Processing method, processing system, intelligent terminal and storage medium

Abstract

The invention provides a processing method, a processing system, an intelligent terminal and a storage medium, and the processing method can be applied to the intelligent terminal, and comprises the steps: creating a session instruction according to a preset rule and historical behavior characteristics, and carrying out the end-side calculation operation according to the session instruction and an application material set, so as to obtain a to-be-distributed application. Through the technical scheme of the invention, the session instruction can be created according to the behavior habit of the user on the end side, and the end side calculation operation is carried out to determine the data of the to-be-distributed application matched with the session instruction from the application material set. Thus, according to the technical scheme of the application, the to-be-distributed application meeting the personalized requirements of the user can be obtained through the end side in the application distribution scene, the QPS of the server is reduced, and the user experience is improved.

Description

Technical Field

[0001] This application relates to the technical field of terminals, and in particular, to a processing method, a processing system, an intelligent terminal, and a storage medium. Background Art

[0002] In some implementations, the application distribution mode mostly relies on the server side for data processing and distribution decision-making. Taking an app store as an example, the app store communicates with the server to obtain data of apps to be distributed from the server. The obtained data of apps to be distributed can be directly displayed in the app store, or can be cached for subsequent call and display. Moreover, in order to ensure the freshness of the apps to be distributed in the app store, the server is usually polled multiple times within a set time period.

[0003] In the process of conceiving and implementing this application, the inventors found that there are at least the following problems: in the scenario of app distribution, requesting data of apps to be distributed from the server in a polling manner will cause a high QPS (Queries Per Second) on the server and a low utilization rate of downloaded data. Therefore, how to reduce the QPS on the server and obtain apps to be distributed that meet the personalized needs of users in the scenario of app distribution is a technical problem that needs to be urgently solved by those skilled in the art.

[0004] The foregoing description is for providing general background information and does not necessarily constitute prior art. Summary of the Invention

[0005] In view of the above technical problems, this application provides a processing method, a processing system, an intelligent terminal, and a storage medium, which can obtain apps to be distributed that meet the personalized needs of users through the terminal side in the app distribution scenario, reduce the QPS on the server, and improve the user experience.

[0006] This application provides a processing method, which can be applied to an intelligent terminal, and includes the steps of: creating a session instruction according to a preset rule and historical behavior characteristics, and performing an end-side calculation operation according to the session instruction and an app material set to obtain an app to be distributed.

[0007] Optionally, the preset rule includes at least one of the following: Obtaining first feature dimension weight information; Obtaining a session instruction template; Arranging and processing the historical behavior characteristics according to the first feature dimension weight information and / or the session instruction template to determine the session instruction.

[0008] Optionally, the end-side calculation operation includes at least one of the following: Inputting the session instruction into an AI model on the end side; Call the application material set through the AI model for calculation to obtain the application to be distributed corresponding to the session instruction.

[0009] Optionally, obtain the application material set according to the first strategy, and the first strategy includes at least one of the following: Determine the preset data dimension; Determine the first quantity; Determine the quantity requirement; Download the application material data according to the preset data dimension and / or the first quantity; Sort all or part of the downloaded application material data according to the preset sorting rule to obtain an application material sorting table; According to the quantity requirement, select all or part of the application material data in the application material sorting table for keyword extraction operation to obtain the keyword information of each selected application material data; According to the quantity requirement, select all or part of the downloaded application material data for keyword extraction operation to obtain the keyword information of each selected application material data; Obtain the application material set according to the keyword information of at least part of the application material data.

[0010] Optionally, the historical behavior characteristics include at least one of the following: application click information, application usage information, installed application list, search keywords.

[0011] Optionally, obtain the installed application list according to the third strategy, and the third strategy includes at least one of the following: Perform application sorting processing according to the second feature dimension weight information and the application usage information of each application to obtain an application sequence; Use the application sequence as the installed application list; Select the second quantity of applications from the application sequence in descending order of weight to form the installed application list.

[0012] Optionally, the method of this application further includes at least one of the following steps: Display the application to be distributed according to the second strategy; In response to the operation on the displayed application to be distributed, count the conversion result of the application to be distributed obtained by the end-side calculation operation; Perform feedback optimization processing according to the conversion result to update the first feature dimension weight information and / or the session instruction template for creating the session instruction.

[0013] Optionally, the second strategy includes at least one of the following: Display the application to be distributed; Obtain the installed applications of associated terminals within a preset range; Differentially display the to-be-distributed application that is the same as the installed application.

[0014] This application also provides a processing system, including an intelligent terminal and a server; the server is used to send application material data to the intelligent terminal; the intelligent terminal is used to construct an application material set according to the obtained application material data, create a session instruction according to a preset rule and historical behavior characteristics, and perform an end-side calculation operation according to the session instruction and the application material set to obtain the to-be-distributed application.

[0015] This application also provides an intelligent terminal, including: a memory and a processor, wherein a computer program is stored on the memory, and when the computer program is executed by the processor, the steps of the processing method described in any one of the above are implemented.

[0016] This application also provides a storage medium, and the storage medium stores a computer program, and when the computer program is executed by the processor, the steps of the processing method described in any one of the above are implemented.

[0017] This application provides a processing method, a processing system, an intelligent terminal and a storage medium. The processing method can be applied to an intelligent terminal and includes the steps of: creating a session instruction according to a preset rule and historical behavior characteristics, and performing an end-side calculation operation according to the session instruction and the application material set to obtain the to-be-distributed application. Through the technical solution of this application, a session instruction can be created according to the user's behavior habits on the end side, and an end-side calculation operation is performed to determine the data of the to-be-distributed application that matches the session instruction from the application material set. In this way, the technical solution of this application can obtain the to-be-distributed application that meets the personalized needs of the user on the end side in the application distribution scenario, reduce the QPS of the server, and improve the user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The drawings here are incorporated into the specification and form a part of this specification, showing the embodiments that conform to this application, and are used together with the specification to explain the principles of this application. In order to more clearly illustrate the technical solutions of the embodiments of this application, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, for those of ordinary skill in the art, other drawings can also be obtained based on these drawings without creative efforts.

[0019] Figure 1 A schematic diagram of the hardware structure of a mobile terminal for implementing various embodiments of this application.

[0020] Figure 2 A schematic diagram of the communication network system architecture provided by the embodiment of this application.

[0021] Figure 3 A schematic flow chart of the processing method shown in the first embodiment of this application.

[0022] Figure 4 It is the first process schematic diagram of the processing method shown in the second embodiment of the present application.

[0023] Figure 5 It is the second process schematic diagram of the processing method shown in the second embodiment of the present application.

[0024] Figure 6 It is the schematic diagram of the application distribution platform exemplified in the embodiment of the present application.

[0025] The realization of the purpose, functional features and advantages of the present application will be further described with reference to the embodiments and the accompanying drawings. Through the above-mentioned accompanying drawings, the clear embodiments of the present application have been shown, and there will be more detailed descriptions hereinafter. These drawings and text descriptions are not intended to limit the scope of the concept of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. Detailed implementation manners

[0026] Here, the exemplary embodiments will be described in detail, and the examples are shown in the accompanying drawings. When the following description refers to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The implementation manners described in the following exemplary embodiments do not represent all implementation manners consistent with the present application. On the contrary, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0027] It should be noted that in this article, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "including a..." does not exclude the existence of another identical element in the process, method, article or device including that element. In addition, components, features, and elements with the same name in different embodiments of the present application may have the same meaning or different meanings, and their specific meanings need to be determined by their explanations in the specific embodiments or further in combination with the context of the specific embodiments.

[0028] It should be understood that although terms such as first, second, and third may be used herein to describe various information, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from each other. For example, without departing from the scope of this document, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the word "if" as used herein may be interpreted as "when" or "while" or "in response to determining". Furthermore, as used in this document, the singular forms "a", "an", and "the" are also intended to include the plural forms unless the context indicates otherwise. It should be further understood that the terms "comprising", "including" indicate the presence of the stated features, steps, operations, elements, components, items, kinds, and / or groups, but do not preclude the presence, occurrence, or addition of one or more other features, steps, operations, elements, components, items, kinds, and / or groups. The terms "or", "and / or", "including at least one of the following" used in this application may be interpreted inclusively, or mean any one or any combination. For example, "including at least one of the following: A, B, C" means "any one of the following: A; B; C; A and B; A and C; B and C; A and B and C", and again, "A, B, or C" or "A, B, and / or C" means "any one of the following: A; B; C; A and B; A and C; B and C; A and B and C". An exception to this definition occurs only when the combination of elements, functions, steps, or operations is inherently mutually exclusive in some way.

[0029] It should be understood that although the steps in the flowcharts in the embodiments of this application are shown in sequence according to the indication of the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless there is a clear indication in this document, the execution of these steps has no strict order limit and can be executed in other orders. Moreover, at least a part of the steps in the figure may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same moment, but can be executed at different moments, and their execution order is not necessarily sequential, but can be executed alternately or in turn with at least a part of other steps or sub-steps or stages of other steps.

[0030] Depending on the context, the words "if", "when" as used herein may be interpreted as "when" or "while" or "in response to determining" or "in response to detecting". Similarly, depending on the context, the phrase "if determined" or "if detecting (stated condition or event)" may be interpreted as "when determined" or "in response to determining" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)".

[0031] It should be noted that in this text, step codes such as S10 and S20 are adopted. The purpose is to more clearly and briefly express the corresponding content, and it does not constitute a substantial limitation in terms of sequence. Those skilled in the art may execute S20 first and then S10 during specific implementation, etc., but all of these should be within the protection scope of this application.

[0032] It should be understood that the specific embodiments described herein are only used to explain this application and are not used to limit this application.

[0033] In the following description, suffixes such as "module", "component", or "unit" used to represent elements are only for the convenience of explaining this application, and they have no specific meaning in themselves. Therefore, "module", "component", or "unit" can be used interchangeably.

[0034] The intelligent terminal can be implemented in various forms. For example, the intelligent terminal described in this application can include intelligent terminals such as mobile phones, tablet computers, laptop computers, palmtop computers, personal digital assistants (PDAs), portable media players (PMPs), navigation devices, wearable devices, intelligent bracelets, pedometers, etc., as well as fixed terminals such as digital TVs and desktop computers.

[0035] In the following description, the mobile terminal will be taken as an example for illustration. Those skilled in the art will understand that except for elements specifically for mobile purposes, the structure according to the embodiments of this application can also be applied to fixed-type terminals.

[0036] Please refer to Figure 1 , which is a schematic diagram of the hardware structure of a mobile terminal for implementing various embodiments of this application. The mobile terminal 100 may include: RF (Radio Frequency) unit 101, WiFi module 102, audio output unit 103, A / V (audio / video) input unit 104, sensor 105, display unit 106, user input unit 107, interface unit 108, memory 109, processor 110, and power supply 111, etc. Those skilled in the art can understand that Figure 1 the mobile terminal structure shown in does not constitute a limitation to the mobile terminal. The mobile terminal may include more or fewer components than shown in the figure, or combine certain components, or have different component arrangements.

[0037] The following will combine Figure 1 to specifically introduce each component of the mobile terminal: The radio frequency unit 101 can be used for receiving and sending information or signals during communication. Specifically, after receiving the downlink information from the base station, it is sent to the processor 110 for processing. Additionally, it sends the uplink data to the base station. Generally, the radio frequency unit 101 includes, but is not limited to, antennas, at least one amplifier, transceivers, couplers, low-noise amplifiers, duplexers, etc. Moreover, the radio frequency unit 101 can also communicate with the network and other devices through wireless communication. The above wireless communication can use any communication standard or protocol, including but not limited to GSM (Global System of Mobile communication), GPRS (General Packet Radio Service), CDMA2000 (Code Division Multiple Access 2000), WCDMA (Wideband Code Division Multiple Access), TD-SCDMA (Time Division-Synchronous Code Division Multiple Access), FDD-LTE (Frequency Division Duplexing - Long Term Evolution), TDD-LTE (Time Division Duplexing - Long Term Evolution), 5G, and 6G, etc.

[0038] WiFi belongs to short-range wireless transmission technology. The mobile terminal can help users send and receive emails, browse the web, and access streaming media through the WiFi module 102, which provides users with wireless broadband Internet access. Although Figure 1 the WiFi module 102 is shown, it can be understood that it is not an essential component of the mobile terminal and can be omitted entirely within the scope of not changing the essence of the invention according to needs.

[0039] The audio output unit 103 can convert the audio data received by the radio frequency unit 101 or the WiFi module 102 or stored in the memory 109 into an audio signal and output it as sound when the mobile terminal 100 is in call signal reception mode, call mode, recording mode, voice recognition mode, broadcast reception mode, etc. Moreover, the audio output unit 103 can also provide audio output related to specific functions executed by the mobile terminal 100 (such as call signal reception sound, message reception sound, etc.). The audio output unit 103 can include speakers, buzzers, etc.

[0040] The A / V input unit 104 is used to receive audio or video signals. The A / V input unit 104 may include a Graphics Processing Unit (GPU) 1041 and a microphone 1042. The GPU 1041 processes the image data of still pictures or videos obtained by an image capturing device (such as a camera) in a video capture mode or an image capture mode. The processed image frames can be displayed on the display unit 106. The image frames processed by the GPU 1041 can be stored in the memory 109 (or other storage media) or transmitted via the radio frequency unit 101 or the WiFi module 102. The microphone 1042 can receive sounds (audio data) via the microphone 1042 in operation modes such as a phone call mode, a recording mode, a voice recognition mode, etc., and can process such sounds into audio data. The processed audio (voice) data can be converted into a format that can be transmitted to a mobile communication base station via the radio frequency unit 101 in the case of the phone call mode and output. The microphone 1042 can implement various types of noise cancellation (or suppression) algorithms to cancel (or suppress) the noise or interference generated during the reception and transmission of audio signals.

[0041] The mobile terminal 100 further includes at least one sensor 105, such as a light sensor, a motion sensor, and other sensors. Optionally, the light sensor includes an ambient light sensor and a proximity sensor. Optionally, the ambient light sensor can adjust the brightness of the display panel 1061 according to the brightness of the ambient light, and the proximity sensor can turn off the display panel 1061 and / or the backlight when the mobile terminal 100 is moved to the ear. As a kind of motion sensor, the accelerometer sensor can detect the magnitude of acceleration in all directions (generally three axes), and can detect the magnitude and direction of gravity when stationary, and can be used for applications that identify the posture of the mobile phone (such as horizontal and vertical screen switching, related games, magnetometer posture calibration), vibration recognition related functions (such as a pedometer, tapping), etc.; as for other sensors that can also be configured on the mobile phone, such as a fingerprint sensor, a pressure sensor, an iris sensor, a molecular sensor, a gyroscope, a barometer, a hygrometer, a thermometer, an infrared sensor, etc., they will not be elaborated here.

[0042] The display unit 106 is used to display the information input by the user or the information provided to the user. The display unit 106 may include a display panel 1061, and the display panel 1061 can be configured in the form of a Liquid Crystal Display (LCD), an Organic Light-Emitting Diode (OLED), etc.

[0043] The user input unit 107 can be used to receive input numeric or character information, and generate key signal inputs related to the user settings and function controls of the mobile terminal. Optionally, the user input unit 107 may include a touch panel 1071 and other input devices 1072. The touch panel 1071, also known as a touch screen, can collect touch operations of the user on or near it (such as operations of the user using a finger, a stylus, or any suitable object or accessory on or near the touch panel 1071), and drive corresponding connection devices according to a preset program. The touch panel 1071 can include two parts: a touch detection device and a touch controller. Optionally, the touch detection device detects the touch orientation of the user, detects the signal brought by the touch operation, and transmits the signal to the touch controller; the touch controller receives the touch information from the touch detection device, converts it into contact coordinates, and then sends it to the processor 110, and can receive and execute commands sent by the processor 110. In addition, various types such as resistive, capacitive, infrared, and surface acoustic wave can be used to implement the touch panel 1071. In addition to the touch panel 1071, the user input unit 107 may further include other input devices 1072. Optionally, the other input devices 1072 may include, but are not limited to, one or more of a physical keyboard, function keys (such as volume control keys, power on / off keys, etc.), a trackball, a mouse, a joystick, etc., and specific details are not limited here.

[0044] Optionally, the touch panel 1071 may cover the display panel 1061. After the touch panel 1071 detects a touch operation on or near it, it transmits the operation to the processor 110 to determine the type of the touch event. Subsequently, the processor 110 provides a corresponding visual output on the display panel 1061 according to the type of the touch event. Although in Figure 1 the touch panel 1071 and the display panel 1061 are implemented as two independent components to realize the input and output functions of the mobile terminal, in some embodiments, the touch panel 1071 and the display panel 1061 may be integrated to realize the input and output functions of the mobile terminal, and specific details are not limited here.

[0045] The interface unit 108 serves as an interface through which at least one external device can be connected to the mobile terminal 100. For example, the external device may include a wired or wireless headset port, an external power supply (or battery charger) port, a wired or wireless data port, a memory card port, a port for connecting a device with an identification module, an audio input / output (I / O) port, a video I / O port, a headset port, and so on. The interface unit 108 can be used to receive inputs from the external device (such as data information, power, etc.) and transmit the received inputs to one or more components within the mobile terminal 100 or can be used to transmit data between the mobile terminal 100 and the external device.

[0046] The memory 109 can be used to store software programs and various data. The memory 109 mainly includes a program storage area and a data storage area. Optionally, the program storage area can store an operating system, application programs required for at least one function (such as a sound playback function, an image playback function, etc.); the data storage area can store data created according to the use of the mobile phone (such as audio data, phone book, etc.). In addition, the memory 109 can include a high-speed random access memory, and can also include a non-volatile memory, such as at least one magnetic disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0047] The processor 110 is the control center of the mobile terminal, connecting various parts of the entire mobile terminal through various interfaces and lines. By running or executing software programs and / or modules stored in the memory 109, and by calling the data stored in the memory 109, it executes various functions of the mobile terminal and processes data, thereby monitoring the mobile terminal as a whole. The processor 110 can include one or more processing units; preferably, the processor 110 can integrate an application processor and a modulation / demodulation processor. Optionally, the application processor mainly processes the operating system, user interface, and application programs, etc., and the modulation / demodulation processor mainly processes wireless communications. It can be understood that the above modulation / demodulation processor may not be integrated into the processor 110.

[0048] The mobile terminal 100 can also include a power supply 111 (such as a battery) for powering each component. Preferably, the power supply 111 can be logically connected to the processor 110 through a power management system, thereby realizing functions such as management of charging, discharging, and power consumption management through the power management system.

[0049] Although Figure 1 not shown, the mobile terminal 100 can also include a Bluetooth module, etc., which will not be elaborated here.

[0050] To facilitate the understanding of the embodiments of the present application, the communication network system on which the mobile terminal of the present application is based will be described below.

[0051] Please refer to Figure 2 , Figure 2 which is an architecture diagram of a communication network system provided by an embodiment of the present application. This communication network system is an LTE system of the general mobile communication technology. This LTE system includes a UE (User Equipment) 201, an E-UTRAN (Evolved UMTS Terrestrial Radio Access Network) 202, an EPC (Evolved Packet Core) 203, and the operator's IP service 204 that are sequentially communicatively connected.

[0052] Optionally, the UE 201 may be the above-mentioned terminal 100, which will not be elaborated here.

[0053] The E-UTRAN 202 includes an eNodeB 2021 and other eNodeBs 2022, etc. Optionally, the eNodeB 2021 may be connected to other eNodeBs 2022 through a backhaul (such as an X2 interface), the eNodeB 2021 is connected to the EPC 203, and the eNodeB 2021 may provide access for the UE 201 to the EPC 203.

[0054] The EPC 203 may include an MME (Mobility Management Entity) 2031, an HSS (Home Subscriber Server) 2032, other MMEs 2033, an SGW (Serving GateWay) 2034, a PGW (PDN Gate Way) 2035, a PCRF (Policy and Charging Rules Function) 2036, etc. Optionally, the MME 2031 is a control node that processes the signaling between the UE 201 and the EPC 203 and provides bearer and connection management. The HSS 2032 is used to provide some registers to manage functions such as a home location register (not shown in the figure) and stores some user-specific information such as service characteristics and data rates. All user data can be sent through the SGW 2034. The PGW 2035 may provide IP address allocation for the UE 201 and other functions. The PCRF 2036 is a policy and charging control policy decision point for service data flows and IP bearer resources, and it selects and provides available policy and charging control decisions for a policy and charging enforcement function unit (not shown in the figure).

[0055] The IP service 204 may include the Internet, an intranet, an IMS (IP Multimedia Subsystem), or other IP services, etc.

[0056] Although the above has been introduced by taking the LTE system as an example, those skilled in the art should be aware that this application is not only applicable to the LTE system, but also applicable to other wireless communication systems, such as GSM, CDMA2000, WCDMA, TD-SCDMA, 5G, and future new network systems (such as 6G), etc., which are not limited here.

[0057] Based on the above mobile terminal hardware structure and communication network system, various embodiments of this application are proposed.

[0058] The first embodiment Refer to Figure 3 , Figure 3 which is a schematic flowchart of the processing method shown in the first embodiment of this application. The processing method of this embodiment can be applied to intelligent terminals (such as mobile phones) and includes the following steps (for example, step S10): S10: Create a session instruction according to a preset rule and historical behavior characteristics, and perform an end-side calculation operation according to the session instruction and the application material set to obtain the application to be distributed.

[0059] Optionally, the session instruction can represent an instruction obtained by choreographing one or more historical behavior characteristics. This instruction can not only reflect the user's behavior habits but also be recognized by a computer program or an AI model and participate in the end-side calculation operation to obtain the application to be distributed that meets the user's personalized needs.

[0060] Optionally, the preset rule can represent a processing rule for processing historical behavior characteristics into data that can participate in the end-side calculation operation, and / or a rule for converting historical behavior characteristics into a specific form that can improve the efficiency of the end-side calculation operation.

[0061] Optionally, the application to be distributed or the application mentioned in this embodiment can represent a software program running on an intelligent terminal for implementing specific functions or services. Optionally, the application to be distributed or the application mentioned in this embodiment includes at least one of the following: ordinary application programs (abbreviated as APPs), fast applications, or light applications (without the need for downloading and installation), etc.

[0062] Optionally, the historical behavior characteristics can represent various operation behaviors or behavior results that can map the user's preferences for applications.

[0063] Optionally, the historical behavior characteristics include but are not limited to at least one of the following: application click information, application usage information, installed application list, search keywords.

[0064] Optionally, the application click information can represent the click behavior on the data of the distributed application, such as the click behavior on the displayed application data in the distributed APP.

[0065] Optionally, the application click information can correspond to the click behavior of a single application or the click behavior of multiple applications.

[0066] Optionally, the application usage information can represent various usage behaviors of the application in the end-side, such as at least one of the following: number of usage times, usage frequency, single usage duration, network status during usage, recent usage time, etc.

[0067] Optionally, the application usage information may correspond to the usage behavior of a single application or the usage behaviors of multiple applications.

[0068] Optionally, the search keywords may represent the keywords input in various search scenarios, such as the search keywords in a distribution APP.

[0069] Optionally, the application material set may represent a database of applications used to support end-side computing operations to obtain applications to be distributed.

[0070] Optionally, the application material set may be directly obtained from the server or obtained after processing the data related to the application sent by the server on the end side.

[0071] The processing method provided in this embodiment can be applied to a smart terminal, including step S10: creating a session instruction according to a preset rule and historical behavior characteristics, and performing an end-side computing operation according to the session instruction and the application material set to obtain an application to be distributed. Through the technical solution of this embodiment, a session instruction can be created according to the user's behavior habits on the end side, and an end-side computing operation can be performed to determine the data of the application to be distributed that matches the session instruction from the application material set. In this way, the technical solution of this embodiment can obtain an application to be distributed that meets the personalized needs of the user on the end side in the application distribution scenario, reducing the QPS of the server and improving the user experience.

[0072] Second Embodiment See Figure 4 , Figure 4 is the first flowchart of the processing method shown in the second embodiment of this application. The processing method of this embodiment can be applied to a smart terminal (such as a mobile phone) and includes the following steps: S10: Creating a session instruction according to a preset rule and historical behavior characteristics, and performing an end-side computing operation according to the session instruction and the application material set to obtain an application to be distributed; S20: Displaying the application to be distributed according to the second policy.

[0073] Optionally, the application to be distributed or the application mentioned in this embodiment may represent a software program running on a smart terminal for implementing specific functions or services. Optionally, the application to be distributed or the application mentioned in this embodiment includes at least one of ordinary application programs (abbreviated as APPs), fast applications, or light applications (without the need to download and install).

[0074] Optionally, the historical behavior characteristics may represent various operation behaviors or behavior results that can map the user's preferences for applications.

[0075] Optionally, the historical behavior features include, but are not limited to, at least one of the following: application click information, application usage information, installed application list, and search keywords.

[0076] Optionally, the application click information may characterize the click behavior on the data of the distributed application, such as the click behavior on the data of the displayed application in the distributed APP.

[0077] Optionally, the application click information may correspond to the click behavior of a single application or the click behavior of multiple applications.

[0078] Optionally, the application usage information may characterize various usage features of the applications on the terminal side. For example, at least one of the usage times, usage frequencies, single usage durations, network status during usage, and recent usage times.

[0079] Optionally, the application usage information may correspond to the usage features of a single application or the usage features of multiple applications.

[0080] Optionally, the search keywords may characterize the keywords input in various search scenarios, such as the search keywords in the distributed APP.

[0081] Optionally, the installed application list is obtained according to the third strategy, and the third strategy includes, but is not limited to, at least one of the following: Performing application sorting processing based on the second feature dimension weight information and the application usage information of each application to obtain an application sequence; Using the application sequence as the installed application list; Selecting the second number of applications from the application sequence in descending order of weights to form the installed application list.

[0082] Optionally, the second feature dimension weight information may include the weights of various usage features and / or the requirements for determining the weights. For example, the numerical value of the usage times of an application is its corresponding weight, the weight for usage in the networked state is 1, the weight for usage in the non-networked state is 0.8, the weight for usage within 24 hours is 0.5, etc.

[0083] Optionally, the application sorting processing may characterize sorting each application in descending order or ascending order of weights according to the weights corresponding to the application usage information of each application to obtain an application sequence.

[0084] Optionally, when the number of applications corresponding to the application sequence is less than or equal to the second number, using the application sequence as the installed application list.

[0085] Optionally, when the number of applications corresponding to the application sequence is greater than the second number, select the second number of applications from the application sequence in descending order of weights to form a list of installed applications.

[0086] Optionally, the second number can be any number set, or a specific number set to optimize the efficiency of the end-side computing operation, or the maximum number that can be displayed corresponding to the user interface for displaying the applications to be distributed.

[0087] Optionally, the list of installed applications can represent multiple applications that the user likes to use recently. Optionally, the applications to be distributed obtained by the end-side computing operation can be other applications with attributes similar to those of the applications in the list of installed applications. In this way, the technical solution of this embodiment can achieve obtaining applications to be distributed that meet the personalized needs of users through the end side.

[0088] Optionally, the applications to be distributed obtained by the end-side computing operation can be other applications with attributes different from those of the applications in the list of installed applications. In this way, the technical solution of this embodiment can achieve obtaining diversified applications to be distributed through the end side for subsequent users to try out new ones.

[0089] Optionally, the session instruction can represent an instruction obtained by arranging one or more historical behavior characteristics. This instruction can reflect the user's behavior habits and can also be recognized by a computer program or an AI model and participate in the end-side computing operation to obtain applications to be distributed that meet the personalized needs of the user.

[0090] Optionally, when the AI model is a generative large language model (such as deepseek, chat gpt, etc.), the session instruction can be the text information (or Prompt) input to the AI model. Optionally, the foregoing text information can be used to clearly tell the AI model the specific tasks to be completed.

[0091] Optionally, the preset rule can represent a processing rule for processing historical behavior characteristics into data that can participate in the end-side computing operation, and / or a rule for converting historical behavior characteristics into a specific form that can improve the efficiency of the end-side computing operation.

[0092] Optionally, the preset rule includes at least one of the following: Obtain the weight information of the first feature dimension; Obtain the session instruction template; Arrange and process the historical behavior characteristics according to the weight information of the first feature dimension and / or the session instruction template to determine the session instruction.

[0093] Optionally, the weight information of the first feature dimension can include the weights corresponding to various historical behavior characteristics.

[0094] Optionally, the historical behavior features are arranged according to the first feature dimension weight information and / or the session instruction template to determine the session instruction, including at least one of the following cases: According to the first feature dimension weight information, at least one target historical behavior feature that meets the preset conditions is screened from multiple historical behavior features; The multiple target historical behavior features are assembled in descending order of weight to determine the session instruction; At least one target historical behavior feature is assembled according to the session instruction template to determine the session instruction; The multiple target historical behavior features are filled into the session instruction template in descending order of weight to determine the session instruction.

[0095] Optionally, the preset weight condition can represent various conditions for restricting the number of target historical behavior features by the weight size. In this way, the technical solution of this embodiment can screen the target historical behavior features that meet the preset weight conditions according to the first feature dimension weight information, and can extract the historical behavior features that can better reflect the user's habits or preferences from a large amount of user behavior data, so as to reduce the data processing volume during the end-side calculation operation, and / or enable the end-side calculation operation to more quickly obtain the applications to be distributed that meet the personalized needs of the user.

[0096] Optionally, the larger the value of the weight, the higher the corresponding user preference degree.

[0097] Optionally, the preset conditions include but are not limited to at least one of the following: The weight is greater than the first threshold; After the weights are sorted, the ranking is greater than or equal to the first threshold or the middle ranking.

[0098] Optionally, the multiple target historical behavior features are assembled in descending order of weight to determine the session instruction, which can generate a session instruction that can better reflect the personalized needs of the user, so that the applications to be distributed obtained by subsequent end-side calculation operations can better match the personalized needs of the user, thereby improving the user experience.

[0099] Optionally, at least one target historical behavior feature is assembled according to the session instruction template to determine the session instruction, which can achieve rapid generation of the session instruction and improve the response speed of subsequent end-side calculation operations.

[0100] Optionally, multiple target historical behavior features are filled into the session instruction template in order of weight from high to low to determine the session instruction. In this way, not only can the session instruction be quickly generated, but the session instruction can also better reflect the user's personalized needs, so that subsequent terminal-side computing operations can obtain the application to be distributed more efficiently and can better match the user's personalized needs, thereby improving the user experience.

[0101] Optionally, the application material set may represent a database of related applications used to support end-side computing operations to obtain applications to be distributed.

[0102] Optionally, the application material set may be directly obtained from the server, or obtained by obtaining application-related data sent by the server and then performing data processing on the terminal side.

[0103] Optionally, the application material set is obtained according to a first strategy, where the first strategy includes at least one of the following: Determine the preset data dimensions; determining a first quantity; Determine quantity requirements; Downloading application material data according to a preset data dimension and / or a first quantity; Sorting all or part of the downloaded application material data according to preset sorting rules to obtain an application material sorting table; According to the quantity requirement, all or part of the application material data in the application material sorting table are selected to perform keyword extraction operations, and keyword information of each selected application material data is obtained; According to the quantity requirement, select all or part of the downloaded application material data to perform keyword extraction operation, and obtain keyword information of each selected application material data; An application material set is acquired according to keyword information of at least part of the application material data.

[0104] Optionally, the preset data dimension may correspond to a feature embodied by the conversation instruction.

[0105] Optionally, the preset data dimension may characterize the attributes of the application. Optionally, the preset data dimension includes, but is not limited to, at least one of the following: application identifier (e.g., application name), application data packet identifier (e.g., application package name), application description, application built-in keywords, data validity period, last upgrade time, last update time, number of requests in the server, etc.

[0106] Optionally, the first number may be any set number, or may be a specific multiple of the maximum number of user interfaces that can display the application to be distributed.

[0107] Optionally, the application material data includes specific data corresponding to the preset data dimension.

[0108] Optionally, the preset sorting rule can represent various rules for sorting all or part of the downloaded application material data according to the sorting factor. Optionally, the sorting factor can represent the attributes of various applications with priority sorting characteristics, including the number of end-side displays, the number of end-side clicks, the number of end-side downloads, the most recent update time, etc.

[0109] Optionally, the quantity requirement may include at least one of a specific quantity and a way to determine a specific quantity. The way to determine a specific quantity, for example, all the application material data in the application material sorting table; also, for example, half of all the application material data in the application material sorting table; again, for example, all the downloaded application material data; again, for example, one-third of all the downloaded application material data.

[0110] Optionally, the end-side calculation operation includes at least one of the following: Input the session instruction into the AI model on the end side; Call the application material set through the AI model for calculation to obtain the application to be distributed corresponding to the session instruction.

[0111] In this way, the technical solution of this embodiment can arrange and process the historical behavior characteristics according to the first feature dimension weight information and / or the session instruction template to determine the session instruction; input the session instruction into the AI model on the end side, and call the application material set through the AI model to perform the end-side calculation operation to obtain the corresponding application to be distributed; display the application to be distributed according to the second strategy. Among them, using the AI model on the end side for calculation avoids the delay caused by data transmission to the server, can quickly call the local application material set according to the session instruction, obtain the corresponding application to be distributed and display it, improves the response speed, and enhances the real-time performance of application recommendation. In addition, the end-side calculation operation enables data processing to be completed locally, without uploading user data to the server, effectively protecting user privacy and data security and reducing the risk of data leakage. Optionally, when the generated session instruction is ready, it is input into the AI model, and the AI model will perform calculation operations by calling the locally stored application material set according to its understanding and reasoning ability of the session instruction.

[0112] Optionally, the application material set includes keyword information corresponding to multiple applications. Optionally, when performing the end-side calculation operation, the AI model can perform matching calculations between the session instruction and the keyword information of each application, and thus use the application in the application material set whose keyword information matches the session instruction as the application to be distributed.

[0113] Optionally, the second strategy in step S20 includes but is not limited to at least one of the following: Display the application to be distributed; Obtain the installed applications of associated terminals within a preset range; Differentially display the to-be-distributed applications that are the same as the installed applications.

[0114] In this way, the technical solution of this embodiment can identify the applications in other smart terminals (i.e., associated terminals) in the user interface for displaying the to-be-distributed applications in the scenario where the user carries two smart terminals, so as to avoid the user from downloading repeatedly.

[0115] Optionally, Figure 5 is the second process schematic diagram of the processing method shown in the second embodiment of this application. Refer to Figure 5 This embodiment of the provided processing method may further include the steps: S11: Arrange and process the historical behavior features according to the first feature dimension weight information and / or the session instruction template to determine the session instruction; S12: Perform end-side calculation operations according to the session instruction and the application material set to obtain the to-be-distributed applications; S20: Display the to-be-distributed applications according to the second policy; S30: Respond to the operation on the displayed to-be-distributed applications, and count the conversion results of the to-be-distributed applications obtained by the end-side calculation operations; S40: Perform feedback optimization processing according to the conversion results to update the first feature dimension weight information and / or the session instruction template for creating the session instruction.

[0116] In this way, the technical solution of this embodiment can fill multiple target historical behavior features into the session instruction template in the order of weight from high to low, which can not only achieve the rapid generation of session instructions, but also enable the session instructions to better reflect the personalized needs of users; further, performing end-side calculation operations according to the session instruction and the application material set can quickly obtain one or more to-be-distributed applications that meet the personalized needs of users for subsequent display. Among them, the end-side calculation operations reduce the QPS of the server, and both its algorithm coverage and real-time performance are improved. Further, by detecting the operations of users on the displayed to-be-distributed applications, the conversion results of the to-be-distributed applications can be collected in real time, and feedback optimization processing can be performed according to these conversion results (for example, feedback to the server for optimization processing, or feedback to a specific processing module on the end side for optimization processing) to dynamically adjust the first feature dimension weight information and / or the session instruction template for creating the session instruction to further adapt to the user's behavior preferences, so that the to-be-distributed applications obtained during subsequent end-side calculation operations are more in line with the personalized needs of users, thereby improving the user experience.

[0117] The third embodiment The third embodiment of this application provides a processing system, including: a smart terminal and a server.

[0118] Among them, the server is used to send application material data to the intelligent terminal.

[0119] Among them, the intelligent terminal is used to construct an application material set according to the obtained application material data, create a session instruction according to a preset rule and historical behavior characteristics, and perform end-side calculation operations according to the session instruction and the application material set to obtain the application to be distributed.

[0120] Optionally, the specific implementation manner and beneficial effects of the intelligent terminal can refer to the specific implementation manner of the processing method in the above embodiment, which will not be elaborated here.

[0121] In this way, the processing system provided in this embodiment can create a session instruction according to the user's behavior habits on the end side, and perform end-side calculation operations to determine the data of the application to be distributed that matches the session instruction from the application material set.

[0122] In addition, the processing system provided in this embodiment can also arrange and process the historical behavior characteristics according to the feature dimension weight information and / or the session instruction template to determine the session instruction; input the session instruction into the AI model on the end side, and the AI model calls the application material set to perform end-side calculation operations to obtain the corresponding application to be distributed and display the application to be distributed. Among them, using the AI model on the end side for calculation avoids the delay caused by data transmission to the server, can quickly call the local application material set according to the session instruction, obtain the corresponding application to be distributed and display it, improves the response speed, and enhances the real-time performance of application recommendation. In addition, the end-side calculation operation enables data processing to be completed locally, without uploading user data to the server, effectively protecting the privacy and data security of users and reducing the risk of data leakage. Optionally, when the generated session instruction is ready, it is input into the AI model, and the AI model will perform calculation operations by calling the application material set stored locally according to its understanding and reasoning ability of the session instruction.

[0123] In addition, the processing system provided in this embodiment can also fill multiple target historical behavior features into the session instruction template in descending order of weight. This can not only quickly generate session instructions but also enable the session instructions to better reflect the personalized needs of users. Further, performing end-side computing operations based on the session instructions and the application material set can quickly obtain one or more applications to be distributed that meet the personalized needs of users for subsequent display. Among them, the end-side computing operation reduces the QPS of the server, and both its algorithm coverage and real-time performance are improved. Further, by detecting the operations of users on the displayed applications to be distributed, the conversion results of the applications to be distributed can be collected in real time, and feedback optimization processing can be performed based on these conversion results (such as feedback to the server for optimization processing or feedback to a specific processing module on the end side for optimization processing) to dynamically adjust the feature dimension weights and / or the session instruction template for creating session instructions to further adapt to the user's behavior preferences, so that the applications to be distributed obtained during subsequent end-side computing operations are more in line with the personalized needs of users, thereby enhancing the user experience.

[0124] Based on the technical concept of the technical solution of the above embodiment, the following is an example of a processing system to exemplarily illustrate the above embodiment: The processing system exemplified in the embodiment of the present application includes: an intelligent terminal and a server; wherein, the intelligent terminal may include an end-side feature module, an end-side computing module, an end-side data module, and an end-side feedback module.

[0125] Optionally, the end-side feature module is used to extract historical behavior features according to user behavior records.

[0126] Optionally, the end-side feature module is used to extract user behavior records and generate historical behavior features that meet the requirements of application distribution. In this way, the end-side feature module can obtain the user's behavior preferences in real time, provide historical behavior features that can be used for calculation for the end-side computing module, and does not need to rely on a server (such as a cloud server) and does not need to upload user private data, ensuring data security.

[0127] Optionally, the end-side feature module can obtain the first feature dimension weight information and / or the session instruction template from the server, and perform arrangement processing on the historical behavior features according to the first feature dimension weight information and / or the session instruction template to determine and output a session instruction (such as a prompt) to the end-side computing module, so as to conduct a session with the AI model in the end-side computing module.

[0128] Optionally, the edge-side data module stores the application material data received through the API (the network interface for communicating with the server), and performs label completion on the application material data (such as supplementing keyword information) to provide a computable application material set for the AI model of the edge-side computing module. In this way, the existence of the edge-side data module can accumulate data, reduce the dependence on server access, thereby reducing QPS and lowering costs.

[0129] Optionally, the keyword information (or APP materials) of the applications included in the application material set in the edge-side data module are fed into the AI model of the edge-side computing module, and edge-side computing operations are performed according to the session instructions.

[0130] Optionally, the edge-side computing module is used to call the AI model to perform edge-side computing operations according to the session instructions output by the edge-side feature module and the application material set in the edge-side data module, so that the AI model can output the data of the application to be distributed.

[0131] Optionally, when the edge-side computing module obtains the session instructions, it can add and record the batch number.

[0132] Optionally, the edge-side feedback module is used to detect the browsing behavior and click behavior of the user after the data of the application to be distributed corresponding to the above batch number output by the edge-side computing module is displayed, and statistically evaluate the browsing behavior and click behavior of the user to obtain the conversion result, and according to the conversion result, the weight information of the first feature dimension, the session instruction template, and / or the AI model can be updated and iterated to improve the coverage of the distributed application through the update and iteration of the data.

[0133] The following exemplifies a specific application scenario of a processing system to illustrate the foregoing embodiments by way of example: See Figure 6 , Figure 6 is a schematic diagram of the application distribution platform exemplified in the embodiments of the present application. The application distribution platform (i.e., the processing system) in this example is used to obtain the data displayed in the application store, so that the application-related data subsequently displayed in the application store is more in line with the applications required by the user. It includes: a smart terminal 61 and a server 62; among them, the smart terminal 61 may include an edge-side feature module 610, an edge-side computing module 611, an edge-side data module 612, and an edge-side feedback module 613.

[0134] Optionally, the edge-side feature module 610 is used to extract the historical behavior features of the user for subsequent application distribution.

[0135] Optionally, the behavior types corresponding to the historical behavior features include the user's click behavior on application-related data in the distribution application (such as the application pictures shown in the distribution application), the application usage behavior of the user in the intelligent terminal 61 (such as a mobile phone) (corresponding to the application usage information in the historical behavior features), the list of installed applications, and the search keywords in the distribution application.

[0136] Optionally, after obtaining the user's historical behavior features from the above-mentioned behavior types, the edge feature module 610 can transmit them to the edge computing module 611.

[0137] Optionally, since there are many behaviors in the user's intelligent terminal 61 and they need to be assembled according to weights, the server 62 will send the feature dimension weight information and the session instruction template to the edge feature module 610, and the edge feature module 610 will extract them according to the feature weight values. For example: [The number of times the user opens the application, with a weight of 1 when there is a network, a weight of 0.8 when there is no network, a weight of 0.7 within 24 hours, a weight of 0.5 within 48 hours, and the top 20]; the edge feature module 610 can extract the user's application usage records according to the above weight values and assign weights to each application. For example, when an application has a network and is used within 24 hours, the two weights are added to obtain the weight assignment of the aforementioned application. Finally, the top 20 applications are selected according to the weight ranking to form the list of applications opened by the user or the list of installed applications.

[0138] Optionally, the edge data module 612 is used to obtain the application material data to be displayed from the server 62, extract keywords to obtain keyword information, and thus construct an application material set according to the keyword information of each material to be distributed for the edge computing module 611 to call, or for the edge feedback module 613 to perform maintenance data iteration.

[0139] Optionally, the edge data module 612 downloads a specific number of application material data from the server 62 (each application material data corresponds to a different application), and the specific number is N (N is configurable, such as 20) times the maximum number of applications that can be displayed by the application store at one time. For example, if the specific number is 20 times the maximum number of applications that can be displayed by the application store at one time, and the maximum number of applications that can be displayed by the application store at one time is the pictures of 300 applications, then 6000 application material data of applications are downloaded from the server 62.

[0140] Optionally, the terminal-side data module 612 may adopt a certain strategy to split the keywords in the application material data (a step belonging to data management), wherein the data dimensions of the application material data include application name, application package name, application description text, built-in keywords, data validity period, last upgrade time, server 62 display times and server 62 click times, etc., local display times and local click times of the application pre-maintained by the terminal-side data module 612, and weight sorting is performed on the above data dimensions to obtain a sorted application material sorting table, and the application material data of the first M (the value of M can be dynamically configured according to device performance, for example, 3000) applications in the application material sorting table are taken for keyword extraction to obtain keyword information (or keyword set) of each application, and then an application material set is constructed.

[0141] Optionally, the keywords are derived from, for example, the application name, description, and type, and a word segmentation algorithm is used to obtain the keywords from the name and description.

[0142] Optionally, the terminal-side computing module 611 is used to calculate the application to be distributed that can be displayed to the user from the terminal-side data module 612 according to the user's historical behavior characteristics characterized by the session instructions, and obtain application display data (such as pictures) of the application to be distributed from the server 62. For example, CDN picture splicing is performed based on the application display data to be distributed to obtain the display picture of the application to be distributed, and it is displayed in the user interface of the application store, so that the application store client can distribute it after the user clicks to download.

[0143] Optionally, the functions of the terminal-side computing module 611 are implemented through an AI model. The implementation process uses session instructions as input parameters of the AI ​​model. The AI ​​model calls the data in the application material set for calculation to obtain applications to be distributed and recommended to the application store.

[0144] Example of session directives: The current user has the following applications [application list], the user has clicked [application list] within 24 hours, the user searched for keyword [keyword], and the current holiday is [local holiday]; analyze the data from [March 18th - March 15th] to obtain 300 applications similar to [X application] that contain keywords [AAA, BBB]; requirements: 1. Sort by [the fit between the name and the search term]; 2. [Film and Television] applications are ranked first.

[0145] Note: The text outside "[]" in the case can be the content of the conversation instruction template, and the text inside "[]" is the keyword and feature.

[0146] Optionally, the conversation instructions are sorted by feature weights and combined into a piece of text that can be recognized by an AI model (such as deepseek). This piece of text can be called a prompt. The AI ​​model will understand this text and process it as required.

[0147] Optionally, the end-side feedback module 613 can be used to count the conversion rate of the applications to be distributed calculated by the AI ​​model in the current batch. The end-side feedback module 613 can count the display and clicks of the applications generated by the AI ​​model in the current batch, and count and recall the current batch of the AI ​​model, the clicked application name and the algorithm parameters, and upload them to the server 62, thereby completing the recall optimization.

[0148] The application distribution platform of this example can process the historical behavior features according to the feature dimension weight information and / or the session instruction template through the end-side feature module 610 to determine the session instruction; the session instruction is input into the AI ​​model of the end-side calculation module 611 through the end-side feature module 610, and the application material set in the end-side data module 612 is called by the AI ​​model to perform end-side calculation operations to obtain the corresponding application to be distributed and display it in the user interface of the client of the application store. Among them, the use of the AI ​​model of the end-side calculation module 611 for calculation avoids the delay caused by the data transmission to the server 62, and can quickly call the local application material set according to the session instruction, obtain the corresponding application to be distributed and display it, thereby improving the response speed and enhancing the real-time nature of the application recommendation. In addition, the end-side calculation operation enables data processing to be completed locally without uploading user data to the server 62, effectively protecting the user's privacy and data security, and reducing the risk of data leakage.

[0149] The application distribution platform of this example enables the intelligent terminal 61 to collect the historical behavior characteristics of users, and with the help of the end-side AI model, based on the data centrally sent from the server 62 to the end-side data module 612, intelligent calculations are performed to obtain the distributed applications that meet the personalized needs of users at a lower implementation cost; wherein, the design of the end-side data module 612 can reduce the QPS by half without affecting the distribution effect, save the cost of the server 62, and ensure the real-time nature of the data; wherein, the design of the end feedback module can continuously adjust the feature weights and the conversation instruction templates, thereby optimizing the results given by the subsequent AI model. Therefore, the technical solution of this example can reduce network dependence and improve user experience and conversion rate without affecting the distribution effect.

[0150] An embodiment of the present application also provides an intelligent terminal, including a memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the steps of the processing method in any of the above embodiments are implemented.

[0151] The embodiments of the present application also provide a storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the processing method in any of the above embodiments are implemented.

[0152] In the embodiments of the intelligent terminal and the storage medium provided by the present application, all the technical features of any of the above-mentioned processing method embodiments may be included. The content of the specification expansion and explanation is basically the same as that of the above-mentioned method embodiments, and will not be elaborated here.

[0153] The embodiments of the present application also provide a computer program product, which includes computer program code. When the computer program code runs on a computer, the computer is enabled to execute the methods in various possible implementation manners as described above.

[0154] The embodiments of the present application also provide a chip, which includes a memory and a processor. The memory is used to store a computer program, and the processor is used to call and run the computer program from the memory, so that a device equipped with the chip executes the methods in various possible implementation manners as described above.

[0155] It can be understood that the above scenarios are only examples and do not constitute a limitation on the application scenarios of the technical solutions provided by the embodiments of the present application. The technical solutions of the present application can also be applied to other scenarios. For example, as known to those of ordinary skill in the art, with the evolution of the system architecture and the emergence of new business scenarios, the technical solutions provided by the embodiments of the present application are equally applicable to similar technical problems.

[0156] The serial numbers of the above embodiments of the present application are only for description and do not represent the advantages or disadvantages of the embodiments.

[0157] The steps in the method embodiments of the present application can be adjusted, combined, and deleted according to actual needs.

[0158] The units in the device embodiments of the present application can be combined, divided, and deleted according to actual needs.

[0159] In the present application, for the description of the same or similar term concepts, technical solutions, and / or application scenarios, generally only the first occurrence is described in detail. When it appears repeatedly later, for the sake of brevity, it is generally not elaborated again. When understanding the technical solutions and other contents of the present application, for the same or similar term concepts, technical solutions, and / or application scenarios that are not described in detail later, reference can be made to the relevant detailed descriptions before.

[0160] In the present application, the descriptions of the various embodiments have their own emphases. For the parts not elaborated or recorded in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0161] The technical features of the technical solution of the present application can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the various technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as falling within the scope recorded in the present application.

[0162] Through the description of the above embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus a necessary general hardware platform. Of course, it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art can be embodied in the form of a software product. The computer software product is stored in a storage medium as described above (such as ROM / RAM, magnetic disk, optical disc), and includes several instructions for causing a terminal device (which can be a mobile phone, computer, server, controlled terminal, or network device, etc.) to execute the methods of each embodiment of the present application.

[0163] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions according to the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a storage medium, or transmitted from one storage medium to another storage medium. For example, the computer instructions can be transmitted from a website, computer, server, or data center to another website, computer, server, or data center in a wired manner (such as coaxial cable, optical fiber, digital subscriber line) or a wireless manner (such as infrared, wireless, microwave, etc.). The storage medium can be any available medium that can be accessed by a computer, or a data storage device such as a server or data center that includes one or more integrated available media. The available medium can be a magnetic medium (such as a floppy disk, storage disk, magnetic tape), an optical medium (such as a DVD), or a semiconductor medium (such as a solid state disk (SSD)), etc.

[0164] The above are only the preferred embodiments of the present application, and do not limit the patent scope of the present application accordingly. Any equivalent structure or equivalent process transformation made by using the content of the specification and drawings of the present application, or directly or indirectly applied in other related technical fields, shall be included in the patent protection scope of the present application by the same token.

Claims

1. A processing method, characterized in that, Including the steps: Create a session instruction according to a preset rule and historical behavior characteristics, and perform an edge-side calculation operation according to the session instruction and an application material set to obtain a to-be-distributed application.

2. The processing method according to claim 1, characterized in that, The preset rule includes at least one of the following: Obtain first feature dimension weight information; Obtain a session instruction template; According to the first feature dimension weight information and / or the session instruction template, arrange and process the historical behavior characteristics to determine the session instruction.

3. The processing method according to claim 1, wherein The edge-side calculation operation includes at least one of the following: Input the session instruction into an AI model on the edge side; Call the application material set through the AI model for calculation to obtain the to-be-distributed application corresponding to the session instruction.

4. The processing method according to claim 1, characterized in that Obtain the application material set according to a first strategy, and the first strategy includes at least one of the following: Determine a preset data dimension; Determine a first quantity; Determine a quantity requirement; Download application material data according to the preset data dimension and / or the first quantity; Sort all or part of the downloaded application material data according to a preset sorting rule to obtain an application material sorting table; According to the quantity requirement, select all or part of the application material data in the application material sorting table for keyword extraction operation to obtain keyword information of each selected application material data; According to the quantity requirement, select all or part of the downloaded application material data for keyword extraction operation to obtain keyword information of each selected application material data; Obtain the application material set according to keyword information of at least part of the application material data.

5. The processing method according to any one of claims 1 to 4, wherein The historical behavior characteristics include at least one of the following: application click information, application usage information, installed application list, search keyword; and / or, Obtain the installed application list according to a third strategy, and the third strategy includes at least one of the following: Perform application sorting processing according to second feature dimension weight information and the application usage information of each application to obtain an application sequence; Use the application sequence as the installed application list; Select a second quantity of applications from the application sequence in descending order of weight to form the installed application list.

6. The processing method according to any one of claims 1 to 4, characterized in that It further includes at least one of the following steps: Display the to-be-distributed application according to a second strategy; In response to an operation on the displayed to-be-distributed application, count the conversion result of the to-be-distributed application obtained by the edge-side calculation operation; Perform feedback optimization processing according to the conversion result to update the first feature dimension weight information and / or the session instruction template for creating the session instruction.

7. The processing method according to claim 6, characterized in that The second strategy includes at least one of the following: Display the to-be-distributed application; Obtain the installed applications of associated terminals within a preset range; Differentially display the to-be-distributed applications that are the same as the installed applications.

8. A processing system, characterized in that, Including an intelligent terminal and a server; The server is used to send application material data to the intelligent terminal; The intelligent terminal is used to construct an application material set according to the obtained application material data, create a session instruction according to a preset rule and historical behavior characteristics, and perform an end-side calculation operation according to the session instruction and the application material set to obtain an application to be distributed.

9. An intelligent terminal, characterized in that, It includes: A memory and a processor, where a computer program is stored on the memory, and when the computer program is executed by the processor, the steps of the processing method described in any one of claims 1 to 7 are implemented.

10. A storage medium, characterized in that, A computer program is stored on the storage medium, and when the computer program is executed by a processor, the steps of the processing method described in any one of claims 1 to 7 are implemented.

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