Data processing method and apparatus

Abstract

Disclosed in the present application are a data processing method and an apparatus. The method comprises: acquiring first sensing data and second sensing data, the first sensing data being from a sensor, the first sensing data comprising sensing data of a first user, the second sensing data being obtained by using communication and sensing technology, the second sensing data comprising sensing data of at least two users, and the at least two users comprising the first user; and, on the basis of the first sensing data and the second sensing data, determining sensing data of a target user, the target user being one or more of the at least two users. The present application specifies an application solution of communication and sensing technology in multi-user scenarios, thus enriching application scenarios of communication and sensing technology; moreover, the present application also enables accurate identification of sensing data of target users in multi-user scenarios, thereby avoiding the problem of overlapping of sensing data of different users caused by range resolution limitations and remarkably improving the accuracy of communication and sensing technology.

Description

A data processing method and apparatus Technical Field

[0001] This application relates to the field of communication technology, and in particular to a data processing method and apparatus. Background Technology

[0002] Communication sensing technology refers to the technology that uses wireless communication signals to achieve sensing functions. Currently, communication sensing technology is widely used in many fields. For example, in areas such as smart homes and health management, technologies that use wireless communication signals for respiratory monitoring and sleep quality monitoring are gradually gaining attention.

[0003] However, existing technologies do not cover the application of communication sensing technology in multi-user scenarios. Summary of the Invention

[0004] This application provides a data processing method and apparatus, clarifies the application scheme of communication sensing technology in multi-user scenarios, improves the application scenarios of communication sensing technology, and also enhances the accuracy of communication sensing technology.

[0005] In a first aspect, a data processing method is provided, comprising: acquiring first sensing data and second sensing data, wherein the first sensing data originates from a sensor (i.e., the first sensing data is sensor data), the first sensing data includes sensing data of a first user, and the second sensing data is obtained using communication sensing technology (i.e., the second sensing data is communication sensing data), the second sensing data includes sensing data of at least two users, the at least two users including the first user; and determining sensing data of a target user based on the first sensing data and the second sensing data, wherein the target user is one or more of the at least two users.

[0006] This application clarifies the application scheme of communication sensing technology in multi-user scenarios. Specifically, it combines sensor data (i.e., first sensing data) to determine the communication sensing data of the target user from the communication sensing data (i.e., second sensing data) of multiple users, thus improving the application scenarios of communication sensing technology. Furthermore, this scheme can accurately identify the communication sensing data of the target user in multi-user scenarios, avoiding the problem of overlapping communication sensing data of multiple users under distance resolution limitations, and can significantly improve the accuracy of communication sensing technology.

[0007] In one possible design, before determining the target user's perception data based on the first perception data and the second perception data, first information can also be obtained, which is used to indicate a multi-user scenario.

[0008] In this way, the data processing method of this application can be executed again in multi-user scenarios, which can save device power consumption.

[0009] In one possible design, before obtaining the first information, a second information can also be output, which is used to ask or indicate whether the inquiry is a multi-user scenario.

[0010] In this way, multi-user scenarios can be determined based on the user, which can improve the accuracy of multi-user scenario identification.

[0011] In one possible design, outputting the second information may include: outputting the second information based on third sensing data, wherein the acquisition time of the third sensing data is no later than the acquisition time of the second sensing data. The third sensing data can be sensor data or communication sensing data, without limitation.

[0012] In this way, it is possible to identify multi-user scenarios based on perception data, which can be achieved by using existing hardware conditions without adding additional hardware costs.

[0013] In one possible design, the acquisition time of the first sensing data and the acquisition time of the second sensing data at least partially overlap.

[0014] In this way, invalid judgments can be avoided on sensing data collected at different time intervals with large time gaps, thereby reducing power consumption waste.

[0015] In one possible design, the sensor includes a wearable device. Of course, this is just an example and is not limited to this.

[0016] In one possible design, determining the target user's perceptual data based on the first and second perceptual data may include: performing feature matching on the second and first perceptual data, whereby the target user's perceptual data consists of the perceptual data in the second perceptual data that fails to match the features of the first perceptual data. In other words, the target user has perceptual data in the second perceptual data, but not in the first perceptual data.

[0017] In this way, it is possible to filter out the communication sensing data of users who do not have sensor data from the communication sensing data of multiple users.

[0018] In one possible design, determining the target user's perceptual data based on the first and second perceptual data can include: performing feature matching on the second and first perceptual data, whereby the target user's perceptual data is the perceptual data in the second perceptual data that successfully matches the features of the first perceptual data. In other words, the target user has perceptual data in both the first and second perceptual data.

[0019] In this way, communication sensing data of users with sensor data can be filtered out from the communication sensing data of multiple users.

[0020] In one possible design, the identity information of the target user can also be determined based on the identity information of the user corresponding to the first perception data.

[0021] In this way, the identity information of the target user can be identified, which is beneficial to applying the target user's perception data to other scenarios related to the target user and improving the user experience.

[0022] In one possible design, alarm information can also be output based on the target user's perception data. The alarm information is used to indicate abnormalities in the target user's perception data. For example, for target users who do not have sensor data (such as users who are not wearing wearable devices), the user's health or sleep status can be identified based on communication perception data, and an alarm can be issued when the user's health or sleep status is abnormal.

[0023] In this way, anomaly monitoring of the target user's perceived data is achieved, which can improve the user experience.

[0024] In one possible design, if the running status of the application associated with the target user does not match the target user's perceived data, then the running status of the application associated with the target user is adjusted based on the target user's perceived data. The application includes an alarm clock.

[0025] In this way, the perception data of non-target users can be prevented from interfering with the running status of applications associated with target users, which can further improve the user experience.

[0026] In a second aspect, a data processing apparatus is provided, including modules / units / technical means for performing the method as described in the first aspect or any possible design of the first aspect. Exemplarily, the apparatus may include:

[0027] The acquisition module is used to acquire first sensing data and second sensing data. The first sensing data comes from a sensor and includes the sensing data of a first user. The second sensing data is obtained using communication sensing technology and includes the sensing data of at least two users, including the first user.

[0028] The processing module is used to determine the perception data of the target user based on the first perception data and the second perception data, wherein the target user is one or more of at least two users.

[0029] Thirdly, a data processing apparatus is provided, comprising: at least one processor; and a communication interface communicatively connected to the at least one processor; wherein the at least one processor executes instructions stored in a memory to cause the method described in the first aspect or any possible design of the first aspect to be performed.

[0030] Fourthly, a computer-readable storage medium is provided, wherein a computer program or instructions are stored therein, which, when executed, cause the method described in the first aspect or any possible design of the first aspect to be implemented.

[0031] Fifthly, a computer program product is provided, including instructions that, when run on a computer, cause the method described in the first aspect or any possible design of the first aspect to be implemented.

[0032] Sixthly, a data processing system is provided, comprising:

[0033] A sensor is used to collect first sensing data, which includes the sensing data of a first user.

[0034] A communication device for collecting second sensing data, the second sensing data including sensing data of at least two users, the at least two users including a first user;

[0035] A first device for performing the method as described in the first aspect or any possible design of the first aspect.

[0036] It is understandable that sensors, communication devices, and the primary device can be deployed separately in two or more physical devices, or integrated into one physical device, without restriction.

[0037] The technical effects of the second to sixth aspects mentioned above are described in the first aspect and will not be repeated here. Attached Figure Description

[0038] Figures 1A to 1D are schematic diagrams of several communication sensing scenarios applicable to the embodiments of this application;

[0039] Figure 2 is a flowchart of a data processing method provided in an embodiment of this application;

[0040] Figures 3A and 3B are schematic diagrams of output alarm information;

[0041] Figures 4A and 4B are schematic diagrams of infant care scenarios;

[0042] Figure 5A is a schematic diagram of multi-user perception data;

[0043] Figure 5B is a schematic diagram of multi-user scenario confirmation;

[0044] Figure 6 is a specific example of an embodiment of this application;

[0045] Figure 7 is a schematic diagram of the structure of a data processing device provided in an embodiment of this application;

[0046] Figure 8 is a schematic diagram of another data processing device provided in an embodiment of this application;

[0047] Figure 9 is a schematic diagram of another chip structure provided in an embodiment of this application. Detailed Implementation

[0048] First, let's introduce some of the technical terms used in this application.

[0049] 1) Communication sensing technology:

[0050] Also known as communication sensing, wireless sensing, or wireless sensing technology, it is a technology that uses wireless communication signals (which can be simply referred to as wireless signals or communication signals) to achieve sensing functions. Communication sensing transmits wireless communication signals and collects data after these signals have been reflected, scattered, and transmitted through multiple paths in the environment, thereby obtaining information such as the distribution, size, quantity, temperature, and human behavior of objects in the environment. This data carries environmental information, and after complex signal processing, the sensed environment can be reconstructed on a computer, allowing for the identification of objects and people in the environment, and the detection of temperature, human movements, and even breathing and heart rate.

[0051] Integrated sensing and communication (ISAC) technology is a specific example of sensing technology. Also known as integrated sensing technology, ISAC refers to the integration of electromagnetic signals used for communication and those used for sensing. ISAC technology can enhance the performance of both sensing and communication functions through mutual assistance and amplification.

[0052] 2) Perceived data: refers to information about the real world that the device perceives.

[0053] Perceptual data can include information about the real world acquired using sensors; this type of perceptual data is called sensor data or sensing data. Sensors can be cameras, microphones, temperature sensors, etc., and can sense various types of data. Perceptual data covers many types of data, including images, sound, temperature, humidity, and pressure. For example: visual perception data: visual information acquired through devices such as cameras and infrared sensors, which can be used for applications such as image recognition and target tracking; sound perception data: sound information acquired through devices such as microphones and sound wave sensors, which can be used for applications such as speech recognition and sound analysis; environmental perception data: environmental information acquired through devices such as temperature sensors and humidity sensors, which can be used for applications such as weather forecasting and environmental monitoring; motion perception data: motion information acquired through devices such as accelerometers and gyroscopes, which can be used for applications such as motion detection and posture recognition, and so on.

[0054] Sensing data can also include information about the real world obtained using communication sensing technology; this type of sensing data can be called communication sensing data or wireless sensing data, etc.

[0055] 3) User perception data:

[0056] Also known as user-specific sensory data, this refers to information perceived by the device that reflects the user's physiological characteristics. These physiological characteristics include, but are not limited to, the user's height, weight, temperature, respiratory rate, heart rate, movement behavior, voice, and so on.

[0057] 4) Distance resolution refers to the ability to distinguish between two or more targets at different distances. For example, distance resolution can be the minimum distance between two objects that a device can distinguish; when the distance between two objects is less than this minimum distance, the device cannot distinguish between the two objects.

[0058] 5) In the embodiments of this application, the number of nouns, unless otherwise specified, refers to "singular nouns or plural nouns," that is, "one or more." "At least one" means one or more, and "more than one" means two or more. "And / or" describes the relationship between related objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist simultaneously, or B exists alone, where A and B can be singular or plural. The character " / " generally indicates that the related objects before and after are in an "or" relationship. For example, A / B means: A or B. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c means: a, b, c, a and b, a and c, b and c, or a and b and c, where a, b, and c can be single or multiple.

[0059] 6) In the embodiments of this application, ordinal numbers such as "first" and "second" are used to distinguish multiple objects and are not used to limit the size, content, order, timing, priority, or importance of the multiple objects. For example, the third parameter and the second parameter can be the same parameter or different parameters, and such names do not indicate differences in the content, application scenario, priority, or importance of the two parameters. In addition, the numbering of steps in the various embodiments described in this application is only to distinguish different steps and is not used to limit the order of steps.

[0060] The application scenarios of this application will be introduced below.

[0061] The technical solutions provided in this application can be applied to various communication sensing scenarios, such as indoor communication sensing scenarios, vehicle-mounted communication sensing scenarios, or other communication sensing scenarios in wide-area wireless networks or local area wireless networks. This application does not impose any limitations. The specific wireless communication technologies used in communication sensing include, but are not limited to, Spark Link (or Near Link), Wi-Fi, Bluetooth, Bluetooth Low Energy (BLE), Ultra Wide Bandwidth (UWB), and frequency-modulated continuous wave (FMCW) millimeter-wave radar technologies. Among these, Spark Link technology includes, but is not limited to, synchronous low latency broadband (SLB) technology, synchronous low energy (SLE) technology, synchronous link positioning (SLP) technology, and synchronous link zero-power (SLZ) technology.

[0062] Referring to Figures 1A to 1D, these are schematic diagrams illustrating some possible application scenarios provided by embodiments of this application. The scenarios include devices with communication sensing capabilities (hereinafter referred to as communication devices), such as watches, mobile phones, or routers. These communication devices can acquire user (or human body) perception data in the environment by transmitting wireless communication signals and collecting data after these signals have undergone environmental reflection, scattering, and multipath transmission. User perception data includes, but is not limited to, at least one of the user's respiratory rate, heart rate, or movement status. This user perception data can be used for sleep monitoring, health monitoring, or controlling applications associated with the user, etc.

[0063] It is understood that the user in this application embodiment is mainly a human being. In actual applications, the user can also be other objects with life characteristics, such as animals (e.g., cats, dogs).

[0064] In fields such as smart homes and health management, technologies like respiratory monitoring and sleep quality monitoring are widely used. Smart devices on the market, such as smartwatches and sleep monitors, largely rely on sensors (such as photoelectric plethysmography (PGG) and accelerometers (ACC)) to monitor users' heart rate and activity. However, the application of these devices in multi-user environments is limited, especially when users are not wearing the device, making it impossible to collect user sensory data and thus distinguish and monitor each user's specific situation. Therefore, technologies for respiratory monitoring and sleep quality monitoring using non-contact sensors are gradually gaining attention.

[0065] Communication sensing technology enables non-contact respiratory detection and sleep quality monitoring. This technology primarily utilizes narrowband wireless communication signals (such as Wi-Fi) for sensing. However, the use of narrowband wireless communication signals has limitations in terms of distance resolution, particularly for individual identification in densely populated environments. Due to the limited signal bandwidth, aliasing within the distance resolution range makes it impossible to distinguish the respiratory signals of different individuals at close range. This phenomenon is especially pronounced in multi-user scenarios, particularly when multiple users have similar breathing frequencies, making it exceptionally difficult to identify the breathing patterns of a specific individual.

[0066] Taking Wi-Fi communication sensing as an example, Wi-Fi communication sensing mainly infers the position and motion state of objects in the environment by analyzing the reflection, scattering and refraction of Wi-Fi signals. However, in scenarios where multiple people share similar spaces, especially when the actions and physiological characteristics (such as breathing) of human beings are similar, it becomes very difficult to distinguish and identify each individual.

[0067] Specifically, breathing is a cyclical physiological process, and its impact on Wi-Fi signals is typically manifested as small fluctuations within the frequency range. When multiple users are in the same environment with similar breathing patterns, the Wi-Fi sensing system cannot effectively distinguish the subtle differences between users due to the similar signal reflection and transmission paths. This leads to significant errors in complex multi-user scenarios, and the system may even fail to correctly detect and differentiate between multiple individuals. Furthermore, communication sensing technology relies on signal fluctuations to infer human position and movement; however, subtle human movements, such as the minute undulations of breathing, are easily masked by background noise or other environmental factors (such as interference from furniture or other devices). This interference significantly impacts the accuracy and robustness of sensing in multi-user environments, especially under dynamic conditions and with similar physiological characteristics.

[0068] In summary, although communication sensing technology performs well in single-user scenarios, it has limitations in distance resolution in multi-user scenarios, which can lead to the inability to distinguish between different users at close range.

[0069] Furthermore, communication sensing technology mainly focuses on single-user scenarios and has not yet covered applications in multi-user scenarios.

[0070] In view of this, the technical solution of this application embodiment is provided, clarifying the application of communication sensing technology in multi-user scenarios: by coordinating communication sensing technology and sensor (such as wearable devices) technology, the sensing data of target users can be accurately identified in multi-user scenarios, solving the problem of aliasing of multi-user communication sensing data under distance resolution limitations, and significantly improving the accuracy and individual identification capability of communication sensing technology. When the technical solution of this application embodiment is applied to scenarios such as multi-user respiratory monitoring and sleep quality monitoring, it can enhance the intelligence and practicality of user health management and meet the growing health management needs of users. It is understood that the technical solution of this application embodiment is not limited to multi-user respiratory monitoring and sleep quality monitoring scenarios, but can also be applied to other scenarios.

[0071] Referring to Figure 2, which is a flowchart of a data processing method provided in an embodiment of this application, the method is taken as being executed by a first device. The first device can be a sensor, a communication device, or other devices besides sensors and communication devices, without limitation. As shown in Figure 2, the method includes the following steps S201 to S202:

[0072] S201, Acquire first perception data and second perception data.

[0073] The first sensed data originates from the sensor; or, the first sensed data is collected by the sensor; or, the first sensed data is obtained using sensor technology. In other words, the first sensed data is sensor data.

[0074] The first perception data includes the perception data of the first user. In specific implementations, the first perception data may include the perception data of one or more users. For example, the first perception data may only contain the perception data of the first user; or, in addition to the perception data of the first user, the first perception data may also include the perception data of other users, such as the perception data of the second user.

[0075] In one possible example, the sensor includes a wearable device, or the sensor is located within a wearable device (i.e., the wearable device includes the sensor). For example, the first user's perception data is collected by sensors such as PGG and ACC in a wearable device worn by the first user (such as a smart bracelet, smart glasses, headphones, etc.).

[0076] In another possible example, the sensor includes a medical device worn or installed on the user's body, or the sensor is located within a medical device worn or installed on the user's body. For example, the first user's sensory data is collected by a medical device installed on the first user's body. Such medical devices include, but are not limited to: ventilators, implantable hearing devices (such as cochlear implants, vibrating sound bridges, bone-anchored hearing aids (BAHA), auditory brainstem implants, etc.), artificial hearts (an auxiliary device that uses mechanical or biomechanical means to partially or completely replace the natural heart in supplying blood to the human body), and so on.

[0077] Of course, the above two examples are just illustrations, and the actual situation is not limited to these. For ease of description, wearable devices will be the primary example in the following text.

[0078] The second sensing data originates from communication sensing data; or, the second sensing data is collected by communication devices; or, the second sensing data is obtained using communication sensing technology. In other words, the second sensing data is communication sensing data. For example, the second sensing data is ISAC data.

[0079] The second perception data includes perception data from at least two users, including the first user. It is understood that when the first perception data includes more than just the first user's perception data, the number of identical users corresponding to the first and second perception data can be one or more. For example, if the first perception data includes both the first and second user's perception data, the second perception data may or may not include the second user's perception data. For instance, the second perception data may include the first user's perception data and the third user's perception data, or it may include the first user's perception data, the second user's perception data, and the third user's perception data, and so on.

[0080] In one possible implementation, the sensor and the first device are the same device (or the first device includes the sensor, or the sensor includes the first device), and the first device acquiring the first sensing data includes: the first device using sensor technology to collect the first sensing data. In another possible implementation, the sensor and the first device are not the same device (or the first device does not include the sensor, or the sensor does not include the first device), and the first device acquiring the first sensing data includes: the first device acquiring the first sensing data from the sensor (e.g., the first device receiving first sensing data from the sensor).

[0081] In one possible implementation, the communication device and the first device are the same device (or the first device includes the communication device, or the communication device includes the first device), and the first device acquiring the second sensing data includes: the first device collecting the second sensing data using communication sensing technology. In another possible implementation, the communication device and the first device are not the same device (or the first device does not include the communication device, or the communication device does not include the first device), and the first device acquiring the second sensing data includes: the first device acquiring the second sensing data from the communication device (e.g., the first device receiving second sensing data from the communication device).

[0082] In one possible implementation, the sensor and the communication device are the same device (or the communication device includes the sensor, or the sensor includes the communication device), meaning the sensor (or communication device) uses sensor technology to collect first sensing data and uses communication sensing technology to collect second sensing data. In another possible implementation, the sensor and the communication device are not the same device (or the communication device does not include the sensor, or the sensor does not include the communication device), meaning the sensor uses sensor technology to collect first sensing data, and the communication device uses communication sensing technology to collect second sensing data.

[0083] The following, referring to Figures 1A to 1D, lists several possible scenarios:

[0084] Referring to Figure 1A, the first device, sensor, and communication device are all watches worn by user A. The watch uses sensor technology to collect user A's perception data (i.e., first perception data), and the watch uses communication sensing technology to collect user A's perception data and user B's perception data (i.e., second perception data). The watch determines the target user's perception data based on the first and second perception data (i.e., executes S202).

[0085] Referring to Figure 1B, the first device and communication device are both mobile phones, and the sensor is a watch worn by user A. The watch can use sensor technology to collect user A's perception data (i.e., the first perception data), and the mobile phone can use communication sensing technology to collect user A's perception data and user B's perception data (i.e., the second perception data). The watch can send the first perception data to the mobile phone, and the mobile phone can determine the target user's perception data based on the first and second perception data (i.e., execute S202); or, the mobile phone can send the second perception data to the watch, and the watch can determine the target user's perception data based on the first and second perception data (i.e., execute S202).

[0086] Referring to Figure 1C, the communication device is a router, the sensor is a watch worn by user A, and the first device is a mobile phone. The watch can use sensor technology to collect user A's perception data (i.e., first perception data), and the router can use communication sensing technology to collect user A's perception data and user B's perception data (i.e., second perception data). The watch can send the first perception data to the mobile phone, and the router can send the second perception data to the mobile phone. The mobile phone can determine the target user's perception data based on the first and second perception data (i.e., execute S202). Of course, the functions performed by the router and the mobile phone can also be interchanged, i.e., the communication device is the mobile phone, and the first device is the router.

[0087] Referring to Figure 1D, the communication device is a router, the sensors include a watch worn by user A and glasses worn by user C, and the first device is a mobile phone. The watch can use sensor technology to collect user A's perception data, and the glasses can use sensor technology to collect user C's perception data. The first perception data includes the perception data of user A collected by the watch and the perception data of user C collected by the glasses. The router can use communication sensing technology to collect the perception data of user A, user B, and user C (i.e., the second perception data). The watch can send user A's perception data to the mobile phone, the glasses can send user C's perception data to the mobile phone, and the router can send the second perception data to the mobile phone. The mobile phone can determine the perception data of the target user based on the first and second perception data (i.e., execute S202). Of course, the functions performed by the router and the mobile phone can also be interchanged, that is, the communication device is the mobile phone, and the first device is the router.

[0088] It should be understood that Figures 1A to 1D above are merely some possible scenario examples, and are not limited to these in practice.

[0089] In one possible design, the acquisition time of the first sensing data and the acquisition time of the second sensing data at least partially overlap. Here, the acquisition time of the first sensing data refers to the time when the sensor collects the first sensing data, or the time when the first device acquires the first sensing data; the acquisition time of the second sensing data refers to the time when the communication device collects the second sensing data, or the time when the first device acquires the second sensing data.

[0090] It is understood that the first sensing data and acquisition time can be one or more time periods (i.e., the first sensing data includes sensing data from one or more time periods, which can be continuous or discontinuous), and the second sensing data and acquisition time can also be one or more time periods (i.e., the second sensing data includes sensing data from one or more time periods, which can be continuous or discontinuous). The acquisition time of the first sensing data and the acquisition time of the second sensing data at least partially overlap means that the first sensing data and the second sensing data include sensing data from at least the same time period. For example, if the first sensing data was collected from 16:35:00 to 16:40:00 on December 31, 2024, and the second sensing data was collected from 16:37:00 to 16:41:00 on December 31, 2024, then both the first and second sensing data include sensing data from the time period of 16:37:00 to 16:40:00 on December 31, 2024.

[0091] In this way, invalid judgments can be avoided on sensing data collected at different time intervals with large time gaps, thereby reducing power consumption waste.

[0092] S202. Determine the target user's perception data based on the first perception data and the second perception data.

[0093] The target users are one or more of the at least two users.

[0094] In one possible implementation, the target user has perceptual data in the second perceptual data, but no perceptual data in the first perceptual data. As an example, the target user is a user who is not wearing a wearable device, for example, the target user is user B in the scenario of Figure 1A, Figure 1B, Figure 1C, or Figure 1D.

[0095] In this implementation, the first device determines the target user's perception data based on the first perception data and the second perception data. This may include: the first device performing feature matching on the second perception data and the first perception data, and the target user's perception data being the perception data in the second perception data that fails to match the features of the first perception data.

[0096] There are several ways for the first device to perform feature matching between the second and first sensed data. Here is a possible example:

[0097] Example 1: When the user confirms a two-person scenario, the first device processes and analyzes the first and second sensory data respectively, identifying the user's breathing frequency corresponding to the two sensory data. The user's breathing frequency corresponding to the first sensory data is 20 breaths per minute. There are two user breathing frequencies corresponding to the second sensory data: User A: 20 breaths per minute, User B: 30 breaths per minute. The user's breathing frequency corresponding to the first sensory data is the same as the user A's breathing frequency in the second sensory data. Therefore, it is determined that the user A's sensory data in the first and second sensory data matches successfully, while the user B's sensory data in the first and second sensory data does not match. User B is the target user.

[0098] Example 2: When the user confirms a two-person scenario, the first device processes and analyzes the first and second sensory data respectively, identifying the user's breathing frequency corresponding to the two sensory data. The user's breathing frequency corresponding to the first sensory data is 20 breaths per minute, and there are two user breathing frequencies corresponding to the second sensory data: User A: 21 breaths per minute, User B: 30 breaths per minute. The user's breathing frequency corresponding to the first sensory data is closer to the breathing frequency of User A in the second sensory data. Therefore, it is determined that the sensory data of User A in the first and second sensory data matches successfully, while the sensory data of User B in the first and second sensory data does not match. User B is the target user.

[0099] Specifically, if the user's breathing rate corresponding to the first sensing data is 20 breaths per minute, and there is only one user's breathing rate corresponding to the second sensing data, such as 20 breaths per minute, then it can be confirmed that the breathing rates of both users in the second sensing data are 20 breaths per minute.

[0100] Furthermore, in this implementation, the first device can also combine the target user's perception data in the first perception data and the target user's perception data in the second perception data to determine the user's information. For example, if the sensor detects user A's breathing rate as 20 breaths per minute and the communication device detects user A's breathing rate as 22 breaths per minute, then the average of the two, 21 breaths per minute, can be taken as the breathing detection result for user A. This can further improve the accuracy of the device's perception.

[0101] This implementation method allows for the filtering of communication perception data from multiple users' communication perception data in multi-user scenarios, specifically filtering out the communication perception data of users who do not have sensor data. For example, in the scenarios shown in Figures 1A to 1D, the communication perception data of user B who is not wearing a wearable device can be filtered out.

[0102] In another possible implementation, the target user has perceptual data in the first perceptual data and also has perceptual data in the second perceptual data. As an example, the target user is a user wearing a wearable device, for example, the target user is user A in the scenario of Figure 1A, Figure 1B, or Figure 1C, or the target user is user A and / or user C in the scenario of Figure 1D.

[0103] In this implementation, the first device determines the target user's perceived data based on the first and second perceived data. This can include: the first device performing feature matching on the second and first perceived data, with the target user's perceived data being the perceived data in the second perceived data that successfully matches the features of the first perceived data. The specific implementation method for the first device to perform feature matching on the second and first perceived data can be found above and will not be repeated here.

[0104] This implementation method allows for the filtering of communication perception data from multiple users' communication perception data in multi-user scenarios. For example, in the scenarios shown in Figures 1A to 1D, the communication perception data of user B who is not wearing a wearable device can be filtered out.

[0105] The above technical solutions clarify the application of communication sensing technology in multi-user scenarios. Specifically, it combines sensor data (i.e., first sensing data) to determine the target user's communication sensing data from multi-user communication sensing data (i.e., second sensing data), thus improving the application scenarios of communication sensing technology. Furthermore, this technical solution can accurately identify the target user's communication sensing data in multi-user scenarios (the target device can be a user wearing a wearable device or a user not wearing a wearable device), avoiding the problem of overlapping communication sensing data among multiple users under distance resolution limitations, and significantly improving the accuracy of communication sensing technology.

[0106] In one possible design, the first device can also determine the target user's identity information based on the user's identity information corresponding to the first sensing data.

[0107] For example, the target user is user A wearing a watch in any of the scenarios shown in Figures 1A to 1C. The watch stores user A's identity information (such as account identifier, nickname, name, etc.). The first device can obtain user A's identity information and associate user A's identity information with user A's communication perception data.

[0108] For example, the target users are user A wearing a watch and user C wearing glasses in the scene shown in Figure 1D. The watch stores user A's identity information (such as user A's account identifier, nickname, name, etc.), and the glasses store user C's identity information (such as user C's account identifier, nickname, name, etc.). The first device can obtain user A's identity information and user C's identity information, and associate user A's identity information with user A's communication perception data, and associate user C's identity information with user C's communication perception data.

[0109] In this way, the identity information of the target user can be clearly identified, which will facilitate the management of communication-aware data.

[0110] In one possible design, the first device can also output alarm information based on the target user's perception data, and the alarm information is used to indicate that the target user's perception data is abnormal.

[0111] For example, the first device outputs an alarm message when the target user's perceived data is abnormal. For instance, the target user's perceived data includes their respiratory rate, and the first device can output an alarm message regarding the respiratory rate when the target user's respiratory rate exceeds a set respiratory rate range. The set respiratory rate range could be, for example, between 35 and 45 breaths per minute for an infant and between 12 and 20 breaths per minute for an adult.

[0112] For example, the target user's perceived data includes the target user's heart rate. The first device can output alarm information about the heart rate when the target user's heart rate exceeds a set heart rate range, and so on. For example, a normal heart rate for an infant is generally between 120-140 beats per minute, and for an adult it is between 60-100 beats per minute.

[0113] Of course, the above two are just examples, and the actual situation is not limited to these.

[0114] In practice, the first device can output alarm information through its own human-computer interaction device. For example, if the first device has a display screen, it can display text or image prompts on the screen; if the first device has a speaker, it can play voice prompts through the speaker; or the first device can drive a vibration motor to alert the user through body vibration, and so on. The first device can also output alarm information through other devices. For example, the first device can send alarm information to other devices such as the second device, and the second device can output alarm information (or output processed alarm information) through its human-computer interaction device. For example, it can display text or image prompts on the second device's display screen, play voice prompts through the second device's speaker, or drive the second device's motor to vibrate, and so on.

[0115] For example, in any of the scenarios shown in Figures 1A to 1D, user B is an infant, who is not suitable to wear wearable devices, while user A is the caregiver of the infant and is wearing a wearable device, such as a watch. Using the embodiments of this application, the infant's communication perception data can be filtered from the communication perception data of multiple users based on sensor data, thereby obtaining the infant's individual communication perception data to aid in sleep monitoring, health monitoring, and other tasks.

[0116] Referring to Figure 1A, taking respiratory rate as an example of sensor data: The watch uses sensor technology to collect sensor data from user A (user A is an adult) (the first sensor data includes user A's sensor data). The watch also uses communication sensing technology to simultaneously collect communication sensing data from two people (i.e., the second sensor data includes communication sensing data from user A and user B). Based on user A's sensor data, the watch determines user B's communication sensing data from the two people's communication sensing data. Based on user B's communication sensing data, the watch determines user B's (user B is an infant) respiratory rate to be 30 breaths per minute. The normal respiratory rate of an infant is approximately between 35 and 45 breaths per minute. If user B's respiratory rate is abnormal, the watch can vibrate and display a text prompt such as "Child's breathing is slow" on the display screen to promptly remind user A to pay attention to user B's condition, as shown in Figure 3A.

[0117] Referring to Figure 1B, taking heart rate as an example of perceived data: The watch collects sensor data from user A (user A is an adult) using sensor technology (the first perceived data includes user A's sensor data). The mobile phone simultaneously collects the communication perceived data of both users using communication sensing technology (i.e., the second sensing data includes the communication perceived data of user A and user B). The mobile phone obtains user A's sensor data from the watch and determines user B's communication perceived data from the two users' communication perceived data based on user A's sensor data. Based on user B's communication perceived data, user B's (user B is an infant) heart rate is determined to be 90 beats per minute. However, an infant's normal heart rate is generally 120-140 beats per minute. Since user B's heart rate is abnormal, the mobile phone can play a voice prompt such as "The child's heart is beating fast," and can also send an alarm message to the watch to drive the watch to vibrate and display a text prompt such as "The child's heart is beating fast," so as to promptly remind user A to pay attention to user B's status, as shown in Figure 3B.

[0118] Of course, Figures 3A and 3B are just some possible scenario examples. In practical applications, more devices can be combined to output alarm information.

[0119] This design approach enables anomaly monitoring of target user perception data in multi-user scenarios, thereby improving user experience.

[0120] In one possible design, the first device can also detect whether the running status of the application associated with the target user matches the target user's perception data. If the running status of the application associated with the target user does not match the target user's perception data, the running status of the application associated with the target user is adjusted according to the target user's perception data.

[0121] Specifically, the running status of the application associated with the target user corresponds to the target user's perceived data. Matching the running status of the application associated with the target user and the target user's perceived data means that the running status of the application associated with the target user and the target user's perceived data satisfy this correspondence. Mismatching the running status of the application associated with the target user and the target user's perceived data means that the running status of the application associated with the target user and the target user's perceived data do not satisfy this correspondence.

[0122] Taking a communication device as an example, the sound effects of an alarm clock application (hereinafter referred to as an alarm clock) correspond to user actions. For instance, the action of a user staying in bed corresponds to sound effect A, and the action of a user getting up corresponds to sound effect B. Alternatively, the action of a user staying in bed may correspond to sound effect A, but the action of a user getting up may not have a corresponding sound effect. The alarm clock can detect user actions through communication sensing technology. At the set alarm time, the alarm clock can adjust the alarm sound effect in real time based on the detected user actions and the correspondence between the sound effects and user actions.

[0123] Taking a baby care scenario as an example, there are user A and user B. User B is the baby, and user A is the adult caring for the baby. User A is wearing a watch, and the account logged in on both the alarm clock and the watch is user A's account. When the alarm goes off, user A has already gotten up, while user B is still asleep.

[0124] Figure 4A illustrates a scenario where the data processing method of this application is not used. If the data processing method is not used, for example, if the alarm clock only acquires communication perception data, and the collected communication perception data consists of multiple users (e.g., user A and user B), the following scenarios may occur: 1) User actions cannot be recognized; 2) It is detected that a user has not yet gotten up, so the alarm clock sound effect for user A (when they are still in bed) is played, but user A has actually already gotten up, and the running status of the alarm clock application associated with user A does not match user A's perception data. Furthermore, the alarm clock sound effect also affects user B.

[0125] Figure 4B shows a scenario example of using the data processing method of this application: If the data processing method of this application is used, for example, in addition to collecting communication perception data from multiple people (such as user A and user B), the alarm clock can also obtain sensor data of user A from the watch. Based on the sensor data of user A, the alarm clock determines the communication perception data of user A from the communication perception data of multiple people. Based on the communication perception data of user A, it determines that user A has woken up. The alarm clock plays the sound effect after user A wakes up, so that the running status of the alarm clock application associated with user A matches the perception data of user A. Alternatively, the alarm clock does not play the sound effect, so that the running status of the alarm clock application associated with user A matches the perception data of user A, while avoiding affecting user B.

[0126] As can be understood, Figures 4A and 4B use the example of an alarm clock associated with user A. In practical applications, the alarm clock can also be associated with user B (the association method is not limited, for example, user A can configure user B's identity information in the alarm clock), so that the alarm clock can adjust the sound effect in real time according to user B's status.

[0127] Furthermore, in practical applications, the application is not limited to alarm clocks. For example, it can also be an application for controlling air conditioners, refrigerators, televisions, etc. This application does not impose any restrictions.

[0128] This design approach avoids interference from non-target-aware data on the operational status of applications associated with the target user, thereby further improving the user experience.

[0129] In one possible design, before determining the target user's perception data based on the first perception data and the second perception data, the first device also acquires first information, which is used to indicate a multi-user scenario.

[0130] In one possible example, the first information is generated by the first device. For instance, the first device receives input from a user, and generates the first information based on that input, which is an operation to confirm a multi-user scenario. Taking the scenario shown in Figure 1A as an example, the first device is a watch; user A performs an input operation on the watch to confirm the multi-user scenario, and the watch generates the first information based on that input. Taking the scenario shown in Figure 1B as an example, the first device is a mobile phone; user A or user B performs an input operation on the mobile phone to confirm the multi-user scenario, and the mobile phone generates the first information based on that input. Of course, these are just two possible examples, and the actual implementation is not limited to these.

[0131] In another possible example, the first information is sent by another device, such as the first device receiving first information from a second device. Taking the scenario shown in Figure 1B as an example, the first device is a mobile phone, and the second device is a watch. User A performs an input operation on the watch to confirm the multi-user scenario. The watch generates first information based on this input operation and sends it to the mobile phone, which then receives it. Taking the scenario shown in Figure 1C as an example, the first device is a mobile phone, and the second device is glasses. User C performs an input operation on the glasses to confirm the multi-user scenario. The glasses generate first information based on this input operation and send it to the mobile phone, which then receives it. Of course, these are just two possible examples, and the actual scenario is not limited to these.

[0132] Optionally, before acquiring the first information, the first device may also output second information, which may be used to query or indicate whether the scenario is a multi-user scenario.

[0133] In specific implementation, the first device outputs the second information, which can be done by the first device outputting the second information (the second information is used to inquire whether it is a multi-user scenario) through a human-computer interaction device, such as displaying text or images on a screen and playing voice through a speaker; or, the first device can send the second information (the second information is used to inquire whether it is a multi-user scenario) to other devices such as the second device, so that the second device outputs corresponding information based on the first information through the human-computer interaction device, such as the second device displaying text or images on a screen and playing voice through a speaker.

[0134] Optionally, the first device may output second information based on third sensing data, wherein the acquisition time of the third sensing data is no later than the acquisition time of the second sensing data. Specifically, the third sensing data may be the second sensing data, or it may be sensing data collected before the second sensing data, or it may include the second sensing data (and may also include sensing data collected before or after the second sensing data), and so on. The third sensing data may originate from sensors or from communication sensing data, without limitation.

[0135] Specifically, when the first device detects that there are likely multiple users in the scene based on the third perception data, it outputs third information. For example, the first device collects the perception data of users in the scene through communication sensing technology, accumulates it for a period of time to obtain third perception data, performs frequency domain analysis, time domain analysis or artificial intelligence (AI) processing on the third perception data, detects that the third perception data has the characteristics of multiple people, and then outputs third information to further confirm with the user whether it is a multi-user scene.

[0136] For example, the sensing data is a breathing signal. Figure 5A is a schematic diagram of the breathing signal after frequency domain processing. The horizontal axis is the breathing frequency and the vertical axis is the Fourier Transform (FFT) intensity. There are two peaks in the figure, indicating that there may be breathing signals from two people.

[0137] For example, the watch collects sensory data (such as breathing rate) of users within 10 meters based on communication sensing technology. Through frequency domain analysis, it is determined that the sensory data has multi-user characteristics. The watch outputs second information, such as displaying the text "Multi-user scenario?" on the display screen, as shown in Figure 5B. After the user enters a confirmation operation on the watch's display screen (such as the user clicking the "Yes" control), the watch generates first information based on the confirmation operation. The watch confirms that the current scenario is a multi-user scenario based on the first information.

[0138] Of course, Figure 5B is just one possible example, and the actual situation is not limited to this.

[0139] This design approach allows the data processing method of this application (such as the method in S201 to S202) to be executed only in multi-user scenarios, avoiding execution of the data processing method of this application in single-user scenarios, thus saving device power consumption.

[0140] It is understood that the above embodiments can be implemented individually or in combination, and this application does not impose any restrictions.

[0141] For example, see Figure 6 for a possible example of a combined implementation:

[0142] S601, Scene Confirmation;

[0143] For example, receiving user actions and determining whether the current scenario is a single-user scenario or a multi-user scenario based on the user actions;

[0144] In a single-user scenario, such as user A wearing a wearable device, then S602 to S605 are executed:

[0145] S602. Obtain sensor data and communication sensing data from user A;

[0146] For example, acquiring sensor data of user A collected by wearable devices worn by user A, and acquiring communication perception data of user A collected by communication devices;

[0147] S603. Combining the sensor data and communication sensing data of user A, output the monitoring results corresponding to user A (such as health status, sleep quality, etc.);

[0148] If it is a multi-user scenario, such as including user A and user B, where user A wears a wearable device and user B does not wear a wearable device, then execute S604 to S606:

[0149] S604. Acquire sensor data of user A, and acquire multi-user (such as user A and user B) communication sensing data collected by the communication device (corresponding to S201 above).

[0150] S605. Determine the communication sensing data of user B from the multi-user communication sensing data based on the sensor data of user A (corresponding to S202 above).

[0151] S606. Based on the communication perception data of user B, output the monitoring results (such as health status, sleep quality, etc.) corresponding to user B.

[0152] Furthermore, if user B's monitoring results are abnormal, S607 can also be executed:

[0153] S607. Output alarm information for user B.

[0154] For details on how to implement each of the above steps, please refer to the relevant content above.

[0155] This example demonstrates how sensory data from users not wearing wearable devices can be identified, enabling health monitoring, sleep tracking, and other functions. This solution enhances the intelligence and practicality of user health management, better meeting users' growing health management needs.

[0156] The methods provided by the embodiments of this application have been described above with reference to the accompanying drawings. The apparatus provided by the embodiments of this application will be described below with reference to the accompanying drawings.

[0157] Based on the same technical concept, embodiments of this application provide a data processing apparatus, which includes a module / unit / means for executing the method executed by the first device in the above method embodiments. This module / unit / means can be implemented in software, or in hardware, or implemented by hardware executing corresponding software.

[0158] For example, referring to FIG7, the device may include an acquisition module 701 and a processing module 702.

[0159] The acquisition module 701 is used to acquire first sensing data and second sensing data. The first sensing data comes from a sensor and includes the sensing data of a first user. The second sensing data is obtained using communication sensing technology and includes the sensing data of at least two users, including the first user.

[0160] The processing module 702 is used to determine the perception data of a target user based on the first perception data and the second perception data, wherein the target user is one or more of at least two users.

[0161] This device can combine sensor data (i.e., first sensing data) to determine the target user's communication sensing data from multi-user communication sensing data (i.e., second sensing data), thus improving the application scenarios of communication sensing technology. Furthermore, this device can accurately identify the target user's communication sensing data in multi-user scenarios, avoiding the problem of overlapping multi-user communication sensing data under distance resolution limitations, and can significantly improve the accuracy of communication sensing technology.

[0162] In one possible design, the acquisition module 701 can also be used to: acquire first information before the processing module 702 determines the target user's perception data based on the first perception data and the second perception data, the first information being used to indicate a multi-user scenario.

[0163] Thus, the device can execute the data processing method of this application in a multi-user scenario, which can save device power consumption.

[0164] In one possible design, the device further includes an output module 703 for outputting second information before the acquisition module 701 acquires the first information. The second information is used to query or indicate whether the query is a multi-user scenario.

[0165] In this way, the device can determine multi-user scenarios based on users, which can improve the accuracy of multi-user scenario recognition.

[0166] In one possible design, the processing module 702 can also be used to: control the output module 703 to output second information based on the third sensing data, wherein the acquisition time of the third sensing data is no later than the acquisition time of the second sensing data.

[0167] In this way, the device can identify multi-user scenarios based on sensing data, and the device only needs to utilize existing hardware conditions without adding additional hardware costs.

[0168] In one possible design, the acquisition time of the first sensing data and the acquisition time of the second sensing data at least partially overlap.

[0169] In this way, the device can avoid making invalid judgments on sensing data collected at different time intervals with long time intervals, thereby reducing power consumption waste.

[0170] In one possible design, the sensor includes a wearable device. Of course, this is just an example and is not limited to this.

[0171] In one possible design, the processing module 702 is used to: perform feature matching on the second perception data and the first perception data, wherein the perception data of the target user is the perception data in the second perception data that fails to match the features of the first perception data.

[0172] In this way, the device can filter out the communication sensing data of users who do not have sensor data from the communication sensing data of multiple users.

[0173] In one possible design, the processing module 702 is used to: perform feature matching on the second perception data and the first perception data, wherein the perception data of the target user is the perception data in the second perception data that successfully matches the features of the first perception data.

[0174] In this way, the device can filter out the communication sensing data of users with sensor data from the communication sensing data of multiple users.

[0175] In one possible design, the processing module 702 can also be used to: determine the identity information of the target user based on the identity information of the user corresponding to the first perception data.

[0176] In this way, the device can identify the target user's identity information, which is beneficial for applying the target user's perception data to other scenarios related to the target user, thereby improving the user experience.

[0177] In one possible design, the processing module 702 can also be used to: control the output module 703 to output alarm information based on the target user's perception data, the alarm information being used to indicate that the target user's perception data is abnormal.

[0178] In this way, the device can monitor for anomalies in the target user's perceived data, thereby improving the user experience.

[0179] In one possible design, the processing module 702 can also be used to: adjust the running state of the application associated with the target user based on the target user's perception data if the running state of the application associated with the target user does not match the target user's perception data. For example, the application includes an alarm clock.

[0180] In this way, the device can avoid non-target perception data from interfering with the operation of applications associated with the target user, thereby further improving the user experience.

[0181] In a specific example, the acquisition module 701 may include one or more of a sensor module, a communication sensing module, or a communication module. For example, if the acquisition module 701 includes a sensor, then the acquisition of first sensing data by the acquisition module 701 may specifically involve the sensor collecting the first sensing data; if the acquisition module 701 includes a communication sensing module, then the acquisition of second sensing data by the acquisition module 701 may specifically involve the communication sensing module collecting the first sensing data; if the acquisition module 701 includes a communication module, then the acquisition of first sensing data by the acquisition module 701 may specifically involve the communication module receiving the first sensing data from the sensor, and the acquisition of second sensing data by the acquisition module 701 may specifically involve the communication module receiving second sensing data from a communication device, and so on.

[0182] In a specific example, the processing module 702 may include a target recognition module, a scene recognition module, etc. The target recognition module can determine the target user's perception data from the second perception data based on the first perception data, and the scene recognition module can identify multi-user scenes based on the third perception data.

[0183] In a specific example, the output module 703 may include one or more of the following: a human-computer interaction module (such as a display screen or a speaker) and a communication module. For example, the output module 70 outputs information (such as second information), which may specifically be displaying text or images on the display screen, playing voice through the speaker, or the communication module sending information to other devices (such as a second device), without limitation.

[0184] It is understandable that the modules represented by dashed lines in Figure 7 are optional configurations for the device.

[0185] It should be understood that all relevant content of each step involved in the above method embodiments can be referenced from the functional description of the corresponding functional module, and will not be repeated here.

[0186] In practical implementation, the above-mentioned device can take many product forms. Several possible product forms are introduced below.

[0187] As shown in Figure 8, this application embodiment also provides a data processing apparatus, including:

[0188] At least one processor 801; and a communication interface 803 communicatively connected to the at least one processor 801; the at least one processor 801 causes the device to perform the method steps in the above method embodiments through the communication interface 803 by executing instructions stored in the memory 802.

[0189] When the device executes the method steps in the above method embodiments, the processor 801 can be used to implement the functions of the above processing module 702, and the communication interface 803 can be used to implement the functions of the above acquisition module 701 or output module 703.

[0190] The processor 801 may include a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor.

[0191] It should be noted that when the processor is a general-purpose processor, DSP, ASIC, FPGA, or other programmable logic device, discrete gate or transistor logic device, or discrete hardware component, the memory (storage module) can be integrated into the processor.

[0192] The memory 802 can be volatile memory or non-volatile memory, or it can include both. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of RAM are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous linked dynamic random access memory (SLDRAM), and direct rambus RAM (DR RAM).

[0193] The communication interface 803 can be a transceiver or an input / output interface, etc.

[0194] The memory 802 may be located outside the device; alternatively, the device may include the memory 802. The memory 802 is connected to the processor 801. The memory 802 stores instructions that can be executed by the processor 801. The processor 801 and the memory 802 may be coupled through an interface circuit or integrated together; no limitation is imposed here.

[0195] Optionally, the device may also include a sensor 805 for acquiring the first sensing data. The device may also include a display screen, a speaker, and other human-computer interaction devices.

[0196] It is understood that the devices indicated by dashed lines in Figure 8 (such as memory 802, sensor 805, etc.) are optional for the device.

[0197] This application embodiment does not limit the specific connection medium between the processor 801, memory 802, and communication interface 803. In Figure 8, the processor 801, memory 802, and communication interface 803 are connected via a bus 804, which is represented by a thick line. The connection methods between other components are for illustrative purposes only and are not intended to be limiting. The bus can be categorized as an address bus, data bus, control bus, etc. For ease of illustration, only one thick line is used in Figure 8, but this does not indicate that there is only one bus or one type of bus.

[0198] Based on the same technical concept, this application also provides a chip, as shown in Figure 9. This chip may include logic circuitry and input / output interfaces. Optionally, it may also include a memory. The input / output interfaces can be used to receive code instructions (stored in the memory, which can be read directly from the memory or through other devices) and transmit them to the logic circuitry; the logic circuitry can be used to execute the code instructions to perform the methods described in the above method embodiments.

[0199] It should be understood that the processor mentioned in the embodiments of this application can be implemented in hardware or software. When implemented in hardware, the processor can be a logic circuit, integrated circuit, etc. When implemented in software, the processor can be a general-purpose processor, implemented by reading software code stored in memory.

[0200] It should be noted that the memories described herein are intended to include, but are not limited to, these and any other suitable types of memories.

[0201] Based on the same technical concept, embodiments of this application also provide a computer-readable storage medium storing a computer program or instructions, which, when executed by a device, implements the method steps described in the above method embodiments.

[0202] Based on the same technical concept, this application also provides a computer program product, which stores instructions that, when run on a computer, cause the computer to execute the method steps described in the above method embodiments.

[0203] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0204] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to this application. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in one or more blocks of the flowchart illustrations and / or one or more blocks of the block diagrams.

[0205] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means that implement the functions specified in one or more flowcharts and / or one or more block diagrams.

[0206] These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, such that the instructions, which execute on the computer or other programmable apparatus, provide steps for implementing the functions specified in one or more flowcharts and / or one or more block diagrams.

Claims

1. A data processing method, characterized in that, include: Acquire first sensing data and second sensing data. The first sensing data comes from a sensor and includes the sensing data of a first user. The second sensing data is obtained using communication sensing technology and includes the sensing data of at least two users, including the first user. The perception data of the target user is determined based on the first perception data and the second perception data, wherein the target user is one or more of the at least two users.

2. The method as described in claim 1, characterized in that, Before determining the target user's perception data based on the first perception data and the second perception data, the method further includes: Obtain first information, which is used to indicate a multi-user scenario.

3. The method as described in claim 2, characterized in that, Before obtaining the first information, the method further includes: Output a second piece of information, which is used to ask or indicate whether the scenario is a multi-user scenario.

4. The method as described in claim 3, characterized in that, The output of the second information includes: The second information is output based on the third sensing data, and the acquisition time of the third sensing data is no later than the acquisition time of the second sensing data.

5. The method according to any one of claims 1-4, characterized in that, The acquisition time of the first sensing data and the acquisition time of the second sensing data overlap at least partially.

6. The method according to any one of claims 1-5, characterized in that, The sensors include wearable devices.

7. The method according to any one of claims 1-6, characterized in that, Determining the target user's perception data based on the first perception data and the second perception data includes: Feature matching is performed on the second perception data and the first perception data, and the perception data of the target user is the perception data in the second perception data that fails to match the features of the first perception data.

8. The method according to any one of claims 1-6, characterized in that, Determining the target user's perception data based on the first perception data and the second perception data includes: Feature matching is performed on the second perception data and the first perception data, and the perception data of the target user is the perception data in the second perception data that successfully matches the features of the first perception data.

9. The method as described in claim 8, characterized in that, Also includes: The identity information of the target user is determined based on the user's identity information corresponding to the first perceived data.

10. The method according to any one of claims 1-9, characterized in that, Also includes: An alarm message is output based on the target user's perception data, and the alarm message is used to indicate that the target user's perception data is abnormal.

11. The method according to any one of claims 1-10, characterized in that, Also includes: If the running status of the application associated with the target user does not match the target user's perception data, then the running status of the application associated with the target user is adjusted according to the target user's perception data.

12. The method as described in claim 11, characterized in that, The application includes an alarm clock.

13. A data processing apparatus, characterized in that, include: The acquisition module is used to acquire first sensing data and second sensing data. The first sensing data comes from a sensor and includes the sensing data of a first user. The second sensing data is obtained using communication sensing technology and includes the sensing data of at least two users, including the first user. The processing module is configured to determine the perception data of a target user based on the first perception data and the second perception data, wherein the target user is one or more of the at least two users.

14. The apparatus as claimed in claim 13, characterized in that, The acquisition module is further configured to: acquire first information before the processing module determines the target user's perception data based on the first perception data and the second perception data, wherein the first information is used to indicate a multi-user scenario.

15. The apparatus as claimed in claim 14, characterized in that, The device further includes: The output module is used to output second information before the acquisition module acquires the first information. The second information is used to ask or indicate whether it is a multi-user scenario.

16. The apparatus as claimed in claim 15, characterized in that, The processing module is also used for: The output module is controlled to output the second information based on the third sensing data, and the acquisition time of the third sensing data is no later than the acquisition time of the second sensing data.

17. The apparatus according to any one of claims 13-16, characterized in that, The acquisition time of the first sensing data and the acquisition time of the second sensing data overlap at least partially.

18. The apparatus according to any one of claims 14-17, characterized in that, The sensors include wearable devices.

19. The apparatus according to any one of claims 14-18, characterized in that, The processing module is used for: Feature matching is performed on the second perception data and the first perception data, and the perception data of the target user is the perception data in the second perception data that fails to match the features of the first perception data.

20. The apparatus according to any one of claims 14-18, characterized in that, The processing module is used for: Feature matching is performed on the second perception data and the first perception data, and the perception data of the target user is the perception data in the second perception data that successfully matches the features of the first perception data.

21. The apparatus as claimed in claim 20, characterized in that, The processing module is also used for: The identity information of the target user is determined based on the user's identity information corresponding to the first perceived data.

22. The apparatus according to any one of claims 14-21, characterized in that, The processing module is also used for: The control output module outputs alarm information based on the target user's perception data, and the alarm information is used to indicate that the target user's perception data is abnormal.

23. The apparatus according to any one of claims 14-22, characterized in that, The processing module is also used for: If the running status of the application associated with the target user does not match the target user's perception data, then the running status of the application associated with the target user is adjusted according to the target user's perception data.

24. The apparatus as claimed in claim 23, characterized in that, The application includes an alarm clock.

25. A data processing apparatus, characterized in that, include: Includes at least one processor; and a communication interface that is communicatively connected to the at least one processor; The at least one processor executes the method as described in any one of claims 1-12 by executing instructions stored in the memory.

26. A computer-readable storage medium, characterized in that, The storage medium stores a computer program or instructions that, when executed, enable the method described in any one of claims 1-12 to be implemented.

27. A computer program product, characterized in that, Includes instructions that, when executed on a computer, cause the method described in any one of claims 1-12 to be implemented.

28. A data processing system, characterized in that, include: A sensor is used to collect first sensing data, which includes the sensing data of a first user. A communication device for collecting second sensing data, the second sensing data including sensing data of at least two users, the at least two users including the first user; A first device is configured to perform the method as described in any one of claims 1-12.

Images