WO2023061413A1 - Sensor data processing method and apparatus, wearable electronic device, and storage medium - Google Patents

Abstract

The present application relates to the technical field of electronic devices, and discloses a sensor data processing method and apparatus, a wearable electronic device, and a storage medium. The method comprises: obtaining a first data table, wherein the first data table comprises multiple pieces of first data recorded by a sensor, each piece of first data comprises: a behavior event identifier of one sensor, a measurement value corresponding to the behavior event identifier, and a timestamp corresponding to the measurement value, and any two pieces of first data comprise different behavior event identifiers; and under the condition that multiple pieces of second data having the same timestamp exist in the multiple pieces of first data, merging the multiple pieces of second data in the first data table into one piece of data, and obtaining a second data table, wherein the merging process comprises: reserving behavior event identifiers in the multiple pieces of second data and measurement values corresponding to the behavior event identifiers, and reserving a timestamp.

Description

Sensor data processing method, device, wearable electronic device and storage medium

Cross References to Related Applications

This application claims the priority of the Chinese patent application with the application number 202111205494.3 and the title "sensor data processing method, device, wearable electronic device and storage medium" submitted to the China Patent Office on October 15, 2021, the entire content of which is passed References are incorporated in this application.

technical field

The application belongs to the technical field of electronic equipment, and in particular relates to a sensor data processing method, device, wearable electronic equipment and storage medium.

Background technique

With the development of mobile Internet technology and the upgrading of the hardware configuration of electronic devices, the functions of wearable electronic devices such as smart watches and bracelets are becoming more and more perfect. For example, various sensors such as heart rate sensors, acceleration sensors, Ambient temperature sensors and ECG sensors enable wearable electronic devices to measure human body signals such as heart rate and blood oxygen and count sports data.

In the prior art, in order to realize various functions of the wearable electronic device, it is necessary to store and upload the raw data generated by each sensor in the wearable electronic device, which will take up a lot of storage space, and when uploading user equipment (such as a smart phone) , the transfer takes a long time.

Contents of the invention

The purpose of the embodiments of the present application is to provide a sensor data processing method, device, wearable electronic device, and storage medium, which can solve the problems in the prior art that sensor data takes up a lot of storage space and takes a long time to transmit.

In the first aspect, the embodiment of the present application provides a sensor data processing method, the method comprising:

Obtain a first data table, wherein the first data table includes multiple pieces of first data recorded by the sensor, and each piece of first data includes: a behavior event identifier of a sensor, a measurement value corresponding to the behavior event identifier, and a measurement value Corresponding timestamps, the behavior event identifiers contained in any two first data are different;

In the case where there are multiple pieces of second data with the same timestamp in the multiple pieces of first data, combining the multiple pieces of second data in the first data table into one piece of data to obtain a second data table; Wherein, the merging process includes: retaining the behavior event identifiers in the multiple pieces of second data and the measurement values corresponding to the behavior event identifiers, and retaining a time stamp.

In the second aspect, the embodiment of the present application provides a sensor data processing device, the device includes:

An acquisition module, configured to acquire a first data table, wherein the first data table includes a plurality of pieces of first data recorded by the sensor, and each piece of first data includes: a behavior event identifier of a sensor, a behavior event identifier corresponding to The measured value and the timestamp corresponding to the measured value, the behavior event identifiers contained in any two first data are different;

A merging module, configured to combine the multiple pieces of second data in the first data table into one piece of data when there are multiple pieces of second data with the same timestamp in the multiple pieces of first data, to obtain The second data table; wherein, the merging process includes: retaining the behavior event identifiers in the multiple pieces of second data and the measurement values corresponding to the behavior event identifiers, and retaining a time stamp.

In a third aspect, an embodiment of the present application provides an electronic device, the electronic device includes a processor, a memory, and a program or instruction stored in the memory and operable on the processor, and the program or instruction is The processor implements the steps of the method described in the first aspect when executed.

In a fourth aspect, an embodiment of the present application provides a readable storage medium, on which a program or an instruction is stored, and when the program or instruction is executed by a processor, the steps of the method described in the first aspect are implemented .

In the fifth aspect, the embodiment of the present application provides a chip, the chip includes a processor and a communication interface, the communication interface is coupled to the processor, and the processor is used to run programs or instructions, so as to implement the first aspect The steps of the method.

In this embodiment of the application, for the raw data generated by each sensor in the wearable electronic device, the raw data that occurred at the same time point can be combined, and only one timestamp is retained, while the redundant timestamp is removed, so that the sensor data can be guaranteed Under the premise that the integrity can be restored, the space occupation of sensor data is reduced, and the transmission time is shortened.

Description of drawings

FIG. 1 is a flow chart of a sensor data processing method provided by an embodiment of the present application;

Fig. 2 is a flow chart of another sensor data processing method provided by the embodiment of the present application;

Fig. 3 is a flowchart of another sensor data processing method provided by the embodiment of the present application;

Fig. 4 is a flowchart of another sensor data processing method provided by the embodiment of the present application;

Fig. 5 is a structural block diagram of a sensor data processing device provided by an embodiment of the present application;

FIG. 6 is a schematic structural diagram of an electronic device provided by an embodiment of the present application;

FIG. 7 is a schematic diagram of a hardware structure of an electronic device implementing various embodiments of the present application.

specific embodiment

The following will clearly describe the technical solutions in the embodiments of the present application with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, but not all of them. All other embodiments obtained by persons of ordinary skill in the art based on the embodiments in this application belong to the protection scope of this application.

The terms "first", "second" and the like in the specification and claims of the present application are used to distinguish similar objects, and are not used to describe a specific sequence or sequence. It should be understood that the terms so used are interchangeable under appropriate circumstances such that the embodiments of the application can be practiced in sequences other than those illustrated or described herein, and that references to "first," "second," etc. distinguish Objects are generally of one type, and the number of objects is not limited. For example, there may be one or more first objects. In addition, "and/or" in the specification and claims means at least one of the connected objects, and the character "/" generally means that the related objects are an "or" relationship.

Take smart watches as an example. In today's smart watches, a variety of sensors are commonly used to measure human body signals (such as heart rate, blood oxygen), and statistical motion data (such as step counting), from raw sensor data to algorithm output, and then The interface shows that the entire path is relatively long, and a large amount of sensor data (also called "sensorlog") needs to be recorded to ensure problem analysis and feedback.

At present, smart watches mainly rely on Bluetooth to upload sensorlog to user devices (such as mobile phones). If the sensorlog is too large, it will take up more storage space for smart watches. On the other hand, the current sensorlog system of smart watches can record data for 24 hours The amount is about 300MB. If the Bluetooth transmission speed is calculated as 150KB/S, the entire transmission time is approximately 300*1024/150=2048 seconds=34 minutes. The entire transmission process takes a long time and affects the user experience.

In order to solve the above technical problems, embodiments of the present application provide a sensor data processing method, device, wearable electronic device, and storage medium.

The sensor data processing method provided by the embodiment of the present application will be described in detail below through specific embodiments and application scenarios with reference to the accompanying drawings.

For ease of understanding, some concepts involved in the embodiments of the present application are firstly introduced.

A PPG sensor, also known as an "optical heart rate sensor", is a sensor for heart rate detection that uses electro-optic plethysmography (PPG) to measure heart rate and other biometric indicators;

ACC sensor, also known as "acceleration sensor", is a sensor capable of measuring acceleration;

AMB sensor, also known as "ambient light sensor", is a sensor that can sense the surrounding light conditions;

ECG sensor, also known as "cardiac sensor", is a sensor that can sense the action potential waveform of cells in different regions of the heart and convert it into a usable output signal.

Next, the sensor data processing method provided by the embodiment of the present application will be introduced.

It should be noted that the sensor data processing method provided by the embodiment of the present application is suitable for wearable electronic devices. In practical applications, the wearable electronic devices may include: smart watches, smart bracelets, etc., which are not discussed in the embodiments of the present application. limited.

Fig. 1 is a flowchart of a sensor data processing method provided by an embodiment of the present application. As shown in Fig. 1, the method may include the following steps: step 101 and step 102, wherein,

In step 101, the first data table is obtained, wherein the first data table includes multiple pieces of first data recorded by the sensor, and each piece of first data includes: a behavior event identifier of a sensor, and a measurement value corresponding to the behavior event identifier The time stamps corresponding to the measured values, the behavior event identifiers contained in any two pieces of first data are different.

In the prior art, the raw data generated by each sensor in the wearable electronic device is stored in the form of a first data table, and each piece of first data in the first data table is a piece of raw data.

sensor data type data value timestamp Behavioral event flags for sensors Measurements timestamp

Table 1

Table 1 shows the storage format of the data in the first data table. It can be seen from Table 1 that each piece of raw data generated by the sensor is recorded as a row, and each row contains a sensor's behavior event identifier, behavior event The measured value and the timestamp used to represent the occurrence time of the behavioral event belong to three columns respectively. Among them, the 32-bit length is used when recording the behavioral event identifier, the 32-bit length is used when recording the measured value, and the 64-bit length is used when recording the timestamp. The unit is ms.

For ease of understanding, take the scene where a wearable electronic device measures the user's blood oxygen and heart rate as an example, and describe the first data table as an example. When measuring the user's blood oxygen and heart rate, various indicator lights (including green light, red light and Infrared lamp) light-emitting time point, each indicator light emits light according to the set time point. At the same time, the relevant sensors in the wearable electronic device will execute the action of collecting the light value reflected by the skin of the user's arm, as well as the action of collecting the light value of the user's current environment. After that, the collected light value, the collection time Points and identifications of collection behavior events are stored in the first data table.

As shown in Table 2 below, Table 2 is the first data table in the scenario of measuring the user's blood oxygen and heart rate.

Table 2

Starting from the third row in Table 2, each row is used to store a piece of raw data from the sensor. For example, the third row from left to right is: behavior event identifier "PPG-G", measured value "PPG green light value" and Timestamp (specific value not shown), wherein, PPG-G indicates that the PPG sensor performs the action of collecting the green light value reflected by the arm skin, the PPG green light value indicates the green light value collected by the PPG sensor, and the time stamp indicates the green light value collected by the PPG sensor. point in time. In the same way, we can know the meaning of the sensor data in the other rows below.

In step 102, in the case where there are multiple pieces of second data with the same time stamp in the multiple pieces of first data, the multiple pieces of second data in the first data table are combined into one piece of data to obtain a second data table; wherein , the merging process includes: retaining the behavior event identifiers in the multiple pieces of second data and the measurement values corresponding to the behavior event identifiers, and retaining a time stamp.

Through the analysis of the above table 2, it is found that: green light value/green light ambient light, infrared value/infrared ambient light, red light value/red light ambient light, these data may occur at the same time point, so the timestamp is the same , that is, the time stamp has room for optimization. In view of the above situation, in the embodiment of the present application, multiple pieces of data with the same time stamp can be combined into one piece of data, and recorded in one line in the data table, so as to reduce redundant lines in the data table.

In an example, still take Table 2 as an example, according to the characteristics of the PPG sensor, when the indicator light is on, the back of the PD watch will receive the ambient light and the reflected light value of the arm skin at the same time, so the 1PPG-G and 3AMB in Table 2 can be combined Merge the data of the row where -G is located, merge the data of the row where 4PPG-IR and 7AMB-IR are located, and merge the data of the row where 5PPG-R and 6AMB-R are located, and optimize 3AMB-G, 6AMB-R and 7 The time stamp of the row where the AMB-IR is located saves space, and the second data table shown in Table 3 below is obtained, wherein the strikethrough in the table is used to indicate that the data of the row is deleted.

table 3

As shown in Table 3, when merging the data of the rows where 1PPG-G and 3AMB-G are located, the behavior event identifiers "PPG-G" and "AMB-G" are reserved (in a row with a length of 32 bits to represent PPG-G&AMB- G), keep the measured values "PPG green light value" and "green light ambient light", but only keep one timestamp, so that the data that needs to be stored in two lines before can be stored in only one line.

Similarly, when merging the data of the rows where 4PPG-IR and 7AMB-IR are located, the behavior event identifiers "PPG-IR" and "AMB-IR" are reserved (indicates PPG-IR&AMB-IR in a row with a length of 32 bits), The measured values "PPG infrared value" and "infrared ambient light" are kept, but only one timestamp is kept, so that the data that used to be stored in two lines can now be stored in only one line.

When merging the data in the row where 5PPG-R and 6AMB-R are located, keep the behavior event identifiers "PPG-R" and "AMB-R" (indicate PPG-R&AMB-R in a row with 32bit length), and keep the measured value "PPG red light value" and "red light ambient light", but only one timestamp is reserved, so that the data that needs to be stored in two lines before can be stored in only one line.

In the embodiment of the present application, by merging the redundant rows in Table 2 through the above-mentioned method of Table 3, 8*3+4*3=36 bytes can be saved, where 8 is the byte length of the timestamp, and 4 is the behavior The length in bytes of the event ID.

In another example, by analyzing data usage scenarios, it is found that when measuring blood oxygen, infrared/red light coexist; when measuring heart rate, green light/infrared coexist; therefore red light/green light/infrared can occur at the same time , sharing the same time stamp, table 3 can be further optimized into table 4 below. Specifically, the data in the rows of 1PPG-G&AMB-G, 4PPG-IR&AMB-IR and 5PPG-R&AMB-R in Table 3 are merged, and the behavior event identifiers "PPG-G", "AMB-G", "PPG- R", "AMB-R", "PPG-IR" and "AMB-IR" (specifically, express PPG-G&AMB-G&PPG-R&AMB-R&PPG-IR&AMB-IR in a line with a length of 32 bits), keep the measured value" PPG Green Light Value", "Green Light Ambient Light", "PPG Red Light Value", "Red Light Ambient Light", "PPG Infrared Value" and "Infrared Ambient Light", only one timestamp is reserved, so it needs to use three lines to The stored data can now be stored in only one line. In addition, a data type identification field can be added under the measurement value directory to identify different types of measurement values in a row, and the length of the identification field can be 8 bits.

Table 4

In the embodiment of the present application, by combining the redundant rows in Table 2 through the method of Table 4 above, 8*5+4*5=60 bytes can be saved, where 8 is the byte length of the timestamp, and 4 is the behavior The length in bytes of the event ID.

It can be seen from the above embodiment that in this embodiment, for the raw data generated by each sensor in the wearable electronic device, the raw data generated at the same time point can be combined, only one timestamp is retained, and the redundant timestamp is removed, so that On the premise of ensuring the integrity and recovery of sensor data, reduce the space occupation of sensor data and shorten the transmission time.

Considering that the timestamp of each row in the data table is an absolute timestamp, in ms, it is 64bit long, but in wearable electronic devices, generally speaking, the sensor is based on a fixed frequency (25/50/100Hz) For sampling, the time difference between the two sets of data is about 40ms, so using an absolute timestamp of 64bit length will waste a lot of space, and there is a problem of information redundancy. In this case, a differential mechanism can be used to correct the time in the data table Click to optimize. Correspondingly, FIG. 2 is a flow chart of another sensor data processing method provided by the embodiment of the present application. As shown in FIG. 2 , the method may include the following steps: step 201, step 202 and step 203, wherein,

In step 201, the first data table is obtained, wherein the first data table includes multiple pieces of first data recorded by the sensor, and each piece of first data includes: a behavior event identifier of a sensor, and a measurement value corresponding to the behavior event identifier The time stamps corresponding to the measured values, the behavior event identifiers contained in any two pieces of first data are different.

In step 202, in the case that there are multiple pieces of second data with the same time stamp in the multiple pieces of first data, the multiple pieces of second data in the first data table are combined into one piece of data to obtain a second data table; wherein , the merging process includes: retaining the behavior event identifiers in the multiple pieces of second data and the measurement values corresponding to the behavior event identifiers, and retaining a time stamp.

Step 201 and step 202 in the embodiment of the present application are similar to steps 101 and 102 in the embodiment shown in FIG. 1 , and will not be repeated here.

In step 203, for each pair of adjacent two data in the second data table, calculate the difference between the time stamps in the two data, and replace the time stamp in the next piece of data in the two data with the time Poke the difference to get the third data table.

In the embodiment of this application, the 64-bit timestamp of each row in the data table can be optimized into a differential time mechanism. The default timestamp is differential time, and a reference time type is added, as shown in Table 5 below.

sensor data type data value timestamp 3 64bit complete time T1 - 2ACC 32bitX, 32bitY, 32bitZ timestamp (differential)

table 5

Among them, the differential time can be expressed in the following form: Byte0+Byte1～Byte 7, Byte0 is used to indicate the number of bytes used by the differential time, and Byte1～Byte7 is used to indicate the specific byte in 1～7 occupied by the differential time. In an example, the mechanism shown in Table 5 may be used to process Table 4 to obtain the third data table shown in Table 6.

Table 6

Among them, the differential time of each row is the time of the new data minus the time of the same type of data in the previous row. The absolute time of each row is equal to Tn=T1+T_diff_1+T_diff_2+...T_diff_n-1.

It can be seen that in the embodiment of the present application, the differential mechanism is used to optimize the time stamp in the sensor data, which can further reduce the space occupation of the sensor data and shorten the transmission time on the premise of ensuring the integrity and recovery of the sensor data.

Considering that the data sheets mainly contain data generated by PPG sensors, ECG sensors, AMB sensors, and ACC sensors, the measured values in these data are complete measurement data, and these sensors do not collect data at each sampling time point. There are drastic changes. In view of this situation, the difference mechanism can be used to optimize the measured values in the data table, specifically, the relative difference of the measured value data before and after the sensor data is sampled is saved in the data table to save storage space. Correspondingly, FIG. 3 is a flow chart of another sensor data processing method provided by the embodiment of the present application. As shown in FIG. 3 , the method may include the following steps: step 301, step 302, step 303 and step 304, wherein,

In step 301, the first data table is obtained, wherein the first data table includes multiple pieces of first data recorded by the sensor, and each piece of first data includes: a behavior event identifier of a sensor, and a measurement value corresponding to the behavior event identifier The time stamps corresponding to the measured values, the behavior event identifiers contained in any two pieces of first data are different.

In step 302, in the case that there are multiple pieces of second data with the same time stamp in the multiple pieces of first data, the multiple pieces of second data in the first data table are combined into one piece of data to obtain a second data table; wherein , the merging process includes: retaining the behavior event identifiers in the multiple pieces of second data and the measurement values corresponding to the behavior event identifiers, and retaining a time stamp.

In step 303, for each pair of adjacent two data in the second data table, calculate the difference of the time stamps in the two data, and replace the time stamp in the next piece of data in the two data with the time Poke the difference to get the third data table.

Steps 301 to 303 in the embodiment of the present application are similar to steps 201 to 203 in the embodiment shown in FIG. 2 , and will not be repeated here.

In step 304, for each piece of data corresponding to the target sensor in the sensor in the third data table, the difference between the measured values collected at two adjacent sampling time points is calculated, and the sequenced value of the two sampling time points is The measurement value in a piece of data is replaced by the difference value of the measurement value to obtain the fourth data table.

In the embodiment of the present application, the target sensor may include: a heart rate sensor, an acceleration sensor, an ambient light sensor, and an electrocardiogram sensor.

In an example, the fourth data table shown in Table 7 can be obtained by performing measurement value optimization on the third data table shown in Table 6.

Table 7

It can be seen that in the embodiment of the present application, the differential mechanism is used to optimize the measurement value of a specific sensor in the sensor data, which can further reduce the space occupation of the sensor data and shorten the transmission time under the premise of ensuring the integrity and recovery of the sensor data .

Considering that the sensor in the wearable electronic device collects data according to a fixed sampling rate, caches it in the first-in-first-out (FIFO, First In First Out) memory, and then reads it centrally by the micro control unit (MCU, Micro Controller Unit), if according to The storage format in the first data table requires many tags and timestamps. In view of this situation, a batch FIFO mechanism can be adopted to store sensor data in combination. Correspondingly, FIG. 4 is a flowchart of another sensor data processing method provided by the embodiment of the present application. As shown in FIG. 4 , the method may include the following steps: step 401, step 402, step 403, step 404 and step 405 ,in,

In step 401, the first data table is obtained, wherein the first data table includes multiple pieces of first data recorded by the sensor, and each piece of first data includes: a behavior event identifier of a sensor, and a measurement value corresponding to the behavior event identifier The time stamps corresponding to the measured values, the behavior event identifiers contained in any two pieces of first data are different.

In step 402, in the case that there are multiple pieces of second data with the same time stamp in the multiple pieces of first data, the multiple pieces of second data in the first data table are combined into one piece of data to obtain a second data table; wherein , the merging process includes: retaining the behavior event identifiers in the multiple pieces of second data and the measurement values corresponding to the behavior event identifiers, and retaining a time stamp.

In step 403, for each pair of adjacent two data in the second data table, calculate the difference of the time stamps in the two data, and replace the time stamp in the next piece of data in the two data with the time Poke the difference to get the third data table.

In step 404, for each piece of data corresponding to the target sensor in the sensor in the third data table, calculate the difference between the measured values collected at two adjacent sampling time points, and sort the difference between the two sampling time points The measurement value in a piece of data is replaced by the difference value of the measurement value to obtain the fourth data table.

Steps 401 to 404 in the embodiment of the present application are similar to steps 301 to 304 in the embodiment shown in FIG. 3 , and will not be repeated here.

In step 405, for each piece of data corresponding to the target sensor in the fourth data table, a batch type label is added to each piece of data to obtain the fifth data table, and each piece of data in the fifth data table and the corresponding batch type label , stored in batches in first-in first-out memory.

In an example, the fifth data table shown in Table 8 can be obtained by adding tags to the fourth data table shown in Table 7.

Table 8

In the embodiment of this application, since the time interval of each sampling time point of the sensor can be known, the number of time stamps used can be reduced through batch storage, and the sensor data can be further reduced under the premise of ensuring that the integrity of the sensor data is recoverable. The amount of space occupied by the data, and shorten the transmission time.

Use the method in the embodiment shown in Fig. 1 to Fig. 4 to optimize the original sensor data, and can reduce the space occupied by the disk (compressed from 300MB to 100MB, compressing rate of 33%), reduce transmission time (from 30 minutes to 10 minutes, shortened by 300%), improve user experience, reduce hardware memory requirements, and save costs.

It should be noted that, the sensor data processing method provided in the embodiment of the present application may be executed by a sensor data processing device, or a control module in the sensor data processing device for executing the loading sensor data processing method. In the embodiment of the present application, the sensor data processing device provided in the embodiment of the present application is described by taking the sensor data processing device executing the loading sensor data processing method as an example.

Fig. 5 is a structural block diagram of a sensor data processing device provided by an embodiment of the present application. As shown in Fig. 5, the sensor data processing device 500 may include: an acquisition module 501 and a combination module 502, wherein,

The acquisition module 501 is configured to acquire a first data table, wherein the first data table includes a plurality of pieces of first data recorded by the sensor, and each piece of first data includes: a behavior event identifier of a sensor, a corresponding behavior event identifier The measured value and the timestamp corresponding to the measured value, the behavior event identifiers contained in any two first data are different;

a merging module 502, configured to merge the multiple pieces of second data in the first data table into one piece of data when there are multiple pieces of second data with the same timestamp in the multiple pieces of first data, A second data table is obtained; wherein, the merging process includes: retaining the behavior event identifiers in the multiple pieces of second data and the measurement values corresponding to the behavior event identifiers, and retaining a time stamp.

It can be seen from the above embodiment that in this embodiment, for the raw data generated by each sensor in the wearable electronic device, the raw data generated at the same time point can be combined, only one timestamp is retained, and the redundant timestamp is removed, so that On the premise of ensuring the integrity and recovery of sensor data, reduce the space occupation of sensor data and shorten the transmission time.

Optionally, as an embodiment, the sensor data processing device 500 may further include:

The first calculation module is used to calculate the difference between the time stamps in the two pieces of data for each pair of adjacent two pieces of data in the second data table;

The first replacement module is configured to replace the time stamp in the last piece of data among the two pieces of data with the difference between the time stamps to obtain a third data table.

Optionally, as an embodiment, the sensor data processing device 500 may further include:

The second calculation module is used to calculate the difference between the measured values collected at two adjacent sampling time points for each piece of data corresponding to the target sensor in the sensor in the third data table;

The second replacement module is configured to replace the measurement value in the last piece of data at the two sampling time points with the difference between the measurement values to obtain a fourth data table.

Optionally, as an embodiment, the sensor data processing device 500 may further include:

Adding a module, for each piece of data corresponding to the target sensor in the fourth data table, adding a batch type label to each piece of data to obtain a fifth data table;

The storage module is configured to store each piece of data in the fifth data table and the corresponding batch type label in batches into the FIFO memory.

The sensor data processing device in the embodiment of the present application may be a device, or a component, an integrated circuit, or a chip in a terminal. The device may be a mobile electronic device or a non-mobile electronic device. Exemplarily, the mobile electronic device may be a mobile phone, a tablet computer, a notebook computer, a handheld computer, a vehicle electronic device, a wearable device, an ultra-mobile personal computer (ultra-mobile personal computer, UMPC), a netbook or a personal digital assistant (personal digital assistant). assistant, PDA), etc., non-mobile electronic devices can be servers, network attached storage (Network Attached Storage, NAS), personal computer (personal computer, PC), television (television, TV), teller machine or self-service machine, etc., this application Examples are not specifically limited.

The sensor data processing device in the embodiment of the present application may be a device with an operating system. The operating system may be an Android operating system, an iOS operating system, or other possible operating systems, which are not specifically limited in this embodiment of the present application.

The sensor data processing device provided in the embodiment of the present application can implement various processes implemented in the method embodiment in FIG. 1 , and details are not repeated here to avoid repetition.

Optionally, as shown in FIG. 6 , the embodiment of the present application further provides an electronic device 600, including a processor 601, a memory 602, and programs or instructions stored in the memory 602 and operable on the processor 601, When the program or instruction is executed by the processor 601, each process of the above-mentioned embodiment of the sensor data processing method can be achieved, and the same technical effect can be achieved. To avoid repetition, details are not repeated here.

It should be noted that the electronic devices in the embodiments of the present application include the above-mentioned mobile electronic devices and non-mobile electronic devices.

FIG. 7 is a schematic diagram of a hardware structure of an electronic device implementing an embodiment of the present application.

The electronic device 700 includes, but is not limited to: a radio frequency unit 701, a network module 702, an audio output unit 703, an input unit 704, a sensor 705, a display unit 706, a user input unit 707, an interface unit 708, a memory 709, and a processor 710, etc. part.

Those skilled in the art can understand that the electronic device 700 can also include a power supply (such as a battery) for supplying power to various components, and the power supply can be logically connected to the processor 710 through the power management system, so that the management of charging, discharging, and function can be realized through the power management system. Consumption management and other functions. The structure of the electronic device shown in FIG. 7 does not constitute a limitation to the electronic device. The electronic device may include more or fewer components than shown in the figure, or combine some components, or arrange different components, and details will not be repeated here. .

The processor 710 is configured to acquire a first data table, wherein the first data table includes multiple pieces of first data recorded by the sensor, and each piece of first data includes: a behavior event identifier of a sensor, a behavior event identifier corresponding to The measured value and the timestamp corresponding to the measured value, the behavior event identifier contained in any two pieces of first data are different; in the case that there are multiple pieces of second data with the same time stamp in the multiple pieces of first data, the The multiple pieces of second data in the first data table are merged into one piece of data to obtain a second data table; wherein the merging process includes: retaining the behavior event identifiers in the multiple pieces of second data and the corresponding behavior event identifiers measured value, and retain a timestamp.

It can be seen that in the embodiment of the present application, for the raw data generated by each sensor in the wearable electronic device, the raw data generated at the same time point can be combined, and only one timestamp is retained, and the redundant timestamp is removed, so that the sensor Under the premise that the data integrity can be restored, the space occupation of sensor data is reduced, and the transmission time is shortened.

Optionally, as an embodiment, the processor 710 is further configured to, for each pair of adjacent two pieces of data in the second data table, calculate the difference between the time stamps in the two pieces of data; The time stamp in the last piece of data among the pieces of data is replaced by the difference of the time stamps to obtain the third data table.

Optionally, as an embodiment, the processor 710 is further configured to, for each piece of data corresponding to the target sensor in the sensor in the third data table, calculate the sum of the measured values collected at two adjacent sampling time points difference: replacing the measured value in the last piece of data at the two sampling time points with the difference between the measured values to obtain a fourth data table.

Optionally, as an embodiment, the processor 710 is further configured to, for each piece of data corresponding to the target sensor in the fourth data table, add a batch type label to each piece of data to obtain a fifth data table; Each piece of data in the fifth data table and the corresponding batch type label are stored in batches in the FIFO memory.

It should be understood that, in the embodiment of the present application, the input unit 704 may include a graphics processor (Graphics Processing Unit, GPU) 7041 and a microphone 7042, and the graphics processor 7041 is used for the image capture device ( Such as the image data of the still picture or video obtained by the camera) for processing. The display unit 706 may include a display panel 7061, and the display panel 7061 may be configured in the form of a liquid crystal display, an organic light emitting diode, or the like. The user input unit 707 includes a touch panel 7071 and other input devices 7072 . The touch panel 7071 is also called a touch screen. The touch panel 7071 may include two parts, a touch detection device and a touch controller. Other input devices 7072 may include, but are not limited to, physical keyboards, function keys (such as volume control keys, switch keys, etc.), trackballs, mice, and joysticks, which will not be repeated here. Memory 709 may be used to store software programs as well as various data, including but not limited to application programs and operating systems. The processor 710 may integrate an application processor and a modem processor, wherein the application processor mainly processes an operating system, user interface, application program, etc., and the modem processor mainly processes wireless communication. It can be understood that the foregoing modem processor may not be integrated into the processor 710 .

The embodiment of the present application also provides a readable storage medium, on which a program or instruction is stored, and when the program or instruction is executed by the processor, each process of the above embodiment of the sensor data processing method is realized, and can achieve The same technical effects are not repeated here to avoid repetition.

Wherein, the processor is the processor in the electronic device described in the above embodiments. The readable storage medium includes computer readable storage medium, such as computer read-only memory (Read-Only Memory, ROM), random access memory (Random Access Memory, RAM), magnetic disk or optical disk, etc.

The embodiment of the present application further provides a chip, the chip includes a processor and a communication interface, the communication interface is coupled to the processor, and the processor is used to run programs or instructions to implement the above embodiment of the sensor data processing method Each process, and can achieve the same technical effect, in order to avoid repetition, will not repeat them here.

It should be understood that the chips mentioned in the embodiments of the present application may also be called system-on-chip, system-on-chip, system-on-a-chip, or system-on-a-chip.

It should be noted that, in this document, the term "comprising", "comprising" or any other variation thereof is intended to cover a non-exclusive inclusion such that a process, method, article or apparatus comprising a set of elements includes not only those elements, It also includes other elements not expressly listed, or elements inherent in the process, method, article, or device. Without further limitations, an element defined by the phrase "comprising a ..." does not preclude the presence of additional identical elements in the process, method, article, or apparatus comprising that element. In addition, it should be pointed out that the scope of the methods and devices in the embodiments of the present application is not limited to performing functions in the order shown or discussed, and may also include performing functions in a substantially simultaneous manner or in reverse order according to the functions involved. Functions are performed, for example, the described methods may be performed in an order different from that described, and various steps may also be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.

Through the description of the above embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus a necessary general-purpose hardware platform, and of course also by hardware, but in many cases the former is better implementation. Based on such an understanding, the technical solution of the present application can be embodied in the form of computer software products, which are stored in a storage medium (such as ROM/RAM, magnetic disk, etc.) , optical disc), including several instructions to enable a terminal (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in various embodiments of the present application.

The embodiments of the present application have been described above in conjunction with the accompanying drawings, but the present application is not limited to the above-mentioned specific implementations. The above-mentioned specific implementations are only illustrative and not restrictive. Those of ordinary skill in the art will Under the inspiration of this application, without departing from the purpose of this application and the scope of protection of the claims, many forms can also be made, all of which belong to the protection of this application.

Claims (13)

A sensor data processing method, the method comprising: Obtain a first data table, wherein the first data table includes multiple pieces of first data recorded by the sensor, and each piece of first data includes: a behavior event identifier of a sensor, a measurement value corresponding to the behavior event identifier, and a measurement value Corresponding timestamps, the behavior event identifiers contained in any two first data are different; In the case where there are multiple pieces of second data with the same timestamp in the multiple pieces of first data, combining the multiple pieces of second data in the first data table into one piece of data to obtain a second data table; Wherein, the merging process includes: retaining the behavior event identifiers in the multiple pieces of second data and the measurement values corresponding to the behavior event identifiers, and retaining a time stamp. The method according to claim 1, wherein, after the step of merging the multiple pieces of second data in the first data table into one piece of data to obtain a second data table, further comprising: For each pair of adjacent two pieces of data in the second data table, calculate the difference between the time stamps in the two pieces of data; The timestamp in the last piece of data among the two pieces of data is replaced with the difference between the timestamps to obtain a third data table. The method according to claim 2, wherein, after the step of replacing the timestamp in the last piece of data in the two pieces of data with the difference between the timestamps to obtain the third data table, Also includes: For each piece of data corresponding to the target sensor in the sensor in the third data table, calculate the difference between the measured values collected at two adjacent sampling time points; The measurement value in the last piece of data at the two sampling time points is replaced by the difference of the measurement values to obtain a fourth data table. The method according to claim 3, wherein, in the step of replacing the measured value in the last piece of data in the two sampling time points with the difference between the measured values to obtain the fourth data table After that, also include: For each piece of data corresponding to the target sensor in the fourth data table, add a batch type label to each piece of data to obtain a fifth data table; Store each piece of data in the fifth data table and the corresponding batch type label in batches into the FIFO memory. A sensor data processing device, said device comprising: An acquisition module, configured to acquire a first data table, wherein the first data table includes a plurality of pieces of first data recorded by the sensor, and each piece of first data includes: a behavior event identifier of a sensor, a behavior event identifier corresponding to The measured value and the timestamp corresponding to the measured value, the behavior event identifiers contained in any two first data are different; A merging module, configured to combine the multiple pieces of second data in the first data table into one piece of data when there are multiple pieces of second data with the same timestamp in the multiple pieces of first data, to obtain The second data table; wherein, the merging process includes: retaining the behavior event identifier and the measurement value corresponding to the behavior event identifier in the multiple pieces of second data, and retaining a time stamp. The device according to claim 5, wherein the device further comprises: The first calculation module is used to calculate the difference between the time stamps in the two pieces of data for each pair of adjacent two pieces of data in the second data table; The first replacement module is configured to replace the time stamp in the last piece of data among the two pieces of data with the difference between the time stamps to obtain a third data table. The device according to claim 6, wherein the device further comprises: The second calculation module is used to calculate the difference between the measured values collected at two adjacent sampling time points for each piece of data corresponding to the target sensor in the sensor in the third data table; The second replacement module is configured to replace the measurement value in the last piece of data at the two sampling time points with the difference between the measurement values to obtain a fourth data table. The device according to claim 7, wherein the device further comprises: Adding a module, for each piece of data corresponding to the target sensor in the fourth data table, adding a batch type label to each piece of data to obtain a fifth data table; The storage module is configured to store each piece of data in the fifth data table and the corresponding batch type label in batches into the FIFO memory. An electronic device, wherein the electronic device includes a processor, a memory, and a program or instruction stored on the memory and operable on the processor, when the program or instruction is executed by the processor, the following The step of the method described in any one of claims 1 to 4. A readable storage medium, wherein a program or an instruction is stored on the readable storage medium, and when the program or instruction is executed by a processor, the steps of the method according to any one of claims 1 to 4 are realized. A chip, wherein the chip includes a processor and a communication interface, the communication interface is coupled to the processor, and the processor is used to run a program or an instruction, to achieve any one of claims 1 to 4 Methods. A computer program product, wherein the program product is stored in a non-volatile storage medium, and the program product is executed by at least one processor to implement the method according to any one of claims 1 to 4 step. An electronic device, wherein the electronic device is configured to implement the steps of the method according to any one of claims 1 to 4.

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