WO2023061357A1

WO2023061357A1 - Data processing method, related apparatus, and communication system

Info

Publication number: WO2023061357A1
Application number: PCT/CN2022/124595
Authority: WO
Inventors: 刘江明
Original assignee: 华为技术有限公司
Priority date: 2021-10-14
Filing date: 2022-10-11
Publication date: 2023-04-20
Also published as: CN115981756A

Abstract

The present application provides a data processing method, a related apparatus, and a communication system. In the method, a data acquisition device can, according to a rule of a data dictionary, report data to a data display device. The data display device can identify the data according to the rule of the data dictionary, analyze and display the data, and synchronize the data to a cloud server and open the data to a third-party application. The cloud server and the third-party application can both identify the data according to the rule of the data dictionary. The described method can facilitate the reuse of codes for different types of data accessing a data display device, a cloud server, and a third-party application as defined according to rules of a data dictionary, thus reducing the workload of the developers and improving the efficiency of data access.

Description

Data processing method, related device and communication system

This application claims priority to a Chinese patent application with application number 202111198750.0 and application title "Data Processing Method, Related Device, and Communication System" filed with the China Patent Office on October 14, 2021, the entire contents of which are incorporated herein by reference Applying.

technical field

The present application relates to the technical field of terminals, and in particular to a data processing method, a related device and a communication system.

Background technique

With the development of Internet of Things technology, there are more and more types of devices such as smart wearable devices and smart home devices. These devices can collect various data such as the user's sports health data and family status data. The main function of these devices is to collect data, and may not have the ability of data analysis and display, or the ability of data analysis and display is weak. These devices can send the collected data to electronic devices with data analysis and display capabilities, such as mobile phones and tablet computers. However, with the increase in the types of data acquisition devices and data types, the access of new types of data to data display devices requires multiple developers to modify the code logic to ensure that the newly added types of data can be presented in the data display device and do not interfere with Existing data conflicts. This makes the data access efficiency of new types low, and the workload of developers is heavy.

Contents of the invention

This application provides a data processing method, a related device and a communication system. The method defines the data collected by the data collection device according to the rules of the data dictionary, and the codes of different types of data access data display devices, cloud servers and third-party applications can be reused. This can reduce the workload of developers when generating new types of data access data display devices, cloud servers and third-party applications, and improve the efficiency of data access.

In a first aspect, the present application provides a data processing method. In the method, a first device receives first data from a first data collection device. The first data includes a first data identifier and a first sample value of the first type of data, and conforms to the rules of the first configuration file. A first profile is defined according to a first rule. The first configuration file may include a first data identifier, the first data identifier may be used to identify the first type of data, and the first data identifier is unique among the data identifiers defined according to the first rule. The first device may identify that the first data belongs to the first type of data according to the first data identifier and the first configuration file.

The above-mentioned first rule includes the definition and description of one or more of attributes such as data identification, data name, data storage method, and data processing method. That is, the first rule can be used to provide a unified definition method for different types of data. The above data identifiers defined according to the first rule are unique among all the data identifiers defined according to the first rule and do not conflict with each other. Wherein, the above-mentioned first rule may be a rule of a data dictionary.

In a possible implementation manner, the first configuration file may include data type definition rules for the first type of data. The data type definition rule may include the above-mentioned first data identifier. The above-mentioned first data conforms to the rules of the first configuration file, and specifically may be a data type definition rule conforming to the above-mentioned first type of data. The data type definition rules for the above-mentioned first type of data may be solidified in the first data collection device. When the first sampling value of the above-mentioned first type of data is collected, the first data collection device can associate the first data identifier with the first sampling value according to the data type definition rule of the first type of data, obtain the above-mentioned first data, and send the The first device sends first data.

With reference to the first aspect, in some embodiments, the first device may receive the second data from the second data collection device, the second data includes the second data identifier and the second sampled value of the second type of data, and conforms to the first The rules of the second configuration file, the second configuration file is defined according to the first rule, the second configuration file contains the second data identifier, the second data identifier is used to identify the second type of data, and the second data identifier is in accordance with the first rule Unique within the defined data ID. The first device may identify that the second data belongs to the second type of data according to the second data identifier and the second configuration file. Wherein, the program for identifying the first data in the first device is the same as the program for identifying the second data. The program for identifying data in the first device is the code for identifying data.

For the method for the second data collection device to obtain the second data, reference may be made to the aforementioned method for the first data collection device to obtain the first data.

In some embodiments, the above-mentioned first configuration file and the above-mentioned second configuration file may be two different configuration files. That is, each type of data defined according to the rules of the data dictionary can correspond to a configuration file. Optionally, the above-mentioned first configuration file and the above-mentioned second configuration file may also be different parts of one configuration file. That is, the definition content corresponding to each type of data defined according to the rules of the data dictionary can be written into a configuration file.

It can be seen from the above embodiments that, using the rules of the above data dictionary, in the case of adding a new type of data, the first device can identify the newly added type of data by using the program for identifying existing types of data. This can reduce the workload of developers during the process of data collected by the data collection device being connected to the first device, and improve the efficiency of data connection.

With reference to the first aspect, in some embodiments, the first configuration file may further include a fusion strategy for the first type of data. For example, multi-source fusion strategy and homologous fusion strategy.

Wherein, in the case of receiving the same type of data collected by multiple different data collection devices at the same time, the first device may, according to the multi-source fusion policy defined in the configuration file of this type of data, The data collected by different data collection devices is used for data fusion.

Specifically, the above-mentioned first sampling value is collected by the first data collection device at the first time. The first device also receives M sample values of the first type of data, and the M sample values are respectively collected by M data collection devices at the first time. M is a positive integer. The first device determines from the first configuration file that the data fusion strategy for the first type of data is the first fusion strategy, and determines a sampling value from the first sampling value and M sampling values according to the first fusion strategy as the first The sampling value of type data at the first time; the first fusion strategy is any of the following: take the maximum value, take the minimum value, take the earliest value stored in the first device, and take the latest time stored in the first device The value of the first data collection device and the sampling value collected by the device with the highest device priority among the M data collection devices.

Among them, when the data acquisition device determines that a type of data cannot be collected immediately to obtain the result, but needs to be processed by using multiple data collected in the previous period of time to obtain the result, the first device can The same-source fusion strategy defined in the configuration file performs data fusion on multiple data collected by the same data collection device.

In some embodiments, the second configuration file may further include a fusion strategy for the second type of data. The first device may also perform data fusion on the sampled values of the second type of data according to the fusion policy in the second configuration file. It can be understood that since the above-mentioned first type of data and the above-mentioned second type of data are both defined according to the rules of the data dictionary and have different data identifiers, when the fusion strategy of the first type of data is the same as the fusion strategy of the second type of data , the first device may call the same program for performing data fusion to perform data fusion on the first type of data and the second type of data. That is, the program for data fusion of different types of data defined according to the rules of the data dictionary can be reused. This can reduce the workload of developers and improve the efficiency of data access to the first device.

With reference to the first aspect, in some embodiments, the first configuration file may further include statistics policies for the first type of data.

Wherein, the first device may perform data statistics on sampling values of a type of data within a period of time according to a statistical strategy defined in a configuration file of the type of data.

Specifically, the first device may determine from the first configuration file that the data statistics strategy of the first type of data is the first statistics strategy, and the first statistics strategy includes one or more of the following: calculating the maximum value, calculating the minimum value, and calculating the average value , sum, calculate variance, calculate the number of sampled values, determine the sampled value with the latest acquisition time. The first device may perform data statistics on N sampling values of the first type of data collected within the first time period according to the first statistical strategy, where N is a positive integer.

In some embodiments, the second configuration file may also include statistics policies for the second type of data. It can be understood that since the above-mentioned first type of data and the above-mentioned second type of data are both defined according to the rules of the data dictionary and have different data identifiers, when the statistical strategy of the first type of data is the same as the statistical strategy of the second type of data , the first device may invoke the same program for performing data statistics to perform data statistics on the first type of data and the second type of data. That is, the program used to perform data statistics on different types of data defined according to the rules of the data dictionary can be reused. This can reduce the workload of developers and improve the efficiency of data access to the first device.

With reference to the first aspect, in some embodiments, the above-mentioned first type of data may be single point data. Single-point data can mean that the data collection device collects only one type of data when collecting data at a time. For example, heart rate data, body temperature data, and weight data are all single-point data. Single point data can contain a field. For example, heart rate data includes a field called heart rate. Single-point data can have data identifiers. Fields contained in single-point data can have field identifiers. Since the single-point data only contains one field, the above-mentioned first data identifier used to identify the first type of data may be the data identifier of the first type of data as the single-point data, or may also be a field of a field contained in the first type of data logo.

In some other embodiments, the above-mentioned first type of data may be a field in multipoint data. Multi-point data can mean that the data collection equipment can obtain multiple types of data at a time for data collection. For example, blood pressure data is multi-point data. Multipoint data can contain multiple fields. For example, blood pressure data can contain two fields: diastolic, systolic. Multipoint data can have data identifiers. Each field included in the multi-point data can have a different field identifier. The above-mentioned first data identifier used to identify the first type of data may be a field identifier. Optionally, when sampling values of multiple fields contained in a multi-point data are collected, the data acquisition device may also use the data identifier of the multi-point data, the field identifiers of the multiple fields contained in the multi-point data, and the The sampled values of the multiple fields are sent to the first device.

With reference to the first aspect, in some embodiments, the first device includes a display device, the first configuration file includes a first display strategy for the first type of data, and the first display strategy includes one or more of the following: a first display form , the first display position. The first device may display the received sampling value of the first type of data on the first user interface according to the first display strategy through the above display means.

With reference to the first aspect, in some embodiments, the second configuration file includes a second display strategy for the second type of data, and the second display strategy is the same as the first display strategy. The first device may also use the display device to display the sampled value of the second type of data received on the second user interface according to the first display strategy; wherein, it is used to display the sampled value of the first type of data according to the first display strategy The procedure for is the same as the procedure for displaying the sampled values of the second type of data according to the first display strategy. For example, the above-mentioned first display form is a line chart, and the first device may invoke the same program for drawing a line chart to draw the sampled values of the first type of data and the sampled values of the second type of data.

It can be seen that the programs for displaying different types of data defined according to the rules of the data dictionary can be reused. This can reduce the workload of developers and improve the efficiency of data access to the first device.

With reference to the first aspect, in some embodiments, the first configuration file further includes first description information of the first type of data. The first description information may be, for example, the name of the first type of data. The first device may send third data to the cloud server, where the third data includes first description information and sampling values of the first type of data. The cloud server can store the above third data. In this way, the first type of data cloud synchronization can be realized.

In some embodiments, the second configuration file further includes second description information of the second type of data. The second description information may be, for example, the name of the second type of data. The first device may send data including the second description information and sampled values of the second type of data to the cloud server. The cloud server may store sampled values of the second type of data.

Wherein, since the above-mentioned first type of data and the above-mentioned second type of data are defined according to the rules of the data dictionary and have different data identifiers, the first device can send the above-mentioned third data and the above-mentioned 2. Data describing the information and sampled values of the second type of data. That is to say, when a new type of data is generated, the developer does not need to additionally develop a data interaction interface in the first device suitable for accessing the new type of data to the cloud server. This can reduce the workload of developers when adding new types of data to the cloud server, and improve the efficiency of data access.

With reference to the first aspect, in some embodiments, a first application is installed on the first device, the first application has permission to obtain the first type of data, and the first configuration file includes first description information of the first type of data. The first device may provide the fourth data to the first application in response to the first application's request for the first type of data. The fourth data includes the first description information and sample values of the first type of data.

In some embodiments, the above-mentioned first application program has permission to obtain the first type of data. The second configuration file includes second description information of the second type of data. The first device may provide data including the second description information and the sampled value of the second type of data to the first application program in response to the first application program's request for acquiring the first type of data.

Wherein, since the above-mentioned first-type data and the above-mentioned second-type data are both defined according to the rules of the data dictionary and have different data identifiers, the first device can provide the first application program with the above-mentioned fourth data and the The second description information and the data of the sampling value of the second type of data. That is to say, when a new type of data is generated, the developer does not need to additionally develop a data opening interface in the first device suitable for opening the newly added type of data to the first application program. This can reduce the workload of the developer when the new type of data is opened to the first application program, and improve the efficiency of data access.

With reference to the first aspect, in some embodiments, the first device is a cloud server. The cloud server may send fifth data to the data display device, where the fifth data includes the first data identifier and the sampling value of the first type of data. That is to say, the first data collection device can send the collected data to the cloud server according to the rules of the data dictionary. The cloud server can identify the data from the data collection device according to the configuration file following the rules of the data dictionary, and send the data from the data collection device to the data display device. The data shows that configuration files that follow the rules of the data dictionary are stored in the device. The data display device can identify the received data according to the configuration file following the rules of the data dictionary, and process the data according to the display strategy, data fusion strategy, data statistics strategy and other rules in the configuration file. For example, the data display device may identify that the fifth data belongs to the first type of data according to the first data identifier and the first configuration file. Further, the data display device may sample the first type of data according to the display strategy in the first configuration file.

In some embodiments, the first device is a cloud server. The first configuration file includes first description information of the first type of data. The cloud server can send the sixth data to the data display device. The sixth data may include the first description information and sample values of the first type of data. After receiving the sixth data, the data display device may determine that the sixth data belongs to the first type of data according to the first description information in the sixth data. That is, the data display device may not identify the type of the sixth data according to the configuration file following the rules of the data dictionary. Wherein, the configuration file following the rules of the data dictionary is stored in the data display device. The data display device may determine rules such as a display policy, a data statistics policy, and a data fusion policy of the first type of data according to the first configuration file, and process the sampled values of the first type of data.

In some embodiments, the first device is a cloud server. The cloud server can send the sixth data to the data display device. In addition, the cloud server may also send the display policy, data fusion policy, data statistics policy and other rules in the first configuration file to the data display device. In this way, the data display device does not need to store configuration files that comply with the rules of the data dictionary. Wherein, when the data display device receives the sixth data, it can be determined according to the first description information in the sixth data that the sixth data belongs to the first type of data. The data display device can process the first type of data according to the received rule for processing the first type of data.

With reference to the first aspect, in some embodiments, the first data collection device may be any of the following devices: smart watch, smart bracelet, body fat scale, smart glasses, thermometer, blood pressure monitor, heart rate monitoring device.

In combination with the first aspect, in some embodiments, the first type of data can be any of the following data: walking data, running data, swimming data, cycling data, sleep data, weight data, pressure data, heart rate data, blood pressure data , body temperature data, blood oxygen data, blood sugar data.

In a second aspect, the present application provides a data processing method. Wherein, the first data collection device can obtain the first data, the first data includes the first data identifier and the first sampling value of the first type of data, and conforms to the rules of the first configuration file, and the first configuration file is based on the first rule Defined, the first configuration file includes a first data identifier, the first data identifier is used to identify the first type of data, and the first data identifier is unique among the data identifiers defined according to the first rule. The first data collection device sends the first data to the first device.

Wherein, the above-mentioned first rule may be a rule of a data dictionary.

With reference to the second aspect, in some embodiments, the first data collection device may be any of the following devices: smart watch, smart bracelet, body fat scale, smart glasses, thermometer, blood pressure monitor, heart rate monitoring device.

In combination with the second aspect, in some embodiments, the first type of data may be any of the following data: walking data, running data, swimming data, cycling data, sleep data, weight data, pressure data, heart rate data, blood pressure data , body temperature data, blood oxygen data, blood sugar data.

In a third aspect, the present application provides a communication system. The communication system may include a first data collection device and a data display device. Wherein, the first data acquisition device can be used to obtain the first data and send the first data to the data display device; the first data includes the first data identifier and the first sampling value of the first type of data, and conforms to the first configuration file The first configuration file is defined according to the first rule. The first configuration file contains the first data identifier. The first data identifier is used to identify the first type of data. The first data identifier is defined in accordance with the first rule. ID is unique. The data display device can be used to identify that the first data belongs to the first type of data according to the first data identifier and the first configuration file.

Wherein, the above-mentioned first rule may be a rule of a data dictionary.

With reference to the third aspect, in some embodiments, the communication system may further include a cloud server, and the first configuration file includes first description information of the first type of data. The data display device can also be used to send the third data to the cloud server, the third data includes the first description information and the sampling value of the first type of data. The cloud server can be used to store the third data.

In a fourth aspect, the present application provides a device. The above-mentioned device includes: a communication device, a memory, and a processor. The above-mentioned memory can be used to store a computer program, and the above-mentioned processor can be used to call the above-mentioned computer program, so that the above-mentioned device executes any possible implementation method according to the first aspect or the second aspect.

In a fifth aspect, the present application provides a chip, the chip is applied to a device, the chip includes one or more processors, and the processor is used to invoke computer instructions so that the device performs any one of the first aspect or the second aspect. possible implementation methods.

In a sixth aspect, the present application provides a computer program product containing instructions, which is characterized in that, when the above computer program product is run on a device, the device is made to execute any possible implementation method as in the first aspect or the second aspect .

In the seventh aspect, the present application provides a computer-readable storage medium, including instructions, which is characterized in that, when the above-mentioned instructions are run on a device, the device is made to perform any possible implementation method according to the first aspect or the second aspect .

It can be understood that the device provided in the fourth aspect, the chip provided in the fifth aspect, the computer program product provided in the sixth aspect, and the computer-readable storage medium provided in the seventh aspect are all used to execute the method provided in the embodiment of the present application . Therefore, the beneficial effects that it can achieve can refer to the beneficial effects in the corresponding method, and will not be repeated here.

Description of drawings

FIG. 1 is a schematic structural diagram of a communication system 10 provided by an embodiment of the present application;

FIG. 2A is a schematic structural diagram of an electronic device 100 provided in an embodiment of the present application;

FIG. 2B is a software structural block diagram of an electronic device 100 provided by an embodiment of the present application;

FIG. 3 is a schematic structural diagram of another communication system 30 provided by an embodiment of the present application;

FIG. 4 is a schematic structural diagram of another communication system 40 provided by an embodiment of the present application;

5A to 5C are schematic diagrams of some electronic devices 100 displaying body temperature data provided by the embodiment of the present application;

FIG. 6 is a schematic structural diagram of another communication system 60 provided by an embodiment of the present application.

Detailed ways

The technical solutions in the embodiments of the present application will be described clearly and in detail below in conjunction with the accompanying drawings. Among them, in the description of the embodiments of this application, unless otherwise specified, "/" means or means, for example, A/B can mean A or B; "and/or" in the text is only a description of associated objects The association relationship indicates that there may be three kinds of relationships, for example, A and/or B, which may indicate: A exists alone, A and B exist at the same time, and B exists alone. In addition, in the description of the embodiment of the present application , "plurality" means two or more than two.

Hereinafter, the terms "first" and "second" are used for descriptive purposes only, and cannot be understood as implying or implying relative importance or implicitly specifying the quantity of indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of these features. In the description of the embodiments of the present application, unless otherwise specified, the "multiple" The meaning is two or more.

Please refer to FIG. 1 . FIG. 1 is a schematic structural diagram of a communication system 10 provided in an embodiment of the present application.

As shown in FIG. 1 , a communication system 10 may include an electronic device 100 , one or more data collection devices, a cloud server 300 and a three-party application server 301 . in:

The one or more data collection devices mentioned above may include a smart watch 201 , a smart bracelet 202 , a body fat scale 203 , and smart glasses 204 . Not limited to the equipment used to collect the user's exercise and health data, the above-mentioned data collection equipment may also include smart home equipment such as smart air conditioners, smart refrigerators, air purifiers, water purifiers, water immersion monitors, and gas monitors. These smart home devices can be used to collect user's home status data. Not limited to the above sports health data and family status data, the data collected by the data collection device may also include device configuration data, device working status, and other data. The embodiment of the present application does not limit the specific type of the above-mentioned data acquisition device.

The aforementioned exercise health data may include steps, calories, calories, exercise records, sleep, weight, stress, heart rate, body temperature, blood sugar, blood pressure, etc. The above-mentioned family status data may include the temperature at home, ingredients in the smart refrigerator, air quality index, water quality, gas usage status and so on.

In some embodiments, the data collection device has a limited area for displaying data (such as a display screen), and can simply present the data collected by itself and send the collected data to the electronic device 100 (such as a mobile phone). The electronic device 100 can analyze the data from the data collection device, and present rich analysis results and relevant suggestions. For example, the smart watch 201 can be used to collect the user's heart rate. The smart watch 201 may only display the user's heart rate at the current moment. The heart rate collected by the smart watch 201 can be sent to the electronic device 100 . The electronic device 100 can calculate the user's resting heart rate, heart rate range, time period of high heart rate, time period of low heart rate and other analysis results according to the user's heart rate within a period of time, and provide suggestions for improving heart health.

Optionally, the data collection device does not have the ability to display data. The data collection device may also only collect data, and send the collected data to the electronic device 100 . The electronic device 100 can analyze and display the received data.

The electronic device 100 can be used as a data display device to analyze the data from the data collection device, and present rich analysis results and relevant suggestions. The electronic device 100 may have a display device. For example, the display. Electronic device 100 may be equipped with

Or portable electronic devices with other operating systems, such as mobile phones, tablet computers, etc., can also be laptop computers (Laptop) with touch-sensitive surfaces or touch panels, desktop computers with touch-sensitive surfaces or touch panels, etc. Portable Electronic Devices. The embodiment of the present application does not limit the specific type of the electronic device 100 .

A device management application (application, APP) for managing the data collection device may be installed in the electronic device 100 . Exemplarily, in the case that the data collection device is a device for collecting sports and health data, the above-mentioned device management APP for managing the data collection device may include a sports and health APP. In the case that the data collection device is a device that collects family status data, the above-mentioned device management APP for managing the data collection device may include a smart home APP (such as a smart life APP).

A binding relationship may be established between the electronic device 100 and one or more data collection devices. Wherein, a binding relationship may be established between the electronic device 100 and the data collection device through an account. That is, the account registered on the APP used to control the data collection device in the electronic device 100 is the same as the account registered on the data collection device. Optionally, a binding relationship may also be established between the electronic device 100 and the data collection device by scanning a two-dimensional code, Bluetooth pairing, and the like. The embodiment of the present application does not specifically limit the method for establishing a binding relationship between the electronic device 100 and the data collection device.

The data collection device may send the data collected by itself to an electronic device (such as the electronic device 100 ) that has established a binding relationship with itself. Wherein, the data collection device can send the data collected by itself to the electronic device 100 through a short-distance communication connection such as a Bluetooth communication connection, a wireless fidelity (Wi-Fi) communication connection, a ZigBee communication connection, or the like.

The electronic device 100 can also upload the data from the data collection device to the cloud server 300 . Optionally, the electronic device 100 may also upload the analysis results and related suggestions of the data collected by the data collection device to the cloud server 300 .

The cloud server 300 may receive and store data from the electronic device 100 . Wherein, the cloud server 300 may be an application server of the above-mentioned device management APP for managing data collection devices. The cloud server 300 may store the binding relationship between the electronic device 100 and the data collection device. In some embodiments, the cloud server 300 can be used for the electronic device 100 to remotely control the data collection device.

In some embodiments, the data collection device may also upload the data collected by itself to the cloud server 300 through the network. For example, the data collection device can be connected to the Internet through the network access device. Alternatively, the data collection device has a mobile data access function (for example, a SIM card is inserted into the data collection device). The cloud server 300 can store the data collected by the data collection device, and send the data collected by the data collection device to a data display device (such as the electronic device 100 ) that has a binding relationship with the data collection device. The data display device can analyze the data from the data acquisition device, and present the analysis results of the data and relevant suggestions.

In the subsequent embodiments of this application, the scenario where the data collection device sends data to the data display device that has established a binding relationship with itself, and the data display device sends the data of the data collection device to the cloud server 300 as an example will be introduced.

In some embodiments, the APP installed in the electronic device 100 may include a device management APP for managing the data collection device, or may include a third-party APP. Wherein, the device management APP may be an APP matched with the data collection device. The data collection device can send the data collected by itself to the device management APP in the electronic device 100 for managing itself. The data collection device and the device management APP that manages the data collection device may be developed and manufactured by the same manufacturer. The third-party APP may be an APP other than the device management APP. The three-party APP can request permission to obtain the data collected by the data collection device. After obtaining the open permission of the data collected by the above-mentioned data collection equipment, the third-party APP can obtain the data collected by the above-mentioned data collection equipment through the open interface, and use the data collected by the data collection equipment to realize its own business. The above-mentioned open interface may be provided by the device management APP.

Exemplarily, the electronic device 100 is installed with a sports health APP and a YY sports APP. Wherein, the sports health APP may be a device management APP in the electronic device 100 , and may be used to manage data collection devices that have a binding relationship with the electronic device 100 . For example, smart watch 201, smart bracelet 202, etc. The YY sports APP can be a three-party APP in the electronic device 100 . Through the open interface provided by the Sports Health APP, the YY Sports APP can obtain data collected by smart watches 201, smart bracelets 202 and other data collection devices managed by the Sports Health APP, and use these data to realize its own business. For example, to provide users with exercise suggestions, etc.

The third-party APP can obtain the data collected by the data collection device through the open interface provided by the above-mentioned device management APP. The third-party APP can also upload the data collected by the data collection device and the data generated in the process of implementing related business processes using the data collected by the data collection device to the third-party application server 301 . The third-party application server 301 may store the above-mentioned data from the third-party APP. Wherein, the process of the third-party APP sending data to the third-party application server 301 may specifically be a process in which the electronic device (such as the electronic device 100 ) installed with the third-party APP sends the data of the third-party APP to the third-party application server 301 . The embodiment of the present application does not limit the specific implementation manner of sending data from the electronic device 100 to the server (such as the cloud server 300 and the third-party application server 301 ).

Please refer to FIG. 2A . FIG. 2A exemplarily shows a schematic structural diagram of an electronic device 100 provided by an embodiment of the present application.

As shown in Figure 2A, the electronic device 100 may include a processor 110, an external memory interface 120, an internal memory 121, a universal serial bus (universal serial bus, USB) interface 130, a charging management module 140, a power management module 141, and a battery 142 , antenna 1, antenna 2, mobile communication module 150, wireless communication module 160, audio module 170, speaker 170A, receiver 170B, microphone 170C, earphone jack 170D, sensor module 180, button 190, motor 191, indicator 192, camera 193 , a display screen 194, and a subscriber identification module (subscriber identification module, SIM) card interface 195, etc. The sensor module 180 may include a pressure sensor 180A, a gyroscope sensor 180B, an air pressure sensor 180C, a magnetic sensor 180D, an acceleration sensor 180E, a distance sensor 180F, a proximity light sensor 180G, a fingerprint sensor 180H, a temperature sensor 180J, a touch sensor 180K, an ambient light sensor 180L, bone conduction sensor 180M, etc.

It can be understood that, the structure illustrated in the embodiment of the present invention does not constitute a specific limitation on the electronic device 100 . In other embodiments of the present application, the electronic device 100 may include more or fewer components than shown in the figure, or combine certain components, or separate certain components, or arrange different components. The illustrated components can be realized in hardware, software or a combination of software and hardware.

The processor 110 may include one or more processing units, for example: the processor 110 may include an application processor (application processor, AP), a modem processor, a graphics processing unit (graphics processing unit, GPU), an image signal processor (image signal processor, ISP), controller, video codec, digital signal processor (digital signal processor, DSP), baseband processor, and/or neural network processor (neural-network processing unit, NPU), etc. Wherein, different processing units may be independent devices, or may be integrated in one or more processors.

The controller can generate an operation control signal according to the instruction opcode and timing signal, and complete the control of fetching and executing the instruction.

A memory may also be provided in the processor 110 for storing instructions and data. In some embodiments, the memory in processor 110 is a cache memory. The memory may hold instructions or data that the processor 110 has just used or recycled. If the processor 110 needs to use the instruction or data again, it can be called directly from the memory. Repeated access is avoided, and the waiting time of the processor 110 is reduced, thus improving the efficiency of the system.

The USB interface 130 is an interface conforming to the USB standard specification, specifically, it can be a Mini USB interface, a Micro USB interface, a USB Type C interface, and the like. The USB interface 130 can be used to connect a charger to charge the electronic device 100 , and can also be used to transmit data between the electronic device 100 and peripheral devices. It can also be used to connect headphones and play audio through them. This interface can also be used to connect other electronic devices, such as AR devices.

The charging management module 140 is configured to receive a charging input from a charger. Wherein, the charger may be a wireless charger or a wired charger. While the charging management module 140 is charging the battery 142 , it can also provide power for electronic devices through the power management module 141 .

The power management module 141 is used for connecting the battery 142 , the charging management module 140 and the processor 110 . The power management module 141 receives the input from the battery 142 and/or the charging management module 140 to provide power for the processor 110 , the internal memory 121 , the display screen 194 , the camera 193 , and the wireless communication module 160 .

The wireless communication function of the electronic device 100 can be realized by the antenna 1 , the antenna 2 , the mobile communication module 150 , the wireless communication module 160 , a modem processor, a baseband processor, and the like.

Antenna 1 and Antenna 2 are used to transmit and receive electromagnetic wave signals. Each antenna in electronic device 100 may be used to cover single or multiple communication frequency bands. Different antennas can also be multiplexed to improve the utilization of the antennas. For example: Antenna 1 can be multiplexed as a diversity antenna of a wireless local area network. In other embodiments, the antenna may be used in conjunction with a tuning switch.

The mobile communication module 150 can provide wireless communication solutions including 2G/3G/4G/5G applied on the electronic device 100 . The mobile communication module 150 may include at least one filter, switch, power amplifier, low noise amplifier (low noise amplifier, LNA) and the like. The mobile communication module 150 can receive electromagnetic waves through the antenna 1, filter and amplify the received electromagnetic waves, and send them to the modem processor for demodulation. The mobile communication module 150 can also amplify the signals modulated by the modem processor, and convert them into electromagnetic waves through the antenna 1 for radiation. In some embodiments, at least part of the functional modules of the mobile communication module 150 may be set in the processor 110 . In some embodiments, at least part of the functional modules of the mobile communication module 150 and at least part of the modules of the processor 110 may be set in the same device.

The wireless communication module 160 can provide wireless local area networks (wireless local area networks, WLAN) (such as wireless fidelity (Wireless Fidelity, Wi-Fi) network), bluetooth (bluetooth, BT), global navigation satellite, etc. applied on the electronic device 100. System (global navigation satellite system, GNSS), frequency modulation (frequency modulation, FM), near field communication technology (near field communication, NFC), infrared technology (infrared, IR) and other wireless communication solutions. The wireless communication module 160 may be one or more devices integrating at least one communication processing module. The wireless communication module 160 receives electromagnetic waves via the antenna 2 , frequency-modulates and filters the electromagnetic wave signals, and sends the processed signals to the processor 110 . The wireless communication module 160 can also receive the signal to be sent from the processor 110 , frequency-modulate it, amplify it, and convert it into electromagnetic waves through the antenna 2 for radiation.

The electronic device 100 realizes the display function through the GPU, the display screen 194 , and the application processor. The GPU is a microprocessor for image processing, and is connected to the display screen 194 and the application processor. GPUs are used to perform mathematical and geometric calculations for graphics rendering. Processor 110 may include one or more GPUs that execute program instructions to generate or change display information.

The display screen 194 is used to display images, videos and the like. The display screen 194 includes a display panel. The display panel can be a liquid crystal display (LCD), an organic light-emitting diode (OLED), an active matrix organic light emitting diode or an active matrix organic light emitting diode (active-matrix organic light emitting diode, AMOLED), flexible light-emitting diode (flex light-emitting diode, FLED), Miniled, MicroLed, Micro-oLed, quantum dot light emitting diodes (quantum dot light emitting diodes, QLED), etc. In some embodiments, the electronic device 100 may include 1 or N display screens 194 , where N is a positive integer greater than 1.

The electronic device 100 can realize the shooting function through an ISP, a camera 193, a video codec, a GPU, a display screen 194, and an application processor.

The ISP is used for processing the data fed back by the camera 193 . For example, when taking a picture, open the shutter, the light is transmitted to the photosensitive element of the camera through the lens, and the light signal is converted into an electrical signal, and the photosensitive element of the camera transmits the electrical signal to the ISP for processing, and converts it into an image visible to the naked eye.

Camera 193 is used to capture still images or video. The object generates an optical image through the lens and projects it to the photosensitive element. The photosensitive element converts the light signal into an electrical signal, and then transmits the electrical signal to the ISP to convert it into a digital image signal. The ISP outputs the digital image signal to the DSP for processing. DSP converts digital image signals into standard RGB, YUV and other image signals. In some embodiments, the electronic device 100 may include 1 or N cameras 193 , where N is a positive integer greater than 1.

Digital signal processors are used to process digital signals. In addition to digital image signals, they can also process other digital signals. For example, when the electronic device 100 selects a frequency point, the digital signal processor is used to perform Fourier transform on the energy of the frequency point.

Video codecs are used to compress or decompress digital video. The electronic device 100 may support one or more video codecs. In this way, the electronic device 100 can play or record videos in various encoding formats, for example: moving picture experts group (moving picture experts group, MPEG) 1, MPEG2, MPEG3, MPEG4 and so on.

The NPU is a neural-network (NN) computing processor. By referring to the structure of biological neural networks, such as the transfer mode between neurons in the human brain, it can quickly process input information and continuously learn by itself. Applications such as intelligent cognition of the electronic device 100 can be realized through the NPU, such as image recognition, face recognition, speech recognition, text understanding, and the like.

The internal memory 121 may include one or more random access memories (random access memory, RAM) and one or more non-volatile memories (non-volatile memory, NVM).

Random access memory can include static random-access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (synchronous dynamic random access memory, SDRAM), double data rate synchronous Dynamic random access memory (double data rate synchronous dynamic random access memory, DDR SDRAM, such as the fifth generation DDR SDRAM is generally called DDR5SDRAM), etc.;

Non-volatile memory may include magnetic disk storage devices, flash memory (flash memory).

According to the operating principle, flash memory can include NOR FLASH, NAND FLASH, 3D NAND FLASH, etc. According to the potential order of storage cells, it can include single-level storage cells (single-level cell, SLC), multi-level storage cells (multi-level cell, MLC), triple-level cell (TLC), quad-level cell (QLC), etc., can include universal flash storage (English: universal flash storage, UFS) according to storage specifications , embedded multimedia memory card (embedded multi media Card, eMMC), etc.

The random access memory can be directly read and written by the processor 110, and can be used to store executable programs (such as machine instructions) of an operating system or other running programs, and can also be used to store data of users and application programs.

The non-volatile memory can also store executable programs and data of users and application programs, etc., and can be loaded into the random access memory in advance for the processor 110 to directly read and write.

The external memory interface 120 can be used to connect an external non-volatile memory, so as to expand the storage capacity of the electronic device 100 . The external non-volatile memory communicates with the processor 110 through the external memory interface 120 to implement a data storage function. For example, files such as music and video are stored in an external non-volatile memory.

The electronic device 100 can implement audio functions through the audio module 170 , the speaker 170A, the receiver 170B, the microphone 170C, the earphone interface 170D, and the application processor. Such as music playback, recording, etc.

The audio module 170 is used to convert digital audio information into an analog audio signal output, and is also used to convert an analog audio input into a digital audio signal. The audio module 170 may also be used to encode and decode audio signals. In some embodiments, the audio module 170 may be set in the processor 110 , or some functional modules of the audio module 170 may be set in the processor 110 .

Speaker 170A, also referred to as a "horn", is used to convert audio electrical signals into sound signals. Electronic device 100 can listen to music through speaker 170A, or listen to hands-free calls.

Receiver 170B, also called "earpiece", is used to convert audio electrical signals into sound signals. When the electronic device 100 receives a call or a voice message, the receiver 170B can be placed close to the human ear to receive the voice.

The microphone 170C, also called "microphone" or "microphone", is used to convert sound signals into electrical signals.

The earphone interface 170D is used for connecting wired earphones. The earphone interface 170D can be a USB interface 130, or a 3.5mm open mobile terminal platform (OMTP) standard interface, or a cellular telecommunications industry association of the USA (CTIA) standard interface.

The pressure sensor 180A is used to sense the pressure signal and convert the pressure signal into an electrical signal. In some embodiments, pressure sensor 180A may be disposed on display screen 194 .

The gyro sensor 180B can be used to determine the motion posture of the electronic device 100 . In some embodiments, the angular velocity of the electronic device 100 around three axes (ie, x, y and z axes) may be determined by the gyro sensor 180B.

The air pressure sensor 180C is used to measure air pressure. In some embodiments, the electronic device 100 calculates the altitude based on the air pressure value measured by the air pressure sensor 180C to assist positioning and navigation.

The magnetic sensor 180D includes a Hall sensor. The electronic device 100 may use the magnetic sensor 180D to detect the opening and closing of the flip leather case.

The acceleration sensor 180E can detect the acceleration of the electronic device 100 in various directions (generally three axes). When the electronic device 100 is stationary, the magnitude and direction of gravity can be detected. It can also be used to identify the posture of electronic devices, and can be used in applications such as horizontal and vertical screen switching, pedometers, etc.

The distance sensor 180F is used to measure the distance. The electronic device 100 may measure the distance by infrared or laser. In some embodiments, when shooting a scene, the electronic device 100 may use the distance sensor 180F for distance measurement to achieve fast focusing.

Proximity light sensor 180G may include, for example, light emitting diodes (LEDs) and light detectors, such as photodiodes. The light emitting diodes may be infrared light emitting diodes. The electronic device 100 emits infrared light through the light emitting diode. Electronic device 100 uses photodiodes to detect infrared reflected light from nearby objects. When sufficient reflected light is detected, it may be determined that there is an object near the electronic device 100 . When insufficient reflected light is detected, the electronic device 100 may determine that there is no object near the electronic device 100 .

The ambient light sensor 180L is used for sensing ambient light brightness. The electronic device 100 can adaptively adjust the brightness of the display screen 194 according to the perceived ambient light brightness.

The fingerprint sensor 180H is used to collect fingerprints. The electronic device 100 can use the collected fingerprint characteristics to implement fingerprint unlocking, access to application locks, take pictures with fingerprints, answer incoming calls with fingerprints, and the like.

The temperature sensor 180J is used to detect temperature. In some embodiments, the electronic device 100 uses the temperature detected by the temperature sensor 180J to implement a temperature treatment strategy. For example, when the temperature reported by the temperature sensor 180J exceeds the threshold, the electronic device 100 may reduce the performance of the processor located near the temperature sensor 180J, so as to reduce power consumption and implement thermal protection.

The touch sensor 180K is also called "touch device". The touch sensor 180K can be disposed on the display screen 194, and the touch sensor 180K and the display screen 194 form a touch screen, also called a “touch screen”. The touch sensor 180K is used to detect a touch operation on or near it. The touch sensor can pass the detected touch operation to the application processor to determine the type of touch event. Visual output related to the touch operation can be provided through the display screen 194 . In other embodiments, the touch sensor 180K may also be disposed on the surface of the electronic device 100 , which is different from the position of the display screen 194 .

The bone conduction sensor 180M can acquire vibration signals. In some embodiments, the bone conduction sensor 180M can acquire the vibration signal of the vibrating bone mass of the human voice. The bone conduction sensor 180M can also contact the human pulse and receive the blood pressure beating signal.

The keys 190 include a power key, a volume key and the like. The key 190 may be a mechanical key. It can also be a touch button. The electronic device 100 can receive key input and generate key signal input related to user settings and function control of the electronic device 100 .

The motor 191 can generate a vibrating reminder. The motor 191 can be used for incoming call vibration prompts, and can also be used for touch vibration feedback.

The indicator 192 can be an indicator light, and can be used to indicate charging status, power change, and can also be used to indicate messages, missed calls, notifications, and the like.

The SIM card interface 195 is used for connecting a SIM card. The SIM card can be connected and separated from the electronic device 100 by inserting it into the SIM card interface 195 or pulling it out from the SIM card interface 195 . The electronic device 100 may support 1 or N SIM card interfaces, where N is a positive integer greater than 1. SIM card interface 195 can support Nano SIM card, Micro SIM card, SIM card etc. Multiple cards can be inserted into the same SIM card interface 195 at the same time. The types of the multiple cards may be the same or different. The SIM card interface 195 is also compatible with different types of SIM cards. The SIM card interface 195 is also compatible with external memory cards. The electronic device 100 interacts with the network through the SIM card to implement functions such as calling and data communication. In some embodiments, the electronic device 100 adopts an eSIM, that is, an embedded SIM card. The eSIM card can be embedded in the electronic device 100 and cannot be separated from the electronic device 100 .

The software system of the electronic device 100 may adopt a layered architecture, an event-driven architecture, a micro-kernel architecture, a micro-service architecture, or a cloud architecture. The embodiment of the present application takes the Android system with a layered architecture as an example to illustrate the software structure of the electronic device 100 .

FIG. 2B is a block diagram of the software structure of the electronic device 100 according to the embodiment of the present application.

The layered architecture divides the software into several layers, and each layer has a clear role and division of labor. Layers communicate through software interfaces. In some embodiments, the Android system is divided into four layers, which are respectively the application program layer, the application program framework layer, the Android runtime (Android runtime) and the system library, and the kernel layer from top to bottom.

The application layer can consist of a series of application packages.

As shown in Figure 3, the application package can include applications such as camera, gallery, calendar, call, map, navigation, WLAN, Bluetooth, music, video, sports and health. In addition to the sports and health APP, the application package can also contain more applications for managing data collection equipment.

The application framework layer provides an application programming interface (application programming interface, API) and a programming framework for applications in the application layer. The application framework layer includes some predefined functions.

As shown in Figure 3, the application framework layer can include window manager, content provider, view system, phone manager, resource manager, notification manager, etc.

A window manager is used to manage window programs. The window manager can get the size of the display screen, determine whether there is a status bar, lock the screen, capture the screen, etc.

Content providers are used to store and retrieve data and make it accessible to applications. The data may include video, images, audio, calls made and received, data collected from data collection devices (such as exercise health data, family status data) and the like.

The view system includes visual controls, such as controls for displaying text, controls for displaying pictures, and so on. The view system can be used to build applications. A display interface can consist of one or more views. For example, a display interface including a text message notification icon may include a view for displaying text and a view for displaying pictures.

The phone manager is used to provide communication functions of the electronic device 100 . For example, the management of call status (including connected, hung up, etc.).

The resource manager provides various resources for the application, such as localized strings, icons, pictures, layout files, video files, and so on.

The notification manager enables the application to display notification information in the status bar, which can be used to convey notification-type messages, and can automatically disappear after a short stay without user interaction. For example, the notification manager is used to notify the download completion, message reminder, etc. The notification manager can also be a notification that appears on the top status bar of the system in the form of a chart or scroll bar text, such as a notification of an application running in the background, or a notification that appears on the screen in the form of a dialog window. For example, prompting text information in the status bar, issuing a prompt sound, vibrating the electronic device, and flashing the indicator light, etc.

In some embodiments, the electronic device 100 may store the data sent by the data collection device in the content provider. The sports health APP can obtain the data from the data collection device from the content provider, and analyze the data from the data collection device. The sports health APP can call the above-mentioned view system to display the data from the data collection equipment and the analysis results of these data, etc.

Android Runtime includes core library and virtual machine. The Android runtime is responsible for the scheduling and management of the Android system.

The core library consists of two parts: one part is the function function that the java language needs to call, and the other part is the core library of Android.

The application layer and the application framework layer run in virtual machines. The virtual machine executes the java files of the application program layer and the application program framework layer as binary files. The virtual machine is used to perform functions such as object life cycle management, stack management, thread management, security and exception management, and garbage collection.

A system library can include multiple function modules. For example: surface manager (surface manager), media library (Media Libraries), 3D graphics processing library (eg: OpenGL ES), 2D graphics engine (eg: SGL), etc.

The surface manager is used to manage the display subsystem and provides the fusion of 2D and 3D layers for multiple applications.

The media library supports playback and recording of various commonly used audio and video formats, as well as still image files, etc. The media library can support a variety of audio and video encoding formats, such as: MPEG4, H.264, MP3, AAC, AMR, JPG, PNG, etc.

The 3D graphics processing library is used to implement 3D graphics drawing, image rendering, compositing, and layer processing, etc.

2D graphics engine is a drawing engine for 2D drawing.

The kernel layer is the layer between hardware and software. The kernel layer includes at least a display driver, a camera driver, an audio driver, and a sensor driver.

The workflow of the software and hardware of the electronic device 100 will be exemplarily described below in conjunction with capturing and photographing scenes.

When the touch sensor 180K receives a touch operation, a corresponding hardware interrupt is sent to the kernel layer. The kernel layer processes touch operations into original input events (including touch coordinates, time stamps of touch operations, and other information). Raw input events are stored at the kernel level. The application framework layer obtains the original input event from the kernel layer, and identifies the control corresponding to the input event. Take the touch operation as a touch click operation, and the control corresponding to the click operation is the control of the camera application icon as an example. The camera application calls the interface of the application framework layer to start the camera application, and then starts the camera driver by calling the kernel layer. Camera 193 captures still images or video.

A data processing method will be introduced below by taking the communication system 30 composed of the electronic device 100 , the smart watch 201 and the cloud server 300 in the communication system 10 as an example.

Please refer to FIG. 3 . FIG. 3 exemplarily shows a schematic structural diagram of a communication system 30 .

As shown in FIG. 3 , the smart watch 201 may include a data ① collection module 310 , a data ② collection module 311 , a data ① packaging module 312 , and a data ② packaging module 313 . Wherein, the data ① collection module 310 can be used to collect data ①. The data ② collection module 311 can be used to collect data ②. It can be understood that the smart watch 201 has hardware devices for collecting data ① and data ②. For example, in the case that the data ① is heart rate, the data ① acquisition module 310 may include a heart rate monitoring device.

Not limited to the data ① collection module 310 and the data ② collection module 311 mentioned above, the smart watch 201 may also include more or less data collection modules.

The data ① encapsulation module 312 can be used for encapsulating the collected data ① according to the data encapsulation protocol agreed between the smart watch 201 and the electronic device 100 .

The data ② encapsulation module 313 can be used for encapsulating the collected data ② according to the data encapsulation protocol agreed between the smart watch 201 and the electronic device 100 .

The above data encapsulation protocol for encapsulating data ① and data ② can determine the meaning of each field in the encapsulated data. The above data encapsulation protocols for encapsulating data ① and data ② may be the same or different.

Exemplarily, the above data ① is heart rate, and the above data ② is pressure. The data ① collection module 310 collects the user's heart rate as 81. The data ② collection module 311 collects the pressure of the user as 52. The data ① packaging module 312 can package the heart rate data. The encapsulated heart rate data may include "0181". The data ② packaging module 313 can package the pressure data. Packed pressure data may include "0152".

The smart watch 201 can send the packaged data ① and data ② to the electronic device 100 .

It can be understood that the smart watch 201 also includes a communication module (such as a Bluetooth communication module). The data ① encapsulation module 312 and the data ② encapsulation module 313 can transmit the encapsulated data to the communication module in the smart watch 201 . For example, a Bluetooth communication connection is established between the smart watch 201 and the electronic device 100 . The Bluetooth communication module in the smart watch 201 can send the encapsulated data ① and data ② to the electronic device 100 according to the Bluetooth communication protocol. The electronic device 100 may also include a communication module. The communication module in the electronic device 100 can receive the data sent from the smart watch 201, and transmit the data sent from the smart watch 201 to a corresponding application program (such as a sports health APP). In order to reflect the data processing process more clearly, the embodiment of the present application simplifies the data transmission process in the communication system 30 .

The electronic device 100 may include a sports and health APP. Not limited to sports and health APP, the electronic device 100 may also include other device management APPs for managing data collection devices. In the subsequent embodiments of the present application, the sports health APP is specifically used as an example for illustration.

The sports health APP may include a device management module 314, one or more data storage and cloud synchronization modules, one or more data calculation modules, and one or more business realization modules.

The device management module 314 may include one or more data decapsulation modules. For example, the data ① decapsulation module 314A and the data ② decapsulation module 314B. The data ① decapsulation module 314A can be used to decapsulate the encapsulated data ① according to the data encapsulation protocol agreed between the smart watch 201 and the electronic device 100 . The data ② decapsulation module 314B can be used for decapsulating the encapsulated data ② according to the data encapsulation protocol agreed between the smart watch 201 and the electronic device 100 .

Exemplarily, the data ① decapsulation module 314A may decapsulate the encapsulated heart rate data "0181" to obtain the following heart rate data "data type: heart rate, data value 81". Wherein, the above-mentioned "data type: heart rate" can be determined by the data ① decapsulation module 314A according to the first two digits "01" in "0181". The above "data value 81" may be determined by the data ① decapsulation module 314A according to the last two digits "81" in "0181".

The data ② decapsulation module 314B can decapsulate the encapsulated pressure data "0152" to obtain the following pressure data "data type: pressure, data value 52". Wherein, the above-mentioned "data type: pressure" can be determined by the data② decapsulation module 314B according to the first two digits "01" in "0152". The above "data value 52" may be determined by the data ② decapsulating module 314B according to the last 2 digits "52" in "0152".

It can be seen that the encapsulated heart rate data and encapsulated pressure data cannot be decapsulated through the same data decapsulation module in the Sports Health APP, otherwise there will be situations where the meaning of the data content "01" cannot be determined.

It can be understood that when data collection devices such as the smart watch 201 use different data encapsulation protocols for encapsulating different data, the same data content in different data may have different meanings, and the sports health APP in the electronic device 100 needs to follow the Different data encapsulation protocols are used to decapsulate data to determine what the received data represents.

Not limited to the above-mentioned data encapsulation module, the device management module 314 may also include other modules for managing sports and health data collection devices such as the smart watch 201 .

The data storage and cloud synchronization module in the Sports Health APP may include a data ① storage and cloud synchronization module 316 , and a data ② storage and cloud synchronization module 317 . Among them, the data ① storage and cloud synchronization module 316 can be used to store the data ① obtained after being decapsulated by the data ① decapsulation module 314A, and upload the data ① to the cloud server 300 . The data ② storage and cloud synchronization module 317 can be used to store the data ② obtained after being decapsulated by the data ② decapsulation module 314B, and upload the data ② to the cloud server 300 .

The data calculation module in the sports health APP can include a data ① calculation module 318 and a data ② calculation module 319 . Among them, the data ① calculation module 318 can obtain data ① from the data ① storage and cloud synchronization module 316, and perform one or more of the following calculations on the data ①: determine the mean value, determine the variation range, determine the maximum value, determine the minimum value, Determine the sum and determine the number of counts of data ① within a period of time. The data ② calculation module 319 can obtain the data ② from the data ② storage and cloud synchronization module 317, and perform calculation processing on the data ②.

A business realization module can be used to realize a business. The above business can represent the services that the Sports Health APP can provide to users. The business realization module in the sports health APP may include a business A realization module 320 and a business B realization module 321 . For example, the business A implementation module can be used to present heart rate-related data and stress-related data. The service B implementation module may be used to present recommendations for maintaining physical health determined according to heart rate-related data and stress-related data. Then, both the service A implementation module 320 and the service B implementation module 321 can obtain heart rate-related data from the data ① calculation module 318 and pressure-related data from the data ② calculation module 319 .

In some embodiments, the sports health APP may further include one or more data opening modules (not shown in FIG. 3 ). For example, Data ① Open Module and Data ② Open Module. The data open module can be used to provide an open interface for third-party APPs to obtain corresponding data.

The cloud server 300 may include a data ① storage module 322 and a data ② storage module 323 . The data ① storage module 322 can be used to store data ①. The data ② storage module 323 can be used to store data ②. Among them, the sports health APP can upload data ① and data ② to the cloud server 300 according to the data encapsulation protocol agreed with the cloud server 300 . Further, the cloud server 300 can determine and store the meaning of the received data according to the data encapsulation protocol agreed between the Sports Health APP and the cloud server 300 .

From the communication system 30 shown in FIG. 3 above, it can be seen that since different types of data in the smart watch 201 may be encapsulated according to different data encapsulation protocols, and the data collected by multiple data collection devices such as the smart watch 201 may be Encapsulated according to different data encapsulation protocols, the sports health APP in the electronic device 100 needs to use different data encapsulation protocols to identify the meanings represented by different data. That is, the sports health APP needs to include modules for managing data encapsulated with different data encapsulation protocols. Then, when a new type of data is added to be displayed by the electronic device 100, and the cloud server 300 stores and opens it to the third-party APP, the developers of the sports health APP need to develop identification data, analysis data, Code that processes data such as rendering data and open data. The cloud server 300 and the three-party APP also need to add codes for processing the newly added type of data according to the data encapsulation protocol adopted by the newly added type of data.

It can be seen that when adding a new type of data to access data display devices, cloud servers, and third-party APPs in the above processing method, multiple developers need to modify and adapt the code for processing data. This makes developers have a heavy workload and low data access efficiency.

An embodiment of the present application provides a data processing method. In this method, the data acquisition device can report the data A to the data display device according to the data type definition rules in the data dictionary. The data display device can determine the specific content of the received data A according to the data type definition rules in the data dictionary, and analyze the data A according to the data processing rules in the data dictionary. The data display device can display data A and its analysis results, and can also upload data A to the cloud server and open it to third-party APPs. Wherein, the cloud server can determine the specific content of the received data A according to the data type definition rules in the data dictionary, and store it. The third-party APP can determine the specific content of the received data A according to the data type definition rules in the data dictionary, and use the data A to realize the required business of the third-party APP.

The above-mentioned data A may be sports health data, family status data and other types of data. The embodiment of the present application does not limit the specific type of data A.

The above rules of the data dictionary (such as data type definition rules, data processing rules, etc.) can be applied to many different types of data. It can be seen that, using the rules of the above data dictionary, in the case of adding a new type of data, the new type of data is connected to the data display device, cloud server, and third-party APP, and the data display device is connected to the newly added type. Codes that process data such as analyzing and displaying data can reuse codes that process existing types of data. This can reduce the workload of developers and improve the efficiency of data access.

In order to facilitate the understanding of the data processing method provided by this application, the concept of the data dictionary involved in this application will be introduced below.

Data dictionary refers to the definition and description of data items, data structure, data storage, and processing logic. The content contained in the data dictionary can refer to the content shown in Table 1 below.

Table 1

The data dictionary can include data type definition rules and data processing rules.

The data type definition rule may include the following data items in the data type definition in Table 1 above: typeID, subType, name, description, type, sensitivityLevel, validatePolicies, displayPolicies.

Wherein, the format of the data item typeID can be, for example, 00100001, the first 3 digits can identify the grouping of the data type, and the last 5 digits can be used to ensure the global uniqueness of the typeID. In this way, the data display device, the cloud server, and the third-party APP can determine the type of the data according to the type identifier of the data. The embodiment of the present application does not limit the format of the data item typeID.

The data item subType is an optional data item. In some embodiments, one type of data can also be divided into multiple subtypes. Data corresponding to the multiple subtypes may have data subtype identifiers. For example, the item of sports records can include multiple subtypes of data: running records, swimming records, mountaineering records, cycling records, and so on.

The data item name can be an English string. For example, the name of sleep data can be "SLEEP_RECORD". The data item name can be used as a unique access identifier in the code.

The data item type can include single sampling data category, sequence sampling data category and other data categories. The above-mentioned single-sampling data category may indicate that the data is sampled once and a data value of a category can be determined. For example, a heart rate value can be obtained every time the data collection device collects heart rate data. The aforementioned collection of heart rate once may mean that heart rate collection is performed at one moment. The one-shot data category may also be referred to as single-point data. The above-mentioned sequence sampling data category may indicate that data values of multiple categories can be determined once the data is sampled. For example, each time the data acquisition device collects the data of running records, it can obtain data values of multiple categories such as running speed average, running mileage, and calorie consumption. The aforementioned collection of a running record may mean collecting running-related data during the time period from the start of the run to the end of the run.

The data item sensitivityLevel can be used to determine the sensitivity level of data. The above sensitivity level can be defined according to the degree of impact that data leakage or misuse may have on individuals, organizations or the public. For example, if a type of data is leaked or misused may bring unrecoverable or catastrophic adverse effects on individuals, organizations or the public, the sensitivityLevel of this type of data may be 4. If a type of data is leaked or misused may have a significant adverse impact on individuals, organizations or the public, the sensitivityLevel of this type of data can be 3. If a type of data is leaked or misused that may have serious adverse effects on individuals, organizations or the public, the sensitivityLevel of this type of data can be 2. If a type of data is leaked or misused may have a limited degree of adverse impact on individuals, organizations or the public, the sensitivityLevel of this type of data can be 1. If a type of data is leaked or misused and may have negligible adverse effects on individuals, organizations or the public, then the sensitivityLevel of this type of data can be 0. The specific value of the sensitivityLevel is not limited in this embodiment of the application.

The data item validatePolicies is an optional data item. The data item validatePolicies can be used to verify the rationality of the data collection equipment. For example, the validatePolicies for body temperature data can be body temperature ≥ 34 and body temperature ≤ 42. If the device management APP used to manage the data collection device receives body temperature data with a value of 45, the APP can determine that the body temperature data is abnormal data. The device management APP used to manage the data collection device can process (such as delete) the abnormal data, so as to reduce the situation of presenting the abnormal data to the user.

The data item displayPolicies can be used to determine the display policy of the data. The value list of displayPolicies can include display form displayForm and display position displayPosition.

For example, if the displayForm of a type of data is 1, the data display device may display the sampling value of this type of data in the form of a line graph. If the displayForm of one type of data is 2, the data display device can display the sampling value of this type of data in the form of a histogram. If the displayForm of one type of data is 3, the data display device can display the sampling value of this type of data in the form of a pie chart. If the displayForm of a type of data is 4, the data display device can display the sampling value of this type of data in the form of a radar chart. If the displayForm of one type of data is 5, the data display device can display the sampling value of this type of data in the form of a scatter diagram.

If the displayPosition of a type of data is Left, the data display device may display the sampled value of this type of data in a display form A (such as a line graph) on the left side of the user interface. If the displayPosition of one type of data is Right, the data display device may display the sampling value of this type of data in display form A on the right side of the user interface. If the displayPosition of one type of data is Top, the data display device may display the sampling value of this type of data in display form A on the top of the user interface. If the displayPosition of one type of data is Bottom, the data display device may display the sampling value of this type of data in display form A at the bottom of the user interface.

Not limited to the displayForm and displayPosition listed above, more displayForm and displayPosition may be included in the data dictionary. The embodiment of the present application does not limit the values of displayForm and displayPosition. The data dictionary can also represent different displayForms and different displayPositions through other characters such as data and letters.

The values of the above displayForm and the above displayPosition are only exemplary descriptions of the present application, and shall not limit the present application.

In some embodiments, displayPolicies may include one of displayForm and displayPosition. Optionally, not limited to displayForm and displayPosition, displayPolicies may also include more rules for instructing the data display device to display data, such as alignment and so on.

In some embodiments, in addition to including the above-mentioned display form and display position of sampling values of a type of data displayed over a period of time, the display policy displayPolicies may also include statistical data (such as the maximum value) of this type of data displayed , minimum value, mean value, etc.) display form and display position.

A data type definition rule may also contain a data item field list fields. A type of data can contain one or more fields in the field list. For example, the field list of single-point data heart rate can contain one field, which is heart rate. The field list of multipoint data blood pressure can contain two fields, systolic blood pressure and diastolic blood pressure. The above-mentioned multi-point data may indicate that the data collection device can obtain multiple types of data by performing data collection at one moment.

Data processing rules may contain other data items contained in Table 1 than the data type definition rules. For example, each data item in a field definition and each data item in a statistics policy definition.

Among them, one of the fields in the above field list fields can have the data items included in the field definition in Table 1: fieldType, fieldName, fieldDescription, format, unit(cn), unit(en), fieldOptional, mergePolicy, dataSourcePriority, mergePolicySameSource, statPolicies . That is, the data items included in the field definition in Table 1 can describe a field in the field list fields from different dimensions.

The data item fieldType can be used to identify different types of fields under one type of data. The format of the fieldType of a field can be 00100001001, the first 8 bits can be the typeID of the type of data to which this field belongs, and the last 3 bits can be used to ensure that the fieldType is unique among the fields contained in the data corresponding to the above typeID.

The data item fieldName can be an English character string. For example, the fieldName of body temperature can be "bodyTemperature".

The data item fieldOptional can be used to indicate whether a field is optional. For example, the value "M" of the data item fieldOptional may indicate that a field is mandatory. The value "O" of the data item fieldOptional may indicate that a field is optional. When a type of data is defined according to the rules of the data dictionary, the mandatory fields contained in this type of data must be defined.

The data item mergePolicy can be used to determine the policy for processing the same type of data collected from multiple data collection devices at the same time. That is, the multi-source fusion strategy. The multi-source fusion strategy may include taking the maximum value (MAX), taking the minimum value (MIN), taking the new value (NEW), taking the old value (OLD), and according to the priority of the data source (SOURCE_PRIORITY). The above-mentioned data source may represent a source of data and a data collection device.

The data item dataSourcePriority is an optional data item that can be used to determine the priority of the data source. The data item dataSourcePriority needs to be defined only when the value of the above data item mergePolicy is SOURCE_PRIORITY.

The data item mergePolicySameSource can be used to determine the policy for processing data of the same type collected from a data collection device. That is, the homologous fusion strategy. For example, in the scenario where the smart watch 201 determines whether the user falls asleep at time A, the smart watch 201 needs to use multiple sleep-related data monitored before time A to determine whether the user falls asleep at time A. That is to say, when the data acquisition device determines that one type of data cannot be collected and obtained immediately, but needs to be processed by multiple data collected in the previous period to obtain the result, the data display device can use the above-mentioned homologous The fusion strategy determines a method for processing the multiple data collected in the preceding period of time. The homologous fusion strategy may include taking the maximum value (MAX), taking the minimum value (MIN), taking the new value (NEW), and taking the old value (OLD).

The data item statPolicies can represent a list of statistical policies for a field. One or more statistical strategies can be included in the statistical strategy list of a field. For example, a statistical strategy to calculate the maximum value, a statistical strategy to calculate the minimum value, a statistical strategy to calculate the average value, a statistical strategy to calculate the sum, and so on.

Each statistical policy may have the data items included in the statistical policy definition in Table 1: statType, statFieldName, statFormat, statPolicy. in:

The data item statType can be used to identify the statistical data type of a field. For the format of the data item statType, please refer to the data format of the aforementioned data item fieldType.

The data item statPolicy can be used to determine the statistical policy of a field. Statistical strategies may include calculating the maximum value (MAX), calculating the minimum value (MIN), calculating the average value (AVG), summing (SUM), counting (COUNT), calculating the variance (SD), and taking a new value (LAST).

The embodiment of the present application does not limit the names of the data items in the data dictionary. Not limited to the data items listed in Table 1 above, more or less data items may be included in the data dictionary.

Not limited to the above-mentioned digital dictionary, the above-mentioned rules for providing a unified definition method for different types of data may also have other names. This embodiment of the present application does not limit it.

Different types of data can be defined as metadata according to the data dictionary shown in Table 1. The metadata corresponding to one type of data can be used to determine the value of each data item of this type of data. Then, the metadata corresponding to different types of data whose metadata is defined according to the data dictionary may have the same or similar data structure. Metadata corresponding to different types of data having the same data structure may indicate that the types of data items contained in these metadata are the same. The metadata corresponding to different types of data are similar, which can indicate that among these metadata, some metadata contain optional data items in the data dictionary, and some metadata do not include optional data items in the data dictionary.

Since the metadata corresponding to different types of data can have the same or similar data structure, the code used to calculate and present new types of data in the device management APP (such as sports and health APP) used to manage data collection equipment can be reused The code for data of an existing type. For example, codes for data fusion and statistics, etc. This can reduce the workload of developers and improve the efficiency of data access.

Based on the above data dictionary, a data processing method provided by the embodiment of the present application is introduced here by taking the communication system 40 composed of the electronic device 100, the smart watch 201 and the cloud server 300 in the communication system 10 as an example.

Please refer to FIG. 4 , which exemplarily shows a schematic structural diagram of a communication system 40 .

(1) The data collected by the smart watch 201 is connected to the electronic device 100 .

As shown in FIG. 4 , the smart watch 201 may include a data ① collection module 410 , a data ② collection module 411 , and a data reporting module 412 . Wherein, the data ① collection module 410 and the data ② collection module 411 can refer to the introduction of the aforementioned communication system 30 . The data reporting module 412 can be used to report the data ① to the sports health APP in the electronic device 100 according to the data type definition rules contained in the metadata corresponding to the data ①, and define the rules according to the data type definition rules contained in the metadata corresponding to the data ② , and report the data ② to the sports health APP in the electronic device 100 .

In a possible implementation, the smart watch 201 can be written with the data type definition rules contained in the metadata corresponding to the above data ① and the data type definition rules contained in the metadata corresponding to the data ② before leaving the factory. Optionally, the above-mentioned data type definition rules adapted to data ① and data ② may also be written into smart watch 201 through software update in smart watch 201 after smart watch 201 leaves the factory.

When the data ① is collected, the smart watch 201 can determine the data type definition rules that match the data ①. The smart watch 201 can report the data ① to the sports health APP in the electronic device 100 through the data reporting module 412 according to the data type definition rules adapted to the data ①.

(2) The electronic device 100 processes the data of the data collection device.

The electronic device 100 may include a sports and health APP. The sports health APP may include a data management module 413, a data dictionary 414, a business A realization module 415, and a business B realization module 416. Wherein, the data management module 413 can obtain data ① and data ② reported by the data reporting module 412 in the smart watch 201 . It can be understood that during the process of data ① and data ② being transmitted from the data reporting module 412 to the data management module 413, the data ① and data ② have also passed through the smart watch 201 and other modules (such as Bluetooth communication modules, etc.) in the electronic device 100. deal with. The embodiment of the present application simplifies the data transmission process in the communication system 40 in order to more clearly reflect the data processing process. The embodiment of the present application does not limit the specific method of transferring data ① and data ② from the data reporting module 412 to the data management module 413 , and from the data management module 413 to the data storage module 417 in the cloud server 300 .

The data dictionary 414 may contain configuration files for data ① and configuration files for data ②. A configuration file of a type of data may follow the definition content in the metadata corresponding to this type of data. That is, the configuration file of data ① may be determined according to the metadata corresponding to data ①. The configuration file of data ② may be determined according to metadata corresponding to data ②. A configuration file of a type of data can be used by the device management APP to identify the received data of this type, data processing, cloud synchronization, and open to third-party APPs for processing.

When receiving data ① and data ② from the data reporting module 412 , the data management module 413 can obtain the configuration file of data ① and the configuration file of data ② from the data dictionary 414 . Wherein, the data ① reported by the data reporting module 412 includes the data item typeID of the data ①, and the data ② includes the data item typeID of the data ②. The data management module 413 can obtain the configuration file of data ① from the data dictionary 414 according to the data item typeID of data ①, and obtain the configuration file of data ② from the data dictionary 414 according to the data item typeID of data ②.

According to the data type definition rules in the configuration file of ① and the configuration file of data ②, the data management module 413 can identify the specific content of data ① and data ②. According to the data processing rules in the configuration file of ① and the configuration file of data ②, the data management module 413 can perform calculation processing such as data fusion and data statistics on data ① and data ②.

It can be known from the above Table 1 that the data processing rules in the data dictionary may include data fusion rules and data statistics rules. The above data fusion rules may include data items mergePolicy, dataSourcePriority, mergePolicySameSource. The above-mentioned data statistics rule may include a data item statPolicy.

(a) Data Fusion

In a possible implementation, the data management module 413 may first determine whether there is data ① reported by a data collection device other than the smart watch 201, and the smart watch 201 and other data collection devices collect data ① at the same time. If the data management module 413 only receives the data ① collected by the smart watch 201 , the data management module 413 can store the data ① collected by the smart watch 201 .

If the data management module 413 receives multiple data ① collected by multiple data collection devices such as the smart watch 201 at time A, the data management module 413 can determine according to the data item mergePolicy of the multi-source fusion policy in the configuration file of the data ①, Which data ① among the above-mentioned plurality of data ① is used as the accurate data at time A.

For example, when the data item mergePolicy in the configuration file of data ① is NEW, the data management module 413 may use the data ① with the latest writing time among the above-mentioned multiple data ① as the merged data, that is, the accurate data at time A. The above writing time may be the time when data is written into the storage module of the electronic device 100 .

For another example, when the data item mergePolicy in the configuration file of data ① is OLD, the data management module 413 may use the data ① with the oldest writing time among the above-mentioned multiple data ① as the merged data.

For another example, when the data item mergePolicy in the configuration file of data ① is MAX, the data management module 413 may use the data ① with the largest value among the above-mentioned multiple data ① as the merged data.

For another example, when the data item mergePolicy in the configuration file of data ① is MIN, the data management module 413 may use the data ① with the smallest value among the above-mentioned multiple data ① as the merged data.

For another example, in the case that the data item mergePolicy in the configuration file of data ① is SOURCE_PRIORITY, the data management module 413 can use the data ① collected by the device with the highest device priority among multiple data collection devices such as the smart watch 201 as the merged data ①. data.

Wherein, when the data item mergePolicy is SOURCE_PRIORITY, the configuration file of data ① includes the data item dataSourcePriority. dataSourcePriority can be used to determine device priority.

In a possible implementation manner, the device priority determined by dataSourcePriority is a priority determined according to the category of the data collection device. For example, when collecting blood pressure data, the priority of the sphygmomanometer is higher than that of the smart watch. The data management module 413 can determine the types of the above-mentioned multiple data collection devices that collect data ① at time A, and select the data ① collected by the data collection device with the highest category priority according to the device priority determined by dataSourcePriority. If among the above-mentioned multiple data collection devices that collect data ① at time A, there are multiple data collection devices that are all data collection devices with the highest category priority, the data management module 413 can combine other fusion strategies (such as MAX, MIN, NEW , OLD, etc.) select one of the data ① collected by the data acquisition device with the highest category priority as the fused data.

In a possible implementation manner, the device priority determined by dataSourcePriority may include two levels of priority: the priority determined according to the type of the data collection device, and the priority determined according to the model of the data collection device. For the above priority determined according to the type of the data collection device, reference may be made to the description of the foregoing embodiments. The above priority determined according to the model of the data collection device can be used to determine the priority of each type of data collection device. For example, when collecting blood pressure data, a data collection device classified as a blood pressure monitor has multiple models: a model A blood pressure monitor, a model B blood pressure monitor, and the like. Model A blood pressure monitors have higher priority than Model B blood pressure monitors. Then, the data management module 413 can first use the priority determined according to the type of the data collection device to select the data ① collected by the data collection device with the highest category priority from the above-mentioned multiple data collection devices that collect data ① at time A. Further, if there are multiple data collection devices with the highest category priority, the data management module 413 can determine the models of the data collection devices with the highest category priority, and use the above-mentioned priority determined according to the model of the data collection device Select the data collected by the data collection device with the highest model priority①.

In a possible implementation manner, the device priority determined by dataSourcePriority may only include the priority determined according to the model of the data collection device. The data management module 413 can determine the models of the above-mentioned multiple data collection devices that collect data ① at time A, and select the data ① collected by the data collection device with the highest model priority according to the device priority determined by dataSourcePriority.

For the method for the data management module 413 to perform data fusion on data ②, please refer to the above-mentioned introduction to data fusion on data ①. I won't go into details here.

The above multi-source fusion strategy can reduce the situation that multiple data collection devices collect the same type of data at the same time, resulting in data conflicts. Not limited to the multi-source fusion strategies listed above, more multi-source fusion strategies may also be included in the data dictionary.

In some embodiments, when the data management module 413 receives multiple data of the same type collected by a data collection device, the data management module 413 may, according to the homologous fusion policy in the configuration file of this type of data The data item mergePolicySameSource performs data fusion on these multiple data of the same type. For the method of performing homologous fusion of data by the data management module 413, reference may be made to the above-mentioned introduction of multi-source fusion of data.

(b) Statistics

In a possible implementation manner, the data management module 413 may perform data statistics on multiple data ① within a period of time according to the data item statPolicy of the data statistics policy in the configuration file of the data ①. The aforementioned period of time may be, for example, one hour, one day, and so on. The embodiment of the present application does not limit the length of the above period of time.

Wherein, in the case that the data management module 413 only receives the data ① collected by the smart watch 201 , the multiple data ① in the above period of time may be the multiple data ① collected by the smart watch 201 in this period of time. When the data management module 413 receives the data ① collected by multiple data collection devices such as the smart watch 201, the multiple data ① in the above-mentioned period of time may be collected within the above-mentioned period of time, and have undergone the above-mentioned multi-source fusion processing Multiple data of ①.

For example, in the case that the data item statPolicy in the configuration file of data ① is AVG, the data management module 413 can calculate the average value of multiple data ① in the above-mentioned period of time.

For another example, when the data item statPolicy in the configuration file of data ① is SUM, the data management module 413 may calculate the sum of multiple data ① in the above-mentioned period of time.

For another example, when the data item statPolicy in the configuration file of data ① is MAX, the data management module 413 may determine the maximum value of multiple data ① within the above period of time.

For another example, when the data item statPolicy in the configuration file of data ① is MIN, the data management module 413 may determine the minimum value of multiple data ① within the above-mentioned period of time.

For another example, in the case where the data item statPolicy in the configuration file of data ① is SD, the data management module 413 may calculate the variance of multiple data ① within the above-mentioned period of time.

For another example, when the data item statPolicy in the configuration file of data ① is COUNT, the data management module 413 may calculate the number of data of multiple data ① within the above-mentioned period of time.

For another example, in the case that the data item statPolicy in the configuration file of data ① is LAST, the data management module 413 may determine the latest values of multiple data ① within the above-mentioned period of time. The latest value may be the latest data collected within a period of time.

Not limited to the data statistics strategies listed above, more data statistics strategies may be included in the data dictionary.

The data management module 413 performs data statistics on the data according to the rules of data statistics, which can facilitate the business implementation module to realize the display requirements of various types of statistical data. For example, display the average value and sum of the data, and display the data in the form of a line graph, a histogram, etc.

For the services to be implemented by the service A realization module 415 and the service B realization module 416, reference may be made to the introduction in the aforementioned communication system 30 shown in FIG. 3 . Both the business A implementation module 415 and the business B implementation module 416 can obtain data ①, data ②, statistical results of data ① under various data statistical strategies, and statistical results of data ② under various data statistical strategies from the data management module 413 Wait for the data.

Exemplarily, the service A realization module 415 may be used to present data related to data ① and data related to data ②. The business A implementation module can determine the display strategy of data ① from the configuration file of data ① (such as displayed in a line graph at the top of the user interface), and determine the display strategy of data ② from the configuration file of data ② (such as displayed in the user interface displayed as a histogram at the top of the ). The service A implementation module 415 can display the data ① obtained from the data management module 413 according to the display strategy of data ①, and display the data ② obtained from the data management module 413 according to the display strategy of data ②.

In addition, if the configuration file of data ① defines a display strategy for statistical data of data ① (such as displaying the average value on the lower side of the line graph of data ① in the user interface), the business A implementation module can also obtain data from the data management module 413 Various statistical data of ①, and display according to the display strategy of the statistical data of data ①. For the method of displaying statistical data of data ② by the module of business A, please refer to the method of displaying statistical data of data ① above. I won't go into details here.

It can be understood that the user interface for displaying data related to data ① and the user interface for displaying data related to data ② are different user interfaces.

(3) The electronic device 100 uploads the data collected by the data collection device to the cloud server 300, and opens it to the third-party APP.

In some embodiments, the data management module 413 may send the data ① to the cloud server 300 according to the data type definition rules in the configuration file of the data ①. The cloud server 300 may include a data storage module 417 . When the above data ① is received, the cloud server 300 can determine the specific content of the data ① according to the data type definition rules contained in the metadata corresponding to the data ①, and store the data ① in the data storage module 417.

For the process of cloud server 300 receiving data ② and storing data ② in data storage module 417 , please refer to the introduction of cloud server 300 storing data ① in data storage module 417 .

In some embodiments, the data management module 413 may open the data ① to third-party APPs according to the data type definition rules in the configuration file of the data ①. The third-party APP can determine the specific content of data ① according to the data type definition rules contained in the metadata corresponding to data ①, and use data ① to realize its own business needs.

It can be seen from the above-mentioned embodiments that when different types of data are defined according to the rules of the data dictionary, the metadata corresponding to different types of data may have the same or similar data structure, and the different types of data have The data item typeID is globally unique. Then, during the process of connecting different types of data to the device management APP in the electronic device 100, the interface for data interaction between the data collection device and the electronic device 100 can be shared. During the cloud synchronization process of different types of data, the electronic device 100 and the cloud server The interface for data exchange between 300s can be shared, and the open interface provided by the device management APP during the process of opening different types of data to third-party APPs can also be shared. When a new type of data is added, the newly added type of data access electronic device 100, cloud server 300 and related interfaces open to third-party APPs can be connected with existing types of data access electronic device 100, cloud server 300 and open to third-party APPs. The relevant interfaces of the three-party APP are the same. The above-mentioned existing types of data are data defined according to the rules of the above-mentioned data dictionary. That is to say, when the newly added type of data is connected to the electronic device 100, the cloud server 300 and opened to third-party APPs, developers do not need to develop an additional access interface suitable for the newly added type of data. This can reduce the workload of developers when adding new types of data access, and improve the efficiency of data access.

In some embodiments, configuration files of one or more types of data may be stored in the cloud server 300 . A configuration file of a type of data may be determined according to metadata corresponding to this type of data. When a new type of data is generated, the cloud server 300 may send the configuration file of the new type of data to the electronic device 100 . A device management APP (such as a sports and health APP) in the electronic device 100 can use the configuration file of the newly added type of data to update the data dictionary. In this way, when receiving the new type of data collected by the data collection device, the device management APP can use the updated data dictionary to determine the content of the new type of data, and according to the configuration file of the new type of data The newly added type of data is processed for calculation and display.

In some embodiments, when a new type of data is generated, the electronic device 100 may update the version of the device management APP in response to a user operation for updating the version of the device management APP. The data dictionary in the device management APP after the version update can contain the configuration file of the newly added type of data.

The following takes the newly added body temperature data as an example to illustrate the data processing process of connecting the data collected by the data collection device to the data display device, cloud server and open to the third-party APP.

(1) Define metadata corresponding to body temperature data according to the rules of the data dictionary.

The metadata corresponding to the body temperature data can be used to determine the values of various data items of the body temperature data. The metadata corresponding to the body temperature data can refer to the content shown in Table 2 below.

Table 2

For the value of each data item in Table 2, reference may be made to the introduction of the data dictionary in the foregoing embodiments. I won't go into details here.

(2) Determine the configuration file of the body temperature data according to the metadata corresponding to the body temperature data.

Profiles for body temperature data can include:

It can be seen that the configuration file of the body temperature data defines the value of each data item of the body temperature data. Since the body temperature data is single-point data, the data item field list fields in the body temperature data configuration file contains only one field, that is, the field name "fieldName" is "bodyTemperature", and the field data type identifier "fieldType" is "400011975". The configuration file of the body temperature data may also include a data fusion strategy and a statistics strategy for a field in the body temperature data. For example, the statistical policy list "statPolicies" in the above configuration file contains four statistical policies: calculating the average value (AVG), calculating the maximum value (MAX), calculating the minimum value (MIN), and counting (COUNT).

The configuration file of the above body temperature data can be used by the device management APP to identify the received body temperature data, and perform data fusion and data statistics processing on the body temperature data.

(3) Solidify the data type definition rules in the configuration file of body temperature data to the body temperature data acquisition device.

In a possible implementation manner, before the body temperature data collection device leaves the factory, the manufacturer of the body temperature data collection device may write the data type definition rules in the body temperature data configuration file into the body temperature data collection device. Optionally, the data type definition rules in the above body temperature data configuration file may also be written into the body temperature data acquisition device when the software is updated after the body temperature data acquisition device leaves the factory.

The above data type definition rules may include data items: typeID, name, type, sensitivityLevel, validatePolicies, displayPolicy. Wherein, one or more of the above data items name, type, sensitivityLevel, validatePolicies, and displayPolicy may not be written into the body temperature data collection device.

Wherein, the above-mentioned data type definition rule may also include a data item field list fields. The manufacturer of the body temperature data collection device can also write one or more data items under the field definitions in the body temperature data configuration file to the body temperature data collection device. Since the body temperature data is single-point data, the body temperature data has only one field, that is, body temperature. The manufacturer of the body temperature data collection device can write the data items in the field definition: fieldType, unit(en) into the body temperature data collection device. Then, the body temperature data acquisition device can report body temperature data according to the above data type definition rules, and report the fields contained in the body temperature data according to the data items in the field definition.

(4) The body temperature data collection device reports the collected body temperature data to the data display device according to the data type definition rules of the body temperature data.

When the body temperature data is collected, the body temperature data acquisition device can describe the body temperature data according to the data type definition rules of the body temperature data, and send the described data to the data display device. The data display device may be a device that has a binding relationship with the body temperature data collection device.

In a possible implementation manner, the described body temperature data may include not only the type identifier typeID, but also the field data type identifier fieldType and the sampling value of the field.

For example, the described body temperature data can be:

"typeID":400011

"fieldType":400011975

"start_time": 1626836477471

"end_time": 1626836477471

"value":36.0

It can be understood that the above "start_time" and "end_time" may represent the start time and end time of this body temperature data collection respectively. The body temperature data described above by the body temperature data collection device may also contain more data items. Such as name, type, sensitivityLevel, validatePolicies, etc.

In another possible implementation manner, since the body temperature data is single-point data, the body temperature data acquisition device may only uniquely identify the body temperature data by using the field data type identifier fieldType of the fields contained in the body temperature data. That is, the described body temperature data may contain the field data type identifier fieldType and the sampled value of the field, but not the type identifier typeID.

For example, the described body temperature data can be:

"fieldType":400011975

"start_time": 1626836477471

"end_time": 1626836477471

"value":36.0

When the data described by the data collection device is multi-point data, the data collection device can use the data type identifier typeID to uniquely identify the data, and use the field data type identifier fieldType to identify different fields contained in the data. For example, blood pressure data is multi-point data. The blood pressure data described according to the rules of the data dictionary can include the type identifier typeID of the blood pressure data, the start time and end time of the collection, the field data type identifier fieldType of the diastolic pressure field of the blood pressure data, the sampling value of the diastolic pressure field, and the blood pressure data The field data type of the field systolic pressure identifies fieldType, and the sampling value of the field systolic pressure.

(5) The data display device uses the configuration file of the body temperature data to identify the body temperature data reported by the body temperature data collection device, and calculates and displays the body temperature data.

Here, the data display device is the electronic device 100, and the device management APP used to manage the body temperature data collection device in the data display device is a sports health APP as an example for illustration.

The electronic device 100 stores the configuration file of the above body temperature data. When the electronic device 100 receives the body temperature data reported by the body temperature data collection device, the sports health APP in the electronic device 100 can use the configuration file of the body temperature data to identify the specific content of the body temperature data.

For example, according to the data item "fieldType": 400011975 in the received data, the Sports Health APP can recognize that the data is body temperature data. Among them, the sports health APP can determine that the data whose fieldType is 400011975 is the body temperature data whose field name is bodyTemperature according to the configuration file of body temperature data.

In some embodiments, when the data collection device reports the data collected by itself to the data display device, it may also report its own device identification to the data display device. The device identification of each type of data acquisition device is globally unique. For example, the body temperature data collection device of model A has a device identifier of 71 . The device identifier of the body temperature data collection device of model B is 72 . The device identifier of the model C blood pressure data acquisition device is 73 . The embodiment of the present application does not limit the format of the device identification of the data collection device.

After the sports health APP recognizes the specific content in the body temperature data, it can store the body temperature data in the data storage module in the electronic device 100 . Exemplarily, the storage format of the body temperature data in the data storage module of the electronic device 100 can refer to the content shown in the following Table 3:

IdID	start_timestart_time	end_timeend_time	fieldTypefieldType	valuevalue	device_iddevice_id	sync_statussync_status
11	16268364774711626836477471	16268364774711626836477471	400011975400011975	36.036.0	7171	00
22	16268364774811626836477481	16268364774811626836477481	400011975400011975	36.236.2	7171	00

table 3

Wherein, each row in Table 3 may represent a record of body temperature data. A record of body temperature data may represent body temperature data collected by a body temperature data collection device at a time. The Id in Table 3 may represent the sequence number of the record corresponding to the row. The device_id in Table 3 may represent the device identifier of the data acquisition device. The sync_status in Table 3 can indicate the cloud synchronization status of body temperature data. For example, a sync_status of 0 may indicate that the body temperature data has not been uploaded to the cloud server. A sync_status of 1 indicates that the body temperature data has been uploaded to the server. Not limited to the contents listed in Table 3, the storage format of body temperature data may also include more or less contents. For example, data name, data unit, data sensitivity level, etc.

It can be known from the foregoing embodiments that all data whose metadata is defined according to the rules of the data dictionary have a globally unique type identifier typeID and have the same or similar data structure. Then, the Sports Health APP can identify all the data reported according to the rules of the data dictionary through the same data identification module. The code used to implement the above data identification module is the program used to identify the above data reported according to the rules of the data dictionary. The above-mentioned data identification module may be equivalent to an interface for data interaction between the data display device and the data collection device. When a new type of data is added, and the new type of data is reported according to the rules of the data dictionary, the new type of data can reuse the data interaction interface of the existing type of data during the reporting process. Developers only need to define configuration files for new types of data according to the rules of the data dictionary, without additionally developing data interaction interfaces suitable for new types of data. This can reduce the workload of developers when new types of data are connected to the data display device, and improve the efficiency of data connection.

In some embodiments, the body temperature data configuration file includes a data item validation rule set validatePolicies. The Sports Health APP can verify the body temperature data stored in the data storage module according to validatePolicies to determine whether there is any abnormality in the body temperature data.

In some embodiments, the sports health APP can determine to perform data fusion and data statistics on the body temperature data according to the configuration file of the body temperature data.

Exemplarily, when it is determined that multiple body temperature data stored in the data storage module have multi-source conflicts, the sports health APP can use the data item multi-source fusion policy mergePolicy contained in the body temperature data configuration file to Body temperature data for data fusion. The aforementioned multiple body temperature data with multi-source conflicts may include body temperature data collected at the same time by multiple models of body temperature data collection devices.

It can be seen from the configuration file of body temperature data that the mergePolicy is "SOURCE_PRIORITY", that is, the merged data is determined according to the priority of the data acquisition device. The configuration file of body temperature data contains the data item device priority dataSourcePriority. The Sports Health APP can select the body temperature data collected by the body temperature data collection device with the highest device priority among multiple body temperature data with multi-source conflicts as the fused data according to the dataSourcePriority. For example, dataSourcePriority is [1,129,384,57,72,71]. The device priority determined by the dataSourcePriority is the priority determined according to the model of the data collection device. The device ID in the array [1,129,384,57,72,71] can identify a specific model of data acquisition device. Among them, the body temperature data collection device whose device ID is 1 has the highest priority. The body temperature data collection device whose device ID is 71 has the lowest priority. If the above-mentioned multiple body temperature data with multi-source conflicts come from the body temperature data collection device whose device ID is 129 and the body temperature data collection device whose device ID is 71 . Then, the Sports Health APP can select the body temperature data collected by the body temperature data collection device whose device ID is 129 as the fused body temperature data.

In some embodiments, the device priority determined by the above dataSourcePriority is the priority determined according to the type of body temperature data collection device. The value of dataSourcePriority may include the device class identifier. A device category identifier can identify a category of body temperature data collection equipment. The Sports Health APP can select the body temperature data collected by the body temperature data collection device with the highest category priority as the fused body temperature data. The embodiment of the present application does not limit the representation method of the device class identifier. For example, the device class identifier may be represented by one or more characters such as numbers and letters.

The above fused body temperature data can be used for data statistics, data display and other processing.

The configuration file of the above body temperature data contains four statistical strategies of MAX, MIN, AVG and COUNT. The sports health APP can perform statistical processing corresponding to the above four statistical strategies on the body temperature data within a period of time, and store the statistically processed statistical body temperature data in the data storage module in the electronic device 100 . Exemplarily, the storage format of the statistical body temperature data in the data storage module of the electronic device 100 can refer to the content shown in the following Table 4:

IdID	datedate	fieldTypefieldType	statTypestatType	valuevalue	sync_statussync_status
11	2021072220210722	400011975400011975	400011740400011740	37.037.0	00
22	2021072220210722	400011975400011975	400011854400011854	38.038.0	00
33	2021072220210722	400011975400011975	400011708400011708	36.036.0	00
44	2021072220210722	400011975400011975	400011375400011375	2.02.0	00

Table 4

Wherein, each row in Table 4 may represent a record of statistical body temperature data. A record of statistical body temperature data can represent a period of time, for example, within a day, the sports health APP will collect statistical data on the received body temperature data according to a statistical strategy. The Id in Table 4 may indicate the need of the record corresponding to the row. The date in Table 4 may indicate the time period of the body temperature data collection time for data statistics. For example, if the date is 20210722, it can mean that the body temperature data collected within July 22, 2021 will be counted. For other contents in Table 4, reference may be made to the introduction of the foregoing embodiments.

In some embodiments, the sports health APP can determine the display strategy of the body temperature data according to the configuration file of the body temperature data. For example, in the body temperature data configuration file, the data item display policy displayPolicies is [1, Top], which can indicate that the body temperature data needs to be displayed in the form of a line chart and displayed at the top of the user interface. The Sports Health APP can call the code for drawing a line graph to draw a line graph of body temperature data over a period of time, and display the line graph on the top of the user interface. Optionally, the sports health APP can also display statistical body temperature data (such as maximum value, minimum value, etc.).

FIG. 5A to FIG. 5C exemplarily show schematic diagrams of scenarios in which the electronic device 100 displays body temperature data and collects body temperature data.

As shown in FIG. 5A , the electronic device 100 may display a user interface 510 . User interface 510 displays a page on which application icons are placed. The page may include multiple application icons. For example, smart life application icons, sports and health application icons 511, YY sports application icons 512, and ZZ health application icons 513. In response to a user operation, such as a touch operation, on the above-mentioned application icon, the electronic device 100 may start an application program corresponding to the application icon. It can be understood that the sports and health APP corresponding to the sports and health application icon 511 may be a device management APP for managing body temperature data collection devices. Both the YY Sports APP corresponding to the YY Sports application icon and the ZZ Health APP corresponding to the ZZ Health application icon 513 may be three-party APPs. In response to user operations acting on the sports and health application icon 511, the electronic device 100 may display a user interface 520 shown in FIG. 5B.

As shown in FIG. 5B , the user interface 520 may contain multiple cards. A card can be used to present one type of data. For example, heart rate card 521 may be used to present heart rate data. Body temperature card 522 may be used to present body temperature data. Pressure cards 523 may be used to present pressure data. Athletic log card 524 may be used to present athletic log data. In response to a user operation, such as a touch operation, on a card, the electronic device 100 may display more detailed content of the data corresponding to the card. For example, the sampling value of the data within a period of time, the statistical data of the data under different statistical strategies, etc. In response to user operations acting on the body temperature card 522, the electronic device 100 may display the user interface 530 shown in FIG. 5C.

As shown in FIG. 5C , the user interface 530 may include a time option area 531 , a body temperature data display area 532 , a body temperature mean card 533 and a body temperature range card 534 . Wherein, the time option area 531 may contain multiple time options. When a time option is selected, body temperature data in the time period corresponding to this time option can be displayed in the form of a line graph in the body temperature data display area 532 . For example, when the time option indicating January 15 is selected, the body temperature data display area 532 may display a line chart of all body temperature data collected by the body temperature data collection device on January 15. When the time option indicating January is selected, the body temperature data display area 532 may display a line chart of all body temperature data collected by the body temperature data collection device in January. In this way, the user can view body temperature data in different time periods through different time options in the time option area 531 .

The average body temperature card 533 can be used to display the average value of body temperature data for a period of time (eg within a day).

The body temperature range card 534 can be used to display the range of body temperature data fluctuations for a period of time (eg, within a day). The range of body temperature data fluctuations within a period of time can be determined by the minimum and maximum values of body temperature data within this period of time.

The above user interfaces shown in FIG. 5A to FIG. 5C are only exemplary illustrations of the data display device displaying body temperature data in the embodiment of the present application, and should not be construed as limiting the present application.

If the above-mentioned heart rate data, body temperature data, pressure data, and exercise record data are defined as metadata according to the rules of the data dictionary, the above-mentioned body temperature data is regarded as a new type of data, and the above-mentioned heart rate data, pressure data, and exercise record data are used as existing type of data, then, in the Sports Health APP, data fusion, data statistics (average body temperature, body temperature range as shown in Figure 5C) and data display (body temperature data display area 532 in the final body temperature data display area 532 as shown in Figure 5C) are performed in the Sports Health APP. Line chart) codes can reuse the codes of Sports Health APP to process existing types of data.

It can be seen from the above-mentioned embodiments that when different types of data are defined as metadata according to the rules of the data dictionary, the code for data fusion (such as a multi-source fusion strategy) for these different types of data in the Sports Health APP is Reusable, the code for data statistics (such as statistical strategies) of these different types of data is reusable, and the code for data display of these different types of data (such as line chart display data) is also reusable . When a new type of data is added, and the new type of data is defined as metadata according to the rules of the data dictionary, the code for data fusion, data statistics, and data display of the newly added type of data can be reused Code for processing existing types of data in the Sports Health APP. Developers may only need to modify the part of the code used to identify data (such as the type identifier typeID, etc.) in the code that processes existing types of data in the sports health APP to identify the content of the newly added type of data, without modifying the Code logic for processing data, and develop code suitable for processing the newly added type of data. This can reduce the workload of developers when new types of data are connected to the data display device, and improve the efficiency of data connection.

(6) The data display device sends the body temperature data to the cloud server according to the data type definition rules of the body temperature data.

In some embodiments, the data display device can send the body temperature data in the data storage module that has not been synchronized to the cloud to the cloud server. The cloud server may be an application server of a device management APP for managing body temperature data collection devices. The cloud server can identify the received data as body temperature data and specific content in the body temperature data according to the data type definition rules of the body temperature data.

Wherein, after receiving the body temperature data reported by the body temperature data acquisition device, the data display device can correlate the sampled value of the body temperature data with the name of the body temperature data identified according to the configuration file of the body temperature data, and then send it to the cloud server. In this way, the cloud server can directly store the received body temperature data, instead of identifying whether the received data belongs to body temperature data according to the data type definition rules of the body temperature data before storing it.

In some embodiments, the data display device can also download the body temperature data from the cloud server. In the case where the data shows that the device uninstalls the device management APP and then downloads the device management APP again, the data shows that only part of the body temperature data may be stored in the device. Then, the data shows that the device can download the temperature data that is in the cloud server from the cloud server, but the data shows that the device does not have body temperature data.

Refer to the introduction of different types of data defined by metadata according to the rules of the data dictionary connected to the data display device by the data acquisition device. When different types of data defined by the metadata according to the rules of the data dictionary are connected to the cloud server by the data display device, An interactive interface for data upload can be shared, and when the data is downloaded from the cloud server to the data display device, an interactive interface for data download can be shared. In this way, when a new type of data is generated, the developer does not need to separately develop an interactive interface suitable for transmitting the new type of data between the data display device and the cloud server every time. This can reduce the workload of developers when adding new types of data to the cloud server, and improve the efficiency of data access.

(7) The data display device opens the body temperature data to the third-party APP according to the data type definition rules of the body temperature data.

In some embodiments, the three-party APP may request the open permission to acquire the body temperature data from the device management APP in the data display device for managing the body temperature data collection device. In the case of requesting open permission to obtain body temperature data, the device management APP can provide an open interface for body temperature data for the third-party APP. Among them, the device management APP can open the body temperature data to the third-party APP through the open interface of the body temperature data. Among them, the body temperature data open to the third-party APP can be described according to the data type definition rules of body temperature data. The three-party APP can identify the acquired data as body temperature data and the specific content of the body temperature data according to the data type definition rules of body temperature data. Optionally, the data display device may also associate the name of the body temperature data in the configuration file of the body temperature data with the sampling value of the body temperature data, and open it to third-party APPs. In this way, the third-party APP does not need to identify whether the acquired data belongs to the body temperature data according to the configuration file of the body temperature data.

In some embodiments, the three-party APP may contain data type definition rules for different types of data. Wherein, the above-mentioned different types of data may be defined as metadata according to the rules of the data dictionary. Then, the codes for identifying the above-mentioned different types of data in the three-party APP can be reused, and the codes for processing the above-mentioned different types of data can also be reused. In this way, in the case of a newly added type of data, the code used to identify the newly added type of data and the code used to process the newly added type of data can be reused in the three-party APP and the existing type The code corresponding to the data. This can reduce the workload of the developer of the third-party APP when new types of data are connected to the third-party APP, and improve the efficiency of data access.

Another communication system 60 provided by the embodiment of the present application will be introduced below based on the above processing process of body temperature data.

As shown in FIG. 6 , the communication system 60 may include a body temperature data collection device 610 , an electronic device 100 , and a cloud server 300 . in:

The body temperature data collection device 610 may include a body temperature data collection module 611 and a data reporting module 612 . The body temperature data collection module 611 can be used to collect body temperature data and send the body temperature data to the data reporting module 612 . In the data reporting module 612, the data type definition rules in the configuration file with body temperature data are written. The data reporting module 612 can report to the sports health APP in the electronic device 100 according to the rules of the data dictionary.

Wherein, the process of reporting the body temperature data by the data reporting module 612 according to the rules of the data dictionary may be a process of describing the body temperature data according to the data type definition rules in the configuration file of the body temperature data, and sending the described body temperature data.

The electronic device 100 may include sports and health APPs and three-party APPs. The sports health APP mentioned above may be a device management APP for managing the body temperature data collection device 610 . The sports health APP may include a data calculation module 621 , a data storage module 622 , a data presentation module 623 , a data opening module 624 and a data dictionary 625 .

The data dictionary 625 may contain configuration files of body temperature data. The sports health APP can use the body temperature data configuration file in the data dictionary 625 to identify the body temperature data reported by the body temperature data collection device 610 according to the rules of the data dictionary.

The data calculation module 621 can perform calculation processing such as data fusion and data statistics on the body temperature data according to the configuration file of the body temperature data.

The data storage module 622 can store body temperature data. For the storage format of the body temperature data in the data storage module 622, reference may be made to the introduction of the foregoing embodiments.

The data display module 623 can be used to display multiple sampled values of body temperature data and statistical values of body temperature data obtained through data statistics. In some embodiments, the displayed body temperature data may be data obtained through data fusion by the data calculation module 621 .

The above data open module 624 can be used to provide an open interface for body temperature data for three-party APPs.

It can be known from the foregoing embodiments that all data whose metadata is defined according to the rules of the data dictionary have a globally unique type identifier typeID and have the same or similar data structure. Then, not limited to the above-mentioned body temperature data, the sports health APP can also use the above-mentioned data calculation module 621, data storage module 622, data display module 623 and data open module 624 to correspond to other data defined by metadata according to the rules of the data dictionary. management. That is, the data calculation module 621 , data storage module 622 , data display module 623 and data opening module 624 can be reused. When a new type of data is added, and the added type of data is reported according to the rules of the data dictionary, developers do not need to develop additional modules suitable for managing the added type of data.

The sports health APP can open the body temperature data to third-party APPs that have the authority to obtain body temperature data through the body temperature data open interface provided by the data open module 624 . The above-mentioned body temperature data open to the third-party APP may be obtained after being described according to the data type definition rules of body temperature data. The body temperature data described above may include a type identifier of the body temperature data and a sampling value of the body temperature data. The third-party APP can identify the acquired body temperature data according to the data type definition rules of the body temperature data, and use the body temperature data to realize its own demand business. Optionally, the Sports Health APP can also associate the name of the body temperature data in the configuration file of the body temperature data with the sampling value of the body temperature data, and open it to third-party apps. In this way, the third-party APP does not need to identify whether the acquired data belongs to the body temperature data according to the configuration file of the body temperature data.

The Sports Health APP can also perform cloud synchronization on body temperature data. Wherein, the sports health APP can describe according to the data type definition rules of the body temperature data, and send the described body temperature data to the cloud server 300 . The body temperature data described above may include a type identifier of the body temperature data and a sampling value of the body temperature data. The cloud server 300 can identify and store the received body temperature data according to the data type definition rules of the body temperature data. Optionally, the Sports Health APP can also associate the sampled value of the body temperature data with the name of the body temperature data identified according to the configuration file of the body temperature data, and then send it to the cloud server. In this way, the cloud server can directly store the received body temperature data, instead of identifying whether the received data belongs to body temperature data according to the data type definition rules of the body temperature data before storing it.

It can be seen that using the rules of the data dictionary designed in the embodiment of the present application, in the case of adding a type of data, the newly added type of data is connected to the data display device, cloud server, third-party APP, and the data display device. Codes for processing data of the newly added type such as analysis and display may reuse codes for processing existing types of data. This can reduce the workload of developers and improve the efficiency of data access.

As mentioned above, the above embodiments are only used to illustrate the technical solutions of the present application, and are not intended to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: it can still understand the foregoing The technical solutions described in each embodiment are modified, or some of the technical features are replaced equivalently; and these modifications or replacements do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the various embodiments of the application.

Claims

A data processing method, characterized in that the method comprises:

The first device receives the first data from the first data acquisition device, the first data includes the first data identifier and the first sampling value of the first type of data, and conforms to the rules of the first configuration file, the first The configuration file is defined according to the first rule, the first configuration file contains the first data identifier, the first data identifier is used to identify the first type of data, and the first data identifier is used according to the uniqueness among the data identifiers defined by the first rule above;

The first device identifies, according to the first data identifier and the first configuration file, that the first data belongs to the first type of data.
The method according to claim 1, wherein the first rule is a rule of a data dictionary.
The method according to claim 1 or 2, said method further comprising:

The first device receives the second data from the second data collection device, the second data includes a second data identifier and a second sampling value of the second type of data, and conforms to the rules of the second configuration file, the The second configuration file is defined according to the first rule, the second configuration file contains the second data identifier, the second data identifier is used to identify the second type of data, and the second data The identifier is unique among the data identifiers defined according to the first rule;

The first device identifies that the second data belongs to the second type of data according to the second data identifier and the second configuration file; wherein, the first device is used to identify the first The procedure for the data is the same as for identifying said second data.
The method according to any one of claims 1-3, wherein the first sampling value is collected by the first data acquisition device at the first time, and the method further comprises:

The first device receives M sample values of the first type of data, and the M sample values are respectively collected by M data collection devices at the first time; the M is a positive integer;

The first device determines from the first configuration file that the data fusion strategy of the first type of data is a first fusion strategy, and according to the first fusion strategy, from the first sampled value and the M A sampling value is determined from the sampling values as the sampling value of the first type of data at the first time; the first fusion strategy is any of the following: take the maximum value, take the minimum value, take and store in the The earliest value of the time of the first device, the latest value stored in the first device, the value collected by the device with the highest priority among the first data collection device and the M data collection devices sample value.
The method according to any one of claims 1-4, wherein the method further comprises:

The first device determines from the first configuration file that the data statistics strategy of the first type of data is a first statistics strategy, and the first statistics strategy includes one or more of the following: calculating the maximum value, calculating the minimum value value, calculate the mean, sum, calculate the variance, calculate the number of sampled values, determine the sampled value with the latest collection time;

The first device performs data statistics on N sampling values of the first type of data within a first time period of collection time according to the first statistical strategy, where N is a positive integer.
The method according to any one of claims 1-5, wherein the first device includes a display device, the first configuration file includes a first display strategy for the first type of data, and the first The display strategy includes one or more of the following: the first display form, the first display position; the method also includes:

The first device displays the received sampling value of the first type of data on the first user interface according to the first display strategy through the display device.
The method according to claim 6, wherein the second configuration file includes a second display strategy for the second type of data, and the second display strategy is the same as the first display strategy; the method further include:

The first device displays the received sampling value of the second type of data on the second user interface according to the first display strategy through the display device; The procedure for displaying the sampled values according to the first display strategy is the same as the procedure for displaying the sampled values of the second type of data according to the first display strategy.
The method according to any one of claims 1-7, wherein the first configuration file includes first description information of the first type of data, and the method further comprises:

The first device sends third data to the cloud server, where the third data includes the first description information and a sampling value of the first type of data.
The method according to any one of claims 1-8, wherein a first application program is installed in the first device, and the first application program has permission to obtain the first type of data, so The first configuration file includes first description information of the first type of data, and the method further includes:

The first device provides the fourth data to the first application in response to the first application's request to obtain the first type of data; the fourth data includes the first description information and the sampled value of the first type of data.
The method according to any one of claims 1-5, wherein the first device is a cloud server, and the method further comprises:

The cloud server sends fifth data to the data display device, where the fifth data includes the first data identifier and a sampling value of the first type of data.
The method according to any one of claims 1-10, wherein the first data collection device is any of the following devices: smart watch, smart bracelet, body fat scale, smart glasses, thermometer, blood pressure Meter, heart rate monitoring equipment.
The method according to any one of claims 1-11, wherein the first type of data is any of the following data: walking data, running data, swimming data, cycling data, sleep data, body weight data , pressure data, heart rate data, blood pressure data, body temperature data, blood oxygen data, blood sugar data.
A data processing method, characterized in that the method comprises:

The first data acquisition device acquires the first data, the first data includes the first data identifier and the first sampling value of the first type of data, and conforms to the rules of the first configuration file, and the first configuration file is according to the first Defined by a rule, the first configuration file includes the first data identifier, the first data identifier is used to identify the first type of data, and the first data identifier is defined in accordance with the first rule Uniqueness in data identification;

The first data collection device sends the first data to the first device.
The method according to claim 13, wherein the first rule is a rule of a data dictionary.
The method according to claim 13 or 14, wherein the first data collection device is any of the following devices: smart watch, smart bracelet, body fat scale, smart glasses, thermometer, blood pressure monitor, heart rate monitor equipment.
The method according to any one of claims 13-15, wherein the first type of data is any of the following data: walking data, running data, swimming data, riding data, sleep data, body weight data , pressure data, heart rate data, blood pressure data, body temperature data, blood oxygen data, blood sugar data.
A communication system, characterized in that the communication system includes a first data acquisition device and a data display device, wherein,

The first data collection device is configured to acquire first data and send the first data to the data display device; the first data includes a first data identifier and a first sampling value of a first type of data , and conform to the rules of the first configuration file, the first configuration file is defined according to the first rule, the first configuration file contains the first data identifier, and the first data identifier is used to identify the The first type of data, the first data identifier is unique among the data identifiers defined according to the first rule;

The data display device is configured to identify that the first data belongs to the first type of data according to the first data identifier and the first configuration file.
The communication system according to claim 17, wherein the communication system further comprises a cloud server, and the first configuration file includes first description information of the first type of data;

The data display device is further configured to send third data to the cloud server, where the third data includes the first description information and sampling values of the first type of data;

The cloud server is used to store the third data.
A device, characterized in that the device comprises a memory and a processor, wherein the memory is used to store a computer program, and the processor is used to call the computer program, so that the device performs claims 1-12 or The method of any one of claims 13-16.
A computer-readable storage medium, comprising instructions, wherein, when the instructions are run on a device, the device is made to execute the method according to any one of claims 1-12 or 13-16.
A computer program product, characterized in that the computer program product includes computer instructions, and when the computer instructions are run on a device, the device executes any one of claims 1-12 or 13-16. described method.