CN119026103A

CN119026103A - Crowd type identification method and device

Info

Publication number: CN119026103A
Application number: CN202310613419.3A
Authority: CN
Inventors: 门慧超; 张轶博; 刘兴宇
Original assignee: Honor Device Co Ltd
Current assignee: Honor Device Co Ltd
Priority date: 2023-05-26
Filing date: 2023-05-26
Publication date: 2024-11-26

Abstract

The application provides a crowd type identification method and device, which can improve the accuracy of identifying the crowd type of a user by terminal equipment. The method comprises the following steps: under the condition that first biological data of a user when the terminal equipment is used currently is obtained, determining whether the user is a first type user or not based on the first biological data, wherein the first biological data comprises at least one of sound data, breathing data, fingerprint data or face data, and the first type user is a user with the use time and/or the use times of the terminal equipment exceeding a first preset threshold value; in the case that the user is a first type user, acquiring historical data of the terminal device, wherein the historical data comprises historical use data of an application program on the terminal device and second biological data of the user when the terminal device is used in the historical mode, and the second biological data comprises at least one of sound data, breathing data or face data; based on the historical data, it is determined whether the crowd type to which the user belongs is a target crowd type.

Description

Crowd type identification method and device

Technical Field

The present application relates to the field of terminal technologies, and in particular, to a crowd type identification method and apparatus.

Background

When the user uses the terminal device, the terminal device can identify the crowd type of the user, for example, whether the user is a minor, so that more reasonable service is provided for the user. For example, when the user opens the game application of the terminal device, the terminal device may identify whether the user is a minor, and if the user is a minor, the terminal device may limit the time for which the user uses the game application to not exceed a preset time period. Currently, terminal devices typically acquire facial features of a user from image data of the user; and determining the crowd type of the user according to the facial features of the user.

However, the accuracy of identifying the crowd type of the user from the facial features of the user is low, making the user experience poor.

Disclosure of Invention

The application provides a crowd type identification method and device, which can improve the accuracy of identifying the crowd type of a user by terminal equipment, thereby improving the user experience.

In a first aspect, a crowd type identification method is provided, including: under the condition that first biological data of a user when the terminal equipment is used currently is acquired, determining whether the user is a first type user or not based on the first biological data, wherein the first biological data comprises at least one of sound data, breathing data, fingerprint data or face data, and the first type user is a user with the use time and/or the use times of the terminal equipment exceeding a first preset threshold value; acquiring historical data of the terminal equipment under the condition that the user is the first type of user, wherein the historical data comprises historical use data of an application program on the terminal equipment and second biological data of the user when the user uses the terminal equipment in a historical manner, and the second biological data comprises at least one of sound data, breathing data or face data; based on the historical data, whether the crowd type to which the user belongs is a target crowd type is determined.

According to the crowd type identification method, whether the user is a user using the terminal equipment for a long time is determined according to the biological data when the user uses the terminal equipment currently; acquiring historical biological data of the user and historical use data of a historical use application program under the condition that the user is the user using the terminal equipment for a long time; and determining whether the crowd type of the user belongs to the target crowd type through the historical biological data and the historical use data, so that the terminal equipment can determine the crowd type of the user through the biological data, can further assist in determining whether the user belongs to the crowd type through the combination of the historical use data, and can improve the accuracy of the crowd type identification method and improve the user experience through the combination of the historical use data and the biological data.

It should be appreciated that the first type of user may also be referred to as a long-term user, or as a owner. The first preset threshold may be a preset duration and/or number of times. The user history using the terminal device may refer to that the user uses the terminal device at any time before the current time, or may refer to that the user uses the terminal device at any time before the user uses the terminal device this time. The time and/or number of uses of the terminal device by the user may include the time and/or number of uses of the terminal device this time. The use time and/or the use number refers to the use time and/or the use number of the current terminal device used by the user historically. The target crowd type can be various crowd types such as minors, old people, teachers, females and the like.

In certain implementations of the first aspect, the determining, based on the historical data, whether the crowd type to which the user belongs is a target crowd type includes: in the case where the first biological data includes at least one of sound data, respiratory data, or face data, it is determined whether the group type to which the user belongs is the target group type based on the history data and the first biological data.

It should be understood that the terminal device may determine the age group to which the user belongs through at least one of face data, sound data, or face data of the user. In the case that the first biological data does not include sound data, breathing data and face data, the accuracy of determining the age group to which the user belongs by the terminal device through the fingerprint data is low. Thus, in the case where the first biometric data includes at least one of sound data, respiratory data, or face data, the terminal device determines whether the group type to which the user belongs is a target group type based on the first biometric data, the second biometric data, and the historical use data. Thus, the accuracy of the crowd type identification method can be further improved.

In certain implementations of the first aspect, the determining, based on the historical data and the first biological data, whether the group type to which the user belongs is the target group type includes: fusing the first biological data and the second biological data to obtain fused biological data; determining first age information of the user based on the fused biometric data; based on the first age information and the historical usage data, it is determined whether the crowd type to which the user belongs is the target crowd type.

It should be appreciated that fusing the first biometric data and the second biometric data may also be referred to as merging or stitching the first biometric data and the second biometric data. The fused biometric data includes at least one of face data, voice data, or respiratory data. The fused biometric data includes biometric data of the user currently using the terminal device and biometric data of the user historically using the terminal device, which helps to improve accuracy of the first age information of the user determined by the terminal device.

In certain implementations of the first aspect, the target population type is a minor type; the method further comprises the steps of: determining second age information of the user based on the historical usage data; the determining whether the crowd type to which the user belongs is the target crowd type includes: based on the first age information and the second age information, it is determined whether the group type to which the user belongs is the minor type.

It should be appreciated that the lifestyle and habits of people of different age groups may be different, such that the historical usage data stored in the terminal device may be different, and thus the terminal device may determine the second age information of the user based on the historical usage data. It will be appreciated that the biological characteristics of a part of the adolescent population are similar to those of an adult, and therefore, the terminal device has a lower accuracy in determining whether the adolescent population and the adult similar to the juvenile biological characteristics are of the juvenile type based on the fused biological characteristics. Under the auxiliary effect of the historical usage data, the terminal equipment can further determine second age information of the user based on the historical usage data, so that the accuracy of determining whether the crowd type to which the user belongs is of the minors or not by the terminal equipment can be improved based on the first age information and the second age information. Illustratively, the first age information indicates that the user is an adult, and that the age group to which the user belongs is expected to be 20-25 years old; the historical usage data indicates that the place where the user enters and exits every day on the working day is a school, the time period of using the terminal device is 20:00 to 21:00, the application program with the largest using times is the game application program, and the terminal device indicates that the user belongs to middle school students, namely minors based on the second age information determined by the historical usage data. Because the age group of the user determined by the terminal device based on the fusion biological data is 20-25 years old, and the biological characteristics of the crowd in the age group of 20-25 years old are close to the biological characteristics of part of adolescent crowd, the accuracy of the first age information determined by the terminal device based on the fusion biological data is lower. In combination with the historical usage data, the user enters and exits a student every day on the workday, however, an adult of 20-25 years old is more likely to be a college student, the place where the user enters and exits every day should be the university, and in combination with the application program with the largest use times of the user, the terminal device can determine that the user is a student with a higher probability. Thus, the final judgment result of the terminal device is that the user is a minor.

In certain implementations of the first aspect, the determining the first age information of the user based on the fused biometric data includes: under the condition that the fusion biological data comprises face data and voice data, inputting the fusion biological data into a face-human speaking voice model to obtain the first age information, wherein the face-human speaking voice model is obtained by weighting and fusing an output result of the face model and an output result of the human speaking voice model; or under the condition that the fusion biological data comprises face data and breathing data, inputting the fusion biological data into a face-breathing model to obtain the first age information, wherein the face-breathing model is obtained by carrying out weighted fusion on an output result of the face model and an output result of the breathing model; or, in the case that the fused biological data includes face data, voice data and breathing data, inputting the face data and the voice data in the fused biological data to the face-human speaking voice model, and obtaining the first age information.

It should be understood that the face-human speech sound model is capable of determining first age information of the user based on the face data and the sound data, has functions of the face model and the human speech sound model, and performs weighted fusion of an output result of the face model and an output result of the human speech sound model to obtain first biological information. The face-breathing model can determine first age information of the user based on the face data and the breathing data, has functions of the face model and the breathing model, and performs weighted fusion on an output result of the face model and an output result of the human speaking voice model to obtain first biological information. The face model is a model capable of outputting age information of the user based on the face data, and can comprise a face decision function by which the age information of the user can be determined based on the face data; the human speech sound model is a model capable of outputting age information of a user based on sound data; the breathing model is a model capable of outputting age information of a user based on breathing data.

In certain implementations of the first aspect, the face data is valid face data after filtering.

It should be understood that, the face data of the user obtained by the terminal device may have invalid face data such as poor definition, image data that does not include the complete face of the user, and the face feature vector or the face feature matrix cannot be obtained based on the invalid face data. Therefore, the terminal equipment can screen the face data first, screen the obtained effective face data, and remove the ineffective face data. In this way, the accuracy of the first age information is facilitated to be improved.

In certain implementations of the first aspect, the method further comprises: acquiring interaction data of the user when the terminal device is currently used under the condition that the first biological data is not acquired, wherein the interaction data comprises at least one of sensor data, touch screen data or application program type data; and determining whether the crowd type to which the user belongs is the target crowd type based on the interaction data.

It should be appreciated that in the case where the first biological data is not acquired, the terminal device cannot determine whether the user is the first type of user based on the first biological data, and thus the terminal device cannot acquire the history data of the user used the terminal device in history, in which case the terminal device can acquire the interaction data of the user when the terminal device is currently used. The interactive data of the user when using the terminal device currently may refer to at least one of sensor data, touch screen data or application type data obtained by the terminal device in the process of using the terminal device by the user. The sensor data may include data of sensors such as pressure sensors, temperature sensors, and the like; the touch screen data can comprise data such as sliding length, area and the like of a display screen of the finger terminal equipment of the user when the display screen slides; the application type data may include data such as an application type and/or an application name used when the user uses the terminal device this time.

In certain implementations of the first aspect, the method further comprises: acquiring interaction data of the user when the user currently uses the terminal equipment under the condition that the user is not the first type user, wherein the interaction data comprises at least one of sensor data, touch screen data or application program type data; and determining whether the crowd type to which the user belongs is the target crowd type or not based on the interaction data and/or the first biological data.

It should be appreciated that, in the case where the user is not the first type of user, the terminal device cannot obtain the historical data of the user for which the user uses the terminal device in a historical manner, so that the terminal device may obtain the interaction data of the user when the terminal device is currently used, and determine whether the crowd type to which the user belongs is the target crowd type based on the interaction data and/or the first biological data.

In certain implementations of the first aspect, before the determining whether the crowd type to which the user belongs is the target crowd type, the method further includes: based on the interaction data, selecting the expansion interaction data of the first age group from the expansion interaction data of a plurality of age groups, wherein the expansion interaction data of the plurality of age groups are obtained by clustering sample interaction data, and the sample interaction data comprises at least one of sensor data, touch screen data or application program type data of a plurality of users using the terminal equipment in a historical time period; fusing the interactive data and the expanded interactive data of the first age group to obtain fused interactive data; the determining whether the crowd type to which the user belongs is the target crowd type includes: and determining whether the user is of the target crowd type based on the fused interaction data.

It should be appreciated that the duration of the historical time period may be any duration. The plurality of users may include users of different ages. After clustering a plurality of users in a historical time period by using at least one of sensor data, touch screen data or application type data of the terminal equipment, expanded interaction data of a plurality of age groups can be obtained. The extended interaction data includes at least one of sensor data, touch screen data, or application type data. For example, sensor data, touch screen data and application type data of 1000 users using the terminal device in a historical time period are clustered to obtain extended interaction data of ages 10 to 20 years old, extended interaction data of ages 20 to 40 years old, extended interaction data of ages 40 to 60 years old and extended interaction data of ages 60 to 70 years old; the extended interaction data of different age groups are respectively different.

In certain implementations of the first aspect, before the selecting the extended interaction data for the first age group from the extended interaction data for the plurality of age groups, the method further includes: performing feature extraction on the sample interaction data by using an age model to obtain feature data; and performing cluster analysis on the characteristic data to obtain the extended interaction data of the plurality of age groups.

It should be understood that the terminal device may perform feature extraction on the sensor data, the touch screen data, and the application type data in the sample interaction data through the age model, respectively. Specifically, the terminal device inputs the sensor data, the touch screen data and the application program type data in the sample interaction data into an age model respectively, and the age model can output the characteristic data of three modes, namely the characteristic data of the sensor mode, the characteristic data of the touch screen mode and the characteristic data of the application program type mode. The feature data may be a feature vector or a feature matrix. The three modalities may be referred to as a sensor modality, a touch screen modality, and an application type modality, respectively. The age model may be a deep learning convolution model. The feature data output by the age model may include different age tags, respectively. The age tag may refer to information indicating any one of ages, or information indicating one age group. The terminal equipment clusters the characteristic data of the three modes to obtain the expansion interaction data of a plurality of age groups, and the expansion interaction data of the plurality of age groups can also be called as codebook characteristics of the plurality of age groups. Each of the plurality of age groups may include different extended interaction data, respectively. Extended interaction data for multiple age groups may also be referred to as a feature pattern library.

In certain implementations of the first aspect, before fusing the interaction data with the extended interaction data for the first age group, the method further includes: performing dimension reduction processing on the interaction data to obtain dimension reduction interaction data; performing dimension reduction processing on the expansion interaction data of the first age group to obtain dimension reduction expansion interaction data; the fusing the interaction data and the expanded interaction data of the first age group comprises the following steps: and fusing the dimension reduction interaction data and the dimension reduction expansion interaction data to obtain the fused interaction data.

It will be appreciated that the dimensions of the interaction data and the expanded interaction data may be reduced by a dimension reduction process. Performing dimension reduction processing on the interaction data can mean performing dimension reduction processing on three modes of data in the interaction data respectively, namely reducing the dimension of sensor data in the interaction data; the dimension of touch screen data in the interaction data is reduced; and reducing the dimension of the application program type data in the interaction data.

In certain implementations of the first aspect, the interaction data includes N pieces of touch screen data that are the inverse of a time before the time at which the interaction data was acquired, N being a positive integer.

It should be appreciated that N may be a predetermined positive integer.

In certain implementations of the first aspect, the interaction data includes M valid touch screen data after filtering, where M is a positive integer.

It should be understood that M is any positive integer less than or equal to N. The M pieces of valid touch screen data may be data obtained by screening N pieces of touch screen data that are the inverse of the time before the time when the interaction data is acquired.

In certain implementations of the first aspect, the method further comprises: determining a time interval between adjacent touch screen data in N pieces of touch screen data according to the generation time of each piece of touch screen data in N pieces of touch screen data before the time of acquiring the interaction data, wherein N is a positive integer greater than or equal to M; according to the time interval, determining a person changing time node; and determining touch screen data after the time node of the person changing closest to the time of acquiring the interaction data in the time node of the person changing as the M pieces of effective touch screen data.

It should be understood that the generation time of each piece of touch screen data may refer to the time when the user touches the screen so that the terminal device obtains and stores the touch screen data. The adjacent touch screen data refers to any two adjacent pieces of touch screen data after the N pieces of touch screen data are ordered according to the generation time. Illustratively, the N pieces of touch screen data include one piece of touch screen data of 10:00 at a time of generation 2023, 1 month, 1 day, 10:05, 2023, 1 month, 1 day, 9:00, and 2023, 1 month, 1 day, 12:00; after the N pieces of touch screen data are ordered, the N pieces of touch screen data are sequentially arranged from the early to the late according to the time sequence: one piece of touch screen data of 2023, 1, 9:00, one piece of touch screen data of 2023, 1, 10:00, one piece of touch screen data of 2023, 1, 10:05, and one piece of touch screen data of 2023, 1, 12:00; the time interval between two adjacent pieces of touch screen data refers to the time interval between one piece of touch screen data of 2023, 1 month, 1 day, 9:00 and one piece of touch screen data of 2023, 1 month, 1 day, 10:00, one piece of touch screen data of 2023, 1 month, 1 day, 10:05, and one piece of touch screen data of 2023, 1 month, 1 day, 12:00.

In certain implementations of the first aspect, before determining whether the crowd type to which the user belongs is a target crowd type, the method further includes: determining feedback time delay based on the service requirement of the current service of the terminal equipment; acquiring third biological data of the user using the terminal device in a preset time period when the feedback time delay is greater than or equal to a second preset threshold, wherein the third biological data comprises at least one of sound data, breathing data or face data; the determining, based on the historical data, whether the crowd type to which the user belongs is a target crowd type includes: based on the historical data and the third biometric data, it is determined whether the type of crowd to which the user belongs is the target crowd type.

It should be understood that the current service of the terminal device may refer to a service that triggers the crowd type recognition method, for example, in the case that the user uses the payment service of the terminal device, the crowd type recognition method is required to recognize whether the user is an minor, and the current service of the terminal device may refer to the payment service. The second preset threshold may be a preset duration, for example 5min, etc. The preset time period may be a preset duration, for example, 3min, 10min, etc. In one example, the second preset threshold is 6min, the preset time period is 5min, when the user uses the payment service, it needs to determine whether the user is a minor, and the feedback delay of the payment service is 50s, and then the feedback delay of the current service is smaller than the second preset threshold, so that the terminal device will not acquire the third biological data, but determine whether the crowd type to which the user belongs is a minor type based on the history data.

In a second aspect, a crowd type identification device is provided for performing the method in any one of the possible implementation manners of the first aspect. In particular, the apparatus comprises means for performing the method in any one of the possible implementations of the first aspect described above.

In a third aspect, there is provided a further crowd type recognition device comprising a processor coupled to a memory, operable to execute instructions in the memory to implement a method as in any one of the possible implementations of the first aspect. Optionally, the apparatus further comprises a memory. Optionally, the apparatus further comprises a communication interface, the processor being coupled to the communication interface.

In one implementation, the apparatus is a terminal device. When the apparatus is a terminal device, the communication interface may be a transceiver, or an input/output interface.

In another implementation, the apparatus is a chip configured in a terminal device. When the apparatus is a chip configured in a terminal device, the communication interface may be an input/output interface.

In a fourth aspect, there is provided a processor comprising: input circuit, output circuit and processing circuit. The processing circuit is configured to receive a signal via the input circuit and transmit a signal via the output circuit, such that the processor performs the method of any one of the possible implementations of the first aspect.

In a specific implementation flow, the processor may be a chip, the input circuit may be an input pin, the output circuit may be an output pin, and the processing circuit may be a transistor, a gate circuit, a trigger, various logic circuits, and the like. The input signal received by the input circuit may be received and input by, for example and without limitation, a receiver, the output signal may be output by, for example and without limitation, a transmitter and transmitted by a transmitter, and the input circuit and the output circuit may be the same circuit, which functions as the input circuit and the output circuit, respectively, at different times. The embodiment of the application does not limit the specific implementation modes of the processor and various circuits.

In a fifth aspect, a processing device is provided that includes a processor and a memory. The processor is configured to read instructions stored in the memory and to receive signals via the receiver and to transmit signals via the transmitter to perform the method of any one of the possible implementations of the first aspect.

Optionally, the processor is one or more, and the memory is one or more.

Alternatively, the memory may be integrated with the processor or the memory may be separate from the processor.

In a specific implementation process, the memory may be a non-transient (non-transitory) memory, for example, a Read Only Memory (ROM), which may be integrated on the same chip as the processor, or may be separately disposed on different chips.

It should be appreciated that the related data interaction flow may be, for example, a flow of sending indication information from a processor, and the receiving capability information may be a flow of receiving input capability information by the processor. Specifically, the data output by the processing may be output to the transmitter, and the input data received by the processor may be from the receiver. Wherein the transmitter and receiver may be collectively referred to as a transceiver.

The processing means in the fifth aspect may be a chip, and the processor may be implemented by hardware or by software, and when implemented by hardware, the processor may be a logic circuit, an integrated circuit, or the like; when implemented in software, the processor may be a general-purpose processor, implemented by reading software code stored in a memory, which may be integrated in the processor, or may reside outside the processor, and exist separately.

In a sixth aspect, there is provided a computer program product comprising: a computer program (which may also be referred to as code, or instructions) which, when executed, causes a computer to perform the method of any one of the possible implementations of the first aspect.

In a seventh aspect, a computer readable storage medium is provided, which stores a computer program (which may also be referred to as code, or instructions) which, when run on a computer, causes the computer to perform the method of any one of the possible implementations of the first aspect.

Drawings

Fig. 1 is a schematic structural diagram of a terminal device according to an embodiment of the present application;

fig. 2 is a software architecture block diagram of a terminal device according to an embodiment of the present application;

fig. 3 is a schematic diagram of an application scenario provided in an embodiment of the present application;

Fig. 4 is a flow chart of a crowd type recognition method according to an embodiment of the application;

Fig. 5 is a schematic process diagram of a crowd type recognition method when a user is a first type user according to an embodiment of the present application;

Fig. 6 is a schematic process diagram of a crowd type recognition method provided in the embodiment of the present application when a user is a first type user or is not a first type user;

FIG. 7 is a schematic diagram illustrating a process of a crowd type recognition method when a user is not a first type user or does not acquire first biological data according to an embodiment of the present application;

FIG. 8 is a schematic process diagram of another crowd type identification method according to an embodiment of the application;

fig. 9 is an interface schematic diagram of a display interface of a terminal device according to an embodiment of the present application;

FIG. 10 is a schematic block diagram of a crowd type recognition device according to an embodiment of the application;

Fig. 11 is a schematic block diagram of another crowd type recognition device according to an embodiment of the application.

Detailed Description

The technical scheme of the application will be described below with reference to the accompanying drawings.

In embodiments of the present application, the words "first," "second," and the like are used to distinguish between identical or similar items that have substantially the same function and effect. For example, the first value and the second value are merely for distinguishing between different values, and are not limited in order. It will be appreciated by those of skill in the art that the words "first," "second," and the like do not limit the amount and order of execution, and that the words "first," "second," and the like do not necessarily differ.

It should be noted that, in the embodiments of the present application, words such as "exemplary" or "such as" are used to mean serving as an example, instance, or illustration. Any embodiment or design described herein as "exemplary" or "for example" should not be construed as preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary" or "such as" is intended to present related concepts in a concrete fashion.

In the embodiments of the present application, "at least one" means one or more, and "a plurality" means two or more. "and/or", describes an association relationship of an association object, and indicates that there may be three relationships, for example, a and/or B, and may indicate: a alone, a and B together, and B alone, wherein a, B may be singular or plural. The character "/" generally indicates that the context-dependent object is an "or" relationship. "at least one of" or the like means any combination of these items, including any combination of single item(s) or plural items(s). For example, at least one (one) of a, b, or c may represent: a, b, c, a-b, a-c, b-c, or a-b-c, wherein a, b, c may be single or plural.

The terminal device provided by the embodiment of the application is a terminal device with a touch display screen, and can be a mobile phone, a tablet personal computer (pad), a desktop computer, a notebook computer and the like. The embodiment of the application does not limit the specific technology and the specific equipment form adopted by the terminal equipment.

In order to better understand the terminal device in the embodiment of the present application, the hardware structure of the terminal device in the embodiment of the present application is described in detail below with reference to fig. 1.

Fig. 1 is a schematic structural diagram of a terminal device 100 according to an embodiment of the present application. As shown in fig. 1, the terminal device 100 may include a processor 110, an external memory interface 120, an internal memory 121, a universal serial bus (universal serial bus, USB) interface 130, a charge 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, an earphone interface 170D, a sensor module 180, a key 190, a motor 191, an indicator 192, a camera 193, a display 194, a subscriber identity module (subscriber identification module, SIM) card interface 195, and the like. The sensor module 180 may include a pressure sensor 180A, a gyro sensor 180B, an air pressure sensor 180C, a magnetic sensor 180D, an acceleration 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, and the like.

It is to be understood that the structure illustrated in the embodiment of the present application does not constitute a specific limitation on the terminal device 100. In other embodiments of the application, terminal device 100 may include more or less components than illustrated, or certain components may be combined, or certain components may be split, or different arrangements of components. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

The processor 110 may include one or more processing units, such as: the processor 110 may include an application processor (application processor, AP), a modem processor, a graphics processor (graphics processing unit, GPU), an image signal processor (IMAGE SIGNAL processor, ISP), a controller, a video codec, a digital signal processor (DIGITAL SIGNAL processor, DSP), a baseband processor, and/or a neural-Network Processor (NPU), etc. Wherein the different processing units may be separate devices or may be integrated in one or more processors.

The controller can generate operation control signals according to the instruction operation codes and the time sequence signals to finish the control of instruction fetching and instruction execution.

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

In some embodiments, the processor 110 may include one or more interfaces. The interfaces may include an integrated circuit (inter-INTEGRATED CIRCUIT, I2C) interface, an integrated circuit built-in audio (inter-INTEGRATED CIRCUIT SOUND, I2S) interface, a pulse code modulation (pulse code modulation, PCM) interface, a universal asynchronous receiver transmitter (universal asynchronous receiver/transmitter, UART) interface, a mobile industry processor interface (mobile industry processor interface, MIPI), a general-purpose input/output (GPIO) interface, a subscriber identity module (subscriber identity module, SIM) interface, and/or a universal serial bus (universal serial bus, USB) interface, among others.

The I2C interface is a bi-directional synchronous serial bus comprising a serial data line (SERIAL DATA LINE, SDA) and a serial clock line (derail clock line, SCL). In some embodiments, the processor 110 may contain multiple sets of I2C buses. The processor 110 may be coupled to the touch sensor 180K, charger, flash, camera 193, etc., respectively, through different I2C bus interfaces. For example: the processor 110 may be coupled to the touch sensor 180K through an I2C interface, so that the processor 110 and the touch sensor 180K communicate through an I2C bus interface to implement a touch function of the terminal device 100.

The I2S interface may be used for audio communication. In some embodiments, the processor 110 may contain multiple sets of I2S buses. The processor 110 may be coupled to the audio module 170 via an I2S bus to enable communication between the processor 110 and the audio module 170. In some embodiments, the audio module 170 may transmit an audio signal to the wireless communication module 160 through the I2S interface, to implement a function of answering a call through the bluetooth headset.

PCM interfaces may also be used for audio communication to sample, quantize and encode analog signals. In some embodiments, the audio module 170 and the wireless communication module 160 may be coupled through a PCM bus interface. In some embodiments, the audio module 170 may also transmit audio signals to the wireless communication module 160 through the PCM interface to implement a function of answering a call through the bluetooth headset. Both the I2S interface and the PCM interface may be used for audio communication.

The UART interface is a universal serial data bus for asynchronous communications. The bus may be a bi-directional communication bus. It converts the data to be transmitted between serial communication and parallel communication. In some embodiments, a UART interface is typically used to connect the processor 110 with the wireless communication module 160. For example: the processor 110 communicates with a bluetooth module in the wireless communication module 160 through a UART interface to implement a bluetooth function. In some embodiments, the audio module 170 may transmit an audio signal to the wireless communication module 160 through a UART interface, to implement a function of playing music through a bluetooth headset.

The MIPI interface may be used to connect the processor 110 to peripheral devices such as a display 194, a camera 193, and the like. The MIPI interfaces include camera serial interfaces (CAMERA SERIAL INTERFACE, CSI), display serial interfaces (DISPLAY SERIAL INTERFACE, DSI), and the like. In some embodiments, processor 110 and camera 193 communicate through a CSI interface to implement the photographing function of terminal device 100. The processor 110 and the display 194 communicate via a DSI interface to implement the display function of the terminal device 100.

The GPIO interface may be configured by software. The GPIO interface may be configured as a control signal or as a data signal. In some embodiments, a GPIO interface may be used to connect the processor 110 with the camera 193, the display 194, the wireless communication module 160, the audio module 170, the sensor module 180, and the like. The GPIO interface may also be configured as an I2C interface, an I2S interface, a UART interface, an MIPI interface, etc.

The USB interface 130 is an interface conforming to the USB standard specification, and may specifically be a Mini USB interface, a Micro USB interface, a USB Type C interface, or the like. The USB interface 130 may be used to connect a charger to charge the terminal device 100, or may be used to transfer data between the terminal device 100 and a peripheral device. And can also be used for connecting with a headset, and playing audio through the headset. The interface may also be used to connect other terminal devices, such as AR devices, etc.

It should be understood that the interfacing relationship between the modules illustrated in the embodiment of the present application is only illustrative, and does not constitute a structural limitation of the terminal device 100. In other embodiments of the present application, the terminal device 100 may also use different interfacing manners, or a combination of multiple interfacing manners in the foregoing embodiments.

The charge management module 140 is configured to receive a charge input from a charger. The charger can be a wireless charger or a wired charger. In some wired charging embodiments, the charge management module 140 may receive a charging input of a wired charger through the USB interface 130. In some wireless charging embodiments, the charge management module 140 may receive wireless charging input through a wireless charging coil of the terminal device 100. The charging management module 140 may also supply power to the terminal device through the power management module 141 while charging the battery 142.

The power management module 141 is used for connecting the battery 142, and the charge management module 140 and the processor 110. The power management module 141 receives input from the battery 142 and/or the charge management module 140 to power the processor 110, the internal memory 121, the display 194, the camera 193, the wireless communication module 160, and the like. The power management module 141 may also be configured to monitor battery capacity, battery cycle number, battery health (leakage, impedance) and other parameters. In other embodiments, the power management module 141 may also be provided in the processor 110. In other embodiments, the power management module 141 and the charge management module 140 may be disposed in the same device.

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

The antennas 1 and 2 are used for transmitting and receiving electromagnetic wave signals. Each antenna in the terminal device 100 may be used to cover a single or multiple communication bands. Different antennas may also be multiplexed to improve the utilization of the antennas. For example: the antenna 1 may be multiplexed into a diversity antenna of a wireless local area network. In other embodiments, the antenna may be used in conjunction with a tuning switch.

The mobile communication module 150 may provide a solution including 2G/3G/4G/5G wireless communication applied to the terminal device 100. The mobile communication module 150 may include at least one filter, switch, power amplifier, low noise amplifier (low noise amplifier, LNA), etc. The mobile communication module 150 may receive electromagnetic waves from the antenna 1, perform processes such as filtering, amplifying, and the like on the received electromagnetic waves, and transmit the processed electromagnetic waves to the modem processor for demodulation. The mobile communication module 150 can amplify the signal modulated by the modem processor, and convert the signal into electromagnetic waves through the antenna 1 to radiate. In some embodiments, at least some of the functional modules of the mobile communication module 150 may be disposed in the processor 110. In some embodiments, at least some of the functional modules of the mobile communication module 150 may be provided in the same device as at least some of the modules of the processor 110.

The modem processor may include a modulator and a demodulator. The modulator is used for modulating the low-frequency baseband signal to be transmitted into a medium-high frequency signal. The demodulator is used for demodulating the received electromagnetic wave signal into a low-frequency baseband signal. The demodulator then transmits the demodulated low frequency baseband signal to the baseband processor for processing. The low frequency baseband signal is processed by the baseband processor and then transferred to the application processor. The application processor outputs sound signals through an audio device (not limited to the speaker 170A, the receiver 170B, etc.), or displays images or video through the display screen 194. In some embodiments, the modem processor may be a stand-alone device. In other embodiments, the modem processor may be provided in the same device as the mobile communication module 150 or other functional module, independent of the processor 110.

The wireless communication module 160 may provide solutions for wireless communication including wireless local area network (wireless local area networks, WLAN) (e.g., wireless fidelity (WIRELESS FIDELITY, wi-Fi) network), bluetooth (BT), global navigation satellite system (global navigation SATELLITE SYSTEM, GNSS), frequency modulation (frequency modulation, FM), near field communication (NEAR FIELD communication, NFC), infrared (IR), etc., applied on the terminal device 100. The wireless communication module 160 may be one or more devices that integrate at least one communication processing module. The wireless communication module 160 receives electromagnetic waves via the antenna 2, modulates the electromagnetic wave signals, filters the electromagnetic wave signals, and transmits the processed signals to the processor 110. The wireless communication module 160 may also receive a signal to be transmitted from the processor 110, frequency modulate it, amplify it, and convert it to electromagnetic waves for radiation via the antenna 2.

In some embodiments, antenna 1 and mobile communication module 150 of terminal device 100 are coupled, and antenna 2 and wireless communication module 160 are coupled, such that terminal device 100 may communicate with a network and other devices via wireless communication techniques. The wireless communication techniques can include the Global System for Mobile communications (global system for mobile communications, GSM), general packet radio service (GENERAL PACKET radio service, GPRS), code division multiple access (code division multiple access, CDMA), wideband code division multiple access (wideband code division multiple access, WCDMA), time division code division multiple access (time-division code division multiple access, TD-SCDMA), long term evolution (long term evolution, LTE), BT, GNSS, WLAN, NFC, FM, and/or IR techniques, among others. The GNSS may include a global satellite positioning system (global positioning system, GPS), a global navigation satellite system (global navigation SATELLITE SYSTEM, GLONASS), a beidou satellite navigation system (beidou navigation SATELLITE SYSTEM, BDS), a quasi zenith satellite system (quasi-zenith SATELLITE SYSTEM, QZSS) and/or a satellite based augmentation system (SATELLITE BASED AUGMENTATION SYSTEMS, SBAS).

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

The display screen 194 is used to display images, videos, and the like. The display 194 includes a display panel. The display panel may employ a Liquid Crystal Display (LCD) CRYSTAL DISPLAY, an organic light-emitting diode (OLED), an active-matrix organic LIGHT EMITTING diode (AMOLED), a flexible light-emitting diode (FLED), miniled, microLed, micro-oLed, a quantum dot LIGHT EMITTING diode (QLED), or the like. In some embodiments, the terminal device 100 may include 1 or N display screens 194, N being a positive integer greater than 1.

The terminal device 100 may implement a photographing function through an ISP, a camera 193, a video codec, a GPU, a display screen 194, an application processor, and the like.

The ISP is used to process data fed back by the camera 193. For example, when photographing, the shutter is opened, light is transmitted to the camera photosensitive element through the lens, the optical signal is converted into an electric signal, and the camera photosensitive element transmits the electric signal to the ISP for processing and is converted into an image visible to naked eyes. ISP can also optimize the noise, brightness and skin color of the image. The ISP can also optimize parameters such as exposure, color temperature and the like of a shooting scene. In some embodiments, the ISP may be provided in the camera 193.

The camera 193 is used to capture still images or video. The object generates an optical image through the lens and projects the optical image onto the photosensitive element. The photosensitive element may be a charge coupled device (charge coupled device, CCD) or a Complementary Metal Oxide Semiconductor (CMOS) phototransistor. The photosensitive element converts the optical signal into an electrical signal, which is then transferred to the ISP to be converted into a digital image signal. The ISP outputs the digital image signal to the DSP for processing. The DSP converts the digital image signal into an image signal in a standard RGB, YUV, or the like format. In some embodiments, the terminal device 100 may include 1 or N cameras 193, N being a positive integer greater than 1.

The digital signal processor is used for processing digital signals, and can process other digital signals besides digital image signals. For example, when the terminal device 100 selects a frequency bin, the digital signal processor is used to fourier transform the frequency bin energy, or the like.

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

The NPU is a neural-network (NN) computing processor, and can rapidly process input information by referencing a biological neural network structure, for example, referencing a transmission mode between human brain neurons, and can also continuously perform self-learning. Applications such as intelligent awareness of the terminal device 100 may be implemented by the NPU, for example: image recognition, face recognition, speech recognition, text understanding, etc.

The external memory interface 120 may be used to connect an external memory card, such as a Micro SD card, to realize expansion of the memory capability of the terminal device 100. The external memory card communicates with the processor 110 through an external memory interface 120 to implement data storage functions. For example, files such as music, video, etc. are stored in an external memory card.

The internal memory 121 may be used to store computer executable program code including instructions. The internal memory 121 may include a storage program area and a storage data area. The storage program area may store an application program (such as a sound playing function, an image playing function, etc.) required for at least one function of the operating system, etc. The storage data area may store data (such as audio data, phonebook, etc.) created during use of the terminal device 100, and the like. In addition, the internal memory 121 may include a high-speed random access memory, and may further include a nonvolatile memory such as at least one magnetic disk storage device, a flash memory device, a universal flash memory (universal flash storage, UFS), and the like. The processor 110 performs various functional applications of the terminal device 100 and data processing by executing instructions stored in the internal memory 121 and/or instructions stored in a memory provided in the processor.

The terminal device 100 may implement audio functions through an audio module 170, a speaker 170A, a receiver 170B, a microphone 170C, an earphone interface 170D, an application processor, and the like. Such as music playing, recording, etc.

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

The speaker 170A, also referred to as a "horn," is used to convert audio electrical signals into sound signals. The terminal device 100 can listen to music or to handsfree talk through the speaker 170A.

A receiver 170B, also referred to as a "earpiece", is used to convert the audio electrical signal into a sound signal. When the terminal device 100 receives a call or voice message, it is possible to receive voice by approaching the receiver 170B to the human ear.

Microphone 170C, also referred to as a "microphone" or "microphone", is used to convert sound signals into electrical signals. When making a call or transmitting voice information, the user can sound near the microphone 170C through the mouth, inputting a sound signal to the microphone 170C. The terminal device 100 may be provided with at least one microphone 170C. In other embodiments, the terminal device 100 may be provided with two microphones 170C, and may implement a noise reduction function in addition to collecting sound signals. In other embodiments, the terminal device 100 may be further provided with three, four or more microphones 170C to collect sound signals, reduce noise, identify the source of sound, implement directional recording functions, etc.

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

The pressure sensor 180A is used to sense a pressure signal, and may convert the pressure signal into an electrical signal. In some embodiments, the pressure sensor 180A may be disposed on the display screen 194. The pressure sensor 180A is of various types, such as a resistive pressure sensor, an inductive pressure sensor, a capacitive pressure sensor, and the like. The capacitive pressure sensor may be a capacitive pressure sensor comprising at least two parallel plates with conductive material. The capacitance between the electrodes changes when a force is applied to the pressure sensor 180A. The terminal device 100 determines the intensity of the pressure according to the change of the capacitance. When a touch operation is applied to the display 194, the terminal device 100 detects the intensity of the touch operation according to the pressure sensor 180A. The terminal device 100 may also calculate the position of the touch from the detection signal of the pressure sensor 180A. In some embodiments, touch operations that act on the same touch location, but at different touch operation strengths, may correspond to different operation instructions. For example: and executing an instruction for checking the short message when the touch operation with the touch operation intensity smaller than the first pressure threshold acts on the short message application icon. And executing an instruction for newly creating the short message when the touch operation with the touch operation intensity being greater than or equal to the first pressure threshold acts on the short message application icon.

The gyro sensor 180B may be used to determine a motion gesture of the terminal device 100. In some embodiments, the angular velocity of the terminal device 100 about three axes (i.e., x, y, and z axes) may be determined by the gyro sensor 180B. The gyro sensor 180B may be used for photographing anti-shake. Illustratively, when the shutter is pressed, the gyro sensor 180B detects the angle of shake of the terminal apparatus 100, calculates the distance to be compensated for by the lens module according to the angle, and allows the lens to counteract the shake of the terminal apparatus 100 by the reverse movement, thereby realizing anti-shake. The gyro sensor 180B may also be used for navigating, somatosensory game scenes.

The air pressure sensor 180C is used to measure air pressure. In some embodiments, the terminal device 100 calculates altitude from barometric pressure values measured by the barometric pressure sensor 180C, aiding in positioning and navigation.

The magnetic sensor 180D includes a hall sensor. The terminal device 100 can detect the opening and closing of the flip cover using the magnetic sensor 180D. In some embodiments, when the terminal device 100 is a folder, the terminal device 100 may detect opening and closing of the folder according to the magnetic sensor 180D. And then according to the detected opening and closing state of the leather sheath or the opening and closing state of the flip, the characteristics of automatic unlocking of the flip and the like are set.

The acceleration sensor 180E can detect the magnitude of acceleration of the terminal device 100 in various directions (typically three axes). The magnitude and direction of gravity may be detected when the terminal device 100 is stationary. The method can also be used for identifying the gesture of the terminal equipment, and is applied to the applications such as horizontal and vertical screen switching, pedometers and the like.

A distance sensor 180F for measuring a distance. The terminal device 100 may measure the distance by infrared or laser. In some embodiments, the terminal device 100 may range using the distance sensor 180F to achieve fast focusing.

The proximity light sensor 180G may include, for example, a Light Emitting Diode (LED) and a light detector, such as a photodiode. The light emitting diode may be an infrared light emitting diode. The terminal device 100 emits infrared light outward through the light emitting diode. The terminal device 100 detects infrared reflected light from a nearby object using a photodiode. When sufficient reflected light is detected, it can be determined that there is an object in the vicinity of the terminal device 100. When insufficient reflected light is detected, the terminal device 100 may determine that there is no object in the vicinity of the terminal device 100. The terminal device 100 can detect that the user holds the terminal device 100 close to the ear to talk by using the proximity light sensor 180G, so as to automatically extinguish the screen for the purpose of saving power. The proximity light sensor 180G may also be used in holster mode, pocket mode to automatically unlock and lock the screen.

The ambient light sensor 180L is used to sense ambient light level. The terminal device 100 may adaptively adjust the brightness of the display 194 based on the perceived ambient light level. The ambient light sensor 180L may also be used to automatically adjust white balance when taking a photograph. The ambient light sensor 180L may also cooperate with the proximity light sensor 180G to detect whether the terminal device 100 is in a pocket to prevent false touches.

The fingerprint sensor 180H is used to collect a fingerprint. The terminal device 100 can utilize the collected fingerprint characteristics to realize fingerprint unlocking, access an application lock, fingerprint photographing, fingerprint incoming call answering and the like.

The temperature sensor 180J is for detecting temperature. In some embodiments, the terminal device 100 performs a temperature processing strategy using the temperature detected by the temperature sensor 180J. For example, when the temperature reported by the temperature sensor 180J exceeds a threshold, the terminal device 100 performs a reduction in the performance of a processor located near the temperature sensor 180J in order to reduce power consumption to implement thermal protection. In other embodiments, when the temperature is below another threshold, the terminal device 100 heats the battery 142 to avoid the low temperature causing the terminal device 100 to shut down abnormally. In other embodiments, when the temperature is below a further threshold, the terminal device 100 performs boosting of the output voltage of the battery 142 to avoid abnormal shutdown caused by low temperatures.

The touch sensor 180K, also referred to as a "touch device". The touch sensor 180K may be disposed on the display screen 194, and the touch sensor 180K and the display screen 194 form a touch screen, which is also called a "touch screen". The touch sensor 180K is for detecting a touch operation acting thereon or thereabout. The touch sensor may communicate the detected touch operation to the application processor to determine the touch event type. Visual output related to touch operations may be provided through the display 194. In other embodiments, the touch sensor 180K may also be disposed on the surface of the terminal device 100 at a different location than the display 194.

The bone conduction sensor 180M may acquire a vibration signal. In some embodiments, bone conduction sensor 180M may acquire a vibration signal of a human vocal tract vibrating bone pieces. The bone conduction sensor 180M may also contact the pulse of the human body to receive the blood pressure pulsation signal. In some embodiments, bone conduction sensor 180M may also be provided in a headset, in combination with an osteoinductive headset. The audio module 170 may analyze the voice signal based on the vibration signal of the sound portion vibration bone block obtained by the bone conduction sensor 180M, so as to implement a voice function. The application processor may analyze the heart rate information based on the blood pressure beat signal acquired by the bone conduction sensor 180M, so as to implement a heart rate detection function.

The keys 190 include a power-on key, a volume key, etc. The keys 190 may be mechanical keys. Or may be a touch key. The terminal device 100 may receive key inputs, generating key signal inputs related to user settings and function controls of the terminal device 100.

The motor 191 may generate a vibration cue. The motor 191 may be used for incoming call vibration alerting as well as for touch vibration feedback. For example, touch operations acting on different applications (e.g., photographing, audio playing, etc.) may correspond to different vibration feedback effects. The motor 191 may also correspond to different vibration feedback effects by touching different areas of the display screen 194. Different application scenarios (such as time reminding, receiving information, alarm clock, game, etc.) can also correspond to different vibration feedback effects. The touch vibration feedback effect may also support customization.

The indicator 192 may be an indicator light, may be used to indicate a state of charge, a change in charge, a message indicating a missed call, a notification, etc.

The SIM card interface 195 is used to connect a SIM card. The SIM card may be contacted and separated from the terminal apparatus 100 by being inserted into the SIM card interface 195 or by being withdrawn from the SIM card interface 195. The terminal device 100 may support 1 or N SIM card interfaces, N being a positive integer greater than 1. The SIM card interface 195 may support Nano SIM cards, micro SIM cards, and the like. The same SIM card interface 195 may be used to insert multiple cards simultaneously. The types of the plurality of cards may be the same or different. The SIM card interface 195 may also be compatible with different types of SIM cards. The SIM card interface 195 may also be compatible with external memory cards. The terminal device 100 interacts with the network through the SIM card to realize functions such as call and data communication. In some embodiments, the terminal device 100 employs esims, namely: an embedded SIM card. The eSIM card can be embedded in the terminal device 100 and cannot be separated from the terminal device 100. The software system of the terminal device 100 may employ a layered architecture, an event driven architecture, a micro-core architecture, a micro-service architecture, or a cloud architecture. In the embodiment of the application, taking an Android system with a layered architecture as an example, a software structure of the terminal device 100 is illustrated.

Fig. 2 is a software configuration block diagram of the terminal device 100 according to the embodiment of the present application.

The layered architecture divides the software into several layers, each with distinct roles and branches. The layers communicate with each other through a software interface. In some embodiments, the Android system is divided into four layers, from top to bottom, an application layer, an application framework layer, an Zhuoyun rows (Android runtime) and system libraries, and a kernel layer, respectively.

The application layer may include a series of application packages. As shown in fig. 2, the application package may include applications for cameras, gallery, calendar, phone calls, maps, navigation, WLAN, bluetooth, music, video, short messages, etc.

The application framework layer provides an application programming interface (application programming interface, API) and programming framework for the application of the application layer. The application framework layer includes a number of predefined functions. As shown in FIG. 2, the application framework layer may include a window manager, a content provider, a view system, a telephony manager, a resource manager, a notification manager, and the like.

The window manager is used for managing window programs. The window manager can acquire the size of the display screen, judge whether a status bar exists, lock the screen, intercept the screen and the like.

The content provider is used to store and retrieve data and make such data accessible to applications. The data may include video, images, audio, calls made and received, browsing history and bookmarks, phonebooks, etc.

The view system includes visual controls, such as controls to display text, controls to display pictures, and the like. The view system may be used to build applications. The display interface may be composed of one or more views. For example, a display interface including a text message notification icon may include a view displaying text and a view displaying a picture.

The telephony manager is used to provide the communication functions of the terminal device 100. Such as the management of call status (including on, hung-up, etc.).

The resource manager provides various resources for the application program, such as localization strings, icons, pictures, layout files, video files, and the like.

The notification manager allows the application to display notification information in a status bar, can be used to communicate notification type messages, can automatically disappear after a short dwell, and does not require user interaction. Such as notification manager is used to inform that the download is complete, message alerts, etc. The notification manager may also be a notification in the form of a chart or scroll bar text that appears on the system top status bar, such as a notification of a background running application, or a notification that appears on the screen in the form of a dialog window. For example, a text message is prompted in a status bar, a prompt tone is emitted, the terminal equipment vibrates, and an indicator light blinks.

Android run time includes a core library and virtual machines. Android runtime is responsible for scheduling and management of the android system.

The core library consists of two parts: one part is a function which needs to be called by java language, and the other part is a core library of android.

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

The system library may include a plurality of functional modules. For example: surface manager (surface manager), media Libraries (Media Libraries), three-dimensional graphics processing Libraries (e.g., openGL ES), 2D graphics engines (e.g., SGL), etc.

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

Media libraries support a variety of commonly used audio, video format playback and recording, still image files, and the like. The media library may support a variety of audio and video encoding formats, such as MPEG4, h.264, MP3, AAC, AMR, JPG, PNG, etc.

The three-dimensional graphic processing library is used for realizing three-dimensional graphic drawing, image rendering, synthesis, layer processing and the like.

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

The kernel layer is a layer between hardware and software. The kernel layer is used for driving the hardware so that the hardware works. The kernel layer at least includes a display driver, a screen driver, an image processor (graphics processing unit, GPU) driver, a camera, and a sensor driver, which is not limited in this embodiment of the present application. For example, the screen driver may drive a screen bright or off screen.

In the process of using the terminal equipment, the terminal equipment can identify the crowd type of the user, so that more reasonable service is provided for the user. The crowd type can be minors, adults, old people, middle school students, college students or a certain occupation, etc. One such occupation may be a teacher, chef, etc. Fig. 3 is a schematic diagram of an application scenario 300 according to an embodiment of the present application. As shown in fig. 3, during the process of playing a game through a mobile phone, as shown in an interface (a) in fig. 3, the user performs game recharging, and when the recharging is selected by 200 yuan, the display interface of the mobile phone jumps to an interface (b) in fig. 3, and at the interface (b), the user can select a payment mode to pay. However, since events such as a current minors game refill, live viewing, etc. are frequent, the cell phone can identify the crowd type of the user, for example, whether the user is a minor, before the user makes a payment. When the handset obtains the user's minor confidence, for example, the user's minor confidence is 90%, the handset determines that the user is minor. At this time, if the user inputs the password correctly in the interface (b) shown in fig. 3, the mobile phone may further display a parent account number and a password verification interface, and if verification is passed, the payment is successful; otherwise, the payment fails.

Currently, terminal devices typically acquire facial features of a user from image data of the user; and inputting the facial features of the user into the face model to obtain the crowd type of the user. However, such a crowd type recognition method is low in accuracy, so that the user experience is poor. For example, the terminal device determines whether the user is a minor based on facial features of the user, assuming that the user belongs to a adolescent minor, the terminal device may determine that the user is an adult because the facial features of the minor, partially adolescent, are similar to those of the adult. Also, when the terminal device determines whether the user is an elderly person, the terminal device may determine the middle-aged person as an elderly person because part of the facial features of the middle-aged person are close to the elderly person. Therefore, for the confusing crowd types such as puberty, middle-aged people, etc., the terminal device may be in error.

In order to solve the above problems, the present application provides a crowd type recognition method, when acquiring biological data of a user, for example, face data, sound data, respiration data, fingerprint data, etc. when the user uses a terminal device, the terminal device can determine whether the user is a user who uses the terminal device for a long time according to the biological data of the user; if the user is a user who uses the terminal equipment for a long time, the terminal equipment acquires historical biological data of the user when the terminal equipment is used in a historical manner and historical use data of an application program stored in the terminal equipment; the terminal device determines the crowd type of the user based on the historical usage data, the historical biological data and the biological data, such that, on the one hand, the terminal device determines the age group of the user based on the historical biological data and the biological data; on the other hand, the terminal device can determine habit information of the user, such as life habit information, work habit information and the like, based on the historical usage data, and then the terminal device can further determine crowd types of the user based on the habit information of the user and the age bracket of the user determined according to the biological data and the historical biological data, so that accuracy of the crowd type identification method is improved, and user experience is improved.

The crowd type recognition method of the application is described in detail below with reference to fig. 4 to 9. The execution body of the embodiment shown in the present application may be a terminal device, the hardware structure of the terminal device may be shown in fig. 1, and the software structure may be shown in fig. 2. The crowd type recognition method according to the embodiment of the application will be described in detail below by taking a terminal device as an execution subject.

It should be understood that the terminal device may be the terminal device itself, a chip, a system on a chip or a processor supporting the terminal device to implement the crowd type recognition method, or a logic module or software capable of implementing all or part of the functions of the terminal device, which is not specifically limited in the present application.

Fig. 4 is a flowchart of a crowd type recognition method 400 according to an embodiment of the application. As shown in fig. 4, the method 400 includes the steps of:

S401, under the condition that first biological data of a user when the terminal equipment is currently used is obtained, whether the user is a first type user or not is determined based on the first biological data, wherein the first biological data comprises at least one of sound data, breathing data, fingerprint data or face data, and the first type user is a user with the use time and/or the use times of the terminal equipment exceeding a first preset threshold value.

It should be understood that the current use of the terminal device by the user may refer to a process from the current start of using the terminal device by the user to the current time. Illustratively, when the current time is 2023, 1, 15, 10:00, and the user unlocks the terminal device and starts using the terminal device at 2023, 1, 15, 9:00, then the time for the user to currently use the terminal device may be 2023, 1, 15, 9:00, to 2023, 1, 15, 10:00. Correspondingly, the user history using the terminal device may refer to a process that the user uses the terminal device before using the terminal device this time. Illustratively, when the current time is 2023, 1, 15, 10:00, and the user unlocks the terminal device and starts using the terminal device at 2023, 1, 15, 9:00, the time for which the user has used the terminal device historically may be any time period before 2023, 1, 15, 9:00.

In one possible embodiment, the first biometric data may be obtained after the user unlocks the terminal device, and at this time, the user may refer to a period of time from a time when the first biometric data of the user is obtained to a time when the terminal device is unlocked last time when the user is currently using the terminal device. For example, when the user unlocks the mobile phone at 2023, 1.1.11:00 and triggers the method 400 at 2023, 1.1.12:00, i.e. the mobile phone acquires the first biological data at 2023, 1.1.12:00, the first biological data is the biological data acquired by the mobile phone from 2023, 1.1.11:00 to 2023, 1.1.1.12:00. The first biometric data may include biometric data collected by the terminal device when the user unlocks the terminal device.

It will be appreciated that after the user unlocks the terminal device, the terminal device may trigger the method 400 in a different situation to obtain the first biometric data. In one case, after unlocking the terminal device, the terminal device may trigger the method 400 when using certain services in the terminal device, i.e. to obtain the first biometric data, e.g. when the user uses the terminal device for payment services, the terminal device collects the first biometric data. In another case, the terminal device periodically triggers the method 400 to obtain the first biological data. For example, the terminal device triggers the method 400 every 48 hours to obtain the first biological data.

In another possible embodiment, the first biometric data may be obtained when the user unlocks the terminal device, and based on the first biometric data, it is determined whether the user is a first type of user. The mobile phone is unlocked by face recognition, so that the mobile phone can acquire a face image of the user, obtain face data based on the face image, and further determine whether the user is a first type user according to the face data.

It should be understood that the unlocking terminal device may also be referred to as a kernel, and the user may unlock the terminal device by means of face unlocking, password unlocking, fingerprint unlocking, etc., which is not limited in particular by the present application.

The voice data may be audio data obtained by the terminal device during a user speaking, a voice call, a singing, a video call, using a voice assistant, and the like. The face data may be image data obtained by the terminal device during the process of unlocking the terminal device, video call, and the like. The breathing data may be audio data obtained by the terminal device in any process of obtaining audio data of the user, such as voice call and video call of the user. The fingerprint data may be sensor data obtained by the terminal device during a process of unlocking the terminal device by the user through the fingerprint, and the like.

The first type of user may also be referred to as a long-term user or as a owner. The first preset threshold may be a preset duration and/or number of times. In the case that the first type of user is a user whose usage time for the terminal device exceeds a first preset threshold, the first preset threshold is a preset duration, for example, 20h, and in the case that the terminal device determines that the total duration of the user history using the terminal device exceeds 20h, the user is the first type of user. In case the first type of user is a user whose number of uses of the terminal device exceeds a first preset threshold, the first preset threshold is a preset number of times, for example 10 times, and in case the terminal device determines that the number of times the user has used the terminal device historically exceeds 10 times, the user is a first type of user. In the case that the first type of user is a user whose usage time and usage number of the terminal device exceed a first preset threshold, the first preset threshold includes a preset duration and a preset number of times, for example, the first preset threshold includes 20h and 10 times, and in the case that the total duration of the user history using the terminal device exceeds 20h and the total number of times history using the terminal device exceeds 10 times, the user is the first type of user.

It can be understood that the user history using the terminal device may refer to that the user uses the terminal device at any time before the current time, and may also refer to that the user uses the terminal device at any time before the user uses the terminal device this time. The time and/or number of uses of the terminal device by the user may include the time and/or number of uses of the terminal device this time. The use time and/or the use number refers to the use time and/or the use number of the current terminal device used by the user historically.

In one possible embodiment, the first type of user is a user whose usage time and/or number of usage times of the terminal device exceeds a first preset threshold value within a first preset history period, which is a period of time before the current time. In an exemplary embodiment, the first preset history period is one week before the current time, and the user is a long-term user of the terminal device, that is, the user is a first type user, when the use time and/or the use number of the terminal device exceeds the first preset threshold value in the previous week.

S402, in the case that the user is a first type user, historical data of the terminal device are obtained, the historical data comprise historical use data of an application program on the terminal device and second biological data of the user when the terminal device is used in the historical mode, and the second biological data comprise at least one of sound data, breathing data or face data.

It should be understood that the historical usage data of the application program on the terminal device may refer to any historical usage data of the application program stored by the terminal device, such as data of a name of the application program, a time when the user uses the application program, a service performed by the user through the application program, and the like. Optionally, the historical usage data is historical usage data of a preset type of application on the terminal device. By way of example, the preset type of application may include a navigation type application, a take-away type application, and correspondingly, the historical usage data may include geographic location data and take-away food type data. Optionally, the historical usage data is historical usage data of an application program on the terminal device within a second preset historical period of time. Illustratively, if the second preset historical time period is one month before the current time, the historical usage data is the usage data of the application program stored by the terminal device in the previous month.

In case it is determined that the user currently using the terminal device is a first type of user, the terminal device may acquire second biometric data of the user cached by the terminal device when the user historically uses the terminal device. For example, the user may have voice calls while historically using the terminal device, and the terminal device may buffer the user's voice data and breath data; the user can unlock the terminal device through face recognition when using the terminal device in history, and the terminal device can buffer the face data of the user.

S403, determining whether the crowd type of the user belongs to the target crowd type or not based on the historical data.

It should be appreciated that the target crowd type may be any of a variety of crowd types including minors, elderly people, teachers, females, and the like. The historical data comprises second biological data and historical use data, the terminal equipment can obtain biological characteristic data through the second biological data, and the biological characteristic data can be data such as biological characteristic vectors or biological characteristic matrixes; the terminal equipment can obtain historical habit characteristic data of the user through the historical use data, wherein the historical habit characteristic data can also be habit characteristic vectors or habit characteristic matrixes and other data; then, the terminal device determines whether the crowd type to which the user belongs is the target crowd type based on the biometric data and the history habit feature data.

As an alternative embodiment, S403 may be implemented as follows: in the case where the first biometric data includes at least one of sound data, respiratory data, or face data, it is determined whether the group type to which the user belongs is a target group type based on the history data and the first biometric data.

As an alternative embodiment, determining whether the crowd type to which the user belongs is the target crowd type based on the history data and the first biological data may be implemented by: fusing the first biological data and the second biological data to obtain fused biological data; determining first age information of the user based on the fused biometric data; based on the first age information and the historical usage data, it is determined whether the crowd type to which the user belongs is a target crowd type.

The first age information may include information of an age group to which the user currently using the terminal device belongs, and may also include probability information of the user belonging to the age group, for example, the first age information includes 30-40 and 60%, and the probability of the user belonging to 30 to 40 years is 60%. The first age information may also include a plurality of age group information, and a probability that the user belongs to each of the plurality of age groups, for example, the first age information includes: 30-40 and 60%,20-30 and 40%, the probability that the user belongs to the age of 30 to 40 is 60%, and the probability that the user belongs to the age of 20 to 30 is 40%. The first age information may also include other information that can be used to indicate age information of the user, e.g., the first age information may also be a feature vector or a feature matrix.

The historical usage data may include user's lifestyle information such as the type of place frequently active, the type of food most frequently purchased, web pages most frequently browsed, the type of book most frequently read, etc.; the historical usage data may also include habit information of the user using the terminal device, such as the type of application used the most frequently, the period of time the terminal device is most frequently used, and so on. The terminal equipment combines the historical use data and the fusion biological data, so that whether the crowd type of the user belongs to the target crowd type can be determined more accurately. Illustratively, the terminal device determines whether the user currently using the terminal device is a teacher through the method 400, and on one hand, the terminal device may determine the age group to which the user belongs by fusing the biometric data, for example, the terminal device determines that the age group to which the user belongs is most likely 30 to 40 years old; on the other hand, the terminal device can determine that the place where the user most frequently goes is a school through the history use data, so that the terminal device can determine that the user is a teacher based on the history use data and the fusion biometric data.

Optionally, determining whether the crowd type to which the user belongs is the target crowd type based on the first age information and the historical usage data includes: determining habit information of the user based on the historical usage data; based on the first age information and the habit information, whether the crowd type to which the user belongs is a target crowd type is determined.

It should be understood that the historical usage data may be usage data of an application program buffered in the terminal device during a historical period of time, and habit information may be determined based on the historical usage data. Habit information may include user's lifestyle information such as the type of place of regular activity, the type of food most frequently purchased, the type of book read for the longest time, etc.; the habit information may also include habit information of using the terminal device by the user, such as the type of application used the most frequently, the period of time during which the terminal device is most frequently used, and the like.

In one possible embodiment, the target population type is a minor type; the method 400 further comprises: determining second age information of the user based on the habit information; based on the first age information and the historical usage data, determining whether the crowd type to which the user belongs is a target crowd type includes: based on the first age information and the second age information, it is determined whether the group type to which the user belongs is a minor type.

It should be appreciated that the lifestyle and habits of people of different age groups may be different, such that the historical usage data is different, and thus the terminal device may determine the second age information of the user based on the historical usage data. It can be understood that the biological characteristics of a part of adolescent population are close to those of adults, and therefore, when the terminal device determines whether the population of adolescent population and the population type to which the adults close to the biological characteristics of minors belong is a minors type based on the fused biological characteristics, the accuracy is low. Under the auxiliary effect of the historical usage data, the terminal equipment can further determine second age information of the user based on the historical usage data, so that the accuracy of determining whether the crowd type to which the user belongs is of the minors or not by the terminal equipment can be improved based on the first age information and the second age information. Illustratively, the first age information indicates that the user is an adult, and that the age group to which the user belongs is expected to be 20-25 years old; the historical usage data indicates that the place where the user enters and exits every day on the working day is a school, the time period of using the terminal device is 20:00 to 21:00, the application program with the largest using times is the game application program, and the terminal device indicates that the user belongs to middle school students, namely minors based on the second age information determined by the historical usage data. Because the age group of the user determined by the terminal device based on the fusion biological data is 20-25 years old, and the biological characteristics of the crowd in the age group of 20-25 years old are close to the biological characteristics of part of adolescent crowd, the accuracy of the first age information determined by the terminal device based on the fusion biological data is lower. In combination with historical usage data, the user goes in and out of a university daily on weekdays, however, an adult aged 20-25 is more likely to be a college student, and the place in and out of each day should be university; and secondly, the application program with the largest use times of the user is combined with historical use data such as game application programs, and the terminal equipment can determine that the user is a student with higher probability. Thus, the final judgment result of the terminal device is that the user is of the minors type.

It will be appreciated that when the target crowd type is not minors, but other crowd types related to age, for example, crowd types of elderly people, middle aged people, children, etc., the terminal device may also determine whether the crowd type to which the user belongs is the target crowd type by the same method.

In one possible embodiment, determining the first age information of the user based on the fused biometric data includes:

Under the condition that the fusion biological data comprises face data and voice data, inputting the fusion biological data into a face-human speaking voice model to obtain first age information, wherein the face-human speaking voice model is obtained by carrying out weighted fusion on an output result of the face model and an output result of the human speaking voice model; or alternatively, the first and second heat exchangers may be,

Under the condition that the fusion biological data comprises face data and breathing data, inputting the fusion biological data into a face-breathing model to obtain first age information, wherein the face-breathing model is obtained by weighting and fusing an output result of the face model and an output result of the breathing model; or alternatively, the first and second heat exchangers may be,

In the case that the fused biometric data includes face data, voice data, and respiratory data, the face data and the voice data in the fused biometric data are input to a face-human speech model to obtain first age information.

It should be understood that the face-human speech sound model is capable of determining the first age information of the user based on the face data and the sound data, has functions of the face model and the human speech sound model, and performs weighted fusion of an output result of the face model and an output result of the human speech sound model, such as feature data, confidence level, and the like, to obtain the first biological information. The face-breathing model can determine first age information of the user based on the face data and the breathing data, has functions of the face model and the breathing model, and performs weighted fusion on an output result of the face model and an output result of a human speaking voice model, such as feature data, confidence level and the like, so as to obtain first biological information. The face model is a model capable of outputting age information of a user based on face data, the face model can comprise a face decision function, the age information of the user can be determined based on the face data through the face decision function, and the face decision function can be a combined decision form of multitasking output of the face model; the human speech sound model is a model capable of outputting age information of a user based on sound data; the breathing model is a model capable of outputting age information of a user based on breathing data.

According to the technical scheme, the face model and the human speaking voice model are fused to obtain the face-human speaking voice model, and the first age information is obtained based on the face data and the voice data; or the face model and the breathing model are fused to obtain a face-breathing model, and then the first age information is obtained based on the face data and the breathing data, so that the sound data or the breathing data are used in combination with the face data, the age information of the user is determined based on biological data of various dimensions, and the accuracy of the first age information can be improved. Alternatively, in the case where sound data and respiratory data are present at the same time, sound data is preferentially selected, and face data and sound data are input to the face-human speaking sound model, so that the first age information of the user can be more accurately determined than by the face data and respiratory data.

Optionally, in the case that the fused biological data includes face data and voice data, before the fused biological data is input to the face-human speaking voice model, the face data may be converted into a face feature vector and the voice data may be converted into a voice feature vector through different convolutional neural networks, respectively; or converting the face data into a face feature matrix and converting the sound data into a sound feature matrix. The face-to-human speech acoustic model is capable of determining first age information of the user based on the input face feature vector and the sound feature vector, or the face feature matrix and the sound feature matrix.

Optionally, in the case that the fused biological data includes face data and respiratory data, before the fused biological data is input to the face-respiratory model, the face data may be converted into a face feature vector and the respiratory data may be converted into a respiratory feature vector through different convolutional neural networks or other types of machine learning models, respectively; or converting the face data into a face feature matrix and converting the breathing data into a breathing feature matrix. The face-respiration model can determine first age information of the user based on the input face feature vector and respiration feature vector, or the face feature matrix and the respiration feature matrix.

In a possible implementation manner, in the case that the fused biological data includes face data, the terminal device obtains a face feature vector or a face feature matrix based on the face data; and inputting the face feature vector or the face feature matrix into a face model, a face-breathing model or a face-human speaking voice model to obtain first age information of the user.

In a possible embodiment, in the case where the fused biometric data includes voice data, the terminal device obtains a voice feature vector or a voice feature matrix based on the voice data; and inputting the voice feature vector or the voice feature matrix into a human speaking voice model or a human face-human speaking voice model to obtain first age information of the user.

In a possible embodiment, in the case of fusion of biological data comprising respiratory data, the terminal device obtains a respiratory feature vector or a respiratory feature matrix based on the respiratory data; and inputting the breathing characteristic vector or the breathing characteristic matrix into a human speaking voice model or a human face-breathing model to obtain first age information of the user.

In a possible embodiment, in the case of fusion of biological data comprising sound data and respiration data, the terminal device obtains a sound feature vector or a sound feature matrix based on the sound data; and inputting the voice feature vector or the voice feature matrix into a human speaking voice model or a human face-human speaking voice model to obtain first age information of the user.

In one possible implementation, the face data in the fused biometric data is the valid face data after screening.

It should be understood that the face data of the user obtained by the terminal device may include invalid face data such as poor definition, image data that does not include the entire face of the user, and the terminal device may not obtain a face feature vector or a face feature matrix based on the invalid face data. Therefore, the terminal equipment can screen the face data first, remove invalid face data and screen to obtain valid face data. In this way, the accuracy of the first age information is facilitated to be improved.

As an alternative embodiment, prior to S403, the method 400 further includes: determining feedback delay based on the service requirement of the current service of the terminal equipment; acquiring third biological data of the user using the terminal device in a preset time period under the condition that the feedback time delay is greater than or equal to a second preset threshold value, wherein the third biological data comprises at least one of sound data, breathing data or face data; based on the historical data, determining whether the crowd type to which the user belongs is the target crowd type includes: based on the historical data and the third biometric data, it is determined whether the group type to which the user belongs is the target group type.

It should be understood that the current service of the terminal device may refer to a service that triggers the crowd type recognition method, for example, in the case that the user uses the payment service of the terminal device, the crowd type recognition method is required to recognize whether the user is an minor, and the current service of the terminal device may refer to the payment service. The second preset threshold may be a preset duration, for example 5s, etc. The preset time period may be a preset duration, for example, 3s, 10s, etc. In one example, the second preset threshold is 6s, the preset time period is 5s, when the user uses the payment service, it needs to determine whether the user is a minor, and the feedback delay of the payment service is 1s, and then the feedback delay of the current service is smaller than the second preset threshold, so that the terminal device will not acquire the third biological data, but determine whether the crowd type to which the user belongs is a minor type based on the history data. In another example, the second preset threshold is 6s, the preset time period is 5s, the user opens the first game application program, the terminal device judges whether the user is a minor, and if the user is a minor, a down notification message is displayed when the duration of using the first game application program by the user is 2 hours, and the down notification message is used for reminding the user that the account number which is currently logged in will be automatically down and exit the game interface, so that the duration of playing the game by the user is limited. For the game service, the feedback delay is 30s, so the feedback delay of the current service is greater than the second preset threshold, and therefore the terminal device will continue to acquire the third biological data of the user within 5 s. And determining whether the group type to which the user belongs is a minor type based on the history data and the third biometric data.

The crowd type recognition method will be further described below with reference to fig. 5, taking the target crowd type as the minor type and the user as the first type.

Fig. 5 is a schematic process diagram of a crowd type recognition method 500 when a user is a first type user according to an embodiment of the present application. As shown in fig. 5, the face data may include at least one of face data in the first biometric data, face data in the second biometric data, or face data in the third biometric data; the sound data may include at least one of sound data in the first biometric data, sound data in the second biometric data, or sound data in the third biometric data; the respiration data may include at least one of respiration data in the first biometric data, respiration data in the second biometric data, or respiration data in the third biometric data. After the terminal equipment inputs the face data into the face-image model, the face-image model can output face feature vectors; after the terminal device inputs the sound data or the breathing data into the audio model, the audio model can output sound feature vectors or breathing feature vectors, specifically, the sound data is input into the audio model, the audio model outputs sound feature vectors, and after the breathing data is input into the audio model, the audio model outputs breathing feature vectors. In the presence of sound data and respiration data, the terminal device inputs the sound data to the audio model. Then, the terminal device inputs the voice feature vector or the respiratory feature vector and the face feature vector to the biometric fusion model, so that the fused biometric can be output, and inputs the fused biometric to the face-human speech model or the face-respiratory model, so that the face-human speech model or the face-respiratory model can output the first age information. The terminal device inputs the history use data and the first age information to a bio-user portrayal fusion model, which is capable of outputting whether the user is of a minor type or a non-minor type. Optionally, before the historical usage data is input into the biological-user portrait fusion model, the feature vector of the historical usage data is acquired through a deep learning convolution model; the feature vector of the history use data and the first age information are input to the bio-user portrayal fusion model, and the user is output as a minor type or not.

It should be understood that the face-image model and the audio model may be models such as a deep learning convolution model, and the model types of the face-image model and the audio model are not particularly limited in the present application. The biometric fusion model may fuse a face feature vector with a voice feature vector, or combine a face feature vector with a respiratory feature vector. The bio-user portrayal fusion model is capable of determining second age information of the user based on the historical usage data and determining whether the user is of the minors type based on the first age information and the second age information.

As an alternative embodiment, the method 400 further comprises: under the condition that the first biological data is not acquired, acquiring interaction data of a user when the terminal equipment is currently used, wherein the interaction data comprises at least one of sensor data, touch screen data or application program type data; based on the interaction data, it is determined whether the crowd type to which the user belongs is a target crowd type.

It should be appreciated that in the case where the first biological data is not acquired, it cannot be determined whether the user is a first type of user based on the first biological data, and thus, the terminal device cannot acquire history data of historical use of the terminal device by the user. In such a case, the terminal device may acquire interaction data of the user when the terminal device is currently used. The interactive data of the user when using the terminal device currently may refer to at least one of sensor data, touch screen data or application type data obtained by the terminal device in the process of using the terminal device by the user. The sensor data may include data of sensors such as pressure sensors, temperature sensors, and the like; the touch screen data can comprise data such as sliding length, area and the like of a display screen of the finger terminal equipment of the user when the display screen slides; the application type data may include data such as an application type and/or an application name used when the user uses the terminal device this time.

As an alternative embodiment, the method 400 further comprises: under the condition that the user is not a first type user, acquiring interaction data of the user when the user currently uses the terminal equipment, wherein the interaction data comprises at least one of sensor data, touch screen data or application program type data; based on the interaction data and/or the first biological data, whether the crowd type to which the user belongs is a target crowd type or not is determined.

In one possible implementation, in a case where the first biological data includes at least one of face data, sound data, or respiratory data, determining whether a group type to which the user belongs is a target group type based on the first biological data; in the case that the first biological data does not include face data, sound data, and breathing data, it is determined whether the crowd type to which the user belongs is a target crowd type based on the interaction data.

It should be appreciated that the accuracy of the terminal device in determining whether the crowd type to which the user belongs is the target crowd type may be higher based on at least one of face data, sound data, or breathing data in the first biometric data than the interaction data. Therefore, in the case where the first biological data includes at least one of face data, sound data, or breathing data, it is determined whether the crowd type to which the user belongs is the target crowd type based on the first biological data, so that the accuracy of the crowd type recognition method can be improved.

As an alternative embodiment, in the case where the first biometric data is not acquired and/or the user is not a first type of user, before determining whether the crowd type to which the user belongs is the target crowd type, the method 400 further includes: based on the interaction data, selecting the expansion interaction data of the first age group from the expansion interaction data of a plurality of age groups, wherein the expansion interaction data of the plurality of age groups are obtained by clustering sample interaction data, and the sample interaction data comprises at least one of sensor data, touch screen data or application program type data of a plurality of users using the terminal equipment in a historical time period; fusing the interactive data with the extended interactive data of the first age group to obtain fused interactive data; determining whether the crowd type to which the user belongs is the target crowd type includes: based on the fused interaction data, it is determined whether the user is of the target crowd type.

It should be appreciated that the terminal devices used by the plurality of users over the historical time period may be different, and may be, for example, a tablet, a smart watch, a notebook, a cell phone, etc. The duration of the history period may be any duration. The plurality of users may include users of different ages. After clustering a plurality of users in a historical time period by using at least one of sensor data, touch screen data or application type data of the terminal equipment, expanded interaction data of a plurality of age groups can be obtained. The extended interaction data includes at least one of sensor data, touch screen data, or application type data. For example, sensor data, touch screen data and application type data of 1000 users using the terminal device in a historical time period are clustered to obtain extended interaction data of ages 10 to 20 years old, extended interaction data of ages 20 to 40 years old, extended interaction data of ages 40 to 60 years old and extended interaction data of ages 60 to 70 years old; the extended interaction data of different age groups are respectively different.

In one possible implementation, the first age group is determined by a classification algorithm based on the interaction data and the expanded interaction data for the plurality of age groups. It should be appreciated that, through the classification algorithm, it may be determined that the age of the user may belong to the first age group, and thus, the expanded interaction data and the interaction of the first age group are fused to obtain fused interaction data. The classification algorithm may be a K-nearest neighbor (KNN) classification algorithm or the like. Because the interactive data is the data acquired by the terminal equipment when the user uses the terminal equipment currently, the interactive data acquired by the terminal equipment may be less in a shorter period of time. The interactive data of the user can be expanded by fusing the expanded interactive data and the interactive data of the first age group, so that whether the user is of the target crowd type can be more accurately determined by fusing the interactive data.

In one possible implementation, before selecting the expanded interaction data of the first age group from the expanded interaction data of the plurality of age groups, the method 400 further includes: extracting characteristics of the sample interaction data by using an age model to obtain characteristic data; and carrying out cluster analysis on the characteristic data to obtain the expanded interaction data of a plurality of age groups.

It should be understood that the terminal device may perform feature extraction on the sensor data, the touch screen data, and the application type data in the sample interaction data through the age model, respectively. Specifically, the terminal device inputs the sensor data, the touch screen data and the application program type data in the sample interaction data into an age model respectively, and the age model can output the characteristic data of three modes, namely the characteristic data of the sensor mode, the characteristic data of the touch screen mode and the characteristic data of the application program type mode. The feature data may be a feature vector or a feature matrix. The three modalities may be referred to as a sensor modality, a touch screen modality, and an application type modality, respectively. The age model may be a deep learning convolution model. The feature data output by the age model may include different age tags, respectively. The age tag may refer to information indicating any one of ages, or information indicating one age group. The terminal equipment clusters the characteristic data of the three modes to obtain the expansion interaction data of a plurality of age groups, and the expansion interaction data of the plurality of age groups can also be called as codebook characteristics of the plurality of age groups. Each of the plurality of age groups may include different extended interaction data, respectively. Extended interaction data for multiple age groups may also be referred to as a feature pattern library. The clustering analysis may be performed by different clustering methods, for example, an unsupervised clustering method may be used, where spatial clustering (density-based spatial clustering of applications with noise, DBSCAN), K mean point (Kmeans) clustering methods may be applied to density-based noise, and K means that sample interaction data may be aggregated into K clusters by Kmeans.

Fusing the interactive data with the extended interactive data of the first age group can refer to fusing the feature data of the interactive data with the extended interactive data of the first age group. The characteristic data of the interaction data may also be obtained by means of an age model. Specifically, the interactive data and the expanded interactive data of the first age group are fused, namely, the characteristic data of the sensor mode in the interactive data and the characteristic data of the sensor mode in the expanded interactive data of the first age group are tiled and spliced; the method comprises the steps of tiling and splicing characteristic data of a touch screen mode in the interaction data and characteristic data of the touch screen mode in the expanded interaction data of a first age group; and tiling and splicing the characteristic data of the application program type mode in the interaction data and the characteristic data of the application program type mode in the extended interaction data of the first age group.

The crowd type identification method when the user is a first type user or is not a first type user is further described below with reference to fig. 6.

Fig. 6 is a schematic process diagram of a crowd type recognition method 600 when a user is a first type user or is not a first type user according to an embodiment of the present application. As shown in fig. 6, in the case where the terminal device acquires the first biological data, it is determined whether the user is a first type user based on the first biological data. In the case that the user is a first type of user, the feature data of the biometric data, for example, the biometric vector, may be obtained based on the biometric data of the user; based on the history use data, feature data of the history use data, such as history use feature vectors, is obtained. Then, the terminal device determines whether the crowd type to which the user belongs is the target crowd type based on the biometric vector and the history use feature vector. It is understood that in the case where the first biological data includes at least one of face data, sound data, or respiratory data, the biological data includes first biological data and second biological data; in the case that the first biological data includes at least one of face data, sound data, or respiratory data, and the feedback delay of the current service is greater than or equal to a second preset threshold, the biological data includes first biological data, second biological data, and third biological data; when the first biological data does not comprise face data, voice data and breathing data, and the feedback time delay of the current service is greater than or equal to a second preset threshold value, the biological data comprises second biological data and third biological data; and under the condition that the first biological data does not comprise face data, voice data and breathing data and the feedback time delay of the current service is smaller than a second preset threshold value, the biological data comprises second biological data.

As shown in fig. 6, in the case where the user is not the first type user, the terminal device acquires the interactive data of the user currently using the terminal device. The terminal equipment classifies the interactive data based on the interactive data and the expanded interactive data of a plurality of age groups, and determines the expanded interactive data of a first age group; the terminal equipment fuses the expansion interactive data and the interactive data of the first age group to obtain fused interactive data; and finally, the terminal equipment determines whether the crowd type of the user belongs to the target crowd type or not based on the fusion interaction data and/or the biological characteristic data. It is understood that in the case where the first biological data includes at least one of face data, sound data, or respiration data, the biological data includes the first biological data; and under the condition that the feedback time delay of the current service is greater than or equal to a second preset threshold value, the biological data comprises third biological data. And under the condition that the first biological data does not comprise face data, sound data and breathing data and the feedback time delay of the current service is smaller than a second preset threshold value, the terminal equipment determines whether the crowd type of the user belongs to the target crowd type or not based on the fusion interaction data.

In one possible implementation, before fusing the interaction data with the expanded interaction data of the first age group, the method 400 further includes: performing dimension reduction processing on the interaction data to obtain dimension reduction interaction data; performing dimension reduction processing on the expansion interaction data of the first age group to obtain dimension reduction expansion interaction data; fusing the interaction data with the expanded interaction data of the first age group, including: and fusing the dimension-reducing interaction data and the dimension-reducing expansion interaction data to obtain fused interaction data.

It will be appreciated that the dimensions of the interaction data and the expanded interaction data may be reduced by a dimension reduction process. Performing dimension reduction processing on the interaction data can mean performing dimension reduction processing on three modes of data in the interaction data respectively, namely reducing the dimension of sensor data in the interaction data; the dimension of touch screen data in the interaction data is reduced; and reducing the dimension of the application program type data in the interaction data. Optionally, before performing dimension reduction processing on the interaction data, obtaining feature data of the interaction data based on the interaction data; and performing dimension reduction processing on the characteristic data of the interaction data. Specifically, the dimension of the characteristic data of the sensor mode in the interaction data is reduced; the dimension of the characteristic data of the touch screen mode in the interaction data is reduced; and reducing the dimension of the characteristic data of the application program type mode in the interaction data.

The dimension reduction process may reduce the variety of data included in the data of the three modalities, and the sensor data includes, for example, the three-dimensional data of the pressure sensor data, the temperature sensor data, and the gyro sensor data; by performing dimension reduction processing on the sensor data, the dimension data of the gyroscope sensor data can be reduced, and the two dimensions data of the sensor data and the temperature sensor data can be obtained. Alternatively, the dimension reduction process may be performed by a method such as principal component analysis (PRINCIPAL COMPONENT ANALYSIS, PCA) or partial least squares discriminant analysis (PARTIAL LEAST square DISCRIMINANT ANALYSIS, PLSDA), and the method for the dimension reduction process is not particularly limited in the present application.

The dimension reduction interactive data and the dimension reduction expansion interactive data are fused, so that the interactive data of each mode in the dimension reduction interactive data can be tiled and spliced with the data which is the same as each mode in the expansion interactive data respectively. Illustratively, the dimension-reduced interaction data includes feature data of a sensor modality after dimension reduction, feature data of a touch screen modality after dimension reduction, and feature data of an application program type modality after dimension reduction; the dimension-reduced expansion data comprise expansion feature data of a sensor mode after dimension reduction, expansion feature data of a touch screen mode after dimension reduction and expansion feature data of an application program type mode after dimension reduction. The terminal equipment tiles and splices the feature data of the sensor mode after the dimension reduction and the extended feature data of the sensor mode after the dimension reduction; tiling and splicing the feature data of the touch screen mode after the dimension reduction with the extended feature data of the touch screen mode after the dimension reduction; and tiling and splicing the feature data of the application program type mode after the dimension reduction with the extended feature data of the application program type mode after the dimension reduction.

In a possible implementation manner, classifying and encoding the feature vector of each mode in the dimension-reduction expansion interaction data, and solving orthogonal public feature data of the feature vector of each mode in the dimension-reduction expansion interaction data; fusing the dimension-reduced interaction data and the dimension-reduced expansion interaction data to obtain fused interaction data, wherein the method comprises the following steps of: and respectively tiling and splicing the orthogonal public feature data of the feature vector of each mode in the dimension-reduction expansion interaction data and the feature vector of each mode in the dimension-reduction interaction data to obtain fusion interaction data. Where the orthogonal common features may refer to the common features of the feature vectors of each modality.

Illustratively, the dimension-reduced extended interaction data includes feature vectors of a sensor modality, feature vectors of a touch screen modality, and feature vectors of an application type modality; the terminal equipment respectively solves orthogonal public feature data of the feature vector of the sensor mode, orthogonal public feature data of the feature vector of the touch screen mode and orthogonal public feature data of the feature vector of the application program type mode in the dimension reduction expansion interaction data; the terminal equipment tiles and splices orthogonal public feature data of the feature vector of the sensor mode in the dimension-reduction expansion interaction data and the feature vector of the sensor mode in the dimension-reduction interaction data; tiling and splicing orthogonal public feature data of the feature vector of the touch screen mode in the dimension-reduction expansion interaction data and the feature vector of the touch screen mode in the dimension-reduction interaction data; and tiling and splicing the orthogonal public feature data of the feature vector of the application program type mode in the dimension-reducing expansion interaction data and the feature vector of the application program type mode in the dimension-reducing interaction data. Wherein the orthogonal common feature data may be an orthogonal common feature vector.

As an alternative embodiment, the interaction data includes N pieces of touch screen data that are the inverse of the moment before the moment the interaction data is acquired, where N is a positive integer.

It should be appreciated that N may be a predetermined positive integer.

As an optional embodiment, the interaction data comprises M pieces of effective touch screen data after screening, wherein M is a positive integer.

Specifically, the method for screening out valid touch screen data may be as follows.

In one possible implementation, the method 400 further includes: determining the time interval between adjacent touch screen data in N pieces of touch screen data according to the generation time of each piece of touch screen data in N pieces of touch screen data before the time of acquiring the interaction data, wherein N is a positive integer greater than or equal to M; according to the time interval, determining a person changing time node; and determining touch screen data after the time node of the person changing closest to the time of acquiring the interactive data in the time node of the person changing as M pieces of effective touch screen data.

It should be understood that the generation time of each piece of touch screen data may refer to the time when the user touches the screen so that the terminal device obtains and stores the touch screen data. The adjacent touch screen data refers to any two adjacent pieces of touch screen data after the N pieces of touch screen data are ordered according to the generation time. Illustratively, the N pieces of touch screen data include one piece of touch screen data of 10:00 at a time of generation 2023, 1 month, 1 day, 10:05, 2023, 1 month, 1 day, 9:00, and 2023, 1 month, 1 day, 12:00; after the N pieces of touch screen data are ordered, the N pieces of touch screen data are sequentially arranged from the early to the late according to the time sequence: one piece of touch screen data of 2023, 1, 9:00, one piece of touch screen data of 2023, 1, 10:00, one piece of touch screen data of 2023, 1, 10:05, and one piece of touch screen data of 2023, 1, 12:00; the time interval between two adjacent pieces of touch screen data refers to the time interval between one piece of touch screen data of 2023, 1 month, 1 day, 9:00 and one piece of touch screen data of 2023, 1 month, 1 day, 10:00, one piece of touch screen data of 2023, 1 month, 1 day, 10:05, and one piece of touch screen data of 2023, 1 month, 1 day, 12:00. According to the time interval, the determining the change time node specifically may be: and determining two adjacent touch screen data with time intervals greater than or equal to the preset time length as a change time node. In the case where two adjacent touch screen data are determined as the change time node, the terminal device may determine that the two adjacent touch screen data are generated when different users touch the screen according to the change time node. Under the condition that the number of the time nodes for changing the people is 1, the time node for changing the people is the time node for changing the people closest to the moment of acquiring the interactive data; when the number of the change time nodes is greater than 1, the generation time of the adjacent touch screen data corresponding to the plurality of change time nodes is different, and at this time, the terminal device may refer to the generation time of the touch screen data, which is closer to (may be farther from) the time of acquiring the interaction data, among the generation time of the two adjacent touch screen data corresponding to each of the plurality of change time nodes, and determine one of the plurality of change time nodes closest to the time of acquiring the interaction data. The touch screen data with the generation time being closer to the time of acquiring the interaction data and the touch screen data with the generation time being after the generation time of the touch screen data are M pieces of effective touch screen data.

Illustratively, assuming that the time interval between two adjacent pieces of touch screen data includes a time interval between one piece of touch screen data of 2023 month 1 day 9:00 and one piece of touch screen data of 2023 month 1 day 10:00, a time interval between one piece of touch screen data of 2023 month 1 day 10:00 and one piece of touch screen data of 2023 month 1 day 10:05, a time interval between one piece of touch screen data of 2023 month 1 day 10:05 and one piece of touch screen data of 2023 month 1 day 10:25, a time interval between one piece of touch screen data of 2023 month 1 day 10:25 and one piece of touch screen data of 2023 month 1 day 10:28, and the preset time length is 10min, the number of time nodes for a swap is two, which are, in order, one piece of touch screen data of 2023 month 1 day 9:00 and one piece of touch screen data of 2023 month 1 day 10:00, and one piece of touch screen data of 2023 month 1 day 10:25 of year 1:25, respectively; taking the generation time of the touch screen data, which is closer to the time of acquiring the interaction data, of the adjacent touch screen data in the two time nodes as a reference, namely taking the generation time of one piece of the touch screen data of 2023, 1 month, 1 day, 10:00 in the one piece of the touch screen data of 2023, 1 month, 1 day, 10:05 in the one piece of the touch screen data of 2023, 1 month, 1 day, 10:25 as a reference; one piece of touch screen data of 2023, 1 month, 1 day, 10:05 and one piece of touch screen data of 2023, 1 month, 1 day, 10:25 are more proximate to the time point at which the interaction data is acquired; therefore, if one piece of touch screen data of 10:25 on month 1 of 2023 and one piece of touch screen data after the generation time of the touch screen data are M pieces of valid touch screen data, the M pieces of valid touch screen data are one piece of touch screen data of 10:25 on month 1 of 2023 and one piece of touch screen data of 10:28 on month 1 of 2023.

In a possible implementation manner, a pseudo tag method is combined with an anti-learning method, and based on the equipment types of the terminal equipment currently used by the user, adaptive migration processing is performed on the extended interaction data and the weak models of the multiple age groups, so that the extended interaction data of the multiple age groups are more suitable for the equipment to identify the crowd types of the user.

It should be understood that the adaptive migration process may be a process of changing sample interaction data according to the device type of the terminal device currently used by the user. Exemplary sample interaction data includes a plurality of users using terminal device touch screen data over a historical period of time; in the sample interaction data, the terminal devices used by the plurality of users in the historical time period can comprise a tablet computer, a smart watch and other devices, assuming that the terminal device currently used by the user is a mobile phone, and in the case that the mobile phone needs to determine whether the crowd type to which the user belongs is the target crowd type based on the fused interaction data, because touch screen data in the sample interaction data can be stored when the plurality of users use the terminal devices such as the tablet computer, the smart watch and other devices, and the sizes of the touch screens of the tablet computer, the smart watch and the mobile phone are different, for example, the sliding length of the user on the display screen of the tablet computer can be obviously longer than the sliding length of the user on the display screen of the mobile phone, and the sliding length of the user on the display screen of the smart watch can be obviously shorter than the sliding length on the display screen of the mobile phone. Therefore, the touch screen data in the expanded interactive data of the first age group obtained based on the sample interactive data is spliced to the touch screen data in the interactive data, so that the touch screen data in the expanded interactive data and the touch screen data in the interactive data are not adapted. In view of this, the terminal device may perform adaptive migration processing on the sample interaction data according to the type of the terminal device currently used by the user, for example, to increase or decrease the sliding length data on the display screen in the touch screen data in the sample interaction data in equal proportion.

The method for determining whether the crowd type to which the user belongs is the target crowd type by the terminal device in the case that the user is not the first type user or the first biological data is not acquired is further described below with reference to fig. 7.

Fig. 7 is a schematic diagram illustrating a process of a crowd-type recognition method 700 when a user is not a first type user or does not acquire first biological data according to an embodiment of the application. As shown in fig. 7, the sample interaction data includes sensor data, touch screen data, and application type data of a plurality of users using the terminal device for a history period. The terminal device can obtain sensor characteristic data of the sensor data, touch screen characteristic data of the touch screen data and application program type characteristic data of the application program type data through the age model respectively, wherein the characteristic data can be a characteristic vector or a characteristic matrix. And then, the terminal equipment clusters the sensor characteristic data, the touch screen characteristic data and the application program type characteristic data respectively to obtain the expansion interaction data of a plurality of age groups.

As shown in fig. 7, the interaction data includes sensor data, touch screen data and application type data of the terminal device currently used by the user, and the sensor data, the touch screen data and the application type data in the interaction data are respectively input into a trained deep learning convolution model to obtain sensor feature data, touch screen feature data and application type feature data, where the feature data may be feature vectors or feature matrices. And then, respectively classifying the sensor characteristic data, the touch screen characteristic data and the application program type characteristic data into the expansion interaction data of a plurality of age groups by a KNN classification method.

Optionally, the terminal device may perform dimension reduction processing on the sensor feature data, the touch screen feature data, and the application type feature data in the extended interaction data of the multiple age groups, and solve an orthogonal public feature of the dimension reduced sensor feature data, an orthogonal public feature of the dimension reduced touch screen feature data, and an orthogonal public feature of the dimension reduced application type feature data. Then, the terminal equipment tiling and splicing the orthogonal public features of the sensor feature data after the dimension reduction in the expansion interaction data of the first age group and the sensor feature data in the interaction data; orthogonal public features of the touch screen feature data after dimension reduction in the expanded interactive data of the first age group are tiled and spliced with the touch screen feature data in the interactive data; orthogonal public features of the application program type feature data after dimension reduction in the extended interaction data of the first age group are tiled and spliced with the application program type feature data in the interaction data, so that fusion interaction data are obtained. The fused interaction data may also be referred to as code feature data. And finally, the terminal equipment determines whether the crowd type to which the user belongs is minors or not based on the fusion interaction data and/or the biological data. The biometric data may include first biometric data and/or third biometric data. Optionally, in the case that the first biological data and the third biological data are not acquired, the terminal device determines whether the crowd type to which the user belongs is minors based on the fused interaction data; in the case where the first biometric data includes at least one of face data, voice data, or respiratory data, and/or the third biometric data is acquired, the terminal device may determine whether the crowd type to which the user belongs is minors based on the fusion of the interactive data and the biometric data, or based on the biometric data.

Optionally, under the condition that the terminal device determines whether the crowd type to which the user belongs is minors based on the fusion interaction data and the biological data, the terminal device can firstly fuse the fusion interaction data with the characteristic data of the biological data to obtain spliced characteristic data; inputting the spliced characteristic data into an interaction-biological model, and determining whether the crowd type of the user belongs to is the juvenile type. Wherein the interaction-biological model may be denoted as model B, and the interaction-biological model may include a face-human speaking voice model, a breath-human speaking voice model, a weak model, and a bio-interaction model, wherein the face-human speaking voice model or the breath-human speaking voice model may determine the first age information according to the feature data of the first biological data; the weak model can determine habit information of the user using the terminal equipment according to the fused interaction data; the bio-interaction model may determine whether the crowd type to which the user belongs is a target crowd type according to the first age information and habit information of the user using the terminal device. The habit information may be a feature vector or a feature matrix.

Fig. 8 is a process diagram of another crowd type recognition method 800 according to an embodiment of the application. As shown in fig. 8, in the case where the terminal device obtains the first biological data, it is determined whether the user is the first type user based on the first biological data, specifically, the terminal device may input the first biological data to the owner identification model, and the owner identification model outputs that the user is the first type user or is not the first type user. Then, the terminal device may determine, based on the service requirement of the current service, a feedback delay of the current service, and determine whether the feedback delay of the current service is greater than or equal to a second preset threshold. In the case that the user is a first type user, acquiring historical usage data and second biometric data of the user; and acquiring third biological data under the condition that the feedback time delay of the current service is greater than or equal to a second preset threshold value. The terminal device determines whether a group type to which the user belongs is a target group type based on the historical usage data and the biometric data, the biometric data including second biometric data, and in a case where the first biometric data includes at least one of face data, sound data, or respiratory data, the biometric data further includes the first biometric data; and under the condition that the feedback time delay of the current service is greater than or equal to a second preset threshold value, the biological data further comprises third biological data.

Specifically, the terminal device determines whether the crowd type to which the user belongs is the target crowd type based on the history use data and the biological data, by: on the one hand, the terminal equipment inputs the face data in the biological data into a face-image model, and the face-image model outputs a face feature vector; the terminal device inputs sound data or breathing data in the biological data into an audio model, and the audio model outputs sound characteristic vectors or breathing characteristic vectors. Then, in the case where the biological data includes sound data and face data, the terminal device inputs the face feature vector and the sound feature vector into a face-human speaking sound model capable of outputting the first age information; in the case that the biological data includes respiratory data and face data, the terminal device inputs the face feature vector and the respiratory feature vector into a face-respiratory model, the face-respiratory model being capable of outputting first age information; in the case that the biological data includes face data, the terminal device may input a face feature vector into at least one of a face model, a face-human speaking voice model, or a face breathing model, to obtain first age information; in the case that the biological data includes voice data, the terminal device may input a voice feature vector into a face-human speaking voice model to obtain first age information; under the condition that the biological data comprises breathing data, the terminal equipment can input a breathing characteristic vector into a face-breathing model to obtain first age information; in case the biometric data comprises sound data and breathing data, the terminal device may input the sound feature vector into a face-human speaking sound model, resulting in the first age information. The first age information may be an age group to which the user belongs and a probability that the user belongs to the age group, or may be a feature vector or a feature matrix. On the other hand, the terminal device can obtain the feature vector of the historical usage data through the deep learning convolution model, then the feature vector of the historical usage data is input into the user portrait model, the user portrait model can output life habit information of the user and/or habit information of the terminal device based on the historical usage data, for example, the type of food most purchased by the user, the type of webpage most browsed by the user and the like, and the habit information can be the feature vector or the feature matrix. Finally, the terminal device may determine, based on the first age information and the habit information, whether the crowd type to which the user belongs is the target crowd type, and specifically, the terminal device may input the first age information and the habit information into an age-habit model, and the age-habit model outputs whether the crowd type to which the user belongs is the target crowd type.

As shown in fig. 8, in case the user is not the first type user, the terminal device may determine whether the group type to which the user belongs is the target group type based on the biometric data and/or the interaction data. In the case where the first biometric data includes at least one of face data, voice data, or respiratory data, the biometric data includes the first biometric data; and under the condition that the feedback time delay of the current service is greater than or equal to a second preset threshold value, the biological data further comprises third biological data.

Optionally, in the case that the first biological data includes at least one of face data, sound data or breathing data, and/or the feedback delay of the current service is greater than or equal to a second preset threshold, the terminal device determines whether the group type to which the biological data belongs is the target group type based on the biological data. It should be appreciated that, the specific embodiment of determining whether the crowd type to which the user belongs is the target crowd type based on the biological data is the same as the case where the user is the first type user, and reference may be made to the above, and details thereof will not be repeated.

And under the condition that the first biological data does not comprise face data, sound data and breathing data and the feedback time delay of the current service is smaller than a second preset threshold value, the terminal equipment determines whether the crowd type of the user belongs to the target crowd type or not based on the interaction data. Specifically, the terminal device inputs sensor data in the interaction data to an inertial measurement unit (inertial measurement unit, IMU) model, and outputs a feature vector of the sensor data; the terminal equipment inputs touch screen data in the interaction data to the sliding model and outputs feature vectors of the touch screen data; the terminal device inputs the application type data in the interaction data to an Application (APP) model, and outputs a feature vector of the application type data. The IMU model, the sliding model, and the APP model may be deep learning convolution models. Then, the terminal device inputs the feature vector of the sensor data, the feature vector of the touch screen data, and the feature vector of the application type data to the weak model, and the weak model can input whether the crowd type to which the user belongs is the target crowd type or not.

It can be understood that the face data in the embodiment of the present application may include face image data obtained by a terminal device when a user is used to unlock the terminal device from a face; the voice data and the breath data can be cached in the terminal equipment when the user uses services such as voice call, video call, voice assistant and the like, and can also be obtained when the terminal equipment needs to determine whether the crowd type to which the user belongs is the target crowd type; the sensor data can be data cached by the terminal equipment when the user calls part of APP by using the terminal equipment, or can be data acquired under the condition that the terminal equipment needs to determine whether the crowd type to which the user belongs is the target crowd type; the touch screen data can be acquired by the terminal equipment and stored in the cache in the form of a sliding window when the user touches the display screen of the terminal equipment, for example, the data size of the touch screen data stored in the terminal equipment is certain, and the touch screen data furthest from the current moment is correspondingly deleted along with the generation of new touch screen data; when the terminal equipment needs to determine whether the crowd type of the user is the target crowd type based on the touch screen data, the touch screen data stored in the cache is called, namely N pieces of data of the reciprocal of the moment before the touch screen data are acquired are called; the application type data can be collected when the user uses the application of the terminal device, and in the form of a sliding window, the application type data of the buffer memory is called under the condition that the terminal device needs to determine whether the crowd type to which the user belongs is the target crowd type based on the application type data.

As an optional embodiment, in a case where the crowd type recognition switch is in an on state, the terminal device determines whether the crowd type to which the user belongs is the target crowd type periodically or when the current service of the terminal device is a preset service type. It should be understood that the crowd type identification switch may set a selection button in the interface for the system of the terminal device, as shown by selection button 901 in fig. 9. When the user clicks the selection button 901, the crowd type recognition switch may be turned on or off.

It should be understood that various models related to the embodiments of the present application, such as a face-image model, a face model, a human speaking voice model, a biometric fusion model, a biometric-user portrait fusion model, an age model, a weak model, and the like, may be models such as a neural network model, a deep learning convolution model, and the like, which may be obtained through deep learning, and the present application is not limited thereto.

It should be understood that various models related to the embodiments of the present application, for example, a face-image model, a face model, a human speaking voice model, a biometric fusion model, a biometric-user portrait fusion model, an age model, a weak model, etc., may be named as other names, and the models capable of achieving the corresponding effects may be used as the models.

It should be understood that the sequence numbers of the above methods do not mean the order of execution, and the order of execution of the processes should be determined by the functions and internal logic thereof.

The crowd type recognition method according to the embodiment of the application is described in detail above with reference to fig. 3 to 9, and the crowd type recognition device according to the embodiment of the application is described in detail below with reference to fig. 10 and 11. The crowd type recognition device comprises a module or unit for executing the corresponding part of each embodiment. The modules or units may be software, hardware, or a combination of software and hardware. The crowd type recognition device is only briefly illustrated below, and for implementation details of the scheme, reference may be made to the description of the foregoing method embodiment, which is not repeated herein.

Fig. 10 is a schematic block diagram of a crowd type recognition device 1000 according to an embodiment of the application. As shown in fig. 10, the apparatus 1000 includes: an acquisition module 1001 and a processing module 1002.

In a possible implementation manner, the apparatus 1000 may be used to implement steps corresponding to the terminal device in the foregoing method embodiment.

An obtaining module 1001, configured to determine, based on first biometric data, whether the user is a first type user when the first biometric data is obtained when the user is currently using the device 1000, where the first biometric data includes at least one of sound data, breathing data, fingerprint data, or face data, and the first type user is a user whose usage time and/or usage number of the device 1000 exceeds a first preset threshold; in the case where the user is a first type of user, acquiring historical data of the device 1000, the historical data including historical usage data of an application on the device 1000 and second biometric data of the user when the device 1000 is used historically, the second biometric data including at least one of sound data, respiratory data, or face data; the processing module 1002 is configured to determine, based on the history data, whether the crowd type to which the user belongs is the target crowd type.

Optionally, the processing module 1002 is specifically configured to: in the case where the first biometric data includes at least one of sound data, respiratory data, or face data, it is determined whether the group type to which the user belongs is a target group type based on the history data and the first biometric data.

Optionally, the processing module 1002 is specifically configured to: fusing the first biological data and the second biological data to obtain fused biological data; determining first age information of the user based on the fused biometric data; based on the first age information and the historical usage data, it is determined whether the crowd type to which the user belongs is a target crowd type.

Optionally, the target population type is a minor type; the processing module 1002 is further configured to: determining second age information of the user based on the historical usage data; the processing module 1002 is specifically configured to: based on the first age information and the second age information, it is determined whether the group type to which the user belongs is a minor type.

Optionally, the processing module 1002 is specifically configured to: under the condition that the fusion biological data comprises face data and voice data, inputting the fusion biological data into a face-human speaking voice model to obtain first age information, wherein the face-human speaking voice model is obtained by carrying out weighted fusion on an output result of the face model and an output result of the human speaking voice model; or under the condition that the fusion biological data comprises face data and breathing data, inputting the fusion biological data into a face-breathing model to obtain first age information, wherein the face-breathing model is obtained by weighting and fusing an output result of the face model and an output result of the breathing model; or, in the case that the fused biometric data includes face data, voice data, and respiratory data, inputting the face data and the voice data in the fused biometric data to a face-human speaking voice model to obtain the first age information.

Optionally, the face data is effective face data after screening.

Optionally, the obtaining module 1001 is further configured to: in the case where the first biological data is not acquired, acquiring interactive data of the user when the device 1000 is currently used, the interactive data including at least one of sensor data, touch screen data, or application type data; the processing module 1002 is further configured to: based on the interaction data, it is determined whether the crowd type to which the user belongs is a target crowd type.

Optionally, the obtaining module 1001 is further configured to: in the case that the user is not the first type of user, acquiring interaction data of the user when the device 1000 is currently used, the interaction data including at least one of sensor data, touch screen data, or application type data; the processing module 1002 is further configured to: based on the interaction data and/or the first biological data, whether the crowd type to which the user belongs is a target crowd type or not is determined.

Optionally, the processing module 1002 is further configured to: based on the interaction data, selecting extended interaction data of a first age group from extended interaction data of a plurality of age groups, the extended interaction data of the plurality of age groups being obtained by clustering sample interaction data, the sample interaction data including at least one of sensor data, touch screen data, or application type data of the device 1000 used by a plurality of users in a historical time period; fusing the interactive data with the extended interactive data of the first age group to obtain fused interactive data; the processing module 1002 is specifically configured to: based on the fused interaction data, it is determined whether the user is of the target crowd type.

Optionally, the processing module 1002 is further configured to: extracting characteristics of the sample interaction data by using an age model to obtain characteristic data; and carrying out cluster analysis on the characteristic data to obtain the expanded interaction data of a plurality of age groups.

Optionally, the processing module 1002 is further configured to: performing dimension reduction processing on the interaction data to obtain dimension reduction interaction data; performing dimension reduction processing on the expansion interaction data of the first age group to obtain dimension reduction expansion interaction data; the processing module 1002 is specifically configured to: and fusing the dimension-reducing interaction data and the dimension-reducing expansion interaction data to obtain fused interaction data.

Optionally, the interaction data includes N pieces of touch screen data of reciprocal number N before the time of acquiring the interaction data, where N is a positive integer.

Optionally, the interaction data includes M pieces of effective touch screen data after screening, where M is a positive integer.

Optionally, the processing module 1002 is further configured to: determining the time interval between adjacent touch screen data in N pieces of touch screen data according to the generation time of each piece of touch screen data in N pieces of touch screen data before the time of acquiring the interaction data, wherein N is a positive integer greater than or equal to M; according to the time interval, determining a person changing time node; and determining touch screen data after the time node of the person changing closest to the time of acquiring the interactive data in the time node of the person changing as M pieces of effective touch screen data.

Optionally, the processing module 1002 is further configured to: determining a feedback delay based on a service requirement of a current service of the apparatus 1000; acquiring third biometric data of the user using the device 1000 within a preset time period, the third biometric data including at least one of sound data, respiratory data, or face data, if the feedback delay is greater than or equal to a second preset threshold; the processing module 1002 is specifically configured to: based on the historical data and the third biometric data, it is determined whether the group type to which the user belongs is the target group type.

It should be appreciated that the apparatus 1000 herein is embodied in the form of functional modules. The term module herein may refer to an Application Specific Integrated Circuit (ASIC), an electronic circuit, a processor (e.g., a shared, dedicated, or group processor, etc.) and memory that execute one or more software or firmware programs, a combinational logic circuit, and/or other suitable components that support the described functionality. In an alternative example, it will be understood by those skilled in the art that the apparatus 1000 may be specifically a terminal device in the foregoing embodiment, and the apparatus 1000 may be configured to perform each flow and/or step corresponding to the terminal device in the foregoing method embodiment, which is not described herein for avoiding repetition.

The apparatus 1000 has a function of implementing the corresponding steps executed by the terminal device in the method; the above functions may be implemented by hardware, or may be implemented by hardware executing corresponding software. The hardware or software includes one or more modules corresponding to the functions described above.

In an embodiment of the present application, the apparatus 1000 in fig. 10 may also be a chip, for example: SOC. Correspondingly, the processing module 1002 may be a transceiver circuit of the chip, which is not limited herein.

Fig. 11 shows a schematic block diagram of another crowd type recognition device 1100 provided by an embodiment of the application. The apparatus 1100 comprises a processor 1101, a transceiver 1102 and a memory 1103. The processor 1101, the transceiver 1102 and the memory 1103 are in communication with each other through an internal connection path, the memory 1103 is used for storing instructions, and the processor 1101 is used for executing the instructions stored in the memory 1103 to control the transceiver 1102 to send and/or receive signals.

It should be understood that the apparatus 1100 may be specifically configured as a terminal device in the foregoing embodiment, and may be configured to perform the steps and/or flows corresponding to the terminal device in the foregoing method embodiment. The memory 1103 may optionally include read only memory and random access memory, and provide instructions and data to the processor. A portion of the memory may also include non-volatile random access memory. For example, the memory may also store information of the device type. The processor 1101 may be configured to execute instructions stored in a memory, and when the processor 1101 executes instructions stored in the memory, the processor 1101 is configured to perform the steps and/or processes of the method embodiments described above. The transceiver 1102 may include a transmitter that may be used to implement various steps and/or processes for performing transmit actions corresponding to the transceiver and a receiver that may be used to implement various steps and/or processes for performing receive actions corresponding to the transceiver.

It is to be appreciated that in embodiments of the application, the processor may be a central processing unit (central processing unit, CPU), which may also be 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, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.

In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in a processor or by instructions in the form of software. The steps of a method disclosed in connection with the embodiments of the present application may be embodied directly in a hardware processor for execution, or in a combination of hardware and software modules in the processor for execution. The software modules may be located in a random access memory, flash memory, read only memory, programmable read only memory, or electrically erasable programmable memory, registers, etc. as well known in the art. The storage medium is located in a memory, and the processor executes instructions in the memory to perform the steps of the method described above in conjunction with its hardware. To avoid repetition, a detailed description is not provided herein.

The present application also provides a computer readable storage medium for storing a computer program for implementing the method shown in the above-described method embodiments.

The present application also provides a computer program product comprising a computer program (which may also be referred to as code, or instructions) which, when run on a computer, performs the method as shown in the method embodiments described above.

Those of ordinary skill in the art will appreciate that the various illustrative modules and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.

It will be clearly understood by those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described system, apparatus and module may refer to corresponding procedures in the foregoing method embodiments, which are not repeated herein.

In the several embodiments provided by the present application, it should be understood that the disclosed systems, devices, and methods may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, and for example, the division of the modules is merely a logical function division, and there may be additional divisions when actually implemented, for example, multiple modules or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or modules, which may be in electrical, mechanical, or other forms.

The modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical modules, i.e., may be located in one place, or may be distributed over a plurality of network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist alone physically, or two or more modules may be integrated into one module.

The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a read-only memory (ROM), a random access memory (random access memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.

The foregoing is merely a specific implementation of the present application, but the scope of the embodiments of the present application is not limited thereto, and any person skilled in the art may easily think about changes or substitutions within the technical scope of the embodiments of the present application, and all changes and substitutions are included in the scope of the embodiments of the present application. Therefore, the protection scope of the embodiments of the present application shall be subject to the protection scope of the claims.

Claims

1. A method for identifying crowd types, comprising:

In the case of obtaining first biometric data of a user when currently using a terminal device, determining whether the user is a first type of user based on the first biometric data, the first biometric data including at least one of voice data, breathing data, fingerprint data or face data, and the first type of user is a user whose usage time or number of usages of the terminal device exceeds a first preset threshold;

When the user is a user of the first type, acquiring historical data of the terminal device, the historical data including historical usage data of the application on the terminal device and second biometric data of the user when the terminal device was historically used, the second biometric data including at least one of voice data, breathing data or face data;

Based on the historical data, it is determined whether the type of population to which the user belongs is a target population type.

2. The method according to claim 1, characterized in that the determining, based on the historical data, whether the type of population to which the user belongs is the target population type comprises:

In the case where the first biological data includes at least one of voice data, breathing data or face data, it is determined whether the type of population to which the user belongs is the target population type based on the historical data and the first biological data.

3. The method according to claim 2, characterized in that the determining whether the type of population to which the user belongs is the target population type based on the historical data and the first biological data comprises:

fusing the first biological data and the second biological data to obtain fused biological data;

Determining first age information of the user based on the fused biometric data;

Based on the first age information and the historical usage data, it is determined whether the population type to which the user belongs is the target population type.

4. The method according to claim 3, characterized in that the target population type is a minor type;

The method further comprises:

determining second age information of the user based on the historical usage data;

The determining whether the type of population to which the user belongs is the target population type includes:

Based on the first age information and the second age information, it is determined whether the type of population to which the user belongs is the minor type.

5. The method according to claim 3 or 4, characterized in that the determining the first age information of the user based on the fused biometric data comprises:

In the case where the fused biometric data includes face data and voice data, the fused biometric data is input into a face-human speech voice model to obtain the first age information, wherein the face-human speech voice model is obtained by weighted fusion of an output result of the face model and an output result of the human speech voice model; or

In the case where the fused biometric data includes face data and respiration data, the fused biometric data is input into a face-respiration model to obtain the first age information, wherein the face-respiration model is obtained by weighted fusion of an output result of the face model and an output result of the respiration model; or

In the case where the fused biometric data includes face data, voice data and breathing data, the face data and voice data in the fused biometric data are input into the face-human speech voice model to obtain the first age information.

6. The method according to claim 5 is characterized in that the facial data is valid facial data that has been screened.

7. The method according to any one of claims 1 to 6, characterized in that the method further comprises:

If the first biometric data is not obtained, obtaining interaction data of the user when currently using the terminal device, the interaction data including at least one of sensor data, touch screen data or application type data;

Based on the interaction data, it is determined whether the type of population to which the user belongs is the target population type.

8. The method according to any one of claims 1 to 6, characterized in that the method further comprises:

In a case where the user is not a user of the first type, acquiring interaction data of the user when currently using the terminal device, the interaction data including at least one of sensor data, touch screen data, or application type data;

Based on the interaction data and/or the first biological data, it is determined whether the type of population to which the user belongs is the target population type.

9. The method according to claim 7 or 8, characterized in that before determining whether the type of population to which the user belongs is the target population type, the method further comprises:

Based on the interaction data, selecting extended interaction data of a first age group from extended interaction data of multiple age groups, wherein the extended interaction data of the multiple age groups are obtained by clustering sample interaction data, wherein the sample interaction data includes at least one of sensor data, touch screen data, or application type data of a terminal device used by multiple users in a historical time period;

fusing the interaction data with the extended interaction data of the first age group to obtain fused interaction data;

Based on the fused interaction data, determine whether the user is the target population type.

10. The method according to claim 9, characterized in that before selecting the extended interaction data of the first age group from the extended interaction data of multiple age groups, the method further comprises:

Using the age model, extracting features from the sample interaction data to obtain feature data;

Cluster analysis is performed on the characteristic data to obtain extended interaction data of the multiple age groups.

11. The method according to claim 9 or 10, characterized in that before fusing the interaction data with the extended interaction data of the first age group, the method further comprises:

Performing dimensionality reduction processing on the interaction data to obtain dimensionality-reduced interaction data;

Performing dimensionality reduction processing on the extended interaction data of the first age group to obtain dimensionality-reduced extended interaction data;

The fusing the interaction data with the extended interaction data of the first age group includes:

The dimension reduction interaction data and the dimension reduction extension interaction data are fused to obtain the fused interaction data.

12 . The method according to claim 7 , wherein the interaction data comprises the reciprocal N pieces of touch screen data before the moment when the interaction data is acquired, where N is a positive integer.

13 . The method according to claim 7 , wherein the interaction data comprises M pieces of screened valid touch screen data, where M is a positive integer.

14. The method according to claim 13, characterized in that the method further comprises:

Determine, according to the generation time of each touch screen data in the reciprocal N touch screen data before the moment of acquiring the interaction data, the time interval between adjacent touch screen data in the N touch screen data, where N is a positive integer greater than or equal to M;

Determine a time node for substitution according to the time interval;

The touch screen data after the person-changing time node closest to the moment of acquiring the interaction data among the person-changing time nodes are determined as the M pieces of valid touch screen data.

15. The method according to any one of claims 1 to 14, characterized in that before determining whether the group type to which the user belongs is the target group type, the method further comprises:

Determining a feedback delay based on a service requirement of a current service of the terminal device;

When the feedback delay is greater than or equal to a second preset threshold, obtaining third biometric data of the user using the terminal device within a preset time period, the third biometric data including at least one of voice data, breathing data or face data;

The determining, based on the historical data, whether the type of population to which the user belongs is a target population type includes:

Based on the historical data and the third biological data, it is determined whether the type of population to which the user belongs is the target population type.

16. A crowd type recognition device, characterized by comprising a module for executing the method according to any one of claims 1 to 15.

17. A crowd type recognition device, characterized in that it comprises: a processor, the processor is coupled to a memory, the memory is used to store a computer program, when the processor calls the computer program, the device executes the method as described in any one of claims 1 to 15.

18. A computer-readable storage medium, characterized in that it is used to store a computer program, wherein the computer program includes instructions for implementing the method according to any one of claims 1 to 15.

19. A computer program product, characterized in that the computer program product comprises computer program code, and when the computer program code is executed on a computer, the computer is enabled to implement the method according to any one of claims 1 to 15.