other scenes. The second system can correspond to other scenes, and the first system can correspond to sports scenes and sleep scenes. When the image data corresponds to other scenes, the wearable device can enter the collaborative working mode of the first system and the second system.

[0075] In the above system control method, after the wearable device obtains the motion recognition result and the voice recognition result, it can match the motion recognition result with the motion trigger condition, and match the voice recognition result with the voice trigger condition, so that the user can accurately match and execute system control whether through voice or action.

[0076] Figure 5 FIG. 1 is a flow chart of a system control method in another embodiment. This embodiment relates to an implementation method of a wearable device entering a collaborative working mode. Based on the above embodiment, Figure 5 As shown, the above S103 includes:

[0077] S401. Send an indication message to the second system through a data transmission channel between the first system and the second system; the indication message is used to instruct the second system to enter a working mode.

[0078] The first system and the second system in the wearable device include a data transmission channel, so that the system management module in the first system can exchange data with the system management module in the second system. When any recognition result meets the preset conditions, the wearable device can send an indication message to the second system through the first system. After receiving the indication message, the second system in the dormant state can be adjusted to the working state, and a response message can be returned to the first system through the data transmission channel.

[0079] Accordingly, the wearable device receives a response message returned by the second system in the first system.

[0080] S402: Receive a response message returned by the second system based on the indication message.

[0081] After the first system receives the corresponding message returned by the second system, it can determine whether the second system is successfully adjusted to the working state according to the above response message.

[0082] S403: If the response message indicates that the second system is in working mode, control the first system to enter sleep mode.

[0083] If the response message indicates that the second system is in the working mode, the first system is controlled to enter the sleep mode, thereby reducing the power consumption of the wearable device.

[0084] In the above system control method, when the wearable device enters the collaborative working mode, it is not necessary to restart the wearable device, thereby avoiding the low system response speed caused by the long system restart time. In the above system control method, the wearable device can realize the system entering the collaborative working mode by recognizing the user's actions, voice, etc., providing functional linkage between the first system and the second system, and also allowing the first system to use the system resources in the second system to realize more business functions and improve user experience.

[0085] Figure 6 FIG. 1 is a flow chart of a system control method in another embodiment. This embodiment relates to an implementation method of a wearable device acquiring sensor data. Based on the above embodiment, Figure 6 As shown, the above S101 includes:

[0086] S501: Detect whether the wearable device is in a user-worn state.

[0087] Before obtaining sensor data, the wearable device can first detect whether the wearable device is in a user-worn state. The wearable device can obtain the sensing data collected by the capacitive sensor set on the wearable device; then calculate the distance value between the wearable device and the user based on the sensing data; and determine whether the wearable device is in a user-worn state based on the distance value. If the distance value is less than or equal to the preset distance threshold, it is determined that the wearable device is in a user-worn state; if the distance value is greater than the preset distance threshold, it is determined that the wearable device is not in a user-worn state.

[0088] The wearable device may detect whether the wearable device is being worn by a user before collecting one type of sensor data; or the wearable device may detect the wearable device being worn by a user before collecting each type of sensor data. For example, the wearable device may detect whether the wearable device is being worn by a user before collecting motion sensor data.

[0089] S502: If the wearable device is in a state of being worn by the user, sensor data is acquired based on the first system.

[0090] If the wearable device is in the state of being worn by the user, the wearable device can obtain the above sensor data and start the recognition model to identify and process the sensor data. If the wearable device is not in the state of being worn, the wearable device can assume that the user is not using the device and does not need to control the system, so there is no need to collect sensor data or start the recognition model, thereby reducing the power consumption of the wearable device.

[0091] In the above-mentioned system control method, the wearable device executes the step of acquiring sensor data when being worn by a user, which can avoid the increase in device power consumption caused by collecting sensor data, starting the recognition model, etc. when the user is not using the wearable device, thereby reducing the power consumption of the wearable device and extending the standby time of the wearable device.

[0092] In one embodiment, based on the above embodiment, Figure 7 As shown, the system control method includes:

[0093] S601, detecting whether the wearable device is in a user-worn state; if so, executing S602.

[0094] S602: Acquire motion posture data and audio data based on the first system.

[0095] S603: Calculate acceleration information and posture angle information according to the motion posture data.

[0096] S604: Use the acceleration information and the posture angle information as inputs of the action recognition model to obtain action recognition results.

[0097] S605: Determine whether the action type corresponding to the action recognition result matches the preset target action type. If so, execute S610.

[0098] S606: Use the audio data as input of a speech recognition model to extract speech information from the audio data.

[0099] S607: Convert the voice information into text to obtain a voice recognition result.

[0100] S608: Perform keyword extraction operation on the speech recognition result.

[0101] S609: Determine whether the keyword carried in the speech recognition result matches the preset target keyword. If so, execute S610.

[0102] S610: Send an indication message to the second system through a data transmission channel between the first system and the second system.

[0103] S611. Receive a response message returned by the second system based on the indication message.

[0104] S612: Determine whether the response message indicates that the second system is in working mode. If so, execute S613.

[0105] S613: Control the first system to enter a sleep mode.

[0106] The implementation principle and technical effect of the above system control method are similar to those of the above embodiment and will not be repeated here.

[0107] It should be understood that although Figure 2-7 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, Figure 2-7 At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

[0108] Figure 8 FIG. 1 is a structural block diagram of a system control device according to an embodiment of the present invention. Figure 8 As shown, it is applied to a wearable device, the wearable device includes a first processor and a second processor, wherein the first processor is used to run a first system, the second processor is used to run a second system, the power consumption of the second processor is lower than the power consumption of the first processor, and the system control device includes:

[0109] An acquisition module 10, configured to acquire sensor data based on the first system;

[0110] The collaborative working module 20 is used to enter the collaborative working mode of the first system and the second system when any recognition result among the recognition results corresponding to the sensor data meets a preset condition.

[0111] In one embodiment, based on the above embodiment, Fig. 9 As shown, the sensor data includes motion posture data collected by the motion sensor and audio data collected by the audio sensor.

[0112] In one of the embodiments, based on the above embodiment, the above device also includes a recognition module 30 which is specifically used to: call a recognition model that matches the sensor data to perform recognition processing on the sensor data to obtain a set of recognition results; wherein the recognition result set includes motion recognition results corresponding to the motion posture data and speech recognition results corresponding to the audio data.

[0113] In one embodiment, based on the above embodiment, Fig.10 As shown, the identification module 30 includes:

[0114] A calculation unit 301 is used to calculate acceleration information and posture angle information of the wearable device according to the motion posture data;

[0115] The recognition unit 302 is used to use the acceleration information and the posture angle information as inputs of the action recognition model to obtain the action recognition result.

[0116] In one embodiment, based on the above embodiment, Fig.11 As shown, the identification module 30 further includes:

[0117] An extraction unit 303, used to use the audio data as an input of a speech recognition model to extract speech information from the audio data;

[0118] The conversion unit 304 is used to convert the voice information into text to obtain a voice recognition result.

[0119] In one of the embodiments, based on the above embodiments, the preset conditions include action trigger conditions corresponding to the action sensor and voice trigger conditions corresponding to the audio sensor, the action trigger conditions include multiple target action types that can trigger the system to enter the collaborative work mode, and the voice trigger conditions include multiple target keywords that can trigger the system to enter the collaborative work mode.

[0120] In one of the embodiments, based on the above embodiment, the above collaborative working module 20 is specifically used to: when the action type corresponding to the action recognition result matches the preset target action type, determine that the action recognition result meets the preset condition.

[0121] In one of the embodiments, based on the above embodiment, the collaborative working module 20 is also used to: perform keyword extraction operation on the speech recognition result; when the keyword carried in the speech recognition result matches the preset target keyword, determine that the speech recognition result meets the preset condition.

[0122] In one of the embodiments, based on the above embodiment, the collaborative working module 20 is further used to: send an indication message to the second system through a data transmission channel between the first system and the second system; the indication message is used to instruct the second system to enter a working mode.

[0123] In one embodiment, based on the above embodiment, Fig.12As shown, the above device also includes a sleep module 40, which is used to: receive a response message returned by the second system based on the indication message; and control the first system to enter the sleep mode when the response message indicates that the second system is in the working mode.

[0124] In one embodiment, based on the above embodiment, the wearable device is a wearable device such as Fig.13 As shown, the acquisition module 10 includes:

[0125] The detection unit 101 is used to detect whether the wearable device is in a state of being worn by a user;

[0126] The acquisition unit 102 is used to acquire sensor data based on the first system when the wearable device is in a state of being worn by a user.

[0127] In one of the embodiments, based on the above embodiment, the sensor data also includes image data collected by a camera.

[0128] In one of the embodiments, based on the above embodiment, the recognition result set also includes the scene type in which the wearable device is located; the above recognition module 30 is specifically used to: use the image data as the input of the image recognition model to obtain the scene type corresponding to the image data.

[0129] In one of the embodiments, based on the above embodiment, the preset condition further includes at least one target scene type corresponding to the second system.

[0130] The division of the various modules in the above-mentioned system control device is only for illustration. In other embodiments, the system control device may be divided into different modules as needed to complete all or part of the functions of the above-mentioned system control device.

[0131] For the specific definition of the system control device, please refer to the definition of the system control method above, which will not be repeated here. Each module in the above system control device can be implemented in whole or in part by software, hardware and a combination thereof. Each of the above modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to each of the above modules.

[0132] Fig.14 FIG. 1 is a schematic diagram of the internal structure of a wearable device in one embodiment. Fig.14As shown, the wearable device includes a first processor, a second processor and a memory connected through a system bus. The first processor is used to run the first system, and the second processor is used to run the second system. The power consumption of the second processor is lower than that of the first processor, wherein the first processor and the second processor are used to provide computing and control capabilities to support the operation of the entire wearable device. The memory may include a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The computer program can be executed by the processor to implement a system control method provided in each of the following embodiments. The internal memory provides a cached operating environment for the operating system computer program in the non-volatile storage medium.

[0133] The implementation of each module in the system control device provided in the embodiment of the present application can be in the form of a computer program. The computer program can be run on a terminal or a server. The program module constituted by the computer program can be stored in the memory of the electronic device. When the computer program is executed by the processor, the steps of the method described in the embodiment of the present application are implemented.

[0134] The embodiment of the present application also provides a computer-readable storage medium, one or more non-volatile computer-readable storage media containing computer-executable instructions, when the computer-executable instructions are executed by one or more processors, the processors execute the steps of the system control method.

[0135] A computer program product comprising instructions, when executed on a computer, causes the computer to execute a system control method.

[0136] Any reference to memory, storage, database or other medium used in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM), which is used as an external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0137] The above embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several modifications and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A system control method, characterized in that: Applied to a wearable device, the wearable device includes a first processor and a second processor, wherein the first processor is used to run a first system, and the second processor is used to run a second system, and the method includes: acquiring sensor data based on the first system; Identify the sensor data to obtain an identification result corresponding to the sensor data; Determine whether to enter a collaborative working mode between the first system and the second system according to the recognition result.

2. The method according to claim 1, characterized in that The identifying the sensor data to obtain an identification result corresponding to the sensor data includes: Calling a recognition model that matches the sensor data to perform recognition processing on the sensor data to obtain a recognition result set; and / or, The determining whether to enter the collaborative working mode between the first system and the second system according to the recognition result includes: If any of the recognition results corresponding to the sensor data meets a preset condition, the collaborative working mode is entered.

3. The method according to claim 1 or 2, characterized in that: The sensor data includes audio data collected by an audio sensor and / or motion gesture data collected by a motion sensor.

4. The method according to claim 3, characterized in that The step of identifying the sensor data to obtain an identification result corresponding to the sensor data; and determining whether to enter a collaborative working mode between the first system and the second system according to the identification result, includes: The audio data is recognized and processed by a speech recognition model to obtain a speech recognition result corresponding to the audio data, and the speech recognition result is matched with a corresponding preset condition to determine whether to enter the collaborative working mode.

5. The method according to claim 3, characterized in that: The step of identifying the sensor data to obtain an identification result corresponding to the sensor data; and determining whether to enter a collaborative working mode between the first system and the second system according to the identification result, includes: Calculating acceleration information and posture angle information according to the motion posture data; Using the acceleration information and the posture angle information as inputs of an action recognition model to obtain an action recognition result; The action recognition result is matched with the corresponding preset conditions to determine whether to enter the collaborative working mode.

6. The method according to claim 3, characterized in that The recognition result includes a motion recognition result corresponding to the motion posture data and / or a speech recognition result corresponding to the audio data; If any of the recognition results corresponding to the sensor data meets a preset condition, it includes: If the action type corresponding to the action recognition result matches the preset target action type, determining that the action recognition result meets the preset condition; and / or, If the keyword carried in the speech recognition result matches the preset target keyword, it is determined that the speech recognition result meets the preset condition.

7. The method according to claim 1 or 2, characterized in that: When entering the collaborative working mode, the first system exchanges data with the second system; and / or, After entering the collaborative working mode, the first system and the second system work simultaneously; and / or, After entering the collaborative working mode, the first system or the second system is controlled to enter a sleep mode according to the requirements of the current business processing capability.

8. The method according to claim 1 or 2, characterized in that: The wearable device includes one or more of a motion sensor, a biosensor, and an environmental sensor; the sensor data includes data collected by one sensor or data collected by multiple sensors.

9. A system control device, characterized in that: Applied to a wearable device, the wearable device includes a first processor and a second processor, wherein the first processor is used to run a first system, and the second processor is used to run a second system, including: An acquisition module, used for acquiring sensor data based on the first system; An identification module, used to identify the sensor data and obtain an identification result corresponding to the sensor data; A collaborative working module is used to determine whether to enter a collaborative working mode between the first system and the second system according to the recognition result.

10. A wearable device, characterized in that: It includes a memory and a first processor and a second processor, wherein the first processor is used to run a first system, and the second processor is used to run a second system. A computer program is stored in the memory, and when the computer program is executed by the first processor, the first processor executes the steps of the system control method as described in any one of claims 1 to 8.

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