CN113127086A - Data processing method and system, wearable device and storage medium - Google Patents
Data processing method and system, wearable device and storage medium Download PDFInfo
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Abstract
The invention provides a data processing method, a system, wearable equipment and a storage medium, wherein the method comprises the following steps: when the main processor is in a dormant state, acquiring current data through a sensor; processing the current data through a sensor hub, and judging the data type of the current data, wherein the data type comprises: original data stream, state class data and notification class data; awakening the main processor according to the data type; and when the main processor is in an awakening state, performing data processing through the main processor. The wearable intelligent device and the method aim at solving the problem that the power consumption of the whole wearable intelligent device is overlarge due to long-term operation of the main processor of the conventional wearable intelligent device, effectively reduce the operation power consumption of the wearable device, and balance the real-time performance and the low power consumption characteristic of the wearable device.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a data processing method, a data processing system, wearable equipment and a storage medium.
Background
With the wide application of smart phones and smart wearable devices, the energy consumption problem of the smart phones and smart wearable devices is also concerned beyond the functions and processing speed of the smart phones and smart wearable devices. The main processor functions of the smart phone and the smart wearable device are very powerful, but the corresponding power consumption is also very large, and in most scenes, the smart phone and the smart wearable device are in a dormant state to save the power consumption. However, some motion-related algorithms require long-term computation, which results in the processor not being able to sleep and thus consuming too much power. At present, the problem that the wearable intelligent device consumes too much power because a main processor of the wearable device cannot sleep due to long-term execution of motion-related algorithms is urgently needed to be solved, and the real-time performance and the low-power consumption characteristic of data are balanced.
Disclosure of Invention
In order to solve the above problems, the present invention provides a data processing method, a data processing system, a wearable device, and a storage medium, which solve the problem of excessive power consumption caused by the fact that a main processor of the wearable smart device cannot sleep after executing an algorithm for a long time, thereby effectively reducing the energy consumption of the wearable smart device.
In order to achieve the above object of the present invention, the present invention is achieved by the following technical solutions:
the invention provides a data processing method, which is characterized by comprising the following steps:
when the main processor is in a dormant state, acquiring current data through a sensor;
processing the current data through a sensor hub, and judging the data type of the current data, wherein the data type comprises: original data stream, state class data and notification class data;
awakening the main processor according to the data type;
and when the main processor is in an awakening state, performing data processing through the main processor.
The invention classifies the data into three types of original data flow, state data and notification data by classifying the data into different data types, and executes corresponding data processing by the main processor or the sensor concentrator according to the characteristics of different data and the working state of the main controller. When the main processor is in a dormant state, the sensor hub processes data which are not required to be processed by the main processor, the running time of the main processor is reduced, the main processor is in a dormant state in more time, the sensor hub with lower power consumption processes the data in the dormant state, the problem of overlarge power consumption of the wearable intelligent device is greatly reduced, various data types are set, all application scenes are included, and the real-time performance of the data and the low power consumption performance of the wearable intelligent device are balanced.
Further, the data processing method further includes:
when the main processor is in a dormant state, if the data type is an original data stream, receiving the original data stream through the sensor hub;
and when the main processor is in an awakening state, if the data type is an original data stream, receiving the original data stream through the main processor.
Further, the data processing method further includes:
when the main processor is in a dormant state, if the data type is state data, the state data is received through the sensor concentrator and is cached to obtain cached data;
and when the main processor is in an awakening state and if the data type is the state class data, receiving the state class data and the cache data through the main processor.
Further, the waking up the main processor according to the data type in the data processing method further includes:
and when the data type is notification type data, waking up the main processor, and switching the working state of the main processor from the dormant state to a wake-up state.
Further, the data processing method further includes:
and when the main processor is in an awakening state, if the data type does not contain notification data within a preset time threshold value, sleeping the main processor.
The present invention also provides a data processing system comprising: a main processor, a sensor hub, a sensor;
when the main processor is in a dormant state, acquiring current data through a sensor, processing the current data through a sensor hub, and judging the data type of the current data, wherein the data type comprises: original data flow, state class data and notification class data, and awakening the main processor according to the data type;
and when the main processor is in an awakening state, performing data processing through the main processor.
A further described data processing system further comprises:
when the main processor is in a dormant state, if the data type is an original data stream, receiving the original data stream through the sensor hub;
when the main processor is in an awakening state, if the data type is an original data stream, receiving the original data stream through the main processor;
when the main processor is in a dormant state, if the data type is state data, the state data is received through the sensor concentrator and is cached to obtain cached data;
and when the main processor is in an awakening state and if the data type is the state class data, receiving the state class data and the cache data through the main processor.
Further, the data processing system further includes:
when the main processor is in a dormant state, if the data type is notification type data, awakening the main processor;
and when the main processor is in an awakening state, if the data type does not contain notification data within a preset time threshold value, sleeping the main processor.
The invention also provides wearable equipment comprising the data processing system.
The invention also provides a storage medium, wherein at least one instruction is stored in the storage medium, and the instruction is used for realizing the operation executed by the data processing method.
The invention provides a data processing method, a data processing system, wearable equipment and a storage medium, which at least have the following gain effects:
1) by introducing the sensor hub, the working state of the main processor is switched according to different data types in the running process of the wearable device, the work of data acquisition and calculation is handed to the sensor hub with low power consumption under the condition that the main processor is dormant, and the main processor is awakened to report the data when conditions are met, so that the running power consumption of the wearable device is reduced.
2) The data types are distinguished through design, different data processing modes are switched according to different data types, various scene requirements are met, and the real-time performance and the low power consumption characteristic of the data can be balanced.
Drawings
The above features, technical features, advantages and implementations of a data processing method, system, wearable device and storage medium will be further described in the following detailed description of preferred embodiments in a clearly understandable manner, with reference to the accompanying drawings.
FIG. 1 is a flow chart of one embodiment of a data processing method of the present invention;
FIG. 2 is a flow chart of another embodiment of a data processing method of the present invention;
FIG. 3 is a flow chart of yet another embodiment of a data processing method of the present invention;
FIG. 4 is a flow chart of yet another embodiment of a data processing method of the present invention;
FIG. 5 is a flow chart of another embodiment of a data processing method of the present invention;
FIG. 6 is a schematic diagram of one embodiment of a data processing system of the present invention;
reference numbers in the figures: 10-sensor, 20-main processor, 30-sensor hub.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the present application. However, it will be apparent to one skilled in the art that the present application may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.
It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
For the sake of simplicity, the drawings only schematically show the parts relevant to the present invention, and they do not represent the actual structure as a product. In addition, in order to make the drawings concise and understandable, components having the same structure or function in some of the drawings are only schematically illustrated or only labeled. In this document, "one" means not only "only one" but also a case of "more than one".
It should be further understood that the term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.
In addition, in the description of the present application, the terms "first", "second", and the like are used only for distinguishing the description, and are not intended to indicate or imply relative importance.
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following description will be made with reference to the accompanying drawings. It is obvious that the drawings in the following description are only some examples of the invention, and that for a person skilled in the art, other drawings and embodiments can be derived from them without inventive effort.
Example 1
One embodiment of the present invention, as shown in fig. 1, is a data processing method, including:
s100, when the main processor is in a dormant state, current data are collected through the sensor.
Specifically, data is mainly collected by various types of sensors, and the data collected by various types of sensors includes: acceleration, angular velocity, GPS positioning, motion amplitude, current step number, magnetic force, ambient light sensation, proximity light sensation, air pressure, humidity, ultraviolet light, PM2.5, biological characteristics and the like.
S200, processing the current data through a Sensor hub (Sensor hub), and judging the data type of the current data, wherein the data type comprises the following steps: raw data stream, state class data, notification class data.
Specifically, the main processor and the sensor concentrator are two processors, wherein a main processor chip has very powerful functions and carries most of algorithms required by the wearable intelligent equipment in the using process; the sensor concentrator is a solution based on a low-power consumption MCU and a light-weight RTOS operating system, and is mainly used for connecting and processing data from various sensor devices.
Illustratively, the processing according to the data collected by the sensor comprises identifying the motion state and outputting a corresponding instruction. For example, the motion state is identified as walking, running, riding or the like according to the data such as the acceleration, the angular velocity and the like, or the hand-raising and screen-lighting instruction is output according to the data such as the motion amplitude, the acceleration and the like, or the position information is judged according to the data such as the current step number, the acceleration and the like.
Specifically, the raw class data stream is a physical quantity directly acquired by a sensor, and includes: velocity, acceleration, angular velocity, current step count, GPS location, environmental parameters, biological signs, and the like; the state class data is data which is obtained by performing algorithm processing on data acquired by a sensor, expresses the current state and does not need to be reported, and comprises the following steps: motion state, biological sign state, environmental state, and the like; the notification data is data to be reported, and includes: switching of motion states, change of biological signs, change of environmental states, abnormal GPS positioning, preset motion trail and the like.
S300 wakes up the main processor according to the data type.
S400, when the main processor is in the wake-up state, the main processor performs data processing.
The wearable intelligent device power consumption problem caused by the fact that the main processor of the wearable device cannot sleep due to long-term algorithm execution can be solved. Through the classification of the data, different data processing methods are executed based on different data types and different motion states of the main processor, and the power consumption of the wearable device is effectively reduced.
Example 2
Another embodiment of the present invention, as shown in fig. 2, is a data processing method, including:
s100, when the main processor is in a dormant state, current data are collected through the sensor.
Specifically, data is mainly collected by various types of sensors, and the data collected by various types of sensors includes: speed, acceleration, angular velocity, current step number, motion amplitude, GPS positioning, ambient light sensation, proximity light sensation, barometric pressure, humidity, ultraviolet light, PM2.5, biological characteristics, and the like.
S210, processing the current data through the sensor hub, judging that the data type of the current data is an original data stream, and receiving the original data stream through the sensor hub.
Specifically, the main processor and the sensor concentrator are two processors, wherein a main processor chip has very powerful functions and carries most of algorithms required by the wearable intelligent equipment in the using process; the sensor concentrator is a solution based on a low-power consumption MCU and a light-weight RTOS operating system, and is mainly used for connecting and processing data from various sensor devices.
Illustratively, the sensor hub processes the current data including identifying a motion state and outputting a corresponding instruction. For example, the motion state is identified as walking, running, riding or the like according to the data such as the acceleration, the angular velocity and the like, or the hand-raising and screen-lighting instruction is output according to the data such as the motion amplitude, the acceleration and the like, or the position information is judged according to the data such as the current step number, the acceleration and the like.
Specifically, the raw class data stream is a physical quantity directly acquired by a sensor, and includes: speed, acceleration, angular velocity, current step number, motion amplitude, GPS location, ambient light perception, proximity light perception, barometric pressure, humidity, ultraviolet light, PM2.5, biological signs, and the like; and when the data type is judged to be the original data stream, the sensor concentrator performs algorithm processing according to the original data stream acquired by the sensor to identify the motion state and output a corresponding instruction. For example, the motion state is identified as walking, running, riding or the like according to the data such as the acceleration, the angular velocity and the like, or the hand-raising and screen-lighting instruction is output according to the data such as the motion amplitude, the acceleration and the like, or the position information is judged according to the data such as the current step number, the acceleration and the like.
S300 wakes up the main processor according to the data type.
Specifically, when the data type is notification type data, the main processor is awakened.
S410, when the main processor is in the awakening state, if the data type is the original data stream, the original data stream is received through the main processor.
Example 3
Another embodiment of the present invention, as shown in fig. 3, is a data processing method, including:
s100, when the main processor is in a dormant state, current data are collected through the sensor.
Specifically, data is mainly collected by various types of sensors, and the data collected by various types of sensors includes: speed, acceleration, angular velocity, current step number, motion amplitude, GPS positioning, ambient light sensation, proximity light sensation, barometric pressure, humidity, ultraviolet light, PM2.5, biological characteristics, and the like.
S220, processing the current data through the sensor concentrator, and receiving the state data through the sensor concentrator and performing caching processing to obtain cached data when the data type of the current data is judged to be the state data.
Specifically, the main processor and the sensor concentrator are two processors, wherein a main processor chip has very powerful functions and carries most of algorithms required by the wearable intelligent equipment in the using process; the sensor concentrator is a solution based on a low-power consumption MCU and a light-weight RTOS operating system, and is mainly used for connecting and processing data from various sensor devices.
Illustratively, the sensor hub processes the current data including identifying a motion state and outputting a corresponding instruction. For example, the motion state is identified as walking, running, riding or the like according to the data such as the acceleration, the angular velocity and the like, or the hand-raising and screen-lighting instruction is output according to the data such as the motion amplitude, the acceleration and the like, or the position information is judged according to the data such as the current step number, the acceleration and the like.
Specifically, the sensor concentrator is provided with a cache sensor, and when acquiring state class data, the cache sensor comprises: when the motion state, the biological sign state, the environment state and the like do not need to immediately awaken the data of the main processor, the data are cached and processed by the cache Sensor in the Sensor hub, and the cached data are uploaded to the main processor after the main processor is awakened.
S300 wakes up the main processor according to the data type.
Specifically, when the data type is notification type data, the main processor is awakened.
S420, when the main processor is in the wake-up state, if the data type is the original data stream, receiving the state class data and the cache data through the main processor.
Example 4
An embodiment of the present invention, as shown in fig. 4, is a data processing method, including:
s100, when the main processor is in a dormant state, current data are collected through the sensor.
Specifically, data is mainly collected by various types of sensors, and the data collected by various types of sensors includes: acceleration, angular velocity, GPS positioning, motion amplitude, current step number, magnetic force, ambient light sensation, proximity light sensation, air pressure, humidity, ultraviolet light, PM2.5, biological characteristics and the like.
S230, processing the current data through the sensor hub, judging the data type of the current data to be notification data, and receiving the notification data through the sensor hub.
Specifically, the main processor and the sensor concentrator are two processors, wherein a main processor chip has very powerful functions and carries most of algorithms required by the wearable intelligent equipment in the using process; the sensor concentrator is a solution based on a low-power consumption MCU and a light-weight RTOS operating system, and is mainly used for connecting and processing data from various sensor devices.
Illustratively, the motion state is identified and corresponding instructions are output according to the data collected by the sensor through algorithm processing. For example, the motion state is identified as walking, running, riding or the like according to the data such as the acceleration, the angular velocity and the like, or the hand-raising and screen-lighting instruction is output according to the data such as the motion amplitude, the acceleration and the like, or the position information is judged according to the data such as the current step number, the acceleration and the like.
Specifically, the notification data includes a preset motion track such as a hand raising bright screen, feature identification such as a face identification bright screen, a pupil identification bright screen and a fingerprint identification bright screen, motion state changes such as a riding state information prompt and a violent motion state information prompt, environmental state changes such as a PM2.5 overhigh information prompt, an air temperature abnormal information prompt and an ultraviolet overhigh information prompt, and physiological sign changes such as a body temperature overhigh information prompt and a pulse overtime information prompt.
S300 wakes up the main processor according to the data type.
S430 receiving, by the main processor, the notification class data when the main processor is in the awake state.
And S500, if the data type does not contain the notification data within a preset time threshold, the main processor is dormant.
For example, the preset time threshold may be 15s, 30s, 60s, 2min, 10min, 30min, or the like, or may be set autonomously by the user. When the main processor is in an awakening state, the original data stream, the state class data and the notification class data are uploaded to the main processor for processing, when the data types received by the main processor within the preset time threshold do not contain the notification class data, the main processor is dormant, the original data stream, the state class data and the notification class data are uploaded to the sensor hub for data processing, and the data processing process refers to the data processing method described in the embodiment during the dormant main processing.
The embodiment can solve the problem that the power consumption of the wearable device is too large when the main processor of the wearable device continuously processes various data, and can effectively reduce the energy consumption of the wearable device. By introducing the sensor hub, the data acquisition and calculation work is handed to the sensor hub with low power consumption under the condition that the main processor is dormant, and the main processor is awakened to report the data when the conditions are met, so that the low-power-consumption data processing method is realized. And the data types are designed and distinguished, various scene requirements are met, and the real-time performance and the low power consumption characteristic of the data can be balanced.
Example 5
An embodiment of the present invention, as shown in fig. 5, is a data processing method, including:
s100, when the main processor is in a dormant state, current data are collected through the sensor.
Specifically, data is mainly collected by various types of sensors, and the data collected by various types of sensors includes: acceleration, angular velocity, GPS positioning, motion amplitude, current step number, magnetic force, ambient light sensation, proximity light sensation, air pressure, humidity, ultraviolet light, PM2.5, biological characteristics and the like.
S200, processing the current data through a Sensor hub (Sensor hub), and judging the data type of the current data, wherein the data type comprises the following steps: raw data stream, state class data, notification class data.
Specifically, the main processor and the sensor concentrator are two processors, wherein a main processor chip has very powerful functions and carries most of algorithms required by the wearable intelligent equipment in the using process; the sensor concentrator is a solution based on a low-power consumption MCU and a light-weight RTOS operating system, and is mainly used for connecting and processing data from various sensor devices.
Illustratively, the processing according to the data collected by the sensor comprises identifying the motion state and outputting a corresponding instruction. For example, the motion state is identified as walking, running, riding or the like according to the data such as the acceleration, the angular velocity and the like, or the hand-raising and screen-lighting instruction is output according to the data such as the motion amplitude, the acceleration and the like, or the position information is judged according to the data such as the current step number, the acceleration and the like.
Specifically, the raw class data stream is a physical quantity directly acquired by a sensor, and includes: velocity, acceleration, angular velocity, current step count, GPS location, environmental parameters, biological signs, and the like; the state class data is data which is obtained by performing algorithm processing on data acquired by a sensor, expresses the current state and does not need to be reported, and comprises the following steps: motion state, biological sign state, environmental state, and the like; the notification data is data to be reported, and includes: switching of motion states, change of biological signs, change of environmental states, abnormal GPS positioning, preset motion trail and the like.
S300 wakes up the main processor according to the data type.
S400, when the main processor is in the wake-up state, the main processor performs data processing.
And S500, if the data type does not contain the notification data within a preset time threshold, the main processor is dormant.
For example, the preset time threshold may be 15s, 30s, 60s, 2min, 10min, 30min, or the like, or may be set autonomously by the user. When the main processor is in an awakening state, the original data stream, the state class data and the notification class data are uploaded to the main processor for processing, and when the data types received by the main processor within the preset time threshold do not contain the notification class data, the main processor is dormant, and the original data stream, the state class data and the notification class data are uploaded to the sensor hub for data processing.
In this embodiment, for the subsequent processing of the data processing method in the foregoing embodiment, when the main processor is in the wake state, the main processor is switched from the wake state to the sleep state. When the main processor is in the wake-up state and the data type received within the preset time threshold does not contain notification data, the main processor is switched to the sleep state, the main processor does not process data in the sleep state, power consumption is reduced, the sensor hub with lower power consumption processes data, and the data processing method is the data processing method described in the embodiment.
Example 6
One embodiment of the present invention, as shown in FIG. 6, is a data processing system comprising:
a sensor 10.
Specifically, data is mainly collected by various types of sensors, and the data collected by various types of sensors includes: acceleration, angular velocity, GPS positioning, motion amplitude, current step number, magnetic force, ambient light sensation, proximity light sensation, air pressure, humidity, ultraviolet light, PM2.5, biological characteristics and the like.
A main processor 20.
Specifically, the main processor chip is very powerful and carries most of the algorithms required by the wearable smart device in the using process.
A sensor hub 30.
In particular, a sensor hub is a solution based on a combination of software and hardware on top of a low power MCU and a lightweight RTOS operating system, whose main function is to connect and process data from various sensor devices.
When the main processor is in a dormant state, acquiring current data through a sensor, processing the current data through a sensor hub, and judging the data type of the current data, wherein the data type comprises: original data stream, state class data, notification class data, and awakening the main processor according to the data type.
Specifically, the raw class data stream is a physical quantity directly acquired by a sensor, and includes: velocity, acceleration, angular velocity, current step count, GPS location, environmental parameters, biological signs, and the like; the state class data is data which is obtained by performing algorithm processing on data acquired by a sensor, expresses the current state and does not need to be reported, and comprises the following steps: motion state, biological sign state, environmental state, and the like; the notification data is data to be reported, and includes: switching of motion states, change of biological signs, change of environmental states, abnormal GPS positioning, preset motion trail and the like.
Illustratively, the processing according to the data collected by the sensor comprises identifying the motion state and outputting a corresponding instruction. For example, the motion state is identified as walking, running, riding or the like according to the data such as the acceleration, the angular velocity and the like, or the hand-raising and screen-lighting instruction is output according to the data such as the motion amplitude, the acceleration and the like, or the position information is judged according to the data such as the current step number, the acceleration and the like.
And when the main processor is in the wake-up state, performing data processing through the main processor.
The wearable intelligent device power consumption problem caused by the fact that the main processor of the wearable device cannot sleep due to long-term algorithm execution can be solved. Through the classification of the data, different data processing methods are executed based on different data types and different motion states of the main processor, and the power consumption of the wearable device is effectively reduced.
In this embodiment, the data processing system further includes:
and when the main processor is in a dormant state, if the data type is the original data stream, receiving the original data stream through the sensor hub.
Illustratively, the sensor hub carries out data processing according to the data collected by the sensor to identify the motion state and output a corresponding instruction. For example, the motion state is identified as walking, running, riding or the like according to the data such as the acceleration, the angular velocity and the like, or the hand-raising and screen-lighting instruction is output according to the data such as the motion amplitude, the acceleration and the like, or the position information is judged according to the data such as the current step number, the acceleration and the like.
And when the main processor is in an awakening state, if the data type is the original data stream, the original data stream is received through the main processor.
In this embodiment, the data processing system further includes:
when the main processor is in a dormant state, if the data type is state data, the state data is received through the sensor concentrator and is cached to obtain cached data.
Illustratively, the sensor hub is provided with a cache sensor, and when acquiring the state class data, the cache sensor includes: when the motion state, the biological sign state, the environment state and the like do not need to immediately wake up the data of the main processor, the data are cached and processed by the cache sensor in the sensor hub, and the cached data are uploaded to the main processor for processing after the main processor is awakened.
When the main processor is in the awakening state, if the data type is the state type data, the state type data and the cache data are received through the main processor.
In this embodiment, the data processing system further includes:
and when the main processor is in a dormant state, if the data type is the notification type data, waking up the main processor.
Illustratively, the notification data includes preset motion tracks such as a hand raising bright screen, feature identification such as a face identification bright screen, a pupil identification bright screen and a fingerprint identification bright screen, motion state changes such as a riding state information prompt and a violent motion state information prompt, environmental state changes such as a PM2.5 overhigh information prompt, an air temperature abnormal information prompt and an ultraviolet overhigh information prompt, and physiological sign changes such as an overhigh information prompt and an overtravel information prompt.
And when the main processor is in an awakening state, if the data type does not contain notification data within a preset time threshold, sleeping the main processor.
For example, the preset time threshold may be 15s, 30s, 60s, 2min, 10min, 30min, or the like, or may be set autonomously by the user. When the main processor is in an awakening state, the original data stream, the state class data and the notification class data are uploaded to the main processor for processing, and when the data types received by the main processor within the preset time threshold do not contain the notification class data, the main processor is dormant, and the original data stream, the state class data and the notification class data are uploaded to the sensor hub for data processing.
The embodiment can solve the problem that the overall power consumption of the wearable device is overlarge when the main processor of the wearable device continuously processes different types of data, and can effectively reduce the energy consumption of the wearable device. By introducing the sensor hub, the data acquisition and calculation work is handed to the sensor hub with low power consumption under the condition that the main processor is dormant, and the main processor is awakened to report the data when the conditions are met, so that the low-power-consumption motion algorithm is realized. And the data types are designed and distinguished, various scene requirements are met, and the real-time performance and the low power consumption characteristic of the data can be balanced.
Example 7
In an embodiment of the present invention, a wearable device includes the data processing system in the above embodiment.
In this embodiment, the wearable device may be a desktop computer, a notebook, a palm computer, a tablet computer, a mobile phone, a human-computer interaction screen, or the like. The wearable device includes a system, not limited to the sensor 10, the main processor 20, and the sensor hub 30. Those skilled in the art will appreciate that the data processing system of fig. 6 is merely an example of a data processing system in a wearable device and does not constitute a limitation of wearable devices, which may include more or fewer components than those described in the data processing system, or some components in combination, or different components, such as: the wearable device may also include memory, input/output interfaces, a display device, a network access device, a communication bus, a communication interface, and the like. A communication interface and a communication bus, and may also include an input/output interface. The wearable device comprises the data processing system in the embodiment, and the data processing method in the corresponding method embodiment can be realized.
The main Processor 20 in the data Processing system included in the wearable device may be a Central Processing Unit (CPU), or may be other general-purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA) or other Programmable logic device, a discrete Gate or transistor logic device, a discrete hardware component, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The wearable device comprises a sensor hub in a data processing system for processing data of each sensor 10, the sensor 10 may be an acceleration sensor, a gyroscope sensor, a magnetometer sensor, an ambient light sensor, a proximity light sensor, a barometer sensor, a hygrometer sensor, an ultraviolet sensor, a PM2.5 sensor. According to different terminal equipment and service scene requirements, the current sensor concentrator framework can be mainly divided into three types, namely an MCU built-in type, an MCU external type and an MCU independent type. The hardware components of the sensor hub mainly comprise a low-power consumption MCU, such as ARM7, ARM9 and cortex M series.
Example 8
An embodiment of the present invention is a storage medium, in which at least one instruction is stored, where the instruction is used to implement the operations performed by the data processing method according to the embodiment. For example, the storage medium may be a read-only memory (ROM), a Random Access Memory (RAM), a compact disc read-only memory (CD-ROM), a magnetic tape, a floppy disk, an optical data storage device, and the like.
They may be implemented in program code that is executable by a computing device such that it is executed by the computing device, or separately, or as individual integrated circuit modules, or as a plurality or steps of individual integrated circuit modules. Thus, the present invention is not limited to any specific combination of hardware and software.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or recited in detail in a certain embodiment.
Those of ordinary skill in the art will appreciate that the various illustrative elements and steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In the embodiments provided in the present application, it should be understood that the disclosed data processing method, system, wearable device and storage medium may be implemented in other ways. For example, the above-described embodiments of a data processing method, system, wearable device, and storage medium are merely illustrative, and for example, the division of the modules is only a logical division, and other divisions may be realized in practice, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be through some interfaces, indirect coupling or communication connection of devices or units or integrated circuits, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
It should be noted that the above embodiments can be freely combined as necessary. The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.
Claims (10)
1. A data processing method, comprising:
when the main processor is in a dormant state, acquiring current data through a sensor;
processing the current data through a sensor hub, and judging the data type of the current data, wherein the data type comprises: original data stream, state class data and notification class data;
awakening the main processor according to the data type;
and when the main processor is in an awakening state, performing data processing through the main processor.
2. A data processing method according to claim 1, further comprising:
when the main processor is in a dormant state, if the data type is an original data stream, receiving the original data stream through the sensor hub;
and when the main processor is in an awakening state, if the data type is an original data stream, receiving the original data stream through the main processor.
3. A data processing method according to claim 1, further comprising:
when the main processor is in a dormant state, if the data type is state data, the state data is received through the sensor concentrator and is cached to obtain cached data;
and when the main processor is in an awakening state and if the data type is the state class data, receiving the state class data and the cache data through the main processor.
4. The data processing method of claim 1, wherein waking up the main processor according to the data type further comprises:
and when the data type is notification type data, waking up the main processor, and switching the working state of the main processor from the dormant state to a wake-up state.
5. A data processing method according to claim 1, further comprising:
and when the main processor is in an awakening state, if the data type does not contain notification data within a preset time threshold value, sleeping the main processor.
6. A data processing system, comprising:
a sensor;
a main processor;
a sensor hub;
when the main processor is in a dormant state, acquiring current data through a sensor, processing the current data through a sensor hub, and judging the data type of the current data, wherein the data type comprises: original data flow, state class data and notification class data, and awakening the main processor according to the data type;
and when the main processor is in an awakening state, performing data processing through the main processor.
7. A data processing system according to claim 6, further comprising:
when the main processor is in a dormant state, if the data type is an original data stream, receiving the original data stream through the sensor hub;
when the main processor is in an awakening state, if the data type is an original data stream, receiving the original data stream through the main processor;
when the main processor is in a dormant state, if the data type is state data, the state data is received through the sensor concentrator and is cached to obtain cached data;
and when the main processor is in an awakening state and if the data type is the state class data, receiving the state class data and the cache data through the main processor.
8. A data processing system according to claim 6, further comprising:
when the main processor is in a dormant state, if the data type is notification type data, awakening the main processor;
and when the main processor is in an awakening state, if the data type does not contain notification data within a preset time threshold value, sleeping the main processor.
9. A wearable device comprising the data processing system of any of claims 6-8.
10. A storage medium having stored therein at least one instruction for carrying out operations performed by a control method according to any one of claims 1 to 5.
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| CN202110534578.5A CN113127086A (en) | 2021-05-17 | 2021-05-17 | Data processing method and system, wearable device and storage medium |
| PCT/CN2021/097182 WO2022241824A1 (en) | 2021-05-17 | 2021-05-31 | Data processing method, system, wearable device, and storage medium |
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| CN103619056A (en) * | 2013-12-02 | 2014-03-05 | 华为终端有限公司 | Method and terminal for reporting sensor data |
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Application publication date: 20210716 |