CN116738033B - Method and device for recommending service - Google Patents

Abstract

The application provides a method and a device for recommending service, which are beneficial to reducing the power consumption of terminal equipment so as to improve the user experience. The method comprises the following steps: collecting a plurality of pieces of use data of a target user for a target service in a first historical time period, wherein the plurality of pieces of use data comprise a plurality of time points; determining a difference between each two of the plurality of time points; clustering the plurality of pieces of usage data based on the difference value to obtain at least one cluster of usage data, wherein each cluster of usage data in the at least one cluster of usage data comprises a plurality of target time points; and determining at least one target time period based on the at least one cluster of use data so that the terminal equipment can perform conditional judgment of a service recommendation algorithm of the target service in the at least one target time period.

Description

Method and device for recommending services

Technical Field

The present application relates to the field of terminal technology, and in particular to a method and device for recommending services.

Background Art

With the promotion of various applications in terminal devices, terminal devices have become indispensable devices in users' lives. Users can use payment, navigation and other services through terminal devices. However, when users use various services in terminal devices, they usually need to click to enter the application first, and then click to enter different pages within the application, which makes the operation process complicated.

In order to simplify the operation process of users using service functions in terminal devices, artificial intelligence (AI) algorithms can be used to learn the rules of users using service functions and predict users' usage intentions. When the terminal device predicts through the AI algorithm that the user intends to use the service functions in the terminal device, the terminal device can actively recommend the service to the user.

However, the use of AI algorithms will cause high power consumption of terminal devices. There is an urgent need to provide a method that can reduce the power consumption of terminal devices while recommending services to users.

Summary of the invention

The present application provides a method and apparatus for recommending services, which are beneficial to reducing the power consumption of terminal devices, thereby improving user experience.

In a first aspect, a method for recommending services is provided, the method comprising: collecting multiple usage data of a target service by a target user within a first historical time period, the multiple usage data comprising multiple time points; determining a difference between every two time points in the multiple time points; clustering the multiple usage data based on the difference to obtain at least one cluster of usage data, each cluster of usage data in the at least one cluster of usage data comprising multiple target time points; determining at least one target time period based on the at least one cluster of usage data, so that a terminal device performs conditional judgment of a service recommendation algorithm for the target service within the at least one target time period.

The method for recommending services provided by the present application obtains at least one target time period by clustering multiple pieces of usage data. The at least one target time period is used for the conditional judgment of the service recommendation algorithm of the target service by the terminal device. When the current time of the terminal device used by the target user belongs to the target time period, it indicates that the target user may have the intention to use the target service at this time, and the terminal device can call the service recommendation algorithm for conditional judgment. When the current time of the terminal device used by the target user does not belong to the target time period, it indicates that the target user does not have the intention to use the target service at this time, and the terminal device does not need to call the service recommendation algorithm. In this way, while simplifying the operation process of the user using the service in the terminal device, the power consumption of the terminal device is reduced, and the user experience is improved.

It should be understood that the target user is the object of exploration and can be a user who uses any service in the terminal device. The target service is the service to be explored, such as payment, navigation, music, etc. The multiple pieces of usage data refer to the multiple pieces of data information generated when the target user uses the target service in the first historical time period, and each piece of usage data in the multiple pieces of usage data includes a time point.

In certain implementations of the first aspect, determining at least one target time period based on the at least one cluster of usage data includes: determining at least one time period based on the multiple target time points included in each cluster of usage data; determining the confidence of each time period based on the number of days information corresponding to the first historical time period and the number of target time points included in each time period in the at least one time period; determining whether the confidence of each time period is greater than or equal to a first preset threshold; and if there is a first time period in the at least one time period whose confidence is greater than or equal to the first preset threshold, determining the first time period as the target time period.

It should be understood that the above-mentioned day information refers to the number of days included in the time period referred to by the first historical time period. For example, when the first historical time period is 2021/04/06 08:00:00 to 2021/04/10 08:00:00, the day information can be 4 days. A time period corresponds to a confidence level, and the confidence level of a time period is used to indicate the reliability of the time period. The higher the confidence level, the more reliable the time period. The above-mentioned first preset threshold is a preset number greater than 0. When the confidence level of a time period is greater than or equal to the first preset threshold, it means that the reliability of the time period is high and can be used as a target time period, that is, it can be used for conditional judgment of the service recommendation algorithm for the target service of the terminal device.

In some implementations of the first aspect, the confidence of the second time period in the at least one time period is determined by the following formula:

Among them, Z is the confidence level, Q is the day value corresponding to the day information corresponding to the first historical time period, _Yi is the day value corresponding to the i-th section of day information among the n sections of day information divided by the day information, _Y1 + _Y2 +...+ _Yn =Q, _Xi is the number of time points included in the i-th section of day information in the second time period, i∈{1,2,...n}, _ωi is the weight of the time points included in the i-th section of day information in the second time period, _ω1 + _ω2 +...+ _ωn =1.

It should be understood that the second time period is any one of the at least one time period. n and ω _i are preset values. ω _i can be determined according to the size of _Xi. The larger _{Xi is} , the more time points there are in the second time period in the i-th day information, and the larger ω _i can be.

In certain implementations of the first aspect, determining at least one time period based on the multiple target time points included in each cluster of usage data, includes: determining the at least one time period based on the maximum and minimum values of the multiple target time points included in each cluster of usage data in the at least one cluster of usage data.

It should be understood that if multiple clusters of usage data are obtained after clustering, the number of time periods obtained based on the multiple clusters of usage data is multiple and equal to the number of the multiple clusters of usage data, that is, one cluster of usage data can obtain one time period.

In certain implementations of the first aspect, the multiple usage data also include multiple geographic coordinates, and each cluster of usage data also includes multiple target geographic coordinates; the method also includes: calculating the distance between every two geographic coordinates in the multiple geographic coordinates; determining the clustering measurement value between every two usage data in the multiple usage data based on the distance and the difference; clustering the multiple usage data based on the difference includes: clustering the multiple usage data based on the clustering measurement value to obtain the at least one cluster of usage data; determining at least one target time period based on the at least one cluster of usage data includes: determining the at least one target time period and at least one geographic area based on the at least one cluster of usage data, so that the terminal device performs conditional judgment of the service recommendation algorithm for the target service within the at least one target time period and within the at least one geographic area.

It should be understood that the geographic coordinates are two-dimensional coordinates, the geographic location coordinates (X, Y) of the target user when using the target service, where X is longitude and Y is latitude. Each of the multiple usage data includes a time point and a geographic coordinate.

By combining time information and geographic information, the data processing device can more accurately determine the target user's intention to use the target service, thereby reducing the number of invalid judgments of the terminal device, further reducing the power consumption of the terminal device, and improving the user experience.

In certain implementations of the first aspect, determining a clustering metric between every two pieces of usage data in the multiple pieces of usage data based on the distance and the difference includes: obtaining a first maximum value and a first minimum value in the distance between every two geographic coordinates; determining a dimensionless distance between every two geographic coordinates based on the first maximum value, the first minimum value, and the distance between every two geographic coordinates; obtaining a second maximum value and a second minimum value in the difference between every two time points; determining a dimensionless difference between every two time points based on the second maximum value, the second minimum value, and the difference between every two time points; determining a clustering metric between every two pieces of usage data based on the dimensionless distance between every two geographic coordinates and the dimensionless difference between every two time points.

It should be understood that the dimensionless distance is a value without a unit obtained by dedimensionalizing the distance between each two geographic coordinates, and the dimensionless difference is a value without a unit obtained by dedimensionalizing the difference between each two time points.

In certain implementations of the first aspect, the dimensionless distance between each two geographic coordinates is determined by the following formula: Wherein, _Gr is the dimensionless distance, _Gi is the distance between each two geographic coordinates, _Gmax is the first maximum value, and _Gmin is the first minimum value; the dimensionless difference between each two time points is determined by the following formula: Wherein, _Tr is the dimensionless difference, _Ti is the difference between each two time points, _Tmax is the second maximum value, and _Tmin is the second minimum value.

It should be understood that since the units of the difference between each two time points and the distance between each two geographic coordinates are different, when calculating the clustering metric value based on the above difference and distance, the above difference and distance need to be de-dimensionalized first. The clustering metric value between each pair of usage data is calculated based on the difference between the time points of the two usage data and the distance between the geographic coordinates of the two usage data.

In certain implementations of the first aspect, determining the at least one geographic area based on the at least one cluster of usage data includes: determining the at least one geographic area according to the maximum longitude, maximum latitude, minimum longitude, and minimum latitude among a plurality of target geographic coordinates included in each cluster of usage data in the at least one cluster of usage data.

It should be understood that the terminal device can determine a geographic area based on the maximum longitude, maximum latitude, minimum longitude and minimum latitude in multiple target geographic coordinates of each cluster usage data, or determine a geographic area by combining multiple target geographic coordinates with administrative areas, such as street areas, shopping mall areas, etc.

In certain implementations of the first aspect, determining the difference between every two time points in the multiple time points includes: converting each of the multiple time points into a second-level value based on information on hours, minutes, and seconds contained in the multiple time points; and determining the difference between the second-level values corresponding to every two time points as the difference between the every two time points.

It should be understood that the information of hours, minutes and seconds is the moment information contained in the time point. For example, when the time point is 2021/04/08 09:01:00, the hour information is 09 hours, the minute information is 01 minutes, and the second information is 00 seconds. The second level value is the value obtained by converting the information of hours, minutes and seconds contained in the time point into seconds.

In a second aspect, a device for recommending services is provided, which is used to execute the method in any possible implementation of the first aspect. Specifically, the device includes a module for executing the method in any possible implementation of the first aspect.

In a third aspect, the present application provides another device for recommending services, including a processor, the processor is coupled to a memory, and can be used to execute instructions in the memory to implement the method in any possible implementation of the first aspect. Optionally, the device also includes a memory. Optionally, the device also includes a communication interface, and the processor is 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 device is a chip configured in a terminal device. When the device is a chip configured in a terminal device, the communication interface may be an input/output interface.

In a fourth aspect, a processor is provided, comprising: an input circuit, an output circuit, and a processing circuit. The processing circuit is used to receive a signal through the input circuit and transmit a signal through the output circuit, so that the processor executes the method in any possible implementation of the first aspect.

In the specific implementation process, the processor can be a chip, the input circuit can be an input pin, the output circuit can be an output pin, and the processing circuit can be a transistor, a gate circuit, a trigger, and various logic circuits. The input signal received by the input circuit can be, for example, but not limited to, received and input by a receiver, and the signal output by the output circuit can be, for example, but not limited to, output to a transmitter and transmitted by the transmitter, and the input circuit and the output circuit can be the same circuit, which is used as an input circuit and an output circuit at different times. The embodiments of the present application do not limit the specific implementation methods of the processor and various circuits.

In a fifth aspect, a processing device is provided, comprising a processor and a memory. The processor is used to read instructions stored in the memory, and can receive signals through a receiver and transmit signals through a transmitter to execute the method in any possible implementation of the first aspect.

Optionally, the number of the processors is one or more, and the number of the memories is one or more.

Optionally, the memory may be integrated with the processor, or the memory may be provided separately from the processor.

In the specific implementation process, the memory can be a non-transitory memory, such as a read-only memory (ROM), which can be integrated with the processor on the same chip or can be set on different chips respectively. This application does not limit the type of memory and the setting method of the memory and the processor.

It should be understood that the relevant data interaction process, such as sending indication information, can be a process of outputting indication information from a processor, and receiving capability information can be a process of receiving input capability information from a processor. Specifically, the processed output data can be output to a transmitter, and the input data received by the processor can come from a receiver. Among them, the transmitter and the receiver can be collectively referred to as a transceiver.

The processing device in the fifth aspect mentioned above can be a chip. The processor can be implemented by hardware or by software. When implemented by hardware, the processor can be a logic circuit, an integrated circuit, etc.; when implemented by software, the processor can be a general-purpose processor, which is implemented by reading the software code stored in the memory. The memory can be integrated in the processor or can be located outside the processor and exist independently.

In a sixth aspect, a computer program product is provided, the computer program product comprising: a computer program (also referred to as code, or instruction), which, when executed, enables a computer to execute a method in any possible implementation of the first aspect.

In a seventh aspect, a computer-readable storage medium is provided, which stores a computer program (also referred to as code, or instructions) which, when executed on a computer, enables the computer to execute a method in any possible implementation of the first aspect.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG1 is a schematic diagram of the structure of a terminal device provided in an embodiment of the present application;

FIG2 is a software structure block diagram of a terminal device according to an embodiment of the present application;

FIG3 is a flow chart of a method for recommending services provided in an embodiment of the present application;

FIG4 is a schematic diagram of a model of a clustering algorithm provided in an embodiment of the present application;

FIG5 is a flow chart of another method for recommending services provided in an embodiment of the present application;

FIG6 is a schematic diagram of a clustering algorithm according to an embodiment of the present application;

FIG. 7 is a second schematic diagram of the process of the clustering algorithm provided in an embodiment of the present application;

FIG8 is a schematic diagram of the structure of an apparatus for recommending services provided in an embodiment of the present application;

FIG. 9 is a schematic diagram of the structure of another device for recommending services provided in an embodiment of the present application.

DETAILED DESCRIPTION

The technical solution in this application will be described below in conjunction with the accompanying drawings.

In the embodiments of the present application, words such as "first" and "second" are used to distinguish the same or similar items with substantially the same functions and effects. For example, the first value and the second value are only used to distinguish different values, and do not limit their order. Those skilled in the art can understand that words such as "first" and "second" do not limit the quantity and execution order, and words such as "first" and "second" do not necessarily limit them to be different.

It should be noted that in the embodiments of the present application, words such as "exemplarily" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplarily" or "for example" in the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplarily" or "for example" is intended to present related concepts in a specific way.

In the embodiments of the present application, "at least one" refers to one or more, and "plurality" refers to two or more. "And/or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and/or B can represent: A exists alone, A and B exist at the same time, and B exists alone, where A and B can be singular or plural. The character "/" generally indicates that the objects associated before and after are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can represent: a, b, c, a-b, a--c, b-c, or a-b-c, where a, b, c can be single or multiple.

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

The terminal device may be a device that provides voice/data connectivity to users, such as a handheld device or vehicle-mounted device with wireless connection function. At present, some examples of terminal devices include: mobile phones, tablet computers, laptop computers, PDAs, mobile internet devices (MID), wearable devices, virtual reality (VR) devices, augmented reality (AR) devices, wireless terminals in industrial control, wireless terminals in self driving, wireless terminals in remote medical surgery, wireless terminals in smart grids, wireless terminals in transportation safety, wireless terminals in smart cities, wireless terminals in smart homes, cellular phones, cordless phones, session initiation protocol (SIP) phones, wireless local loop (WLL) stations, personal digital assistants (PDAs), handheld devices with wireless communication functions, computing devices or other processing devices connected to wireless modems, vehicle-mounted devices, wearable devices, terminal devices in 5G networks or future evolved public land mobile communication networks (public land mobile The present application does not limit the terminal equipment in the network, PLMN, etc.

As an example but not limitation, in the present application, the terminal device may be a terminal device in the Internet of Things (IoT) system. The Internet of Things is an important part of the future development of information technology. Its main technical feature is to connect objects to the network through communication technology, thereby realizing an intelligent network of human-machine interconnection and object-to-object interconnection. For example, the terminal device in the embodiment of the present application may be a wearable device. Wearable devices may also be called wearable smart devices, which are a general term for wearable devices that are designed and developed using wearable technology for daily wear, such as glasses, gloves, watches, clothing and shoes. Wearable devices are portable devices that can be worn directly on the body or integrated into the user's clothes or accessories. Wearable devices are not only a hardware device, but also can realize powerful functions through software support, data interaction, and cloud interaction. Broadly speaking, wearable smart devices include full-featured, large-size, and can realize complete or partial functions without relying on smartphones, such as smart watches or smart glasses, etc., as well as those that only focus on a certain type of application functions and need to be used in conjunction with other devices such as smartphones, such as various types of smart bracelets and smart jewelry for vital sign monitoring.

As an example but not limitation, in the embodiments of the present application, the terminal device may also be a terminal device in machine type communication (MTC). In addition, the terminal device may also be a vehicle-mounted module, a vehicle-mounted module, a vehicle-mounted component, a vehicle-mounted chip or a vehicle-mounted unit, etc., which is built into the vehicle as one or more components or units. The vehicle can implement the method provided by the present application through the built-in vehicle-mounted module, vehicle-mounted module, vehicle-mounted component, vehicle-mounted chip or vehicle-mounted unit, etc. Therefore, the embodiments of the present application may also be applied to the Internet of Vehicles, such as vehicle to everything (V2X), long term evolution-vehicle (LTE-V), vehicle-to-vehicle (V2V) technology, etc.

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 in conjunction with Figure 1.

Fig. 1 is a schematic diagram of the structure of a terminal device 100 provided in 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 (USB) interface 130, a charging management module 140, a power management module 141, a battery 142, an antenna 1, an antenna 2, a mobile communication module 150, a wireless communication module 160, an audio module 170, a speaker 170A, a receiver 170B, a microphone 170C, an earphone interface 170D, a sensor module 180, a button 190, a motor 191, an indicator 192, a camera 193, a display screen 194, and a subscriber identification module (SIM) card interface 195, etc. The sensor module 180 may include a pressure sensor 180A, a gyroscope sensor 180B, an air pressure sensor 180C, a magnetic sensor 180D, an acceleration sensor 180E, a distance sensor 180F, a proximity light sensor 180G, a fingerprint sensor 180H, a temperature sensor 180J, a touch sensor 180K, an ambient light sensor 180L, a bone conduction sensor 180M, etc.

It is 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 present application, the terminal device 100 may include more or fewer components than shown in the figure, or combine certain components, or split certain components, or arrange the components differently. The components shown in the figure may be implemented in hardware, software, or a combination of software and hardware.

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

The controller can generate operation control signals according to the instruction operation code and timing signal to complete the control of instruction fetching and execution.

The processor 110 may also be provided with a memory for storing instructions and data. In some embodiments, the memory in the processor 110 is a cache memory. The memory may store instructions or data that the processor 110 has just used or cyclically used. If the processor 110 needs to use the instruction or data again, it may be directly called from the memory. This avoids repeated access, reduces the waiting time of the processor 110, and thus improves the efficiency of the system.

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

The I2C interface is a bidirectional synchronous serial bus, including a serial data line (SDA) and a serial clock line (SCL). In some embodiments, the processor 110 may include multiple groups of I2C buses. The processor 110 may be coupled to the touch sensor 180K, the charger, the flash, the camera 193, etc. through different I2C bus interfaces. For example: the processor 110 may be coupled to the touch sensor 180K through the I2C interface, so that the processor 110 communicates with the touch sensor 180K through the I2C bus interface, thereby realizing the touch function of the terminal device 100.

The I2S interface can be used for audio communication. In some embodiments, the processor 110 can include multiple I2S buses. The processor 110 can be coupled to the audio module 170 via the I2S bus to achieve communication between the processor 110 and the audio module 170. In some embodiments, the audio module 170 can transmit an audio signal to the wireless communication module 160 via the I2S interface to achieve the function of answering a call through a Bluetooth headset.

The PCM interface can also be used for audio communication, sampling, quantizing and encoding analog signals. In some embodiments, the audio module 170 and the wireless communication module 160 can be coupled via a PCM bus interface. In some embodiments, the audio module 170 can also transmit audio signals to the wireless communication module 160 via the PCM interface to realize the function of answering calls via a Bluetooth headset. Both the I2S interface and the PCM interface can be used for audio communication.

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

The MIPI interface can be used to connect the processor 110 with peripheral devices such as the display screen 194 and the camera 193. The MIPI interface includes a camera serial interface (CSI), a display serial interface (DSI), etc. In some embodiments, the processor 110 and the camera 193 communicate via the CSI interface to implement the shooting function of the terminal device 100. The processor 110 and the display screen 194 communicate via the DSI interface to implement the display function of the terminal device 100.

The GPIO interface can be configured by software. The GPIO interface can be configured as a control signal or as a data signal. In some embodiments, the GPIO interface can 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, etc. The GPIO interface can also be configured as an I2C interface, an I2S interface, a UART interface, a MIPI interface, etc.

The USB interface 130 is an interface that complies with the USB standard specification, and specifically can be a Mini USB interface, a Micro USB interface, a USB Type C interface, etc. The USB interface 130 can be used to connect a charger to charge the terminal device 100, and can also be used to transmit data between the terminal device 100 and peripheral devices. It can also be used to connect headphones to play audio through the headphones. The interface can also be used to connect other terminal devices, such as AR devices, etc.

It is understandable that the interface connection relationship between the modules illustrated in the embodiment of the present application is only a schematic illustration and does not constitute a structural limitation on the terminal device 100. In other embodiments of the present application, the terminal device 100 may also adopt different interface connection methods in the above embodiments, or a combination of multiple interface connection methods.

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

The power management module 141 is used to connect the battery 142, the charging management module 140 and the processor 110. The power management module 141 receives input from the battery 142 and/or the charging management module 140, and supplies power to the processor 110, the internal memory 121, the display screen 194, the camera 193, and the wireless communication module 160. The power management module 141 can also be used to monitor parameters such as battery capacity, battery cycle number, battery health status (leakage, impedance), etc. In some other embodiments, the power management module 141 can also be set in the processor 110. In other embodiments, the power management module 141 and the charging management module 140 can also be set in the same device.

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

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

The mobile communication module 150 can provide solutions for wireless communications including 2G/3G/4G/5G applied to the terminal device 100. The mobile communication module 150 may include at least one filter, a switch, a power amplifier, a low noise amplifier (LNA), etc. The mobile communication module 150 can receive electromagnetic waves from the antenna 1, and filter, amplify, and process the received electromagnetic waves, and transmit them to the modulation and demodulation processor for demodulation. The mobile communication module 150 can also amplify the signal modulated by the modulation and demodulation processor, and convert it into electromagnetic waves for radiation through the antenna 1. In some embodiments, at least some of the functional modules of the mobile communication module 150 can be set in the processor 110. In some embodiments, at least some of the functional modules of the mobile communication module 150 can be set 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. Among them, the modulator is used to modulate the low-frequency baseband signal to be sent into a medium-high frequency signal. The demodulator is used to demodulate the received electromagnetic wave signal into a low-frequency baseband signal. The demodulator then transmits the demodulated low-frequency baseband signal to the baseband processor for processing. After the low-frequency baseband signal is processed by the baseband processor, it is passed to the application processor. The application processor outputs a sound signal through an audio device (not limited to a speaker 170A, a receiver 170B, etc.), or displays an image or video through a display screen 194. In some embodiments, the modem processor may be an independent device. In other embodiments, the modem processor may be independent of the processor 110 and be set in the same device as the mobile communication module 150 or other functional modules.

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

In some embodiments, the antenna 1 of the terminal device 100 is coupled to the mobile communication module 150, and the antenna 2 is coupled to the wireless communication module 160, so that the terminal device 100 can communicate with the network and other devices through wireless communication technology. The wireless communication technology may include global system for mobile communications (GSM), general packet radio service (GPRS), code division multiple access (CDMA), wideband code division multiple access (WCDMA), time-division code division multiple access (TD-SCDMA), long term evolution (LTE), BT, GNSS, WLAN, NFC, FM, and/or IR technology. The GNSS may include a global positioning system (GPS), a global navigation satellite system (GLONASS), a Beidou navigation satellite system (BDS), a quasi-zenith satellite system (QZSS) and/or a satellite based augmentation system (SBAS).

The terminal device 100 implements the display function through a GPU, a display screen 194, and an application processor. The GPU is a microprocessor for image processing, which connects the display screen 194 and the application processor. The GPU is used to perform mathematical and geometric calculations for graphics rendering. The 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, etc. The display screen 194 includes a display panel. The display panel can be a liquid crystal display (LCD), an organic light-emitting diode (OLED), an active-matrix organic light emitting diode or an active-matrix organic light emitting diode (AMOLED), a flexible light-emitting diode (FLED), Miniled, MicroLed, Micro-oLed, a quantum dot light-emitting diode (QLED), etc. In some embodiments, the terminal device 100 may include 1 or N display screens 194, where N is a positive integer greater than 1.

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

ISP is used to process the data fed back by camera 193. For example, when taking a photo, the shutter is opened, and the light is transmitted to the camera photosensitive element through the lens. The light signal is converted into an electrical signal, and the camera photosensitive element transmits the electrical signal to ISP for processing and converts it into an image visible to the naked eye. ISP can also perform algorithm optimization on the noise, brightness, and skin color of the image. ISP can also optimize the exposure, color temperature and other parameters of the shooting scene. In some embodiments, ISP can be set in camera 193.

The camera 193 is used to capture still images or videos. The object generates an optical image through the lens and projects it onto the photosensitive element. The photosensitive element can be a charge coupled device (CCD) or a complementary metal-oxide-semiconductor (CMOS) phototransistor. The photosensitive element converts the optical signal into an electrical signal, and then passes the electrical signal 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 other format. In some embodiments, the terminal device 100 may include 1 or N cameras 193, where N is a positive integer greater than 1.

The digital signal processor is used to process digital signals, and can process not only digital image signals but also other digital signals. For example, when the terminal device 100 is selecting a frequency point, the digital signal processor is used to perform Fourier transform on the frequency point energy.

Video codecs are used to compress or decompress digital videos. The terminal device 100 may support one or more video codecs. Thus, the terminal device 100 may play or record videos in various coding formats, such as Moving Picture Experts Group (MPEG) 1, MPEG2, MPEG3, MPEG4, etc.

NPU is a neural network (NN) computing processor. By drawing on the structure of biological neural networks, such as the transmission mode between neurons in the human brain, it can quickly process input information and can also continuously self-learn. Through NPU, applications such as intelligent cognition of the terminal device 100 can be realized, such as image recognition, face recognition, voice recognition, text understanding, etc.

The external memory interface 120 can be used to connect an external memory card, such as a Micro SD card, to expand the storage capacity of the terminal device 100. The external memory card communicates with the processor 110 through the external memory interface 120 to implement a data storage function. For example, files such as music and videos are stored in the external memory card.

The internal memory 121 can be used to store computer executable program codes, which include instructions. The internal memory 121 may include a program storage area and a data storage area. Among them, the program storage area may store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc. The data storage area may store data created during the use of the terminal device 100 (such as audio data, a phone book, etc.), etc. In addition, the internal memory 121 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, a universal flash storage (UFS), etc. The processor 110 executes various functional applications and data processing of the terminal device 100 by running instructions stored in the internal memory 121 and/or instructions stored in a memory provided in the processor.

The terminal device 100 can implement audio functions such as music playing and recording through the audio module 170, the speaker 170A, the receiver 170B, the microphone 170C, the headphone jack 170D, and the application processor.

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

The speaker 170A, also called a "speaker", is used to convert an audio electrical signal into a sound signal. The terminal device 100 can listen to music or listen to a hands-free call through the speaker 170A.

The receiver 170B, also called a "handset", is used to convert audio electrical signals into sound signals. When the terminal device 100 receives a call or voice message, the voice can be received by placing the receiver 170B close to the human ear.

Microphone 170C, also called "microphone" or "microphone", is used to convert sound signals into electrical signals. When making a call or sending a voice message, the user can speak by putting their mouth close to the microphone 170C to input the sound signal into the microphone 170C. The terminal device 100 can be provided with at least one microphone 170C. In other embodiments, the terminal device 100 can be provided with two microphones 170C, which can not only collect sound signals but also realize noise reduction function. In other embodiments, the terminal device 100 can also be provided with three, four or more microphones 170C to collect sound signals, reduce noise, identify the source of sound, realize directional recording function, etc.

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

The pressure sensor 180A is used to sense the pressure signal and can convert the pressure signal into an electrical signal. In some embodiments, the pressure sensor 180A can be set on the display screen 194. There are many types of pressure sensors 180A, such as resistive pressure sensors, inductive pressure sensors, capacitive pressure sensors, etc. The capacitive pressure sensor can be a parallel plate including at least two conductive materials. When a force acts on the pressure sensor 180A, the capacitance between the electrodes changes. The terminal device 100 determines the intensity of the pressure according to the change in capacitance. When a touch operation acts on the display screen 194, the terminal device 100 detects the touch operation intensity according to the pressure sensor 180A. The terminal device 100 can also calculate the touch position according to the detection signal of the pressure sensor 180A. In some embodiments, touch operations acting on the same touch position but with different touch operation intensities can correspond to different operation instructions. For example: when a touch operation with a touch operation intensity less than the first pressure threshold acts on the short message application icon, an instruction to view the short message is executed. When a touch operation with a touch operation intensity greater than or equal to the first pressure threshold acts on the short message application icon, an instruction to create a new short message is executed.

The gyroscope sensor 180B can be used to determine the motion posture of the terminal device 100. In some embodiments, the angular velocity of the terminal device 100 around three axes (i.e., x, y, and z axes) can be determined by the gyroscope sensor 180B. The gyroscope sensor 180B can be used for anti-shake shooting. For example, when the shutter is pressed, the gyroscope sensor 180B detects the angle of the terminal device 100 shaking, calculates the distance that the lens module needs to compensate based on the angle, and allows the lens to offset the shaking of the terminal device 100 through reverse movement to achieve anti-shake. The gyroscope sensor 180B can also be used for navigation and somatosensory game scenes.

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

The magnetic sensor 180D includes a Hall sensor. The terminal device 100 can use the magnetic sensor 180D to detect the opening and closing of the flip leather case. In some embodiments, when the terminal device 100 is a flip phone, the terminal device 100 can detect the opening and closing of the flip cover according to the magnetic sensor 180D. Then, according to the detected opening and closing state of the leather case or the opening and closing state of the flip cover, the flip cover automatic unlocking and other features are set.

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

The distance sensor 180F is used to measure the distance. The terminal device 100 can measure the distance by infrared or laser. In some embodiments, when shooting a scene, the terminal device 100 can use the distance sensor 180F to measure the distance 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 uses a photodiode to detect infrared reflected light from nearby objects. When sufficient reflected light is detected, it can be determined that there is an object near the terminal device 100. When insufficient reflected light is detected, the terminal device 100 can determine that there is no object near the terminal device 100. The terminal device 100 can use the proximity light sensor 180G to detect that the user holds the terminal device 100 close to the ear to talk, so as to automatically turn off the screen to save power. The proximity light sensor 180G can also be used in leather case mode, and pocket mode automatically unlocks and locks the screen.

The ambient light sensor 180L is used to sense the ambient light brightness. The terminal device 100 can adaptively adjust the brightness of the display screen 194 according to the perceived ambient light brightness. The ambient light sensor 180L can also be used to automatically adjust the white balance when taking pictures. The ambient light sensor 180L can also cooperate with the proximity light sensor 180G to detect whether the terminal device 100 is in a pocket to prevent accidental touch.

The fingerprint sensor 180H is used to collect fingerprints. The terminal device 100 can use the collected fingerprint characteristics to achieve fingerprint unlocking, access application locks, fingerprint photography, fingerprint answering calls, etc.

The temperature sensor 180J is used to detect temperature. In some embodiments, the terminal device 100 uses the temperature detected by the temperature sensor 180J to execute a temperature processing strategy. For example, when the temperature reported by the temperature sensor 180J exceeds a threshold, the terminal device 100 reduces the performance of the processor located near the temperature sensor 180J to reduce power consumption and implement thermal protection. In other embodiments, when the temperature is lower than another threshold, the terminal device 100 heats the battery 142 to avoid abnormal shutdown of the terminal device 100 due to low temperature. In other embodiments, when the temperature is lower than another threshold, the terminal device 100 performs a boost on the output voltage of the battery 142 to avoid abnormal shutdown caused by low temperature.

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

The bone conduction sensor 180M can obtain a vibration signal. In some embodiments, the bone conduction sensor 180M can obtain a vibration signal of a vibrating bone block of the vocal part of the human body. The bone conduction sensor 180M can also contact the human pulse to receive a blood pressure beat signal. In some embodiments, the bone conduction sensor 180M can also be set in an earphone and combined into a bone conduction earphone. The audio module 170 can parse out a voice signal based on the vibration signal of the vibrating bone block of the vocal part obtained by the bone conduction sensor 180M to realize a voice function. The application processor can parse the heart rate information based on the blood pressure beat signal obtained by the bone conduction sensor 180M to realize a heart rate detection function.

The key 190 includes a power key, a volume key, etc. The key 190 may be a mechanical key or a touch key. The terminal device 100 may receive key input and generate key signal input related to user settings and function control of the terminal device 100.

Motor 191 can generate vibration prompts. Motor 191 can be used for incoming call vibration prompts, and can also be used for touch vibration feedback. For example, touch operations acting on different applications (such as taking pictures, audio playback, etc.) can correspond to different vibration feedback effects. For touch operations acting on different areas of the display screen 194, motor 191 can also correspond to different vibration feedback effects. Different application scenarios (for example: time reminders, receiving messages, alarm clocks, games, etc.) can also correspond to different vibration feedback effects. The touch vibration feedback effect can also support customization.

Indicator 192 may be an indicator light, which may be used to indicate charging status, power changes, messages, missed calls, notifications, etc.

The SIM card interface 195 is used to connect the SIM card. The SIM card can be connected to and separated from the terminal device 100 by inserting the SIM card interface 195 or pulling it out from the SIM card interface 195. The terminal device 100 can support 1 or N SIM card interfaces, where N is a positive integer greater than 1. The SIM card interface 195 can support Nano SIM card, Micro SIM card, SIM card, etc. Multiple cards can be inserted into the same SIM card interface 195 at the same time. The types of the multiple cards can be the same or different. The SIM card interface 195 can also be compatible with different types of SIM cards. The SIM card interface 195 can also be compatible with external memory cards. The terminal device 100 interacts with the network through the SIM card to realize functions such as calls and data communications. In some embodiments, the terminal device 100 uses an eSIM, i.e., 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 can adopt a layered architecture, an event-driven architecture, a micro-core architecture, a micro-service architecture, or a cloud architecture. The embodiment of the present application takes the Android system with a layered architecture as an example to illustrate the software structure of the terminal device 100.

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

The layered architecture divides the software into several layers, each with clear roles and division of labor. The layers communicate with each other through software interfaces. In some embodiments, the Android system is divided into four layers, from top to bottom, namely, the application layer, the application framework layer, the Android runtime and system library, and the kernel layer.

The application layer may include a series of application packages. As shown in FIG2 , the application package may include applications such as camera, gallery, calendar, call, map, navigation, WLAN, Bluetooth, music, video, short message, etc.

The application framework layer provides an application programming interface (API) and a programming framework for the applications in the application layer. The application framework layer includes some predefined functions. As shown in Figure 2, the application framework layer may include a window manager, a content provider, a view system, a phone manager, a resource manager, a notification manager, etc.

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

Content providers are used to store and retrieve data and make it accessible to applications. The data may include videos, images, audio, calls made and received, browsing history and bookmarks, phone books, etc.

The view system includes visual controls, such as controls for displaying text, controls for displaying images, etc. The view system can be used to build applications. A display interface can be composed of one or more views. For example, a display interface including a text notification icon can include a view for displaying text and a view for displaying images.

The phone manager is used to provide communication functions of the terminal device 100, such as management of call status (including connection, disconnection, etc.).

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

The notification manager enables applications to display notification information in the status bar. It can be used to convey notification-type messages and can disappear automatically after a short stay without user interaction. For example, the notification manager is used to notify download completion, message reminders, etc. The notification manager can also be a notification that appears in the system top status bar in the form of a chart or scroll bar text, such as notifications of applications running in the background, or a notification that appears on the screen in the form of a dialog window. For example, a text message is displayed in the status bar, a prompt sound is emitted, the terminal device vibrates, the indicator light flashes, etc.

Android Runtime includes core libraries 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 the function that needs to be called by the Java language, and the other part is the Android core library.

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

The system library may include multiple functional modules, such as surface manager, media library, 3D graphics processing library (such as OpenGL ES), 2D graphics engine (such as SGL), etc.

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

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

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

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

The kernel layer is a layer between hardware and software. The kernel layer is used to drive the hardware to make the hardware work. The kernel layer at least includes display driver, screen driver, graphics processing unit (GPU) driver, camera, and sensor driver, etc., which are not limited in the embodiments of the present application. For example, the screen driver can drive the screen to turn on or off.

A variety of application software can be installed and run in the terminal device, such as calendar, gallery, music, weather, etc. Users can choose to use different application software according to different needs. Specifically, when a user uses the service functions in the terminal device, such as payment services, navigation services, etc., it is usually necessary to first open the application software that can use the service, and then perform various operation processes in sequence. For example, when a user needs to pay money to a merchant, the user can select an application software with a payment function, open the application software, and scan the merchant's QR code. At this time, the payment interface will be displayed on the user's terminal device, and then the user can enter the amount to complete the payment. For another example, when a user needs to use the navigation function, the user can open the map software, enter the target address in the corresponding location of the map software, and then click the navigation function to navigate.

Because the operation process is relatively complicated when the user uses the service in the terminal device. Therefore, in order to simplify the operation process, the terminal device can learn the rules of the user when using the service function in the terminal device through the AI algorithm. The AI algorithm can also be called a service recommendation algorithm. The terminal device can obtain the user's behavior information, such as location information, time information, etc., and determine whether the user has the intention to use the service in the terminal device based on the behavior information. When the AI algorithm predicts that the user has the intention to use the service in the terminal device, the terminal device can actively recommend the service to the user, simplify the user's operation process, and improve the user experience. For example, before using the payment service function at noon every day, the user will leave the workstation and go from the 20th floor to the restaurant on the 1st floor. When the AI algorithm detects that the user's geographical location has moved from the workstation to the restaurant, the terminal device can recommend the payment service to the user. For example, the terminal device can display a payment notification message on the interface so that the user can click to pay.

At present, terminal devices usually use AI algorithms to predict the user's intention to use services at regular intervals, for example, predicting the user's intention to use services every 30 seconds. However, the current prediction method causes the terminal device to consume a lot of power.

In order to solve the above problems, the present application provides a method for recommending services, which analyzes and processes multiple usage data of the target user using the target service in the terminal device, determines the time period when the target user uses the target service, and uses the time period as a trigger condition. When the current time falls within the above time period, the terminal device is triggered to call the AI algorithm. In this way, while simplifying the operation process of the user using the service in the terminal device, it is helpful to reduce the power consumption of the terminal device, thereby improving the user experience.

The following is a detailed description of the method for recommending services in the embodiment of the present application in conjunction with Figures 3 to 6. The method can be executed by a device with a data processing function, such as a server, a network device, a terminal device, etc., and the present application does not make specific restrictions on this. The execution subject of the embodiment of the present application is collectively referred to as a "data processing device" and the above method is described in detail.

FIG3 is a flow chart of a method 300 for recommending services provided in an embodiment of the present application. The hardware structure of the terminal device involved in the method 300 may be as shown in FIG1 , and the software structure may be as shown in FIG2 . The method 300 includes the following steps:

S301. Collect multiple usage data of a target service by a target user in a first historical time period, where the multiple usage data include multiple time points.

It should be understood that the above-mentioned first historical time period is any one or more past time periods, for example, 2021/04/06 08:00:00 to 2021/05/06 08:00:00, or the sum of the two time periods of 2021/04/06 08:00:00 to 2021/04/10 08:00:00 and 2021/04/12 08:00:00 to 2021/04/17 08:00:00, etc. The target user is the object of exploration and can be a user who uses any service in the terminal device. The target service is the service to be explored, such as payment, navigation, music, etc. The multiple usage data refers to the multiple data information generated when the target user uses the target service in the first historical time period, and each of the multiple usage data includes a time point.

Exemplarily, for user A, the target service is the payment service, the first historical time period is from 2021/04/06 08:00:00 to 2021/05/06 08:00:00, during this period, user A used the payment service once at 2021/04/07 08:00:03 and once at 2021/04/08 08:01:00, then the multiple usage data are two usage data, one of which includes the time point 2021/04/07 08:00:03, and the other includes the time point 2021/04/08 08:01:00.

S302: Determine the difference between every two time points in the above-mentioned multiple time points.

The above-mentioned every two time points refer to the combination of each time point in the multiple time points with the remaining time points. Assuming that the number of multiple time points is a, there are a*(a-1)/2 combinations of every two time points, and the number of the above-mentioned differences is a*(a-1)/2. For example, a=3, and the multiple time points are point 1, point 2, and point 3, then every two time points include three combinations of point 1 and point 2, point 2 and point 3, and point 1 and point 3.

Optionally, the data processing device may ignore the date information in the time point and calculate the difference between the hours, minutes or seconds contained in the two time points. Exemplarily, the above S302 may also be implemented in the following manner: according to the information of the hours, minutes and seconds contained in the multiple time points, each of the multiple time points is converted into a second-level value; the difference between the second-level values corresponding to each two time points is determined as the difference between each two time points.

The above-mentioned information of hours, minutes and seconds is the moment information contained in the time point. For example, when the time point is 2021/04/08 01:02:03, the hour information is 01 hours, the minute information is 01 minutes, and the second information is 03 seconds. The second-level value is the value obtained by converting the information of hours, minutes and seconds contained in the time point into seconds, where 1 hour is 3600 seconds and 1 minute is 60 seconds. The second-level value can be obtained by calculating in the following way: 1*3600+2*60+3=3723, that is, the second-level value corresponding to the above-mentioned time point 2021/04/08 01:02:03 is 3723. After converting the time point information into second-level values, the difference between the time points is the difference between the second-level values corresponding to the two time points. For example, time point 1 and time point 2 are 2021/04/08 01:02:03 and 2021/04/08 01:03:03 respectively. The second-level values corresponding to time point 1 and time point 2 are 3723 and 3783, and the difference between the two time points is 60.

In other possible implementations, the difference may also be the hourly difference between every two time points. In the above example, the two time points are 2021/04/08 01:02:03 and 2021/04/08 01:03:03, and the difference between the two time points is 0. In other possible implementations, the difference may also be the minute-level difference between every two time points. In the above example, the two time points are 2021/04/08 01:02:03 and 2021/04/08 01:03:03, and the difference between the two time points is 1.

S303: Clustering the plurality of pieces of usage data based on the difference to obtain at least one cluster of usage data, wherein each cluster of usage data in the at least one cluster of usage data includes a plurality of target time points.

The clustering of the embodiment of the present application can be achieved by a clustering algorithm. The clustering algorithm, or group analysis, is a statistical analysis method for studying classification problems. Through clustering, relatively discrete usage data in multiple usage data can be discarded to obtain at least one cluster of usage data. Among them, the discarded usage data can be called noise points, and the ratio between the noise points and the number of multiple usage data is called the noise ratio. For example, the noise ratio can be 5%. The noise ratio can be adjusted by presetting the model parameters in the clustering algorithm. The model parameters can be preset.

FIG4 is a schematic diagram of a model of a clustering algorithm provided by an embodiment of the present application. As shown in FIG4 , assuming that the above-mentioned multiple pieces of usage data are points 1 to 10, two clusters of usage data can be obtained after clustering, one cluster of usage data is points 1 to 6, the other cluster of usage data is points 8, 9 and 10, and the noise point is point 7. The above-mentioned model parameters include the radius of the circle shown in FIG4 , and the minimum value of the number of points included in each circle.

It should be understood that each piece of usage data in each cluster of usage data includes a time point, which is called a target time point. In FIG4 , for a cluster of usage data points 1 to 6, the target time point included in the cluster of usage data is the time point included in each piece of usage data corresponding to points 1 to 6. For another cluster of usage data points 8, 9, and 10, the corresponding target time point is the time point included in each piece of usage data corresponding to points 8, 9, and 10.

S304: Determine at least one target time period based on at least one cluster of usage data, so that the terminal device performs conditional judgment of a service recommendation algorithm for a target service within the at least one target time period.

Specifically, each cluster of usage data in the above-mentioned at least one cluster of usage data includes multiple target time points. According to the above-mentioned multiple target time points, at least one target time period can be obtained. The target time period can also be called a "time fence" or other names, which is not limited in the embodiments of the present application. The terminal device can perform conditional judgment of the service recommendation algorithm of the target service according to the at least one target time period. In one possible implementation, if the current time belongs to the target time period, the terminal device calls the service recommendation algorithm of the target service for further judgment; otherwise, the terminal device does not call the service recommendation algorithm of the target service. For example, if the target time period is 08:00:00 to 09:00:00, then if the current time is 08:00:01, the terminal device calls the service recommendation algorithm of the target service to determine whether to recommend the target service to the target user.

The method for recommending services provided in the embodiment of the present application obtains at least one target time period by clustering multiple usage data of the target user using the target service, and the terminal device can perform conditional judgment of the service recommendation algorithm of the target service based on the at least one target time period. When the current time of the terminal device used by the target user belongs to the target time period, it means that the target user may have the intention to use the target service at this time, and the terminal device can call the service recommendation algorithm to perform conditional judgment. When the current time of the terminal device used by the target user does not belong to the target time period, it means that the target user does not have the intention to use the target service at this time, and the terminal device does not need to call the service recommendation algorithm. In this way, while simplifying the operating process of the user using the service in the terminal device, the power consumption of the terminal device is reduced, and the user experience is improved.

As an optional embodiment, the above S304 can be implemented in the following ways: determine at least one time period based on the multiple target time points included in each cluster of usage data; determine the confidence of each time period based on the number of days information corresponding to the first historical time period and the number of target time points included in each time period in at least one time period; determine whether the confidence of each time period is greater than or equal to a first preset threshold; if there is a first time period in at least one time period whose confidence is greater than or equal to the first preset threshold, determine the first time period as the target time period.

Specifically, the data processing device can determine at least one time period based on the maximum and minimum values of the multiple target time points included in each cluster of usage data in at least one cluster of usage data. Exemplarily, assuming that the maximum value of the multiple target time points included in a cluster of usage data is 2021/04/30 08:50:00 and the minimum value is 2021/04/01 08:01:00, then the time period corresponding to the cluster usage data is 08:01:00 to 08:50:00. It should be understood that if multiple clusters of usage data are obtained after clustering, the number of time periods obtained based on the multiple clusters of usage data is multiple and equal to the number of the multiple clusters of usage data, that is, one cluster of usage data can obtain one time period.

The above-mentioned day information refers to the number of days included in the time period referred to by the first historical time period. For example, when the first historical time period is 2021/04/06 08:00:00 to 2021/04/10 08:00:00, the day information can be 4 days. A time period corresponds to a confidence level, and the confidence level of a time period is used to indicate the reliability of the time period. The higher the confidence level, the more reliable the time period. The above-mentioned first preset threshold is a preset number greater than 0. When the confidence level of a time period is greater than or equal to the first preset threshold, it means that the reliability of the time period is high and can be used as a target time period, that is, it can be used for conditional judgment of the service recommendation algorithm for the target service of the terminal device.

By calculating the confidence level, the data processing device can use the confidence level to measure the reliability of each of the at least one time period mentioned above, so that the target time period with a confidence level greater than or equal to the first preset threshold value can be used for the conditional judgment of the service recommendation algorithm of the terminal device for the target service, so that the data processing device can make a more accurate judgment on the target user's intention to use the target service, which helps to further reduce the power consumption of the terminal device and improve the user experience.

When the confidence of a time period is less than the first preset threshold, it indicates that the reliability of the time period is low, and the time period cannot be used for conditional judgment of the service recommendation algorithm for the target service by the terminal device. The data processing device can calculate the proportion of time periods whose confidence is less than the first preset threshold in the at least one time period, and according to the proportion, the data processing device can determine whether to reselect the historical time period for clustering and obtain a new time period.

In a possible implementation, when the proportion of time periods in the above at least one time period whose confidence is less than the first preset threshold is greater than the second preset threshold, the data processing device may collect multiple pieces of usage data of the target user for the target service in the second historical time period, and obtain at least one new time period by clustering the multiple pieces of usage data. The data processing device may calculate the confidence of each time period in the new at least one time period, and use the time period with a confidence greater than or equal to the first preset threshold as the target time period, which is used for the conditional judgment of the service recommendation algorithm for the target service by the terminal device.

It should be understood that the second historical time period includes more days than the first historical time period, and/or the second historical time period is closer to the current time than the first historical time period.

Exemplarily, assuming that the at least one time period includes time period 1, time period 2, and time period 3, and the corresponding confidences are 12, 13, and 15, respectively, and the first preset threshold is 14, and the second preset threshold is 50%, then the confidences of time period 1 and time period 2 among the three time periods are less than the first preset threshold. The proportion of time periods whose confidences among the three time periods are less than the first preset threshold is 2/3, which is greater than the second preset threshold. At this time, the data processing device can re-collect multiple usage data of the target user using the target service in the second historical time period, and obtain at least one new time period based on the multiple usage data. For example, assuming that the first historical time period is from 2021/04/06 08:00:00 to 2021/04/13 08:00:00, the data processing device can collect multiple usage data of the target user using the target service in the second historical time period corresponding to 2021/04/06 08:00:00 to 2021/04/30 08:00:00, and obtain at least one new time period. Alternatively, assuming that the first historical time period is from 2021/04/06 08:00:00 to 2021/04/30 08:00:00, the reference value of the plurality of usage data for the time when the target user currently uses the target service may be small. Therefore, the data processing device may collect a plurality of usage data of the target user using the target service in a second historical time period with a more recent date, for example, the second historical time period may be from 2021/08/06 08:00:00 to 2021/09/30 08:00:00, and obtain at least one new time period based on the plurality of usage data.

In a possible implementation, the data processing device may further segment each of the at least one time period obtained based on the at least one cluster of usage data, and calculate the confidence based on the segmented time periods. The confidence of the second time period in the at least one time period is determined by the following formula:

Among them, Z is the confidence, Q is the day value corresponding to the day information corresponding to the first historical time period, _Yi is the day value corresponding to the i-th section of day information among the n sections of day information divided into the day information, _Y1 + _Y2 +...+ _Yn =Q, _Xi is the number of time points included in the i-th section of day information in the second time period, i∈{1,2,…n}, _ωi is the weight of the time points included in the i-th section of day information in the second time period, _ω1 + _ω2 +…+ _ωn =1.

It should be understood that the second time period is any one of the at least one time period. n and ω _i are preset values. ω _i can be determined according to the size of _Xi. The larger _{Xi is} , the more time points there are in the second time period in the i-th day information, and the larger ω _i can be.

In a specific example, assume that the first historical time period is from 2021/04/06 08:00:00 to 2021/04/13 08:00:00, and Q is 7. If n=3, the first historical time period is divided into 3 sections, wherein the first section is from 2021/04/06 08:00:00 to 2021/04/08 08:00:00, the second section is from 2021/04/08 08:00:00 to 2021/04/10 08:00:00, and the third section is from 2021/04/10 08:00:00 to 2021/04/13 08:00:00. Accordingly, _Y1 is 2, _Y2 is 2, and _Y3 is 3. Assume that the second time period is 12:00:00-13:00:00, _X1 is the sum of the number of time points included in 12:00:00-13:00:00 every day from 2021/04/06 08:00:00 to 2021/04/8 08:00:00, _X2 is the sum of the number of time points included in 12:00:00-13:00:00 every day from 2021/04/08 08:00:00 to 2021/04/10 08:00:00, and _X3 is the sum of the number of time points included in 12:00:00-13:00:00 every day from 2021/04/10 08:00:00 to 2021/04/13 08:00:00. Assume that _X1 is 6, _X2 is 10, _X3 is 15, _ω1 is 0.2, _ω2 is 0.3, and _ω3 is 0.5. Then the confidence level Z of the second time period is (6/2*0.2+10/2*0.3+15/3*0.5)*7=32.2.

The above-mentioned method for recommending services utilizes the time information (i.e., time points) in the usage data within the first historical time period for clustering. In another possible implementation method, the time information (i.e., time points) and geographic information (such as geographic coordinates) in the usage data within the first historical time period can also be used for clustering. This implementation method is introduced in detail below.

FIG5 is a flow chart of another method 500 for recommending services provided in an embodiment of the present application. The hardware structure of the terminal device involved in the method 500 may be as shown in FIG1 , and the software structure of the terminal device may be as shown in FIG2 . The method 500 includes the following steps:

S501: Collect multiple usage data of a target service by a target user in a first historical time period, where the multiple usage data include multiple time points and multiple geographic coordinates.

Exemplarily, the geographic coordinates are two-dimensional coordinates, the geographic location coordinates (X, Y) of the target user when using the target service, where X is longitude and Y is latitude. Each of the multiple usage data includes a time point and a geographic coordinate. The introduction of the first historical time period and the time point can refer to S301 in the above and Figure 3, which will not be repeated here.

S502: Determine the difference between every two time points in the multiple time points.

It should be understood that the specific implementation of S502 is the same as that of S302 in FIG. 3 , and will not be described in detail herein.

S503: Calculate the distance between every two geographic coordinates in the plurality of geographic coordinates.

Similar to S502, every two geographic coordinates refer to the combination of each geographic coordinate in the plurality of geographic coordinates with the remaining geographic coordinates. Assuming that the number of geographic coordinates is m, the number of every two geographic coordinate combinations is m*(m-1)/2, and the number of the above distances is m*(m-1)/2. The distance in the embodiment of the present application can be understood as the spherical distance between two geographic coordinates. Exemplarily, assume that the geographic coordinates of point A are ( _X1 , _Y1 ), the geographic coordinates of point B are ( _X2 , _Y2 ), the center of the earth is point O, the radius of the earth is R, point C is a point with the same longitude as point A and a latitude of 0, and the geographic coordinates of point C are ( _X1 , 0), point D is a point with the same longitude as point B and a latitude of 0, and the geographic coordinates of point D are ( _X2 , 0), and the spherical distance d between point A and point B is R*arccos(cos∠AOC*cos∠BOD*cos∠DOC+sin∠AOC*sin∠BOD).

S504: Determine a clustering metric value between every two pieces of usage data in the plurality of usage data according to the distance and the difference.

The clustering metric is a value that needs to be referenced during the clustering process of multiple usage data. The data processing device determines whether the clustering metric between two usage data is less than or equal to the preset model parameter in the clustering algorithm, such as the radius of the preset circle shown in Figure 4. In an embodiment of the present application, the clustering metric between each two usage data is calculated based on the difference between the time points of the two usage data and the distance between the geographic coordinates. For example, the time point included in the first usage data is 2021/04/08 08:00:00, and the geographic coordinates are (80, 90), and the time point included in the second usage data is 2021/04/08 08:01:00, and the geographic coordinates are (77, 86). The difference between the time point corresponding to the first usage data and the time point corresponding to the second usage data is 60, and the distance between the geographic coordinates corresponding to the first usage data and the geographic coordinates corresponding to the second usage data is 5. The clustering metric between the first usage data and the second usage data can be calculated based on 60 and 5.

S505 . Cluster the plurality of usage data based on the clustering metric value to obtain at least one cluster of usage data, wherein each cluster of usage data in the at least one cluster of usage data includes a plurality of target time points and a plurality of target geographic coordinates.

It should be understood that the specific implementation of S505 is similar to S303 in FIG. 3 . Since each usage data includes a time point and a geographic coordinate, each cluster of usage data includes multiple target time points and multiple target geographic coordinates.

S506: Determine at least one target time period and at least one geographical area based on at least one cluster of usage data, so that the terminal device performs conditional judgment of a service recommendation algorithm for a target service within the at least one target time period and within the at least one geographical area.

It should be understood that the specific implementation of S506 is similar to S304 in FIG. 3 . Since each cluster of usage data includes multiple target time points and multiple target geographic coordinates, the terminal device can determine a time period according to the multiple target time points of each cluster of usage data, and the specific implementation method is similar to S304. The data processing device can also determine a geographical area according to the multiple target geographic coordinates included in each cluster of usage data, and the geographical area can also be called a "geo-fence".

Specifically, the data processing device may determine at least one geographic area based on the maximum longitude, maximum latitude, minimum longitude and minimum latitude of multiple target geographic coordinates included in each cluster of usage data in at least one cluster of usage data. Exemplarily, the data processing device may determine a geographic area based on the maximum longitude, maximum latitude, minimum longitude and minimum latitude of multiple target geographic coordinates of each cluster of usage data. In another possible implementation, the data processing device may also determine at least one geographic area based on the multiple target geographic coordinates included in each cluster of usage data in at least one cluster of usage data in combination with administrative areas. The administrative area may be a street area, a shopping mall area, etc. The data processing device first determines an area based on the above-mentioned multiple target geographic coordinates, and then determines a more accurate geographic area in combination with the administrative areas near the area.

At least one target time period and at least one geographic area are used for conditional judgment of the service recommendation algorithm of the target service by the terminal device. When the geographical location of the target user belongs to at least one geographic area, and the current time belongs to at least one time period, it means that the target user may have the intention to use the target service at this time, and the terminal device can call the service recommendation algorithm of the target service to determine whether to recommend the target service to the target user. When the geographical location of the target user does not belong to at least one geographic area, or the current time does not belong to at least one time period, it means that the target user does not have the intention to use the target service at this time, and the terminal device does not need to call the service recommendation algorithm.

The method for recommending services provided in the embodiment of the present application obtains at least one target time period and at least one geographical area by clustering multiple usage data of the target user using the target service, and the terminal device can make conditional judgments of the service recommendation algorithm of the target service based on the at least one target time period and at least one geographical area. The method combines time information and geographical information, so that the data processing device can more accurately determine the intention of the target user to use the target service, thereby reducing the number of invalid judgments of the terminal device, further reducing the power consumption of the terminal device, and improving the user experience.

As an optional embodiment, the above S504 can be implemented in the following manner: obtain the first maximum value and the first minimum value in the distance between every two geographic coordinates; determine the dimensionless distance between every two geographic coordinates based on the first maximum value, the first minimum value and the distance between every two geographic coordinates; obtain the second maximum value and the second minimum value in the difference between every two time points; determine the dimensionless difference between every two time points based on the second maximum value, the second minimum value and the difference between every two time points; determine the clustering measure between every two usage data based on the dimensionless distance between every two geographic coordinates and the dimensionless difference between every two time points.

It should be understood that the dimensionless distance is a value without a unit obtained by dedimensionalizing the distance between each two geographic coordinates, and the dimensionless difference is a value without a unit obtained by dedimensionalizing the difference between each two time points.

In one possible implementation, the dimensionless distance between every two geographic coordinates is determined by the following formula: Among them, _Gr is the dimensionless distance, _Gi is the distance between every two geographic coordinates, _Gmax is the first maximum value, and _Gmin is the first minimum value.

In one possible implementation, the dimensionless difference between every two time points is determined by the following formula: Wherein, _Tr is the dimensionless difference, _Ti is the difference between every two time points, _Tmax is the second maximum value, and _Tmin is the second minimum value.

Since the unit of the difference between each two time points and the unit of the distance between each two geographic coordinates are different, when the clustering metric is calculated based on the above difference and distance, it is necessary to first de-dimensionalize the above difference and distance. The clustering metric between each two usage data is calculated based on the difference between the time points of the two usage data and the distance between the geographic coordinates of the two usage data. In an embodiment of the present application, the clustering metric can be calculated by the following formula: D = α*G _r + β*T _r , where D is the clustering metric, α is the weight of the distance, β is the weight of the difference, and α+β=1.

For example, it is assumed that the plurality of usage data are first usage data, second usage data, third usage data, and fourth usage data. The first usage data includes a first time point and a first geographic coordinate; the second usage data includes a second time point and a second geographic coordinate; the third usage data includes a third time point and a third geographic coordinate; and the fourth usage data includes a fourth time point and a fourth geographic coordinate.

If the difference between the first time point and the second time point is 61; the difference between the first time point and the third time point is 70; the difference between the first time point and the fourth time point is 65; the difference between the second time point and the third time point is 62; the difference between the second time point and the fourth time point is 60; the difference between the third time point and the fourth time point is 63. The units of the above differences are all seconds. The second maximum value is 70, and the second minimum value is 60. The dimensionless difference T _r corresponding to the difference between the first time point and the second time point = (61-60)/(70-60) = 0.1.

If the distance between the first geographic coordinate and the second geographic coordinate is 6; the distance between the first geographic coordinate and the third geographic coordinate is 5; the distance between the first geographic coordinate and the fourth geographic coordinate is 10; the distance between the second geographic coordinate and the third geographic coordinate is 12; the distance between the second geographic coordinate and the fourth geographic coordinate is 10; the distance between the third geographic coordinate and the fourth geographic coordinate is 13. The units of the above distances can all be meters, decimeters, etc. The first maximum value is 13, and the first minimum value is 5. The dimensionless distance _Gr corresponding to the distance between the first geographic coordinate and the second geographic coordinate is (6-5)/(13-5)=0.125.

In the above example, the clustering metric value of the first usage data and the second usage data is calculated based on the dimensionless difference between the first time point and the second time point and the dimensionless distance between the first geographic coordinate and the second geographic coordinate. Specifically, in the above example, assuming that α is 0.2 and β is 0.8, the clustering metric value D between the first usage data and the second usage data is 0.2*0.125+0.8*0.1=0.105.

The following is a detailed introduction of the method for recommending services in an embodiment of the present application using two examples.

Example 1: Clustering based on time information

The data processing device collects the usage data of the target user using the payment service in the historical time period from 2022/04/06 to 2022/04/13. The number of usage data is 10, and they are represented by points 1 to 10 respectively. Each usage data includes a time point. The ten times are: 2022/04/06 08:00:03, 2022/04/07 08:01:04, 2022/04/08 08:02:03, 2022/04/09 08:04:03, 2022/04/10 08:05:03, 2022/04/11 08:02:36, 2022/04/10 12:00:00, 2022/04/11 12:01:03, 2022/04/12 12:02:03, 2022/04/13 12:02:23.

According to the information of hours, minutes and seconds contained in the ten time points, the ten time points are converted into second-level values. For example, the information of hours, minutes and seconds contained in the time point 2022/04/06 08:00:03 corresponding to point 1 is 08:00:03, one hour is 3600 seconds, one minute is 60 seconds, and the second-level value corresponding to this time point is 28803. The second-level values corresponding to points 1 to 10 are: 28803, 28864, 28923, 29043, 29103, 28956, 43200, 43263, 43323, 43343.

Determine two model parameters in the clustering algorithm: the preset minimum number of payment events N and the preset maximum time distance difference M, and then input the second-level values corresponding to the above points 1 to 10 into the clustering algorithm for clustering to obtain at least one time period. Assume that N = 3 and M = 100. N is the minimum number of time points included in each preset time period. M is the maximum time distance between the time points included in each preset time period.

The specific implementation process of the clustering algorithm of the embodiment of the present application is described below in conjunction with Figures 6 and 7.

FIG6 is a process diagram 1 of a clustering algorithm provided in an embodiment of the present application. As shown in FIG6 , the clustering process of the above 10 usage data is as follows:

First, calculate the difference between every two second-level values. For example, the second-level values corresponding to point 1 and point 2 are 28803 and 28864 respectively, and the difference between the two second-level values is 61. Similarly, the time distance between point 2 and point 3 is 59. According to the above method, the difference between every two points is calculated in turn, and 45 differences will be obtained.

As shown in FIG6(a), any time point among the ten points is selected as the center of the circle, for example, point 2 is selected as the center of the circle, and the radius is M, to obtain the first circular graph. Determine whether the number of usage data covered in the first circular graph is greater than or equal to N. The first circular graph covers point 1, point 2, point 3, and point 6. If the number of usage data covered in the first circular graph is greater than N, the first circular graph is used as the target circular graph.

As shown in Figure 6(b), since the first circular graph is the target circular graph, a new circular graph is obtained by taking the other points in the first circular graph except point 2 as the center and M as the radius, and then determining whether the new circular graph can be used as the target circular graph. Through judgment, the number of points covered by the circular graphs with points 1 and 3 as the centers is less than 3, and cannot be used as the target circular graph. The number of points covered by the circular graph with point 6 as the center is equal to 3, and can be used as the target circular graph.

As shown in Figure 6(c), since the circular diagram obtained with point 6 as the center is the target circular diagram, and the target circular diagram covers points 2, 6 and 4, point 2 has been used as the center to obtain the first circular diagram, therefore, continue to make a circle with point 4 as the center, the circular diagram obtained covers points 6, 4 and 5, the number is equal to 3, and the circular diagram obtained with point 4 as the center can be used as the target circular diagram. Continue to make a circle with point 5 as the center, the circular diagram obtained covers points 4 and 5, the number is less than 3, then the circular diagram obtained with point 5 as the center cannot be used as the target circular diagram. At this time, all the points covered in the target circular diagram have been used as the center to make a circle. The usage data covered by all target circular diagrams is a cluster of usage data, and the time points included in the cluster of usage data are multiple target time points. The first time period is determined based on the multiple target time points. The multiple target time points include six target time points included in the usage data corresponding to points 1 to 6, the largest time point among the six target time points is the time point corresponding to point 5, and its corresponding second-level value is 29103, and the smallest time point is the time point corresponding to point 1, and its corresponding second-level value is 28803. The largest second-level value and the smallest second-level value are respectively converted into time points containing hour, minute and second information to obtain a first time period. The first time period is 08:00:03 to 08:05:03.

As shown in Figure 6(d), in the process of determining the first time period, points 7, 8, 9, and 10 are not used as the center of the circle, so the four second-level values are isolated use data. Take any one of the isolated use data as the center of the circle and M as the radius to obtain a circular diagram, and repeat the above target circular diagram acquisition process. For example, take point 8 as the center of the circle, and the obtained circular diagram covers points 7, 8, 9, and 10, so this circular diagram can be used as the target circular diagram.

As shown in Figure 6(e), circles are drawn with points 7, 9 and 10 as the center, and the circles drawn with points 9 and 10 as the center can all be used as target circles, while the circle drawn with point 7 as the center cannot be used as the target circle. According to the maximum and minimum values of the second-level values corresponding to the four usage data of points 7, 8, 9 and 10, the second time period is obtained, which is from 12:00:00 to 12:02:23.

FIG7 is a second schematic diagram of the process of the clustering algorithm provided in the embodiment of the present application. As shown in FIG7 , the process includes the following steps:

As shown in FIG7(a), ten circular diagrams are obtained by taking each point from point 1 to point 10 as the center and M as the radius.

As shown in FIG7(b), determine whether the number of usage data covered by the ten circular graphs is greater than or equal to N. If so, the circular graph is used as the target circular graph. The circular graph obtained with point 2, point 6, point 4, point 8, point 9 and point 10 as the center is the target circular graph. The set of intersecting target circular graphs is used as a density reachable circular graph. For example, the sum of the circular graphs obtained with point 2, point 6 and point 4 as the center is the density reachable circular graph, and the sum of the circular graphs obtained with point 8, point 9 and point 10 as the center is the density reachable circular graph. The usage data covered by each density reachable circular graph is a cluster of usage data, and the time points included in the cluster of usage data are multiple target time points. Two time periods can be determined according to the multiple target time points included in each cluster of usage data in the two clusters of usage data. Specifically, the first time period can be obtained according to the density reachable circular graph obtained with point 2, point 6 and point 4 as the center; the second time period can be obtained according to the density reachable circular graph obtained with point 8, point 9 and point 10 as the center. The process of obtaining a time period according to multiple target time points is similar to the process shown in FIG6( c ), and will not be described in detail here.

The terminal device can perform conditional judgment of the service recommendation algorithm of the payment service within the above-mentioned first time period and second time period, and can also calculate the confidence of the first time period and the second time period, and further select the target time period to perform conditional judgment of the service recommendation algorithm of the payment service, which will not be repeated here.

Example 2: Clustering based on time and geographic information

When multiple usage data include multiple time points and multiple geographic coordinates, the data processing device can collect the usage data of the target user using the payment service in the historical time period of 2022/04/06-2022/04/13. The number of usage data is 10, and the ten usage data are represented by points 1 to 10 respectively. Each usage data contains a time point and a geographic coordinate. Exemplarily, the time point included in the usage data corresponding to point 1 may be 2022/04/06 08:00:03, and the included geographic coordinates may be (100,90); the time point included in the usage data corresponding to point 2 may be 2022/04/07 08:01:03, and the included geographic coordinates may be (101,90). Other points are similar and are not listed here one by one.

According to the information of hours, minutes and seconds contained in the ten time points, the ten time points are converted into second-level values. The second-level value of the time point 2022/04/06 08:00:03 is 28803, and the second-level value of the time point 2022/04/07 08:01:03 is 28863. The specific conversion method is similar to the implementation method in process 700, which will not be repeated here.

Determine two model parameters in the clustering algorithm: the preset minimum number of payment events N and the preset maximum difference in target distance (M), cluster the ten usage data, obtain trigger information, and obtain at least one time period and at least one geographical area.

The clustering process of the embodiment of the present application is as follows:

Calculate the distance between every two geographic coordinates. For example, the distance between the geographic coordinates contained in the usage data corresponding to point 1 and the usage data corresponding to point 2 is the distance between the coordinates (101,90) and the coordinates (100,90), which is 1. Calculate the difference between every two second-level values, and use the difference as the difference between the time points. The difference between the time points contained in the first usage data and the second usage data is the difference between 28863 and 28803, which is 60. The number of distances and differences is 45, and there is a one-to-one correspondence between the two, that is, the difference between the time points of every two usage data corresponds to the distance between the geographic coordinates of the two usage data. For example, if the distance between the geographic coordinates contained in the usage data corresponding to point 1 and the usage data corresponding to point 2 is 1, and the difference between the time points contained is 60, then the distance 1 corresponds to the difference 60.

De-dimensionalize the distance between each two geographic coordinates to obtain the dimensionless distance. The dimensionless distance is calculated by the formula Calculated. Assuming that the maximum value of the above 45 distances is 9 and the minimum value is 0, the distance between the geographic coordinates of the usage data corresponding to point 1 and the usage data corresponding to point 2 is 1, and the dimensionless distance between point 1 and point 2 after dimensionless is 1/9. Dedimensionalize the difference between each two time points to obtain the dimensionless difference. The dimensionless difference is calculated by the formula Assuming that the maximum value of the above 45 differences is 100 and the minimum value is 20, the difference between the time points included in the usage data corresponding to point 1 and the usage data corresponding to point 2 is 60, and the dimensionless difference between point 1 and point 2 obtained after dimensionless is 0.5.

Calculate the clustering metric between every two usage data. The clustering metric can be calculated by the following formula: D = α*G _r + β*T _r . Assume that α is 0.2 and β is 0.8, then the clustering metric between the usage data corresponding to point 1 and the usage data corresponding to point 2 is 0.42. A clustering metric is obtained between every two usage data.

Assuming that the model parameters in the clustering algorithm are N=3, M=0.6, clustering is performed based on the above ten usage data.

In a possible implementation, referring to FIG6 , any one of the ten points is selected as the center of a circle, and a circle is drawn with M as the radius, and whether the circle is a target circle is determined by determining whether the number of usage data covered by the circle is greater than or equal to N. Next, at least one time period and at least one geographical area are determined according to the target time point and target geographical coordinates included in the usage data covered by the target circle.

In another possible implementation, referring to FIG. 7 , ten circular graphs are obtained by taking each of the ten time points as the center and M as the radius. The target circular graph and the density reachable circular graph are determined in the same way. Then, at least one time period and at least one geographical area are obtained according to the target time point and the target geographical coordinates included in the usage data covered by each density reachable circular graph.

The terminal device can perform conditional judgment of the service recommendation algorithm for the payment service within the above-mentioned at least one time period and at least one geographical area, and can also calculate the confidence of each time period in at least one time period, further select at least one target time period, and perform conditional judgment of the service recommendation algorithm for the payment service within at least one target time period and at least one geographical area, which will not be repeated here.

It should be understood that the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

The above describes in detail the method for recommending services in an embodiment of the present application in combination with Figures 3 to 7. The following will describe in detail the device for recommending services in an embodiment of the present application in combination with Figures 8 and 9.

Fig. 8 is a schematic diagram of the structure of an apparatus 800 for recommending services provided in an embodiment of the present application. As shown in Fig. 8 , the apparatus 800 includes: a collection module 801 and a processing module 802 .

In a possible implementation, the apparatus 800 is used to implement the steps corresponding to the data processing device in the above method 300.

A collection module 801 is used to collect multiple usage data of a target user for a target service in a first historical time period, where the multiple usage data include multiple time points;

Processing module 802 is used to determine the difference between every two time points among multiple time points; based on the difference, cluster multiple usage data to obtain at least one cluster of usage data, each cluster of usage data in at least one cluster of usage data includes multiple target time points; based on at least one cluster of usage data, determine at least one target time period so that the terminal device performs conditional judgment of the service recommendation algorithm for the target service within at least one target time period.

Optionally, the processing module 802 is specifically used to: determine at least one time period based on multiple target time points included in each cluster of usage data; determine the confidence of each time period based on the number of days information corresponding to the first historical time period and the number of target time points included in each time period in at least one time period; determine whether the confidence of each time period is greater than or equal to a first preset threshold; if there is a first time period in at least one time period whose confidence is greater than or equal to the first preset threshold, determine the first time period as the target time period.

Optionally, the confidence level of the second time period in at least one time period is determined by the following formula:

Among them, Z is the confidence, Q is the day value corresponding to the day information, _Yi is the day value corresponding to the i-th section of day information among the n sections of day information divided into the day information, _Y1 + _Y2 +...+ _Yn =Q, _Xi is the number of time points included in the i-th section of day information in the second time period, i∈{1,2,…n}, _ωi is the weight of the time points included in the i-th section of day information in the second time period, _ω1 + _ω2 +…+ _ωn =1.

Optionally, the processing module 802 is specifically configured to: determine at least one time period based on a maximum value and a minimum value among a plurality of target time points included in each cluster of usage data in at least one cluster of usage data.

Optionally, the multiple usage data also include multiple geographic coordinates, and each cluster of usage data also includes multiple target geographic coordinates; the processing module 802 is also used to: calculate the distance between every two geographic coordinates in the multiple geographic coordinates; determine the clustering measurement value between every two usage data in the multiple usage data based on the distance and the difference; cluster the multiple usage data based on the clustering measurement value to obtain at least one cluster of usage data; based on at least one cluster of usage data, determine at least one target time period and at least one geographic area, so that the terminal device performs conditional judgment of the service recommendation algorithm for the target service within at least one target time period and at least one geographic area.

Optionally, the processing module 802 is specifically used to: obtain the first maximum value and the first minimum value in the distance between every two geographic coordinates; determine the dimensionless distance between every two geographic coordinates based on the first maximum value, the first minimum value and the distance between every two geographic coordinates; obtain the second maximum value and the second minimum value in the difference between every two time points; determine the dimensionless difference between every two time points based on the second maximum value, the second minimum value and the difference between every two time points; determine the clustering measure between every two usage data based on the dimensionless distance between every two geographic coordinates and the dimensionless difference between every two time points.

Optionally, the dimensionless distance between each two geographic coordinates is determined by the following formula: Among them, _Gr is the dimensionless distance, _Gi is the distance between every two geographic coordinates, _Gmax is the first maximum value, and _Gmin is the first minimum value.

Optionally, the dimensionless difference between each two time points is determined by the following formula: Wherein, _Tr is the dimensionless difference, _Ti is the difference between every two time points, _Tmax is the second maximum value, and _Tmin is the second minimum value.

Optionally, the processing module 802 is specifically configured to determine at least one geographic area according to a maximum longitude, a maximum latitude, a minimum longitude, and a minimum latitude in a plurality of target geographic coordinates included in each cluster of usage data in at least one cluster of usage data.

Optionally, the processing module 802 is specifically used to: convert each of the multiple time points into a second-level value based on the information of hours, minutes and seconds contained in the multiple time points; and determine the difference between the second-level values corresponding to every two time points as the difference between every two time points.

It should be understood that the device 800 here is embodied in the form of a functional module. The term "module" here may refer to an application specific integrated circuit (ASIC), an electronic circuit, a processor (such as a shared processor, a proprietary processor or a group processor, etc.) and a memory for executing one or more software or firmware programs, a merged logic circuit and/or other suitable components that support the described functions. In an optional example, those skilled in the art can understand that the device 800 can be specifically a data processing device in the above-mentioned embodiment, and the device 800 can be used to execute the various processes and/or steps corresponding to the data processing device in the above-mentioned method embodiment. To avoid repetition, it will not be repeated here.

The above-mentioned device 800 has the function of implementing the corresponding steps executed by the data processing device in the above-mentioned method; the above-mentioned function can be implemented by hardware, or by hardware executing corresponding software. The hardware or software includes one or more modules corresponding to the above-mentioned functions. For example, the above-mentioned processing module 802 may include a determination module and a clustering module, the determination module can be used to implement the various steps and/or processes for performing the determination action corresponding to the above-mentioned processing module, and the clustering module can be used to implement the various steps and/or processes for performing the clustering action corresponding to the above-mentioned processing module.

In the embodiment of the present application, the device 800 in Fig. 8 may also be a chip, such as a SOC. Correspondingly, the processing module 802 may be a transceiver circuit of the chip, which is not limited here.

9 shows a schematic diagram of the structure of another device 900 for recommending services provided in an embodiment of the present application. The device 900 includes a processor 901, a transceiver 902, and a memory 903. The processor 901, the transceiver 902, and the memory 903 communicate with each other through an internal connection path, the memory 903 is used to store instructions, and the processor 901 is used to execute the instructions stored in the memory 903 to control the transceiver 902 to send and/or receive signals.

It should be understood that the device 900 can be specifically a data processing device in the above-mentioned embodiment, and can be used to execute the various steps and/or processes corresponding to the data processing device in the above-mentioned method embodiment. Optionally, the memory 903 may include a read-only memory and a random access memory, and provide instructions and data to the processor. A part of the memory may also include a non-volatile random access memory. For example, the memory may also store information about the device type. The processor 901 may be used to execute instructions stored in the memory, and when the processor 901 executes the instructions stored in the memory, the processor 901 is used to execute the various steps and/or processes of the above-mentioned method embodiment. The transceiver 902 may include a transmitter and a receiver, the transmitter may be used to implement the various steps and/or processes corresponding to the above-mentioned transceiver for performing the sending action, and the receiver may be used to implement the various steps and/or processes corresponding to the above-mentioned transceiver for performing the receiving action.

It should be understood that in the embodiments of the present application, the processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor may be any conventional processor, etc.

In the implementation process, each step of the above method can be completed by an integrated logic circuit of hardware in a processor or an instruction in the form of software. The steps of the method disclosed in conjunction with the embodiment of the present application can be directly embodied as a hardware processor for execution, or a combination of hardware and software modules in a processor for execution. The software module can be located in a storage medium mature in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The storage medium is located in a memory, and the processor executes the instructions in the memory, and completes the steps of the above method in conjunction with its hardware. To avoid repetition, it is not described in detail here.

The present application also provides a computer-readable storage medium, which is used to store a computer program, and the computer program is used to implement the method shown in the above method embodiment.

The present application also provides a computer program product, which includes a computer program (also referred to as code or instruction). When the computer program runs on a computer, the computer can execute the method shown in the above method embodiment.

Those of ordinary skill in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and modules described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the modules is only a logical function division. There may be other division methods in actual implementation, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or modules, which can be electrical, mechanical or other forms.

The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, that is, they may be located in one place or distributed on multiple 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 physically separately, or two or more modules may be integrated into one module.

If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

The above is only a specific implementation of the present application, but the protection scope of the embodiments of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the embodiments of the present application, which should be included in the protection scope of the embodiments of the present application. Therefore, the protection scope of the embodiments of the present application should be based on the protection scope of the claims.

Claims (19)

1. A method for recommending services, comprising:

Collecting a plurality of pieces of use data of a target user for a target service in a first historical time period, wherein the plurality of pieces of use data comprise a plurality of time points;

Determining a difference between each two of the plurality of time points;

Clustering the plurality of pieces of usage data based on the difference value to obtain at least one cluster of usage data, wherein each cluster of usage data in the at least one cluster of usage data comprises a plurality of target time points;

Determining at least one target time period based on the at least one cluster of usage data, so that the terminal equipment can perform conditional judgment of a service recommendation algorithm of the target service in the at least one target time period;

the determining at least one target time period based on the at least one cluster usage data includes:

Determining at least one time period based on a plurality of target time points included in the usage data of each cluster respectively;

Determining the confidence coefficient of each time period according to the number of the target time points included in each time period in the at least one time period and the number of the days corresponding to the first historical time period respectively;

Determining whether the confidence degree of each time period is larger than or equal to a first preset threshold value;

Determining a first time period as the target time period when the confidence of the first time period is greater than or equal to the first preset threshold value in the at least one time period;

the confidence level for a second time period of the at least one time period is determined by the following formula:

Wherein Z is the confidence, Q is a day value corresponding to the day information, Y _i is a day value corresponding to the i-th day information in the n-th day information, Y ₁+Y₂+…+Y_n＝Q,X_i is the number of time points included in the second time period in the i-th day information, i e {1,2, … n }, ω _i is a weight of the time points included in the second time period in the i-th day information, and ω ₁+ω₂+…+ω_n =1.

2. The method of claim 1, wherein the determining at least one time period based on the plurality of target time points included in the per-cluster usage data, respectively, comprises:

The at least one time period is determined based on a maximum value and a minimum value of a plurality of target time points included in each cluster of the at least one cluster of usage data.

3. The method of claim 1, wherein the plurality of pieces of usage data further comprises a plurality of geographic coordinates, and wherein the per cluster usage data further comprises a plurality of target geographic coordinates;

The method further comprises the steps of:

Calculating a distance between each two geographic coordinates of the plurality of geographic coordinates;

Determining a clustering metric value between every two pieces of usage data in the plurality of pieces of usage data according to the distance and the difference value;

The clustering the plurality of pieces of usage data based on the difference value includes:

based on the clustering metric value, clustering the plurality of pieces of usage data to obtain at least one cluster of usage data;

the determining at least one target time period based on the at least one cluster usage data includes:

Determining the at least one target time period and at least one geographical area based on the at least one cluster usage data, so that the terminal device makes conditional decisions of a service recommendation algorithm for the target service within the at least one target time period and within the at least one geographical area.

4. A method according to claim 3, wherein said determining a cluster metric value between each two of said plurality of usage data based on said distance and said difference value comprises:

acquiring a first maximum value and a first minimum value in the distance between every two geographic coordinates;

Determining the dimensionality removal distance between every two geographic coordinates according to the first maximum value, the first minimum value and the distance between every two geographic coordinates;

acquiring a second maximum value and a second minimum value in the difference value between every two time points;

determining a dimensionality difference value between each two time points according to the second maximum value, the second minimum value and the difference value between each two time points;

and determining a clustering metric value between every two pieces of usage data according to the dimensionality removing distance between every two geographic coordinates and the dimensionality removing difference value between every two time points.

5. The method of claim 4, wherein the dimensionality distance between each two geographic coordinates is determined by the following formula:

Wherein G _r is the dimensionality distance, G _i is the distance between every two geographic coordinates, G _max is the first maximum value, and G _min is the first minimum value.

6. The method of claim 5, wherein the dimensionality difference between each two time points is determined by the following formula:

Wherein T _r is the dimensionality difference, T _i is the difference between every two time points, T _max is the second maximum, and T _min is the second minimum.

7. The method of any of claims 3-6, wherein the determining the at least one geographic area based on the at least one cluster usage data comprises:

and determining the at least one geographical area according to the maximum longitude, the maximum latitude, the minimum longitude and the minimum latitude in a plurality of target geographical coordinates included in each cluster of the at least one cluster of the usage data.

8. The method of claim 7, wherein said determining the difference between each two of the plurality of time points comprises:

converting each of the plurality of time points into a second level value according to the information of the hours, the minutes and the seconds contained in the plurality of time points;

and determining the difference value between the second level values corresponding to every two time points as the difference value between every two time points.

9. An apparatus for recommending services, comprising:

The system comprises an acquisition module, a storage module and a processing module, wherein the acquisition module is used for acquiring a plurality of pieces of use data of a target user on a target service in a first historical time period, and the plurality of pieces of use data comprise a plurality of time points;

A processing module for determining a difference between each two of the plurality of time points; clustering the plurality of pieces of usage data based on the difference value to obtain at least one cluster of usage data, wherein each cluster of usage data in the at least one cluster of usage data comprises a plurality of target time points; and determining at least one target time period based on the at least one cluster of usage data, so that the terminal equipment can perform condition judgment of a service recommendation algorithm of the target service in the at least one target time period;

The processing module is specifically configured to:

Determining at least one time period based on a plurality of target time points included in the usage data of each cluster respectively;

Determining the confidence coefficient of each time period according to the number of the target time points included in each time period in the at least one time period and the number of the days corresponding to the first historical time period respectively;

Determining whether the confidence degree of each time period is larger than or equal to a first preset threshold value;

Determining a first time period as the target time period when the confidence of the first time period is greater than or equal to the first preset threshold value in the at least one time period;

the confidence level for a second time period of the at least one time period is determined by the following formula:

Wherein Z is the confidence, Q is a day value corresponding to the day information, Y _i is a day value corresponding to the i-th day information in the n-th day information, Y ₁+Y₂+...+Y_n＝Q,X_i is the number of time points included in the second time period in the i-th day information, i e {1,2, … n }, ω _i is a weight of the time points included in the second time period in the i-th day information, and ω ₁+ω₂+...+ω_n =1.

10. The apparatus of claim 9, wherein the processing module is specifically configured to:

The at least one time period is determined based on a maximum value and a minimum value of a plurality of target time points included in each cluster of the at least one cluster of usage data.

11. The apparatus of claim 9, wherein the plurality of pieces of usage data further comprises a plurality of geographic coordinates, and wherein the per cluster usage data further comprises a plurality of target geographic coordinates;

the processing module is further configured to:

Calculating a distance between each two geographic coordinates of the plurality of geographic coordinates;

Determining a clustering metric value between every two pieces of usage data in the plurality of pieces of usage data according to the distance and the difference value;

based on the clustering metric value, clustering the plurality of pieces of usage data to obtain at least one cluster of usage data;

Determining the at least one target time period and at least one geographical area based on the at least one cluster usage data, so that the terminal device makes conditional decisions of a service recommendation algorithm for the target service within the at least one target time period and within the at least one geographical area.

12. The apparatus of claim 11, wherein the processing module is specifically configured to:

acquiring a first maximum value and a first minimum value in the distance between every two geographic coordinates;

Determining the dimensionality removal distance between every two geographic coordinates according to the first maximum value, the first minimum value and the distance between every two geographic coordinates;

acquiring a second maximum value and a second minimum value in the difference value between every two time points;

determining a dimensionality difference value between each two time points according to the second maximum value, the second minimum value and the difference value between each two time points;

and determining a clustering metric value between every two pieces of usage data according to the dimensionality removing distance between every two geographic coordinates and the dimensionality removing difference value between every two time points.

13. The apparatus of claim 12, wherein the dimensionality distance between each two geographic coordinates is determined by the following formula:

Wherein G _r is the dimensionality distance, G _i is the distance between every two geographic coordinates, G _max is the first maximum value, and G _min is the first minimum value.

14. The apparatus of claim 13, wherein the dimensionality difference between each two time points is determined by the following formula:

Wherein T _r is the dimensionality difference, T _i is the difference between every two time points, T _max is the second maximum, and T _min is the second minimum.

15. The apparatus according to any one of claims 11-14, wherein the processing module is specifically configured to:

and determining the at least one geographical area according to the maximum longitude, the maximum latitude, the minimum longitude and the minimum latitude in a plurality of target geographical coordinates included in each cluster of the at least one cluster of the usage data.

16. The apparatus of claim 15, wherein the processing module is specifically configured to:

converting each of the plurality of time points into a second level value according to the information of the hours, the minutes and the seconds contained in the plurality of time points;

and determining the difference value between the second level values corresponding to every two time points as the difference value between every two time points.

17. An apparatus for recommending services, comprising: a processor coupled to a memory for storing a computer program which, when invoked by the processor, causes the terminal device to perform the method of any one of claims 1 to 8.

18. A computer readable storage medium storing a computer program comprising instructions for implementing the method of any one of claims 1 to 8.

19. A computer program product comprising computer program code means for causing a computer to carry out the method as claimed in any one of claims 1 to 8 when said computer program code means are run on the computer.