Scene mode starting method, intelligent terminal and storage medium

Abstract

The invention provides a scene mode starting method, an intelligent terminal and a storage medium, is applied to the intelligent terminal, and comprises the following steps: obtaining influence factor data corresponding to at least one preset scene mode, and if the influence factor data meets a starting condition of the corresponding preset scene mode, starting the preset scene mode. More accurate influence factor data are set for different scene modes, whether the corresponding scene mode is started or not is judged according to the influence factor data, the accuracy and the intelligent degree of scene mode starting are improved, and then the user experience is improved.

Description

Technical Field

[0001] The present application relates to the technical field of smart terminals, and in particular to a method for starting a scene mode, a smart terminal and a storage medium. Background Art

[0002] With the rapid development of smart terminal technology, automatically starting the scene mode in the smart terminal can effectively improve the user experience.

[0003] In the related art, usually only simple scene modes are set in the smart terminal, such as night mode, focus mode and driving mode, etc. The smart terminal turns on the corresponding scene mode according to simple judgment conditions pre-set by the user.

[0004] In the process of conceiving and implementing the present application, the inventors discovered that there are at least the following problems: the above-mentioned method is prone to the scene mode being accidentally activated, and the degree of intelligence is low.

[0005] The preceding description is intended to provide general background information and does not necessarily constitute prior art. Summary of the invention

[0006] In response to the above technical problems, the present application provides a scene mode startup method, a smart terminal and a storage medium, which improve the accuracy and intelligence of the scene mode startup of the smart terminal.

[0007] In order to solve the above technical problems, the present application provides a method for starting a scene mode, which is applied to a smart terminal, comprising:

[0008] S10: Obtaining influencing factor data corresponding to at least one preset scene mode;

[0009] S20: If the influencing factor data meets the activation condition of the corresponding preset scene mode, the preset scene mode is activated.

[0010] Optionally, the S20 includes:

[0011] Input the influencing factor data into a matching pre-trained machine learning model, determine whether the influencing factor data meets the start-up conditions of the corresponding preset scene mode, and output the determination result;

[0012] If the judgment result indicates that it is satisfied, the preset scene mode is started.

[0013] Optionally, the influencing factor data includes influencing factor time series data;

[0014] The step of inputting the influencing factor data into a matching pre-trained machine learning model to determine whether the influencing factor data satisfies a start-up condition of a corresponding preset scene mode includes:

[0015] Inputting the time series data of the influencing factors into a matching pre-trained machine learning model;

[0016] The matched pre-trained machine learning model is used to extract influencing factor feature data from the influencing factor time series data, and based on the influencing factor feature data, it is determined whether the start-up conditions of the corresponding preset scene mode are met.

[0017] Optionally, the preset scene mode is a driving mode; the influencing factor data corresponding to the driving mode includes: first acceleration sensor data and / or positioning data;

[0018] The starting of the preset scene mode includes:

[0019] Control the preset notifications and / or applications in the driving mode to be closed, and / or control the preset navigation applications and voice interaction functions to be turned on.

[0020] Optionally, the preset scene mode is a sports mode; the influencing factor data corresponding to the sports mode includes: second acceleration sensor data and / or gyroscope data;

[0021] The starting of the preset scene mode includes:

[0022] Adjust the volume in the exercise mode to a preset volume threshold, and / or control the preset exercise monitoring application and voice interaction function to start.

[0023] Optionally, the preset scene mode is a reading mode; the influencing factor data corresponding to the reading mode includes: light sensor data and / or first terminal operation status data;

[0024] The starting of the preset scene mode includes:

[0025] Adjust the screen brightness in the reading mode to a preset screen brightness threshold and / or adjust the sound mode to silent mode, and / or control the preset notification and / or application to close.

[0026] Optionally, the preset scene mode is a movie watching / game mode; the influencing factor data corresponding to the movie watching / game mode includes: gravity sensing sensor data and / or second terminal operation status data;

[0027] The starting of the preset scene mode includes:

[0028] Control preset notifications and / or application closing in the viewing / gaming mode.

[0029] Optionally, before S10, the step further includes:

[0030] For each preset scene mode, obtain training data corresponding to the matching initial machine learning model, wherein the training data includes a time series sample of the influencing factors and a label indicating whether it is a matching preset scene mode;

[0031] The training data is used to train the matching initial machine learning model until a preset convergence condition is met, and the machine learning model that meets the convergence condition is determined as the matching pre-trained machine learning model.

[0032] The present application also provides an intelligent terminal, comprising: a memory and a processor, wherein a startup program is stored in the memory, and when the startup program is executed by the processor, the steps of the above method are implemented.

[0033] The present application also provides a storage medium, wherein the storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0034] As described above, the scene mode activation method, smart terminal and storage medium of the present application are applied to smart terminals, and include the steps of: obtaining the influencing factor data corresponding to at least one preset scene mode, and activating the preset scene mode if the influencing factor data meets the activation condition of the corresponding preset scene mode. Through the above technical solution, more accurate influencing factor data is set for different scene modes, and whether to activate the corresponding scene mode is determined according to the influencing factor data, thereby improving the accuracy and intelligence of the scene mode activation, thereby improving the user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] The drawings herein are incorporated into the specification and constitute a part of the specification, illustrate embodiments consistent with the present application, and are used together with the specification 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 drawings required for use in the description of the embodiments are briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor.

[0036] Figure 1 A schematic diagram of the hardware structure of a smart terminal provided in each embodiment of the present application;

[0037] Figure 2 A communication network system architecture diagram provided for an embodiment of the present application;

[0038] Figure 3 A schematic diagram of an existing driving mode provided for this application;

[0039] Figure 4 A schematic diagram of a method for starting a scene mode provided in an embodiment of the present application;

[0040] Figure 5 A flowchart of a method for determining whether influencing factor data meets the start-up conditions of a corresponding preset scene mode provided in an embodiment of the present application;

[0041] Fig. 6A A schematic flow chart of a method for activating a driving mode provided in an embodiment of the present application;

[0042] Figure 6B A schematic flow chart of a method for activating a motion mode provided in an embodiment of the present application;

[0043] Figure 6C A flowchart of a method for starting a reading mode provided in an embodiment of the present application;

[0044] Fig.6D A flowchart of a method for starting a movie watching / game mode provided in an embodiment of the present application;

[0045] Figure 7 A flowchart of a method for training a machine learning model provided in an embodiment of the present application;

[0046] Figure 8 A schematic diagram of a module of a scene mode starting device provided in an embodiment of the present application.

[0047] The realization of the purpose, functional features and advantages of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings. The above-mentioned drawings have shown clear embodiments of this application, which will be described in more detail later. These drawings and textual descriptions are not intended to limit the scope of the concept of this application in any way, but to illustrate the concept of this application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0048] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0049] It should be noted that, in this article, the terms "include", "comprises" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also includes 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 "includes a ..." does not exclude the existence of other identical elements in the process, method, article or device including 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.

[0050] It should be understood that, although the terms first, second, third, etc. may be used to describe various information in this article, these information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of this article, 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 can be interpreted as "at the time of" or "when" or "in response to determination". Furthermore, as used in this article, the singular forms "one", "one" and "the" are intended to also include plural forms, unless there is an opposite indication in the context. It should be further understood that the terms "comprising", "including" indicate the existence of features, steps, operations, elements, components, projects, kinds, and / or groups, but do not exclude the existence, occurrence or addition of one or more other features, steps, operations, elements, components, projects, kinds, and / or groups. The terms "or", "and / or", "including at least one of the following" etc. used in this application can be interpreted as inclusive, or mean 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 only occur when a combination of elements, functions, steps or operations are inherently mutually exclusive in some manner.

[0051] It should be understood that, although the various steps in the flowchart in the embodiment of the present application are displayed in sequence according to the indication of the arrows, these steps are not necessarily performed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and it can be performed in other orders. Moreover, at least a portion of the steps in the figure may include a plurality of sub-steps or a plurality of 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 other steps or at least a portion of the sub-steps or stages of other steps.

[0052] 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 determining" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)", depending on the context.

[0053] It should be noted that in this article, step codes such as S401, S402, etc. are used for the purpose of expressing the corresponding content more clearly and concisely, and do not constitute a substantial limitation on the sequence. When implementing the step, those skilled in the art may execute S402 first and then S401, etc., but these should all be within the scope of protection of this application.

[0054] 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.

[0055] In the subsequent description, the suffixes such as "module", "component" or "unit" used to represent elements are only used to facilitate the description of the present application, and have no specific meanings. Therefore, "module", "component" or "unit" can be used in a mixed manner.

[0056] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with the relevant laws, regulations and standards of the relevant countries and regions, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0057] The smart terminal can be implemented in various forms. For example, the smart terminal described in this application may include smart 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.

[0058] The subsequent description will be illustrated by taking a smart terminal as an example, and those skilled in the art will understand that, in addition to components specifically used for mobile purposes, the construction according to the embodiments of the present application can also be applied to fixed-type terminals.

[0059] See also Figure 1 , Figure 1 A schematic diagram of the hardware structure of a smart terminal provided in each embodiment of the present application, the smart terminal 100 may include: a radio frequency (RF) 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 can understand that Figure 1 The structure of the smart terminal shown in the figure does not constitute a limitation on the smart terminal. The smart terminal may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0060] Combine the following Figure 1 A detailed introduction to each component of the smart terminal:

[0061] The radio frequency unit 101 can be used for receiving and sending signals during information transmission or communication. Specifically, after receiving the downlink information of the base station, it is sent to the processor 110 for processing; in addition, the uplink data is sent to the base station. Generally, the radio frequency unit 101 includes but is not limited to an antenna, at least one amplifier, a transceiver, a coupler, a low noise amplifier, a duplexer, etc. In addition, the radio frequency unit 101 can also communicate with the network and other devices through wireless communication. The above-mentioned wireless communications can use any communication standard or protocol, including but not limited to Global System of Mobilecommunication (GSM), General Packet Radio Service (GPRS), Code Division Multiple Access 2000 (CDMA2000), Wideband Code Division Multiple Access (WCDMA), Time Division-Synchronous Code Division Multiple Access (TD-SCDMA), Frequency Division Duplexing-Long Term Evolution (FDD-LTE), Time Division Duplexing-Long Term Evolution (TDD-LTE), 5G and 6G, etc.

[0062] WiFi is a short-range wireless transmission technology. The smart terminal 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 a necessary component of the smart terminal and can be omitted as needed without changing the essence of the invention.

[0063] The audio output unit 103 can convert the 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 smart terminal 100 is in a call signal receiving mode, a call mode, a recording mode, a voice recognition mode, a broadcast receiving mode, etc. Moreover, the audio output unit 103 can also provide audio output related to a specific function performed by the smart terminal 100 (for example, a call signal receiving sound, a message receiving sound, etc.). The audio output unit 103 may include a speaker, a buzzer, etc.

[0064] The A / V input unit 104 is used to receive audio or video signals. The A / V input unit 104 may include a graphics processor (GPU) 1041 and a microphone 1042, and the graphics processor 1041 processes the image data of a static picture or video obtained by an image capture device (such as a camera) in a video capture mode or an image capture mode. The processed image frame can be displayed on the display unit 106. The image frame processed by the graphics processor 1041 can be stored in the memory 109 (or other storage medium) or sent via the radio frequency unit 101 or the WiFi module 102. The microphone 1042 can receive sound (audio data) via the microphone 1042 in a telephone call mode, a recording mode, a voice recognition mode, and other operating modes, and can process such sound into audio data. The processed audio (voice) data can be converted into a format output that can be sent to a mobile communication base station via the radio frequency unit 101 in the case of a telephone call mode. The microphone 1042 can implement various types of noise elimination (or suppression) algorithms to eliminate (or suppress) noise or interference generated in the process of receiving and sending audio signals.

[0065] The smart terminal 100 also 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 smart terminal 100 is moved to the ear. As a type 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. It 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 pedometer, tapping), etc.; as for other sensors that can also be configured on the mobile phone, such as fingerprint sensors, pressure sensors, iris sensors, molecular sensors, gyroscopes, barometers, hygrometers, thermometers, infrared sensors, etc., they will not be repeated here.

[0066] 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.

[0067] The user input unit 107 can be used to receive input digital or character information, and to generate key signal input related to the user settings and function control of the intelligent 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 the user's touch operation on or near it (such as the user's operation on the touch panel 1071 or near the touch panel 1071 using any suitable object or accessory such as a finger, stylus, etc.), and drive the corresponding connection device according to a pre-set program. The touch panel 1071 may include two parts: a touch detection device and a touch controller. Optionally, the touch detection device detects the user's touch orientation, 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 the touch point coordinates, and then sends it to the processor 110, and can receive and execute commands sent by the processor 110. In addition, the touch panel 1071 can be implemented in various types such as resistive, capacitive, infrared, and surface acoustic waves. In addition to the touch panel 1071, the user input unit 107 may also 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, a function key (such as a volume control key, a switch key, etc.), a trackball, a mouse, a joystick, etc., which are not specifically limited here.

[0068] 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. Then, the processor 110 provides corresponding visual output on the display panel 1061 according to the type of touch event. Figure 1 In the figure, the touch panel 1071 and the display panel 1061 are used as two independent components to realize the input and output functions of the smart 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 smart terminal, which is not limited here.

[0069] The interface unit 108 is used as an interface through which at least one external device can be connected to the smart 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 smart terminal 100 or may be used to transmit data between the smart terminal 100 and an external device.

[0070] The memory 109 can be used to store software programs and various data. The memory 109 can mainly include a program storage area and a data storage area. Optionally, the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), 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 disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0071] The processor 110 is the control center of the smart terminal. It uses various interfaces and lines to connect various parts of the entire smart terminal. It executes various functions of the smart terminal and processes data by running or executing software programs and / or modules stored in the memory 109, and calling data stored in the memory 109, so as to monitor the smart terminal as a whole. The processor 110 may include one or more processing units; preferably, the processor 110 may integrate an application processor and a modem processor. Optionally, the application processor mainly processes the operating system, user interface, and application programs, and the modem processor mainly processes wireless communications. It is understandable that the above-mentioned modem processor may not be integrated into the processor 110.

[0072] The intelligent terminal 100 may also 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 through a power management system, thereby managing functions such as charging, discharging, and power consumption through the power management system.

[0073] although Figure 1 Not shown, the smart terminal 100 may also include a Bluetooth module, etc., which will not be described in detail here.

[0074] To facilitate understanding of the embodiments of the present application, the communication network system on which the smart terminal of the present application is based is described below.

[0075] See also Figure 2 , Figure 2 A communication network system architecture diagram is provided for an embodiment of the present application, wherein the communication network system is an LTE system of universal mobile communication technology, and the LTE system includes a user equipment (UE) 201, an evolved UMTS terrestrial radio access network (Evolved UMTS Terrestrial Radio Access Network, E-UTRAN) 202, an evolved packet core network (Evolved Packet Core, EPC) 203 and an operator's IP service 204 that are connected in communication in sequence.

[0076] Optionally, UE201 may be the above-mentioned smart terminal 100, which will not be described in detail here.

[0077] E-UTRAN 202 includes eNodeB 2021 and other eNodeBs 2022 , etc. Optionally, eNodeB 2021 may be connected to other eNodeBs 2022 via a backhaul (eg, an X2 interface), and eNodeB 2021 is connected to EPC 203 , and eNodeB 2021 may provide UE 201 with access to EPC 203 .

[0078] EPC203 may include Mobility Management Entity (MME) 2031, Home Subscriber Server (HSS) 2032, other MMEs 2033, Serving GateWay (SGW) 2034, Packet Data Network Gateway (PDN Gate Way, PGW) 2035 and Policy and Charging Rules Function (PCRF) 2036, etc. Optionally, MME2031 is a control node that processes signaling between UE201 and EPC203, and provides bearer and connection management. HSS2032 is used to provide some registers to manage functions such as home location register (not shown in the figure), and save some 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 flow and IP bearer resources, which selects and provides available policy and charging control decisions for the policy and charging execution function unit (not shown in the figure).

[0079] The IP service 204 may include the Internet, an intranet, an IMS (IP Multimedia Subsystem) or other IP services.

[0080] Although the above introduction takes the LTE system as an example, those skilled in the art should know that the present 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., without limitation here.

[0081] Based on the above-mentioned smart terminal hardware structure and communication network system, various embodiments of the present application are proposed.

[0082] In order to facilitate the understanding of the present application, the prior art that may be involved is first exemplarily described below.

[0083] As the performance of smart terminals continues to improve, the usage scenarios and user needs of smart terminals are becoming increasingly complex. At present, smart terminals usually only have simple scene modes, such as night mode, focus mode, or driving mode. For scene modes, take driving mode as an example. Figure 3 , Figure 3 A schematic diagram of an existing driving mode provided for this application. When the driving mode is started, the smart terminal starts the driving mode based on simple judgment conditions pre-set by the user, such as whether the current position reaches the preset position.

[0084] However, in the above method, the judgment conditions are relatively simple, and it is easy to cause the scene mode to be activated by mistake, and the degree of intelligence is low. For example, when a user passes a certain road section, because the road section is a road section where the user often drives a vehicle, when the smart terminal recognizes the road section, it turns on the driving mode, but in fact, the user is just walking and not driving a vehicle, which causes the driving mode to be triggered by mistake.

[0085] In order to solve the above technical problems, this application proposes a method for starting a scene mode, which is applied to a smart terminal. Figure 4 , Figure 4 A schematic diagram of a method for starting a scene mode provided in an embodiment of the present application, the method comprising:

[0086] S401: Obtain influencing factor data corresponding to at least one preset scene mode.

[0087] In this embodiment, multiple scene modes are preset, including: driving mode, sports mode, reading mode and movie / game mode.

[0088] Optionally, the corresponding influencing factor data in the driving mode includes but is not limited to: first acceleration sensor data and positioning data, etc.

[0089] Optionally, the influencing factor data corresponding to the motion mode includes but is not limited to: second acceleration sensor data and gyroscope data.

[0090] Optionally, the influencing factor data corresponding to the reading mode includes but is not limited to: light sensor data and first terminal operation status data.

[0091] Optionally, the influencing factor data corresponding to the movie watching / game mode includes but is not limited to: gravity sensing sensor data and second terminal operation status data.

[0092] S402: If the influencing factor data meets the activation condition of the corresponding preset scene mode, the preset scene mode is activated.

[0093] In this embodiment, a plurality of machine learning models are set and pre-trained, thereby obtaining pre-trained machine learning models under different scenario modes.

[0094] For any scene mode, the influencing factor data corresponding to the scene mode is input into the matching pre-trained machine learning model to determine whether the influencing factor data meets the activation conditions of the corresponding preset scene mode, and output the judgment result. If the judgment result indicates that it is satisfied, the preset scene mode is activated. If the judgment result indicates that it is not satisfied, the current state of the smart terminal is maintained.

[0095] In the above embodiment of the present application, the influencing factor data corresponding to at least one preset scene mode is obtained, and if the influencing factor data meets the activation condition of the corresponding preset scene mode, the preset scene mode is activated. This embodiment sets more accurate influencing factor data for different scene modes, and then determines whether to activate the corresponding scene mode based on the influencing factor data, thereby improving the accuracy and intelligence of the scene mode activation, thereby improving the user experience.

[0096] Furthermore, based on the above-mentioned embodiments, the following embodiments illustrate the process of inputting the influencing factor data into a matching pre-trained machine learning model to determine whether the influencing factor data meets the start-up conditions of the corresponding preset scene mode.

[0097] See also Figure 5 , Figure 5 A flow chart of a method for determining whether influencing factor data meets the activation condition of a corresponding preset scene mode provided in an embodiment of the present application, wherein the influencing factor data includes influencing factor time series data, and the method includes the following steps:

[0098] S501. Input the time series data of influencing factors into a matching pre-trained machine learning model.

[0099] Optionally, the machine learning model includes but is not limited to a decision tree model, a neural network model, a support vector machine model, etc., and may also be other models, which are not limited in this application.

[0100] S502: Use a matching pre-trained machine learning model to extract influencing factor feature data from the influencing factor time series data, and determine whether the start-up conditions of the corresponding preset scene mode are met based on the influencing factor feature data.

[0101] Optionally, if the machine learning model is a decision tree model. For any preset scene mode, the influencing factor data under the scene mode is obtained, and the influencing factor data includes the influencing factor time series data, for example, data within 10 seconds. The influencing factor time series data is preprocessed, including but not limited to outlier removal, missing value processing, and data type conversion, etc., to obtain the influencing factor time series data after preprocessing. Since the decision tree model will automatically select the features that can best distinguish different categories during the construction process, the influencing factor time series data after preprocessing will be feature selected to obtain the influencing factor feature data used by the model for prediction, such as information gain, information gain rate, and Gini index, etc., and then judge whether the start-up conditions of the corresponding preset scene mode are met based on the influencing factor feature data.

[0102] Optionally, if the machine learning model is a neural network model. For any preset scene mode, the influencing factor data under the scene mode is obtained, and the influencing factor data includes the influencing factor time series data, for example, data within 20 seconds. The influencing factor time series data is preprocessed, including but not limited to standardization and normalization, etc., to obtain the influencing factor time series data after preprocessing. Since the neural network model needs to set an activation function, the activation function will perform feature selection on the influencing factor time series data after preprocessing to obtain the influencing factor feature data used by the model for prediction, such as probability information, etc., and then judge whether the start-up conditions of the corresponding preset scene mode are met based on the influencing factor feature data.

[0103] Optionally, if the machine learning model is a support vector machine model. For any preset scene mode, the time series data of the influencing factors in the scene mode is obtained, and the time series data of the influencing factors after preprocessing is obtained. Since the support vector machine model needs to set a kernel function, the kernel function will perform feature selection on the time series data of the influencing factors after preprocessing to obtain the characteristic data of the influencing factors used by the model for prediction, such as hyperplane information, etc., and then judge whether the start-up conditions of the corresponding preset scene mode are met according to the characteristic data of the influencing factors.

[0104] In the above-mentioned embodiment of the present application, the influencing factor time series data is input into a matching pre-trained machine learning model, the matching pre-trained machine learning model is used to extract the influencing factor feature data from the influencing factor time series data, and based on the influencing factor feature data, it is judged whether the start condition of the corresponding preset scene mode is met. The method of this embodiment improves the accuracy of judging whether to start the preset scene mode by using a pre-trained machine learning model to judge based on the influencing factor time series data, thereby improving the degree of intelligence and user experience.

[0105] Furthermore, based on the above embodiments, the method for starting the scene mode of the present application is described through specific scenarios.

[0106] Optionally, see Fig. 6A , Fig. 6A A flowchart of a method for starting a driving mode provided in an embodiment of the present application is provided, and the method comprises the following steps:

[0107] S601. Obtaining corresponding influencing factor data in a driving mode.

[0108] Optionally, the influencing factor data includes but is not limited to: first acceleration sensor data and / or positioning data, the first acceleration sensor data can be obtained by an accelerometer, and the positioning data can be obtained by a positioning system.

[0109] S602: Input the influencing factor data into a pre-trained machine learning model to determine whether the influencing factor data meets the start-up conditions of the corresponding driving mode.

[0110] The first acceleration sensor data and / or positioning data are input into a machine learning model pre-trained in the driving mode to determine whether the first acceleration sensor data and / or positioning data meet the start-up conditions of the corresponding driving mode.

[0111] S603: If the conditions are met, the preset notifications and / or applications in the driving mode are controlled to be turned off, and / or the preset navigation application and voice interaction function are controlled to be turned on.

[0112] If the conditions are met, it means that the user is driving the vehicle and is in driving state. The smart terminal controls some preset message notifications or applications to be closed to prevent unnecessary interference during driving. It can also control the opening of applications with navigation functions to assist driving. It can also control the opening of voice interaction functions to provide more convenient control operations during driving.

[0113] S604: If not satisfied, maintain the current state of the smart terminal.

[0114] Optionally, see Figure 6B , Figure 6B A schematic flow chart of a method for activating a motion mode provided in an embodiment of the present application, the method comprising the following steps:

[0115] S611, obtaining corresponding influencing factor data in the sports mode.

[0116] Optionally, the influencing factor data includes but is not limited to: second acceleration sensor data and / or gyroscope data, the second acceleration sensor data can be obtained through an accelerometer, and the gyroscope data can be obtained through a gyroscope.

[0117] S612: Input the influencing factor data into a pre-trained machine learning model to determine whether the influencing factor data meets the starting conditions of the corresponding motion mode.

[0118] The second acceleration sensor data and / or the gyroscope data are input into a machine learning model pre-trained in the motion mode to determine whether the second acceleration sensor data and / or the gyroscope data meet the start-up conditions of the corresponding motion mode.

[0119] S613: If the conditions are met, adjust the volume in the sports mode to a preset volume threshold, and / or control a preset sports monitoring application and a voice interaction function to start.

[0120] If the conditions are met, it means that the user is exercising, for example, the user is jogging, skipping rope, or running. The smart terminal adjusts the volume to the preset volume threshold to provide the user with clear voice input, and can also control the opening of sports-related applications to monitor the user's current physical condition and exercise status, and can also control the opening of the voice interaction function to provide more convenient control operations during exercise.

[0121] S614: If not satisfied, maintain the current state of the smart terminal.

[0122] Optionally, see Figure 6C , Figure 6C A flowchart of a method for starting a reading mode provided in an embodiment of the present application is provided, and the method comprises the following steps:

[0123] S621. Obtain corresponding influencing factor data in the reading mode.

[0124] Optionally, the influencing factor data includes but is not limited to: light sensor data and / or first terminal operation status data. The light sensor data can be obtained through a light sensor, and the first terminal operation status data can be an application running in the smart terminal, such as an e-book, web browsing, and documents.

[0125] S622: Input the influencing factor data into a pre-trained machine learning model to determine whether the influencing factor data meets the starting conditions of the corresponding reading mode.

[0126] The light sensor data and / or the first terminal operation status data are input into a machine learning model pre-trained in the reading mode to determine whether the light sensor data and / or the first terminal operation status data meet the start-up conditions of the corresponding reading mode.

[0127] S623. If satisfied, adjust the screen brightness in the reading mode to a preset screen brightness threshold and / or adjust the sound mode to silent mode, and / or control the preset notification and / or application to close.

[0128] If satisfied, it means that the user is reading and is in a reading state. The smart terminal adjusts the screen brightness in reading mode to a preset screen brightness threshold to provide the user with appropriate light. The sound mode can also be adjusted to silent mode and the preset notifications and / or applications can be closed to prevent unnecessary disturbances during reading.

[0129] S624: If not satisfied, maintain the current state of the smart terminal.

[0130] Optionally, see Fig.6D , Fig.6D A flowchart of a method for starting a movie watching / game mode provided in an embodiment of the present application is provided, and the method comprises the following steps:

[0131] S631, obtaining the corresponding influencing factor data in the movie watching / game mode.

[0132] Optionally, the influencing factor data includes but is not limited to: gravity sensor data and / or second terminal operation status data. The gravity sensor data is obtained by a gravity sensor, and the second terminal operation status data may be an application running in the smart terminal, such as a game, a movie, and an animation.

[0133] S632: Input the influencing factor data into a pre-trained machine learning model to determine whether the influencing factor data meets the start-up conditions of the corresponding viewing / game mode.

[0134] The gravity sensing sensor data and / or the second terminal operating status data are input into a machine learning model pre-trained in the viewing / game mode to determine whether the gravity sensing sensor data and / or the second terminal operating status data meet the start-up conditions of the corresponding viewing / game mode.

[0135] S633: If satisfied, control the preset notification and / or application in the movie watching / game mode to be closed.

[0136] If the conditions are met, it means that the user is viewing the smart terminal in landscape mode. The smart terminal controls some preset message notifications or applications to be closed to provide a comfortable viewing environment for the user.

[0137] S634: If not satisfied, maintain the current state of the smart terminal.

[0138] In the above-mentioned embodiments of the present application, by obtaining the influencing factor data corresponding to the driving mode, sports mode, reading mode, and movie / game mode, and inputting the corresponding influencing factor data into the matching machine learning model, the activation of each scene mode is made more accurate and intelligent, which effectively improves the user experience.

[0139] According to any of the above embodiments, the machine learning model used in this application is pre-trained, see Figure 7 , Figure 7 A flow chart of a method for training a machine learning model provided in an embodiment of the present application, the method comprising the following steps:

[0140] S701. For each preset scene mode, obtain training data corresponding to the matching initial machine learning model, where the training data includes time series samples of influencing factors and a label indicating whether it is a matching preset scene mode.

[0141] Optionally, the initial machine learning model includes but is not limited to: a decision tree model, a neural network model, and a support vector machine model.

[0142] Optionally, for the driving mode, the time series samples of influencing factors corresponding to the initial machine learning model obtained include acceleration sensor data of multiple different time periods and / or positioning data of multiple different time periods, and whether the above data matches the label of the driving mode.

[0143] Optionally, for the motion mode, the time series samples of influencing factors corresponding to the initial machine learning model obtained include acceleration sensor data of multiple different time periods and / or gyroscope data of multiple different time periods, and whether the above data matches the label of the motion mode.

[0144] Optionally, for the reading mode, the time series samples of influencing factors corresponding to the initial machine learning model obtained include light sensor data of multiple different time periods and / or terminal operation status data of multiple different time periods, and whether the above data matches the label of the reading mode.

[0145] Optionally, for the movie-watching / game mode, the time series samples of influencing factors corresponding to the initial machine learning model obtained include gravity sensing sensor data of multiple different time periods and / or terminal operation status data of multiple different time periods, and whether the above data matches the label of the movie-watching / game mode.

[0146] It is understandable that the preset scene mode in the present application may also include other modes, which are not limited in the present application.

[0147] S702. Use training data to train the matching initial machine learning model until a preset convergence condition is met, and determine the machine learning model that meets the convergence condition as the matching pre-trained machine learning model.

[0148] For the training data corresponding to any of the above scenario modes, the training data is trained with the initial machine learning model that matches it. When the initial machine learning model meets the convergence conditions, the machine learning model training is completed.

[0149] Optionally, the convergence condition may be that the loss function satisfies a preset loss value, or the number of training times satisfies a preset number of training times, etc.

[0150] The machine learning model in this embodiment can also record and upload its output results during the application process to the cloud, so that other smart terminals can also call the data therein, and can also obtain updated scene patterns from the pattern library in the cloud to identify the updated scene patterns.

[0151] In the above-mentioned embodiment of the present application, the training data corresponding to the matching initial machine learning model is obtained for each preset scene mode, wherein the training data includes a time series sample of the influencing factors and a label indicating whether it is a matching preset scene mode. The matching initial machine learning model is trained using the training data until the preset convergence condition is met, and the machine learning model that meets the convergence condition is determined as the matching pre-trained machine learning model. The method of this embodiment trains the corresponding initial machine model according to the training data under different scene modes, so that the machine learning models under different scene modes obtained after training are more accurate, and thus the judgment result of whether to turn on the corresponding scene mode determined by the machine learning model obtained after training is more accurate.

[0152] In some embodiments of the present application, a scene mode activation device is also provided, referring to Figure 8 , Figure 8 This is a schematic diagram of a module of a scene mode startup device provided in an embodiment of the present application, wherein the scene mode startup device comprises:

[0153] The acquisition module 801 is used to acquire the influencing factor data corresponding to at least one preset scene mode.

[0154] The processing module 802 is configured to start a preset scene mode if the influencing factor data meets the start condition of the corresponding preset scene mode.

[0155] Optionally, the processing module 802 is specifically configured to:

[0156] The influencing factor data is input into the matching pre-trained machine learning model to determine whether the influencing factor data meets the startup conditions of the corresponding preset scene mode and output the judgment result.

[0157] If the judgment result indicates that it is satisfied, the preset scene mode is activated.

[0158] Optionally, the processing module 802 is further specifically configured to:

[0159] Input the time series data of influencing factors into the matching pre-trained machine learning model.

[0160] A matching pre-trained machine learning model is used to extract influencing factor feature data from the influencing factor time series data, and based on the influencing factor feature data, it is determined whether the startup conditions of the corresponding preset scene mode are met.

[0161] Optionally, the preset scene mode is a driving mode, and the influencing factor data corresponding to the driving mode includes: first acceleration sensor data and / or positioning data. The processing module 802 is specifically configured to:

[0162] Control the preset notifications and / or applications in driving mode to be turned off, and / or control the preset navigation applications and voice interaction functions to be turned on.

[0163] Optionally, the preset scene mode is a sports mode, and the influencing factor data corresponding to the sports mode includes: second acceleration sensor data and / or gyroscope data. The processing module 802 is specifically configured to:

[0164] Adjust the volume in exercise mode to a preset volume threshold, and / or control the preset exercise monitoring application and voice interaction function to start.

[0165] Optionally, the preset scene mode is a reading mode, and the influencing factor data corresponding to the reading mode includes: light sensor data and / or first terminal operation status data. The processing module 802 is specifically configured to:

[0166] Adjust the screen brightness in reading mode to a preset screen brightness threshold and / or adjust the sound mode to silent mode, and / or control preset notifications and / or application shutdown.

[0167] Optionally, the preset scene mode is a movie watching / game mode, and the influencing factor data corresponding to the movie watching / game mode includes: gravity sensor data and / or second terminal operation status data. Processing module 802 is specifically used to:

[0168] Control preset notifications and / or app closing in movie / game mode.

[0169] Optionally, the processing module 802 is further configured to:

[0170] For each preset scene mode, the training data corresponding to the matching initial machine learning model is obtained, and the training data includes time series samples of influencing factors and labels indicating whether it is a matching preset scene mode.

[0171] The matching initial machine learning model is trained using the training data until a preset convergence condition is met, and the machine learning model that meets the convergence condition is determined as the matching pre-trained machine learning model.

[0172] As described above, the scene mode activation device provided in the embodiment of the present application can be applied to a smart terminal. For specific implementation steps and implementation effects, please refer to the above embodiment. To avoid redundancy, the description will not be repeated.

[0173] The present application also provides a smart terminal, which includes a memory and a processor. The memory stores a startup program, and when the startup program is executed by the processor, the steps of the scene mode startup method in any of the above embodiments are implemented.

[0174] The present application also provides a storage medium, such as a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the method for starting the scene mode in any of the above embodiments are implemented.

[0175] In the embodiments of the smart terminal and computer-readable storage medium provided in the present application, all technical features of any of the above-mentioned scene mode startup method embodiments may be included, and the expanded and explained contents of the specification are basically the same as those of the above-mentioned method embodiments, and will not be repeated here.

[0176] The embodiment of the present application further provides a computer program product, which includes a computer program code. When the computer program code runs on a computer, the computer executes the methods in the above various possible implementation modes.

[0177] An embodiment of the present application also provides a chip, including 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 modes as described above.

[0178] It is understood that the above scenarios are only examples and do not constitute a limitation on the application scenarios of the technical solutions provided in the embodiments of the present application. The technical solutions of the present application can also be applied to other scenarios. For example, it is known to those skilled in the art that with the evolution of the system architecture and the emergence of new business scenarios, the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems.

[0179] The serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0180] The steps in the method of the embodiment of the present application can be adjusted in order, combined and deleted according to actual needs.

[0181] The units in the device of the embodiment of the present application can be merged, divided and deleted according to actual needs.

[0182] In the present application, the same or similar terminology concepts, technical solutions and / or application scenario descriptions are generally described in detail only 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 the present application, for the same or similar terminology concepts, technical solutions and / or application scenario descriptions that are not described in detail later, reference can be made to the previous related detailed descriptions.

[0183] In the present application, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0184] The various technical features of the technical solution of the present application can be arbitrarily combined. 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 the present application.

[0185] 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 a necessary general hardware platform, and of course 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, 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 above, and includes a number of instructions for a terminal device (which can be a mobile phone, a computer, a server, a controlled terminal, or a network device, etc.) to execute the method of each embodiment of the present application.

[0186] 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 loading and executing computer program instructions on a computer, the process or function according to the embodiment of the present application is 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 computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from a website site, a computer, a server or a data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server, a data center, etc. that contains one or more available media integrated. The available medium can be a magnetic medium, (e.g., a floppy disk, a storage disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state storage disk Solid State Disk (SSD)), etc.

[0187] The above are only preferred embodiments of the present application, and are not intended to 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 method for starting a scene mode, applied to a smart terminal, characterized in that: The method comprises: S10: Obtaining influencing factor data corresponding to at least one preset scene mode; S20: If the influencing factor data meets the activation condition of the corresponding preset scene mode, the preset scene mode is activated.

2. The method according to claim 1, characterized in that The S20 includes: Input the influencing factor data into a matching pre-trained machine learning model, determine whether the influencing factor data meets the start-up conditions of the corresponding preset scene mode, and output the determination result; If the judgment result indicates that it is satisfied, the preset scene mode is started.

3. The method according to claim 2, characterized in that The influencing factor data includes influencing factor time series data; The step of inputting the influencing factor data into a matching pre-trained machine learning model to determine whether the influencing factor data satisfies a start-up condition of a corresponding preset scene mode includes: Inputting the time series data of the influencing factors into a matching pre-trained machine learning model; The matched pre-trained machine learning model is used to extract influencing factor feature data from the influencing factor time series data, and based on the influencing factor feature data, it is determined whether the start-up conditions of the corresponding preset scene mode are met.

4. The method according to claim 1, characterized in that: The preset scene mode is a driving mode; The influencing factor data corresponding to the driving mode includes: first acceleration sensor data and / or positioning data; The starting of the preset scene mode includes: Control the preset notifications and / or applications in the driving mode to be closed, and / or control the preset navigation applications and voice interaction functions to be turned on.

5. The method according to claim 1, characterized in that The preset scene mode is a sports mode; the influencing factor data corresponding to the sports mode includes: second acceleration sensor data and / or gyroscope data; The starting of the preset scene mode includes: Adjust the volume in the exercise mode to a preset volume threshold, and / or control the preset exercise monitoring application and voice interaction function to start.

6. The method according to claim 1, characterized in that The preset scene mode is a reading mode; The influencing factor data corresponding to the reading mode includes: light sensor data and / or first terminal operation status data; The starting of the preset scene mode includes: Adjust the screen brightness in the reading mode to a preset screen brightness threshold and / or adjust the sound mode to silent mode, and / or control the preset notification and / or application to close.

7. The method according to claim 1, characterized in that The preset scene mode is a movie watching / game mode; The influencing factor data corresponding to the movie watching / game mode includes: gravity sensing sensor data and / or second terminal operation status data; The starting of the preset scene mode includes: Control preset notifications and / or application closing in the viewing / gaming mode.

8. The method according to claim 3, characterized in that Before S10, it also includes: For each preset scene mode, obtain training data corresponding to the matching initial machine learning model, wherein the training data includes a time series sample of the influencing factors and a label indicating whether it is a matching preset scene mode; The training data is used to train the matching initial machine learning model until a preset convergence condition is met, and the machine learning model that meets the convergence condition is determined as the matching pre-trained machine learning model.

9. An intelligent terminal, characterized in that: The smart terminal comprises: a memory and a processor, wherein a startup program is stored in the memory, and when the startup program is executed by the processor, the steps of the startup method of the scene mode as claimed in any one of claims 1 to 8 are implemented.

10. A storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by the processor, the steps of the method for starting the scene mode according to any one of claims 1 to 8 are implemented.

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