Method for intelligently searching and killing Androdi background process through AI and electronic equipment

Abstract

The invention relates to the technical field of electronic communication, in particular to an AI intelligent Android background process searching and killing method and electronic equipment, and the method comprises the steps: starting an AI tracking module, and recording the use frequency, duration and time period of an application; user behavior portraits are constructed and perfected according to the data, and dependence and preference are reflected. And tracking a background application state, and predicting a use interval. If the interval exceeds the threshold value, the system automatically terminates the background process, and the memory is released. According to the method, unimportant background processes can be recognized and cleared in time, smooth operation of the system is guaranteed, action can be performed without waiting for memory shortage, and system jamming or crash is avoided. The AI technology is utilized, a user behavior portrait is constructed, a searching and killing strategy is dynamically adjusted, optimization is carried out along with time, and personalized and efficient background management is provided.

Description

Technical Field

[0001] The present invention relates to the field of electronic communication technology, and in particular to a method and electronic device for intelligently detecting and killing Android background processes using AI. Background Art

[0002] One of the design concepts of Android is that when an application is exited, the process corresponding to the application will not be terminated immediately, but will continue to remain in the system. This design allows the system to respond quickly when the user starts the application again, thereby improving the user experience. However, this design concept also brings a problem: each process has its own independent memory address space. As the number of applications opened by users gradually increases, the memory used by the system will also increase. As a result, the system memory may become increasingly tight, eventually leading to insufficient memory. Therefore, a method is needed to effectively manage all processes and decide when to release the process based on a certain strategy to ensure the stable operation of the system and improve the user experience.

[0003] At present, the mainstream background process management method is to use lmkd (Low Memory Killer Daemon). When the system detects that the memory is tight, lmkd will be activated. It will decide which processes should be killed based on the oom_score_adj value of the process. The higher the oom_score_adj value of the process, the easier it is to be killed when the memory is tight. Since background processes usually no longer interact directly with users, their oom_score_adj values ​​are relatively large, so they are more likely to be killed when the memory is tight. However, this strategy will only trigger the cleanup of background processes when the system memory is really tight. This means that some background processes will continue to occupy memory resources even if the system memory is not tight if they are not used for a long time after being used by the user. This situation will not only reduce the fluency of the system, but also affect the overall user experience. Therefore, how to reasonably manage background processes and release unnecessary memory resources while ensuring user experience has become an urgent problem to be solved in Android system optimization. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention discloses an AI intelligent method for detecting and killing Android background processes and an electronic device, which are used to solve the above problems.

[0005] The present invention is achieved through the following technical solutions:

[0006] In a first aspect, the present invention provides a method for AI intelligently detecting and killing Android background processes, comprising the following steps:

[0007] Start the AI ​​tracking module. When a user opens and uses an app, it records the usage frequency, usage duration, and usage time period of the app.

[0008] Based on the collected data, a detailed behavior profile is constructed and dynamically improved over time. The profile can also reflect the user's dependence on different applications and usage preferences.

[0009] When the user switches the app to the background, the AI ​​module will continue to track the background running status of the app and predict the time interval for the user to use the app next time based on the behavior profile;

[0010] If the predicted time interval exceeds the preset threshold, the system will automatically kill the background process of the application to release memory resources.

[0011] Furthermore, in the method, the preset threshold includes a time threshold and a resource threshold. The time threshold is used to determine how long the user has not used the application before background process killing is performed. The resource threshold is used to determine the current system resource usage. When the resource usage exceeds the preset value, the process will be killed even if the time threshold is not reached.

[0012] Furthermore, in the method, when predicting the time interval for the next time a user uses an application, the AI ​​module adjusts the prediction strategy based on the user's historical usage habits, current time period, and application type factors to improve the accuracy of the prediction.

[0013] Furthermore, the resource usage of background processes is monitored in real time. When it is found that the resource usage of a process exceeds the preset safety threshold, the system will automatically trigger the killing mechanism and send a notification to the user to inform him of the current killing process and the reason, ensuring the user's transparency and control over system operations.

[0014] Furthermore, in the method, a second confirmation is performed before killing the background process, prompting the user whether the application really needs to be closed to avoid accidentally killing an application that the user actually needs.

[0015] Furthermore, in the method, user feedback and usage data are collected every N hours, the method for constructing the behavior profile is optimized through machine learning technology, and the resource threshold is dynamically adjusted according to the hardware configuration and current operating status of the user's device.

[0016] Furthermore, the method records and analyzes the frequency and reasons for the background process being killed, provides optimization suggestions to users, synchronizes data with the cloud server, and uses big data analysis technology to continuously optimize the prediction algorithm of the AI ​​module to improve the accuracy and efficiency of killing.

[0017] Furthermore, in the method, the operating status of the system is checked regularly to ensure that the prediction algorithm and detection and killing strategy of the AI ​​module are always in the best state. When an abnormal situation is detected, it will automatically repair it or remind the user to intervene manually to ensure the stability and reliability of the system.

[0018] Furthermore, the method provides a detailed background process management report, including a list of processes to be killed, cause analysis, and optimization suggestions, and provides an intuitive operating experience through a graphical user interface (GUI). Users can view and manage background processes, and the most appropriate setting options are intelligently recommended based on user operating habits.

[0019] In a second aspect, the present invention provides an electronic device comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the memory is coupled to the processor, and when the processor executes the computer program, the method for AI intelligently detecting and killing Android background processes as described in the first aspect is implemented.

[0020] The beneficial effects of the present invention are:

[0021] The present invention has the function of instantly identifying and clearing background processes that are relatively unimportant to users, ensuring the smooth operation of the system. This process does not need to wait until the system memory resources become tight before starting to execute, thereby effectively avoiding the system freeze or crash caused by insufficient memory.

[0022] By using advanced AI tracking technology, the present invention can build a user's behavior profile and dynamically adjust the killing strategy based on the user's actual behavior. Over time, this strategy will become more and more in line with the user's actual needs, thereby providing a more personalized and efficient background process management experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0024] Figure 1 It is a principle and step diagram of a method for AI intelligently detecting and killing Android background processes;

[0025] Figure 2 It is a flowchart of a method for AI intelligently detecting and killing Android background processes;

[0026] Figure 3It is a functional block diagram of an electronic device. DETAILED DESCRIPTION

[0027] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0028] In one embodiment, please refer to Figure 1 As shown, a method for AI intelligently detecting and killing Android background processes is provided, including the following steps:

[0029] Start the AI ​​tracking module. When a user opens and uses an app, it records the usage frequency, usage duration, and usage time period of the app.

[0030] Based on the collected data, a detailed behavior profile is constructed and dynamically improved over time. The profile can also reflect the user's dependence on different applications and usage preferences.

[0031] When the user switches the app to the background, the AI ​​module will continue to track the background running status of the app and predict the time interval for the user to use the app next time based on the behavior profile;

[0032] If the predicted time interval exceeds the preset threshold, the system will automatically kill the background process of the application to release memory resources.

[0033] In one embodiment, the preset threshold includes two main aspects: a time threshold and a resource threshold. The role of the time threshold is to determine how long a user has not used an application before the system will automatically perform a background process check and kill operation. Specifically, this time threshold can be set according to actual needs so as to release system resources when the user has not used the application for a long time. The resource threshold is used to determine the resource usage of the current system. When the system resource usage exceeds the preset resource threshold, the system will immediately perform a background process check and kill operation, even if the time threshold is not reached, to ensure the reasonable allocation of system resources and the smooth operation of the application.

[0034] To further improve the accuracy of predicting the time interval when a user will next use an app, the AI ​​module takes multiple factors into consideration. First, it analyzes the user's historical usage habits and uses data mining and pattern recognition techniques to find out the patterns and preferences of the user's app usage. Second, the AI ​​module takes into account the current time period, because the user's behavior patterns may be different in different time periods. For example, users may use certain apps more frequently in the morning and evening time periods on weekdays. Finally, the AI ​​module also takes into account the type of app, as different types of apps have different importance and usage frequencies in users' daily lives. By combining these factors, the AI ​​module can dynamically adjust its prediction strategy to improve the accuracy and reliability of the prediction.

[0035] Before actually performing the background process killing operation, the system will perform a secondary confirmation operation. Specifically, the system will send a prompt message to the user, asking the user whether the user really needs to close the application. The purpose of this prompt message is to avoid accidentally killing applications that the user actually needs to continue running. In this way, users can understand the upcoming operations of the system in a timely manner and make choices based on their actual needs. This not only prevents accidental killings, but also improves the user experience and ensures that users will not miss important application notifications or functions due to the system automatically killing background processes.

[0036] In one embodiment, we use advanced AI technology to accurately track the user's usage behavior for various applications and build a detailed user behavior profile based on this. When an application is transferred to the background due to a change in the user's current needs, the system will scientifically predict the time node when the application will be activated next time based on this behavior profile. If the prediction results show that the next usage interval of the application significantly exceeds the normal expectation, the system will take immediate action and intelligently terminate the application's running process in the background, thereby effectively releasing system memory resources and ensuring a smooth and unobstructed overall operating environment.

[0037] When the user re-enables the app at some point in the future, the system will automatically guide it through the necessary initialization process. Given the long time span between two uses, the interference caused by this process to the user's current experience is kept at a low level. It is worth emphasizing that as AI technology continues to deepen user behavior tracking and accumulate data, the constructed user behavior portrait will become more accurate, making the system's management decisions on background processes more in line with the user's actual needs and minimizing the impact on the user's daily operations.

[0038] In one embodiment, referring to Figure 2 The specific scheme shown is as follows:

[0039] Through AI tracking technology, we can track user behavior, build a behavioral profile of users using the app, and continuously improve the behavioral profile over time;

[0040] The user no longer interacts directly with the app and the app goes into the background.

[0041] Based on the behavioral portrait, predict the time interval for the user to use the app next time.

[0042] The predicted interval is compared with the threshold. If it is greater than the threshold, the background process corresponding to the application is killed. The threshold here should be set in advance. The value is also a time interval. The user needs to reinitialize the application when opening it again after the threshold time, but the user does not feel strange. For example, it can be set to 12 hours or 6 hours.

[0043] Clean up background processes immediately to free up memory resources.

[0044] In one embodiment, we will actively collect user feedback and usage data every N hours to better understand user needs and behavior patterns. By using advanced machine learning technology, we continuously optimize the method of building user behavior portraits to more accurately capture user interests and preferences. At the same time, we will also dynamically adjust resource thresholds based on the hardware configuration and current operating status of the user's device to ensure that the device runs in the best condition.

[0045] In addition, we will also record and analyze in detail the frequency and reasons why background processes are killed. With this data, we can provide users with targeted optimization suggestions to help them improve device performance and user experience. At the same time, we will also synchronize data with the cloud server in real time and use big data analysis technology to continuously optimize the prediction algorithm of the AI ​​module to improve the accuracy and efficiency of killing.

[0046] To ensure the stability and reliability of the system, we regularly check the operating status of our system. In this way, we can ensure that the prediction algorithm and killing strategy of the AI ​​module are always in the best condition. Once any abnormality is detected, the system will automatically repair it or remind the user to intervene manually to ensure the stable operation of the system. Through these measures, we are committed to providing users with an efficient, reliable and safe usage environment.

[0047] In one embodiment, real-time monitoring of the resource usage of background processes is a very important function, which can help us discover possible problems in the system in a timely manner. When the system detects that the resource usage of a background process exceeds a preset safety threshold, it will automatically trigger a killing mechanism to ensure the stable operation of the system. In order to ensure the transparency and control of the user over the system operation, the system will also send a notification to the user to inform him of the current killing process and the specific reason.

[0048] In addition, the system will also provide a detailed background process management report, which includes not only a list of processes that have been killed, but also a detailed analysis of the causes and optimization suggestions. In this way, users can not only understand the current status of the system, but also perform corresponding optimization operations based on the suggestions in the report, thereby improving the operating efficiency of the system.

[0049] In order to provide a more intuitive and convenient operating experience, the system also displays the management interface of the background process through a graphical user interface (GUI). Users can view and manage the background process through this interface, which is very intuitive and simple to operate. The system will also intelligently recommend the most appropriate setting options based on the user's operating habits to help users manage the background process more efficiently. In this way, users can not only easily grasp the operating status of the system, but also make personalized settings according to their needs, so as to obtain the best user experience.

[0050] In one embodiment, referring to Figure 3 As shown, the present invention provides an electronic device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, the memory is coupled to the processor, and when the processor executes the computer program, the method of AI intelligently detecting and killing Android background processes described in the first aspect is implemented.

[0051] In summary, the present invention has the function of instantly identifying and clearing background processes that are relatively unimportant to the user, ensuring the smooth operation of the system. This process does not need to wait until the system memory resources become tight to start execution, thereby effectively avoiding system freezes or crashes caused by insufficient memory.

[0052] By using advanced AI tracking technology, the present invention can build a user's behavior profile and dynamically adjust the killing strategy based on the user's actual behavior. Over time, this strategy will become more and more in line with the user's actual needs, thereby providing a more personalized and efficient background process management experience.

[0053] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for intelligently detecting and killing Android background processes using AI, characterized in that: The following steps are involved: Start the AI ​​tracking module. When a user opens and uses an app, it records the usage frequency, usage duration, and usage time period of the app. Based on the collected data, a detailed behavior profile is constructed and dynamically improved over time. The profile can also reflect the user's dependence on different applications and usage preferences. When the user switches the app to the background, the AI ​​module will continue to track the background running status of the app and predict the time interval for the user to use the app next time based on the behavior profile; If the predicted time interval exceeds the preset threshold, the system will automatically kill the background process of the application to release memory resources.

2. The method for AI intelligently detecting and killing Android background processes according to claim 1 is characterized in that: In the method, the preset thresholds include a time threshold and a resource threshold. The time threshold is used to determine how long a user has not used an application before background process detection and killing is performed. The resource threshold is used to determine the current system resource usage. When the resource usage exceeds the preset value, the process will be detected and killed even if the time threshold is not reached.

3. The method for AI intelligently detecting and killing Android background processes according to claim 1 is characterized in that: In the method, when predicting the time interval for the next use of an application by a user, the AI ​​module adjusts the prediction strategy based on the user's historical usage habits, current time period, and application type factors to improve the accuracy of the prediction.

4. The method for AI intelligently detecting and killing Android background processes according to claim 1 is characterized in that: In the method, a second confirmation is performed before killing the background process to prompt the user whether the application really needs to be closed, so as to avoid accidentally killing an application that the user actually needs.

5. The method for AI intelligently detecting and killing Android background processes according to claim 1 is characterized in that: In the method, user feedback and usage data are collected every N hours, the method for constructing the behavior profile is optimized through machine learning technology, and the resource threshold is dynamically adjusted according to the hardware configuration and current operating status of the user's device, where N is a positive integer.

6. The method for AI intelligently detecting and killing Android background processes according to any one of claims 1 to 5, characterized in that: In the method, the frequency and reasons for the background process being killed are recorded and analyzed, optimization suggestions are provided to users, and data is synchronized with the cloud server. By using big data analysis technology, the prediction algorithm of the AI ​​module is continuously optimized to improve the accuracy and efficiency of killing.

7. The method for AI intelligently detecting and killing Android background processes according to claim 1, characterized in that: In the method, the resource usage of background processes is monitored in real time. When it is found that the resource usage of a process exceeds a preset safety threshold, the system will automatically trigger a killing mechanism and send a notification to the user to inform him of the current killing process and the reason, thereby ensuring the user's transparency and control over system operations.

8. The method for AI intelligently detecting and killing Android background processes according to claim 1, characterized in that: In the method, user feedback and usage data are collected every N hours, and the method for constructing behavior portraits is continuously optimized through machine learning technology. The resource threshold is dynamically adjusted according to the hardware configuration and current operating status of the user's device to ensure that the best memory management effect can be achieved on different devices.

9. The method for AI intelligently detecting and killing Android background processes according to claim 1, characterized in that: In the method, a detailed background process management report is provided, including a list of processes to be killed, cause analysis, and optimization suggestions, and an intuitive operation experience is provided through a graphical user interface (GUI). Users can view and manage background processes, and the most appropriate setting options are intelligently recommended based on user operating habits.

10. An electronic device, characterized in that: The invention comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. The memory is coupled to the processor, and when the processor executes the computer program, the method for AI intelligently detecting and killing Android background processes as described in any one of claims 1 to 9 is implemented.

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