Data processing method, device, equipment and medium for wearable device

Abstract

The present application discloses a data processing method, device, equipment and medium for a wearable device, which obtains data to be processed, performs data screening on the data to be processed based on a preset step interval to obtain new data to be processed; reduces the preset step interval to obtain a new preset step interval, and performs the steps of performing data screening on the data to be processed based on the preset step interval to obtain new data to be processed, until the reduced new preset step interval is less than or equal to the preset step threshold, and determines the data obtained by the data screening as target data, so as to determine whether there is an event in the data to be processed based on the target data. The present application can determine the part of the data to be processed with the greatest possibility of having an event as the target data on the basis of reducing the data processing time and reducing the power consumption of the device, and effectively improve the efficiency of the wearable device in searching for events based on the data obtained by the sensor.

Description

Technical Field

[0001] The present application relates to the technical field of wearable devices, and in particular to a data processing method, apparatus, device and medium for a wearable device. Background Art

[0002] With the development and progress of science and technology, consumers have higher and higher requirements for electronic consumer products, hoping that the product interface will be more intuitive, more convenient and more intelligent. Especially in the field of acoustic headphones, the form of headphones has been subversively changed from wired to smarter and more portable TWS (True Wireless Stereo) products such as TWS headphones. This includes the credit of many intelligent algorithms, such as algorithms that intelligently identify whether the user is wearing, double-clicking or single-clicking operations.

[0003] The intelligent recognition solution uses sensors such as GSENSOR (gravity sensor) and cap sensor (capacitive sensor) to provide data to MCU (Microcontroller Unit) in real time at a certain rate. Regardless of whether it uses vibration characteristics or physical characteristics such as dielectric constant, when the user has an action event, the sensitive physical characteristics of these sensor devices will change, and the data obtained by the sensor will also change suddenly. Further, through the algorithm, the characteristics of the mutation data are extracted, and then the algorithm is used to output what action this mutation is, so as to realize intelligent judgment of the user's action. Traditional algorithms all process all the sensor data, monitor the data one by one, and determine whether there is a sensitive change in each data to search for events. This will cause the device to have high power consumption and long data processing time, resulting in low efficiency of wearable devices such as wireless headphones when searching for events based on data obtained by sensors. Summary of the invention

[0004] The main purpose of the present application is to provide a data processing method, apparatus, device and medium for a wearable device, aiming to solve the technical problem of low efficiency of wearable devices when searching for events based on data acquired by sensors.

[0005] To achieve the above-mentioned purpose, an embodiment of the present application provides a data processing method for a wearable device, and the data processing method for a wearable device includes:

[0006] Acquire data to be processed, and perform data screening on the data to be processed based on a preset step interval to obtain new data to be processed;

[0007] The preset step interval is reduced to obtain a new preset step interval, and the data to be processed is screened based on the preset step interval to obtain new data to be processed, until the new preset step interval obtained by reduction is less than or equal to the preset step threshold, and the data obtained by data screening is determined as target data, so as to determine whether there is an event in the data to be processed based on the target data.

[0008] Preferably, the step of screening the data to be processed based on a preset step interval to obtain new data to be processed includes:

[0009] Segmenting the data to be processed according to the starting point of the data to be processed and the preset step interval to obtain a plurality of data segmentation points;

[0010] Respectively obtain the data values ​​corresponding to each of the data segmentation points;

[0011] The data to be processed is screened according to each of the data values ​​to obtain new data to be processed.

[0012] Preferably, the step of screening the data to be processed according to each of the data values ​​to obtain new data to be processed includes:

[0013] Compare the data values, and determine the data value with the largest value among the data values ​​as the target data value;

[0014] The data to be processed is screened according to the target data value to obtain new data to be processed.

[0015] Preferably, the step of screening the data to be processed according to the target data value to obtain new data to be processed includes:

[0016] Determine the data segmentation point corresponding to the target data value as the target data segmentation point;

[0017] The data to be processed is screened based on the target data segmentation point to obtain new data to be processed.

[0018] Preferably, the step of screening the data to be processed based on the target data segmentation point to obtain new data to be processed includes:

[0019] Determine a data segmentation point before and after the target data segmentation point;

[0020] The data between the previous data segmentation point and the next data segmentation point are selected from the data to be processed as new data to be processed.

[0021] Preferably, the step of performing data segmentation on the data to be processed according to the starting point of the data to be processed and the preset step interval to obtain a plurality of data segmentation points comprises:

[0022] Taking the starting point of the data to be processed as the starting point and taking the preset step interval as the data interval, the data to be processed is divided into multiple station data, the starting point of the data to be processed is determined as the first data division point, and the starting point of each station data is respectively determined as the second data division point, and multiple data division points are formed by the first data division point and each of the second data division points.

[0023] Preferably, after the step of reducing the preset step interval to obtain a new preset step interval, the method further comprises:

[0024] The new preset step length interval obtained by reduction is compared with the preset step length threshold to determine the size relationship between the new preset step length interval and the preset step length threshold.

[0025] To achieve the above-mentioned purpose, the present application further provides a data processing device for a wearable device, the data processing device for the wearable device comprising:

[0026] An acquisition module is used to acquire data to be processed, and to screen the data to be processed based on a preset step interval to obtain new data to be processed;

[0027] A screening module is used to reduce the preset step interval to obtain a new preset step interval, and perform data screening on the data to be processed based on the preset step interval to obtain new data to be processed, until the new preset step interval obtained by reduction is less than or equal to a preset step threshold, and the data obtained by data screening is determined as target data, so as to determine whether there is an event in the data to be processed based on the target data.

[0028] Furthermore, to achieve the above-mentioned purpose, the present application also provides a data processing device for a wearable device, wherein the data processing device for the wearable device comprises a memory, a processor, and a data processing program for the wearable device stored in the memory and executable on the processor, and when the data processing program for the wearable device is executed by the processor, the steps of the data processing method for the wearable device are implemented.

[0029] Furthermore, to achieve the above-mentioned purpose, the present application also provides a medium, which is a computer-readable storage medium, and a data processing program for a wearable device is stored on the computer-readable storage medium. When the data processing program for the wearable device is executed by a processor, the steps of the data processing method for the wearable device are implemented.

[0030] Furthermore, to achieve the above-mentioned purpose, the present application also provides a computer program product, including a computer program, which implements the steps of the above-mentioned data processing method for the wearable device when executed by a processor.

[0031] The embodiment of the present application provides a data processing method, device, equipment and medium for a wearable device, which obtains data to be processed, performs data screening on the data to be processed based on a preset step interval to obtain new data to be processed; reduces the preset step interval to obtain a new preset step interval, and performs the steps of data screening on the data to be processed based on the preset step interval to obtain new data to be processed, until the reduced new preset step interval is less than or equal to the preset step threshold, and determines the data obtained by the data screening as target data, so as to determine whether there is an event in the data to be processed based on the target data. The present application performs multiple data screening on the data to be processed by gradually reducing the step interval, and can determine the part of the data to be processed with the greatest possibility of having an event as the target data on the basis of reducing the data processing time and reducing the power consumption of the device, thereby effectively improving the efficiency of the wearable device in searching for events based on the data obtained by the sensor. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 This is a schematic diagram of the structure of the hardware operating environment involved in the embodiment of the data processing method for the wearable device of the present application;

[0033] Figure 2 This is a flow chart of the first embodiment of the data processing method for a wearable device of the present application;

[0034] Figure 3 A schematic diagram of a scenario of the first embodiment of the data processing method for a wearable device of the present application;

[0035] Figure 4 This is a flow chart of the second embodiment of the data processing method for a wearable device of the present application;

[0036] Figure 5 This is a schematic diagram of the functional modules of a preferred embodiment of the data processing device of the wearable device of the present application.

[0037] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0038] It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0039] The embodiment of the present application provides a data processing method for a wearable device, which includes acquiring data to be processed, filtering the data to be processed based on a preset step interval to obtain new data to be processed; reducing the preset step interval to obtain a new preset step interval, and performing the step of filtering the data to be processed based on the preset step interval to obtain new data to be processed, until the reduced new preset step interval is less than or equal to the preset step threshold, and determining the data obtained by the data filtering as target data, so as to determine whether there is an event in the data to be processed based on the target data. The present application performs multiple data filtering on the data to be processed by gradually reducing the step interval, and can determine the part of the data to be processed with the greatest possibility of having an event as the target data on the basis of reducing the data processing time and reducing the power consumption of the device, thereby effectively improving the efficiency of the wearable device in searching for events based on the data obtained by the sensor.

[0040] like Figure 1 As shown, Figure 1 It is a schematic diagram of the structure of a data processing device of a wearable device in the hardware operating environment involved in the embodiment of the present application.

[0041] In the subsequent description, the suffixes such as "module", "component" or "unit" used to represent elements are only used to facilitate the description of the present application, and have no specific meanings. Therefore, "module", "component" or "unit" can be used in a mixed manner.

[0042] The data processing device of the wearable device in the embodiment of the present application can be a PC, or a portable terminal device such as a tablet computer or a portable computer.

[0043] like Figure 1 As shown, the data processing device of the wearable device may include: a processor 1001, such as a CPU, a network interface 1004, a user interface 1003, a memory 1005, and a communication bus 1002. Among them, the communication bus 1002 is used to realize the connection and communication between these components. The user interface 1003 may include a display screen (Display), an input unit such as a keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface and a wireless interface. The network interface 1004 may optionally include a standard wired interface and a wireless interface (such as a WI-FI interface). The memory 1005 may be a high-speed RAM memory, or a stable memory (non-volatile memory), such as a disk memory. The memory 1005 may also be a storage device independent of the aforementioned processor 1001.

[0044] Those skilled in the art will understand that Figure 1The data processing device structure of the wearable device shown in the figure does not constitute a limitation on the data processing device of the wearable device, and may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0045] like Figure 1 As shown, the memory 1005 as a storage medium may include an operating system, a network communication module, a user interface module, and a data processing program for the wearable device.

[0046] exist Figure 1 In the device shown, the network interface 1004 is mainly used to connect to the background server and communicate data with the background server; the user interface 1003 is mainly used to connect to the client (user end) and communicate data with the client; and the processor 1001 can be used to call the data processing program of the wearable device stored in the memory 1005 and perform the following operations:

[0047] Acquire data to be processed, and perform data screening on the data to be processed based on a preset step interval to obtain new data to be processed;

[0048] The preset step interval is reduced to obtain a new preset step interval, and the data to be processed is screened based on the preset step interval to obtain new data to be processed, until the new preset step interval obtained by reduction is less than or equal to the preset step threshold, and the data obtained by data screening is determined as target data, so as to determine whether there is an event in the data to be processed based on the target data.

[0049] Furthermore, the step of screening the data to be processed based on a preset step interval to obtain new data to be processed includes:

[0050] Segmenting the data to be processed according to the starting point of the data to be processed and the preset step interval to obtain a plurality of data segmentation points;

[0051] Respectively obtain the data values ​​corresponding to each of the data segmentation points;

[0052] The data to be processed is screened according to each of the data values ​​to obtain new data to be processed.

[0053] Furthermore, the step of screening the data to be processed according to each of the data values ​​to obtain new data to be processed includes:

[0054] Compare the data values, and determine the data value with the largest value among the data values ​​as the target data value;

[0055] The data to be processed is screened according to the target data value to obtain new data to be processed.

[0056] Furthermore, the step of screening the data to be processed according to the target data value to obtain new data to be processed includes:

[0057] Determine the data segmentation point corresponding to the target data value as the target data segmentation point;

[0058] The data to be processed is screened based on the target data segmentation point to obtain new data to be processed.

[0059] Furthermore, the step of screening the data to be processed based on the target data segmentation point to obtain new data to be processed includes:

[0060] Determine a data segmentation point before and after the target data segmentation point;

[0061] The data between the previous data segmentation point and the next data segmentation point are selected from the data to be processed as new data to be processed.

[0062] Furthermore, the step of segmenting the data to be processed according to the starting point of the data to be processed and the preset step interval to obtain a plurality of data segmentation points includes:

[0063] Taking the starting point of the data to be processed as the starting point and taking the preset step interval as the data interval, the data to be processed is divided into multiple station data, the starting point of the data to be processed is determined as the first data division point, and the starting point of each station data is respectively determined as the second data division point, and multiple data division points are formed by the first data division point and each of the second data division points.

[0064] Further, after the step of reducing the preset step interval to obtain a new preset step interval, the processor 1001 may be used to call a data processing program of the wearable device stored in the memory 1005, and perform the following operations:

[0065] The new preset step length interval obtained by reduction is compared with the preset step length threshold to determine the size relationship between the new preset step length interval and the preset step length threshold.

[0066] In order to better understand the above technical solution, exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0067] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0068] Reference Figure 2 , Figure 2 A flowchart of a data processing method for a wearable device provided in the first embodiment of the present application. In this embodiment, the data processing method for the wearable device includes the following steps:

[0069] Step S10, obtaining data to be processed, and screening the data to be processed based on a preset step interval to obtain new data to be processed;

[0070] In this embodiment, the data processing method of the wearable device is applied to the data processing system of the wearable device, wherein the data processing system of the wearable device can be deployed in TWS products such as TWS headphones, smart watches, smart bracelets and other wearable devices. In this embodiment, the data processing system of the wearable device is deployed in TWS headphones as an example. Among them, the wearable device (specifically TWS headphones in this embodiment) has sensors such as gravity sensors and / or capacitance sensors, which can obtain the gravity and / or capacitance data generated by the wearable device when the user performs events such as wearing, double-clicking or single-clicking operations, so as to search whether there is actually an event based on the acquired gravity and / or capacitance data, and respond to the event in time when an event occurs. However, traditional algorithms all process all the data of the sensor, monitor the data one by one, and determine whether each data has sensitive changes to search whether there is an event. This will cause the device to have high power consumption and long data processing time, resulting in low efficiency of wearable devices such as wireless headphones when searching for events based on data obtained by sensors. Based on the above problems, the present application provides a data processing method for a wearable device. By gradually reducing the step interval to perform multiple data screening on the data to be processed, the part of the data to be processed with the highest possibility of containing events can be determined as the target data on the basis of reducing the data processing time and reducing the power consumption of the device, thereby effectively improving the efficiency of the wearable device in searching for events based on the data obtained by the sensor.

[0071] Specifically, the data to be processed is obtained through the sensor preset in the TWS headset. It can be understood that if the sensor preset in the TWS headset is a gravity sensor, the data to be processed obtained is gravity data; and if the sensor preset in the TWS headset is a capacitive sensor, the data to be processed obtained is capacitive data. It should be noted that the sensor acquires data in chronological order, so there is a correlation between the order of data in the data to be processed and time. After the data to be processed is obtained, the data to be processed is searched for the first time (i.e., the first data screening is performed), which can be specifically: the data to be processed is segmented according to the starting point of the data to be processed and the preset step interval, and multiple data segmentation points are obtained, wherein the preset step interval is the value of the time or data interval number set according to actual needs, and should be less than half of the event duration, and the event duration can be a time value summarized based on actual processing experience. Furthermore, the data values ​​corresponding to each data segmentation point are obtained respectively, the data value with the largest value is determined from each data value, and the data between the previous data segmentation point and the next data segmentation point of the data segmentation point corresponding to the data value with the largest value is filtered out from the data to be processed as new data to be processed, so as to complete the first event search. Through a jumping search method, data corresponding to a range narrowed to a certain extent is determined from the overall data to be processed as new data to be processed, which can reduce the amount of data processing, reduce the data processing time, and reduce the power consumption of the wearable device, thereby effectively improving the efficiency of the wearable device in event search based on data obtained by the sensor.

[0072] Step S20, reducing the preset step interval to obtain a new preset step interval, and performing data screening on the data to be processed based on the preset step interval to obtain new data to be processed, until the new preset step interval obtained by reduction is less than or equal to the preset step threshold, and determining the data obtained by data screening as target data, so as to determine whether there is an event in the data to be processed based on the target data.

[0073] After completing the first search for the data to be processed and screening the data to be processed to obtain new data to be processed, in order to ensure the accuracy of data screening, in the subsequent one or more data screening processes, in this embodiment, the preset step interval used in the first data screening needs to be reduced. The specific reduced step interval value can be set according to actual needs. For example, it can be set to an arithmetic decreasing, geometric decreasing, or other reduction method. For example, the initial preset step interval can be 5, and the preset step interval is reduced to 4 when the second data screening is performed. The preset step size is further reduced to 3 when the third data screening is performed, and so on. Further, after reducing the initial preset step interval to obtain a new preset step interval, the step of performing data screening on the data to be processed based on the preset step interval to obtain new data to be processed is performed, specifically, the step of performing data screening on the new data to be processed based on the new preset step interval to obtain updated data to be processed, and in each intermediate process of reducing the preset step interval and performing data screening, the current latest preset step interval is compared with the preset step interval threshold, and when the current latest preset step interval is less than or equal to the preset step threshold, the last data screening is performed, and the data obtained by this screening is determined as the target data, so as to output the target data to an algorithm for accurately determining whether there is an event in the data, and the algorithm uses the target data as input for further information confirmation; or in certain circumstances, the target data is directly determined as data in which an event occurs. By performing multiple data screening on the data to be processed in a manner of gradually reducing the step interval, the part of the data to be processed with the greatest possibility of having an event can be determined as the target data on the basis of reducing the data processing time and reducing the power consumption of the device, effectively improving the efficiency of the wearable device in searching for events based on the data obtained by the sensor.

[0074] It should be noted that after the step of reducing the preset step interval to obtain a new preset step interval, the step further includes:

[0075] Step A: Compare the new preset step interval obtained by reduction with the preset step threshold to determine the size relationship between the new preset step interval and the preset step threshold.

[0076] After reducing the preset step interval to obtain a new preset step interval, it is also necessary to compare the new preset step interval with the preset step interval threshold to determine the size relationship between the new preset step interval and the preset step interval threshold, specifically, determine whether the new preset step interval obtained by reduction is greater than the preset step threshold, or less than or equal to the preset step threshold, so as to continue to perform data screening more than once (i.e., greater than or equal to twice) when the new preset step interval obtained by reduction is greater than the preset step threshold, and when the new preset step interval obtained by reduction is less than or equal to the preset step threshold, perform the last data screening, and determine the data obtained by the data screening as the target data, so as to accurately determine whether there is an event in the data to be processed according to the target data. By comparing the new preset step interval obtained by reduction with the preset step threshold, it can be accurately determined whether data screening needs to be performed again or multiple data screenings need to be performed, and on the basis of reducing data processing time and reducing device power consumption, the part of the data to be processed with the greatest possibility of having an event can be determined as the target data, effectively improving the efficiency of the wearable device in searching for events based on the data obtained by the sensor.

[0077] In a specific embodiment of the application, refer to Figure 3 , Figure 3 This is a scene diagram of the first embodiment of the data processing method for the wearable device of the present application. A group of data to be processed is obtained from the sensor, a starting point S1 is determined (i.e., the initial data segmentation point, which can be set according to actual needs or set by default as the starting point of a group of data), a preset step interval d is determined (the initial value of the preset step interval d should be less than half of the event duration), a first search is performed in the direction of time (i.e., the first data screening), the data value E1 (i.e., the gravity value or the capacitance value) at the S1 position is recorded, and then the next station S1+d (i.e., the next data segmentation point) is jumped to, the data value E2 of the data segmentation point is recorded, E1 and E2 are compared, the maximum value is retained, and the next station (i.e., the next data segmentation point, such as S1+2d, S1+3d, S1+4d, S1+5d, etc.) is continuously jumped to by analogy until all the data in the data to be processed are scanned and a maximum value is obtained after completion, for example Figure 3 The first search is S1+4d. Then the second search (i.e., the second data screening) is started. The starting point of the second search is determined by the maximum point position of the previous search. The starting point is the previous station of the maximum value, and the end point is the next station of the maximum value (specifically, it can include data segmentation points such as S2+d2, S2+2d2, S2+3d2, S2+4d2, etc.). For example: Figure 3In the second search, the starting point S2 is the previous station S1+3d of the maximum point S1+4d of the first search, and the end point of the second search is the next station S1+5d of the maximum point S1+4d of the first search. The starting point of the second search is S2 (S1+3d), the step size d is reduced to d2, and the second search is performed again. The end point is S2+4d2, and its maximum value position is recorded as S2+2d2. The search is repeated in this way (i.e., data screening is repeated) until the preset step size interval is reduced to less than or equal to the preset step size threshold Lth, the search is ended and the maximum value position of the last search is returned, such as Figure 3 The position of S3+d3 in the third search is the final result, i.e., the event endpoint (the third search includes data segmentation points such as S3, S3+d3, S3+2d3, S3+3d3, and d3 is the preset step interval obtained by further reducing d2). After obtaining the event endpoint, the position of S3+d3 is taken as the center, and the data before and after (the actual maximum duration of the event, which can be set according to actual needs or obtained based on experience) is intercepted as the target data and output to the algorithm.

[0078] The present embodiment provides a data processing method, apparatus, device and medium for a wearable device, which obtains data to be processed, performs data screening on the data to be processed based on a preset step interval to obtain new data to be processed; reduces the preset step interval to obtain a new preset step interval, and performs the steps of data screening on the data to be processed based on the preset step interval to obtain new data to be processed, until the reduced new preset step interval is less than or equal to the preset step threshold, and determines the data screened as target data to determine whether there is an event in the data to be processed based on the target data. The present application performs multiple data screening on the data to be processed by gradually reducing the step interval, and can determine the part of the data to be processed with the greatest possibility of having an event as the target data on the basis of reducing the data processing time and reducing the power consumption of the device, thereby effectively improving the efficiency of the wearable device in searching for events based on the data obtained by the sensor.

[0079] This embodiment abandons the traditional method of processing data one by one, uses the smallest unit to sample data, records the data changes between the two sampling points before and after, uses a special mechanism to determine the center position of the event, and then extracts the surrounding data and inputs it into the algorithm to further extract information. Minimizing data processing greatly optimizes computing power and power consumption. Being able to quickly screen a batch of data instead of monitoring each data is of great help to embedded systems that are very sensitive to resources and power consumption. Specifically, by specifying half of the minimum amount of data that generates an event as a unit scale, sampling data at intervals of the unit, using a fast algorithm to determine the time position of the event center, and then extracting the data before and after the middle position as the input of the algorithm, the data processing requirements can be effectively reduced, achieving a win-win situation in terms of resources and power consumption.

[0080] Further, refer to Figure 4 Based on the first embodiment of the data processing method for the wearable device of the present application, a second embodiment of the data processing method for the wearable device of the present application is proposed. In the second embodiment, the step of filtering the data to be processed based on the preset step interval to obtain new data to be processed includes:

[0081] Step S11, segmenting the data to be processed according to the starting point of the data to be processed and the preset step interval to obtain a plurality of data segmentation points;

[0082] Step S12, respectively obtaining data values ​​corresponding to each of the data segmentation points;

[0083] Step S13, screening the data to be processed according to each of the data values ​​to obtain new data to be processed.

[0084] After obtaining the data to be processed, the starting point of the data to be processed is used as the first data segmentation point, and the data to be processed is segmented with the preset step interval as the data interval, and the data segmentation point corresponding to each segmentation position during data segmentation is determined to obtain multiple data segmentation points. Further, after obtaining multiple data segmentation points, the data values ​​of the data at the corresponding positions of the data to be processed at each data segmentation point are respectively obtained. It should be noted that if the data to be processed is gravity data, the gravity value is obtained; if the data to be processed is capacitance data, the capacitance value is obtained. After obtaining the data values ​​corresponding to each data segmentation point, each data value is compared, and the data value with the largest value among each data value is determined as the target data value; the data segmentation point corresponding to the target data value is determined as the target data segmentation point; the previous data segmentation point and the next data segmentation point of the target data segmentation point are determined; and the data between the previous data segmentation point and the next data segmentation point are selected from the data to be processed as new data to be processed. By using a jumping search method, a data range is first determined from the large-scale data (i.e., the data to be processed) (i.e., new data to be processed is filtered out), avoiding filtering all the data to be processed one by one through traditional algorithms, reducing computing power and power consumption, and reducing data processing time, effectively improving the efficiency of wearable devices in event search based on data obtained by sensors.

[0085] Furthermore, the step of segmenting the data to be processed according to the starting point of the data to be processed and the preset step interval to obtain a plurality of data segmentation points includes:

[0086] Step S111, taking the starting point of the data to be processed as the starting point and taking the preset step interval as the data interval to divide the data to be processed into multiple station data, determining the starting point of the data to be processed as the first data division point, and respectively determining the starting point of each station data as the second data division point, and forming multiple data division points by the first data division point and each of the second data division points.

[0087] After obtaining the data to be processed, the starting point of the data to be processed is taken as the starting point, wherein the starting point can be the first data point of the data to be processed set by default, or can be a data point set according to actual needs, and the data to be processed is divided into multiple station data with a preset step interval as the data interval, and the starting point of the data to be processed is determined as the first data segmentation point, and the starting point of each station data is determined as the second data segmentation point, that is, starting from the starting point of the data to be processed, the data to be processed is divided once every preset step, thereby dividing the data to be processed into multiple station data according to the preset step interval. And further, the starting point of each station data is determined as the second data segmentation point of the station data, and multiple data segmentation points are formed by a first data segmentation point and one or more second data segmentation points. By using a jumping search method, a data range is first determined from the large-scale data (i.e., the data to be processed) (i.e., new data to be processed is filtered out), which can avoid filtering all the data to be processed one by one through the traditional algorithm, reduce computing power and power consumption, and reduce data processing time, effectively improving the efficiency of wearable devices when searching for events based on data obtained by sensors.

[0088] Furthermore, the step of screening the data to be processed according to each of the data values ​​to obtain new data to be processed includes:

[0089] Step S131, comparing the data values, and determining the data value with the largest value among the data values ​​as the target data value;

[0090] Step S132, screening the data to be processed according to the target data value to obtain new data to be processed.

[0091] After obtaining the data values ​​corresponding to each data segmentation point, the data values ​​are compared to determine the data size relationship between the data values, determine the data value with the largest value among the data values, and determine the data value with the largest value among the data values ​​as the target data value. Figure 3As shown, in this embodiment, the first search can also be performed first, the data value E1 at the position S1 is recorded, and then the next station S1+d is jumped to, the data value E2 of the data segmentation point is recorded, E1 and E2 are compared, the maximum value is retained, and the next station is jumped to the next station (the next station includes S1+2d, S1+3d, S1+4d, S1+5d) in sequence, until all the data in the data to be processed (all data segmentation points) are scanned, and a maximum value is obtained after completion, which is the target data value. Further, the data segmentation point corresponding to the target data value is determined as the target data segmentation point; the previous data segmentation point and the next data segmentation point of the target data segmentation point are determined; and the data between the previous data segmentation point and the next data segmentation point are selected from the data to be processed as new data to be processed. By utilizing a jumping search method to first determine a data range (i.e., filter out new data to be processed) from large-scale data (i.e., data to be processed), it is possible to avoid filtering all data to be processed one by one through traditional algorithms, thereby reducing computing power and power consumption, and reducing data processing time, thereby effectively improving the efficiency of wearable devices in searching for events based on data obtained from sensors.

[0092] Furthermore, the step of screening the data to be processed according to the target data value to obtain new data to be processed includes:

[0093] Step S1321, determining the data segmentation point corresponding to the target data value as the target data segmentation point;

[0094] Step S1322: Screen the data to be processed based on the target data segmentation point to obtain new data to be processed.

[0095] After comparing each data value and determining the data value with the largest value among each data value as the target data value, since the target data value is the data value with the largest value among each data value, the data representing the data value and its vicinity are the data with the greatest possibility of the existence of an event in the data to be processed, that is, the time center of gravity position, so the data segmentation point corresponding to the target data value is found, and the data segmentation point is determined as the target data segmentation point. Further, the previous data segmentation point and the next data segmentation point of the target data segmentation point are determined; the data between the previous data segmentation point and the next data segmentation point are filtered from the data to be processed as new data to be processed. By using a jumping search method, a data range is first determined from the large-scale data (i.e., the data to be processed) (i.e., the new data to be processed is filtered out), which can avoid filtering all the data to be processed one by one through traditional algorithms, reduce computing power and power consumption, and reduce data processing time, effectively improving the efficiency of wearable devices when searching for events based on data obtained by sensors.

[0096] Furthermore, the step of screening the data to be processed based on the target data segmentation point to obtain new data to be processed includes:

[0097] Step S13221, determining a data segmentation point before and after the target data segmentation point;

[0098] Step S13222: Filter the data between the previous data segmentation point and the next data segmentation point from the data to be processed as new data to be processed.

[0099] After the data segmentation point corresponding to the target data value is determined as the target data segmentation point, the previous data segmentation point and the next data segmentation point of the target data segmentation point are determined. Specifically, the target data segmentation point and the preset step interval are subjected to a difference operation to obtain the previous data segmentation point of the target data segmentation point, and the target data segmentation point and the preset step interval are subjected to a sum operation to obtain the next data segmentation point of the target data segmentation point. Furthermore, data between a previous data segmentation point and a subsequent data segmentation point are filtered out from the data to be processed as new data to be processed. Specifically, a data range can be formed by the previous data segmentation point and the subsequent data segmentation point, and data outside the data range in the data to be processed is filtered out. The remaining data is within the data range, and the remaining data is used as new data to be processed, so as to facilitate the subsequent use of a method of reducing the preset step interval to reduce the scale, determine the position of the midpoint of the event, and determine the target data of the part of the data with the greatest possibility of representing the existence of the event in the data to be processed, so as to avoid the algorithm from filtering the data one by one, reduce computing power and power consumption, and reduce data processing time, thereby effectively improving the efficiency of wearable devices in searching for events based on data acquired by sensors.

[0100] This embodiment can segment the data to be processed according to the starting point of the data to be processed and the preset step interval to obtain multiple data segmentation points; respectively obtain the data values ​​corresponding to each data segmentation point; and filter the data to be processed according to each data value to obtain new data to be processed. By using a jumping search method to first determine a data range (i.e., filter out new data to be processed) from large-scale data (i.e., data to be processed), it is possible to avoid filtering all data of the data to be processed one by one through traditional algorithms, reduce computing power and power consumption, and reduce data processing time, effectively improving the efficiency of wearable devices in searching for events based on data obtained by sensors.

[0101] Furthermore, the present application also provides a data processing device for a wearable device.

[0102] Reference Figure 5 , Figure 5This is a schematic diagram of the functional modules of the first embodiment of the data processing device of the wearable device of the present application.

[0103] The data processing device of the wearable device comprises:

[0104] An acquisition module 10 is used to acquire data to be processed, and to filter the data to be processed based on a preset step interval to obtain new data to be processed;

[0105] The screening module 20 is used to reduce the preset step interval to obtain a new preset step interval, and perform data screening on the data to be processed based on the preset step interval to obtain new data to be processed, until the new preset step interval obtained by reduction is less than or equal to a preset step threshold, and the data obtained by the data screening is determined as target data, so as to determine whether there is an event in the data to be processed based on the target data.

[0106] In addition, the present application also provides a medium, which is preferably a computer-readable storage medium, on which a data processing program for a wearable device is stored. When the data processing program for the wearable device is executed by a processor, the steps of each embodiment of the data processing method for the wearable device are implemented.

[0107] In addition, the present application also provides a computer program product, including a computer program, which, when executed by a processor, implements the steps of each embodiment of the data processing method for the wearable device described above.

[0108] In the embodiments of the data processing device, computer-readable storage medium and computer program product of the wearable device of the present application, all the technical features of the various embodiments of the data processing method of the above-mentioned wearable device are included, and the description and explanation content are basically the same as those of the various embodiments of the data processing method of the above-mentioned wearable device, and will not be repeated here.

[0109] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element.

[0110] The serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0111] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus the necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, disk, CD), and includes several instructions to enable a terminal device (which can be a fixed terminal, such as an IoT smart device, including smart air conditioners, smart lights, smart power supplies, smart routers and other smart homes; it can also be a mobile terminal, including smart phones, wearable networked AR / VR devices, smart speakers, self-driving cars and many other networked devices) to execute the methods described in the various embodiments of the present application.

[0112] The above are only preferred embodiments of the present application, and are not intended to limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A data processing method for a wearable device, characterized in that: The data processing method of the wearable device includes: Acquire data to be processed, and perform data screening on the data to be processed based on a preset step interval to obtain new data to be processed; The preset step interval is reduced to obtain a new preset step interval, and the data to be processed is screened based on the preset step interval to obtain new data to be processed, until the new preset step interval obtained by reduction is less than or equal to the preset step threshold, and the data obtained by data screening is determined as target data, so as to determine whether there is an event in the data to be processed based on the target data.

2. The data processing method of the wearable device according to claim 1, characterized in that: The step of filtering the data to be processed based on the preset step interval to obtain new data to be processed comprises: Segmenting the data to be processed according to the starting point of the data to be processed and the preset step interval to obtain a plurality of data segmentation points; Respectively obtain the data values ​​corresponding to each of the data segmentation points; The data to be processed is screened according to each of the data values ​​to obtain new data to be processed.

3. The data processing method of the wearable device according to claim 2, characterized in that: The step of filtering the data to be processed according to each of the data values ​​to obtain new data to be processed comprises: Compare the data values, and determine the data value with the largest value among the data values ​​as the target data value; The data to be processed is screened according to the target data value to obtain new data to be processed.

4. The data processing method of the wearable device according to claim 3, characterized in that: The step of screening the data to be processed according to the target data value to obtain new data to be processed comprises: Determine the data segmentation point corresponding to the target data value as the target data segmentation point; The data to be processed is screened based on the target data segmentation point to obtain new data to be processed.

5. The data processing method of the wearable device according to claim 4, characterized in that: The step of filtering the data to be processed based on the target data segmentation point to obtain new data to be processed includes: Determine a data segmentation point before and after the target data segmentation point; The data between the previous data segmentation point and the next data segmentation point are selected from the data to be processed as new data to be processed.

6. The data processing method for a wearable device according to any one of claims 2 to 5, characterized in that: The step of segmenting the data to be processed according to the starting point of the data to be processed and the preset step interval to obtain a plurality of data segmentation points comprises: Taking the starting point of the data to be processed as the starting point and taking the preset step interval as the data interval, the data to be processed is divided into multiple station data, the starting point of the data to be processed is determined as the first data division point, and the starting point of each station data is respectively determined as the second data division point, and multiple data division points are formed by the first data division point and each of the second data division points.

7. The data processing method for a wearable device according to any one of claims 1 to 5, characterized in that: After the step of reducing the preset step interval to obtain a new preset step interval, the method further includes: The new preset step length interval obtained by reduction is compared with the preset step length threshold to determine the size relationship between the new preset step length interval and the preset step length threshold.

8. A data processing device for a wearable device, characterized in that: The data processing device of the wearable device comprises: An acquisition module is used to acquire data to be processed, and to screen the data to be processed based on a preset step interval to obtain new data to be processed; A screening module is used to reduce the preset step interval to obtain a new preset step interval, and perform data screening on the data to be processed based on the preset step interval to obtain new data to be processed, until the new preset step interval obtained by reduction is less than or equal to a preset step threshold, and the data obtained by data screening is determined as target data, so as to determine whether there is an event in the data to be processed based on the target data.

9. A data processing device for a wearable device, characterized in that: The data processing device of the wearable device includes a memory, a processor, and a data processing program for the wearable device stored in the memory and executable on the processor. When the data processing program for the wearable device is executed by the processor, the steps of the data processing method for the wearable device as described in any one of claims 1 to 7 are implemented.

10. A medium, the medium being a computer-readable storage medium, characterized in that: The computer-readable storage medium stores a data processing program for the wearable device, and when the data processing program for the wearable device is executed by the processor, the steps of the data processing method for the wearable device according to any one of claims 1 to 7 are implemented.

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