Data processing method, electronic device and computer-readable storage medium

Abstract

The present application is applicable to the technical field of terminals, and particularly relates to a data processing method, an electronic device and a computer-readable storage medium. In the method, an electronic device can acquire first data and, on the basis of first target data among the first data, determine a first target scenario; and, after determining the first target scenario on the basis of the first target data, the electronic device can determine second target data associated with the first target scenario, so as to determine first information corresponding to the first target scenario on the basis of the first target data and the second target data, such that on the basis of the first information, a user can clearly know an association relationship between the data (for example, the first target data and the second target data) corresponding to the first target scenario, and more clearly know the health condition of the user, meeting the requirements of the user and improving user experience.

Description

Data processing methods, electronic devices and computer-readable storage media

[0001] This application claims priority to Chinese Patent Application No. 202411719987.2, filed with the State Intellectual Property Office of China on November 26, 2024, entitled "Data Processing Method, Electronic Device and Computer-Readable Storage Medium", the entire contents of which are incorporated herein by reference. Technical Field

[0002] This application belongs to the field of terminal technology, and in particular relates to data processing methods, electronic devices and computer-readable storage media. Background Technology

[0003] With the continuous development of electronic technology, electronic devices are becoming increasingly feature-rich. For example, electronic devices can acquire users' exercise data such as steps, running distance, running time, or energy expenditure, and can analyze and display each piece of data separately. They can also acquire users' health data such as sleep, heart rate, emotional health, blood pressure, blood oxygen, body temperature, or stress, and analyze and display each piece of health data separately, allowing users to understand their exercise or health status. However, current analysis methods generally analyze each piece of exercise or health data separately, obtaining analytical information corresponding to each individual data point. This means the analytical information is only related to the individual data points and cannot meet user needs, resulting in a poor user experience. Summary of the Invention

[0004] This application provides a data processing method, an electronic device, and a computer-readable storage medium, which can solve the problem that the analyzed information is only related to the individual data and cannot meet the user's needs, resulting in a poor user experience.

[0005] In a first aspect, embodiments of this application provide a data processing method applied to an electronic device, the method comprising:

[0006] Get the first data;

[0007] Based on the first target data, a first target scenario is determined; the first target data is the data in the first data.

[0008] Determine the second target data associated with the first target scene; the second target data is data in the first data, and the second target data is different from the first target data;

[0009] Based on the first target data and the second target data, the first information corresponding to the first target scenario is determined.

[0010] In the data processing method described above, the electronic device can acquire first data and determine a first target scenario based on the first target data within the first data. After determining the first target scenario based on the first target data, the electronic device can determine second target data associated with the first target scenario and determine first information corresponding to the first target scenario based on the first target data and the second target data. This allows the user to clearly understand the relationships between the data corresponding to the first target scenario based on the first information, and to better understand their own health status, thereby meeting user needs and improving the user experience.

[0011] For example, the first data may include one or more of the following: exercise and health data, environmental data, and usage data. Exercise and health data may include exercise data and health data. For example, exercise and health data may include one or more of the following: sleep heart rate, resting heart rate, sleep heart rate variability, sleep onset time, sleep end time, sleep duration, blood oxygen saturation, blood pressure, body temperature, weight, respiratory rate, mood, stress, steps, exercise duration, activity hours, activity calories, or menstrual cycle.

[0012] In one embodiment, both the first target data and the second target data can be data from the first data. For example, the first target data may include one or more of the first data. That is, the electronic device can determine the first target scene based on one or more of the first data. Similarly, the second target data may include one or more of the first data. That is, the first target scene can be associated with one or more of the second target data.

[0013] In another embodiment, the first target data and the second target data can be data from different datasets. For example, the first target data can be data from a first dataset, and the second target data can be data from other datasets (e.g., third datasets). That is, the electronic device can also acquire the third dataset. After determining the first target scene based on the first target data in the first dataset, the electronic device can determine the second target data associated with the first target scene based on the third dataset.

[0014] In some embodiments, the electronic device stores a scene library, which includes at least one preset scene and triggering conditions corresponding to each preset scene;

[0015] The step of determining the first target scenario based on the first target data includes:

[0016] The first target scenario is determined based on the first target data and the triggering conditions corresponding to each preset scenario; the first target scenario is a scenario among the preset scenarios.

[0017] In the data processing method provided in this embodiment, scenarios can be preset, and the preset scenarios (i.e., preset scenarios) can be saved in an electronic device or in another device communicatively connected to the electronic device. That is, a scenario library can be set up in the electronic device or other devices communicatively connected to the electronic device. The scenario library may include at least one preset scenario, master data corresponding to each preset scenario, triggering conditions corresponding to each preset scenario, and second data associated with each preset scenario, etc. For each preset scenario, the master data corresponding to the preset scenario may refer to the data used to trigger the preset scenario. In other words, the master data corresponding to the preset scenario may refer to the data corresponding to the analysis result represented by the preset scenario; that is, the analysis result represented by the preset scenario may be the analysis result obtained by analyzing the master data corresponding to the preset scenario. For each preset scenario, the triggering condition corresponding to the preset scenario may refer to the condition that the master data corresponding to the preset scenario must satisfy when the preset scenario is triggered.

[0018] Therefore, after acquiring the first data, the electronic device can determine the first target data based on the first data and the master data corresponding to each preset scene in the scene library. After determining the first target data, the electronic device can determine the first target scene based on the first target data and the trigger conditions corresponding to each preset scene in the scene library. That is, it can accurately determine the first target data and the first target scene based on the pre-set scene library.

[0019] In one possible implementation, the scene library further includes second data associated with each of the preset scenes;

[0020] The step of determining the second target data associated with the first target scene includes:

[0021] Based on the second data associated with each preset scenario and the first data, the second target data associated with the first target scenario is determined; the second target data is the data in the second data.

[0022] In the data processing method provided by this implementation, the scene library may further include second data associated with each preset scene. For each preset scene, the second data associated with the preset scene may include data affected by the master data corresponding to the preset scene, and / or data that affects the master data corresponding to the preset scene.

[0023] Therefore, after determining the first target scene, since the first target scene can be a scene from a preset scene library, the electronic device can accurately determine the second data associated with the first target scene based on the scene library, and accurately determine the second target data associated with the first target scene based on the first data and the second data associated with the first target scene. The second target data can be data from the first data, and the second target data can be data from the second data associated with the first target scene.

[0024] In one embodiment, determining the second target data associated with the first target scene based on the second data associated with each of the preset scenes and the first data includes:

[0025] Based on the second data associated with each preset scenario and the first data, candidate data associated with the first target scenario is determined, wherein the candidate data is the data in the first data and the candidate data is the data in the second data;

[0026] Determine the data score corresponding to the candidate data;

[0027] Based on the data scores corresponding to the candidate data, the second target data associated with the first target scene is determined; the second target data is the data in the candidate data.

[0028] It should be understood that the data score corresponding to candidate data can be used to characterize the degree of anomaly of the candidate data. Specifically, a higher data score indicates a greater degree of anomaly, while a lower data score indicates a lesser degree of anomaly. Data with a greater degree of anomaly is generally the data that users need to pay attention to.

[0029] Therefore, in the data processing method provided in this embodiment, the electronic device can determine the data score corresponding to the candidate data associated with the first target scene, so as to characterize the degree of abnormality of the candidate data associated with the first target scene through the data score. Based on the degree of abnormality of the candidate data associated with the first target scene, the most important abnormal data that the user needs to pay close attention to can be determined, so as to analyze these abnormal data and present the analysis results to the user, so that the user can quickly understand the situation of abnormal data, understand their own movement and / or health status, etc., which can improve the user experience.

[0030] In one possible implementation, determining the data score corresponding to the candidate data includes:

[0031] Obtain the first and second scoring criteria corresponding to the candidate data, wherein the first scoring criterion is a guideline standard and the second scoring criterion is a user standard;

[0032] The first deviation degree corresponding to the candidate data is determined according to the first scoring criterion, and the second deviation degree corresponding to the candidate data is determined according to the second scoring criterion;

[0033] The data score corresponding to the candidate data is determined based on the first deviation and the second deviation.

[0034] In the data processing method provided by this implementation, the electronic device can determine the first deviation degree corresponding to each candidate data according to the guideline standard corresponding to each candidate data, that is, determine the degree of deviation of each candidate data from the normal range specified by the guideline standard. In addition, the electronic device can also determine the second deviation degree corresponding to each candidate data according to the user standard corresponding to each candidate data, that is, determine the degree of deviation of each candidate data from the normal range specified by the user standard. Thus, based on the first and second deviation degrees corresponding to each candidate data, the data score corresponding to each candidate data can be accurately determined, that is, the degree of abnormality of each candidate data can be accurately determined.

[0035] In another possible implementation, for each candidate data associated with the first target scenario, the electronic device can also obtain a first initial score for each candidate data in the first target scenario, and obtain at least one of a first indicator weight, a first timeliness weight, and a first experience weight corresponding to each candidate data. For each candidate data, the electronic device can accurately determine the data score corresponding to the candidate data based on the first deviation, second deviation, and first initial score corresponding to the candidate data, as well as at least one of the first indicator weight, first timeliness weight, and first experience weight.

[0036] In some embodiments, determining the first information corresponding to the first target scene based on the first target data and the second target data includes:

[0037] Analyze the first target data to determine the first analysis result corresponding to the first target scenario;

[0038] The second target data is analyzed to determine the second analysis result corresponding to the first target scenario;

[0039] Based on the first analysis result and the second analysis result corresponding to the first target scenario, the first information corresponding to the first target scenario is determined.

[0040] In the data processing method provided in this embodiment, after acquiring the first target data, the electronic device can analyze the first target data to determine the first analysis result corresponding to the first target data. Similarly, after acquiring the second target data associated with the first target scene, the electronic device can analyze the second target data to determine the second analysis result corresponding to the second target data. Subsequently, the electronic device can accurately determine the first information corresponding to the first target scene based on the first analysis result corresponding to the first target data and the second analysis result corresponding to the second target data.

[0041] For example, the first information corresponding to the first target scenario may include one or more of the following: interpretation information corresponding to the first target scenario, information indicating the correlation between the first target data and the second target data corresponding to the first target scenario, and suggestion information corresponding to the first target scenario.

[0042] For example, the interpretation information corresponding to the first target scenario can be determined based on the first analysis result corresponding to the first target data. Information indicating the correlation between the first target data and the second target data corresponding to the first target scenario can be determined based on the first analysis result corresponding to the first target data and the second analysis result corresponding to the second target data. Recommendation information corresponding to the first target scenario can be determined based on the first analysis result corresponding to the first target data and the analysis result corresponding to the second target data.

[0043] In other embodiments, after acquiring the first data, the method further includes:

[0044] Analyze the first data to determine the third analysis result corresponding to the first data;

[0045] The step of determining the first information corresponding to the first target scene based on the first target data and the second target data includes:

[0046] Based on the first target data, the first data, and the third analysis result, a first analysis result corresponding to the first target scenario is determined; the first analysis result corresponding to the first target scenario is the analysis result in the third analysis result.

[0047] Based on the second target data, the first data, and the third analysis result, a second analysis result corresponding to the first target scenario is determined, and the second analysis result corresponding to the first target scenario is the analysis result in the third analysis result;

[0048] Based on the first analysis result and the second analysis result corresponding to the first target scenario, the first information corresponding to the first target scenario is determined.

[0049] In the data processing method provided in this embodiment, after acquiring the first data, the electronic device can analyze each piece of data in the first data to obtain a third analysis result corresponding to each piece of data in the first data. After determining the first target data, the electronic device can obtain the first analysis result corresponding to the first target data from the predetermined third analysis result based on the first target data. Similarly, after determining the second target data associated with the first target scene, the electronic device can obtain the second analysis result corresponding to the second target data from the predetermined third analysis result based on the second target data. Subsequently, the electronic device can determine the first information corresponding to the first target scene based on the first analysis result corresponding to the first target data and the second analysis result corresponding to the second target data. This can improve the determination speed and efficiency of the first analysis result corresponding to the first target data and the second analysis result corresponding to the second target data, thereby improving the determination speed and efficiency of the first information and enhancing the user experience.

[0050] In one possible implementation, after determining the first information corresponding to the first target scene based on the first target data and the second target data, the method further includes:

[0051] The first interface is displayed, which includes a first summary card, which includes first information corresponding to the first target scene; or, the first summary card includes first information corresponding to the first target scene, as well as first analysis results and / or second analysis results corresponding to the first target scene.

[0052] In the data processing method provided by this implementation, after determining the first information corresponding to the first target scenario, the electronic device can display the first information corresponding to the first target scenario through a card (or summary card). When displaying the first information corresponding to the first target scenario through the summary card, the electronic device can also display the analysis results corresponding to the first target data and / or the analysis results corresponding to the second target data, which can facilitate users' understanding of the specific situation of the data corresponding to the first target scenario and improve user experience. For example, the electronic device can present the first analysis results corresponding to the first target data and / or the second analysis results corresponding to the second target data through one or more forms such as text, line charts, bar charts, or graphs.

[0053] For example, an electronic device can display, through one or more summary cards, first information corresponding to the first target scenario, first analysis results of the first target data corresponding to the first target scenario, and / or second analysis results corresponding to the second target data on any interface such as a desktop, the negative one screen, or the interface of a certain application.

[0054] In another possible implementation, after determining the first information corresponding to the first target scene based on the first target data and the second target data, the method further includes:

[0055] The first interface is displayed, which includes a first summary card and a second summary card. The first summary card is positioned in front of the second summary card in the first interface.

[0056] The first summary card includes first information corresponding to the first target scene, and the second summary card includes first information corresponding to the second target scene; or, the first summary card includes first information corresponding to the first target scene, and includes first analysis results and / or second analysis results corresponding to the first target scene, and the second summary card includes first information corresponding to the second target scene, and includes first analysis results and / or second analysis results corresponding to the second target scene, wherein the scene priority of the first target scene is higher than the scene priority of the second target scene.

[0057] In the data processing method provided by this implementation, the first data may include multiple first target data, and multiple target scenarios (e.g., a first target scenario and a second target scenario) can be determined based on the multiple first target data. After determining the first information (e.g., first information A) corresponding to the first target scenario and the first information (e.g., first information B) corresponding to the second target scenario, the electronic device can display a first summary card and a second summary card according to the scenario priority corresponding to the target scenario (i.e., the scenario priority corresponding to the first target scenario and the scenario priority corresponding to the second target scenario). The first summary card may only include the first information A corresponding to the first target scenario; or, the first summary card may include the first information A corresponding to the first target scenario, a first analysis result (e.g., analysis result A1) of the first target data corresponding to the first target scenario, and / or a second analysis result (e.g., analysis result A2) of the second target data associated with the first target scenario. The second summary card may only include the first information B corresponding to the second target scenario. Or, the second summary card may include the first information B corresponding to the second target scenario, a first analysis result (e.g., analysis result B1) of the first target data corresponding to the second target scenario, and a second analysis result (e.g., analysis result B2) of the second target data associated with the second target scenario.

[0058] Specifically, when the scenario priority of a target scenario is higher, the summary card corresponding to that target scenario can be displayed on the interface with priority or at the beginning, allowing users to prioritize the first information related to the target scenario with higher priority, thus improving the user experience. For example, when the scenario priority of the first target scenario is higher than that of the second target scenario, when the electronic device displays the first summary card and the second summary card on the interface, the first summary card can be displayed before the second summary card, allowing users to prioritize the first information related to the first target scenario.

[0059] In another possible implementation, after determining the first information corresponding to the first target scene based on the first target data and the second target data, the method further includes:

[0060] The first interface is displayed, which includes a first summary card; the first summary card includes first information corresponding to the first target scene; or, the first summary card includes first information corresponding to the first target scene, as well as first analysis results and / or second analysis results corresponding to the first target scene.

[0061] In response to a first operation detected in the first interface, a second interface is displayed, the second interface including a second summary card; the second summary card includes first information corresponding to the second target scene; or, the second summary card includes first information corresponding to the second target scene, and includes a first analysis result and / or a second analysis result corresponding to the second target scene; the scene priority of the first target scene is higher than the scene priority of the second target scene.

[0062] In the data processing method provided by this implementation, when there are multiple target scenarios, for example, when the target scenarios include a first target scenario and a second target scenario, when displaying the first information, the electronic device can display the summary card (e.g., the first summary card) corresponding to the target scenario with higher scenario priority (e.g., the first target scenario) on any interface such as the desktop, the negative one screen, or the interface of a certain application, so that users can understand the first information corresponding to the target scenario with higher scenario priority first, thereby improving the user experience.

[0063] When displaying the first summary card corresponding to the first target scene, if the first operation (e.g., operation H) is detected, the electronic device can display summary cards corresponding to other scenes on any interface such as the desktop, the negative one screen, or the interface of a certain application. For example, it can display the summary card corresponding to the second target scene (e.g., the second summary card), or it can display the first summary card corresponding to the first target scene and the second summary card corresponding to the second target scene.

[0064] In some embodiments, the scene priority corresponding to the target scene is preset, or determined based on the first target data corresponding to the target scene.

[0065] In one embodiment, the scenario priority corresponding to the target scenario is determined based on the data score corresponding to the first target data of the target scenario.

[0066] In the data processing method provided in this embodiment, when the data score corresponding to the target scene is higher, the scene priority corresponding to the target scene can be higher; when the data score corresponding to the target scene is lower, the scene priority corresponding to the target scene can be lower. The scene priority is determined according to the data score, so that the target scene with a greater degree of abnormality can have a higher scene priority. This allows the first information corresponding to the target scene with a greater degree of abnormality to be displayed in the interface first or earlier, so that users can quickly understand the abnormal target scene and improve the user experience.

[0067] In another possible implementation, after determining the first information corresponding to the first target scene based on the first target data and the second target data, the method further includes:

[0068] Display a first interface, the first interface including a first summary card, the first summary card including first information corresponding to the first target scene;

[0069] In response to a second operation on the first information corresponding to the first target scene, a third interface is displayed, the third interface including a third summary card, the third summary card including the first analysis result and / or the second analysis result corresponding to the first target scene;

[0070] or,

[0071] In response to a third operation on the first information corresponding to the first target scene, a fourth interface is displayed, the fourth interface including a fourth summary card, the fourth summary card including the first analysis result corresponding to the first target scene;

[0072] In response to a fourth operation on the first information corresponding to the first target scene, or in response to a fifth operation detected in the fourth interface, a fifth interface is displayed, the fifth interface including a fifth summary card, the fifth summary card including a second analysis result corresponding to the first target scene.

[0073] In the data processing method provided by this implementation, the electronic device can display first information corresponding to a target scene (e.g., the first target scene), first analysis results corresponding to the first target data, and second analysis results corresponding to the second target data through multiple summary cards.

[0074] For example, the first information corresponding to the first target scenario can be displayed using a summary card (e.g., the first summary card), the first analysis result corresponding to the first target data and the second analysis result corresponding to the second target data can be displayed using another summary card (e.g., the third summary card). Similarly, the first information corresponding to the first target scenario can be displayed using a summary card (e.g., the first summary card), the first analysis result corresponding to the first target data can be displayed using another summary card (e.g., the fourth summary card), and the second analysis result corresponding to the second target data can be displayed using yet another summary card (e.g., the fifth summary card).

[0075] It should be understood that the first information corresponding to the first target scenario can also be displayed through multiple summary cards. Furthermore, when there are multiple first target data points, the first analysis results corresponding to all the first target data points can be displayed uniformly through a single summary card, or the first analysis result corresponding to each of the first target data points can be displayed through a single summary card. Similarly, when there are multiple second target data points, the second analysis results corresponding to all the second target data points can be displayed uniformly through a single summary card, or the second analysis result corresponding to each of the second target data points can be displayed through a single summary card.

[0076] It should be noted that when displaying the first information corresponding to the first target scenario, the first analysis result corresponding to the first target data, and the second analysis result corresponding to the second target data through multiple summary cards, the electronic device can simultaneously display these multiple summary cards on the interface. This allows the user to quickly understand the relevant information of the first target scenario. Alternatively, the electronic device can display each summary card separately on the interface. For example, it can first display the summary card containing the first information corresponding to the first target scenario and then switch the display of summary cards based on user actions.

[0077] In one possible implementation, after determining the first information corresponding to the first target scene, the electronic device can display only the first information corresponding to the first target scene on the interface.

[0078] In some embodiments, the first target scene is associated with one or more second target data.

[0079] In other embodiments, the first target data includes one or more of the first data.

[0080] In the data processing method provided in this embodiment, the electronic device can determine the first target scene based on one or more types of data in the first data.

[0081] In some embodiments, the first data includes one or more of exercise and health data, environmental data, and usage data corresponding to the electronic device.

[0082] In one possible implementation, the exercise health data includes one or more of the following: sleep heart rate, resting heart rate, sleep heart rate variability, sleep onset time, sleep end time, sleep duration, blood oxygen saturation, blood pressure, body temperature, weight, respiratory rate, mood, stress, steps, exercise duration, activity hours, activity calories, or menstrual cycle.

[0083] Secondly, embodiments of this application provide a data processing apparatus applied to an electronic device, the apparatus comprising:

[0084] The first data acquisition module is used to acquire the first data.

[0085] The target scene determination module is used to determine a first target scene based on first target data; the first target data is the data in the first data.

[0086] The target data determination module is used to determine the second target data associated with the first target scene; the second target data is data in the first data, and the second target data is different from the first target data;

[0087] The first information determination module is used to determine the first information corresponding to the first target scene based on the first target data and the second target data.

[0088] In some embodiments, the electronic device stores a scene library, which includes at least one preset scene and triggering conditions corresponding to each preset scene;

[0089] The target scene determination module is specifically used to determine the first target scene based on the first target data and the triggering conditions corresponding to each preset scene; the first target scene is a scene in the preset scene.

[0090] In one possible implementation, the scene library further includes second data associated with each of the preset scenes;

[0091] The target data determination module is specifically used to determine the second target data associated with the first target scene based on the second data associated with each preset scene and the first data; the second target data is the data in the second data.

[0092] In one embodiment, the target data determination module is further configured to: determine candidate data associated with the first target scene based on the second data associated with each preset scene and the first data, wherein the candidate data is data in the first data and the candidate data is data in the second data; determine the data score corresponding to the candidate data; and determine the second target data associated with the first target scene based on the data score corresponding to the candidate data, wherein the second target data is data in the candidate data.

[0093] In one possible implementation, the target data determination module is further configured to obtain a first scoring standard and a second scoring standard corresponding to the candidate data, wherein the first scoring standard is a guideline standard and the second scoring standard is a user standard; determine a first deviation degree corresponding to the candidate data according to the first scoring standard, and determine a second deviation degree corresponding to the candidate data according to the second scoring standard; and determine a data score corresponding to the candidate data according to the first deviation degree and the second deviation degree.

[0094] In some embodiments, the first information determining module is specifically used to analyze the first target data to determine a first analysis result corresponding to the first target scenario; analyze the second target data to determine a second analysis result corresponding to the first target scenario; and determine first information corresponding to the first target scenario based on the first analysis result and the second analysis result corresponding to the first target scenario.

[0095] In one possible implementation, the device further includes:

[0096] A first display module is used to display a first interface, the first interface including a first summary card, the first summary card including first information corresponding to the first target scene; or, the first summary card including the first information corresponding to the first target scene, and including a first analysis result and / or a second analysis result corresponding to the first target scene.

[0097] In another possible implementation, the device further includes:

[0098] The second display module is used to display the first interface, which includes a first summary card and a second summary card. The first summary card is positioned in front of the second summary card in the first interface.

[0099] The first summary card includes first information corresponding to the first target scene, and the second summary card includes first information corresponding to the second target scene; or, the first summary card includes first information corresponding to the first target scene, and includes first analysis results and / or second analysis results corresponding to the first target scene, and the second summary card includes first information corresponding to the second target scene, and includes first analysis results and / or second analysis results corresponding to the second target scene, wherein the scene priority of the first target scene is higher than the scene priority of the second target scene.

[0100] In another possible implementation, the device further includes:

[0101] A third display module is configured to display a first interface, the first interface including a first summary card; the first summary card including first information corresponding to the first target scene; or, the first summary card including the first information corresponding to the first target scene, and including a first analysis result and / or a second analysis result corresponding to the first target scene; in response to a first operation detected in the first interface, a second interface is displayed, the second interface including a second summary card; the second summary card including the first information corresponding to the second target scene; or, the second summary card including the first information corresponding to the second target scene, and including a first analysis result and / or a second analysis result corresponding to the second target scene; the scene priority of the first target scene is higher than the scene priority of the second target scene.

[0102] In some embodiments, the scene priority corresponding to the target scene is preset, or determined based on the first target data corresponding to the target scene.

[0103] In one embodiment, the scenario priority corresponding to the target scenario is determined based on the data score corresponding to the first target data of the target scenario.

[0104] In another possible implementation, the device further includes:

[0105] The fourth display module is used to display a first interface, the first interface including a first summary card, the first summary card including first information corresponding to the first target scene; in response to a second operation on the first information corresponding to the first target scene, a third interface is displayed, the third interface including a third summary card, the third summary card including a first analysis result and / or a second analysis result corresponding to the first target scene;

[0106] or,

[0107] The fourth display module is further configured to display a fourth interface in response to a third operation on the first information corresponding to the first target scene, the fourth interface including a fourth summary card, the fourth summary card including a first analysis result corresponding to the first target scene; and to display a fifth interface in response to a fourth operation on the first information corresponding to the first target scene, or in response to a fifth operation detected in the fourth interface, the fifth interface including a fifth summary card, the fifth summary card including a second analysis result corresponding to the first target scene.

[0108] In some embodiments, the first target scene is associated with one or more second target data.

[0109] In other embodiments, the first target data includes one or more of the first data.

[0110] In some embodiments, the first data includes one or more of exercise and health data, environmental data, and usage data corresponding to the electronic device.

[0111] In one possible implementation, the exercise health data includes one or more of the following: sleep heart rate, resting heart rate, sleep heart rate variability, sleep onset time, sleep end time, sleep duration, blood oxygen saturation, blood pressure, body temperature, weight, respiratory rate, mood, stress, steps, exercise duration, activity hours, activity calories, or menstrual cycle.

[0112] Thirdly, embodiments of this application provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the electronic device implements the data processing method described in any one of the first aspects above.

[0113] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program, which, when executed by a computer, causes the computer to implement the data processing method described in any one of the first aspects above.

[0114] Fifthly, embodiments of this application provide a computer program product, the computer program product including a computer program, which, when executed by an electronic device, causes the electronic device to implement the data processing method described in any one of the first aspects above.

[0115] It is understood that the beneficial effects of the second to fifth aspects mentioned above can be found in the relevant descriptions in the first aspect mentioned above, and will not be repeated here. Attached Figure Description

[0116] Figure 1 is a schematic diagram of an application scenario;

[0117] Figure 2 is a schematic diagram of the structure of an electronic device to which the data processing method provided in the embodiments of this application is applicable;

[0118] Figure 3 is a schematic diagram of the software architecture to which the data processing method provided in the embodiments of this application is applicable;

[0119] Figure 4 is a flowchart illustrating the data processing method provided in an embodiment of this application;

[0120] Figure 5 is a schematic diagram of the application scenario of the second deviation provided in the embodiments of this application;

[0121] Figure 6 is a schematic flowchart of the data processing method provided in an embodiment of this application;

[0122] Figures 7 and 8 are schematic diagrams of application scenarios provided in the embodiments of this application;

[0123] Figure 9 is a schematic diagram of the second application scenario provided in the embodiments of this application;

[0124] Figures 10 and 11 are schematic diagrams of the third application scenario provided in the embodiments of this application;

[0125] Figures 12 and 13 are schematic diagrams of the application scenarios provided in the embodiments of this application;

[0126] Figure 14 is a schematic diagram of the fifth application scenario provided in the embodiments of this application;

[0127] Figure 15 is a schematic diagram of another structure of the electronic device provided in an embodiment of this application. Detailed Implementation

[0128] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0129] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0130] As used in this application specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if detected [the described condition or event]" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once detected [the described condition or event]," or "in response to detection [the described condition or event]."

[0131] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0132] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0133] Furthermore, the term "multiple" mentioned in the embodiments of this application should be interpreted as two or more.

[0134] The steps involved in the data processing method provided in this application are merely examples, and not all steps are mandatory, nor are all contents of each piece of information or message required. They can be added or removed as needed during use. The same step or step or message with the same function in this application can be referenced and learned from each other in different embodiments.

[0135] The business scenarios described in the embodiments of this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided in the embodiments of this application. As those skilled in the art will know, with the evolution of network architecture and the emergence of new business scenarios, the technical solutions provided in the embodiments of this application are also applicable to similar technical problems.

[0136] With the continuous development of electronic technology, electronic devices are becoming increasingly sophisticated in their functions. During daily use, users generate and accumulate a large amount of multi-dimensional exercise and health data. For example, electronic devices can acquire exercise data such as steps, running distance, running time, or energy expenditure, and can analyze and display each data point separately. They can also acquire health data such as sleep, heart rate, emotional well-being, blood pressure, blood oxygen saturation, body temperature, or stress levels, and analyze and display each data point separately, allowing users to understand their exercise or health status. However, typical analysis methods usually analyze each exercise or health data point separately, obtaining analytical information only relevant to the individual data. The human body is not an isolated system; there are interrelationships between various exercise and health data points. Extensive medical and exercise physiology research also indicates that various physiological indicators in the human body have varying degrees of correlation, whether positive or negative. Changes in a single indicator may be caused by changes in other indicators, or may cause changes in other related indicators. In other words, there is generally a correlation between various sports data and various health data. This method of analyzing only individual sports data or individual health data cannot meet user needs and results in a poor user experience.

[0137] For example, please refer to Figure 1, which shows a schematic diagram of an application scenario.

[0138] As shown in Figure 1, during a user's sleep, the electronic device can acquire the user's sleep time (e.g., sleep onset time (e.g., 01:45), wake-up time (e.g., 7:10), deep sleep time, and light sleep time). After acquiring the user's sleep time, the electronic device can analyze the sleep time to obtain corresponding analysis information for this sleep period. When the user views the sleep data, the electronic device can display the sleep time on the display interface and can also display a button to view analysis information, such as a "Sleep Analysis and Suggestions" button. When a click or touch operation is detected on the "Sleep Analysis and Suggestions" button, the electronic device can display the analysis information corresponding to this sleep period. For example, the analysis information may include a sleep quality score, nighttime sleep duration, bed rest time, sleep latency, and sleep efficiency. The analysis information may also include interpretations and suggestions for this sleep period. For example, interpretations and suggestions may include: "Sleep quality is average. Sleep duration is somewhat insufficient, at 5 hours and 25 minutes. Insufficient sleep will affect mental state the next day" and "It is recommended to turn off the lights when sleeping and choose curtains with good light-blocking effect; dim or low light is conducive to falling asleep quickly." It should be understood that while displaying the analysis information corresponding to this sleep, the electronic device can also display corresponding reference information to help users understand the differences between this sleep and the reference information.

[0139] As shown in Figure 1, after detecting a click or touch operation on the "Sleep Analysis and Suggestions" button, the electronic device can display the sleep quality score (e.g., 55 points), nighttime sleep (e.g., 5 hours and 25 minutes), the reference value for nighttime sleep (e.g., 6 to 10 hours), the analysis result of nighttime sleep (e.g., shorter than normal), bed rest duration (e.g., 5 hours and 35 minutes), the reference value for bed rest duration (e.g., 6 to 10.5 hours), the analysis result of bed rest duration (e.g., shorter than normal), sleep latency (e.g., 10 minutes), the reference value for sleep latency duration (e.g., less than 30 minutes), the analysis result of sleep latency duration (e.g., normal), sleep efficiency (e.g., 97%), the reference value for sleep efficiency (e.g., 85% to 100%), and the analysis result of sleep efficiency (e.g., normal), as well as interpretations and suggestions (e.g., "Sleep quality is average. Sleep duration is somewhat insufficient, at 5 hours and 25 minutes. Insufficient sleep will affect mental state the next day," and "It is recommended to turn off the lights when sleeping and choose curtains with good light-blocking effect; dim light or low light is conducive to falling asleep quickly").

[0140] It should be understood that while displaying sleep time, electronic devices may also display buttons for other functions. For example, as shown in Figure 1, the electronic device may display a button for "Record Sleep," a button for "Manual Input" to manually enter sleep data, or a button for "Sleep Music" to set sleep-aid music, and so on.

[0141] In the aforementioned sleep scenario, when acquiring a user's sleep time, the electronic device directly analyzes the sleep time to determine the corresponding analysis information for this sleep period. That is, the analysis information is only related to the sleep time and does not involve other data such as exercise data or health data. In other words, it does not analyze the correlation between sleep time and other data such as exercise data or health data, so users cannot understand the correlation between sleep time and other data. This makes it impossible for users to better understand their own health status, fails to meet user needs, and results in a poor user experience.

[0142] To address the aforementioned problems, embodiments of this application provide a data processing method, an electronic device, and a computer-readable storage medium. In this method, the electronic device can acquire first data and determine a first target scenario based on first target data within the first data. Subsequently, the electronic device can determine second target data associated with the first target scenario and, based on the first and second target data, determine first information corresponding to the first target scenario. The second target data can be data from the first data, and the second target data is different from the first target data. That is, in this embodiment, after determining the first target scenario based on the first target data, the second target data associated with the first target scenario can be determined. Therefore, based on the first and second target data, the first information corresponding to the first target scenario can be determined, allowing users to clearly understand the relationship between the data corresponding to the first target scenario (e.g., the first and second target data) based on the first information. This enables users to better understand their own health status, meeting user needs, improving user experience, and possessing strong usability and practicality.

[0143] In this application embodiment, the electronic device can be a mobile phone, tablet computer, wearable device, in-vehicle device, augmented reality (AR) / virtual reality (VR) device, laptop computer, ultra-mobile personal computer (UMPC), netbook, personal digital assistant (PDA), desktop computer, etc. This application embodiment does not impose any restrictions on the specific type of electronic device.

[0144] The following first describes the electronic device involved in the embodiments of this application. Please refer to Figure 2, which shows a schematic diagram of the structure of the electronic device 200.

[0145] Electronic device 200 may include a processor 210, an external memory interface 220, an internal memory 221, a universal serial bus (USB) interface 230, a charging management module 240, a power management module 241, a battery 242, antenna 1, antenna 2, a mobile communication module 250, a wireless communication module 260, an audio module 270, a speaker 270A, a receiver 270B, a microphone 270C, a headphone jack 270D, a sensor module 280, buttons 290, a camera 291, and a display screen 292, etc. The sensor module 280 may include a pressure sensor 280A, a gyroscope sensor 280B, a barometric pressure sensor 280C, a magnetic sensor 280D, an accelerometer 280E, a proximity sensor 280F, a proximity light sensor 280G, a fingerprint sensor 280H, a temperature sensor 280J, a touch sensor 280K, an ambient light sensor 280L, and a bone conduction sensor 280M, etc.

[0146] It is understood that the structures illustrated in the embodiments of this application do not constitute a specific limitation on the electronic device 200. In other embodiments of this application, the electronic device 200 may include more or fewer components than illustrated, or combine some components, or split some components, or have different component arrangements. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

[0147] Processor 210 may include one or more processing units, such as application processors (APs), modem processors, graphics processing units (GPUs), image signal processors (ISPs), controllers, video codecs, digital signal processors (DSPs), baseband processors, and / or neural network processing units (NPUs). These different processing units may be independent devices or integrated into one or more processors.

[0148] The controller can generate operation control signals based on the instruction opcode and timing signals to complete the control of instruction fetching and execution.

[0149] The processor 210 may also include a memory for storing instructions and data. In some embodiments, the memory in the processor 210 is a cache memory. This memory can store instructions or data that the processor 210 has just used or that are used repeatedly. If the processor 210 needs to use the instruction or data again, it can directly retrieve it from the memory. This avoids repeated accesses, reduces the waiting time of the processor 210, and thus improves the efficiency of the system.

[0150] In some embodiments, the processor 210 may include one or more interfaces. Interfaces may include an inter-integrated circuit (I2C) interface, an inter-integrated circuit sound (I2S) interface, a pulse code modulation (PCM) interface, a universal asynchronous receiver / transmitter (UART) interface, a mobile industry processor interface (MIPI), a general-purpose input / output (GPIO) interface, a subscriber identity module (SIM) interface, and / or a universal serial bus (USB) interface, etc.

[0151] It is understood that the interface connection relationships between the modules illustrated in the embodiments of this application are merely illustrative and do not constitute a structural limitation on the electronic device 200. In other embodiments of this application, the electronic device 200 may also employ different interface connection methods or combinations of multiple interface connection methods as described in the above embodiments.

[0152] The charging management module 240 receives charging input from the charger. The power management module 241 connects the battery 242, the charging management module 240, and the processor 210. The power management module 241 receives input from the battery 242 and / or the charging management module 240, and supplies power to the processor 210, internal memory 221, display screen 292, camera 291, and wireless communication module 260, etc. The wireless communication function of the electronic device 200 can be implemented through antenna 1, antenna 2, mobile communication module 250, wireless communication module 260, modem processor, and baseband processor, etc.

[0153] Antenna 1 and antenna 2 are used to transmit and receive electromagnetic wave signals. Each antenna in electronic device 200 can be used to cover one or more communication frequency bands.

[0154] The mobile communication module 250 can provide solutions for wireless communication, including 2G / 3G / 4G / 5G, applied to the electronic device 200. The mobile communication module 250 may include at least one filter, switch, power amplifier, low noise amplifier (LNA), etc. The mobile communication module 250 can receive electromagnetic waves via antenna 1, and perform filtering, amplification, and other processing on the received electromagnetic waves before transmitting them to a modem processor for demodulation. The mobile communication module 250 can also amplify the signal modulated by the modem processor and convert it into electromagnetic waves for radiation via antenna 1. In some embodiments, at least some functional modules of the mobile communication module 250 may be housed in the processor 210. In some embodiments, at least some functional modules of the mobile communication module 250 and at least some modules of the processor 210 may be housed in the same device.

[0155] The modem processor may include a modulator and a demodulator. The modulator modulates the low-frequency baseband signal to be transmitted into a mid-to-high frequency signal. The demodulator demodulates the received electromagnetic wave signal into a low-frequency baseband signal. The demodulator then transmits the demodulated low-frequency baseband signal to the baseband processor for processing. After processing by the baseband processor, the low-frequency baseband signal is transmitted to the application processor. The application processor outputs sound signals through audio devices (not limited to speaker 270A, receiver 270B, etc.) or displays images or videos through display screen 292. In some embodiments, the modem processor may be a separate device. In other embodiments, the modem processor may be independent of the processor 210 and may be housed in the same device as the mobile communication module 250 or other functional modules.

[0156] The wireless communication module 260 can provide solutions for wireless communication applications on the electronic device 200, including wireless local area networks (WLANs) (such as wireless fidelity (Wi-Fi) networks), Bluetooth (BT), global navigation satellite system (GNSS), frequency modulation (FM), near field communication (NFC), and infrared (IR) technologies. The wireless communication module 260 can be one or more devices integrating at least one communication processing module. The wireless communication module 260 receives electromagnetic waves via antenna 2, performs frequency modulation and filtering of the electromagnetic wave signals, and sends the processed signal to processor 210. The wireless communication module 260 can also receive signals to be transmitted from processor 210, perform frequency modulation and amplification, and convert them into electromagnetic waves for radiation via antenna 2.

[0157] In some embodiments, antenna 1 of electronic device 200 is coupled to mobile communication module 250, and antenna 2 is coupled to wireless communication module 260, enabling electronic device 200 to communicate with networks and other devices via wireless communication technology. The wireless communication technology may include Global System for Mobile Communications (GSM), General Packet Radio Service (GPRS), Code Division Multiple Access (CDMA), Wideband Code Division Multiple Access (WCDMA), Time-Division Code Division Multiple Access (TD-SCDMA), Long Term Evolution (LTE), BT, GNSS, WLAN, NFC, FM, and / or IR technologies, etc. The GNSS may include the Global Positioning System (GPS), the Global Navigation Satellite System (GLONASS), the BeiDou Navigation Satellite System (BDS), the Quasi-Zenith Satellite System (QZSS), and / or satellite-based augmentation systems (SBAS).

[0158] Electronic device 200 implements display functions through a GPU, display screen 292, and application processor. The GPU is a microprocessor for image processing, connected to the display screen 292 and the application processor. The GPU is used to perform mathematical and geometric calculations and for graphics rendering. Processor 210 may include one or more GPUs, which execute program instructions to generate or modify display information.

[0159] Display screen 292 is used to display images, videos, etc. Display screen 292 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 (AMOLED), a flexible light-emitting diode (FLED), a Miniled LED, a MicroLED, a Micro-OLED, a quantum dot light-emitting diode (QLED), etc. In some embodiments, electronic device 200 may include one or N displays 292, where N is a positive integer greater than 1.

[0160] Electronic device 200 can perform shooting functions through ISP, camera 291, video codec, GPU, display screen 292 and application processor.

[0161] The ISP is used to process data fed back by the camera 291. The camera 291 is used to capture still images or videos. In some embodiments, the electronic device 200 may include one or N cameras 291, where N is a positive integer greater than 1.

[0162] A digital signal processor (DSP) is used to process digital signals, including digital image signals and other digital signals. A video codec is used to compress or decompress digital video. Electronic device 200 can support one or more video codecs. Thus, electronic device 200 can play or record video in various encoding formats, such as Moving Picture Experts Group (MPEG) 1, MPEG 2, MPEG 3, MPEG 4, etc.

[0163] An NPU (Neural Processing Unit) is a neural network (NN) computing processor that, by borrowing the structure of biological neural networks, such as the transmission patterns between neurons in the human brain, rapidly processes input information and can continuously learn on its own. NPUs enable intelligent cognitive applications in electronic devices, such as image recognition, facial recognition, speech recognition, and text understanding.

[0164] The external storage interface 220 can be used to connect an external memory card, such as a Micro SD card, to expand the storage capacity of the electronic device 200. The external memory card communicates with the processor 210 through the external storage interface 220 to perform data storage functions. For example, music, video, and other files can be saved on the external memory card.

[0165] Internal memory 221 can be used to store computer executable program code, which includes instructions. Internal memory 221 may include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function (such as sound playback, image playback, etc.), etc. The data storage area may store data created during the use of electronic device 200 (such as audio data, phonebook, etc.). Furthermore, internal memory 221 may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device, flash memory device, universal flash storage (UFS), etc. Processor 210 executes various functional applications and data processing of electronic device 200 by running instructions stored in internal memory 221 and / or instructions stored in memory disposed in the processor.

[0166] Electronic device 200 can implement audio functions such as music playback and recording through audio module 270, speaker 270A, receiver 270B, microphone 270C, headphone jack 270D, and application processor.

[0167] Audio module 270 is used to convert digital audio information into analog audio signal output, and also to convert analog audio input into digital audio signal. Audio module 270 can also be used for encoding and decoding audio signals.

[0168] Buttons 290 include a power button, volume buttons, etc. Buttons 290 can be mechanical buttons or touch-sensitive buttons. Electronic device 200 can receive button input and generate key signal inputs related to user settings and function control of electronic device 200.

[0169] The software system of electronic device 200 can adopt a layered architecture, event-driven architecture, microkernel architecture, microservice architecture, or cloud architecture. For example, the software system of electronic device 200 can adopt a layered architecture such as Android operating system (OS), Harmony OS, or iOS. This application embodiment uses a layered architecture as an example to illustrate the software structure of electronic device 200.

[0170] Figure 3 is a software structure block diagram of an electronic device 200 according to an embodiment of this application.

[0171] A layered architecture divides software into several layers, each with a clear role and function. Layers communicate with each other through software interfaces. In some embodiments, the operating system is divided into four layers, from top to bottom: the application layer, the application framework layer, the runtime and system libraries, and the kernel layer.

[0172] The application layer can include a series of application packages.

[0173] As shown in Figure 3, the application package may include applications such as camera, gallery, calendar, call, map, navigation, WLAN, Bluetooth, music, video, and SMS.

[0174] The application framework layer provides application programming interfaces (APIs) and a programming framework for applications in the application layer. The application framework layer includes some predefined functions.

[0175] As shown in Figure 3, the application framework layer may include a window manager, content provider, view system, phone manager, resource manager, notification manager, etc.

[0176] The window manager is used to manage windowed applications. It can retrieve screen size, determine the presence of a status bar, lock the screen, and capture screenshots, among other things.

[0177] Content providers store and retrieve data, making that data accessible to applications. This data may include videos, images, audio, made and received phone calls, browsing history and bookmarks, phone books, etc.

[0178] A view system includes visual controls, such as controls for displaying text and controls for displaying images. View systems can be used to build applications. A display interface can consist of one or more views. For example, a display interface including a text notification icon could include views for displaying text and views for displaying images.

[0179] The phone manager is used to provide communication functions for electronic devices 200. For example, it manages call status (including connection and disconnection).

[0180] The file explorer provides applications with various resources, such as localized strings, icons, images, layout files, video files, and more.

[0181] The notification manager allows applications to display notifications in the status bar. These notifications can be used to deliver informational messages and can disappear automatically after a short pause, requiring no user interaction. For example, the notification manager can be used to notify users of completed downloads or message alerts. The notification manager can also display notifications as icons or scrolling text in the top status bar, such as notifications from background applications, or as dialog boxes on the screen. Examples include displaying text messages in the status bar, emitting sounds, vibrating electronic devices, and flashing indicator lights.

[0182] Runtime consists of core libraries and a virtual machine. Runtime is responsible for the scheduling and management of the operating system.

[0183] The core library consists of two parts: one part is the functionalities that the Java language needs to call, and the other part is the core library of the operating system.

[0184] The application layer and application framework layer run in a virtual machine. The virtual machine executes the Java files of the application layer and application framework layer as binary files. The virtual machine is used to perform functions such as object lifecycle management, stack management, thread management, security and exception management, and garbage collection.

[0185] System libraries can include multiple functional modules. For example: surface manager, media libraries, 3D graphics processing libraries (e.g., OpenGLES), 2D graphics engines (e.g., SGL), etc.

[0186] The Surface Manager is used to manage the display subsystem and provides the blending of 2D and 3D layers for multiple applications.

[0187] The media library supports playback and recording of various common audio and video formats, as well as still image files. It supports multiple audio and video encoding formats, such as MPEG4, H.264, MP3, AAC, AMR, JPG, and PNG.

[0188] The 3D graphics processing library is used to implement 3D graphics drawing, image rendering, compositing, and layer processing.

[0189] A 2D graphics engine is a graphics engine for 2D drawing.

[0190] The kernel layer is the layer between hardware and software. The kernel layer contains at least the display driver, camera driver, audio driver, and sensor driver.

[0191] The data processing method provided in the embodiments of this application will be described in detail below with reference to the accompanying drawings and specific application scenarios.

[0192] Please refer to Figure 4, which shows a schematic flowchart of a data processing method provided in an embodiment of this application. This method can be applied to electronic devices. As shown in Figure 4, the method may include:

[0193] S401, The electronic device acquires the first data.

[0194] S402. The electronic device determines the first target scenario based on the first target data; the first target data is the data in the first data.

[0195] S403, the electronic device determines the second target data associated with the first target scene; the second target data is the data in the first data, and the second target data is different from the first target data.

[0196] S404. The electronic device determines the first information corresponding to the first target scenario based on the first target data and the second target data.

[0197] In this embodiment, the electronic device can acquire first data and determine a first target scenario based on first target data within the first data. Subsequently, the electronic device can determine second target data associated with the first target scenario and determine first information corresponding to the first target scenario based on the first and second target data. The second target data can be data from the first data, and the second target data is different from the first target data. That is, in this embodiment, after determining the first target scenario based on the first target data, the second target data associated with the first target scenario can be determined. Therefore, based on the first and second target data, the first information corresponding to the first target scenario can be determined, allowing the user to clearly understand the relationship between the data corresponding to the first target scenario based on the first information, and to better understand their own health status, thus meeting user needs and improving user experience.

[0198] It should be noted that the first data may include data collected by the electronic device, and / or may include data shared by other devices with the electronic device.

[0199] For example, electronic devices can collect data through various sensors within the device. For instance, an accelerometer can be used to collect step counts. An ambient light sensor can be used to collect ambient brightness. A temperature sensor can be used to collect temperature data, and so on.

[0200] For example, electronic devices can connect to wearable devices such as smartwatches. These wearable devices can collect data and share it with the electronic device. For instance, smartwatches can collect data such as steps, exercise duration, heart rate, and blood oxygen saturation, and can share this data with the electronic device.

[0201] For example, electronic devices can connect to fitness equipment. During a user's workout, the fitness equipment can collect data and share that data with the electronic device.

[0202] In some embodiments, the first data may include one or more of exercise and health data, environmental data, and usage data. Exercise and health data may include exercise data and health data.

[0203] Exercise data can refer to various data related to exercise. For example, exercise data can include running ability, cycling ability, swimming ability, triathlon endurance, training load, training stress, recovery time, training index, fitness index, fatigue index, VO2 max, body battery, stamina value, activity calories, exercise duration, activity hours, speed, wheelchair mobility, steps, distance, number of floors climbed, speed going up stairs or down stairs, etc.

[0204] Health data can refer to various data related to physiological indicators. For example, health data can include height, weight, body mass index, body composition, waist circumference, hip circumference, waist-to-hip ratio, respiratory rate, maximum vital capacity, one-second rate, apnea-hypopnea index, menstrual cycle, ovulation estimation, menstrual deviation, heart rate, resting heart rate, heart rate variability (HRV), average walking heart rate, recovery heart rate, sleep heart rate, sleep HRV, arterial elasticity, electrocardiogram, body temperature, blood oxygen, blood pressure, ambulatory blood pressure monitoring, average blood pressure throughout the day, average blood pressure during wakefulness, average blood pressure during sleep, nocturnal blood pressure drop rate, blood glucose, blood lipids, uric acid, bone density, muscle mass, mood, stress, wake-up time, bedtime, sleep onset time, sleep end time (or wake-up time), sleep duration, light sleep ratio, deep sleep ratio, deep sleep continuity, REM sleep ratio, sleep quality, sleep latency, sleep efficiency, sleep onset regularity, wake-up regularity, number of awakenings, or awakening duration, etc.

[0205] Environmental data can refer to various data related to the environment. For example, environmental data may include ambient volume, ambient sound exposure, in-ear volume, in-ear sound exposure, ambient brightness, sunshine duration, ultraviolet index, air quality, or weather index, etc.

[0206] Usage data can refer to data on the use of electronic devices. For example, usage data can include the time spent using electronic devices or the duration of use, etc.

[0207] It should be understood that when exercise data includes one or more types, "first data" including exercise data can mean that the first data includes one or more types of exercise data. Similarly, when health data includes one or more types, "first data" including health data can mean that the first data includes one or more types of health data. Likewise, when environmental data includes one or more types, "first data" including environmental data can mean that the first data includes one or more types of environmental data. And when usage data includes one or more types, "first data" including usage data can mean that the first data includes one or more types of usage data, and so on.

[0208] In some embodiments, both the first target data and the second target data can be data from the first data. For example, the first target data may include one or more of the first data. That is, the electronic device can determine the first target scene based on one or more of the first data. Similarly, the second target data may include one or more of the first data. That is, the first target scene can be associated with one or more of the second target data.

[0209] It should be noted that the embodiments of this application do not limit the number of first target data included, and can be determined specifically according to the actual application scenario. For example, the first target data may include one type of first data, depending on the actual application scenario. Similarly, the embodiments of this application do not limit the number of second target data associated with the first target scenario, and can be determined specifically according to the actual application scenario. For example, the first target scenario may be associated with one type of second target data, depending on the actual application scenario. For example, the first target scenario may be associated with two types of second target data, depending on the actual application scenario.

[0210] In this embodiment of the application, the first target data and the second target data are not the same. That is, the first target data and the second target data are not the same data.

[0211] For example, when the first data includes health data A, health data B, and health data C, the first target data may include health data A, and the second target data may include health data B. Alternatively, the first target data may include health data B, the second target data may include health data C, and so on.

[0212] For example, when the first data includes health data A, health data B, health data C, exercise data D, exercise data E, environmental data F, and usage data G, the first target data may include health data A and environmental data F, and the second target data may include exercise data C. Alternatively, the first target data may include health data B and usage data G, and the second target data may include health data C and exercise data D. Or, the first target data may include health data A, environmental data F, and usage data G, and the second target data may include health data B and exercise data C, and so on.

[0213] It should be noted that the first target data and the second target data are both data from the first data and are only used as examples, and should not be construed as limiting the embodiments of this application. In the embodiments of this application, the first target data and the second target data can be data from different data sources. For example, the first target data can be data from the first data source, and the second target data can be data from other data sources (e.g., it can be called third data). That is, the electronic device can also acquire third data. After determining the first target scene based on the first target data in the first data source, the electronic device can determine the second target data associated with the first target scene based on the third data.

[0214] The third data may include data collected by the electronic device, and / or may include data shared between the electronic device and other devices. The third data may also include one or more of the following: exercise data, health data, environmental data, and usage data. The data included in the third data may be partially the same as or completely different from the data included in the first data. For example, the first data may include health data, and the third data may include exercise data, environmental data, and usage data. The following example illustrates this using the scenario where both the first and second target data are data from the first data.

[0215] In this embodiment, a scenario can refer to the representation of the analysis results obtained by analyzing data such as exercise data and / or health data in a certain time dimension. That is, a scenario can be used to represent the analysis results corresponding to exercise data and / or health data in a certain time dimension. Specifically, a first target scenario can be used to represent the analysis results corresponding to first target data in a certain time dimension. It should be understood that the time dimension can be determined according to the actual application scenario, and this embodiment does not limit this. For example, the time dimension may include one or more of days, weeks, months, or years. A scenario can be used to represent the analysis results such as baseline comparison, month-on-month comparison, mean presentation, or trend of exercise data and / or health data in dimensions such as days, weeks, months, or years.

[0216] For example, in the time dimension, which includes day and hour, the first target data can include data from within a single day.

[0217] When the primary target data includes the previous day's sleep time, the analysis results corresponding to the primary target data can be determined based on a comparison between the previous day's sleep time and the sleep baseline. For example, the analysis results may include a normal sleep time (e.g., the previous day's sleep time was no later than the sleep baseline) and staying up late (e.g., the previous day's sleep time was later than the sleep baseline). In this case, the primary target scenario can include a normal sleep time or staying up late. The sleep baseline can be a guideline baseline or a user baseline (also known as a personal baseline). A guideline baseline can be a standard baseline corresponding to sleep time determined based on authoritative guidelines. A user baseline can be a baseline that conforms to the user's personal habits and is determined based on the user's historical sleep times.

[0218] When the primary target data includes the previous day's sleep duration, the analysis results corresponding to the primary target data can be determined based on a comparison between the previous day's sleep duration and the baseline. For example, the analysis results may include ideal sleep duration (e.g., the previous day's sleep duration is close to the baseline), excessive sleepiness (e.g., the previous day's sleep duration is longer than the baseline), or insufficient sleep (e.g., the previous day's sleep duration is shorter than the baseline). In this case, the primary target scenario can include ideal sleep duration, excessive sleepiness, or insufficient sleep. The baseline can be a guideline baseline or a user baseline. The guideline baseline can be a standard baseline corresponding to sleep duration determined according to authoritative guidelines. The user baseline can be a baseline determined based on the user's historical sleep duration and conforming to the user's personal habits.

[0219] For example, when the time dimension includes weeks, the first target data can include data from a week.

[0220] When the primary target data includes weekly sleep onset time, the analysis results corresponding to the primary target data can be determined based on a comparison between weekly sleep onset time and the sleep baseline. For example, the analysis results may include normal sleep onset time during the week (e.g., the number of times the sleep onset time was no later than the sleep baseline was greater than or equal to 5 times in the week, or the average sleep onset time for the week was no later than the sleep baseline), an improving trend in sleep onset time during the week (e.g., the sleep onset time gradually approaching the sleep baseline for the week), a worsening trend in sleep onset time during the week (e.g., the sleep onset time gradually deviating from the sleep baseline for the week), or staying up late during the week (e.g., staying up late more than or equal to 4 times in the week, or the average sleep onset time for the week being later than the sleep baseline for the week). In this case, the primary target scenario could include normal sleep onset time during the week, an improving trend in sleep onset time during the week, a worsening trend in sleep onset time during the week, or staying up late during the week.

[0221] When the primary target data includes weekly sleep duration, the analysis results corresponding to the primary target data can be determined based on a comparison between the weekly sleep duration and the baseline. For example, the analysis results may include ideal weekly sleep duration (e.g., the number of times the weekly sleep duration is close to the baseline is greater than or equal to 5, or the average weekly sleep duration is close to the baseline), a trend of improving weekly sleep duration (e.g., the weekly sleep duration gradually approaches the baseline), a trend of worsening weekly sleep duration (e.g., the weekly sleep duration gradually deviates from the baseline), excessive sleepiness during the week (e.g., the number of times the weekly sleep duration exceeds the baseline is greater than or equal to 4, or the average weekly sleep duration exceeds the baseline), or insufficient weekly sleep (e.g., the number of times the weekly sleep duration is less than the baseline is greater than or equal to 4, or the average weekly sleep duration is less than the baseline). In this case, the primary target scenario may include ideal weekly sleep duration, a trend of improving weekly sleep duration, a trend of worsening weekly sleep duration, excessive sleepiness during the week, or insufficient weekly sleep.

[0222] When the primary target data includes sleep onset and sleep out times for a week, the analysis results corresponding to the primary target data can be determined based on one or more of the following: a comparison of sleep onset time to sleep onset baseline for a week, a comparison of sleep out time to sleep out baseline for a week, a week-on-week comparison of sleep onset time, and a week-on-week comparison of sleep out time. For example, the analysis results may include regular sleep patterns or irregular sleep patterns. In this case, the primary target scenario may include regular sleep patterns or irregular sleep patterns.

[0223] It should be noted that the scenario can be pre-set. That is, the scenario can be pre-set according to the actual application scenario, and the pre-set scenario (hereinafter referred to as the preset scenario for ease of understanding) can be saved in the electronic device or in other devices that are communicatively connected to the electronic device. After determining the first target data, the electronic device can determine the first target scenario based on the preset scenario saved in the electronic device or the preset scenario saved in other devices. In other words, the target scenario (e.g., the first target scenario) in this embodiment can be a scenario from the preset scenarios.

[0224] In some embodiments, the first information corresponding to the first target scenario may be information obtained by analyzing the first target data and the second target data corresponding to the first target scenario. For example, the first information may include one or more of the following: interpretation information corresponding to the first target scenario, information indicating the correlation between the first target data and the second target data corresponding to the first target scenario, and suggestion information corresponding to the first target scenario.

[0225] It should be noted that the specific content of the first information can be determined according to the actual application scenario, and this application embodiment does not impose specific limitations on it. For example, depending on the actual application scenario, the first information can be determined to include interpretation information corresponding to the first target scenario and information used to indicate the correlation between the first target data and the second target data. For example, depending on the actual application scenario, the first information can be determined to include interpretation information corresponding to the first target scenario, information used to indicate the correlation between the first target data and the second target data, and suggestion information corresponding to the first target scenario, etc.

[0226] The following provides a detailed explanation of S402, the process by which the electronic device determines the first target scene based on the first target data, and S403, the process by which the electronic device determines the second target data associated with the first target scene.

[0227] As described above, scenes can be preset and saved in the electronic device or in other devices that are connected to the electronic device. In other words, the electronic device or other devices connected to the electronic device can store a scene library.

[0228] In some embodiments, the scenario library may include at least one preset scenario, master data corresponding to each preset scenario, triggering conditions corresponding to each preset scenario, and data associated with each preset scenario (e.g., second data, or associated data). For each preset scenario, the master data corresponding to the preset scenario may refer to the data used to trigger the preset scenario. That is, the master data corresponding to the preset scenario may refer to the data corresponding to the analysis result represented by the preset scenario, i.e., the analysis result represented by the preset scenario may be the analysis result obtained by analyzing the master data corresponding to the preset scenario. For each preset scenario, the triggering condition corresponding to the preset scenario may refer to the condition that the master data corresponding to the preset scenario must satisfy when the preset scenario is triggered.

[0229] It should be noted that for each preset scenario, the master data corresponding to that preset scenario can include one or more types, and the master data corresponding to that preset scenario and the second data associated with that preset scenario can be different. Different preset scenarios can have different master data; or they can have the same master data, but the triggering conditions corresponding to each preset scenario can be different. That is, there can be multiple preset scenarios in the scenario library that have the same master data, but the triggering conditions corresponding to these multiple preset scenarios are different. In other words, the same master data can trigger different scenarios when different triggering conditions are met.

[0230] For example, the scenario library can include drowsiness, sleep deprivation, and staying up late. The master data for drowsiness and sleep deprivation is sleep duration, but the trigger condition for drowsiness is condition A (e.g., sleep duration greater than 10 hours), and the trigger condition for sleep deprivation is condition B (e.g., sleep duration less than 6 hours). That is, when sleep duration is greater than 10 hours, the electronic device can determine that the triggered scenario is drowsiness. When sleep duration is less than 6 hours, the electronic device can determine that the triggered scenario is sleep deprivation.

[0231] For each preset scenario, the second data associated with that preset scenario may include data affected by the master data corresponding to that preset scenario, and / or data that affects the master data corresponding to that preset scenario. The second data associated with that preset scenario may include supporting data associated with that preset scenario, and / or may include related data associated with that preset scenario. Supporting data associated with that preset scenario may refer to data directly related to the master data corresponding to that preset scenario. Supporting data associated with that preset scenario can be used to determine the master data corresponding to that preset scenario. For example, when the master data corresponding to that preset scenario includes activity calories, the supporting data associated with that preset scenario may include data such as steps and activity duration that can be used to calculate activity calories. For example, when the master data corresponding to that preset scenario includes sleep quality, the supporting data associated with that preset scenario may include data such as sleep duration and deep sleep ratio that can be used to calculate sleep quality. Related data associated with that preset scenario may refer to data indirectly related to the master data corresponding to that preset scenario, that is, data other than supporting data among the data affected by and influencing the master data corresponding to that preset scenario.

[0232] It should be understood that whether the second data associated with each preset scenario is divided into supporting data and related data can be determined according to the actual application scenario, and this application embodiment does not impose any restrictions on this. The following will be illustrated by taking the example of not dividing the second data associated with each preset scenario into supporting data and related data. In addition, the master data corresponding to each preset scenario and the second data associated with each preset scenario can be determined according to the actual application scenario, and this application embodiment does not impose any restrictions on this. For example, the master data corresponding to each preset scenario and the second data associated with each preset scenario can be determined based on professional basis such as authoritative guidelines, academic research or industry standards.

[0233] For example, for each preset scenario, the master data corresponding to the preset scenario may include one or more of the following: exercise data, health data, environmental data, and usage data. The second data associated with the preset scenario may also include one or more of the following: exercise data, health data, environmental data, and usage data.

[0234] It should be noted that the first data and the third data can be data actually acquired by the electronic device. For each preset scenario, the second data associated with that preset scenario can be data theoretically related to that preset scenario. Specifically, for each preset scenario, when the electronic device acquires all the second data associated with that preset scenario, the first data or the third data can include all the second data associated with that preset scenario. When the electronic device acquires only a portion of the second data associated with that preset scenario, the first data or the third data can include only that portion of the second data associated with that preset scenario. When the electronic device does not acquire the second data associated with that preset scenario, the first data or the third data may not include the second data associated with that preset scenario.

[0235] In some embodiments, the first data may include master data corresponding to one or more preset scenarios. After acquiring the first data, the electronic device can determine the first target data based on the first data and the master data corresponding to each preset scenario, and can determine the preset scenario (i.e., the first target scenario) triggered by the first target data based on the first target data and the triggering conditions corresponding to each preset scenario. The first target data may include master data corresponding to one or more preset scenarios.

[0236] For example, the scenario library may include preset scenario A, preset scenario B, preset scenario C, and preset scenario D. The master data corresponding to preset scenario A and preset scenario B can both be data A. The master data corresponding to preset scenario C and preset scenario D can both be data G. The triggering condition for preset scenario A is that data A meets condition A. The triggering condition for preset scenario B is that data A meets condition B. The triggering condition for preset scenario C is that data G meets condition C. The triggering condition for preset scenario D is that data G meets condition D.

[0237] Assuming the first data includes data A, data B, data C, data D, and data E, the electronic device can determine that the first target data includes data A based on the first data and the master data corresponding to each preset scenario in the scenario library (i.e., data A and data G). Assuming data A satisfies condition A, the electronic device can determine that the scenario triggered by data A is preset scenario A. That is, the electronic device can determine that the first target scenario includes preset scenario A.

[0238] Assuming the first data includes data A, data B, data C, data D, data E, data F, and data G, the electronic device can determine that the first target data includes data A and data G based on the first data and the master data corresponding to each preset scenario in the scenario library (i.e., data A and data G). Assuming data A satisfies condition B and data G satisfies condition C, the electronic device can then determine that the scenario triggered by data A is preset scenario B, and the scenario triggered by data G is preset scenario C. In other words, the electronic device can determine that the first target scenario includes preset scenario B and preset scenario C.

[0239] In this embodiment of the application, after determining the first target scene, the electronic device can determine the second data associated with the first target scene based on the scene library, and can determine the second target data associated with the first target scene based on the first data and the second data associated with the first target scene. The second target data can be data from the first data, and the second target data can be data from the second data associated with the first target scene.

[0240] In some embodiments, the electronic device can determine candidate data associated with the first target scene based on the first data and the second data associated with the first target scene, and can determine second target data associated with the first target scene based on the candidate data associated with the first target scene. The candidate data associated with the first target scene can be data from the first data, and the candidate data associated with the first target scene can be data from the second data associated with the first target scene. The second target data associated with the first target scene can be data from the candidate data associated with the first target scene.

[0241] In one possible implementation, the electronic device can determine all candidate data associated with the first target scene as the second target data associated with the first target scene.

[0242] For example, when the first data includes data A, data B, data C, data D, data E, and data F, and the second data associated with the first target scenario includes data A, data C, data D, data F, and data G, the electronic device can determine that the candidate data associated with the first target scenario includes data A, data C, data D, and data F. In this case, the electronic device can determine data A, data C, data D, and data F as the second target data associated with the first target scenario.

[0243] In another possible implementation, the electronic device can determine a portion of the candidate data associated with the first target scene as the second target data associated with the first target scene. That is, the electronic device can select a portion of the candidate data associated with the first target scene as the second target data associated with the first target scene.

[0244] In one embodiment, an electronic device can select a portion of candidate data associated with a first target scene as second target data associated with that first target scene based on the relevance of each candidate data associated with the first target scene. The relevance can be used to characterize the closeness between data and a scene. That is, the relevance of a certain candidate data (e.g., candidate data A) associated with the first target scene can be used to characterize the closeness between candidate data A and the first target scene. For each preset scene, the scene library may also include the relevance of the second data associated with that preset scene. Therefore, after determining each candidate data associated with the first target scene, the electronic device can obtain the relevance of each candidate data associated with the first target scene and determine the second target data associated with the first target scene based on the relevance of each candidate data associated with the first target scene.

[0245] It should be noted that the relevance of the second data associated with each preset scenario can be determined based on the actual application scenario, and this application embodiment does not impose any restrictions on this. For example, the relevance of the second data associated with each preset scenario can be determined based on professional evidence such as authoritative guidelines, academic research, or industry standards.

[0246] For example, an electronic device can identify candidate data with a relevance greater than or equal to a preset threshold (e.g., preset threshold A) from the candidate data associated with the first target scene as second target data associated with the first target scene. The preset threshold A can be determined based on the actual application scenario, and this embodiment does not impose any limitations on it.

[0247] For example, based on the actual application scenario, a preset threshold A or the absolute value of preset threshold A can be determined as 1. This means the electronic device can identify candidate data with a relevance or an absolute value of relevance greater than or equal to 1 from the candidate data associated with the first target scenario as the second target data associated with the first target scenario. Similarly, based on the actual application scenario, a preset threshold A or the absolute value of preset threshold A can be determined as 2. This means the electronic device can identify candidate data with a relevance or an absolute value of relevance greater than or equal to 2 from the candidate data associated with the first target scenario as the second target data associated with the first target scenario, and so on.

[0248] For example, the electronic device can determine the top N candidate data with the highest relevance or absolute value of relevance among the candidate data associated with the first target scene as the second target data associated with the first target scene. N can be an integer greater than or equal to 1. The value of N can be determined according to the actual application scenario, and this application embodiment does not limit it.

[0249] For example, N can be set to 1 based on the actual application scenario, meaning the electronic device can identify the candidate data with the highest relevance or absolute value of relevance among the candidate data associated with the first target scenario as the second target data associated with the first target scenario. Alternatively, N can be set to 2, meaning the electronic device can identify the top two candidate data with the highest relevance or absolute value of relevance among the candidate data associated with the first target scenario as the second target data associated with the first target scenario. And so on.

[0250] For example, an electronic device can store a scene library as shown in Table 1. The values ​​in Table 1 can represent the relevance of the second data associated with each preset scene. This scene library can set preset scenes based on a daily, weekly, monthly, or yearly time dimension, and set the second data associated with each preset scene. That is, as shown in Table 1, the scene library can include, by day (normal sleep onset time, staying up late, ideal sleep duration, excessive sleepiness, and insufficient sleep), by week (normal sleep onset time, staying up late, improving sleep onset time, worsening sleep onset time, regular sleep, irregular sleep, ideal sleep duration, excessive sleepiness, insufficient sleep, improving sleep duration, and worsening sleep duration), by month (ideal sleep duration, excessive sleepiness, and insufficient sleep), and by year (ideal sleep duration, excessive sleepiness, and insufficient sleep).

[0251] It should be understood that for each preset scenario with a day as the time dimension, the second data associated with each preset scenario may include data with a day as the time dimension; or it may include data with a day as the time dimension, as well as one or more of data with a week as the time dimension, data with a month as the time dimension, and data with a year as the time dimension.

[0252] Similarly, for each preset scenario with a week as the time dimension, the second data associated with each preset scenario may include data with a week as the time dimension; or it may include data with a week as the time dimension, as well as one or more of data with a day as the time dimension, data with a month as the time dimension, and data with a year as the time dimension.

[0253] Similarly, for each preset scenario with a month as the time dimension, the second data associated with each preset scenario may include data with a month as the time dimension; or it may include data with a month as the time dimension, as well as one or more of data with a day as the time dimension, data with a week as the time dimension, and data with a year as the time dimension.

[0254] Similarly, for each preset scenario with a year as the time dimension, the second data associated with the preset scenario may include data with a year as the time dimension; or it may include data with a year as the time dimension, as well as one or more of data with a day as the time dimension, data with a week as the time dimension, and data with a month as the time dimension.

[0255] [Revised according to Detailed Rules 26, 2005.08.2026] Table 1

[0256] Suppose that the first data acquired by the electronic device includes the previous day's sleep time (e.g., 1:30 AM). After acquiring the first data, the electronic device can determine the first target data in the first data (e.g., the previous day's sleep time, i.e., sleep time in terms of daytime) based on the first data and the master data corresponding to each preset scenario. Subsequently, the electronic device can determine the preset scenarios corresponding to the sleep time in terms of daytime (e.g., normal sleep time and staying up late in terms of daytime), and can determine the first target scenario triggered by the previous day's sleep time based on the triggering conditions corresponding to normal sleep time in terms of daytime (e.g., sleep time not later than the sleep baseline) and the triggering conditions corresponding to staying up late in terms of daytime (e.g., sleep time later than the sleep baseline). Assuming the sleep baseline is 11:00 PM, the first target scenario triggered by the previous day's sleep time (i.e., 1:30 AM) can be determined to be staying up late in terms of daytime (hereinafter referred to as staying up late A).

[0257] After identifying the primary target scenario as "staying up all night A," the electronic device can determine the secondary data associated with "staying up all night A" based on the scenario library shown in Table 1. For example, the secondary data associated with "staying up all night A" can include daily step count, stress, resting heart rate, activity calories, exercise duration, and the percentage of positive emotions.

[0258] Suppose that the first data acquired by the electronic device also includes the previous day's steps, stress, resting heart rate, activity calories, exercise duration, and percentage of positive emotions. Based on the first data and the second data associated with staying up all night (A), the electronic device can determine that the candidate data associated with staying up all night (A) includes the previous day's steps, stress, resting heart rate, activity calories, exercise duration, and percentage of positive emotions.

[0259] After identifying candidate data associated with staying up late (A), the electronic device can determine the relevance of each candidate data point based on the scenario library shown in Table 1. Based on the relevance, it can then determine the second target data associated with staying up late (A). For example, the electronic device can determine the relevance of staying up late (A) to steps as -1, stress as -2, resting heart rate as -2, activity calories as -1, exercise duration as -1, and the percentage of pleasant emotions as -1.

[0260] For example, among the candidate data associated with the first target scenario, candidate data whose absolute relevance is greater than or equal to a preset threshold A are determined as the second target data associated with the first target scenario. If the preset threshold A is 2, the electronic device can determine that the second target data associated with staying up late A includes stress and resting heart rate in a daily time dimension. If the preset threshold A is 1, the electronic device can determine that the second target data associated with staying up late A includes steps, stress, resting heart rate, activity calories, exercise duration, and the proportion of pleasant emotions in a daily time dimension.

[0261] For example, when identifying the top N candidate data with the highest absolute relevance among the candidate data associated with the first target scenario as the second target data associated with the first target scenario, if N is 1, the electronic device can determine that the second target data associated with staying up late A includes either stress or resting heart rate in a daily time dimension. If N is 2, the electronic device can determine that the second target data associated with staying up late A includes stress and resting heart rate in a daily time dimension. If N is 3, the electronic device can determine that the second target data associated with staying up late A includes stress, resting heart rate, and any one of steps, activity calories, exercise duration, and percentage of pleasant emotions in a daily time dimension.

[0262] In another embodiment, after determining the candidate data associated with the first target scene, the electronic device can determine the data score corresponding to each candidate data, and based on the data score, determine the second target data associated with the first target scene from the candidate data associated with the first target scene. The data score corresponding to the candidate data can be used to characterize the degree of anomaly of the candidate data. A higher data score indicates a greater degree of anomaly in the candidate data; a lower data score indicates a smaller degree of anomaly in the candidate data.

[0263] For example, the electronic device can determine the candidate data with a data score greater than or equal to a preset threshold (e.g., preset threshold B) from the candidate data associated with the first target scene as the second target data associated with the first target scene. Alternatively, the electronic device can determine the top M candidate data with the highest data scores from the candidate data associated with the first target scene as the second target data associated with the first target scene, and so on. M can be an integer greater than or equal to 1. The values ​​of the preset threshold B and M can be determined according to the actual application scenario, and this application embodiment does not limit them.

[0264] It should be understood that data with a high degree of anomaly is generally the data that users need to pay attention to. Electronic devices can determine the most important and attention-grabbing anomalous data for users based on the degree of anomaly of candidate data associated with the primary target scenario. This allows for the analysis of such anomalous data, and the presentation of the analysis results to the user, enabling them to quickly understand the situation of the anomalous data, their own movement status and / or health status, etc., thereby improving the user experience.

[0265] For example, the first data acquired by the electronic device may include the user's sleep time over the past seven days, i.e., sleep time over a weekly time dimension. After acquiring the first data, the electronic device can determine the first target data (e.g., sleep time over a weekly time dimension) in the first data based on the first data and the master data corresponding to each preset scenario. Subsequently, the electronic device can determine the preset scenarios corresponding to the sleep time over a weekly time dimension (e.g., normal sleep time over a weekly time dimension, staying up late, sleep time showing an improving trend, and sleep time showing a worsening trend). Based on the triggering conditions corresponding to normal sleep time over a weekly time dimension (e.g., the number of times the sleep time is not later than the sleep baseline is greater than or equal to 5 times in a week), the triggering conditions corresponding to staying up late (e.g., the number of times the sleep time is later than the sleep baseline is greater than or equal to 4 times in a week), the triggering conditions corresponding to an improving trend in sleep time (e.g., the sleep time gradually approaches the sleep baseline within a week), and the triggering conditions corresponding to a worsening trend in sleep time (e.g., the sleep time gradually deviates from the sleep baseline within a week), the first target scenario triggered by the user's sleep time over the past seven days can be determined. Assuming the baseline for falling asleep is 11:00 PM, and the user fell asleep after 11:30 PM on 4 out of the last 7 days, the electronic device can determine that the first target scenario triggered by the user's sleep time in the last 7 days is staying up late on a weekly time dimension (for ease of understanding, it can be referred to as staying up late B below).

[0266] After determining that the first target scenario includes staying up late (B), the electronic device can determine the second data associated with staying up late (B). For example, based on the scenario library shown in Table 1 above, the electronic device can determine that the second data associated with staying up late (B) includes steps, stress, resting heart rate, activity calories, exercise duration, and percentage of positive emotions on a daily time dimension, as well as steps, stress, resting heart rate, activity calories, exercise duration, and percentage of positive emotions on a weekly time dimension.

[0267] Suppose that the first data acquired by the electronic device also includes the user's steps, stress levels, resting heart rate, calorie expenditure, exercise duration, and percentage of positive emotions over the past seven days. Then, based on the first data and the second data associated with the user's "staying up all night" (B), the electronic device can determine that the candidate data associated with "staying up all night" (B) includes the user's steps, stress levels, resting heart rate, calorie expenditure, exercise duration, and percentage of positive emotions over the past seven days.

[0268] After identifying candidate data associated with "staying up late" (B), the electronic device can determine the data score corresponding to each candidate data point associated with "staying up late" (B), and based on the data scores corresponding to each candidate data point, determine the second target data associated with "staying up late" (B). For example, the electronic device might determine that the data score for steps taken in the last seven days is 15 points, the data score for stress in the last seven days is 12 points, the data score for resting heart rate in the last seven days is 33 points, the data score for activity calories in the last seven days is 16 points, the data score for exercise duration in the last seven days is 15 points, and the data score for the percentage of pleasant emotions in the last seven days is 25 points.

[0269] For example, when candidate data with a score greater than or equal to 20 is identified as the second target data associated with the first target scenario, the electronic device can determine that the second target data associated with staying up late B includes the resting heart rate and the percentage of pleasant emotions in the past seven days.

[0270] For example, when the candidate data with the highest data score among the candidate data associated with the first target scenario is determined as the second target data associated with the first target scenario, the electronic device can determine that the second target data associated with staying up all night B includes the resting heart rate of the last seven days.

[0271] In another embodiment, after determining the candidate data associated with the first target scene, the electronic device can obtain the relevance of each candidate data associated with the first target scene and determine the data score corresponding to each candidate data associated with the first target scene. Subsequently, the electronic device can determine the second target data associated with the first target scene based on the relevance of each candidate data associated with the first target scene and the data score corresponding to each candidate data. It should be understood that the data score corresponding to each candidate data associated with the first target scene can be used to characterize the degree of abnormality of each candidate data, which is the same as the data score described above. For details, please refer to the foregoing description, which will not be repeated here.

[0272] For example, the electronic device can identify candidate data associated with the first target scene that has a relevance or absolute value of relevance greater than or equal to a preset threshold A, and a data score greater than or equal to a preset threshold B, as the second target data associated with the first target scene. Alternatively, the electronic device can identify the top R candidate data with the largest relevance or absolute value of relevance and the highest data score from the candidate data associated with the first target scene as the second target data associated with the first target scene. Here, R can be an integer greater than or equal to 1.

[0273] In another embodiment, after determining the candidate data associated with the first target scene, the electronic device can obtain the relevance of each candidate data associated with the first target scene, and can determine the top S candidate data with the largest relevance or absolute value of relevance. Subsequently, the electronic device can determine the data score corresponding to the top S candidate data, and can determine the second target data associated with the first target scene based on the data score corresponding to the top S candidate data. That is, the second target data associated with the first target scene can be data from the top S candidate data, so that when determining the second target data associated with the first target scene, the electronic device only needs to determine the data score corresponding to the top S candidate data, which can reduce the computational load of data scoring, improve the performance of the electronic device, improve the speed and efficiency of determining the second target data, and enhance the user experience. Here, S can be an integer greater than or equal to 1.

[0274] For example, the electronic device can identify the candidate data with a data score greater than or equal to a preset threshold (e.g., preset threshold C) from the first S candidate data as the second target data associated with the first target scene. Alternatively, the electronic device can identify the top W candidate data with the highest data scores from the first S candidate data as the second target data associated with the first target scene. Here, W can be an integer greater than or equal to 1, and W can be less than or equal to S.

[0275] It should be understood that the specific values ​​of R, S, and W can be determined according to the actual application scenario, and this application embodiment does not impose any restrictions on them. Furthermore, the preset threshold C and the preset threshold B can be the same or different. The value of the preset threshold C can be determined according to the actual application scenario, and this application embodiment does not impose any restrictions on it.

[0276] It should be noted that the second data associated with each preset scenario described above, including supporting data and related data associated with each preset scenario, is only illustrative and should not be construed as a limitation on the embodiments of this application. In the embodiments of this application, the second data associated with each preset scenario may also only include related data associated with each preset scenario.

[0277] In some embodiments, after determining a first target scene based on first target data, the electronic device can determine supporting data associated with the first target scene based on the first data and the first target scene, and can determine related data associated with the first target scene based on the first data and second data associated with each preset scene in the scene library (e.g., related data associated with each preset scene). Subsequently, the electronic device can determine second target data associated with the first target scene based on the supporting data and / or related data associated with the first target scene. The second target data associated with the first target scene may include supporting data associated with the first target scene, or may include related data associated with the first target scene, or may include both supporting data and related data associated with the first target scene.

[0278] It should be noted that the embodiments of this application do not limit the specific method by which the electronic device determines the supporting data associated with the first target scene based on the first data and the first target scene (e.g., the master data corresponding to the first target scene), and can be determined according to the actual application scenario.

[0279] In one embodiment, after determining the supporting data and / or related data associated with the first target scene, the electronic device can determine the data score (e.g., data score A) corresponding to the supporting data associated with the first target scene, and / or determine the data score (e.g., data score B) corresponding to the related data associated with the first target scene, and can determine the second target data associated with the first target scene based on data score A and data score B.

[0280] For example, when determining that the first target scenario is associated only with supporting data, the electronic device can identify the supporting data with a data score A greater than or equal to a preset threshold B among the supporting data associated with the first target scenario as the second target data associated with the first target scenario; or, the electronic device can identify the top M supporting data with the largest data score A among the supporting data associated with the first target scenario as the second target data associated with the first target scenario.

[0281] For example, when determining that the first target scenario is associated only with relevant data, the electronic device can identify the relevant data with a data score B greater than or equal to a threshold B among the relevant data associated with the first target scenario as the second target data associated with the first target scenario; or, the electronic device can identify the top M relevant data with the largest data score B among the relevant data associated with the first target scenario as the second target data associated with the first target scenario.

[0282] For example, when determining the supporting and related data associated with the first target scenario, the electronic device can identify the supporting data with a data score A greater than or equal to a preset threshold B, and the related data with a data score B greater than or equal to the preset threshold B, as the second target data associated with the first target scenario. Alternatively, the electronic device can identify the top M supporting and / or related data with the highest data score A or data score B among the supporting and related data associated with the first target scenario as the second target data associated with the first target scenario. For instance, after determining the data score A corresponding to the supporting data associated with the first target scenario and the data score B corresponding to the related data associated with the first target scenario, the electronic device can sort the supporting and related data associated with the first target scenario in descending order based on data score A and data score B, and identify the top M data (e.g., supporting and / or related data) in the descending order as the second target data associated with the first target scenario.

[0283] The process of determining the data score corresponding to any data (such as candidate data, supporting data, or related data) associated with the first target scene by an electronic device is described in detail below. Candidate data A will be used as an example for illustration.

[0284] In some embodiments, for candidate data A associated with a first target scene, the electronic device can obtain a first scoring standard and a second scoring standard corresponding to candidate data A, and determine a first deviation degree corresponding to candidate data A based on the first scoring standard, and a second deviation degree corresponding to candidate data A based on the second scoring standard. Subsequently, the electronic device can determine the data score corresponding to candidate data A based on the first deviation degree and the second deviation degree.

[0285] For example, an electronic device can determine the data score corresponding to candidate data A by summing the first deviation and the second deviation. That is, S = S1 + S2. S can be the data score corresponding to candidate data A, S1 can be the first deviation corresponding to candidate data A, and S2 can be the second deviation corresponding to candidate data A.

[0286] For example, an electronic device can determine the data score corresponding to candidate data A by weighting the first deviation and the second deviation. That is, S = S1*Q1 + S2*Q2. Here, Q1 can be the weight corresponding to the first deviation S1, and Q2 can be the weight corresponding to the second deviation S2. It should be understood that Q1 and Q2 can be the same or different. The specific values ​​of Q1 and Q2 can be determined according to the actual application scenario.

[0287] It should be understood that the first scoring criterion for candidate data A can be the guideline standard corresponding to candidate data A. The second scoring criterion for candidate data A can be the user standard corresponding to candidate data A. The guideline standard can be determined based on professional evidence such as authoritative guidelines, academic research, or industry standards. The user standard can be determined based on relevant historical user data. The first and second scoring criteria for candidate data A can be the same or different. For example, when candidate data A is sleep duration, both the guideline standard and the user standard for sleep duration can be 6 to 10 hours. Alternatively, the guideline standard for sleep duration can be 6 to 10 hours, and the user standard for sleep duration can be 7 to 9 hours.

[0288] It should be noted that the data score corresponding to candidate data A can be used to characterize the degree of anomaly of candidate data A. For example, the higher the data score corresponding to candidate data A, the greater the degree of anomaly of candidate data A; the lower the data score corresponding to candidate data A, the smaller the degree of anomaly of candidate data A.

[0289] For example, the first deviation degree corresponding to candidate data A can be used to measure the degree to which candidate data A deviates from the first scoring criterion. When candidate data A meets the first scoring criterion, the first deviation degree corresponding to candidate data A can be small. When candidate data A does not meet the first scoring criterion, the first deviation degree corresponding to candidate data A can be large.

[0290] For example, in a certain application scenario, the first deviation score can be 5 or 20. Specifically, when candidate data A meets the first scoring standard (i.e., candidate data A is within the normal range corresponding to the guideline standard), the first deviation score for candidate data A can be 5. When candidate data A does not meet the first scoring standard (i.e., candidate data A is not within the normal range corresponding to the guideline standard), the first deviation score for candidate data A can be 20.

[0291] Similarly, the second deviation of candidate data A can be used to measure how much candidate data A deviates from the second scoring criterion. When candidate data A meets the second scoring criterion, the second deviation of candidate data A can be small. When candidate data A does not meet the second scoring criterion, the second deviation of candidate data A can be large.

[0292] For example, please refer to Figure 5, which shows a schematic diagram of the application scenario of the second deviation provided in the embodiments of this application.

[0293] As shown in Figure 5, in a certain application scenario, the second deviation can be 1 point, or any value between 10 and 30 points. Specifically, when candidate data A is within the normal range corresponding to the user's standard, the second deviation for candidate data A can be 1 point. When candidate data A is not within the normal range corresponding to the user's standard—for example, when candidate data A is below the minimum value corresponding to the user's standard, or when candidate data A is above the maximum value corresponding to the user's standard—the second deviation for candidate data A can be any value between 10 and 30 points.

[0294] As shown in Figure 5, when candidate data A is not within the normal range corresponding to the user standard, the greater the deviation of candidate data A from the normal range corresponding to the user standard, the larger the second deviation degree corresponding to candidate data A can be. Conversely, the smaller the deviation of candidate data A from the normal range corresponding to the user standard, the smaller the second deviation degree corresponding to candidate data A can be.

[0295] In other embodiments, the electronic device may also acquire an initial score for candidate data A in a first target scenario, and acquire at least one of an indicator weight, a timeliness weight, and an experience weight corresponding to candidate data A. Subsequently, the electronic device may determine the data score corresponding to candidate data A based on the first deviation, the second deviation, and the initial score corresponding to candidate data A, as well as at least one of the indicator weight, the timeliness weight, and the experience weight.

[0296] For example, when determining the data score corresponding to candidate data A based on the first deviation, second deviation, and initial score, as well as the indicator weight, timeliness weight, and experience weight, the electronic device can determine the first data score S corresponding to candidate data A according to the following formula: S=(S_raw+S1+S2)*K*T+N.

[0297] Where S_raw can be the initial score of candidate data A in the first target scenario, S1 can be the first deviation of candidate data A in the first target scenario, S2 can be the second deviation of candidate data A in the first target scenario, K can be the indicator weight, T can be the timeliness weight, and N is the experience weight.

[0298] It should be noted that the initial score S_raw of candidate data A in the first target scenario can be preset. For example, the S_raw corresponding to candidate data A can be set according to the importance of candidate data A to the first target scenario. The more important candidate data A is to the first target scenario, the higher the S_raw can be; the less important candidate data A is to the first target scenario, the lower the S_raw can be. Candidate data A can have different S_raw in different scenarios. The initial score S_raw of candidate data A in the first target scenario can be determined according to the actual application scenario, and this embodiment does not impose any restrictions on this.

[0299] The specific value of the indicator weight K corresponding to the candidate data S can also be determined according to the actual application scenario, and this application embodiment does not impose any restrictions on this. For example, when the candidate data A is data related to premature ventricular contractions and atrial fibrillation, the value of K can be 1.2; when the candidate data A is other data, the value of K can be 1.

[0300] The timeliness weight T corresponding to candidate data A can be determined based on the timeliness of candidate data A. Specifically, the more recent the timeliness of candidate data A, the larger the value of the timeliness weight T can be; the older the timeliness of candidate data A, the smaller the value of the timeliness weight T can be.

[0301] It should be understood that the timeliness of candidate data A can be determined based on the time elapsed between its acquisition or update time and the current time. For example, for candidate data A at the daily or weekly level, the timeliness can be determined by the number of hours between its acquisition or update time and the current time; that is, the fewer hours between the acquisition or update time and the current time, the larger the timeliness weight T can be; conversely, the more hours between the acquisition or update time and the current time, the smaller the timeliness weight T can be. For example, for candidate data A at the monthly or yearly level, the timeliness weight T can increase as the number of candidate data A data points in the current month or year accumulates; alternatively, the timeliness weight T can be determined based on the ratio of the number of days containing candidate data A to the number of days in the current month, or the ratio of the number of days containing candidate data A to the number of days in the current year. Specifically, the greater the proportion of days including candidate data A to the total number of days in the month (or year), the greater the timeliness weight T corresponding to candidate data A can be; conversely, the smaller the proportion of days including candidate data A to the total number of days in the month (or year), the smaller the timeliness weight T corresponding to candidate data A can be. For example, when candidate data A includes the menstrual cycle, the closer candidate data A is to the predicted menstrual cycle, the greater the timeliness weight T corresponding to candidate data A can be; and the farther candidate data A is from the predicted menstrual cycle, the smaller the timeliness weight T corresponding to candidate data A can be.

[0302] The experience weight N corresponding to candidate data A can also be preset. The specific weight can be determined based on the actual application scenario, and this embodiment does not impose any limitations on this.

[0303] The process by which S404 and electronic devices determine the first information corresponding to the first target scenario based on the first target data and the second target data will be described in detail below.

[0304] In some embodiments, after acquiring first target data and second target data associated with a first target scene, the electronic device can analyze the first target data and the second target data to determine first information corresponding to the first target scene. For example, the electronic device can analyze the first target data to determine the analysis result corresponding to the first target data, and can analyze the second target data to determine the analysis result corresponding to the second target data. Subsequently, the electronic device can determine the first information corresponding to the first target scene based on the analysis result corresponding to the first target data and the analysis result corresponding to the second target data.

[0305] For example, the first information corresponding to the first target scenario may include one or more of the following: interpretation information corresponding to the first target scenario, information indicating the relationship between the first target data and the second target data corresponding to the first target scenario, and suggestion information corresponding to the first target scenario.

[0306] It should be noted that the embodiments of this application do not limit the specific method by which the electronic device analyzes the first target data (or the second target data), and can be determined according to the actual application scenario. For example, the data analysis methods in existing sports and health data can be referred to to analyze the first target data (or the second target data) and obtain the analysis results corresponding to the first target data (or the analysis results corresponding to the second target data).

[0307] For example, the analysis of first target data by an electronic device may include one or more of the following: determining the mean of the first target data; comparing the first target data (or the mean of the first target data) with a corresponding data baseline; performing month-on-month analysis on multiple data points in the first target data; and performing trend analysis on multiple data points in the first target data. Similarly, the analysis of second target data by an electronic device may include one or more of the following: determining the mean of the second target data; comparing the second target data (or the mean of the second target data) with a corresponding data baseline; performing month-on-month analysis on multiple data points in the second target data; and performing trend analysis on multiple data points in the second target data. It should be understood that the data baseline corresponding to the first target data may include a guideline baseline corresponding to the first target data, and / or may include a user baseline corresponding to the first target data. The data baseline corresponding to the second target data may also include a guideline baseline corresponding to the second target data, and / or may include a user baseline corresponding to the second target data. The data baseline can be determined according to the actual application scenario.

[0308] It should be noted that the above-described analysis of the first and second target data after determining the second target data corresponding to the first target scenario is merely an illustrative explanation and should not be construed as a limitation on the embodiments of this application. In the embodiments of this application, after obtaining the first data including the first target data and the second target data, the electronic device can directly analyze each data in the first data to determine the analysis results corresponding to each data in the first data. After determining the second target data corresponding to the first target scenario, the electronic device can directly obtain the analysis results corresponding to the first target data and the second target data from the analysis results corresponding to each data in the first data, and can determine the first information corresponding to the first target scenario based on the analysis results corresponding to the first target data and the second target data. This can improve the speed and efficiency of determining the analysis results corresponding to the first target data and the second target data, improve the speed and efficiency of determining the first information, and enhance the user experience.

[0309] In some embodiments, when a first target scenario is associated with multiple second target data, and the second target data associated with the first target scenario includes supporting data and related data, after determining the second target data associated with the first target scenario, the electronic device can determine the supporting data and related data corresponding to the first target scenario from the second target data associated with the first target scenario. It can also determine the first information corresponding to the first target scenario based on the order in which the information corresponding to the supporting data precedes the information corresponding to the related data. For example, it can determine information indicating the association between the first target data and the second target data corresponding to the first target scenario. In other words, when the second target data associated with the first target scenario includes supporting data and related data corresponding to the first target scenario, the electronic device can determine and display the first information in the order from supporting data to related data, so that the content of the supporting data directly related to the first target scenario can be displayed first, allowing the user to quickly understand the specific situation of the first target scenario.

[0310] It should be noted that the above-described determination of supporting data and related data in the second target data associated with the first target scene, and the determination and display of the first information according to the order of supporting data to related data, are merely illustrative explanations and should not be construed as limitations on the embodiments of this application. In the embodiments of this application, it is also possible not to distinguish between supporting data and related data in the second target data associated with the first target scene, but to directly determine and display the first information based on the relevance and / or data score of the second target data. Alternatively, the first information can be determined and displayed according to the order of related data to supporting data, and so on.

[0311] The data processing method provided in the embodiments of this application will be described exemplarily below with reference to the above description. Please refer to Figure 6, which shows a second schematic flowchart of the data processing method provided in the embodiments of this application. This schematic diagram is illustrated by taking the determination of second target data associated with a first target scene based on data scoring as an example.

[0312] As shown in Figure 6, the electronic device can acquire first data. For example, it can acquire data such as heart rate, sleep onset time, sleep heart rate variability, mood, stress, blood oxygen saturation, sleep duration, distance, ambient brightness, steps, body temperature, ambient volume, and exercise duration. After acquiring the first data, the electronic device can determine the first target data based on the first data and the master data corresponding to each preset scene in the scene library. Furthermore, it can determine the first target scene triggered by the first target data based on the first target data and the triggering conditions corresponding to each preset scene.

[0313] After determining the first target scenario, the electronic device can determine the second data associated with the first target scenario based on the second data associated with each preset scenario in the scenario library, and can determine the candidate data associated with the first target scenario based on the first data and the second data associated with the first target scenario.

[0314] After determining the candidate data associated with the first target scenario, the electronic device can determine the data score corresponding to each candidate data associated with the first target scenario based on the first scoring standard (i.e., guide standard), the second scoring standard (i.e., user standard), the initial score, and the indicator weight, timeliness weight, and experience weight corresponding to each candidate data under the first target scenario. Based on the data score, the electronic device can determine the second target data associated with the first target scenario.

[0315] Subsequently, the electronic device can determine the first information corresponding to the first scene based on the first target data and the second target data associated with the first target scene.

[0316] In this embodiment, after determining the first information corresponding to the first target scenario, the electronic device can display the first information corresponding to the first target scenario, or it can display the first information corresponding to the first target scenario based on the user's operation, allowing the user to quickly understand their own health status, etc., based on the first information. For example, the data processing method provided in this embodiment can be a system function; after determining the first information corresponding to the first target scenario, the electronic device can display the first information corresponding to the first target scenario through a pop-up window. For example, the data processing method provided in this embodiment can be a function provided by an application; after determining the first information corresponding to the first target scenario, when it is detected that the user has launched the application, or when it is detected that the user has opened a certain details interface of the application, the electronic device can display the first information corresponding to the first target scenario.

[0317] The process of an electronic device displaying the first information is explained in detail below.

[0318] In some embodiments, after determining the first information corresponding to the first target scene, the electronic device can display the first information through a card (also known as a summary card).

[0319] In one possible implementation, when displaying the first information corresponding to the first target scenario, the electronic device can also display the analysis results corresponding to the first target data and / or the analysis results corresponding to the second target data, facilitating the user's understanding of the specific details of the data corresponding to the first target scenario. This application embodiment does not limit the presentation format of the analysis results corresponding to the first target data and the second target data, which can be determined according to the actual application scenario. For example, depending on the actual application scenario, one or more forms such as text, line charts, bar charts, column charts, or graphs can be used to present the analysis results corresponding to the first target data and / or the second target data.

[0320] In one embodiment, an electronic device may display first information corresponding to a first target scene, as well as analysis results corresponding to first target data and / or analysis results corresponding to second target data, through a summary card.

[0321] For example, when displaying the analysis results corresponding to the first target data and the second target data, the electronic device can display the analysis results corresponding to the first target data and the second target data in the order from the first target data to the second target data. That is, the analysis results corresponding to the first target data can be placed before the analysis results corresponding to the second target data in the summary card, which can help users quickly understand the actual situation of the first target scenario.

[0322] For example, when there are multiple first target data points, the electronic device can display the analysis results corresponding to each first target data point in descending order of relevance (or absolute value of relevance) and / or data score. That is, the higher the relevance (or absolute value of relevance) and / or the higher the data score of the first target data, the earlier it appears in the summary card; conversely, the lower the relevance (or absolute value of relevance) and / or the data score, the later it appears in the summary card. This allows users to quickly understand important data related to the first target scenario, improving the user experience.

[0323] Similarly, when there are multiple second-target data points, the electronic device can display the analysis results corresponding to each second-target data point in descending order of relevance (or absolute value of relevance) and / or data score. That is, the higher the relevance (or absolute value of relevance) and / or the higher the data score of the second-target data, the earlier it appears on the summary card; conversely, the lower the relevance (or absolute value of relevance) and / or the data score, the later it appears on the summary card. This allows users to quickly understand important data related to the first-target scenario, improving the user experience.

[0324] It should be noted that the electronic device described above, which displays the analysis results corresponding to the first target data and the second target data in the order from the first target data to the second target data, and displays the analysis results corresponding to each first target data in descending order of relevance (or the absolute value of relevance) and / or in descending order of data score, or the analysis results corresponding to each second target data, is only illustrative and should not be construed as a limitation on the embodiments of this application. In the embodiments of this application, the electronic device may also display the analysis results corresponding to the first target data and the analysis results corresponding to each second target data in other ways. For example, the electronic device may display the analysis results corresponding to the first target data and the analysis results corresponding to each second target data in a random manner.

[0325] Please refer to Figures 7 and 8, which illustrate an application scenario provided by this application. This application scenario is illustrated by taking as an example a first target data set comprising one data set, a first target scenario associated with a second target data set, and the first information corresponding to the first target scenario including interpretation information corresponding to the first target scenario and information indicating the association between the first target data set and the second target data set.

[0326] Suppose that the electronic device determines that the first target data includes the user's sleep quality from the previous day, and based on the previous day's sleep quality, determines that the first target scenario includes sleep quality needing improvement. The second target data associated with the first target scenario includes the user's sleep heart rate variability (HRV) from the previous day, and the first information corresponding to the first target scenario includes "Your sleep quality was poor yesterday, with a sleep score of 55. Sleep heart rate variability lower than baseline may be related to this." "Your sleep quality was poor yesterday, with a sleep score of 55" can be interpreted as information corresponding to the first target scenario. "Sleep heart rate variability lower than baseline may be related to this" can be information indicating the correlation between the first target data (i.e., the user's sleep quality from the previous day) and the second target data (i.e., the user's sleep HRV from the previous day) corresponding to the first target scenario.

[0327] As shown in Figure 7(a), the electronic device can display the first information corresponding to the first target scenario, the analysis results corresponding to the first target data (i.e., yesterday's sleep quality) (e.g., the analysis results may include "sleep quality 55 points, need improvement"), and the analysis results corresponding to the second target data (i.e. yesterday's sleep HRV) (e.g., the analysis results may include "sleep HRV is 40 milliseconds, lower than the baseline", and a bar chart corresponding to the sleep HRV) through the summary card 710.

[0328] Suppose that the electronic device determines the first target data includes the user's sleep quality from the previous day, and based on the previous day's sleep quality, determines the first target scenario as sleep quality needing improvement. The second target data associated with the first target scenario includes stress, and the first information corresponding to the first target scenario includes "Your sleep quality was poor yesterday, with a sleep score of 55. High stress may negatively impact sleep quality." "Your sleep quality was poor yesterday, with a sleep score of 55" can be considered the interpretation information corresponding to the first target scenario. "High stress may negatively impact sleep quality" can be considered information indicating the correlation between the first target data (i.e., previous day's sleep quality) and the second target data (i.e., previous day's stress) corresponding to the first target scenario.

[0329] As shown in Figure 7(b), the electronic device can display the first information corresponding to the first target scenario, the analysis results corresponding to the first target data (i.e., yesterday's sleep quality) (e.g., the analysis results may include "sleep quality 55 points, need improvement"), and the analysis results corresponding to the second target data (i.e. yesterday's stress) (e.g., the analysis results may include "yesterday's stress 89, too high", and a bar chart corresponding to yesterday's stress) through the summary card 720.

[0330] Suppose that the electronic device determines the first target data to include the user's sleep time over the past 7 days, and determines the first target scenario to include staying up late based on the sleep time over the past 7 days. The second target data associated with the first target scenario includes the user's resting heart rate over the past 7 days, and the first information corresponding to the first target scenario includes "Recently, the user has been staying up late severely, with 6 days of staying up late. The increase in resting heart rate may be related to this." "Recently, the user has been staying up late severely, with 6 days of staying up late" can be interpreted as information corresponding to the first target scenario. "The increase in resting heart rate may be related to this" can be information indicating the correlation between the first target data (i.e., sleep time over the past 7 days) and the second target data (i.e., resting heart rate over the past 7 days) corresponding to the first target scenario.

[0331] As shown in Figure 7(c), the electronic device can display the first information corresponding to the first target scenario, the analysis results corresponding to the first target data (i.e., sleep time in the last 7 days) via the summary card 730 (e.g., the analysis results may include a line graph of "6 days of staying up all night" and the sleep time in the last 7 days), and the analysis results corresponding to the second target data (i.e., resting heart rate in the last 7 days) (e.g., the analysis results may include "resting heart rate is on an upward trend" and a line graph of the resting heart rate in the last 7 days). In addition, the summary card 730 can also display the individual baseline of sleep time (e.g., 23:00-24:00) and the reference range of resting heart rate (e.g., 60-100 beats / minute) for user reference.

[0332] Suppose that the electronic device determines that the first target data includes the number of atrial fibrillation episodes in the user's last 7 days, and determines that the first target scenario includes atrial fibrillation based on the number of atrial fibrillation episodes in the last 7 days. The second target data associated with the first target scenario includes the time to fall asleep in the last 7 days, and the first information corresponding to the first target scenario includes "13 episodes of atrial fibrillation have occurred recently. This may be related to severe sleep deprivation." "13 episodes of atrial fibrillation have occurred recently" can be the interpretation information corresponding to the first target scenario. "This may be related to severe sleep deprivation" can be information indicating the correlation between the first target data (i.e., the number of atrial fibrillation episodes in the last 7 days) and the second target data (i.e., the time to fall asleep in the last 7 days) corresponding to the first target scenario.

[0333] As shown in Figure 8(a), the electronic device can display the first information corresponding to the first target scenario, the analysis results corresponding to the first target data (i.e., the number of atrial fibrillation episodes in the last 7 days) via the summary card 810 (e.g., the analysis results may include a bar chart corresponding to the number of atrial fibrillation episodes for each day in the last 7 days), and the analysis results corresponding to the second target data (i.e., the sleep onset time in the last 7 days) (e.g., the analysis results may include a line chart corresponding to "6 days of staying up all night" and the sleep onset time in the last 7 days). In addition, the summary card 730 can also display the individual baseline of sleep onset time (e.g., 23:00-24:00) for the user's reference.

[0334] Suppose that the electronic device determines the first target data to include the user's sleep quality for the current month, and based on the current month's sleep quality, determines the first target scenario to include an increase in sleep quality. The second target data associated with the first target scenario includes the user's exercise duration for the current month, and the first information corresponding to the first target scenario includes "This month's sleep quality score improved by 5 points compared to last month. This may be related to the increase in exercise duration." "This month's sleep quality score improved by 5 points compared to last month" can be interpreted as information corresponding to the first target scenario. "This may be related to the increase in exercise duration" can be information indicating the correlation between the first target data (i.e., the current month's sleep quality) and the second target data (i.e., the current month's exercise duration) corresponding to the first target scenario.

[0335] As shown in Figure 8(b), the electronic device can display the first information corresponding to the first target scenario, the analysis results corresponding to the first target data (i.e., the sleep quality of the current month) through the summary card 820 (e.g., the analysis results may include "the sleep quality of this month is 90 points, which is 5 points higher than the previous month"), and the analysis results corresponding to the second target data (i.e., the exercise duration of the current month) (e.g., the analysis results may include the average daily exercise duration of this month is 55 minutes, which is 10 points higher than the average daily exercise duration of 45 minutes in the previous month, as well as bar charts of the average daily exercise duration of this month and the average daily exercise duration of the previous month).

[0336] For example, please refer to Figure 9, which illustrates a second application scenario provided by an embodiment of this application. This application scenario uses the association of a first target scenario with two second target data sets as an example. The first information corresponding to the first target scenario includes interpretation information corresponding to the first target scenario and information indicating the association between the first target data and the second target data corresponding to the first target scenario, as illustrated by this example.

[0337] Suppose that the electronic device determines the first target data includes the user's sleep quality from the previous day, and based on the previous day's sleep quality, determines the first target scenario as "excellent sleep quality." The second target data associated with the first target scenario includes the user's sleep heart rate variability (HRV) and stress levels from the previous day. The first information corresponding to the first target scenario includes "Your sleep quality was excellent yesterday, with a sleep score of 90. Higher-than-baseline sleep heart rate variability and lower stress levels may be related to this." "Your sleep quality was excellent yesterday, with a sleep score of 90" can be interpreted as information corresponding to the first target scenario. "Higher-than-baseline sleep heart rate variability and lower stress levels may be related to this" can be information indicating the correlation between the first target data (i.e., the user's sleep quality from the previous day) and the second target data (i.e., the user's sleep heart rate variability and stress levels from the previous day) corresponding to the first target scenario.

[0338] As shown in Figure 9(a), the electronic device can display the first information corresponding to the first target scenario, the analysis results corresponding to the first target data (i.e., yesterday's sleep quality) (e.g., the analysis results may include "sleep quality 90 points, excellent"), the analysis results corresponding to the second target data (e.g., yesterday's sleep HRV) (e.g., the analysis results may include "sleep HRV is 95 milliseconds, higher than the baseline" and a bar chart corresponding to sleep HRV) and the analysis results corresponding to the second target data (e.g., yesterday's stress) (e.g., the analysis results may include "yesterday's stress 29, relaxed" and a bar chart corresponding to stress) through the summary card 910.

[0339] Suppose that the electronic device determines the first target data includes the user's stress level for the current month, and based on the stress level for the current month, determines the first target scenario as increased stress. The second target data associated with the first target scenario includes the user's sleep quality and exercise duration for the current month, and the first information corresponding to the first target scenario includes "Your average daily stress level this month is higher than last month. This may be related to decreased sleep quality and reduced exercise duration." "Your average daily stress level this month is higher than last month" can be interpreted as information corresponding to the first target scenario. "This may be related to decreased sleep quality and reduced exercise duration" can be information indicating the correlation between the first target data (i.e., the user's stress level for the current month) and the second target data (i.e., the user's sleep quality and exercise duration for the current month) corresponding to the first target scenario.

[0340] As shown in Figure 9(b), the electronic device can display the first information corresponding to the first target scenario, the analysis results corresponding to the first target data (i.e., the pressure of the current month) through the summary card 920 (e.g., the analysis results may include "the average daily pressure is 54, which is 5 points higher than the previous month"), the analysis results corresponding to the second target data (e.g., the sleep quality of the current month) (e.g., the analysis results may include the average daily sleep quality of the current month is 75 points, which is 10 points lower than the average daily sleep quality of the previous month is 85 points, as well as the bar chart of the average daily sleep quality of the current month and the bar chart of the average daily sleep quality of the previous month), and the analysis results corresponding to the second target data (e.g., the exercise duration of the current month) (e.g., the analysis results may include the average daily exercise duration of the current month is 19 minutes, which is 16 minutes lower than the average daily exercise duration of the previous month is 35 minutes, as well as the bar chart of the average daily exercise duration of the current month and the bar chart of the average daily exercise duration of the previous month).

[0341] In some embodiments, the electronic device may display, on any interface such as the desktop, the negative one screen, or the interface of a certain application, a summary card that includes first information corresponding to the first target scenario, and analysis results of the first target data corresponding to the first target scenario and / or analysis results of the second target data.

[0342] For example, please refer to Figures 10 and 11, which illustrate a schematic diagram of application scenario three provided by the embodiments of this application. This application scenario is illustrated by taking as an example an electronic device displaying, in the interface of an application (e.g., a sports and health application), first information corresponding to a first target scenario, and a summary card including the analysis results of the first target data corresponding to the first target scenario and / or the analysis results corresponding to the second target data.

[0343] Suppose that the electronic device determines the first target data includes the user's sleep quality from the previous day, and based on the previous day's sleep quality, determines the first target scenario as sleep quality needing improvement. The second target data associated with the first target scenario includes stress, and the first information corresponding to the first target scenario includes "Your sleep quality was poor yesterday, with a sleep score of 55. High stress may negatively impact sleep quality." "Your sleep quality was poor yesterday, with a sleep score of 55" can be considered the interpretation information corresponding to the first target scenario. "High stress may negatively impact sleep quality" can be considered information indicating the correlation between the first target data (i.e., previous day's sleep quality) and the second target data (i.e., previous day's stress) corresponding to the first target scenario.

[0344] As shown in Figure 10, on the homepage of a sports and health application, the electronic device can display the first information corresponding to the first target scenario, the analysis results corresponding to the first target data (i.e., yesterday's sleep quality) (e.g., the analysis results may include "Sleep quality 55 points, needs improvement"), and the analysis results corresponding to the second target data (i.e., yesterday's stress) (e.g., the analysis results may include "Yesterday's stress 89, slightly high", and a bar chart corresponding to yesterday's stress) via summary cards 1010. It should be understood that the homepage of the sports and health application can also display other cards, such as activity record cards, exercise record cards, heart health cards, emotional health cards, and blood pressure cards. Additionally, the homepage of the sports and health application can also display an edit card button.

[0345] Alternatively, as shown in Figure 11, since the first target scenario can be a sleep-related scenario, the electronic device can also display, on the sleep details page, the analysis results corresponding to the first information of the first target scenario and the second target data (i.e., yesterday's stress) via summary card 1110 (for example, the analysis results may include "Yesterday's stress 89, slightly high", and a bar chart corresponding to yesterday's stress). It should be understood that the sleep details page can also display other related sleep data.

[0346] In another embodiment, the electronic device can display first information corresponding to a first target scene, as well as analysis results corresponding to first target data and / or analysis results corresponding to second target data, through multiple summary cards. For example, the electronic device can display the first information corresponding to the first target scene through one or more summary cards, and can also display the analysis results corresponding to the first target data and / or the analysis results corresponding to the second target data through one or more summary cards.

[0347] For example, an electronic device can display the first information corresponding to the first target scenario through summary card A, the analysis results corresponding to the first target data through summary card B, the analysis results corresponding to the second target data A through summary card C1, and the analysis results corresponding to the second target data B through summary card C2, and so on. Alternatively, the electronic device can display the interpretation information and information indicating the correlation between the first target data and the second target data in the first information corresponding to the first target scenario through summary card A1, the suggestion information in the first information corresponding to the first target scenario through summary card A2, the analysis results corresponding to the first target data through summary card B, the analysis results corresponding to the second target data A through summary card C1, and the analysis results corresponding to the second target data B through summary card C2, and so on.

[0348] For example, please refer to Figures 12 and 13, which illustrate an application scenario four provided by an embodiment of this application. This application scenario is illustrated by taking as an example a first target data set comprising one data set, a first target scenario associated with multiple second target data sets, and first information corresponding to the first target scenario including interpretation information corresponding to the first target scenario, information indicating the association between the first target data and the second target data corresponding to the first target scenario, and suggestion information corresponding to the first target scenario.

[0349] Suppose that the electronic device determines the first target data to include the user's sleep time over the past 7 days, and based on this sleep time, determines the first target scenario to include staying up late. The second target data associated with this first target scenario includes sleep heart rate, sleep HRV, and the percentage of unpleasant emotions over the past 7 days. The first piece of information corresponding to this first target scenario includes: "Recently, I have been staying up late quite a bit, which may have adverse effects on my health. The following data changes may be related to staying up late: 1. Sleep heart rate has significantly increased, with 6 days above the baseline; 2. Sleep HRV is showing a downward trend, with 4 days below the baseline; 3. The percentage of unpleasant emotions has significantly increased, with an average of 37%. Health recommendations: 1. Maintain a regular bedtime, going to bed at 11:00 PM every day; 2. Engage in moderate outdoor exercise during the day, ideally 30 minutes." The phrase "Recently, I have been staying up late quite a bit, which may have adverse effects on my health" can be considered the interpretive information for the first target scenario. The following data changes may be related to staying up late: 1. Significantly increased sleep heart rate, with 6 days exceeding personal baseline; 2. Decreasing sleep HRV, with 4 days below personal baseline; 3. Significantly increased percentage of unpleasant emotions, averaging 37%. This information indicates the correlation between the first target data (sleep onset time in the last 7 days) and the second target data (sleep heart rate, sleep HRV, and percentage of unpleasant emotions in the last 7 days) corresponding to the first target scenario. The following health recommendations are provided: 1. Maintain a regular bedtime, going to bed at 11:00 PM every day; 2. Engage in moderate outdoor exercise during the day, ideally 30 minutes. This information corresponds to the recommendations for the first target scenario.

[0350] As shown in Figure 12, the electronic device can display the first information corresponding to the first target scene through the summary card 1210. Alternatively, as shown in Figure 13, the electronic device can display the interpretation information and the information indicating the correlation between the first target data and the second target data corresponding to the first target scene in the first information through the summary card 1211 (Figure 13 illustrates this by placing the information indicating the correlation together in the interpretation information), and can display the suggestion information in the first information corresponding to the first target scene through the summary card 1212.

[0351] As shown in Figures 12 and 13, the electronic device can display the analysis results corresponding to the first target data (i.e., sleep time over the past 7 days) via the summary card 1220. (For example, the analysis results may include "You have been staying up late for the past 7 days, and your sleep time has deviated significantly from your usual routine" and a line graph corresponding to the sleep time over the past 7 days). In addition, the summary card 1220 can also display a personal baseline of sleep time (e.g., 23:00-24:00) for the user's reference.

[0352] The electronic device can display analysis results corresponding to the second target data (such as sleep heart rate over the past 7 days) via summary card 1230. (For example, the analysis results may include "Your sleep heart rate has shown an upward trend over the past 7 days, with 6 days being higher" and a line graph of the sleep heart rate over the past 7 days). In addition, summary card 1230 can also display a personal baseline of sleep heart rate (such as 52-60 beats per minute) for user reference.

[0353] The electronic device can display the analysis results corresponding to the second target data (e.g., sleep HRV over the past 7 days) via summary card 1231. (For example, the analysis results may include "sleep heart rate variability has shown a downward trend over the past 7 days, with 4 days below the personal baseline" and a line graph of the sleep HRV over the past 7 days). In addition, summary card 1231 can also display the personal baseline of sleep HRV (e.g., 28ms-35ms) for user reference.

[0354] The electronic device can display the analysis results corresponding to the second target data (such as the percentage of unpleasant emotions in the last 7 days) through summary cards 1232 (for example, the analysis results may include "the percentage of unpleasant emotions in the last 7 days has shown an upward trend, with the daily average percentage of unpleasant emotions reaching 37%" and a line graph corresponding to the unpleasant emotions in the last 7 days).

[0355] In some embodiments, when displaying the first information corresponding to the first target scenario, the analysis results corresponding to the first target data, and the analysis results corresponding to the second target data through multiple summary cards, the electronic device can simultaneously display these multiple summary cards on any interface such as the desktop, the negative one screen, or the interface of a certain application.

[0356] In other embodiments, when displaying the first information corresponding to the first target scene, the analysis results corresponding to the first target data, and the analysis results corresponding to the second target data through multiple summary cards, the electronic device can display one or more of these multiple summary cards on any interface such as the desktop, the negative one screen, or the interface of a certain application, and can display other summary cards based on the user's relevant operations.

[0357] For example, the electronic device may display a summary card (e.g., summary card A) containing first information corresponding to the first target scene, and may display a summary card containing analysis results corresponding to the first target data and / or analysis results corresponding to the second target data when a related operation is detected.

[0358] In one embodiment, the first information corresponding to the first target scenario can be displayed through a summary card, and the analysis results corresponding to the first target data and the second target data can be displayed through a summary card.

[0359] For example, when displaying a summary card A containing first information corresponding to a first target scenario, if an operation on summary card A (e.g., operation A) is detected, the electronic device can display a summary card (e.g., summary card B) containing analysis results corresponding to the first target data and analysis results corresponding to the second target data.

[0360] In another embodiment, the first information corresponding to the first target scenario can be displayed through a summary card, the analysis results corresponding to each first target data can be displayed through a summary card, and the analysis results corresponding to each second target data can also be displayed through a summary card.

[0361] For example, when displaying summary card A containing first information corresponding to a first target scene, if an operation on summary card A (e.g., operation B1) is detected, the electronic device can display summary card C containing analysis results corresponding to each first target data. When displaying summary card C, if an operation on summary card C (e.g., operation C1) is detected, the electronic device can display summary card D containing analysis results corresponding to each second target data. Alternatively, when displaying summary card A containing first information corresponding to a first target scene, if an operation on summary card A (e.g., operation B2) is detected, the electronic device can display summary card D containing analysis results corresponding to each second target data. When displaying summary card D, if an operation on summary card D (e.g., operation C2) is detected, the electronic device can display summary card C containing analysis results corresponding to each first target data.

[0362] In another embodiment, the first information corresponding to the first target scenario can be displayed through a summary card, the analysis result corresponding to each first target data can be displayed through a summary card, and the analysis result corresponding to each second target data can also be displayed through a summary card.

[0363] For example, when displaying summary card A containing first information corresponding to a first target scenario, if an operation on summary card A (e.g., operation D) is detected, the electronic device can display a summary card (e.g., summary card E) containing the analysis results corresponding to a certain first target data. When displaying summary card E, if an operation on summary card E (e.g., operation E) is detected, the electronic device can display a summary card (e.g., summary card F) containing the analysis results corresponding to another first target data. When displaying summary card F, if an operation on summary card F (e.g., operation F) is detected, the electronic device can display a summary card (e.g., summary card G) containing the analysis results corresponding to a certain second target data. When displaying summary card G, if an operation on summary card G (e.g., operation G) is detected, the electronic device can display a summary card (e.g., summary card H) containing the analysis results corresponding to another second target data, and so on.

[0364] It should be noted that operations A, B1, B2, C1, C2, D, E, F, and G can be determined based on the actual application scenario, and this application embodiment does not impose specific limitations on them. For example, operations A, B1, B2, C1, C2, D, E, F, and G can all be determined as right-sliding operations based on the actual application scenario. That is, when displaying summary card A, if a right-sliding operation is detected, the electronic device can display summary card B. Or, when displaying summary card A, if a right-sliding operation is detected, the electronic device can display summary card C. When displaying summary card C, if a right-sliding operation is detected, the electronic device can display summary card D, and so on.

[0365] In another embodiment, the summary card A containing the first information corresponding to the first target scene may further include a button for viewing the analysis results corresponding to the first target data (for ease of understanding, it can be referred to as button A), and / or a button for viewing the analysis results corresponding to the second target data (for ease of understanding, it can be referred to as button B). When a click or touch operation on button A is detected, the electronic device may display a summary card containing the analysis results corresponding to the first target data. When a click or touch operation on button B is detected, the electronic device may display a summary card containing the analysis results corresponding to one or more second target data.

[0366] Similarly, a summary card containing the analysis results corresponding to a certain first target data may also include one or more buttons such as a button to view the first information, a button to view the analysis results corresponding to the previous first target data, a button to view the analysis results corresponding to the next first target data, and a button to view the analysis results corresponding to a certain second target data.

[0367] Similarly, a summary card containing the analysis results corresponding to a certain second target data point may also include one or more buttons such as those for viewing the analysis results corresponding to a certain first target data point, viewing the analysis results corresponding to the previous second target data point, and viewing the analysis results corresponding to the next second target data point. It should be understood that the electronic device can display the relevant summary card when a click or touch operation on the corresponding button is detected.

[0368] Please refer to Figure 14, which illustrates the fifth application scenario provided by the embodiments of this application. This application scenario can be illustrated by taking the application scenarios shown in Figures 12 and 13 as examples.

[0369] As shown in Figure 14(a), after determining the first information corresponding to the first target scenario, the analysis results corresponding to the first target data, and the analysis results corresponding to the second target data, the electronic device can display the interpretation information in the first information corresponding to the first target scenario (e.g., recent sleep deprivation is serious, which may have an adverse effect on your health) and information used to indicate the correlation between the first target data and the second target data corresponding to the first target scenario (e.g., the following data changes may be related to staying up late: 1. Sleep heart rate is significantly increased, with 6 days higher than the personal baseline; 2. Sleep HRV shows a downward trend, with 4 days lower than the personal baseline; 3. The proportion of unpleasant emotions is significantly increased, with an average of 37%) through the summary card 1410.

[0370] When displaying summary card 1410, if a rightward swipe operation is detected, as shown in Figure 14(b), the electronic device can display the suggested information in the first information corresponding to the first target scenario through summary card 1411 (health suggestions: 1. Regular bedtime, go to bed on time at 23:00 every day; 2. Moderate outdoor exercise during the day, preferably 30 minutes).

[0371] When displaying summary card 1411, if a rightward swipe operation is detected, as shown in (c) of Figure 14, the electronic device can display the analysis results corresponding to the first target data (i.e., the sleep time of the last 7 days) through summary card 1420 (for example, the analysis results may include "the sleep time deviates significantly from your sleep habits" and a line graph corresponding to the sleep time of the last 7 days).

[0372] When displaying summary card 1420, if a rightward swipe operation is detected, as shown in (d) of Figure 14, the electronic device can display the analysis results corresponding to the second target data (e.g., sleep heart rate over the past 7 days) through summary card 1430 (e.g., the analysis results may include "Your sleep heart rate has shown an upward trend over the past 7 days, with 6 days being higher" and a line graph corresponding to the sleep heart rate over the past 7 days).

[0373] When displaying summary card 1430, if a rightward swipe operation is detected, as shown in Figure 14(e), the electronic device can display the analysis results corresponding to the second target data (e.g., sleep HRV over the last 7 days) through summary card 1431 (e.g., the analysis results may include "sleep heart rate variability over the last 7 days shows a downward trend, with 4 days below the individual baseline" and a line graph corresponding to sleep HRV over the last 7 days).

[0374] When displaying summary card 1431, if a rightward swipe operation is detected, as shown in (f) of Figure 14, the electronic device can display the analysis results corresponding to the second target data (e.g., the percentage of unpleasant emotions in the last 7 days) through summary card 1432 (e.g., the analysis results may include "the percentage of unpleasant emotions in the last 7 days has shown an upward trend, with the daily average percentage of unpleasant emotions reaching 37%" and a line graph corresponding to the unpleasant emotions in the last 7 days).

[0375] Additionally, as shown in Figure 14(d), summary card 1430 may include a button for viewing the next second target data (e.g., sleep heart rate variability). Summary card 1431 may be displayed when the electronic device detects a click or touch operation on the sleep heart rate variability button. Similarly, as shown in Figure 14(e), summary card 1431 may include a button for viewing the previous second target data (e.g., sleep heart rate) and a button for viewing the next second target data (e.g., emotional health). Summary card 1430 may be displayed when a click or touch operation on the sleep heart rate button is detected. Alternatively, summary card 1432 may be displayed when a click or touch operation on the emotional health button is detected. Similarly, as shown in Figure 14(f), summary card 1432 may include a button for viewing the previous second target data (e.g., sleep heart rate variability). Summary card 1431 may be displayed when a click or touch operation on the sleep heart rate variability button is detected.

[0376] In some embodiments, the first data may include multiple first target data, and multiple target scenarios (e.g., a first target scenario and a second target scenario) can be determined based on the multiple first target data. After determining the first information corresponding to the first target scenario (hereinafter referred to as first information A for ease of understanding) and the first information corresponding to the second target scenario (hereinafter referred to as first information B for ease of understanding), the electronic device can display a first summary card and a second summary card according to the scenario priority corresponding to the target scenario (i.e., the scenario priority corresponding to the first target scenario and the scenario priority corresponding to the second target scenario). The first summary card may include the first information A corresponding to the first target scenario, the analysis result of the first target data corresponding to the first target scenario (hereinafter referred to as analysis result A1 for ease of understanding), and / or the analysis result of the second target data associated with the first target scenario (hereinafter referred to as analysis result A2 for ease of understanding). The second summary card may include the first information B corresponding to the second target scenario, the analysis result of the first target data corresponding to the second target scenario (hereinafter referred to as analysis result B1 for ease of understanding), and the analysis result of the second target data associated with the second target scenario (hereinafter referred to as analysis result B2 for ease of understanding).

[0377] In one possible implementation, the higher the scene priority of the target scene, the more prominently the summary card corresponding to that target scene can be displayed on the interface. The following example illustrates this, where the scene priority of the first target scene is higher than that of the second target scene.

[0378] In one embodiment, after determining the first information A corresponding to the first target scene and the first information B corresponding to the second target scene, the electronic device can simultaneously display the first summary card and the second summary card on any interface such as the desktop, the negative one screen, or the interface of a certain application, and the position of the first summary card on the interface can be in front of the second summary card.

[0379] In another embodiment, after determining the first information A corresponding to the first target scene and the first information B corresponding to the second target scene, the electronic device can display the first summary card on any interface, such as the desktop, the negative one screen, or the interface of a certain application. While displaying the first summary card, if an operation (e.g., operation H) is detected, the electronic device can display the second summary card on any interface, such as the desktop, the negative one screen, or the interface of a certain application.

[0380] It should be noted that operation H can be determined based on the actual application scenario, and this application embodiment does not impose any limitations on it. For example, when displaying the first summary card, the electronic device may also display a button to view more summary cards. When operation H on the button to view more summary cards is detected, the electronic device may display the second summary card.

[0381] In some embodiments, the scene priority corresponding to the target scene can be preset, or it can be determined based on the first target data corresponding to the target scene. For example, the scene priority corresponding to the target scene can be determined based on the data score of the first target data corresponding to the target scene. The method for determining the data score corresponding to the first target data can refer to the aforementioned methods for determining data scores, and will not be repeated here.

[0382] It should be understood that when the data score corresponding to the target scenario is higher, the scenario priority corresponding to the target scenario can be higher; when the data score corresponding to the target scenario is lower, the scenario priority corresponding to the target scenario can be lower. The scenario priority is determined based on the data score, so that target scenarios with a higher degree of anomaly can have a higher scenario priority. This allows the first information corresponding to target scenarios with a higher degree of anomaly to be displayed first or earlier, making it easier for users to quickly understand the abnormal target scenario and improving the user experience.

[0383] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0384] Corresponding to the data processing method described in the above embodiments, this application also provides a data processing apparatus, the various modules of which can correspondingly implement the various steps of the data processing method.

[0385] It should be noted that the information interaction and execution process between the above-mentioned devices / units are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method embodiments section, and they will not be repeated here.

[0386] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0387] Please refer to Figure 15, which is a schematic diagram of another structure of the electronic device provided in the embodiments of this application. As shown in Figure 15, the electronic device includes at least one memory 1510 (only one is shown in Figure 15 as an example), at least one processor 1520 (only one is shown in Figure 15 as an example), and a computer program 1530 stored in the memory 1510 and executable on the processor 1520. When the processor 1520 executes the computer program 1530, it causes the electronic device to implement the steps in any of the above-described data processing method embodiments.

[0388] This application also provides a computer-readable storage medium storing a computer program, which, when executed by a computer, enables the computer to implement the steps in any of the above-described data processing method embodiments.

[0389] This application provides a computer program product that, when run on an electronic device, enables the electronic device to perform the steps described in any of the above data processing method embodiments.

[0390] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of this application can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable storage medium can include at least: any entity or device capable of carrying computer program code to a device / electronic device, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium. Examples include USB flash drives, portable hard drives, magnetic disks, or optical disks.

[0391] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0392] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0393] In the embodiments provided in this application, it should be understood that the disclosed devices / electronic devices and methods can be implemented in other ways. For example, the device / electronic device embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual couplings or direct couplings or communication connections may be through some interfaces; indirect couplings or communication connections between devices or units may be electrical, mechanical, or other forms.

[0394] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0395] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A data processing method, characterized in that, Applied to electronic devices, the method includes: Get the first data; Based on the first target data, a first target scenario is determined; the first target data is the data in the first data. Determine the second target data associated with the first target scene; the second target data is data in the first data, and the second target data is different from the first target data; Based on the first target data and the second target data, the first information corresponding to the first target scenario is determined.

2. The method according to claim 1, characterized in that, The electronic device stores a scene library, which includes at least one preset scene and triggering conditions corresponding to each preset scene. The step of determining the first target scenario based on the first target data includes: The first target scenario is determined based on the first target data and the triggering conditions corresponding to each preset scenario; The first target scenario is a scenario in the preset scenario.

3. The method according to claim 2, characterized in that, The scene library also includes second data associated with each of the preset scenes; The step of determining the second target data associated with the first target scene includes: Based on the second data associated with each preset scenario and the first data, the second target data associated with the first target scenario is determined; the second target data is the data in the second data.

4. The method according to claim 3, characterized in that, The step of determining the second target data associated with the first target scene based on the second data associated with each preset scene and the first data includes: Based on the second data associated with each preset scenario and the first data, candidate data associated with the first target scenario is determined, wherein the candidate data is the data in the first data and the candidate data is the data in the second data; Determine the data score corresponding to the candidate data; Based on the data scores corresponding to the candidate data, the second target data associated with the first target scene is determined; the second target data is the data in the candidate data.

5. The method according to claim 4, characterized in that, Determining the data score corresponding to the candidate data includes: Obtain the first and second scoring criteria corresponding to the candidate data, wherein the first scoring criterion is a guideline standard and the second scoring criterion is a user standard; The first deviation degree corresponding to the candidate data is determined according to the first scoring criterion, and the second deviation degree corresponding to the candidate data is determined according to the second scoring criterion; The data score corresponding to the candidate data is determined based on the first deviation and the second deviation.

6. The method according to any one of claims 1 to 5, characterized in that, The step of determining the first information corresponding to the first target scene based on the first target data and the second target data includes: Analyze the first target data to determine the first analysis result corresponding to the first target scenario; The second target data is analyzed to determine the second analysis result corresponding to the first target scenario; Based on the first analysis result and the second analysis result corresponding to the first target scenario, the first information corresponding to the first target scenario is determined.

7. The method according to any one of claims 1 to 6, characterized in that, After determining the first information corresponding to the first target scene based on the first target data and the second target data, the method further includes: The first interface is displayed, which includes a first summary card, which includes first information corresponding to the first target scene; or, the first summary card includes first information corresponding to the first target scene, as well as first analysis results and / or second analysis results corresponding to the first target scene.

8. The method according to any one of claims 1 to 6, characterized in that, After determining the first information corresponding to the first target scene based on the first target data and the second target data, the method further includes: The first interface is displayed, which includes a first summary card and a second summary card. The first summary card is positioned in front of the second summary card in the first interface. The first summary card includes first information corresponding to the first target scene, and the second summary card includes first information corresponding to the second target scene; or, the first summary card includes first information corresponding to the first target scene, and includes first analysis results and / or second analysis results corresponding to the first target scene, and the second summary card includes first information corresponding to the second target scene, and includes first analysis results and / or second analysis results corresponding to the second target scene, wherein the scene priority of the first target scene is higher than the scene priority of the second target scene.

9. The method according to any one of claims 1 to 6, characterized in that, After determining the first information corresponding to the first target scene based on the first target data and the second target data, the method further includes: The first interface is displayed, which includes a first summary card; the first summary card includes first information corresponding to the first target scene; or, the first summary card includes first information corresponding to the first target scene, as well as first analysis results and / or second analysis results corresponding to the first target scene. In response to a first operation detected in the first interface, a second interface is displayed, the second interface including a second summary card; the second summary card includes first information corresponding to the second target scene; or, the second summary card includes first information corresponding to the second target scene, and includes a first analysis result and / or a second analysis result corresponding to the second target scene; the scene priority of the first target scene is higher than the scene priority of the second target scene.

10. The method according to claim 8 or 9, characterized in that, The priority of the target scene is either preset or determined based on the first target data corresponding to the target scene.

11. The method according to claim 10, characterized in that, The scenario priority corresponding to the target scenario is determined based on the data score corresponding to the first target data of the target scenario.

12. The method according to any one of claims 1 to 6, characterized in that, After determining the first information corresponding to the first target scene based on the first target data and the second target data, the method further includes: Display a first interface, the first interface including a first summary card, the first summary card including first information corresponding to the first target scene; In response to a second operation on the first information corresponding to the first target scene, a third interface is displayed, the third interface including a third summary card, the third summary card including the first analysis result and / or the second analysis result corresponding to the first target scene; or, In response to a third operation on the first information corresponding to the first target scene, a fourth interface is displayed, the fourth interface including a fourth summary card, the fourth summary card including the first analysis result corresponding to the first target scene; In response to a fourth operation on the first information corresponding to the first target scene, or in response to a fifth operation detected in the fourth interface, a fifth interface is displayed, the fifth interface including a fifth summary card, the fifth summary card including a second analysis result corresponding to the first target scene.

13. The method according to any one of claims 1 to 12, characterized in that, The first target scenario is associated with one or more second target data.

14. The method according to any one of claims 1 to 13, characterized in that, The first target data includes one or more of the first data.

15. The method according to any one of claims 1 to 14, characterized in that, The first data includes one or more of the following: sports and health data, environmental data, and usage data corresponding to the electronic device.

16. The method according to claim 15, characterized in that, The exercise and health data includes one or more of the following: sleep heart rate, resting heart rate, sleep heart rate variability, sleep onset time, sleep end time, sleep duration, blood oxygen saturation, blood pressure, body temperature, weight, respiratory rate, mood, stress, steps, exercise duration, activity hours, activity calories, or menstrual cycle.

17. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it causes the electronic device to implement the data processing method as described in any one of claims 1 to 16.

18. A computer program product, the computer program product comprising a computer program, characterized in that, When the computer program is executed by the electronic device, the electronic device causes the electronic device to implement the data processing method as described in any one of claims 1 to 16.

19. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a computer, it causes the computer to implement the data processing method as described in any one of claims 1 to 16.

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