Method for controlling application operation and electronic device

Abstract

Provided are a method of controlling operation of an application and an electronic device, the method comprising: determining an operation state of the application when a first time or a second time preset for the application arrives, the second time being earlier than the first time and being a preset time length from the first time; and based on the running state of the application, determining whether to limit the running of the application after a first time, where limiting the running of the application indicates forbidding the application, closing the application or forbidding partial functions of the application. Optionally, the method performed by the electronic device may be performed using an artificial intelligence model.

Description

Technical Field

[0001] The present application relates to the field of control technology, and more specifically, to a method and an electronic device for controlling the running of an application. Background Art

[0002] With the popularity and daily use of electronic devices such as mobile phones and tablets, children in many families also have their own electronic devices, and they can use the applications in these electronic devices for learning and / or entertainment. However, the uncontrolled use of applications in electronic devices (for example, game applications) is not good for children's physical and mental health, so it is necessary to limit the time children use applications. Usually, parents can set a time limit for children to use applications through electronic devices to limit the operation of applications when children use applications for a preset period of time, thereby preventing children from becoming addicted to applications.

[0003] When the preset time limit is reached, the prior art directly shuts down the game or removes the limit according to user input, and it cannot intelligently extend the time, which will bring a bad user experience.

[0004] For example, Xiaolan is a 13-year-old kid. After finishing his weekend homework, he finally waited for the entertainment time agreed with his mother. Since his mother used the management software to set a one-hour game time limit for him, Xiaolan could not wait to open a MOBA (Multiplayer Online Battle Arena) battle game and started the game without stopping. With his superb skills and close cooperation with his friends, Xiaolan won one game after another. In the last game, Xiaolan and his friends were fighting against their opponents through unity and cooperation. When they were about to establish an advantage and win the game steadily, the time limit was up.

[0005] If the restrictions were not lifted in time, Xiaolan would be kicked out of the game because the time limit had expired. Due to the sudden numerical disadvantage, they slowly lost the game in which they had the advantage. After hearing the result of the failure and the complaints of his friends, Xiaolan felt ashamed and wronged. He believed that it was because he was forced to abandon his teammates that he lost the game. This caused psychological damage to him.

[0006] If Blue's mother had to go out to buy groceries next, but couldn't bear to let Blue finish the game, she lifted the time limit for Blue and verbally agreed with Blue to finish the game. But when Blue's mother came back from buying groceries, she found that Blue was still lying on the sofa playing games under the temptation of games and friends, and Blue's mother was furious.

[0007] Therefore, how to more reasonably restrict the use of applications to improve the user's application use experience is a problem to be solved urgently in the present invention. Summary of the invention

[0008] An object of the present invention is to provide a method for controlling applications running in an electronic device and an electronic device, so as to at least solve the above-mentioned problems in the related art, or not solve any of the above-mentioned problems.

[0009] According to one aspect of an embodiment of the present invention, a method for controlling an application running in an electronic device is provided, the method comprising: determining a running state of the application when a first time preset for the application is reached or a second time is reached, wherein the second time is earlier than the first time and is a predetermined time length away from the first time; and determining whether to restrict the running of the application after the first time based on the running state of the application, wherein restricting the running of the application indicates disabling the application, closing the application, or disabling some functions of the application.

[0010] According to the embodiments of the present disclosure, when the time preset for the application is reached or before the set time is reached, the application is not directly restricted from running, but the application state is first determined, and then whether to restrict the application from running is determined based on the application running state. In some scenarios where it is not suitable to directly restrict the application running, the embodiments of the present disclosure can obviously bring better user experience to users.

[0011] Optionally, the step of determining the running status of the application includes: obtaining a screenshot or video of the running interface of the application, or obtaining information about the running status of the application from the application; and determining the running status of the application based on the screenshot or video, or the information about the running status of the application.

[0012] Optionally, the method further includes: when a first time pre-set for the application is reached or a second time is reached, before determining the running status of the application, outputting inquiry information for asking whether to extend the running time of the application, and receiving user input for determining whether to extend the running time of the application, wherein the step of determining the running status of the application includes: determining the running status of the application in response to receiving the user input.

[0013] Optionally, the step of determining the running status of the application based on the screenshot or video includes: based on the screenshot or video, using a trained machine learning model to determine the running status of the application.

[0014] Optionally, the method also includes: when it is determined that the running state of the application is a specific running state, determining to restrict the running of the application after a first time; and / or, when it is determined that the running state of the application is not a specific running state, determining not to restrict the running of the application after the first time, and monitoring the running state of the application after the first time, and determining whether to restrict the running of the application based on the monitored running state or the running time of the application after the first time.

[0015] Optionally, the step of determining whether to limit the operation of the application based on the monitored running state or the running time of the application includes: if the monitored running state of the application is a specific running state when the running time of the application does not exceed a preset time length, restricting the use of the application and stopping monitoring the running state of the application; and / or if the monitored running state of the application is not a specific running state when the running time of the application does not exceed the preset time length and the running time of the application reaches the preset time length, restricting the use of the application and stopping monitoring the running state of the application.

[0016] Optionally, the application is a game application, and the specific running state indicates a state in which a running screen of the game application corresponds to a game end scene.

[0017] Optionally, the step of monitoring the running status of the application includes: periodically obtaining screenshots or videos of the running screen of the application, or obtaining the screenshots or videos according to the occupancy rate of specific hardware of the electronic device, and based on the screenshots or videos, using the machine learning model to monitor the running status of the application; or, obtaining information about the running status of the application from the application, and monitoring the running status of the application based on the information about the running status of the application.

[0018] Optionally, the step of acquiring the screenshot or video according to the occupancy rate of specific hardware of the electronic device includes: acquiring the screenshot or video when the change value of the occupancy rate of the CPU or GPU of the electronic device exceeds a threshold value.

[0019] Optionally, the step of outputting an inquiry message for asking whether to request to extend the running time of the application includes: displaying an icon for confirming to extend the running time of the application, wherein the user input indicates an input to the icon. According to another aspect of an embodiment of the present disclosure, an electronic device is provided, comprising: a first determination unit, configured to: determine the running status of the application when a first time pre-set for the application is reached or a second time is reached, wherein the second time is earlier than the first time and is a predetermined time length away from the first time; and a second determination unit, configured to: determine whether to restrict the running of the application after the first time based on the running status of the application, wherein restricting the running of the application indicates disabling the application, closing the application, or disabling some functions of the application.

[0020] Optionally, the first determination unit is configured to: obtain a screenshot or video of the running interface of the application, or obtain information about the running status of the application from the application; and determine the running status of the application based on the screenshot or video, or the information about the running status of the application.

[0021] Optionally, the electronic device also includes: a display unit, configured to output inquiry information for asking whether to extend the running time of the application before determining the running status of the application when a first time pre-set for the application is reached or a second time is reached, and receive user input for determining to extend the running time of the application, wherein the first determination unit is configured to: determine the running status of the application in response to the user's input to the icon.

[0022] Optionally, the first determination unit is configured to: determine the running status of the application based on the screenshot or video using a trained machine learning model.

[0023] Optionally, the second determination unit is further configured to: when it is determined that the running state of the application is a specific running state, determine to restrict the running of the application after a first time; and / or, when it is determined that the running state of the application is not a specific running state, determine not to restrict the running of the application after the first time, monitor the running state of the application after the first time, and determine whether to restrict the running of the application based on the monitored running state or the running time of the application after the first time.

[0024] Optionally, the second determination unit is configured to: if the monitored running state of the application is a specific running state when the running time of the application does not exceed a preset time length, restrict the use of the application and stop monitoring the running state of the application; and / or if the monitored running state of the application is not a specific running state when the running time of the application does not exceed the preset time length and the running time of the application reaches the preset time length, restrict the use of the application and stop monitoring the running state of the application.

[0025] Optionally, the application is a game application, and the specific running state indicates a state in which a running screen of the game application corresponds to a game end scene.

[0026] Optionally, the second determination unit is configured to: periodically obtain screenshots or videos of the running screen of the application, or obtain the screenshots or videos according to the occupancy rate of specific hardware of the electronic device, and based on the screenshots or videos, monitor the running status of the application using the machine learning model; or, obtain information about the running status of the application from the application, and monitor the running status of the application based on the information about the running status of the application.

[0027] Optionally, the second determination unit is configured to: acquire the screenshot or video when a change value of the occupancy rate of the CPU or GPU of the electronic device exceeds a threshold value.

[0028] Optionally, the display unit is configured to: display an icon for confirming extension of the application running time, wherein the user input indicates an input to the icon.

[0029] According to another aspect of an embodiment of the present invention, an electronic device is provided, comprising: at least one processor; and at least one memory storing computer executable instructions, wherein the computer executable instructions, when executed by the at least one processor, cause the at least one processor to execute the method for controlling the running of an application as described herein.

[0030] According to another aspect of an embodiment of the present invention, a computer-readable storage medium storing instructions is provided, wherein when the instructions are executed by at least one processor, the at least one processor is caused to execute the method for controlling the execution of an application as described herein. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] The above and other objects and features of the present invention will become more apparent through the following description in conjunction with the accompanying drawings which exemplarily illustrate an example, in which:

[0032] Figure 1 A flowchart of a method for controlling application execution according to an embodiment of the present disclosure is shown;

[0033] Figure 2 A schematic diagram of determining a game scene stage based on a multimodal large model according to an embodiment of the present disclosure is shown;

[0034] Figure 3 A schematic diagram showing a method of obtaining a game status event from a game application according to an embodiment of the present disclosure is shown;

[0035] Figure 4A A schematic diagram showing the CPU and GPU occupancy rates that vary according to the game scene stage;

[0036] Figure 4B A schematic diagram showing a screenshot operation based on a change in GPU occupancy rate is shown;

[0037] Figure 5A A schematic diagram showing automatic execution of intelligent dynamic delay according to an embodiment of the present disclosure is shown;

[0038] Figure 5B is a diagram showing an example of a graphical user interface for asking whether to perform a delay and for requesting an intelligent dynamic delay according to an embodiment of the present disclosure;

[0039] Figure 6 A diagram showing an example of a pop-up window displayed when the delay ends; and

[0040] Figure 7 A block diagram showing a structure of an electronic device according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0041] Hereinafter, various embodiments of the present disclosure are described with reference to the accompanying drawings, wherein the same reference numerals are used to represent the same or similar elements, features and structures. However, it is not intended that the present disclosure be limited to specific embodiments by the various embodiments described herein, and it is intended that: the present disclosure covers all modifications, equivalents and / or substitutes of the present disclosure, as long as they are within the scope of the attached claims and their equivalents. The terms and words used in the following specification and claims are not limited to their dictionary meanings, but are only used to enable a clear and consistent understanding of the present disclosure. Therefore, it should be apparent to those skilled in the art that the following description of various embodiments of the present disclosure is provided for illustrative purposes only, and not for the purpose of limiting the present disclosure defined by the attached claims and their equivalents.

[0042] It should be understood that the singular includes the plural unless the context clearly indicates otherwise.The terms "include", "comprising" and "having" used herein indicate the presence of disclosed functions, operations or elements, but do not exclude other functions, operations or elements.

[0043] For example, the expression "A or B" or "at least one of A and / or B" may indicate A and B, A or B. For example, the expression "A or B" or "at least one of A and / or B" may indicate (1) A, (2) B, or (3) both A and B.

[0044] In various embodiments of the present disclosure, it is intended that when a component (e.g., a first component) is referred to as being "coupled" or "connected" to another component (e.g., a second component) or being "coupled" or "connected" to another component (e.g., the second component), the component may be directly connected to the other component or may be connected through another component (e.g., a third component). In contrast, when a component (e.g., a first component) is referred to as being "directly coupled" or "directly connected" to another component (e.g., the second component) or being directly coupled to or directly connected to another component (e.g., the second component), there is no other component (e.g., the third component) between the component and the other component.

[0045] The expression "configured to" used in describing various embodiments of the present disclosure may be used interchangeably with expressions such as "suitable for", "having the ability to", "designed to", "suitable for", "manufactured to", and "capable of", for example, depending on the circumstances. The term "configured to" may not necessarily indicate that it is "specially designed to" in terms of hardware. On the contrary, the expression "a device configured to..." in some cases may indicate that the device and another device or part "can..." For example, the expression "a processor configured to perform A, B, and C" may indicate a dedicated processor (e.g., an embedded processor) for performing the corresponding operations or a general-purpose processor (e.g., a central processing unit CPU or an application processor (AP)) for performing the corresponding operations by executing at least one software program stored in a memory device.

[0046] The terms used herein are to describe certain embodiments of the present disclosure, but are not intended to limit the scope of other embodiments. Unless otherwise noted herein, all terms used herein (including technical or scientific terms) may have the same meaning as those generally understood by those skilled in the art. Typically, the terms defined in the dictionary should be considered to have the same meaning as the contextual meaning in the relevant field, and, unless clearly defined herein, should not be understood differently or understood to have an overly formal meaning. In any case, the terms defined in the present disclosure are not intended to be interpreted as excluding embodiments of the present disclosure.

[0047] At least some functions of the device or electronic device provided in the embodiments of the present disclosure can be implemented by an AI model, such as at least one module among multiple modules of the device or electronic device can be implemented by an AI model. Functions associated with AI can be performed by non-volatile memory, volatile memory and processor.

[0048] The processor may include one or more processors. In this case, the one or more processors may be general-purpose processors, such as a central processing unit (CPU), an application processor (AP), etc., or pure graphics processing units, such as a graphics processing unit (GPU), a visual processing unit (VPU), and / or an AI-specific processor, such as a neural processing unit (NPU).

[0049] The one or more processors control the processing of input data according to predefined operating rules or artificial intelligence (AI) models stored in non-volatile memory and volatile memory. The predefined operating rules or artificial intelligence models are provided by training or learning.

[0050] Here, providing by learning means obtaining a predefined operating rule or an AI model with desired characteristics by applying a learning algorithm to a plurality of learning data. The learning can be performed in the device or electronic device itself in which the AI ​​according to the embodiment is executed, and / or can be implemented by a separate server / system.

[0051] The AI ​​model may include multiple neural network layers. Each layer has multiple weight values, and each layer performs neural network calculations by calculating between the input data of the layer (such as the calculation results of the previous layer and / or the input data of the AI ​​model) and the multiple weight values ​​of the current layer. Examples of neural networks include, but are not limited to, convolutional neural networks (CNNs), deep neural networks (DNNs), recurrent neural networks (RNNs), restricted Boltzmann machines (RBMs), deep belief networks (DBNs), bidirectional recurrent deep neural networks (BRDNNs), generative adversarial networks (GANs), and deep Q networks.

[0052] A learning algorithm is a method of using a plurality of learning data to train a predetermined target device (e.g., a robot) to enable, allow, or control the target device to make a determination or prediction. Examples of the learning algorithm include, but are not limited to, supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning.

[0053] The method provided in the present disclosure may involve one or more technical fields such as speech, language, image, video or data intelligence.

[0054] Optionally, when it comes to the field of speech or language, in the method of controlling the operation of an application according to the present disclosure, a speech signal as an analog signal can be received via a speech input device (e.g., a microphone), and the speech portion can be converted into computer-readable text using an automatic speech recognition (ASR) model. The user's speech intention can be obtained by interpreting the converted text using a natural language understanding (NLU) model. The ASR model or the NLU model can be an artificial intelligence model. The artificial intelligence model can be processed by an artificial intelligence dedicated processor designed in a hardware structure specified for artificial intelligence model processing. Language understanding is a technology for recognizing and applying / processing human language / text, including, for example, natural language processing, machine translation, dialogue systems, question answering, or speech recognition / synthesis.

[0055] Optionally, when it comes to the field of images or videos, in the method of controlling the operation of an application according to the present disclosure, output data can be obtained by using image data as input data of an artificial intelligence model. The method of the present disclosure may relate to the field of visual understanding of artificial intelligence technology, which is a technology for recognizing and processing things like human vision, and includes, for example, object recognition, object tracking, image retrieval, human recognition, scene recognition, 3D reconstruction / localization, or image enhancement.

[0056] Optionally, when it comes to the field of intelligent data processing, in the method of controlling the operation of an application according to the present disclosure, in the inference or prediction stage, an artificial intelligence model can be used to perform predictions by using real-time input data. The processor of the electronic device can perform preprocessing operations on the data to convert it into a form suitable for use as an input to the artificial intelligence model. Inference prediction is a technique for logical reasoning and prediction by determining information, including, for example, knowledge-based reasoning, optimization prediction, preference-based planning or recommendation.

[0057] In the present application, an artificial intelligence model can be obtained by training. Here, "obtained by training" means obtaining a predefined operating rule or artificial intelligence model configured to perform a desired feature (or purpose) by training a basic artificial intelligence model with multiple training data through a training algorithm. The artificial intelligence model may include multiple neural network layers. Each of the multiple neural network layers includes multiple weight values, and the neural network calculation is performed by calculating between the calculation result of the previous layer and the multiple weight values.

[0058] The method for controlling applications running in an electronic device described herein can be applied to any scenario where application usage needs to be restricted.

[0059] Figure 1 A flowchart of a method for controlling application execution according to an embodiment of the present disclosure is shown.

[0060] As will be appreciated by those skilled in the art, the methods described herein may be used to control applications running in any type of electronic device (eg, a mobile phone, a tablet, a desktop computer, a notebook computer, etc.).

[0061] Reference Figure 1 In step S101, when a first time preset for the application is reached or a second time is reached, the running state of the application is determined, wherein the second time is earlier than the first time and is a predetermined time length away from the first time.

[0062] In step S102, it is determined whether to restrict the operation of the application after the first time based on the determined operation state of the application, wherein restricting the operation of the application indicates disabling the application, closing the application, or disabling some functions of the application.

[0063] As understood by those skilled in the art, in the prior art, in order to control the time a user (e.g., a child) uses an application (e.g., a game application), the length of time the user is allowed to use the application can be set, and when the length of time reaches a preset time point, use of the application is restricted.

[0064] According to the embodiments of the present disclosure, when the time preset for the application is reached or when the time preset for the application is about to be reached (for example, the second time before the first time), the running of the application is not directly restricted, but the state of the application is first determined, and then whether to restrict the running of the application is determined according to the running state of the application. In some scenarios where it is not suitable to directly restrict the running of the application, the embodiments of the present disclosure can obviously bring better user experience to users.

[0065] As an example, the steps in the method according to an embodiment of the present disclosure may be executed by a management application or system (eg, a Minor Mode module) or a processor in an electronic device.

[0066] As an example, the step of determining the running status of the application includes: obtaining a screenshot or video of the running interface of the application, or obtaining information about the running status of the application from the application; and determining the running status of the application based on the screenshot or video, or the information about the running status of the application.

[0067] As an example, the step of determining the running status of the application based on the screenshot or video includes: based on the screenshot or video, using a trained machine learning model to determine the running status of the application.

[0068] Since the present disclosure determines whether to limit the operation of an application, that is, whether to extend the use or running time of an application, based on a machine learning model, the delay-limited application operation in this article can be referred to as intelligent dynamic delay-limited application operation, and can be simply referred to as intelligent dynamic delay.

[0069] As an example, Figure 1 The method shown may also include: when it is determined that the running state of the application is a specific running state, determining to restrict the running of the application after a first time; and / or, when it is determined that the running state of the application is not a specific running state, determining not to restrict the running of the application after the first time, monitoring the running state of the application after the time, and determining whether to restrict the running of the application based on the monitored running state or the running time of the application after the time.

[0070] As an example, the step of determining whether to limit the operation of the application based on the monitored running state or the running time of the application includes: if the monitored running state of the application is a specific running state when the running time of the application does not exceed the preset time length, restricting the use of the application and stopping monitoring the running state of the application; and / or if the monitored running state of the application is not a specific running state when the running time of the application does not exceed the preset time length and the running time of the application reaches the preset time length, restricting the use of the application and stopping monitoring the running state of the application.

[0071] As an example, the application is a game application, and the specific running state indicates a state in which the running screen of the game application corresponds to a game end scene.

[0072] As an example, the step of monitoring the running status of the application includes: periodically obtaining screenshots or videos of the running screen of the application, or obtaining the screenshots or videos according to the occupancy rate of specific hardware of the electronic device, and based on the screenshots or videos, using the machine learning model to monitor the running status of the application; or, obtaining information about the running status of the application from the application, and monitoring the running status of the application based on the information about the running status of the application.

[0073] As an example, the step of acquiring the screenshot or video according to the occupancy rate of specific hardware of the electronic device includes: acquiring the screenshot or video when the change value of the occupancy rate of the CPU or GPU of the electronic device exceeds a threshold value.

[0074] As an example, Figure 1The method shown may also include: when a first time pre-set for the application is reached or a second time is reached, before determining the running status of the application, outputting inquiry information for asking whether to extend the running time of the application, receiving user input for determining to extend the running time of the application, wherein the step of determining the running status of the application includes: determining the running status of the application in response to receiving the user input.

[0075] As an example, the step of outputting inquiry information for inquiring whether to request extension of the application running time includes: displaying an icon for confirming extension of the application running time, wherein the user input indicates an input to the icon.

[0076] As an example, the following describes the training process of the machine learning model using a game application as an example. It should be understood by those skilled in the art that the game application is only an example and does not limit the present disclosure.

[0077] As an example, a large number of game screenshots (i.e., screenshots of the application screen or application interface when the application is running in an electronic device) can be obtained, and the obtained screenshots can be labeled with labels corresponding to the scene stages. For example, the scene stages can be defined as: idle stage, loading stage, in-game stage, and end stage, and the scenes can be classified accordingly.

[0078] As an example, a deep learning model based on a convolutional neural network (CNN) framework can be used as the machine learning model. CNN can analyze the features of image data and map images with specific features to labels corresponding to scene stages, so as to achieve the effect of outputting labels corresponding to scene stages after inputting specific images. By training the machine learning model based on the acquired screenshots and the labels corresponding to the screenshots, an image recognition model that can predict the scene stage to which the game screenshots belong can be obtained, that is, the machine learning model is trained based on the game screenshots marked with scene stages to obtain a convolutional neural network model that can identify the scene stage through the game screenshots.

[0079] As an example, when using a trained machine learning model, a game screenshot is input into the trained machine learning model to determine the scene stage corresponding to the screenshot through the trained machine learning model.

[0080] As an example, a large number of game screen videos can be obtained, and the obtained videos can be labeled with labels corresponding to the scene stages. By training the machine learning model with the game screen videos marked with the scene stages, a convolutional neural network model can be obtained that can recognize the scene stages through the game screen videos.

[0081] As an example, a machine learning model can be obtained by fine-tuning a large multimodal model (such as ChatGPT-4O, which can recognize multimodal inputs such as video, audio, and text) as a basis.

[0082] Prompts can be written in advance, where the prompts are used to limit the content and format of the big model's answers, etc., and then fine-tuned based on the multimodal big model, and the private domain knowledge base built using the LangChain framework can be used to supplement the big model with background knowledge. The multimodal big model can directly identify and analyze image or video data, and use it as context for content output. Combined with appropriate prompts and relying on the background knowledge supplement of the private domain knowledge base, the multimodal big model can identify the picture or video content of the game and answer what game scene stage this content corresponds to.

[0083] Figure 2 A schematic diagram of determining a game scene stage based on a multimodal large model according to an embodiment of the present disclosure is shown.

[0084] Reference Figure 2 , through the prompt words, and combined with the private domain knowledge base, the multimodal big model can analyze and process the input content in a purposeful manner. The big model can recognize the game screen video and output the data in a formatted manner (for example, JSON format), which describes whether the current screen is in the game scene stage of the end stage.

[0085] As an example, by inputting a screenshot of the game running interface or a video of the interface into a trained multimodal large model, the multimodal large model can output the scene stage corresponding to the screenshot of the interface or the video of the interface.

[0086] As an example, when a trained machine learning model is used to determine that the game application is in a game end scene based on a screenshot or video of the game application running interface, that is, when it is determined that the running state of the game is or indicates that the running screen of the game application is in a state corresponding to the game end scene, it can be determined to restrict the running of the game.

[0087] As an example, when a trained machine learning model is used to determine, based on a screenshot or video of the game application running interface, that the game application is in a game end scene, that is, when it is determined that the running state of the game is not or does not indicate that the running screen of the game application is in a state corresponding to the game end scene, it can be determined that the running of the game is not restricted.

[0088] As an example, the state event of the game can be obtained from the game application, and the running state of the game can be determined based on the state event obtained from the application. For example, it can be determined which stage the game application is in or whether it is in the game end stage based on the state event of the game application.

[0089] As an example, a device (electronic apparatus) manufacturer may cooperate with a game manufacturer. When a game is running on a device, the game application sends game status events to a system component Game Optimization Service (GOS) module of the device, and the Minor Mode module may subscribe to GOS.

[0090] Figure 3 A schematic diagram of obtaining game status events from a game application according to an embodiment of the present disclosure is shown.

[0091] Reference Figure 3 , the third-party game application integrated with the scene software development kit (Scene SDK) sends the game status event to GOS, which is then forwarded by the GOS module to the Minor Mode module. The Minor Mode module can monitor the game status events of the game application to determine the running status of the game application.

[0092] As an example, when the running state of the game application obtained from the application corresponds to the game end scene, it can be determined to restrict the running of the game. Otherwise, it is determined to restrict the running of the game, that is, the user can continue to use the game application.

[0093] As an example, while the game application continues to run after the time, the running status of the application can be monitored. When it is monitored that the running status of the game application corresponds to a game end scenario, the use of the game application can be restricted and the monitoring of the running status of the game application can be stopped.

[0094] As an example, in order to prevent children from escaping supervision due to indefinite dynamic delay, the operation of the game application may be forcibly restricted after the dynamic delay state lasts for a preset time (e.g., 15 minutes). In other words, if the running state of the game application is not monitored to be a state corresponding to the game end scene for a long time, the operation of the game may be forcibly restricted after the preset time after the time, and the monitoring of the running state of the game application may be stopped.

[0095] As an example, the running status of the game application can be monitored by inputting a screenshot or video of the running interface of the game application obtained during monitoring into the above-mentioned trained machine learning model to determine the running status of the game application.

[0096] As an example, during the intelligent dynamic delay process, the Minor mode module can generate screenshots or videos of the application running interface and provide them to the machine learning model to determine the game scene stage. Since continuously obtaining screenshots and videos will lead to a high hardware occupancy rate, the management application or system (for example, the Minor mode module) can determine the timing of obtaining screenshots or videos to reduce the frequency of obtaining screenshots or videos.

[0097] As an example, screenshots or videos may be periodically acquired at predetermined time intervals.

[0098] As an example, an occupancy data access interface provided in the electronic device for monitoring the usage of the CPU (central processing unit) or GPU (graphics processing unit) can be used to monitor the usage of the electronic device's CPU or GPU and other hardware and software resources as the game progresses, and determine the timing of obtaining screenshots or videos based on the usage.

[0099] As an example, you can use a period of time (for example, 1 second) as a unit, with the average occupancy rate of the CPU or GPU as an indicator. If the occupancy rate of any indicator between two adjacent time periods changes greatly, for example, the occupancy rate of a specific hardware in a certain period of time suddenly becomes 50% of the previous period, then it means that the game scene may have changed, and screenshots or videos can be obtained at this time.

[0100] As an example, the average occupancy rate described above is only an example, and it may also be an instantaneous occupancy rate.

[0101] Figure 4A and schematic diagrams showing CPU and GPU occupancy rates that vary according to the game scene stage. Figure 4B A schematic diagram showing taking screenshots based on occupancy change values.

[0102] Reference Figure 4A and Figure 4B , compared to the in-game stage (period 0), the average GPU occupancy rate in the game end stage (period 1) is less than 50% of that in the in-game stage. Therefore, when the average GPU occupancy rate changes by more than the threshold, a screenshot or video of the game running screen can be obtained, because at this time, the game application is likely to be in the game end stage. For example, when entering the game end stage from the in-game stage, the average GPU occupancy rate is reduced by at least 50%, so the operation of obtaining a screenshot or video is performed.

[0103] As an example, when the time is reached, the intelligent dynamic delay operation may be automatically performed.

[0104] Figure 5A A schematic diagram of automatically executing intelligent dynamic delay according to an embodiment of the present disclosure is shown.

[0105] For example, Xiaolan is playing a game on his mobile phone, and his mother sets a one-hour play time for him through Minor Mode. But when the time is about to run out, Xiaolan is still in the middle of a fierce battle, which makes him feel troubled. At this time, if you set the start of intelligent dynamic delay in advance, when the time limit is reached, a pop-up window or notification will be used to remind you that "Intelligent dynamic delay has been started". After clicking "Got it", you can directly return to the game, and the Minor Mode module will directly start dynamic delay and enter the intelligent dynamic delay state.

[0106] As an example, when the time is reached, a query message asking whether to turn on the intelligent dynamic delay may be output, and whether to execute the intelligent dynamic delay may be determined based on user input.

[0107] As an example, the inquiry information may be in text, picture or voice form. For example, the text or icon asking whether to turn on the intelligent dynamic delay may be displayed or a voice asking whether to turn on the intelligent dynamic delay may be output through a voice output unit of an electronic device (e.g., a mobile phone) such as "Do you need to turn on the intelligent dynamic delay?"

[0108] As an example, the user may determine whether to turn on the intelligent dynamic delay by inputting query information.

[0109] As an example, the user may input an icon or voice to inquire whether to turn on the smart dynamic delay.

[0110] For example, the user can click on the icon to confirm the start of intelligent dynamic delay or send out a voice message of "turn on intelligent dynamic delay" to turn on intelligent dynamic delay.

[0111] For example, after Xiaolan finished his weekend homework, he asked to play a MOBA (Multiplayer Online Battle Arena) battle game. After setting a one-hour play limit for him, Lan's mother rushed back to the company to deal with some urgent matters and went out. Xiaolan fought and won repeatedly with his friends. In the middle of the last game, Xiaolan suddenly found that the one-hour play time set by the system prompt was about to arrive, so he requested intelligent dynamic delay. Later, he and his friends won the game through cooperation and unity. With the farewell of Xiaolan and his friends, AI intelligently ended the game and imposed restrictions. Xiaolan put down his mobile phone contentedly and practiced calligraphy. After handling the urgent work, Lan's mother, who was physically and mentally exhausted, returned home and found that Xiaolan was practicing calligraphy obediently. She was very happy for her child's obedience and her fatigue was swept away.

[0112] After turning on the intelligent dynamic delay, by identifying the game scene and determining the time when the game ends, it can ensure that the children's play ends in time after the game ends, thus achieving dynamic delay.

[0113] As an example, when it is determined that the game application is in the specific state (ie, the state corresponding to the game end scene), a prompt message may be output and the running of the application may be restricted after a preset time length (eg, 10 seconds).

[0114] As an example, the management application or system (eg, Minor Mode module) may provide a prompt in the form of a pop-up window or the like to remind the child user that the game has ended and that the game application will be closed and restrictions will be imposed.

[0115] Figure 5B is a diagram illustrating an example of a graphical user interface for inquiring whether to perform a delay and for requesting an intelligent dynamic delay according to an embodiment of the present disclosure.

[0116] Reference Figure 5B When the time set for the game application arrives, the following output can be obtained: Figure 5B The pop-up window for asking whether to execute the delay is shown in the figure above. The user can click the area corresponding to "Request more time", and in response to the user's click operation, the following may be displayed Figure 5B The pop-up window for requesting intelligent dynamic delay is shown in the figure below. Users can click the options in the pop-up window to select the desired extension time or intelligent dynamic delay as needed.

[0117] Those skilled in the art should understand that Figure 5A and Figure 5B The graphical user interfaces in are merely examples and do not limit the present disclosure. Figure 6 A diagram showing an example of a pop-up window displayed when the delay ends.

[0118] Reference Figure 6 , the user can click "End Directly" or "Go to Save", the game application will be closed within 10 seconds, restrictions will be imposed, and intelligent dynamic delay will not be possible again.

[0119] As an example, a captured screenshot or video of the execution screen of the application may be sent to another electronic device, and whether to restrict the execution of the application may be determined based on response information received from the other electronic device with respect to the screenshot or video.

[0120] For example, when a child uses an electronic device to play a game, if the time set for the game application is reached, a screenshot or video may be sent to, for example, an electronic device used by the child's parents. The child's parents determine that the game is in progress by viewing the screenshot or video, and may send information allowing delay to the electronic device used by the child via another electronic device. The electronic device used by the child may determine not to restrict the running of the application based on the received information, otherwise, it may determine to restrict the running of the application.

[0121] As an example, the electronic device may transmit information about the execution status of the monitored application to another electronic device, and determine whether to restrict the execution of the application based on a response message to the information received from the other electronic device.

[0122] Reference above Figures 1 to 6 A method for controlling an application running in an electronic device according to an embodiment of the present disclosure is described. Figure 7 An electronic device according to an embodiment of the present disclosure is described.

[0123] Figure 7 A block diagram showing a structure of an electronic device 700 according to an embodiment of the present disclosure.

[0124] Reference Figure 7 The electronic device 700 may include a first determining unit 701 and a second determining unit 702. As understood by those skilled in the art, the electronic device 700 may further include other components and at least one of the components included in the electronic device 700 may be combined or split.

[0125] As an example, the first determination unit 701 may be configured to determine the running state of the application when a first time or a second time preset for the application is reached, wherein the second time is earlier than the first time and is a predetermined time length away from the first time.

[0126] As an example, the second determination unit 702 may be configured to determine whether to restrict the operation of the application after the time based on the running state of the application, wherein restricting the operation of the application indicates disabling the application, closing the application, or disabling some functions of the application.

[0127] As an example, the first determination unit 701 can be configured to: obtain a screenshot or video of the running interface of the application, or obtain information about the running status of the application from the application; determine the running status of the application based on the screenshot or video, or the information about the running status of the application.

[0128] As an example, the first determination unit 701 may be configured to: determine the running status of the application based on the screenshot or video using a trained machine learning model.

[0129] As an example, the second determination unit 702 may also be configured to: when it is determined that the running state of the application is a specific running state, determine to restrict the running of the application after a first time; and / or, when it is determined that the running state of the application is not a specific running state, determine not to restrict the running of the application after the first time, monitor the running state of the application after the first time, and determine whether to restrict the running of the application based on the monitored running state or the running time of the application after the first time.

[0130] As an example, the second determination unit 702 may be configured to: if the monitored running state of the application is a specific running state when the running time of the application does not exceed a preset time length, restrict the use of the application and stop monitoring the running state of the application; and / or if the monitored running state of the application is not a specific running state when the running time of the application does not exceed the preset time length and the running time of the application reaches the preset time length, restrict the use of the application and stop monitoring the running state of the application.

[0131] As an example, the application is a game application, and the specific running state indicates a state in which the running screen of the game application corresponds to a game end scene.

[0132] As an example, the second determination unit 702 may be configured to: periodically obtain screenshots or videos of the running screen of the application, or obtain the screenshots or videos according to the occupancy rate of specific hardware of the electronic device, and monitor the running status of the application using the machine learning model based on the screenshots or videos; or obtain information about the running status of the application from the application, and monitor the running status of the application based on the information about the running status of the application.

[0133] As an example, the second determining unit 702 may be configured to: when a change value of an occupancy rate of a CPU or a GPU of the electronic device exceeds a threshold, acquire the screenshot or video.

[0134] As an example, the electronic device 700 may also include: a display unit, configured to output inquiry information for asking whether to extend the running time of the application before determining the running status of the application when a first time pre-set for the application is reached or a second time is reached, and receive user input for determining to extend the running time of the application, wherein the first determination unit is configured to: determine the running status of the application in response to the user's input to the icon.

[0135] As an example, the display unit is configured to display an icon for confirming extension of the application running time, wherein the user input indicates an input to the icon.

[0136] According to an embodiment of the present invention, there is provided an electronic device comprising: at least one processor; and at least one memory storing computer executable instructions, wherein the computer executable instructions, when executed by the at least one processor, cause the at least one processor to execute the method for controlling the running of an application as described herein.

[0137] According to an embodiment of the present disclosure, a computer-readable storage medium storing instructions may also be provided, wherein when the instructions are executed by at least one processor, the at least one processor executes the method for controlling the operation of an application according to an embodiment of the present disclosure. Examples of computer-readable storage media include: read-only memory (ROM), random access programmable read-only memory (PROM), electrically erasable programmable read-only memory (EEPROM), random access memory (RAM), dynamic random access memory (DRAM), static random access memory (SRAM), flash memory, non-volatile memory, CD-ROM, CD-R, CD+R, CD-RW, CD+RW, DVD-ROM, DVD-R, DVD+R, DVD-RW, DVD+RW, DVD-RAM, BD-ROM, BD-R, BD-R, BD-RW, , BD-RE, Blu-ray or optical disk storage, hard disk drive (HDD), solid state drive (SSD), card storage (such as, multimedia card, secure digital (SD) card or extreme digital (XD) card), magnetic tape, floppy disk, magneto-optical data storage device, optical data storage device, hard disk, solid state disk and any other device, any other device is configured to store computer programs and any associated data, data files and data structures in a non-transitory manner and provide the computer programs and any associated data, data files and data structures to a processor or computer so that the processor or computer can execute the computer program. The computer program in the above-mentioned computer-readable storage medium can be run in an environment deployed in a computer device such as a client, a host, an agent device, a server, etc. In addition, in one example, the computer program and any associated data, data files and data structures are distributed on a networked computer system, so that the computer program and any associated data, data files and data structures are stored, accessed and executed in a distributed manner by one or more processors or computers.

[0138] According to an embodiment of the present disclosure, a computer program product may also be provided, and instructions in the computer program product may be executed by a processor of a computer device to complete the method for controlling the running of an application described herein.

[0139] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the specification and practicing the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary techniques in the art that are not disclosed in the present disclosure. The specification and examples are intended to be exemplary only, and the true scope and spirit of the present disclosure are indicated by the following claims.

Claims

1. A method for controlling application execution, the method comprising: When a first time preset for the application is reached or a second time is reached, determining the running state of the application, wherein the second time is earlier than the first time and is a predetermined time length away from the first time; and determining whether to restrict the running of the application after the first time based on the running state of the application, Among them, restricting the operation of the application indicates disabling the application, closing the application, or disabling some functions of the application.

2. The method of claim 1, wherein: The step of determining the running status of the application includes: Obtaining a screenshot or video of the running interface of the application, or obtaining information about the running status of the application from the application; and The running state of the application is determined based on the screenshot or video, or the information about the running state of the application.

3. The method of claim 2, further comprising: When a first time preset for the application is reached or a second time is reached, before determining the running state of the application, an inquiry message for asking whether to extend the running time of the application is output, and a user input for determining to extend the running time of the application is received, The step of determining the running state of the application includes: determining the running state of the application in response to receiving the user input.

4. The method of claim 2, wherein: The step of determining the running status of the application based on the screenshot or video includes: Based on the screenshots or videos, the running status of the application is determined using a trained machine learning model.

5. The method of claim 4, further comprising: When it is determined that the running state of the application is a specific running state, determining to restrict the running of the application after a first time; And / or, when it is determined that the running state of the application is not a specific running state, it is determined that the running of the application is not restricted after a first time, and the running state of the application is monitored after the first time, and it is determined whether to restrict the running of the application based on the monitored running state or the running time of the application after the first time.

6. The method of claim 5, wherein: The step of determining whether to limit the running of the application based on the monitored running state or the running time of the application includes: If the monitored running state of the application is a specific running state when the running time of the application does not exceed a preset time length, restricting the use of the application and stopping monitoring the running state of the application; and / or If the monitored running state of the application is not a specific running state when the running time of the application does not exceed the preset time length and the running time of the application reaches the preset time length, the use of the application is restricted and the monitoring of the running state of the application is stopped.

7. The method of claim 5, wherein: The application is a game application, and the specific running state indicates a state where a running screen of the game application corresponds to a game end scene.

8. The method of claim 5, wherein: The step of monitoring the running status of the application includes: Periodically obtaining screenshots or videos of the running screen of the application, or obtaining the screenshots or videos according to the occupancy rate of specific hardware of the electronic device, and monitoring the running status of the application using the machine learning model based on the screenshots or videos; Alternatively, information about the running state of the application is acquired from the application, and the running state of the application is monitored based on the information about the running state of the application.

9. The method of claim 8, wherein: The step of acquiring the screenshot or video according to the occupancy rate of the specific hardware of the electronic device includes: When the change in the occupancy rate of the CPU or GPU of the electronic device exceeds a threshold, the screenshot or video is acquired.

10. The method of claim 3, wherein: The step of outputting inquiry information for inquiring whether to request extension of the application running time includes: displaying an icon for confirming extension of the application running time, wherein the user input indicates an input to the icon.

11. An electronic device comprising: A first determining unit is configured to: determine the running state of the application when a first time preset for the application is reached or a second time is reached, wherein the second time is earlier than the first time and is a predetermined time length from the first time; and a second determining unit configured to determine whether to restrict the running of the application after a first time based on the running state of the application; Among them, restricting the operation of the application indicates disabling the application, closing the application, or disabling some functions of the application.

12. An electronic device comprising: at least one processor; as well as at least one memory storing computer executable instructions, When the computer executable instructions are executed by the at least one processor, the at least one processor is prompted to execute the method for controlling the running of an application as described in any one of claims 1 to 10.

13. A computer-readable storage medium storing instructions, wherein: When the instructions are executed by at least one processor, the at least one processor is caused to execute the method for controlling application execution as described in any one of claims 1 to 10.

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