Data processing method, and first device, storage medium and chip system

Abstract

The present application relates to the technical field of terminals. Provided are a data processing method, and a first device, a storage medium and a chip system. The method comprises: a first device receiving a first operation, wherein the first operation is used for adding an emotion association factor corresponding to a first time period; and the first device displaying a first interface, wherein the first interface comprises a first factor, a second factor, a first mark and a second mark, the first factor and the second factor are emotion association factors, the first factor corresponds to the first mark, the second factor corresponds to the second mark, and the first mark is different from the second mark. In this way, a plurality of factors can be displayed, and a recommendation factor and factors other than the recommendation factor are distinguished by means of different marks, thereby facilitating the confirmation of a user.

Description

Data processing method, first device, storage medium and chip system

[0001] This application claims priority to Chinese Patent Application No. 202411634375.3, filed on November 14, 2024, entitled "Data Processing Method, First Apparatus, Storage Medium and Chip System", the entire contents of which are incorporated herein by reference. Technical Field

[0002] This application relates to the field of terminal technology, and in particular to data processing methods, first devices, storage media, and chip systems. Background Technology

[0003] Currently, electronic devices can detect and record users' emotions. Furthermore, these devices can provide a list of factors to allow users to add and confirm factors related to their emotions. This enables the tracking and recording of emotion-related factors corresponding to a user's emotions.

[0004] For example, electronic devices can display a list of factors that influence emotions, including multiple factors. Users can select some factors from this list and confirm each selection. The electronic device can then record emotion-related factors based on the user's confirmation. However, this process is cumbersome and provides a poor user experience. Summary of the Invention

[0005] This application provides a data processing method, a first device, a storage medium, and a chip system, which can reduce user operations and improve user experience.

[0006] In a first aspect, embodiments of this application provide a data processing method applied to a first device. The method includes: receiving a first operation, the first operation being used to add an emotion-related factor corresponding to a first time period; displaying a first interface, the first interface including a first factor, a second factor, a first marker, and a second marker; wherein the first factor and the second factor are emotion-related factors, the first factor and the first marker correspond to each other, the second factor and the second marker correspond to each other, and the first marker and the second marker are different.

[0007] The first action can be a click, a touch, or any other type of action. The first action can correspond to the triggering actions for adding controls or adding record controls described below; no specific limitations are made here. The first factor can correspond to the recommendation factors described below. The first interface can correspond to the interface displaying recommendation factors described below. The first marker can correspond to the black background, checkmark, etc., described below. The second marker can correspond to the white background described below.

[0008] This allows for the display of multiple factors, with different labels used to distinguish between recommended factors and other factors, making it easier for users to confirm.

[0009] In one possible implementation, a first marker is used to indicate that a first factor is selected, and a second marker is used to indicate that a second factor is not selected; and / or, a first marker is used to indicate that a first factor is a recommended factor, and a second marker is used to indicate that a second factor is not a recommended factor.

[0010] Setting the first factor to be selected reduces the number of times users need to select it, thus reducing user operations. Displaying recommended factors reduces the number of times users need to search for factors, also reducing user operations.

[0011] In one possible implementation, the first marker is used to indicate that the first factor is selected, and the method further includes: receiving a second operation on a first interface, the second operation being used to confirm the added emotion-related factor; and in response to the second operation, adding the first factor as an emotion-related factor corresponding to the first time period.

[0012] The second operation corresponds to the triggering operation for the specified control described below. In this way, the user can set the first factor as the emotion-related factor corresponding to the first time period, thus completing the addition of the emotion-related factor.

[0013] In one possible implementation, the first marker is used to indicate that the first factor is selected, and the method further includes: receiving a third operation on a first interface, the third operation being used to indicate that the first factor is deselected; in response to the third operation, associating the first factor with a third marker, the third marker being used to indicate that the first factor is unselected.

[0014] The third marker can correspond to the checkmark or similar identifier mentioned below. The third action can be a touch, a click, or any type of action; no specific limitations are made here.

[0015] In this way, users can also adjust the factors that are in the selected state (or selected status) and select appropriate factors as added emotional association factors.

[0016] In one possible implementation, the first interface further includes a third factor and a fourth marker, wherein the third factor is an emotion-related factor, the fourth marker corresponds to the third factor, and the fourth marker is used to indicate that the third factor is selected; a fourth operation is received, which is used to confirm the added emotion-related factor; in response to the fourth operation, the third factor is added as an emotion-related factor corresponding to the first time period, wherein the emotion-related factors corresponding to the first time period do not include the first factor.

[0017] The fourth operation can be a touch, a click, or any type of operation; there are no specific limitations here.

[0018] In this way, users can set the first and third factors that are selected as the emotional association factors corresponding to the first time period, thus completing the addition of emotional association factors.

[0019] In one possible implementation, the first marker is used to indicate that the first factor is selected. The method further includes: receiving a fifth operation on a first interface, the fifth operation being used to indicate the selection of a second factor; in response to the fifth operation, associating the second factor with the fifth marker; the fifth marker being used to indicate that the second factor is selected; receiving a sixth operation, the sixth operation being used to confirm the added emotional association factor; in response to the sixth operation, adding the first factor and the second factor as emotional association factors corresponding to the first time period.

[0020] The fifth action can be a touch, a click, or any type of action; there are no specific limitations here.

[0021] In this way, users can also select a second factor as an added emotional association factor.

[0022] In one possible implementation, the first factor is obtained based on a first model and user data; the first model is used to predict sentiment-related factors based on user data.

[0023] In this way, the model can be used to predict factors related to emotions.

[0024] In one possible implementation, the first device is logged into a first account; the user data includes: user data of the first account, and / or, user data of a second account; the second account is an account associated with the first account.

[0025] The first and second accounts can correspond to different users. In this way, the first model can make inferences based on data from different users, which can improve the accuracy of the inference results.

[0026] In one possible implementation, after receiving the first operation, the method further includes: sending first information to a second device, the first information being used to instruct the addition of an emotion-related factor corresponding to a first time period; and receiving a first factor, the first factor being obtained by the second device based on a first model and user data.

[0027] The first device can obtain the first factor from the second device, which can reduce the storage space occupied by the first device and extend the battery life of the first device.

[0028] In one possible implementation, sending the first information to the second device includes: sending the first information to the second device via a third device; the first information is sent by the second device to the first device via the third device.

[0029] The first device can interact with the second device through the third device.

[0030] In one possible implementation, the first model is used to calculate multiple factors based on the first data according to the decision tree; the first data consists of multiple data points randomly selected from user data; the first model is also used to determine the first factor based on the frequency of occurrence of the multiple factors.

[0031] By simulating the human decision-making process using decision trees, we can predict the factors that influence emotions.

[0032] In one possible implementation, the decision tree includes a first decision tree, a second decision tree, and a third decision tree. The first data includes: a first type of data, a second type of data, and a third type of data; the first type of data is data related to social activities; the second type of data is data related to interpersonal relationships; and the third type of data is data related to personal physical and mental health. The first model is specifically used to calculate one or more factors related to social activities based on the first type of data using the first decision tree; the first model is specifically used to calculate one or more factors related to interpersonal relationships based on the second type of data using the second decision tree; and the first model is specifically used to calculate one or more factors related to personal physical and mental health based on the third type of data using the third decision tree.

[0033] By categorizing emotion-related factors into multiple classes and applying different decision trees to each category, the accuracy of decision tree predictions can be improved, thereby enhancing the accuracy of recommendation factors. Furthermore, using multiple decision trees for prediction reduces computational load and increases prediction speed.

[0034] In one possible implementation, the method further includes updating the first model based on the emotional correlation factors corresponding to the first time period.

[0035] In this way, the first model is trained based on the added sentiment-related factors, and personalized settings are applied to improve the accuracy of the recommendation factors output by the first model.

[0036] Secondly, embodiments of this application provide a data processing method applied to a system. The system includes: a first device and a second device. The method includes:

[0037] The first device receives a first operation, which is used to add an emotion-related factor corresponding to a first time period. After the first operation, the first device sends a first message to the second device, which is used to instruct the addition of an emotion-related factor corresponding to the first time period. After receiving the first message, the second device sends a first factor to the first device. The first device displays a first interface, which includes a first factor, a second factor, a first marker, and a second marker. The first factor and the second factor are emotion-related factors. The first factor and the first marker correspond to each other, the second factor and the second marker correspond to each other, and the first marker and the second marker are different.

[0038] In one possible implementation, a first marker is used to indicate that a first factor is selected, and a second marker is used to indicate that a second factor is not selected; and / or, a first marker is used to indicate that a first factor is a recommended factor, and a second marker is used to indicate that a second factor is not a recommended factor.

[0039] In one possible implementation, the first marker is used to indicate that the first factor is selected, and the method further includes: the first device receiving a second operation on the first interface, the second operation being used to confirm the added emotion-related factor; the first device responding to the second operation by adding the first factor as an emotion-related factor corresponding to the first time period.

[0040] In one possible implementation, the first marker is used to indicate that the first factor is selected. The method further includes: the first device receiving a third operation on the first interface, the third operation being used to indicate that the first factor is deselected; the first device responding to the third operation associating the first factor with a third marker, the third marker being used to indicate that the first factor is unselected.

[0041] In one possible implementation, the first interface further includes a third factor and a fourth marker, wherein the third factor is an emotion-related factor, the fourth marker corresponds to the third factor, and the fourth marker is used to indicate that the third factor is selected; a fourth operation is received, which is used to confirm the added emotion-related factor; the first device responds to the fourth operation by adding the third factor as an emotion-related factor corresponding to the first time period, wherein the emotion-related factors corresponding to the first time period do not include the first factor.

[0042] In one possible implementation, the first marker is used to indicate that the first factor is selected. The method further includes: the first device receiving a fifth operation on the first interface, the fifth operation being used to indicate the selection of the second factor; the first device responding to the fifth operation, the second factor corresponding to the fifth marker; the fifth marker being used to indicate that the second factor is selected; the first device receiving a sixth operation, the sixth operation being used to confirm the added emotion-related factor; and in response to the sixth operation, the first factor and the second factor being added as emotion-related factors corresponding to the first time period.

[0043] In one possible implementation, the first factor is obtained by the second device based on the first model and user data; the first model is used to predict emotion-related factors based on the user data.

[0044] In one possible implementation, the first device is logged into a first account; the user data includes: user data of the first account, and / or, user data of a second account; the second account is an account associated with the first account.

[0045] In one possible implementation, the system further includes a third device, and the first device sends first information to the second device, including: the first device sending the first information to the second device through the third device; and the second device sending the first information to the first device through the third device.

[0046] In one possible implementation, the first model is used to calculate multiple factors based on the first data according to the decision tree; the first data consists of multiple data points randomly selected from user data; the first model is also used to determine the first factor based on the frequency of occurrence of the multiple factors.

[0047] In one possible implementation, the decision tree includes a first decision tree, a second decision tree, and a third decision tree. The first data includes: a first type of data, a second type of data, and a third type of data; the first type of data is data related to social activities; the second type of data is data related to interpersonal relationships; and the third type of data is data related to personal physical and mental health. The first model is specifically used to calculate one or more factors related to social activities based on the first type of data using the first decision tree; the first model is specifically used to calculate one or more factors related to interpersonal relationships based on the second type of data using the second decision tree; and the first model is specifically used to calculate one or more factors related to personal physical and mental health based on the third type of data using the third decision tree.

[0048] In one possible implementation, the method further includes: the first device sending emotion-related factors corresponding to a first time period to the second device; and the second device updating the first model based on the emotion-related factors corresponding to the first time period.

[0049] Thirdly, embodiments of this application provide a system comprising: a first device in the method described in the second aspect or any possible implementation of the second aspect, and a second device in the method described in the second aspect or any possible implementation of the second aspect.

[0050] Fourthly, embodiments of this application provide an electronic device including a processor and a memory, the memory for storing code instructions, and the processor for running the code instructions to perform the methods described in the first aspect or any possible implementation of the first aspect.

[0051] Fifthly, embodiments of this application provide a computer-readable storage medium storing a computer program or instructions that, when executed on a computer, cause the computer to perform the methods described in the first aspect or any possible implementation thereof.

[0052] In a sixth aspect, embodiments of this application provide a computer program product including a computer program, which, when run on a computer, causes the computer to perform the methods described in the first aspect or any possible implementation thereof.

[0053] Seventhly, this application provides a chip or chip system including at least one processor and a communication interface, wherein the communication interface and at least one processor are interconnected via a circuit, and the at least one processor is used to run a computer program or instructions to perform the methods described in the first aspect or any possible implementation thereof. The communication interface in the chip may be an input / output interface, pins, or circuits, etc.

[0054] In one possible implementation, the chip or chip system described above in this application further includes at least one memory storing instructions. The memory can be an internal storage unit of the chip, such as a register or cache, or it can be a storage unit of the chip itself (e.g., read-only memory, random access memory, etc.).

[0055] It should be understood that the second to seventh aspects of this application correspond to the technical solutions of the first aspect of this application, and the beneficial effects achieved by each aspect and the corresponding feasible implementation are similar, and will not be repeated here. Attached Figure Description

[0056] Figure 1 is a schematic diagram of a possible data processing interface in a design;

[0057] Figure 2 is a schematic diagram of the structure of a communication system provided in an embodiment of this application;

[0058] Figure 3 is a schematic diagram of the structure of a communication system provided in an embodiment of this application;

[0059] Figure 4A is a schematic diagram of the software architecture of various devices in a system provided in an embodiment of this application;

[0060] Figure 4B is a schematic diagram of the software architecture of various devices in another system provided by an embodiment of this application;

[0061] Figure 4C is a schematic diagram of the software architecture of each device in another system provided by an embodiment of this application;

[0062] Figure 5 is a flowchart illustrating a data processing method provided in an embodiment of this application;

[0063] Figure 6 is a schematic diagram of user data provided in an embodiment of this application;

[0064] Figure 7 is a schematic diagram of the process of obtaining recommendation factors by a recommendation model provided in an embodiment of this application;

[0065] Figure 8A is a schematic diagram of the interface of a wearable device provided in an embodiment of this application;

[0066] Figure 8B is a schematic diagram of the interface of a wearable device for confirming added emotional correlation factors according to an embodiment of this application;

[0067] Figure 8C is a schematic diagram of the interface of another wearable device provided in an embodiment of this application;

[0068] Figure 8D is a schematic diagram of the interface of another wearable device provided in an embodiment of this application;

[0069] Figure 9 is a flowchart illustrating an embodiment of this application for adjusting added emotion-related factors;

[0070] Figure 10 is a schematic diagram of the interface of a wearable device during a factor adjustment process provided in an embodiment of this application;

[0071] Figure 11 is a schematic diagram of the structure of a social model decision tree provided in an embodiment of this application;

[0072] Figure 12 is a schematic diagram of the structure of a decision tree for an interpersonal relationship model provided in an embodiment of this application;

[0073] Figure 13 is a schematic diagram of the structure of a decision tree for a personal mind-body model provided in an embodiment of this application;

[0074] Figure 14 is a schematic diagram of a process for adding emotion-related factors according to an embodiment of this application;

[0075] Figure 15 is a schematic diagram of the interface of an electronic device provided in an embodiment of this application;

[0076] Figure 16 is a flowchart illustrating an embodiment of this application for adjusting added emotion-related factors;

[0077] Figure 17 is a schematic diagram of the interface of an electronic device during a factor adjustment process provided in an embodiment of this application;

[0078] Figure 18 is a flowchart illustrating a data processing method provided in an embodiment of this application;

[0079] Figure 19 is a flowchart illustrating an embodiment of this application for adjusting added emotion-related factors;

[0080] Figure 20 is a schematic diagram of the interface of an electronic device provided in an embodiment of this application;

[0081] Figure 21 is a schematic diagram of the structure of a data processing device provided in an embodiment of this application;

[0082] Figure 22 is a schematic diagram of the hardware structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0083] To facilitate a clear description of the technical solutions in the embodiments of this application, some terms and technologies involved in the embodiments of this application will be briefly introduced below:

[0084] 1. Emotions

[0085] Emotions can be understood as the subjective psychological state experienced by an individual in a specific situation. Emotions are usually triggered by external stimuli or internal cognitive processes and are accompanied by physiological reactions, behavioral manifestations, and subjective experiences.

[0086] Currently, emotions can be identified and analyzed through various methods, such as facial expression analysis, physiological signal monitoring (e.g., heart rate, skin conductance), speech analysis, and text sentiment analysis.

[0087] 2. Emotion-related factors

[0088] Emotion-related factors refer to factors associated with the occurrence of emotions. They can also be understood as events or conditions that significantly influence emotions (e.g., external environmental conditions such as noise and light); or factors that cause changes in or maintenance of emotions. Emotion-related factors can also be called factors associated with emotions, without further specific definition here.

[0089] In this embodiment, factors influencing emotions can include multiple aspects (or multiple dimensions). These multiple aspects can include: interpersonal relationships, social activities, physical and mental health, etc. Any one of these multiple aspects can be further divided into multiple factors. For example, interpersonal relationships can include: partners, neighbors, or friends, etc.; social activities can include: work, family, education, or money, etc.; physical and mental health can include: hobbies, health, illness, weather, etc.

[0090] 3. Electronic equipment

[0091] The electronic devices in this application embodiment may include handheld devices, vehicle-mounted devices, etc., with data processing functions. For example, some electronic devices include: mobile phones, tablets, PDAs, laptops, mobile internet devices (MIDs), wearable devices, virtual reality (VR) devices, augmented reality (AR) devices, wireless terminals in industrial control, wireless terminals in self-driving, wireless terminals in remote medical surgery, wireless terminals in smart grids, wireless terminals in transportation safety, wireless terminals in smart cities, wireless terminals in smart homes, cellular phones, cordless phones, session initiation protocol (SIP) phones, wireless local loop (WLL) stations, personal digital assistants (PDAs), handheld devices with wireless communication capabilities, computing devices or other processing devices connected to wireless modems, in-vehicle devices, terminal devices in Internet of Things (IoT) systems, terminal devices in 5G networks, or future evolution of public land mobile communication networks. Terminal devices in a mobile network (PLMN), etc., are not limited to this in the embodiments of this application.

[0092] The electronic devices in the embodiments of this application may also be referred to as: terminal equipment, user equipment (UE), mobile station (MS), mobile terminal (MT), access terminal, user unit, user station, mobile station, mobile station, remote station, remote terminal, mobile device, user terminal, terminal, wireless communication equipment, user agent, or user device, etc.

[0093] In this embodiment, the electronic device or various network devices include a hardware layer, an operating system layer running on top of the hardware layer, and an application layer running on top of the operating system layer. The hardware layer includes hardware such as a central processing unit (CPU), a memory management unit (MMU), and memory (also called main memory). The operating system can be any one or more computer operating systems that implement business processing through processes, such as Linux, Unix, Android, iOS, or Windows. The application layer includes applications such as browsers, address books, word processing software, and instant messaging software.

[0094] 4. Wearable devices

[0095] Wearable devices are a type of electronic device. They can be worn anywhere on a user's wrist, abdomen, legs, etc. Wearable devices can include smartwatches, smart glasses, smart bracelets, or smart jewelry, etc. No specific limitations are specified here.

[0096] 5. Photoplethysmography (PPG)

[0097] PPG (Potentially Perceptual Glucose) is a non-invasive detection method that uses photoelectric techniques to detect changes in blood volume in living tissue. Specifically, when a light beam of a certain wavelength shines on the user's skin, the beam is transmitted to a photosensor through transmission or reflection. During this process, the blood volume within the blood vessels fluctuates due to the contraction and relaxation of the heart. Therefore, during diastole, the peripheral blood volume decreases, and the photosensor detects a higher light intensity. During systole, the peripheral blood volume increases, and the photosensor detects a lower light intensity. Thus, the light intensity detected by the photosensor changes pulsatially, and this change in light intensity signal can be converted into an electrical signal, i.e., a PPG signal.

[0098] Based on the above description, PPG signals can characterize information such as blood pressure, blood oxygen, brain oxygen, blood glucose, pulse rate, blood oxygen saturation, heart rate, and respiratory rate.

[0099] 6. Other terms

[0100] In the embodiments of the present application, terms such as "first" and "second" are used to distinguish identical or similar items with basically the same functions and effects. For example, the first numerical value and the second numerical value are only used to distinguish different numerical values, and do not limit their order. Those skilled in the art can understand that terms such as "first" and "second" do not limit the quantity and execution order, and the terms such as "first" and "second" do not necessarily limit differences.

[0101] It should be noted that in the embodiments of the present application, words such as "exemplarily" or "for example" are used to represent examples, illustrations or explanations. Any embodiment or design described as "exemplarily" or "for example" in the present application should not be construed as more preferred or more advantageous than other embodiments or designs. Rather, the use of words such as "exemplarily" or "for example" is intended to present related concepts in a specific manner.

[0102] In the embodiments of the present application, "at least one" means one or more, and "multiple" means two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships can exist. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone, where A and B can be singular or plural. The character " / " generally indicates that the associated objects before and after are an "or" relationship. "At least one (item)" or its similar expression refers to any combination of these items, including any combination of single item (item) or plural items (items). For example, at least one (item) of a, b, or c can represent: a, b, c, a - b, a - c, b - c, or a - b - c, where a, b, and c can be single or multiple.

[0103] Currently, electronic devices such as watches and mobile phones can detect and record the emotions of users to facilitate users' understanding of their own emotional states.

[0104] To better understand their own emotional states and help discover potential emotional problems, electronic devices such as mobile phones can also record the emotional correlation factors corresponding to the current emotions to facilitate users' subsequent viewing.

[0105] In a possible design, the electronic device can record the emotional correlation factors in a manner similar to the notepad function. Specifically, the electronic device can display a factor list. The factor list can include factors in multiple aspects such as interpersonal relationships, social activities, personality hobbies, etc. Users can select one or more factors from the factor list as emotional correlation factors.

[0106] However, the operation of this method is cumbersome and the user experience is poor.

[0107] For example, Figure 1 is a schematic diagram of an interface involved in adding emotion-related factors in a possible design. Taking a watch as an example, as shown in Figure 1, the electronic device displays interface 1a, which displays the user's current emotion and a query control 101. This emotion is obtained by the electronic device through analysis of user data. For example, user data may include one or more of the following: blood oxygen saturation, heart rate, respiratory rate, etc.

[0108] When the electronic device receives a trigger operation (e.g., touch operation, click operation, etc.) on interface 1a for query control 101, the electronic device enters interface 1b. Interface 1b includes: emotions over a recent period of time, and associated record control 102.

[0109] When the electronic device receives a trigger operation (e.g., touch operation, click operation, etc.) on interface 1b for the associated record control 102, the electronic device enters interface 1c. Interface 1c includes multiple option tabs, such as Social Activities 103, Interpersonal Relationships, Hobbies, and Others.

[0110] When the electronic device receives a trigger operation (e.g., touch operation, click operation, etc.) for social activity 103 on interface 1c, the electronic device expands the corresponding tab and enters interface 1d. Interface 1d includes multiple factors related to social activity 103, such as "work," "study," "housework," "fitness," and "dating" 104. All these factors are in an unselected (or unselected) state A. State A indicates that they are not selected.

[0111] When the electronic device receives a trigger operation (e.g., touch operation, click operation, etc.) for "Date" 104 on interface 1c, it switches the date from state A to state B, and the electronic device enters interface 1e. In interface 1e, the date has a corresponding check mark 105. The check mark 105 indicates that it is in state B. State B indicates that it is selected.

[0112] When the electronic device receives a trigger operation (e.g., touch operation, click operation, etc.) on the interface 1e for the selected control 106, the factor that is confirmed to be in the selected state is the added emotion-related factor.

[0113] As shown in Figure 1, this method requires users to find emotion-related factors from numerous and varied options. After receiving an instruction to select a factor, the electronic device checks that factor and records it as an emotion-related factor. This process is cumbersome and results in a poor user experience. Furthermore, users may feel overwhelmed and unable to make a decision after viewing numerous options, increasing the user's cost.

[0114] In view of this, embodiments of this application provide a data processing method, an electronic device, a storage medium, and a chip system. During the process of adding emotion-related factors to the electronic device, a recommendation model can be used to analyze and predict user data to obtain recommendation factors. The electronic device can then provide prompts based on these recommendation factors.

[0115] In one possible implementation, when the electronic device subsequently displays multiple factors, it can set the recommended factor to a selected state, while other factors are not selected, thus facilitating the user's identification of emotion-related factors from multiple factors. In other embodiments, the electronic device can also display the recommended factor separately on the interface for prompting, or it can add a mark to the recommended factor to distinguish it from other factors. This application does not specifically limit the prompting method for the recommended factor.

[0116] In this way, recommending factors to users reduces the need for them to search for and select emotion-related factors from multiple options, thus reducing user operations and improving the user's human-computer interaction experience. Furthermore, it reduces the time users spend selecting from numerous options, saving them time in the selection process.

[0117] The methods and apparatus provided in this application can be applied to systems including a first device and a second device. The number of the first device and / or the second device can be one or more.

[0118] The first device can be any type of electronic device that receives user input. The second device can be a device equipped with a recommendation model. The second device can be any type of electronic device, server, etc. This application does not limit the specific form of the first device or the second device.

[0119] In this embodiment, the server can be an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDN), and big data and artificial intelligence platforms.

[0120] In some embodiments, the first device and the second device can establish a communication connection. This communication connection can be of any form, such as any form of wireless communication connection or any form of wired communication connection. For example, a wireless communication connection can include the following forms: Wireless Fidelity (WIFI) communication connection, cellular communication connection, Bluetooth communication connection, near-field communication connection, ultra-wide band (UWB) communication connection, and infrared transmission communication connection, etc. A wired communication connection can include the following forms: Ethernet communication connection, fiber optic communication connection, and universal serial bus (USB) connection, etc.

[0121] In this embodiment of the application, the first device and the second device may establish a communication connection through protocols such as Hypertext Transfer Protocol (HTTP) or Hypertext Transfer Protocol over Secure Socket Layer (HTTPS), and this embodiment of the application does not impose any restrictions on this.

[0122] The first device and the second device can also achieve P2P communication through any form of communication connection. For example, the first device and the second device can achieve P2P communication through wireless transmission, such as Bluetooth transmission, ultra-wide band (UWB) communication, or infrared transmission. The first device and the second device can also achieve P2P communication through wired transmission.

[0123] In other embodiments, both the first device and the second device may establish a communication connection with the third device, thereby enabling data transmission between the first device and the second device through the third device.

[0124] The communication connections established between the first and third devices, and between the third and second devices, are similar to those established between the first and second devices, and will not be described in detail here.

[0125] The above system can be applied to scenarios where emotional association factors are added to wearable devices, as well as to scenarios where emotional association factors are added to electronic devices. Please refer to the scenario descriptions shown in Figures 2 and 3 below for details.

[0126] For example, Figure 2 is a schematic diagram of a first application scenario provided by an embodiment of this application. In this application scenario, the first device is a wearable device 201, the second device is a server 202, and the third device is an electronic device 203.

[0127] After receiving a user's operation, wearable device 201 can transmit first information to server 202 via electronic device 203. This first information indicates the acquisition of emotion-related factors corresponding to emotions within a first time period. Upon receiving this first information, server 202 obtains recommendation factors based on a recommendation model and user data. After obtaining the recommendation factors, server 202 sends them to wearable device 201 via electronic device 203, so that wearable device 201 can set the recommendation factors to a selected state, thereby facilitating user confirmation of the added emotion-related factors.

[0128] It is understood that in the scenario shown in Figure 2, the wearable device 201 interacts with the server 202 through the electronic device 203. In some scenarios, the electronic device 203 may not be present. The wearable device 201 can establish a communication connection with the server 202.

[0129] For example, Figure 3 is a schematic diagram of a second application scenario provided by an embodiment of this application. In this application scenario, the first device is an electronic device 301 and the second device is a server 302.

[0130] Upon receiving a user operation, electronic device 301 can transmit first information to server 302, which indicates the acquisition of emotion-related factors corresponding to the emotion within a first time period. After receiving this first information, server 302 obtains recommendation factors based on a recommendation model and user data. After obtaining the recommendation factors, server 302 sends them to electronic device 301 so that electronic device 301 can set the recommendation factors to a selected state, thereby facilitating user confirmation of the added emotion-related factors.

[0131] It should be understood that the scenarios shown in Figures 2 and 3 above are illustrated using the server as the second device. The second device can also be other devices configured with recommendation models, such as computers, mobile phones, and other electronic devices. The specific implementation is similar to that of the server and will not be elaborated upon here.

[0132] In the above scenario, the recommendation model is set on the second device. In some embodiments, the recommendation model can also be set on the first device. Specifically, after receiving the user's operation, the first device obtains recommendation factors based on the recommendation model and user data. After obtaining the recommendation factors, the first device sets the recommendation factors to a selected state, thereby facilitating the user's confirmation of the added emotion-related factors.

[0133] Understandably, if the recommendation model is set up on the first device, the first device can independently recommend emotion-related factors without interacting with other devices (e.g., the second device), thereby reducing data transmission leaks and improving privacy. Furthermore, the first device can recommend emotion-related factors offline (i.e., without a network connection), making it widely applicable.

[0134] If the recommendation model is set up on the second device, the storage space occupied on the first device can be reduced, extending the battery life of the first device. Furthermore, the computing power of the first device may not be sufficient to support the operation of the recommendation model. Through interaction between the second and first devices, recommendations of sentiment-related factors can be implemented on devices with lower computing power. In some embodiments, the recommendation model is set up on a server. Setting the recommendation model on a server also facilitates model updates and maintenance.

[0135] To better understand the embodiments of this application, the software architecture of each device involved in the embodiments of this application is described below. It should be understood that the above embodiments provide a data processing method for obtaining recommendation factors, which can involve three application scenarios: an application scenario including three devices (as shown in Figure 2), an application scenario including two devices (as shown in Figure 3), and an application scenario including a single device. The software architecture of each device involved in these three scenarios will be described below with reference to Figures 4A to 4C.

[0136] For example, Figure 4A is a schematic diagram of the software structure of various devices provided in an embodiment of this application. As shown in Figure 4A, the system includes: a first device, a second device, and a third device. As shown in Figure 4A, after receiving an operation to instruct the addition of emotion-related factors, the first device can obtain recommendation factors from the second device via the third device to facilitate the user's confirmation of emotion-related factors.

[0137] In some embodiments, the first device may also acquire user data and transmit the user data to the second device through the third device, so that the second device can obtain recommendation factors based on the recommendation model and user data.

[0138] For example, the first device can also acquire the first data through its own hardware and process the first data to obtain user data.

[0139] The first data may include data collected by hardware devices, such as data collected by sensors; user data may include data obtained based on the data collected by the devices, such as the user's physiological characteristic data. Physiological characteristic data includes one or more of the following: blood pressure, blood oxygen, brain oxygen, blood glucose, pulse rate, blood oxygen saturation, heart rate, and respiratory rate. For example, taking the PPG signal from a PPG sensor as an example, the first device can analyze the PPG signal to obtain the user's blood pressure, blood oxygen, brain oxygen, and other physiological characteristic data. Detailed information about the user data can be found in the corresponding description in Figure 6 below, and will not be elaborated here.

[0140] As shown in Figure 4A, the first device may include: a driver, a health service module, and a communication module.

[0141] The driver can acquire first data collected by various hardware devices (e.g., sensors) in the first device and transmit the first data to the health service module. The communication module is used to interact with devices connected to the first device (e.g., a third device) for data exchange.

[0142] The health service module can process the initial data to obtain corresponding user data. The health service module can also report user data to a second device via a third device through the communication module.

[0143] For example, as shown in Figure 4A, the health business module may include: an emotional health module, a sleep module, a blood oxygen module, a heart rate module, etc.

[0144] The emotional health module processes the first data transmitted by the driver to obtain emotional data. The sleep module processes the first data transmitted by the driver to obtain sleep data, such as sleep duration and sleep quality. The blood oxygen module processes the first data transmitted by the driver to obtain blood oxygen data. The heart rate module processes the first data transmitted by the driver to obtain heart rate data.

[0145] In this embodiment, the emotional health module includes an emotion algorithm. The emotion algorithm processes the first data to obtain the emotion.

[0146] The emotional health module can also, upon receiving an instruction to add emotional association factors, obtain recommendation factors from the second device via the communication module and the third device. Furthermore, upon receiving an instruction from the user to adjust the emotional association factors, the emotional health module can transmit the adjusted emotional association factors to the second device, facilitating subsequent training of the recommendation model in the second device using these adjusted factors.

[0147] In some embodiments, the emotional health module includes: an emotional business module, an emotional algorithm module, and a storage module.

[0148] The emotion processing module retrieves initial data from the driver and processes it using the emotion algorithm module. Upon receiving an instruction to add emotion-related factors, the emotion processing module also retrieves recommendation factors from a second device via a communication module and a third device. The emotion processing module can then set these recommendation factors to a selected state, allowing users to easily confirm that the recommended factor is the added emotion-related factor.

[0149] The emotion algorithm module is used to process the first data to obtain the emotion.

[0150] The storage module is used to store added emotion-related factors for easy display later.

[0151] In this embodiment of the application, the third device is used to send the first information to the second device after receiving the first information from the first device. The first information is used to instruct the addition of emotion-related factors.

[0152] The third device is also used to acquire user data and send that user data to the second device. This user data may originate from one or more devices connected to the third device, or it may originate from the third device itself.

[0153] In this embodiment of the application, the account logged in by the device connected to the third device can be the same as the account logged in by the third device, or it can be an account associated with the account logged in by the third device. This embodiment of the application does not specifically limit the source of user data or the accounts logged in by each device.

[0154] For example, the third device includes a fitness and health app. The fitness and health app can acquire user data from multiple devices. These multiple devices may include the third device and devices connected to the third device.

[0155] Sports and health apps can also integrate user data belonging to the same account from multiple devices to facilitate subsequent use of user data based on the account.

[0156] For example, a sports and health application includes: a device management module, a data platform, and a data fusion module.

[0157] The device management module can manage devices connected to the third device. This includes, for example, device connection management, monitoring the connection status of connected devices, and data encryption / decryption. For instance, the device management module can enable communication between the third device and the first device.

[0158] A data platform can store user data from devices connected to a third device. For example, a data platform can store user data from a first device. A data platform can also be called a database.

[0159] The data fusion module can integrate user data belonging to the same account from the user data stored in the data platform.

[0160] In some embodiments, the sports and health application can also implement functions similar to the health business module described above. For example, adding emotion-related factors and calculating emotions. No specific limitations are made here. For instance, the sports and health application may include the aforementioned health business module.

[0161] In this embodiment, the second device is used to obtain recommendation factors based on a recommendation model and user data after receiving the first information. In this embodiment, the recommendation factors are obtained by the second device based on the recommendation model and user data. The second device can obtain user data from a third device, and can also obtain user data from devices connected to the third device through the third device.

[0162] For example, the second device includes a home space. The home space is used to manage user data from each device based on an account. The home space is also used to derive recommendation factors based on user data after receiving first information indicating the addition of emotion-related factors.

[0163] As shown in Figure 4A, the family space may include: an account management module and a recommendation model.

[0164] The account management module allows for the management of stored user data based on accounts. This facilitates the subsequent use of user data by the recommendation model.

[0165] The recommendation model can process user data stored in the account management module to obtain recommendation factors. Specific processing methods are detailed below and will not be elaborated upon here.

[0166] It should be understood that a user's emotions may not only be related to the user themselves, but may also be influenced by other users who are associated with the user. Therefore, the second device may also obtain user data from other users' devices.

[0167] For example, as shown in Figure 4A, the second device can acquire user data from the fourth device. The function and software structure of the fourth device are similar to those of the third device described above, and will not be elaborated further here.

[0168] It should be understood that in this embodiment, different users are distinguished by account number; for example, the first account corresponds to the first user, and the second account corresponds to the second user. In practical applications, different users can also be distinguished by any other means, such as automatically identifying different users. This embodiment does not specifically limit the method of user distinction.

[0169] In some embodiments, the second device can also obtain user data of other users' devices through the third device. For example, as shown in FIG4A, the second device can obtain user data of the fifth device through the third device.

[0170] The function and software structure of the fifth device are similar to those of the first device mentioned above. For details, please refer to the corresponding descriptions above. They will not be elaborated here.

[0171] The following describes the transmission process of user data, the transmission process of the first information used to indicate the addition of emotion-related factors, and the transmission process of recommendation factors, in conjunction with Figure 4A. An example is taken where the first and third devices are both logged into the first account, and the fourth device is logged into the second account associated with the first account.

[0172] The user data transmission process is as follows: The health service module in the first device can send the user data of the first account to the third device via the communication module. The third device can receive the user data of the first account via the device management module. The device management module transmits the user data of the first account to the data platform for storage. The third device then transmits the user data of the first account to the second device.

[0173] In some embodiments, the fourth device transmits user data of the second account to the second device. The fifth device also transmits user data of the second account to the second device via the third device.

[0174] The transmission process of the first information is as follows: After receiving the operation indicating the addition of emotion-related factors, the health service module of the first device sends the first information to the third device via the communication module. The third device then sends the first information to the second device.

[0175] The transmission process of recommendation factors is as follows: After receiving the first information, the second device processes the user data of the first account and / or the user data of the second account through the recommendation model module to obtain recommendation factors. The second device sends the recommendation factors to the third device. The third device then sends the recommendation factors to the first device. Subsequently, the user can confirm on the first device whether the recommendation factor is an added sentiment-related factor.

[0176] Understandably, users may confirm that the added emotional association factor is the recommended factor, or they may confirm that the added emotional association factor is not the recommended factor, or the added emotional association factor may include other factors. In the case that the added emotional association factor is not the recommended factor, or that the added emotional association factor includes other factors, the first device can transmit the added emotional association factor to the second device via the third device.

[0177] For example, if the added emotional association factors include: work, study, dating; and the recommendation factors include: fitness, housework, then the added emotional association factors are not recommendation factors. If the added emotional association factors include: work, study, dating; and the recommendation factors include: study, dating, then the added emotional association factors include recommendation factors and other factors (e.g., work, study). If the added emotional association factors include: work, study, dating; and the recommendation factors include: work, study, dating, dating, then the added emotional association factors are recommendation factors.

[0178] If the added sentiment-related factor is not the recommended factor, or if the added sentiment-related factor includes other factors, the transmission process of the added sentiment-related factor is as follows: The health service module can send the added sentiment-related factor to the third device via the communication module. The third device then sends the added sentiment-related factor to the second device. Subsequently, the second device can train the recommendation model based on the added sentiment-related factor.

[0179] The above embodiments illustrate data transmission between the first device and the second device via a third device. In some embodiments, the first device may also transmit data directly to the second device without going through a third device.

[0180] Specifically, the user data transmission process is as follows: The health service module in the first device can send the user data of the first account to the second device through the communication module.

[0181] The transmission process of the first information is as follows: After receiving the operation to indicate the addition of emotion-related factors, the health business module sends the first information to the second device through the communication module.

[0182] The transmission process of recommendation factors is as follows: After receiving the first information, the second device processes the user data of the first account and the user data of the second account through the recommendation model module to obtain recommendation factors. The second device then sends the recommendation factors to the first device. Subsequently, the user can confirm on the first device whether the recommendation factor is an added sentiment-related factor.

[0183] If the added sentiment-related factor is not the recommended factor, or if the added sentiment-related factor includes other factors, the transmission process of the added sentiment-related factor is as follows: The health service module can send the added sentiment-related factor to the second device through the communication module. Subsequently, the second device can train the recommendation model based on the added sentiment-related factor.

[0184] Figure 4A above illustrates the software architecture of the three devices involved in the data processing method. Figure 4B below illustrates the software architecture of the two devices involved in the data processing method.

[0185] Upon receiving an instruction to add emotion-related factors, the first device can retrieve recommendation factors from the second device to facilitate user confirmation of the added emotion-related factors. The first device can also acquire user data and transmit it to the second device, enabling the second device to subsequently derive recommendation factors based on the recommendation model and the user data.

[0186] As shown in Figure 4B, the first device may include: a driver, a health service module, and a communication module. The functions of the driver, health service module, and communication module can be referred to the descriptions of the corresponding modules in Figure 4A above, and will not be elaborated further here.

[0187] The second device may include: an equipment management module, a data platform, a data fusion module, and a recommendation model. The functions of the equipment management module, data platform, data fusion module, and recommendation model can be found in the descriptions of the corresponding modules in Figure 4A above, and will not be elaborated upon here.

[0188] In some embodiments, the second device may also acquire user data from other users' devices. The functions and software structures of the other users' devices are similar to those of the first, second, and third devices described above, and will not be elaborated upon here.

[0189] The following describes the transmission flow of user data, the transmission flow of the first information used to indicate the addition of emotion-related factors, and the transmission flow of recommendation factors, in conjunction with Figure 4B. Taking the login of the first account on the first device as an example...

[0190] The user data transmission process is as follows: The health service module in the first device can send the user data of the first account to the second device via the communication module. The second device can receive the user data of the first account via the device management module. The device management module transmits the user data of the first account to the data platform for storage. In some embodiments, devices corresponding to other users also send the user data of the second account to the second device. The second device can receive the user data of the second account via the device management module. The device management module transmits the user data of the second account to the data platform for storage.

[0191] The transmission process of the first information is as follows: After receiving the operation to indicate the addition of emotion-related factors, the health business module sends the first information to the second device through the communication module.

[0192] The transmission process of recommendation factors is as follows: After receiving the first information, the second device processes the user data of the first account and the user data of the second account through the recommendation model module to obtain recommendation factors. The second device then sends the recommendation factors to the first device. Subsequently, the user can confirm on the first device whether the recommendation factor is an added sentiment-related factor.

[0193] If the added sentiment-related factor is not the recommended factor, or if the added sentiment-related factor includes other factors, the transmission process of the sentiment-related factor is as follows: The health service module can send the sentiment-related factor to the second device through the communication module. The second device can receive the sentiment-related factor through the device management module. The device management module can train the recommendation model based on the added sentiment-related factor.

[0194] Figures 4A and 4B above illustrate the software architecture of the data processing method involving three and two devices, respectively. The software architecture of the data processing method involving one device will be described below with reference to Figure 4C.

[0195] As shown in Figure 4C, the first device may include: a driver and a sports and health application. The sports and health application may include: a health business module, a device management module, a data fusion module, and a data platform.

[0196] The function of each module can be referred to the description of the corresponding module in Figure 4A above, and will not be repeated in detail here. In some embodiments, the first device may also obtain user data of other users' corresponding devices. The function and software structure of the devices of other users are similar to those of the first device, second device, and third device described above, and can be referred to the corresponding descriptions above, which will not be repeated in detail here.

[0197] The following describes the transmission process of user data, the transmission process of the first information used to indicate the addition of emotion-related factors, and the transmission process of recommendation factors, in conjunction with Figure 4C. The example is the first device logging into the first account.

[0198] The user data transmission process is as follows: The health service module in the first device can transmit the user data of the first account to the data platform for storage. Other users' corresponding devices also send the user data of the second account to the first device. The first device can receive the user data of the second account through the device management module. The device management module then transmits the user data of the second account to the data platform for storage.

[0199] The transmission process of the first information is as follows: After receiving the operation that instructs the addition of emotion-related factors, the health business module sends the first information to the recommendation model.

[0200] The transmission process of recommendation factors is as follows: After receiving the first information, the first device processes the user data of the first account and the user data of the second account in the data platform through the recommendation model module to obtain recommendation factors. Users can then confirm on the first device whether the recommendation factor is an added sentiment-related factor.

[0201] If the added sentiment-related factor is not the recommended factor, or if the added sentiment-related factor includes other factors, the transmission process of the added sentiment-related factor is as follows: The health service module can send the added sentiment-related factor to the first device through the communication module. The first device can receive the added sentiment-related factor through the device management module. The device management module can train the recommendation model based on the added sentiment-related factor.

[0202] It should be understood that the software architecture shown in Figures 4A to 4C is only an example and does not constitute a limitation on the embodiments of this application.

[0203] Figures 4A to 4C above illustrate the software architecture of each device in the system. The data processing method provided in the embodiments of this application will be described below with reference to Figures 5 to 16.

[0204] For example, Figure 5 is a flowchart illustrating the data processing method provided in an embodiment of this application. Taking a wearable device A as the first device, a server as the second device, and an electronic device A as the third device, with both wearable device A and electronic device A logged into a first account, as shown in Figure 5, the method includes: a process of reporting user data to the second device, and a process of adding emotion-related factors. The user data reporting process can correspond to S501-S504; the process of adding emotion-related factors can correspond to S505-S515.

[0205] S501, Wearable device A acquires the first data.

[0206] The first data may include data collected by sensors, characteristic information of the sensor data, and physiological parameters, etc. No specific limitations are made here.

[0207] In some embodiments, wearable device A may include one or more of the following sensors: a PPG sensor, a gyroscope sensor, an accelerometer (ACC) sensor, etc. Physiological data such as blood pressure, blood oxygen, pulse, and heart rate can be obtained from the data collected by the PPG sensor; motion data such as the user's movement speed can be obtained from the data collected by the gyroscope sensor and the accelerometer sensor.

[0208] Wearable device A can collect first data periodically, or it can collect first data upon receiving a user-triggered operation. This application embodiment does not specifically limit the frequency of first data collection by wearable device A, or the operation that triggers data collection.

[0209] For example, wearable device A can periodically collect first data related to physiological characteristics, first data related to exercise, etc.; wearable device A can collect first data related to breathing training after receiving an operation to instruct breathing training.

[0210] S502, Wearable device A obtains user data of the first account based on the first data.

[0211] In this embodiment, user data includes: emotions. Adaptively, wearable device A can display emotions. Emotions can be represented by scores, levels, or other arbitrary forms. For example, emotions can be divided into three categories: pleasant, calm, and unpleasant.

[0212] It should be understood that the three categories of emotions mentioned above are merely examples. Emotions can also be categorized into five types: happiness, sadness, anger, fear, and disgust. Emotions can also be classified in any other way, without specific limitations here.

[0213] For example, Figure 6 is a schematic diagram of user data provided in an embodiment of this application. As shown in Figure 6, user data may include: user physiological data, device usage data, environmental data, data related to interpersonal relationships, etc.

[0214] Physiological data represents the user's physical responses, such as heart rate, respiration, and hormonal changes. Device usage data represents device usage patterns, such as sharing fitness and health information. Environmental data represents external environmental conditions, such as the work environment, living environment, and weather changes. Data related to interpersonal relationships indicates the status of other users associated with the user. Interpersonal relationships can be understood as the relationships between the user and their relatives and friends.

[0215] It's understandable that a safety incident involving a friend or family member could affect a user's emotions. Therefore, their health data can be used to check their condition and determine if they were a contributing factor to the emotional state.

[0216] As shown in Figure 6, physiological data may include: heart health data, exercise data, sleep data, body temperature data, breathing training data, age, gender, mood data, blood pressure data, etc.

[0217] Understandably, cardiac health data is used to indicate a user's cardiac health status. Cardiac health data can include pulse waves, electrocardiograms, and coronary artery disease data.

[0218] It's important to note that a pulse wave is the pressure wave generated when blood is pumped out of the heart and propagates through the arteries with each heart contraction. The frequency of the pulse wave reflects the heart's beating frequency, i.e., heart rate. The morphology and velocity of the pulse wave can indirectly reflect blood pressure. Pulse wave velocity (PWV) can be used to assess the degree of arteriosclerosis and vascular elasticity. The peaks and troughs of the pulse wave can provide information about cardiac function.

[0219] An electrocardiogram (ECG) records the electrical activity of the heart with each heartbeat, reflecting the heart's rhythm and the function of the electrical conduction system. An ECG can provide information about a user's heart rate.

[0220] It's important to note that heart health influences the body's response to stress. Poor heart health can lead to higher stress response levels, thus affecting mood stability. Furthermore, heart health can also affect blood and oxygen supply to the brain, thus impacting mood and cognitive function. Maintaining good heart health is not only beneficial to physical health but can also improve mood and mental well-being.

[0221] In this way, the user's stress level and health status can be confirmed through cardiac health data, and it can then be determined whether stress factors, health factors, etc., are factors affecting emotions.

[0222] Exercise data is used to indicate a user's exercise status. Exercise data may include: running performance index, exercise load, fat loss and body shaping, VO2 max, exercise trajectory, etc.

[0223] The running power index quantifies running intensity by measuring the power consumed by a runner during a run. It is typically measured in watts (W), representing the energy a runner expends per unit of time. The running power index can be used to assess a user's fatigue level, recovery progress, and more.

[0224] Exercise load refers to the stress and pressure exerted on the body by exercise over a period of time. Exercise load can be assessed by combining various factors such as exercise intensity, duration, and frequency. Exercise load can be used to evaluate the body's fatigue level and to determine whether a user is over-exercising or under-exercising.

[0225] Fat loss and body shaping refers to reducing excess body fat and sculpting an ideal physique through a scientific exercise and diet plan. Fat loss and body shaping data refers to the various physiological and behavioral data collected and analyzed during the fat loss and body shaping process. This data can assess a user's physical changes and psychological state during this period.

[0226] Maximum oxygen uptake (VO2max) refers to the maximum amount of oxygen that the human body can take in, transport, and utilize per minute during strenuous exercise.

[0227] Maximum oxygen uptake (VO2max) reflects the cardiopulmonary system and muscles' maximum ability to utilize oxygen during high-intensity exercise. VO2max reflects a user's cardiopulmonary function and aerobic endurance. It can be used to assess exercise intensity, exercise fatigue levels, and more.

[0228] It should be noted that exercise can improve overall mood and increase positive emotional experiences. Different types of exercise (such as aerobic exercise, strength training, and yoga) and different exercise intensities may have different effects on mood.

[0229] Movement trajectories can reflect a user's activity status, such as activity range and distance. For example, the area a user runs and the distance they run. These trajectories can be used to determine if the user has changed their location or traveled. It's important to note that when a user travels, their activity range may change. Therefore, movement trajectory data can be used to infer whether the user has traveled.

[0230] In this way, the user's exercise data can be used to confirm whether the exercise factor is a factor affecting emotions.

[0231] Sleep data is used to indicate sleep patterns. Sleep data can include nighttime sleep data and nap data. Nighttime sleep data indicates a user's sleep patterns during the night. Nighttime sleep data can include sleep score and sleep duration. Nap data indicates a user's sleep patterns during the day.

[0232] It's important to note that sufficient sleep helps stabilize and regulate emotions, reducing mood swings. Insufficient sleep can lead to low mood and increased anxiety.

[0233] In this way, sleep data can be used to confirm whether users stay up late, and then to determine whether staying up late is a factor affecting their mood.

[0234] Body temperature data is used to indicate a user's body temperature. Body temperature data may include: body temperature value over the most recent hour.

[0235] It should be noted that changes in body temperature can affect the activity of neurotransmitters related to mood regulation, the secretion of mood-related hormones, cerebral blood flow, and neural activity in the brain. Neurotransmitters can include serotonin, endorphins, and dopamine. Hormones can include cortisol (a stress hormone) and melatonin (a sleep hormone).

[0236] Specifically, high body temperature may lead to anxiety, agitation, and irritability. Furthermore, high body temperature may cause physical discomfort and fatigue, thus affecting mood and causing feelings of restlessness and unease. Low body temperature may lead to low mood and depression. Additionally, low body temperature may cause physical fatigue and weakness, thus affecting mood and causing feelings of listlessness and frustration.

[0237] In this way, body temperature data can be used to confirm whether the user is sick, and then to determine whether the illness is a factor affecting their mood.

[0238] Breathing training data refers to various physiological and behavioral data collected and analyzed during breathing training. Breathing training data can include: training type, training duration, training effects, etc.

[0239] Understandably, breathing exercises can be used to improve emotional health and mental resilience. Breathing exercises are an important component of mindfulness and meditation practices, helping individuals cultivate inner peace and focus.

[0240] In this way, breathing training data can be used to confirm whether the user is meditating, whether they are healthy, and so on, and then to confirm whether meditation factors, health factors, etc. are factors affecting emotions.

[0241] Age is the length of time a person has lived from birth to the time of calculation.

[0242] Understandably, age can roughly indicate a user's degree of aging. The degree of aging in an individual's tissue structure and physiological functions varies at different ages, resulting in differences in exercise tolerance and fatigue levels. Furthermore, as people age, their emotional experiences and ability to regulate emotions may change.

[0243] In this way, age data can be used to determine whether users meditate, whether they are healthy, and so on, and then to determine whether meditation factors, health factors, etc. are factors that affect emotions.

[0244] Gender can be understood as the user's biological sex. Different biological sexes may correspond to different gender data. For example, women have data related to their menstrual cycle (also known as the menstrual cycle).

[0245] It is understandable that changes in hormone levels at different stages of the menstrual cycle may cause mood swings in users.

[0246] In this way, gender data can be used to confirm users' menstrual cycles, and then to determine whether menstrual cycle factors are factors that affect emotions.

[0247] Emotional data is used to indicate a user's emotions. Understandably, emotional data can be used to determine when emotions change, facilitating the subsequent identification of additional emotion-related factors.

[0248] Blood pressure data is used to indicate a user's blood pressure. Blood pressure data may include: blood pressure risk, average blood pressure, etc.

[0249] It's important to note that blood pressure can affect a user's mood. High blood pressure is closely associated with anxiety and stress, and can lead to mood swings and irritability, impacting daily life and interpersonal relationships. High blood pressure can also cause physical fatigue and lethargy. Low blood pressure can lead to low mood and depression, causing users to feel listless and depressed. Low blood pressure can also cause dizziness and weakness, affecting daily activities and emotional stability.

[0250] In this way, by comparing blood pressure data, it can be confirmed whether the user is sick or under too much stress, and then whether the illness or stress factors are factors affecting emotions.

[0251] As shown in Figure 6, environmental data may include noise data. Noise data is used to indicate the user's environmental conditions. For example, noise data may include noise levels over the past hour.

[0252] It's important to note that noise can activate the sympathetic nervous system, potentially leading to increased heart rate, elevated blood pressure, and elevated cortisol (the stress hormone), thus triggering stress and anxiety. Prolonged noise exposure can cause psychological unease and tension, increasing anxiety. Furthermore, noise may interfere with normal brain function, resulting in mood swings and irritability.

[0253] In a sleep setting, noise can disrupt sleep quality, leading to difficulty falling asleep, sleep interruptions, and early awakenings, which in turn can cause fatigue and lethargy.

[0254] In this way, noise data can be used to identify the user's environment and then determine whether environmental factors are influencing emotions.

[0255] As shown in Figure 6, data related to interpersonal relationships may include one or more of the following: care and support data, health data of relatives and friends, and safety protection data.

[0256] Care and support data can be understood as the data collected and analyzed by the application to track the user's health and activity levels. This data may include: the user's daily steps and walking distance, calorie consumption during different activities, sleep analysis data (sleep patterns, duration, quality, etc.), etc.

[0257] Family and friends health data refers to user data related to family and friends. Family and friends can include parents, children, friends, etc. Examples of family and friends user data include their emotional state and heart health data.

[0258] Safety protection data is used to indicate whether a safety protection event has occurred. A safety protection event can be understood as an event that affects the safety of personnel. Safety protection events can include one or more of the following: emergency rescue events, fall events, abnormal heart rate warning events, etc.

[0259] Figure 6 shows the device usage data, including whether or not exercise and health information has been shared.

[0260] In this way, user behavior can be judged through device usage data. For example, if there is sharing of sports and health information, it can be confirmed that the user is exercising; if there is breathing training, it can be confirmed that the user is meditating, etc.

[0261] It is understandable that a user's emotional changes may be influenced by a variety of factors. The user data described above can be used to analyze a user's emotions from multiple perspectives, including behavior and environment, to identify the factors contributing to these emotions. The user data presented here is merely an example; more or less data may be included, and no specific limitations are set here.

[0262] S503, Wearable device A sends user data of the first account to electronic device A. Adaptively, electronic device A receives user data of the first account from wearable device A.

[0263] In some embodiments, electronic device A can also receive user data from a first account on another device. In some embodiments, electronic device A can also receive user data from a second account on another device. The second account is an account associated with the first account.

[0264] S504. Electronic device A sends user data to the server.

[0265] In this embodiment, the user data may include: user data of a first account. In some embodiments, the user data may also include: user data of a second account.

[0266] For example, electronic device A can obtain user data of the first account from a device logged into the first account (e.g., wearable device A); and obtain user data of the second account from a device logged into the second account. The second account is an account associated with the first account.

[0267] Electronic device A can periodically send user data to the server, or it can send user data to the server upon receiving a user's trigger operation, or it can send user data to the server when the user's emotions change. This application embodiment does not specifically limit the frequency of user data reporting by electronic device A, or the operation that triggers user data reporting.

[0268] S505, Wearable device A receives a first operation, which is used to instruct the addition of emotion-related factors.

[0269] The first operation can be a click operation to add a control, a voice operation to indicate the addition of emotion-related factors, or any type of operation; there are no specific limitations here.

[0270] It should be understood that users can add emotion-related factors for any time period through wearable device A. The arbitrary time period can be the current time period or a past time period. This application embodiment does not specifically limit the time period indicated by the first operation. For example, if the user does not select any time period for emotion, the first operation can be used to instruct the addition of emotion-related factors corresponding to the current time period; if the user selects an emotion corresponding to time period A, the first operation is used to instruct the addition of emotion-related factors corresponding to time period A.

[0271] S506. In response to the first operation, wearable device A sends first information to electronic device A.

[0272] The first information is used to indicate factors associated with the emotion in the first time period. For example, the first information may include: the first time period.

[0273] S507. Electronic device A sends the first message to the server.

[0274] S508: The server obtains recommendation factors based on the recommendation model and user data. The recommendation model is used to predict sentiment-related factors based on user data.

[0275] It is understandable that user data can include: user data corresponding to the first account, and / or, user data corresponding to the second account. This takes into account the emotional influence of related individuals on users, thus improving the accuracy of recommendation factors.

[0276] In some embodiments, the user data in S508 may include: user data for a first time period, and some or all of the user data prior to the first time period. The first time period is the time period indicated by the first operation. For example, taking time period A as the first time period, the user data in S508 may include: user data for time period A, and user data obtainable prior to time period A.

[0277] Understandably, some emotion-related factors may have a lasting impact on a user's mood. For example, staying up late might lead to a user feeling down throughout the day. Furthermore, some emotion-related factors may not be predictable using user data from a short period. For instance, regarding travel, a user might have travel plans spanning multiple days, making it impossible to determine travel decisions based solely on geographical distance. Therefore, recommendation models may require user data from earlier time periods when predicting emotion-related factors.

[0278] In this embodiment of the application, the recommendation model is trained based on a training dataset, which includes user data and sentiment-related factors for each time period.

[0279] For example, Figure 7 is a schematic diagram of a recommendation model for obtaining recommendation factors according to an embodiment of this application. As shown in Figure 7, S508 includes: S508-1 and S508-2.

[0280] S508-1. Randomly select multiple data points and input them into the decision tree to obtain multiple factors.

[0281] Understandably, decision trees use a tree-like graph to represent decision rules and outcomes. Each node represents a feature, and each branch represents a decision result. In this way, decision trees simulate the human decision-making process, enabling the judgment of emotion-related factors. The structure of a decision tree can be seen in Figures 11 to 13 below, which will not be described in detail here.

[0282] It should be understood that randomly selecting data for processing allows the recommendation model to be updated quickly on a smaller subset. Furthermore, it can reduce the likelihood of model overfitting.

[0283] S508-2. Determine the recommendation factors based on the frequency of occurrence of each factor among multiple factors.

[0284] In some embodiments, the recommendation factors can be the top M factors ranked by frequency of occurrence from largest to smallest. This way, after simple sorting, the factors with the highest frequency are used as recommendation factors, consuming minimal computational resources and being simple and easy to implement.

[0285] In other embodiments, the recommendation factors can be factors with a probability value greater than a threshold A, where the probability value is the ratio between the occurrence frequency of each factor and the total occurrence frequency of all factors. This way, the number of recommendation factors is not limited, and recommendation factors are obtained based on probability values, making the recommendations more accurate.

[0286] In other embodiments, the recommendation factor can be a factor that appears more frequently than a threshold B. This eliminates the need for probability calculations, making the method simple and easy to implement.

[0287] Recommendation models can also determine recommendation factors in any other way, without specific limitations here.

[0288] S509. The server sends recommendation factors to electronic device A.

[0289] S510, Electronic device A sends recommendation factors to wearable device A.

[0290] S511. Wearable device A provides suggestions on recommendation factors.

[0291] In this embodiment, wearable device A can provide prompts regarding recommendation factors in any manner. This embodiment does not specifically limit the manner of providing these prompts.

[0292] In one possible implementation, wearable device A displays one or more recommendation factors on the interface, but does not display emotion-related factors other than the recommendation factors.

[0293] In a second possible implementation, wearable device A could also display recommendation factors at the top of the list, while emotion-related factors other than recommendation factors would be displayed at the bottom. This allows for the display of multiple factors, making it easier for users to select emotion-related factors other than recommendation factors.

[0294] In a third possible implementation, wearable device A could also display recommendation factors and emotionally relevant factors (e.g., option background colors) using different markers. This would make it easier for users to distinguish between recommendation factors and emotionally relevant factors.

[0295] For example, Figure 8A is a schematic diagram of the interface of a wearable device provided in an embodiment of this application. Taking a watch as an example, as shown in Figure 8A, the wearable device displays interface 2a, which displays the user's current emotion and a query control 1101. This emotion is obtained by the wearable device through analysis of user data. For example, the user data may include one or more of the following: blood oxygen saturation, heart rate, respiratory rate, etc.

[0296] When the wearable device receives a trigger operation (e.g., touch operation, click operation, etc.) on interface 2a for query control 1101, the wearable device enters interface 2b. Interface 2b includes: emotions over a recent period of time, and associated record control 1102.

[0297] When the wearable device receives a trigger operation (e.g., touch operation, click operation, etc.) on interface 2b for the associated recording control 1102, the wearable device enters interface 2c. Interface 2c may include: a description of the associated recording function, and an option to add the control 1103. The description of the associated recording function could be something like, "Here you can review and record associated effects to track your emotional health." No specific limitations are set here.

[0298] Understandably, if an emotion-related factor is added for the first time, interface 2c can display an explanation of the association record function. If an emotion-related factor has already been added, interface 2c may not display the explanation of the association record function, but will instead display time periods, the emotions corresponding to each time period, and the emotion-related factors corresponding to each time period.

[0299] When the wearable device receives a trigger operation (e.g., touch operation, click operation, etc.) for adding control 1103 at interface 2c, the wearable device can obtain recommendation factors through electronic devices and servers and enter interface 2d, interface 2e or interface 2f.

[0300] Taking the user data as an example, which includes information indicating that the user is in a restaurant, the recommendation factors include "dating" but exclude "work", "study" and "housework". If the wearable device is displayed in a possible implementation mode one, then when the wearable device receives a trigger operation (e.g., touch operation, click operation, etc.) for adding control 1103 on interface 2c, it can enter interface 2d.

[0301] The 2D interface includes "Dating" but excludes factors such as "Work", "Study", and "Household Chores".

[0302] If the wearable device is displayed in possible implementation mode 2, then when the wearable device receives a trigger operation (e.g., touch operation, click operation, etc.) for adding control 1103 on interface 2c, it can enter interface 2e.

[0303] Interface 2e includes factors such as "Dating", "Work", "Study", and "Household Chores". Since "Dating" is a recommendation factor, it is placed above factors such as "Work", "Study", and "Household Chores".

[0304] If the wearable device is displayed in the possible implementation mode three, then when the wearable device receives a trigger operation (e.g., touch operation, click operation, etc.) for adding control 1103 on interface 2c, it can enter interface 2f.

[0305] Interface 2f includes factors such as "Work", "Study", "Dating" (1105) and "Household Chores". Since "Dating" is a recommendation factor, its background color is black; the background colors of "Work", "Study", and "Household Chores" are all white.

[0306] S512. After receiving the operation for indicating the selection factor and the operation for indicating confirmation, the wearable device confirms that the selected factor is an added emotion-related factor.

[0307] For example, as shown in Figure 8B, when wearable device A receives a trigger operation (e.g., touch operation, click operation, etc.) for "Date" on interface 2f, it switches "Date" from state A to state B, and the wearable device enters interface 2g. Interface 2g includes a confirmation control 1106, and factors such as "Work," "Study," "Date," and "Household Chores." "Date" has a checkmark 1107. This checkmark indicates that it is selected.

[0308] When wearable device A receives a trigger operation for confirmation control 1106 on interface 2g, it confirms that the "date" status in state A is an added emotional association factor.

[0309] In summary, wearable device A can display recommendation factors to the user for easy confirmation.

[0310] In some embodiments, when the wearable device A provides suggestions regarding recommendation factors, it can also select all or some of the recommendation factors. Subsequently, upon receiving a confirmation request, the wearable device A can confirm that the selected recommendation factors are added emotion-related factors. This eliminates the need for the user to manually select emotion-related factors, reducing user interaction and improving the user experience.

[0311] For example, as shown in Figure 8C, when the wearable device receives a trigger operation (e.g., touch operation, click operation, etc.) for adding control 1103 on interface 2c, the wearable device can obtain recommendation factors through electronic devices and servers, set the recommendation factors to a selected state, and enter interface 2h. Interface 2h includes: a selection control 1116, and one or more factors. These one or more factors are, for example, "work," "study," "dating" 1108, and "housework."

[0312] Taking user data that includes information indicating the user is in a restaurant as an example, the recommendation factors include "Date," but exclude "Work," "Study," and "Household Chores." For instance, in interface 2h, "Date" has a checkmark (1109). "Work," "Study," and "Household Chores" do not have corresponding checkmarks. A checkmark indicates state B. State B indicates being selected. If there is no checkmark, it indicates state A. State A indicates not being selected.

[0313] When the wearable device receives a trigger operation (e.g., touch operation, click operation, etc.) on the interface 2h for the determination control 1116, it confirms that the "date" in state A is the added emotional association factor.

[0314] In some embodiments, factors can be categorized for easier user retrieval. Adaptively, the wearable device can first display the categories of factors, and upon receiving a user's selection of a category, display one or more factors related to that category.

[0315] For example, as shown in Figure 8D, the wearable device can display interface 2i after interface 2c receives a trigger operation (e.g., touch operation, click operation, etc.) for adding control 1103. Interface 2i includes multiple option tabs, such as social activities 1110, interpersonal relationships, hobbies, and others.

[0316] When the wearable device receives a trigger operation (e.g., touch operation, click operation, etc.) for social activity 1110 on interface 2i, the wearable device expands the corresponding tab and enters interface 2j. Interface 2j may include: a confirmation control 1111, and one or more factors related to social activity 1110. These factors may include "work," "study," and "dating" 1112, etc. In interface 2j, "dating" has a checkmark 1113. "Work," "study," and "housework" do not have corresponding checkmarks.

[0317] When the wearable device receives a trigger operation (e.g., touch operation, click operation, etc.) on interface 2j for the determination control 1111, it confirms that the "date" in state A is an added emotional association factor.

[0318] This eliminates the need for users to manually select emotion-related factors, reducing user operations and improving the user experience.

[0319] In some embodiments, the electronic device can also automatically confirm that the recommendation factor is an emotion-related factor after a period of time (e.g., 3 seconds, 5 seconds, etc.). This can further reduce user operations.

[0320] In summary, wearable device A can display recommended factors as selected factors to the user, making it easier for the user to confirm and reducing user operations.

[0321] Based on the above embodiments, users may consider the recommended factors not to be emotion-related factors and can therefore cancel the recommended factors and select other factors as emotion-related factors. For example, wearable device A may not execute S512, but instead execute S513 and S514. As shown in Figure 9, the process for adjusting emotion-related factors is as follows:

[0322] S513, Wearable device A receives an operation to instruct the adjustment of added emotionally related factors, and adjusts the state of some or all of the factors.

[0323] In some embodiments, wearable device A may receive an operation to instruct the cancellation of certain recommendation factors. In this case, the subsequently added emotion-related factors may not include the cancelled recommendation factors, but may include recommendation factors that have not been cancelled.

[0324] For example, Figure 10 is a schematic diagram of an interface for adjusting added emotional association factors according to an embodiment of this application. As shown in Figure 10, when the wearable device receives a trigger operation (e.g., touch operation, click operation, etc.) for date 1112 on interface 2j, the "date" is switched from state B to state A, and the wearable device enters interface 2k. In this interface 2k, there is no check mark corresponding to "date".

[0325] In other embodiments, wearable device A may receive an operation instructing the selection of a second factor, in which case the subsequently added emotion-related factors may include the second factor.

[0326] For example, as shown in Figure 10, when the wearable device receives a trigger operation (e.g., touch operation, click operation, etc.) for learning on interface 2k, it switches "learning" from state A to state B, and the wearable device enters interface 2l. In interface 2l, "learning" is marked with a check mark 1114.

[0327] S514. Upon receiving an operation to confirm the instruction, the selected factor is added as an emotion-related factor. Adaptively, wearable device A stores the added emotion-related factors.

[0328] The factors that are selected can include all or some of the recommendation factors.

[0329] In some embodiments, the selected factor may further include a second factor. The second factor is a factor added by user instruction.

[0330] For example, as shown in Figure 10, when the wearable device receives a trigger operation (e.g., touch operation, click operation, etc.) on the interface 2l for the determination control 1115, it confirms "learning" as an added emotion-related factor.

[0331] In this way, users can adjust the selected factors and choose appropriate factors as added emotional association factors.

[0332] Based on the above embodiments, the recommendation model can be trained to make the recommendation factors output by the model more accurate. For example, steps S515 to S517 may also be included.

[0333] S515. Wearable device A sends added emotional association factors to electronic device A.

[0334] S516. Electronic device A sends the added emotional correlation factors to the server.

[0335] In some embodiments, wearable device A may send second information to an electronic device. The second information may include: added emotion-related factors; it may also include: removed recommendation factors and / or second factors.

[0336] S517. After receiving the added sentiment-related factors, the server trains the recommendation model based on the added sentiment-related factors.

[0337] The server can train the recommendation model based on the second factor among the added sentiment-related factors. This training, based on the second factor, ensures that subsequent recommendation models can identify the second factor in similar scenarios, reducing the likelihood of users adjusting factors that are currently selected.

[0338] The server can train the recommendation model based on canceled recommendation factors. This reduces the likelihood of subsequent recommendation models receiving canceled recommendation factors in similar scenarios, and also reduces the likelihood of users adding sentiment-related factors later.

[0339] The server can also train the recommendation model based on the number of times each factor is selected. In this way, training the recommendation model based on users' habits of selecting added sentiment-related factors can improve the accuracy of the recommendation model.

[0340] The server can train the model using one or more of the methods described above; no specific limitations are specified here.

[0341] In summary, users can also reselect factors as added emotion-related factors on the wearable device. The system can then train the recommendation model based on the reselected factors, improving the accuracy of the recommendation factors output by the model.

[0342] The above embodiments illustrate the process of adding emotion-related factors. The decision tree described above will be explained below with reference to Figures 11 to 13.

[0343] It should be understood that a decision tree includes multiple nodes, each of which can be used to set judgment conditions related to various emotion-related factors. By using the judgment conditions in each node to identify the corresponding branch for the user data, the corresponding judgment result can be obtained. It is understood that the position of each node in the decision tree is not limited, as long as it can achieve the corresponding judgment.

[0344] In this embodiment, the recommendation model may include multiple decision trees, each of which can be used to judge different categories of emotion-related factors. For example, taking emotion-related factors as divided into social activity factors, interpersonal relationship factors, and personal physical and mental factors, the recommendation model may include a decision tree for social activities, a decision tree for interpersonal relationships, and a decision tree for personal physical and mental factors.

[0345] For example, Figure 11 is a schematic diagram of a decision tree structure for social activities provided in an embodiment of this application. As shown in Figure 11, the decision tree includes multiple nodes related to factors of social activities. Social activities can be understood as some behavioral activities of users, and factors of social activities include exercise, travel, and / or staying up late, etc. Nodes can be used to set judgment conditions related to factors of social activities. By confirming the corresponding branch for user data through the judgment conditions in each node, the judgment result of the corresponding social activity factor can be obtained.

[0346] It is understandable that the position of each node in the decision tree is not limited, as long as it can make the corresponding judgment.

[0347] Taking social activity factors such as exercise, travel, and staying up late as examples, the nodes related to social activity factors can include: whether there was a maximum oxygen uptake in the past 12 hours, whether there was exercise and health sharing, whether the distance between exercise ranges is greater than the preset value B, and whether the sleep duration is greater than the preset value A, etc.

[0348] As shown in Figure 11, the presence of maximum oxygen uptake, sharing of exercise-related health information, and sleep duration exceeding a preset value A in the past 12 hours can be used to determine the exercise factor. Specifically, if there was maximum oxygen uptake and exercise-related health information shared in the past 12 hours, the factor obtained includes: exercise. If there was maximum oxygen uptake but no exercise-related health information shared in the past 12 hours, the factor obtained does not include: exercise. This application embodiment can also determine whether emotions are related to exercise factors through other arbitrary methods, which are not specifically limited here.

[0349] It's understandable that VO2 max typically occurs when a user engages in high-intensity exercise (e.g., running). Therefore, VO2 max can be used to determine whether high-intensity exercise has occurred. Specifically, the presence of VO2 max indicates high-intensity exercise, and exercise can be recommended. The absence of VO2 max indicates that the user has not engaged in high-intensity exercise, and it may not be considered exercise; other factors can be used to make a judgment based on other indicators.

[0350] It's understandable that users typically share their exercise experiences afterward when they feel good, indicating a positive impact on their mood. Therefore, the existence of such sharing suggests that exercise has a positive effect on the user. This allows us to assess whether high-intensity exercise has a positive impact on a user's mood.

[0351] It's understandable that exercise can affect a user's sleep, which in turn can affect their mood. Therefore, in addition to the exercise and health sharing mentioned above, we can also confirm whether exercise has a positive impact on the user by checking their sleep duration. If the user's sleep duration after exercise is greater than the preset value A, it indicates that the user is getting enough sleep, and exercise may be beneficial to the user's sleep, thus having a positive impact. If the user's sleep duration after exercise is less than or equal to the preset value A, it indicates that the user is not getting enough sleep, and exercise may interfere with the user's sleep, thus having a negative impact on the user's mood.

[0352] Therefore, when there is sharing of exercise and health information, and sleep duration exceeds the preset value A, it can be confirmed that exercise has a positive impact on the user, and exercise is recommended as a factor. When there is no sharing of exercise and health information, and sleep duration exceeds the preset value A, the impact of exercise on the user cannot be confirmed, and no recommendation is made. When there is sharing of exercise and health information, and sleep duration is less than or equal to the preset value A, the impact of exercise on the user cannot be confirmed, and no recommendation is made. When there is no sharing of exercise and health information, and sleep duration is less than or equal to the preset value A, it can be inferred from sleep duration that the user is staying up late, and staying up late can be recommended as a factor.

[0353] As shown in Figure 11, if there is no maximum oxygen uptake in the past 12 hours, it indicates that the user has not engaged in high-intensity exercise, and it may not be considered exercise. The travel factor can be determined by checking whether the distance between any two exercise ranges is greater than a preset value B, and whether the sleep duration is greater than a preset value A. Specifically, if the distance between exercise ranges is greater than preset value B and the sleep duration is greater than preset value A, the factor obtained includes travel; if the distance between exercise ranges is less than or equal to preset value B and the sleep duration is less than or equal to preset value A, the factor obtained does not include travel.

[0354] It is understandable that the geographical location of an electronic device typically changes when a user travels. Therefore, the user's location can be determined by measuring the distance between their travel range, thus inferring whether the user has traveled. This application embodiment can also determine whether emotions are related to travel through any other means, without specific limitations here. For example, the distance between the geographical location of the electronic device and the geographical location of the user's permanent address can also be used to confirm whether travel has occurred.

[0355] It's understandable that travel might affect a user's sleep, which in turn could impact their mood. Therefore, in addition to judging the distance between activity areas, the user's sleep duration can also be used to determine whether travel has a positive impact. If the user's sleep duration after travel exceeds a preset value A, it indicates sufficient sleep, and travel may be beneficial to their sleep, thus having a positive impact. If the user's sleep duration after travel is less than or equal to the preset value A, it indicates insufficient sleep, and travel may disrupt their sleep, thus having a negative impact on their mood.

[0356] Therefore, when the distance between activity areas is greater than the preset value B and the sleep duration is greater than the preset value A, travel can be confirmed to have a positive impact on the user, and travel is recommended as a factor. When the distance between activity areas is greater than the preset value B and the sleep duration is less than or equal to the preset value A, the impact of travel on the user cannot be confirmed, and travel is not recommended. When the distance between activity areas is less than or equal to the preset value B and the sleep duration is less than or equal to the preset value A, it can be inferred from the sleep duration that the user stayed up late, and staying up late can be recommended as a factor. When the distance between activity areas is less than or equal to the preset value B and the sleep duration is greater than the preset value A, neither travel nor staying up late occurred, and travel is not recommended.

[0357] It should be understood that the order of the nodes shown in Figure 11 above is only an example, and the order of the nodes can be changed. For example, the factor of staying up late can be judged first, and then the factor of exercise can be judged. There is no specific limitation on the order of judgment of each factor or the order of each node here.

[0358] Figure 11 above illustrates a decision tree using three factors related to social activities: exercise, travel, and staying up late. Factors related to social activities can include more or fewer factors; no specific limitations are made here.

[0359] For example, Figure 12 is a schematic diagram of a decision tree structure for interpersonal relationships provided in an embodiment of this application. As shown in Figure 9, the decision tree includes multiple nodes related to factors of interpersonal relationships. Interpersonal relationships can be understood as the relationship between a user and their relatives and friends. Factors related to interpersonal relationships include, for example, parents, children, and / or friends. Nodes can be used to set judgment conditions related to factors of social activities. By confirming the corresponding branch for user data through the judgment conditions in each node, the judgment result of the corresponding interpersonal relationship factor can be obtained.

[0360] Factors related to interpersonal relationships include parents, children, and friends. Nodes can include: whether a safety incident has occurred involving any relative or friend; whether the relative or friend is calm or happy; whether they are a parent; and whether they are a child.

[0361] As shown in Figure 12, the factors related to relatives and friends can be assessed based on whether a safety precaution was taken, whether the relatives and friends were calm, and whether they were happy. Specifically, if a relative or friend took a safety precaution, and that relative or friend was not calm, the factor obtained includes: that relative or friend. If a relative or friend took a safety precaution, the factor obtained does not include: that relative or friend. If a relative or friend did not take a safety precaution, and that relative or friend was not calm, the factor obtained includes: that relative or friend. If a relative or friend did not take a safety precaution, and that relative or friend was calm, the factor obtained does not include: that relative or friend.

[0362] As is understood, safety alert events are used to indicate when a loved one or family member is in an unusual situation. Unusual situations include, but are not limited to: falls, elevated heart rate, leaving a safe area (e.g., school, home), and making emergency calls. Safety alert events can include one or more of the following: emergency rescue events, location tracking events, fall events, and abnormal heart rate events.

[0363] Whether a friend or family member experiences a safety incident, and the friend's or family member's emotional state, can both influence a user's emotions. For example, if a friend or family member falls, the user may be worried about that friend or family member, and their emotions may become unsettled. If a friend or family member experiences a safety incident but is in a good mood, the user may not be worried, and their emotions may remain calm. If a friend or family member does not experience a fall but is in a bad mood, it may have a negative impact on the user's emotions. Conversely, if a friend or family member does not experience a fall but is in a good mood, it may have a positive impact on the user's emotions. Therefore, the occurrence of a safety incident and the emotional state of friends or family members can be used to determine whether a friend or family member is a factor influencing the user's emotions.

[0364] In some embodiments, the category of relatives and friends can also be determined by nodes such as whether they are parents, children, or friends.

[0365] For example, taking security protection events including location tracking events and fall events as an example, if it is detected that the location of a friend or relative A is not in the safe zone, a location tracking event occurs, and the factors obtained include: the category corresponding to friend or relative A. If it is detected that friend or relative B triggers a fall event, friend or relative B is instructed to fall, and the factors obtained include: the category corresponding to friend or relative B.

[0366] In some embodiments, it is also possible to output the corresponding identifier of the relative or friend without determining the type of relative or friend through a decision tree. For example, the name of the relative or friend.

[0367] For example, Figure 13 is a schematic diagram of a decision tree structure for personal physical and mental health provided in an embodiment of this application. As shown in Figure 10, the decision tree includes multiple nodes related to factors of personal physical and mental health. Personal physical and mental health can be understood as the user's overall physical and psychological state. Factors related to personal physical and mental health include health, illness, and / or meditation, etc. Nodes can be used to set judgment conditions related to factors of personal physical and mental health. By confirming the corresponding branch for user data through the judgment conditions in each node, the corresponding judgment result can be obtained.

[0368] Taking personal physical and mental factors, including health, illness, and meditation, as an example, the nodes related to personal physical and mental factors may include: whether the exercise intensity is greater than the preset value B, whether the stress in the last 20 minutes is greater than the preset value C, whether the sleep score is greater than the preset value D, and whether breathing training is performed, etc.

[0369] As shown in Figure 13, factors such as whether the exercise intensity is greater than preset value B, whether the stress level in the last 20 minutes is greater than preset value C, whether the sleep score is greater than preset value D, and whether breathing training has been performed can all be used to determine health and illness factors. Specifically, if the exercise intensity is greater than preset value B, the stress level in the last 20 minutes is less than or equal to preset value C, the sleep score is greater than preset value D, and breathing training has not been performed, the resulting factors include health. If the exercise intensity is less than or equal to preset value B, the stress level in the last 20 minutes is greater than preset value C, the sleep score is less than or equal to preset value D, and breathing training has not been performed, the resulting factors include illness.

[0370] Understandably, users can typically engage in high-intensity exercise when they are healthy, and low-intensity exercise or no exercise when they are unhealthy, such as when they are sick. Therefore, the user's health status can be determined by whether the exercise intensity exceeds a preset value B.

[0371] Furthermore, when users are in good health, their stress levels are typically low, and their sleep quality is generally good. Therefore, user stress levels and sleep quality can be used to further assess health factors. Moreover, high stress and poor sleep quality can both negatively impact a user's health. In conclusion, health factors are not recommended when users are under high stress or experiencing poor sleep.

[0372] Understandably, breathing exercises are typically conducted when users need to improve their mood and enhance their mental resilience; they are unnecessary if the user is in good health. Therefore, health factors are not recommended when conducting breathing exercises; however, since breathing exercises are an important component of meditation practice, meditation factors can be recommended.

[0373] It is understandable that when a user is ill or in poor health, they cannot engage in high-intensity exercise and can only engage in low-intensity exercise or not exercise at all. Therefore, whether a user is ill can be determined by whether the exercise intensity exceeds a preset value B.

[0374] Furthermore, users are more likely to get sick when under high stress and with poor sleep, and less likely to get sick when under low stress and with good sleep. Therefore, further assessment of illness factors can be made by checking whether stress levels in the last 20 minutes exceed a preset value C and whether sleep scores exceed a preset value D. Moreover, illness may also lead to higher stress levels and poor sleep. In conclusion, factors contributing to illness are not recommended when users are under low stress or have good sleep.

[0375] It should be understood that the number of nodes in the decision tree above, and the judgment method for each node, are only examples. Decision trees can also include more or fewer nodes, and judgments on various factors can be achieved in other ways. No specific limitations are made here.

[0376] The above embodiments illustrate the process of adding emotion-related factors in an application scenario involving three devices. The process of adding emotion-related factors in an application scenario involving two devices will be described below with reference to Figure 14. For example, Figure 14 is a flowchart illustrating an electronic device recording emotion-related factors according to an embodiment of this application. Taking electronic device A as the first device and a server as the second device as an example, as shown in Figure 14, the process includes:

[0377] S1301, Electronic device A receives a first operation, which is used to instruct the addition of emotion-related factors.

[0378] The first operation can be referred to the corresponding description of the first operation in S505 of the process shown in Figure 5 above, which will not be elaborated here.

[0379] S1302. In response to the first operation, electronic device A sends the first information to the server.

[0380] The first information can be referred to in the corresponding description of the first information in S506 of the process shown in Figure 5 above, and will not be elaborated here.

[0381] S1303, The server obtains recommendation factors based on the first model and user data.

[0382] For details, please refer to the corresponding explanation of S508 in the process shown in Figure 5 above, which will not be elaborated here.

[0383] S1304. The server sends recommendation factors to electronic device A.

[0384] S1305. After receiving the recommendation factors, electronic device A provides prompts regarding the recommendation factors.

[0385] The prompting method is similar to that of S511 above. For details, please refer to the corresponding description of S511 above. No further details will be provided here.

[0386] S1306. After receiving the operation for indicating the selection factor and the operation for indicating confirmation, electronic device A confirms that the selected factor is an added emotion-related factor.

[0387] For details, please refer to the corresponding explanation in S512 above; further details will not be provided here.

[0388] In some embodiments, when electronic device A provides a prompt regarding a recommended factor, it can also set the recommended factor to a selected state. Subsequently, electronic device A can confirm, upon receiving an instruction to confirm, that the recommended factor is an added emotion-related factor.

[0389] For example, Figure 15 is a schematic diagram of the interface of an electronic device provided in an embodiment of this application. Taking a mobile phone as an example, as shown in Figure 15, the electronic device displays interface 3a, which displays icons of one or more applications. For example, the icon 1401 of a sports and health application, the icon of a settings application, the icon of a music application, etc.

[0390] When the electronic device receives a trigger operation (e.g., touch operation, click operation, etc.) on interface 3a for icon 1401, the electronic device enters interface 3b. Interface 3b includes multiple health controls, such as exercise record, health clover, heart health 1402, stress, etc.

[0391] When the electronic device receives a trigger operation (e.g., touch operation, click operation, etc.) for heart health 1402 on interface 3b, the electronic device enters interface 3c. This interface 3c includes multiple heart health controls, such as heart rate, electrocardiogram, pulse wave, and emotional health 1403.

[0392] When the electronic device receives a trigger operation (e.g., touch operation, click operation, etc.) for emotional health 1403 at interface 3c, the electronic device enters interface 3d. This interface 3d includes: the trend of emotional state changes over a period of time, the add recording control 1404, and the number of times each emotion-related factor appears over a period of time.

[0393] Electronic devices can record a user's emotional state in a single instance, for a period of time, periodically, or continuously, and display the trend of emotional state changes according to time dimensions such as day, week, month, and year. This facilitates timely feedback to the user on their daily emotional health status. This application does not limit the specific implementation method of the 3D interface.

[0394] As shown in the 3D interface, the frequency of occurrence of each emotion-related factor within a certain period of time can be: 8 times of exercising, 5 times of dating, 3 times of housework, 2 times of studying, and 1 time of arguing.

[0395] In some embodiments, emotional health 1403 can also be set as a health control in interface 3b. In this way, the user can control the electronic device to enter interface 3d by triggering emotional health 1403 in interface 3b.

[0396] When the electronic device receives a trigger operation (e.g., touch operation, click operation, etc.) on the interface 3d for the add record control 1404, it retrieves recommendation factors from the server and adjusts the recommendation factors to state B, then the electronic device enters interface 3e. Interface 3e includes: multiple option labels, a confirmation control 1405, and one or more factors related to each option label. For example, option labels may include: social activities, interpersonal relationships, hobbies, other, etc. For example, one or more factors related to social activities include: "work," "study," "housework," "fitness," "dating," etc. One or more factors related to interpersonal relationships include: "mother," "son," "wife," etc.; one or more factors related to hobbies include: "meditation." Taking the user data including information indicating that the user and their wife are at a restaurant as an example, the recommendation factors include "dating" and "wife," but exclude "work," "study," "housework," "fitness," "mother," "son," and "meditation." "Dating" corresponds to a checkmark 1406; "wife" corresponds to a checkmark 1407. "Work," "study," "housework," "fitness," "mother," "son," and "meditation" do not correspond to any checkmark. A checkmark indicates state B. State B indicates that the item is selected; if there is no checkmark, it indicates state A. State A indicates that the item is not selected.

[0397] When the wearable device receives a trigger operation (e.g., touch operation, click operation, etc.) on the interface 3e for the confirmation control 1405, it confirms that "date" and "wife" are both added emotional association factors.

[0398] This eliminates the need for users to manually select and add emotion-related factors, reducing user operations and improving the user experience.

[0399] In summary, electronic device A can display recommended factors as selected factors to the user, making it easier for the user to confirm and reducing user operations.

[0400] Based on the above embodiments, users may consider the recommended factors to be non-emotionally relevant and can therefore cancel the recommended factors and select other factors as emotionally relevant factors. For example, after S1305, the electronic device may not execute S1306, but instead execute S1307 to S1310.

[0401] As shown in Figure 16, the process for adjusting emotion-related factors is as follows:

[0402] S1307. Electronic device A receives an operation to instruct the adjustment of added emotional correlation factors, and adjusts the state of some or all factors.

[0403] In some embodiments, electronic device A may receive an operation instructing the deselection of certain recommendation factors, in which case the adjusted sentiment-related factors may include recommendation factors that were not deselected.

[0404] For example, Figure 17 is a schematic diagram of an interface for adjusting added emotional association factors according to an embodiment of this application. As shown in Figure 17, when the electronic device receives a trigger operation (e.g., touch operation, click operation, etc.) for dating on interface 3e, the "Dating" is switched from state B to state A, and the electronic device enters interface 3f. In interface 3f, there is no check mark 1406 corresponding to "Dating".

[0405] In other embodiments, wearable device A may receive an operation instructing the selection of a second factor, in which case the subsequently added emotion-related factors may include the second factor.

[0406] For example, as shown in Figure 17, when the electronic device receives a trigger operation (e.g., touch operation, click operation, etc.) for fitness on interface 3f, it switches "Fitness" from state A to state B, and the electronic device enters interface 3g. In interface 3g, "Fitness" has a check mark 1408.

[0407] S1308: Upon receiving an operation to confirm the instruction, the selected factor is added as an emotion-related factor. Adaptively, electronic device A stores the added emotion-related factors.

[0408] The factors that are selected can include all or some of the recommendation factors.

[0409] In some embodiments, the selected factor may further include a second factor. The second factor is a factor added by user instruction.

[0410] For example, as shown in Figure 17, when the electronic device receives a trigger operation (e.g., touch operation, click operation, etc.) on the interface 3g for the determination control 1405, it confirms that "fitness" and "wife" are both added emotional association factors.

[0411] S1309. Electronic device A sends the added emotional correlation factors to the server.

[0412] In some embodiments, electronic device A may send second information to the server. The second information may include: added emotion-related factors; it may also include: removed recommendation factors and / or second factors.

[0413] S1310. After receiving the added sentiment-related factors, the server trains the recommendation model based on the added sentiment-related factors.

[0414] The method for training the recommendation model in S1310 is similar to that for training the recommendation model. For details, please refer to the corresponding explanation of training the recommendation model in S517 of the process shown in Figure 9 above. It will not be elaborated here.

[0415] In summary, users can also reselect factors as added sentiment-related factors on their electronic devices. The system can then train the recommendation model based on these reselected factors, improving the accuracy of the recommendations output by the model.

[0416] The above embodiments illustrate scenarios with 3 devices and 2 devices, respectively. The following describes the flow of the data processing method using 1 device, with reference to Figure 18.

[0417] For example, Figure 18 is a schematic flowchart of a data processing method provided in an embodiment of this application. Taking electronic device A as an example, as shown in Figure 18, the method includes:

[0418] S1801, Received the first operation, which is used to instruct the addition of emotion-related factors.

[0419] The first operation can be referred to the corresponding description of the first operation in S505 of the process shown in Figure 5 above, which will not be elaborated here.

[0420] S1802, In response to the first operation, electronic device A obtains recommendation factors based on the first model and user data.

[0421] For details, please refer to the corresponding explanation of S508 in the process shown in Figure 5 above, which will not be elaborated here.

[0422] S1803. Electronic device A provides prompts based on the recommended factors.

[0423] The prompting method is similar to that of S511 above. For details, please refer to the corresponding description of S511 above. No further details will be provided here.

[0424] S1804. After receiving the operation for indicating the selection factor and the operation for indicating confirmation, electronic device A confirms that the selected factor is the added emotion-related factor.

[0425] For details, please refer to the corresponding explanation in S512 above; further details will not be provided here.

[0426] In some embodiments, when electronic device A provides a prompt regarding a recommended factor, it can also set the recommended factor to a selected state. Subsequently, electronic device A can confirm, upon receiving an instruction to confirm, that the recommended factor is an added emotion-related factor.

[0427] For example, as shown in Figure 15, when the electronic device receives a trigger operation (e.g., touch operation, click operation, etc.) on the add record control 1404 in interface 3d, it retrieves recommendation factors from the server and adjusts the recommendation factors to state B, and the electronic device enters interface 3e. This interface 3e includes: multiple option labels, a confirmation control 1405, and one or more factors associated with each option label.

[0428] Taking user data including information indicating the user and their spouse are at a restaurant as an example, the recommendation factors include "Date" and "Spouse," but exclude "Work," "Study," "Household Chores," "Fitness," "Mother," "Son," and "Meditation." In interface 3e, "Date" has a checkmark 1406; "Spouse" has a checkmark 1407. "Work," "Study," "Household Chores," "Fitness," "Mother," "Son," and "Meditation" do not have corresponding checkmarks. Checkmarks indicate state B. State B indicates selection; if there is no checkmark, it indicates state A. State A indicates not selection.

[0429] When the wearable device receives a trigger operation (e.g., touch operation, click operation, etc.) on the interface 3e for the confirmation control 1405, it confirms that "date" and "wife" are both added emotional association factors.

[0430] This eliminates the need for users to manually select and add emotion-related factors, reducing user operations and improving the user experience.

[0431] In summary, electronic device A can display recommended factors as selected factors to the user, making it easier for the user to confirm and reducing user operations.

[0432] Based on the above embodiments, if a user deems the recommended factor not to be an added emotional association factor, they can cancel the recommended factor and select other factors as the added emotional association factor. For example, after S1803, the electronic device may not execute S1804, but instead execute S1805 to S1807.

[0433] As shown in Figure 19, the process for adjusting emotion-related factors is as follows:

[0434] S1805, Electronic device A receives an operation to instruct the adjustment of added emotional correlation factors, and adjusts the state of some or all factors.

[0435] S1806. Received an operation to indicate confirmation, confirming that the factor in the selected state is an added emotion-related factor.

[0436] S1807. Electronic device A trains the recommendation model based on the added sentiment-related factors.

[0437] The interface can be referred to in the corresponding descriptions above. It will not be described in detail here.

[0438] In this way, users can adjust the selected factors and choose appropriate factors as emotional association factors.

[0439] The above embodiments involve adding emotional association factors corresponding to the current emotion. In some embodiments, users can also add or modify emotional association factors corresponding to a past time period.

[0440] For example, Figure 20 is a schematic diagram of an interface provided in an embodiment of this application. Taking an electronic device as an example, as shown in Figure 20,

[0441] The electronic device displays interface 4a, which includes multiple health controls, such as exercise tracking, health clover, heart health, and emotional health 2001.

[0442] When the electronic device receives a trigger operation (e.g., touch operation, click operation, etc.) for emotional health 2001 on interface 4a, the electronic device enters interface 4b. Interface 4b includes: the trend of emotional state changes over a period of time, associated record controls, and the frequency of occurrence of each emotion-related factor over that period of time. The frequency of occurrence of each emotion-related factor over that period of time can be: 8 times exercising, 5 times dating, 3 times doing housework, 2 times studying, and 1 time arguing.

[0443] When the electronic device receives a trigger operation on interface 3b for the emotion corresponding to time period A (e.g., 12:00-12:10), the electronic device enters interface 4c or interface 4d.

[0444] Taking the example of not adding any emotion-related factors for the emotion in time period A, interface 4c includes: the emotion in time period A and the addition control 2002.

[0445] When the electronic device receives a trigger operation (e.g., touch operation, click operation, etc.) on interface 4c for adding control 2002, it obtains the recommendation factors and sets them to state A, and the electronic device enters interface 4e. Interface 4e includes: multiple option labels, a confirmation control 2003, and multiple factors related to each option label. For example, option labels may include: social activities 2004, interpersonal relationships, hobbies, and others. Taking a recommendation factor that includes "dating" but excludes "work," "study," "housework," and "fitness" as an example, "dating" has a checkmark 2005. "Work," "study," "housework," and "fitness" do not have a corresponding checkmark 2005. The checkmark indicates state B. State B indicates that it is selected; if there is no checkmark, it indicates state A. State A indicates that it is not selected.

[0446] When the electronic device receives a trigger operation (e.g., touch operation, click operation, etc.) on the interface 4e for the determination control 2003, "Dating" is set as an added emotional association factor.

[0447] This eliminates the need for users to manually select and add emotion-related factors, reducing user operations and improving the user experience.

[0448] Taking the addition of emotion-related factors for the emotion of time period A as an example, when the electronic device receives a trigger operation for the emotion corresponding to time period A (e.g., 12:00-12:10) on interface 4b, the electronic device enters interface 4d, which includes: the emotion of time period A, the emotion-related factors corresponding to the emotion of time period A, and the modification control 2006.

[0449] The emotional correlation factors for time period A can be modified based on the operation of the modification control 2006 in interface 4d. When the electronic device receives a trigger operation (e.g., touch operation, click operation, etc.) on the modification control 2006 in interface 4d, it obtains the recommended factors and sets them to state A, and the electronic device enters interface 4e. In some embodiments, the electronic device may not obtain the recommended factors, but instead provide a factor list containing multiple factors on the interface. The user can select some factors from the various factors and confirm the selection of each factor. Afterwards, the electronic device can modify the emotional correlation factors for time period A based on the factors confirmed by the user.

[0450] When the electronic device receives a trigger operation (e.g., touch operation, click operation, etc.) on the interface 4e for the determination control 2003, it confirms that "date" is an emotion-related factor for time period A.

[0451] This eliminates the need for users to manually select and add emotion-related factors, reducing user operations and improving the user experience.

[0452] It should be understood that the interface shown in the above embodiments distinguishes between state A and state B by the presence or absence of a checkmark. However, state A and state B can also be distinguished in any other way. For example, different states can be represented by adjusting the appearance of various factors (e.g., size, text color, background color, etc.). For instance, using text color as an example, black text represents state A, and gray text represents state B. No specific limitations are made here.

[0453] The data processing method of the embodiments of this application has been described above. The apparatus for performing the above method provided in the embodiments of this application is described below. Those skilled in the art will understand that the methods and apparatus can be combined with and referenced by each other, and the related apparatus provided in the embodiments of this application can perform the steps in the above method.

[0454] As shown in Figure 21, which is a schematic diagram of the structure of a data processing device provided in an embodiment of this application, the data processing device may be an electronic device in the embodiment of this application, or a chip or chip system in an electronic device.

[0455] As shown in Figure 21, the data processing device can be used in communication equipment, circuits, hardware components, or chips. The data processing device includes a display unit 2101, a processing unit 2102, and a communication unit 2103. The display unit 2101 supports the display step of the data processing method; the processing unit 2102 supports the information processing step of the data processing device; and the communication unit 2103 supports interaction between the data processing device and other devices.

[0456] For example, when the data processing device is an electronic device, the communication unit 2103 can be a communication interface or an interface circuit. When the data processing device is a chip or chip system within an electronic device, the communication unit 2103 can be a communication interface. For example, a communication interface can be an input / output interface, pins, or circuits, etc.

[0457] The data processing apparatus described in the embodiments of this application may include the units described in the above embodiments.

[0458] Specifically, the processing unit 2102 can be integrated with the display unit 2101, and the processing unit 2102 and the display unit 2101 may communicate with each other.

[0459] In one possible implementation, the data processing apparatus may further include a storage unit 2104. The storage unit 2104 may include one or more memories, which may be devices in one or more devices or circuits used for storing programs or data.

[0460] The storage unit 2104 can exist independently or be connected to the processing unit 2102 via a communication bus. Alternatively, the storage unit 2104 can be integrated with the processing unit 2102.

[0461] Taking the data processing device as an example, which may be a chip or chip system of the electronic device in the embodiments of this application, the storage unit 2104 may store computer-executable instructions for the method of the electronic device, so that the processing unit 2102 executes the method of the electronic device in the above embodiments. The storage unit 2104 may be a register, cache, or random access memory (RAM), etc., and the storage unit 2104 may be integrated with the processing unit 2102. The storage unit 2104 may be a read-only memory (ROM) or other types of static storage devices that can store static information and instructions, and the storage unit 2104 may be independent of the processing unit 2102.

[0462] The data processing device in this embodiment can be used to execute the steps performed in the above method embodiment. Its implementation principle and technical effect are similar, and will not be described again here.

[0463] The data processing method provided in this application can be applied to electronic devices with data processing capabilities. Electronic devices include terminal devices, and the specific device form of the terminal device can be referred to the above-described related information, which will not be repeated here.

[0464] For example, Figure 22 is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. As shown in Figure 22, the electronic device includes: a processor 110, an external memory interface 120, an internal memory 121, a universal serial bus (USB) interface 130, a charging management module 140, a power management module 141, a battery 142, an antenna 1, an antenna 2, a mobile communication module 150, a wireless communication module 160, an audio module 170, a speaker 170A, a receiver 170B, a microphone 170C, a headphone jack 170D, a sensor module 180, buttons 190, a motor 191, an indicator 192, a camera 193, a display screen 194, and a subscriber identification module (SIM) card interface 195, etc. The sensor module 180 may include a pressure sensor 180A, a gyroscope sensor 180B, a barometric pressure sensor 180C, a magnetic sensor 180D, an accelerometer sensor 180E, a distance sensor 180F, a proximity sensor 180G, a fingerprint sensor 180H, a temperature sensor 180J, a touch sensor 180K, an ambient light sensor 180L, a bone conduction sensor 180M, etc.

[0465] It is understood that the structures illustrated in the embodiments of this application do not constitute a specific limitation on the electronic device. In other embodiments of this application, the electronic device 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.

[0466] The processor 110 may include one or more processing units. These processing units may be independent devices or integrated within one or more processors. The processor 110 may also include a memory for storing instructions and data. For example, the processor 110 may store instructions and data related to a data processing method provided in an embodiment of this application.

[0467] Electronic devices utilize GPUs, displays (194), and application processors to achieve display functions. The GPU is a microprocessor for image processing, connecting the displays (194) and the application processor.

[0468] The display screen 194 is used to display images, videos, etc. The display screen 194 includes a display panel. For example, the display screen 194 can display the aforementioned interface, etc., but this embodiment is not limited thereto.

[0469] The mobile communication module 150 and the wireless communication module 160 are used to enable data interaction between the electronic device and other devices. Examples include the transmission of initial information, the transmission of recommendation factors, and the transmission of emotion-related factors.

[0470] Sensor module 180 is used to collect various types of data. For example, gyroscope sensor 180B is used to collect gyroscope data; accelerometer sensor 180E is used to collect acceleration data. Motion data such as the user's speed can be obtained from the gyroscope data and acceleration data.

[0471] This application provides an electronic device, which includes one or more processors and a memory. The memory is coupled to one or more processors and is used to store computer program code, which includes computer instructions. One or more processors call the computer instructions to cause the electronic device to execute the above-described method (so that the electronic device can perform the functions of the electronic device in the above-described method embodiments).

[0472] This application provides a server, which includes one or more processors and a memory; the memory is coupled to one or more processors and is used to store computer program code, which includes computer instructions. One or more processors call the computer instructions to cause the server to execute the above-described method (so that the server implements the functions of the server in the above-described method embodiment).

[0473] This application also provides a computer-readable storage medium for storing a computer program for implementing the methods shown in the above-described method embodiments.

[0474] This application also provides a computer program product, which includes a computer program (also referred to as code or instructions) that, when run on a computer, enables the computer to execute the methods shown in the above method embodiments (so that the computer performs the functions of the electronic device in the above method embodiments, or the functions of the server in the above method embodiments).

[0475] This application provides a chip system including a processor and potentially a memory, for implementing the functions of the electronic device or server described in the method embodiments above. The chip system may be composed of chips or may include chips and other discrete components.

[0476] Those skilled in the art will recognize that the modules 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.

[0477] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, use and processing of the relevant data must comply with the relevant laws, regulations and standards of the relevant countries and regions, and corresponding operation portals are provided for users to choose to authorize or refuse.

[0478] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and modules described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0479] In the embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or modules may be electrical, mechanical, or other forms.

[0480] The modules described as separate components may or may not be physically separate. Similarly, the components shown as modules may or may not be physical modules; they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0481] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0482] If a function is implemented as a software module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0483] The above description is merely a specific embodiment of this application, but the protection scope of the embodiments of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the embodiments of this application should be included within the protection scope of the embodiments of this application. Therefore, the protection scope of the embodiments of this application should be determined by the protection scope of the claims.

Claims

1. A data processing method, characterized in that, Applied to a first device, the method includes: Receive a first operation, which is used to add emotional correlation factors corresponding to the first time period; The first interface is displayed, which includes a first factor, a second factor, a first marker, and a second marker; wherein the first factor and the second factor are emotion-related factors, the first factor corresponds to the first marker, the second factor corresponds to the second marker, and the first marker and the second marker are different.

2. The method according to claim 1, characterized in that, The first marker indicates that the first factor is selected, and the second marker indicates that the second factor is not selected; and / or, The first marker is used to indicate that the first factor is a recommended factor, and the second marker is used to indicate that the second factor is not a recommended factor.

3. The method according to claim 1 or 2, characterized in that, The first marker is used to indicate that the first factor is selected, and the method further includes: The first interface receives a second operation, which is used to confirm the added emotional association factors; In response to the second operation, the first factor is added as an emotion-related factor corresponding to the first time period.

4. The method according to claim 2, characterized in that, The first marker is used to indicate that the first factor is selected, and the method further includes: A third operation is received on the first interface, the third operation being used to instruct the first factor to be deselected; In response to the third operation, the first factor is associated with a third marker, the third marker being used to indicate that the first factor is in an unselected state.

5. The method according to claim 4, characterized in that, The first interface also includes a third factor and a fourth marker. The third factor is an emotion-related factor, and the fourth marker corresponds to the third factor. The fourth marker is used to indicate that the third factor is selected. Receive a fourth operation, which is used to confirm the added emotional association factors; In response to the fourth operation, the third factor is added as an emotion-related factor corresponding to the first time period, wherein the emotion-related factors corresponding to the first time period do not include the first factor.

6. The method according to claim 2, characterized in that, The first marker is used to indicate that the first factor is selected, and the method further includes: The first interface receives a fifth operation, which is used to instruct the selection of the second factor; In response to the fifth operation, the second factor corresponds to the fifth marker; the fifth marker is used to indicate that the second factor is selected. Receive a sixth operation, which is used to confirm the added emotional association factors; In response to the sixth operation, the first factor and the second factor are added as emotion-related factors corresponding to the first time period.

7. The method according to any one of claims 1-6, characterized in that, The first factor is obtained based on the first model and user data; the first model is used to predict sentiment-related factors based on user data.

8. The method according to claim 7, characterized in that, The first device is logged into with a first account; The user data includes: user data of the first account, and / or, user data of the second account; the second account is an account associated with the first account.

9. The method according to claim 7 or 8, characterized in that, After receiving the first operation, the method further includes: Send a first message to the second device, the first message being used to instruct the addition of emotion-related factors corresponding to the first time period; The first factor is received, which is obtained by the second device based on the first model and the user data.

10. The method according to claim 9, characterized in that, Sending the first information to the second device includes: The first information is sent from the third device to the second device; the first factor is sent from the second device to the first device via the third device.

11. The method according to any one of claims 7-10, characterized in that, The first model is used to calculate multiple factors based on the first data using a decision tree; the first data consists of multiple data points randomly selected from the user data. The first model is also used to determine the first factor based on the frequency of occurrence of the plurality of factors.

12. The method according to claim 11, characterized in that, The decision tree includes a first decision tree, a second decision tree, and a third decision tree. The first data includes: a first type of data, a second type of data, and a third type of data; the first type of data is data related to social activities; the second type of data is data related to interpersonal relationships; and the third type of data is data related to personal physical and mental health. The first model is specifically used to calculate one or more factors related to social activities based on the first type of data using the first decision tree; The first model is specifically used to calculate one or more factors related to interpersonal relationships based on the second type of data using the second decision tree; The first model is specifically used to calculate one or more factors related to an individual's physical and mental health based on the third type of data using the third decision tree.

13. The method according to any one of claims 5-12, characterized in that, The method further includes: The first model is updated based on the emotional correlation factors corresponding to the first time period.

14. A first device, characterized in that, The first device includes: one or more processors and memory; The memory is coupled to the one or more processors, the memory being used to store computer program code, the computer program code including computer instructions, the one or more processors invoking the computer instructions to cause the first device to perform the method as described in any one of claims 1 to 13.

15. A chip system, characterized in that, The chip system is applied to a first device or a second device, the chip system including one or more processors, the one or more processors being configured to invoke computer instructions to cause the first device to perform the method as described in any one of claims 1 to 13.

16. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes computer instructions that, when executed on the first device, cause the first device to perform the method as described in any one of claims 1 to 13.

17. A computer program product, characterized in that, The computer program product includes computer program code that, when run on a first device, causes the first device to perform the method as described in any one of claims 1 to 13.

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