Recommendation method, intelligent terminal and storage medium

Abstract

The invention provides a recommendation method, an intelligent terminal and a storage medium, and the method is applied to the intelligent terminal, and comprises the steps: S1, determining a situation feature corresponding to current situation perception data according to a constructed situation knowledge graph; and S2, determining or generating a corresponding recommendation service and / or recommendation operation according to the situation characteristics and the historical behavior data of the user. According to the method and the device, the situation can be accurately identified, active and personalized service recommendation is correspondingly provided, and the user experience is improved.

Description

Technical Field

[0001] The present application relates to the field of terminal technology, and in particular to a recommendation method, a smart terminal and a storage medium. Background Art

[0002] With the rapid development of mobile Internet and sensor technology, the various sensors and applications built into smart terminals can perceive the user's situation in real time.

[0003] During the process of conceiving and implementing this application, the inventors discovered that there are at least the following problems: the existing technology often only uses contextual information alone to recommend services to users, which cannot achieve personalized recommendations and affects user experience.

[0004] The preceding description is intended to provide general background information and does not necessarily constitute prior art. Summary of the Invention

[0005] In response to the above technical problems, the present application provides a recommendation method, an intelligent terminal and a storage medium, which can accurately identify the context and provide proactive and personalized service recommendations, thereby improving the user experience.

[0006] This application provides a recommendation method, which can be applied to a smart terminal, including the following steps: S1, based on the constructed contextual knowledge graph, determines the contextual features corresponding to the current contextual awareness data; S2: Determine or generate corresponding recommended services and / or recommended operations based on the situational features and the user's historical behavior data.

[0007] Optionally, step S2 includes at least one of the following: Build a user behavior sequence model based on historical context features and corresponding user historical behavior data; Inputting the contextual features into the constructed user behavior sequence model to obtain a predicted target behavior sequence; Based on the target behavior sequence, corresponding recommended services and / or recommended operations are determined or generated. Optionally, inputting the contextual features into the constructed user behavior sequence model to obtain a predicted target behavior sequence includes at least one of the following: Get the user's current emotional state; The contextual features and the current emotional state are input into a constructed user behavior sequence model to obtain a predicted target behavior sequence; the user behavior sequence model is constructed based on at least one of the user's historical contextual features, historical emotional state, and corresponding historical behavior data.

[0008] Optionally, the method further includes: Obtaining feedback information on the recommended service and / or the recommended operation; The user behavior sequence model is optimized based on the feedback information.

[0009] Optionally, determining or generating corresponding recommended services and / or recommended operations based on the target behavior sequence includes: Based on the target behavior sequence and the user's health status data, corresponding recommended services and / or recommended operations are determined or generated.

[0010] Optionally, the context-aware data includes at least one of the following: device data, time data, environment data, location data, user physiological state data, and user behavior data.

[0011] Optionally, the method further includes: Access historical situational awareness data; Feature fusion is performed on the historical context perception data to determine or generate the context knowledge graph.

[0012] Optionally, determining or generating corresponding recommended services and / or recommended operations based on the target behavior sequence includes: Sending a recommendation request to a cloud server, wherein the recommendation request includes the target behavior sequence; A recommendation response message returned by the cloud server is received, where the recommendation response message includes a recommended service and / or a recommended operation.

[0013] The present application also provides an intelligent terminal, comprising: a memory and a processor, wherein a processing program is stored in the memory, and the processing program implements the above-mentioned recommendation method when executed by the processor.

[0014] The present application also provides a storage medium storing a computer program, which implements the above-mentioned recommendation method when executed by a processor.

[0015] As described above, the recommendation method of the present application can be applied to smart terminals, including: S1, determining the contextual features corresponding to the current contextual perception data based on the constructed contextual knowledge graph; S2, determining or generating corresponding recommended services and / or recommended operations based on the contextual features and the user's historical behavior data. Through the above technical solution, the smart terminal obtains the contextual features corresponding to the current contextual perception data through the constructed contextual knowledge graph, and then determines or generates the corresponding recommended services and / or recommended operations based on the contextual features and the user's historical behavior data, thereby achieving the purpose of accurately identifying the context and providing proactive and personalized service recommendations, thereby improving the user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The accompanying drawings herein are incorporated into and constitute a part of the specification, illustrate embodiments consistent with the present application, and together with the specification, are used to explain the principles of the present application. In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for describing the embodiments. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without inventive work.

[0017] Figure 1 A schematic diagram of the hardware structure of a mobile terminal for implementing various embodiments of the present application.

[0018] Figure 2 A communication network system architecture diagram provided for an embodiment of the present application.

[0019] Figure 3 FIG. 1 is a flow chart illustrating a recommendation method according to an embodiment.

[0020] Figure 4 FIG. 1 is a schematic structural diagram of a recommendation device according to an embodiment.

[0021] The purpose of this application, its features, and advantages will be further described in conjunction with the embodiments and with reference to the accompanying drawings. The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and the accompanying text are not intended to limit the scope of the present application in any way, but rather to illustrate the concepts of this application to those skilled in the art by reference to specific embodiments. DETAILED DESCRIPTION

[0022] Exemplary embodiments will be described in detail herein, with examples illustrated in the accompanying drawings. In the following description, when referring to the drawings, identical numerals in different figures represent identical or similar elements, unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.

[0023] It should be noted that, in this document, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprising a ..." does not exclude the presence of other identical elements in the process, method, article or device comprising the 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 explanation in the specific embodiment or further combined with the context of the specific embodiment.

[0024] It should be understood that although the terms "first," "second," "third," etc. may be used herein to describe various information, such information should not be limited to these terms. These terms are used solely to distinguish information of the same type from one another. For example, without departing from the scope of this disclosure, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the term "if," as used herein, may be interpreted as "upon," "when," or "in response to a determination." Furthermore, as used herein, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context indicates otherwise. It should be further understood that the terms "comprising" and "including" indicate the presence of the recited features, steps, operations, elements, components, items, types, and / or groups, but do not preclude the presence, occurrence, or addition of one or more other features, steps, operations, elements, components, items, types, and / or groups. The terms "or," "and / or," "including at least one of the following," etc., as used herein, may be interpreted as inclusive, meaning any one or any combination. For example, “comprising at least one of the following: A, B, C” means “any of the following: A; B; C; A and B; A and C; B and C; A and B and C”; and for another example, “A, B or C” or “A, B and / or C” means “any 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 will occur only when a combination of elements, functions, steps or operations are inherently mutually exclusive in some manner.

[0025] It should be understood that, although the various steps in the flowchart in the embodiment of the present application are shown in sequence according to the indication of the arrows, these steps are not necessarily performed in sequence in the order indicated by the arrows. Unless clearly stated herein, the execution of these steps is not strictly limited in order, and they can be performed in other orders. Moreover, at least a portion of the steps in the figure may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and their execution order is not necessarily performed in sequence, but can be performed in turn or alternately with at least a portion of other steps or sub-steps or stages of other steps.

[0026] As used herein, the words "if" and "if" may be interpreted as "at the time of" or "when" or "in response to determining" or "in response to detecting," depending on the context. Similarly, the phrases "if it is determined" or "if (stated condition or event) is detected" may be interpreted as "when it is determined" or "in response to the determination" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)," depending on the context.

[0027] It should be noted that in this article, step codes such as S1 and S2 are used for the purpose of expressing the corresponding content more clearly and concisely, and do not constitute a substantial restriction on the order. When implementing the step, those skilled in the art may execute S2 first and then S1, etc., but these should all be within the scope of protection of this application.

[0028] It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0029] In the subsequent description, the use of suffixes such as "module", "component" or "unit" to represent elements is only for the purpose of facilitating the description of the present application and has no specific meaning. Therefore, "module", "component" or "unit" can be used interchangeably.

[0030] Smart terminals can be implemented in various forms. For example, the smart terminals described in this application may include mobile terminals such as mobile phones, tablet computers, laptop computers, PDAs, portable media players (PMPs), navigation devices, wearable devices, smart bracelets, pedometers, etc., as well as fixed terminals such as digital TVs and desktop computers.

[0031] The subsequent description will be made by taking a mobile terminal as an example. It will be understood by those skilled in the art that, in addition to components specifically used for mobile purposes, the configuration according to the embodiments of the present application can also be applied to fixed-type terminals.

[0032] See also Figure 1 , which is a schematic diagram of the hardware structure of a mobile terminal for implementing various embodiments of the present application. The mobile terminal 100 may include: an RF (Radio Frequency) unit 101, a WiFi module 102, an audio output unit 103, an A / V (Audio / Video) input unit 104, a sensor 105, a display unit 106, a user input unit 107, an interface unit 108, a memory 109, a processor 110, and a power supply 111. Those skilled in the art will understand that Figure 1 The structure of the mobile terminal shown in the figure 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 arrange the components differently.

[0033] The following combination Figure 1 A detailed introduction to the various components of the mobile terminal: The RF unit 101 can be used to send and receive information or receive signals during calls. Specifically, it receives downlink information from the base station and transmits it to the processor 110 for processing. It also transmits uplink data to the base station. Typically, the RF unit 101 includes, but is not limited to, an antenna, at least one amplifier, a transceiver, a coupler, a low-noise amplifier, a duplexer, and more. Furthermore, the RF unit 101 can communicate with the network and other devices via wireless communication. The above-mentioned wireless communications may 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.

[0034] WiFi is a short-range wireless transmission technology. Mobile terminals can help users send and receive emails, browse web pages, and access streaming media through the WiFi module 102. It provides users with wireless broadband Internet access. Figure 1 The WiFi module 102 is shown, but it is understandable that it is not an essential component of the mobile terminal and can be omitted as needed without changing the essence of the invention.

[0035] The audio output unit 103 can convert audio data received by the RF 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 a call signal reception mode, a talk mode, a recording mode, a voice recognition mode, a broadcast reception mode, or the like. Furthermore, the audio output unit 103 can also provide audio output related to a specific function performed by the mobile terminal 100 (e.g., a call signal reception sound, a message reception sound, etc.). The audio output unit 103 may include a speaker, a buzzer, or the like.

[0036] 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 image data from still images or videos captured by an image capture device (such as a camera) in video capture mode or image capture mode. The processed image frames may be displayed on the display unit 106. The image frames processed by the GPU 1041 may be stored in the memory 109 (or other storage medium) or transmitted via the RF unit 101 or the WiFi module 102. The microphone 1042 can receive sound (audio data) in various operating modes, such as phone call mode, recording mode, and voice recognition mode, and process such sound into audio data. In phone call mode, the processed audio (voice) data may be converted into a format that can be transmitted to a mobile communication base station via the RF unit 101. The microphone 1042 may implement various noise cancellation (or suppression) algorithms to eliminate (or suppress) noise or interference generated during the reception and transmission of audio signals.

[0037] The mobile terminal 100 also includes at least one sensor 105, such as a light sensor, a motion sensor, or 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 based on 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 brought to your ear. An accelerometer, a type of motion sensor, can detect acceleration in all directions (typically three axes) and, when stationary, can detect the magnitude and direction of gravity. This can be used for applications that recognize the phone's posture (e.g., switching between landscape and portrait modes, related games, magnetometer posture calibration), vibration recognition-related functions (e.g., pedometer, tapping), and other functions. Other sensors that may be configured on a mobile phone, such as a fingerprint sensor, pressure sensor, iris sensor, molecular sensor, gyroscope, barometer, hygrometer, thermometer, infrared sensor, etc., are not described here.

[0038] The display unit 106 is used to display information input by the user or information provided to the user. The display unit 106 may include a display panel 1061, which may be configured in the form of a liquid crystal display (LCD), an organic light-emitting diode (OLED), or the like.

[0039] The user input unit 107 can be used to receive input digital or character information and generate key signal input related to user settings and function control 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 detect user touch operations on or near it (for example, operations performed on or near the touch panel 1071 using a finger, stylus, or any other suitable object or accessory) and drive corresponding connected devices according to pre-set programs. The touch panel 1071 may include a touch detection device and a touch controller. Optionally, the touch detection device detects the user's touch position and detects signals generated by the touch operation, transmitting the signals to the touch controller. The touch controller receives the touch information from the touch detection device, converts it into touch point coordinates, and then sends it to the processor 110. The touch controller can also receive and execute commands from the processor 110. Furthermore, the touch panel 1071 can be implemented using various types of sensors, including resistive, capacitive, infrared, and surface acoustic wave. 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 keys, etc.), a trackball, a mouse, a joystick, etc., and the specifics are not limited here.

[0040] Optionally, the touch panel 1071 may cover the display panel 1061. When the touch panel 1071 detects a touch operation on or near it, it transmits the information to the processor 110 to determine the type of touch event. The processor 110 then provides a corresponding visual output on the display panel 1061 according to the type of touch event. Figure 1 In the embodiment, the touch panel 1071 and the display panel 1061 are two independent components to realize the input and output functions of the mobile terminal. However, in some embodiments, the touch panel 1071 and the display panel 1061 can be integrated to realize the input and output functions of the mobile terminal, which is not limited here.

[0041] 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 headphone port, etc. The interface unit 108 may be used to receive input (e.g., data information, power, etc.) from an external device and transmit the received input to one or more elements within the mobile terminal 100 or may be used to transmit data between the mobile terminal 100 and an external device.

[0042] Memory 109 can be used to store software programs and various data. Memory 109 may primarily include a program storage area and a data storage area. Optionally, the program storage area may store an operating system and at least one application required for a function (such as sound playback or image playback); the data storage area may store data generated based on the use of the phone (such as audio data and a phone book). Memory 109 may also include high-speed random access memory and non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0043] Processor 110 is the control center of the mobile terminal, connecting all components of the mobile terminal using various interfaces and circuits. By running or executing software programs and / or modules stored in memory 109 and accessing data stored in memory 109, it executes various functions of the mobile terminal and processes data, thereby providing overall monitoring of the mobile terminal. Processor 110 may include one or more processing units; preferably, processor 110 may integrate an application processor and a modem processor. Optionally, the application processor primarily handles the operating system, user interface, and application programs, while the modem processor primarily handles wireless communications. It is understood that the modem processor may not be integrated into processor 110.

[0044] The mobile terminal 100 may further include a power supply 111 (such as a battery) for supplying power to various components. Preferably, the power supply 111 may be logically connected to the processor 110 via a power management system, thereby enabling the power management system to manage charging, discharging, and power consumption.

[0045] although Figure 1 Not shown, the mobile terminal 100 may further include a Bluetooth module, etc., which will not be described in detail here.

[0046] To facilitate understanding of the embodiments of the present application, the communication network system on which the mobile terminal of the present application is based is described below.

[0047] See also Figure 2 , Figure 2 A communication network system architecture diagram is provided for an embodiment of the present application. The communication network system is an LTE system of universal mobile communication technology. The 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 an operator's IP service 204, which are connected in sequence.

[0048] Optionally, UE201 may be the above-mentioned terminal 100, which will not be described in detail here.

[0049] E-UTRAN 202 includes eNodeB 2021 and other eNodeBs 2022 . Optionally, eNodeB 2021 may be connected to other eNodeBs 2022 via a backhaul (eg, an X2 interface). eNodeB 2021 is connected to EPC 203 , and eNodeB 2021 may provide UE 201 with access to EPC 203 .

[0050] 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 GateWay) 2035, and a PCRF (Policy and Charging Rules Function) 2036. Optionally, the MME 2031 is a control node that processes signaling between the UE 201 and the EPC 203, providing bearer and connection management. The HSS 2032 provides registers for managing functions such as the Home Location Register (not shown) and stores user-specific information such as service features and data rates. All user data can be sent through SGW2034. PGW2035 can provide IP address allocation and other functions for UE 201. PCRF2036 is the policy and charging control policy decision point for service data flows and IP bearer resources. It selects and provides available policy and charging control decisions for the policy and charging execution function unit (not shown in the figure).

[0051] The IP service 204 may include the Internet, an intranet, an IMS (IP Multimedia Subsystem), or other IP services.

[0052] Although the above introduction takes the LTE system as an example, those skilled in the art should know that this application is not only applicable to the LTE system, but also 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.

[0053] Based on the above-mentioned mobile terminal hardware structure and communication network system, various embodiments of the present application are proposed.

[0054] Reference Figure 3 , Figure 3 The flowchart of the recommendation method according to an embodiment of the present application is shown. The recommendation method of the embodiment of the present application can be applied to a smart terminal (such as a mobile phone, etc.), including the following steps: S1: Determine the contextual features corresponding to the current contextual awareness data based on the constructed contextual knowledge graph.

[0055] Optionally, the contextual knowledge graph includes dynamic relationships between different entities (such as users, devices, and locations). By matching the current contextual awareness data with the constructed contextual knowledge graph, the contextual features corresponding to the current contextual awareness data can be determined. Contextual features are used to characterize the user's current situation, such as "in a meeting," "driving," "high noise environment," or "high stress state."

[0056] Optionally, context-aware data refers to data that can identify, collect, and utilize environmental context information. Context-aware data may include at least one of the following: device data, environmental data, user physiological status data, and user behavioral data. Optionally, device data indicates data related to the smart terminal itself, including but not limited to acceleration, device posture (e.g., whether it is flipped), device connection status (e.g., whether it is connected to the vehicle), and network status. This data can be obtained using devices such as posture sensors and gyroscopes installed on the smart terminal. Optionally, environmental data includes time data, location data, and spatial data. Time data indicates the current time, such as whether it is a weekday or the current specific time point. Spatial data includes but is not limited to noise, temperature, and the type of space. For example, the type of space can be analyzed using images captured by a camera, noise levels can be determined using sounds collected by a microphone, and the temperature around the smart terminal can be collected using a temperature sensor. Optionally, location data indicates the geographic location of the smart terminal and can be obtained using methods such as a global navigation positioning system installed on the smart terminal or base station positioning. Optionally, user physiological status data refers to indicators related to human physiological functions collected through various sensors or wearable devices, including but not limited to heart rate, blood pressure, body temperature, etc. Optionally, user behavior data refers to data on the user's use or operation of applications on the smart terminal, such as calendar events recorded by calendar applications, meeting events recorded by office applications, chat messages recorded by chat applications, etc. Optionally, before step S1, the method further includes: obtaining current contextual awareness data.

[0057] S2: Determine or generate corresponding recommended services and / or recommended actions based on contextual characteristics and user historical behavior data.

[0058] Optionally, since the user's current situation can be known through contextual features, combined with the user's historical behavior data including the user's behavior data in different situations, it is possible to predict what services the user currently needs, and thus determine or generate corresponding recommended services and / or recommended operations. Optionally, the recommended service can be any function or application that the smart terminal itself can provide, and the recommended operation can be a function that can be achieved by controlling the associated device through the smart terminal. For example, assuming that the contextual features include "driving" and "high temperature environment", and based on the user's historical behavior data, it can be known that the user will turn on the air conditioner and adjust the temperature to a certain temperature value when the temperature in the vehicle is high, and set the wind speed to a fixed wind speed, then a recommended operation of "turn on the air conditioner, set the temperature to a commonly used temperature value and the wind speed to a fixed wind speed" can be generated. For another example, assuming that the contextual features include "using a mobile phone", "in a meeting", and "high noise", a recommended service of "turning on noise reduction mode" can be generated.

[0059] Through the above method, the smart terminal obtains the contextual features corresponding to the current contextual perception data through the constructed contextual knowledge graph, and then determines or generates the corresponding recommended services and / or recommended operations based on the contextual features and user historical behavior data, thereby achieving the purpose of accurately identifying the context and providing proactive and personalized service recommendations, thereby improving the user experience.

[0060] Optionally, step S2 includes at least one of the following: Build a user behavior sequence model based on historical context features and corresponding user historical behavior data; Input the contextual features into the constructed user behavior sequence model to obtain the predicted target behavior sequence; the user behavior sequence model is constructed based on the user's historical contextual features and corresponding historical behavior data; Based on the target behavior sequence, corresponding recommended services and / or recommended actions are determined or generated. Optionally, a user behavior sequence model can be pre-constructed based on the user's historical contextual features and corresponding historical behavior data. For example, reinforcement learning can be used to construct the user behavior sequence model based on the user's historical contextual features and corresponding historical behavior data. After obtaining the user's current contextual features, the contextual features are input into the constructed user behavior sequence model to obtain a target behavior sequence predicted by the user behavior sequence model. The target behavior sequence represents the behaviors or actions that the user may need to perform. Therefore, based on the target behavior sequence, corresponding recommended services and / or recommended actions can be determined or generated. It should be noted that the target behavior sequence may include one or multiple behaviors. For example, suppose a user frequently opens a social networking app on their phone at 9:00 a.m. on weekdays and accesses their friend's status page to view it. The constructed user behavior sequence model can record the correspondence between the contextual features "at work," "morning," and "social networking app" and the behavior sequence "entering the friend's status page." When the contextual features are input, the behavior sequence is output, thereby causing the phone to automatically jump to the friend's status page.

[0061] In this way, recommended services and / or recommended operations can be accurately generated based on contextual characteristics, further improving the personalization of service recommendations and further enhancing the user experience.

[0062] Optionally, the contextual features are input into the constructed user behavior sequence model to obtain a predicted target behavior sequence, including at least one of the following: Get the user's current emotional state; The contextual features and the current emotional state are input into the constructed user behavior sequence model to obtain a predicted target behavior sequence; the user behavior sequence model is constructed based on at least one of the historical contextual features, the historical emotional state and the corresponding user historical behavior data.

[0063] Optionally, the user may need to perform different actions or operations in different emotional states, meaning that the recommended services and / or recommended operations may differ. After obtaining the user's current emotional state through methods such as voice recognition and facial image recognition, the contextual features and current emotional state are input into a constructed user behavior sequence model to obtain a target behavior sequence predicted by the user behavior sequence model. Optionally, the user behavior sequence model can be pre-constructed based on at least one of the user's historical contextual features, historical emotional states, and corresponding historical behavior data. For example, reinforcement learning can be used to construct the user behavior sequence model based on the user's historical contextual features, historical emotional states, and corresponding historical behavior data. For example, assuming a user often purchases a certain snack through a shopping app on their phone in the evening when they are in a bad mood, the constructed user behavior sequence model can record the correspondence between the contextual features "evening," "browsing shopping apps," and the emotional state "unhappy" and the behavior sequence "purchasing a certain snack." When the contextual features and emotional state are input, the behavior sequence is output, thereby displaying a purchase link for the snack when the user opens the shopping app on their phone in the evening. This further enhances the personalization of service recommendations and improves the user experience.

[0064] Optionally, the method further includes: Obtain feedback on recommended services and / or recommended actions; Optimize the user behavior sequence model based on feedback information.

[0065] Optionally, the user will respond to the recommended service and / or recommended operation determined or generated by the smart terminal. For example, if the user currently needs the recommended service and / or recommended operation, the smart terminal will be triggered to execute the recommended service and / or recommended operation, thereby generating feedback information indicating acceptance of the recommended service and / or recommended operation. If the user currently does not need or does not agree with the recommended service and / or recommended operation, the smart terminal will not be triggered to execute the recommended service and / or recommended operation, or the recommended service and / or recommended operation executed by the smart terminal will be adjusted, thereby generating feedback information indicating non-acceptance of the recommended service and / or recommended operation. Optionally, after obtaining feedback information on the recommended service and / or recommended operation, the user behavior sequence model can be optimized based on the feedback information, such as adjusting the weights between the contextual features recorded in the user behavior sequence model and different behavior sequences, etc., to improve the prediction accuracy of the user behavior sequence model.

[0066] Optionally, based on the target behavior sequence, determining or generating corresponding recommended services and / or recommended actions includes: Based on the target behavior sequence and the user's health status data, corresponding recommended services and / or recommended operations are determined or generated.

[0067] Optionally, the user's health status data is used to characterize data related to the user's health, such as whether the user has a physical disease (such as hypoglycemia, hyperglycemia, hypertension, etc.). It can be understood that in the case of a physical disease, it may be necessary to consider the user's diet and / or rest conditions when determining or generating corresponding recommended services and / or recommended operations, so that the recommended services and / or recommended operations are more in line with user needs. For example, assuming that the target behavior sequence includes buying milk tea, and the user's health status data characterizes that the user has high blood sugar, based on the target behavior sequence and the user's health status data, a recommended service for ordering sugar-free or half-sugar milk tea through a food delivery platform can be determined or generated. In this way, in combination with the user's health status data, the corresponding recommended services and / or recommended operations are determined or generated, which further improves the personalization of service recommendations and further enhances the user experience.

[0068] Optionally, the method further includes: Access historical situational awareness data; Perform feature fusion on historical context-aware data to determine or generate contextual knowledge graphs.

[0069] Optionally, the intelligent terminal can actively acquire historical context-awareness data, or receive input historical context-awareness data, and perform feature fusion on the historical context-awareness data to determine or generate a context-awareness graph. Optionally, a spatiotemporal graph neural network can be used to perform feature fusion on the historical context-awareness data to construct a context-awareness graph.

[0070] Optionally, based on the target behavior sequence, determining or generating corresponding recommended services and / or recommended actions includes: Send a recommendation request to the cloud server, the recommendation request includes the target behavior sequence; Receive a recommendation response message returned by the cloud server, where the recommendation response message includes a recommended service and / or a recommended operation.

[0071] Alternatively, if the computing resources of a smart terminal are limited or to reduce computing resource usage, the cloud server can determine or generate the corresponding recommended services and / or recommended operations. Specifically, the smart terminal sends a recommendation request including a target behavior sequence to the cloud server, requesting the cloud server to calculate the services or operations that can be recommended to the user, and receives a recommendation response message including the recommended services and / or recommended operations from the cloud server. This allows for rapid determination or generation of the corresponding recommended services and / or recommended operations, improving the speed of recommendation responses.

[0072] The following is an example of the recommendation method provided in the above embodiment. In this example, the smart terminal is a mobile phone and the scene feature is a scene tag. Figure 4, the recommended device provided in this embodiment includes: 1) Situational Awareness Module Multi-source data collection: Integrate data from mobile terminal devices (sensors, gyroscopes, cameras, microphones, GPS, etc.), IoT devices (temperature and humidity sensors, cameras), wearable devices (heart rate monitoring), user behavior data (calendar, social media, messages, etc.); Context modeling: Spatiotemporal graph neural network (ST-GNN) is used to fuse spatiotemporal features to generate dynamic context maps.

[0073] 2) User Understanding Module Real-time profiling engine: Updates user preferences through online learning and combines affective computing (speech / expression recognition) to infer emotional states. Intent prediction: Build a user behavior sequence model based on reinforcement learning (RL) to predict the next demand.

[0074] 3) Service decision module, including hierarchical decision model and service orchestration engine; Hierarchical decision-making model, including: Scenario layer: Associating scenario tags (such as "at work", "in a meeting", "driving", "in a game", "in a video", "listening to music", etc.) through the context map; Service layer: Dynamically matches service atoms based on scene tags (such as adjusting air conditioning temperature and playing music); Resource layer: schedules edge nodes and cloud computing power to optimize response latency. Service orchestration engine: Models service processes based on Petri nets and supports automatic generation of cross-scenario service chains.

[0075] 4) Execution and feedback module Multimodal interaction: supports mixed interaction of voice, gestures, and AR interfaces, and adopts an intention-based error correction mechanism; Federated learning: Local training of user data, only uploading model parameters to update the model.

[0076] The following describes the recommended method provided by this embodiment in detail through two specific scenarios: Scenario 1: Smart Office Scenario Contextual input: The user's smartwatch detects elevated stress levels, consecutive meetings on the calendar, or ambient noise exceeding a threshold. Recommended service output: Automatically adjust the conference room lighting to a soothing mode and push deep breathing guidance through AR glasses; simultaneously adjust subsequent meeting schedules and recommend a 15-minute break; and send a "user is currently busy" status reminder to collaborative colleagues.

[0077] Scenario 2: Cross-scenario travel services Context migration: Users move from a home environment (e.g., playing news on a smart speaker) to a car environment in the morning. Recommendation service output: News audio is synchronized with the in-car screen, and the playback time is shortened based on road condition predictions; combined with user health data (such as hypoglycemia history), cafes along the way are recommended and low-calorie meals can be reserved.

[0078] In summary, the recommendation method provided in the above embodiments improves context recognition accuracy through the dynamic integration of multimodal data (environment, behavior, etc.) and precise context modeling. A real-time user cognitive model is constructed to capture dynamic intent and emotional state, effectively reducing response latency in user profiling-supported services. Furthermore, a service decision-making mechanism is designed to seamlessly connect across scenarios, supporting adaptive scenario migration and seamless cross-scenario service integration. This enables proactive, personalized service recommendations and reduces the burden of user interaction.

[0079] The above examples are only for reference. In order to avoid redundancy, they will not be listed one by one here. In actual development or application, they can be flexibly combined according to actual needs, but any combination belongs to the technical solution of this application and is covered within the scope of protection of this application.

[0080] An embodiment of the present application further provides an intelligent terminal, comprising a memory and a processor, wherein a processing program is stored in the memory, and when the processing program is executed by the processor, the recommended method in any of the above embodiments is implemented.

[0081] An embodiment of the present application further provides a storage medium having a processing program stored thereon, and when the processing program is executed by a processor, the recommended method in any of the above embodiments is implemented.

[0082] In the embodiments of the smart terminal and storage medium provided in this application, all technical features of any of the above-mentioned recommended method embodiments may be included. The expanded and explained contents of the specification are basically the same as those of the embodiments of the above-mentioned methods and will not be repeated here.

[0083] An embodiment of the present application further provides a computer program product, which includes computer program code. When the computer program code runs on a computer, the computer executes the methods in the various possible implementation modes described above.

[0084] An embodiment of the present application also provides a chip, including a memory and a processor, wherein the memory is used to store computer programs, and the processor is used to call and run the computer programs from the memory, so that a device equipped with the chip executes the methods in the various possible implementation modes as described above.

[0085] It is understood that the above scenarios are merely examples and do not limit the application scenarios of the technical solutions provided in the embodiments of this application. The technical solutions of this application can also be applied to other scenarios. For example, those skilled in the art will appreciate that with the evolution of system architecture and the emergence of new business scenarios, the technical solutions provided in the embodiments of this application will also be applicable to similar technical problems.

[0086] The serial numbers of the above embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0087] The steps in the method of the embodiment of the present application can be adjusted in order, combined and deleted according to actual needs.

[0088] The units in the device of the embodiment of the present application can be merged, divided and deleted according to actual needs.

[0089] In this application, the same or similar terminology, technical solutions and / or application scenario descriptions are generally only described in detail the first time they appear. When they appear again later, they are generally not repeated for the sake of brevity. When understanding the technical solutions and other contents of this application, for the same or similar terminology, technical solutions and / or application scenario descriptions that are not described in detail later, you can refer to the previous relevant detailed descriptions.

[0090] In this application, the description of each embodiment has its own focus. For parts that are not described or recorded in detail in a certain embodiment, please refer to the relevant description of other embodiments.

[0091] The various technical features of the technical solution of this application can be combined arbitrarily. In order to make the description concise, 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, they should be considered to be within the scope of this application.

[0092] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus the necessary general hardware platform, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as mentioned above, and includes a number of instructions for enabling a terminal device (which can be a mobile phone, computer, server, controlled terminal, or network device, etc.) to execute the method of each embodiment of the present application.

[0093] In the above embodiments, all or part of the embodiments can be implemented using software, hardware, firmware, or any combination thereof. When implemented using software, all or part of the embodiments can be implemented in the form of a computer program product. A computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions according to the embodiments of the present application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a storage medium or transmitted from one storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, optical fiber, digital subscriber line) or wireless (e.g., infrared, wireless, microwave, etc.) means. 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 integrates one or more available media. Available media can be magnetic media (e.g., floppy disks, storage disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media (e.g., solid-state disks (SSDs)).

[0094] The above are only preferred embodiments of the present application and do not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A recommendation method, applied to a smart terminal, characterized in that: Including steps: S1, based on the constructed contextual knowledge graph, determines the contextual features corresponding to the current contextual awareness data; S2: Determine or generate corresponding recommended services and / or recommended operations based on the situational features and the user's historical behavior data.

2. The method according to claim 1, wherein Step S2 includes at least one of the following: Build a user behavior sequence model based on historical context features and corresponding user historical behavior data; Inputting the contextual features into the constructed user behavior sequence model to obtain a predicted target behavior sequence; Based on the target behavior sequence, corresponding recommended services and / or recommended operations are determined or generated.

3. The method according to claim 2, wherein Inputting the contextual features into the constructed user behavior sequence model to obtain a predicted target behavior sequence includes at least one of the following: Get the user's current emotional state; Inputting the situational features and the current emotional state into the constructed user behavior sequence model to obtain a predicted target behavior sequence; The user behavior sequence model is constructed based on at least one of the user's historical contextual features, historical emotional state, and corresponding historical behavior data.

4. The method according to claim 2 or 3, wherein: The method further comprises: Obtaining feedback information on the recommended service and / or the recommended operation; The user behavior sequence model is optimized based on the feedback information.

5. The method according to claim 2, wherein The determining or generating corresponding recommended services and / or recommended operations based on the target behavior sequence includes: Based on the target behavior sequence and the user's health status data, corresponding recommended services and / or recommended operations are determined or generated.

6. The method according to claim 1, wherein Context-aware data includes at least one of the following: device data, environmental data, user physiological state data, and user behavior data.

7. The method according to any one of claims 1 to 6, characterized in that The method further comprises: Access historical situational awareness data; Feature fusion is performed on the historical context perception data to determine or generate the context knowledge graph.

8. The method according to claim 2, wherein The determining or generating corresponding recommended services and / or recommended operations based on the target behavior sequence includes: Sending a recommendation request to a cloud server, wherein the recommendation request includes the target behavior sequence; A recommendation response message returned by the cloud server is received, where the recommendation response message includes a recommended service and / or a recommended operation.

9. An intelligent terminal, characterized in that: include: A memory and a processor, wherein a processing program is stored in the memory, and when the processing program is executed by the processor, the recommendation method according to any one of claims 1 to 8 is implemented.

10. A storage medium, characterized in that: The storage medium stores a processing program, which implements the recommendation method according to any one of claims 1 to 8 when executed by a processor.

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