Abstract: Embodiments of the present disclosure describe a memory reclaiming method and a terminal. As discussed with respect to the embodiments described herein, the method may include determining, by a terminal according to a preset rule, a target application program in application programs run on a background, where the target application program is an application program that needs to be cleaned. The method may also include freezing, by the terminal, the target application program, and reclaiming data generated during running of a process of the target application program in memory. The method may also include unfreezing, by the terminal when receiving an input triggering instruction for the target application program, the target application program, and running the target application program.

Abstract: This application provides a method for managing a resource in a computer system and a terminal device. The method includes: obtaining data, where the data includes application sequence feature data related to a current foreground application, and the data further includes at least one of the following real-time data: a system time of the computer system, current status data of the computer system, and current location data of the computer system; selecting, from a plurality of machine learning models based on at least one of the real-time data, a target machine learning model that matches the real-time data; inputting the obtained data into the target machine learning model to rank importance of a plurality of applications installed in the computer system; and performing resource management based on a result of the importance ranking.

Abstract: A memory management method includes determining a memory page that needs to be swapped out of a memory, for each memory page that needs to be swapped out, generating, based on the memory page, a work task reclaiming the memory page, and allocating each work task to a dedicated worker thread for execution.

Abstract: This application provides a mobile service upgrade method. In the method, each service kit in a mobile service is independently packaged into an APK based on a plugin-based idea, and then the kit is upgraded in real time when a user uses an application and the application invokes the kit. Compared with a full silent upgrade of the mobile service, this method shortens upgrade time and improves upgrade flexibility. Further, the APK of the kit includes a dependency relationship between the kit and a dependent kit, and the kit and the dependent kit may be upgraded together during upgrading. This avoids an invocation failure caused because a version cannot meet a requirement.

Abstract: This application discloses an image file obtaining method, including: determining a first value of each of N tasks in program source code (501), where the first value is first duration required for executing each of the N tasks or the first value is a ratio of the first duration to second duration, the second duration is duration required for loading each of the N tasks, and N is a positive integer; sorting the N tasks in descending order of the first values (502); and linking the sorted N tasks to obtain an image file (503). According to the method, after downloading the image file, an embedded apparatus successively loads and executes the N tasks, thereby shortening a startup time period of the embedded apparatus.

Abstract: Embodiments of the present disclosure disclose example broadcast message management methods and a broadcast message management apparatus. One example method includes receiving a broadcast message sent by a target application program, determining a user experience assurance priority of the target application program, and determining a target broadcast queue corresponding to the target application program. The broadcast message is saved in the target broadcast queue, and, when a broadcast message scheduling request is received, the broadcast message stored in the target broadcast queue is scheduled according to a target broadcast message scheduling priority corresponding to the target broadcast queue.

Abstract: A method for managing a trigger on a terminal device, and a terminal device are provided. In response to an operation (S301) of setting a trigger by an application program (181), a trigger manager (235) determines a triggering policy of the trigger based on a use habit of a user, characteristic information of the trigger, and characteristic information of the application program. The triggering policy is accelerating triggering, normal triggering, or delaying triggering. Different triggering policies are executed for triggers having different characteristics, to dynamically change timings for the triggers to trigger scheduled tasks. In this way, a malicious trigger configured to keep an application alive or trigger a task that does not match a true will of the user is prevented to reduce resource consumption, and a trigger beneficial to a system or the user can be triggered earlier to improve user experience.

Abstract: An application start method includes obtaining a target application list when a size of free memory is greater than a preset threshold. The target application list is used to store one or more application identifiers of one or more applications whose memory is released. The application start method further includes starting, in the background, a process of an application identified in the target application list.

Abstract: An example of an apparatus is provided. The apparatus includes a communication interface to receive an image of output from a printing device. The apparatus further includes a memory storage unit connected to the communication interface. The memory storage unit is to store the image of the output. The apparatus also includes a preprocessing engine to process the image. In addition, the apparatus includes a selective search engine to define a search area within the image. The selective search engine defines the search area of the image based on a local defect of unknown size. Furthermore, the apparatus includes a classification engine in communication with the selective search engine. The classification engine is to classify the search area for identification of the local defect.

Abstract: A method and related apparatus for restarting an application being closed because of insufficient memory are provided. The method includes storing application identifiers of the applications which are closed when the free memory of the system is insufficient into a target application list, then when determining the free memory of the system is greater than a preset threshold, automatically starting a process of at least one application indicating by the target application lists.

Abstract: A memory management method includes determining a memory page that needs to be swapped out of a memory, for each memory page that needs to be swapped out, generating, based on the memory page, a work task reclaiming the memory page, and allocating each work task to a dedicated worker thread for execution.

Abstract: This application provides an example resource scheduling method, an example apparatus, and an example computer device. According to this method, a resource management and control policy may be determined and implemented with an aim of improving overall user experience. The overall user experience may be determined based on weights of performance, a temperature, and power consumption in a current application scene of a device, and user experience of the performance, the temperature, and the power consumption.

Abstract: A route selection method and apparatus includes a first mesh node for selecting a route obtains energy consumption information of at least two second mesh nodes, where the second mesh node provides a route for the first mesh node; and the first mesh node selects a route according to the energy consumption information of the at least two second mesh nodes.

Abstract: A memory management method and apparatus to resolve a problem that a memory reclaiming rate is relatively low, severely affecting memory allocation performance in a scenario in which system memory is insufficient. The method includes determining a memory page that needs to be swapped out of a memory, for each memory page that needs to be swapped out, generating, based on the memory page, a work task reclaiming the memory page, and allocating each work task to a dedicated worker thread for execution. In this way, a plurality of central processing units (CPUs) process memory reclaiming in parallel, thereby accelerating memory reclaiming, and improving user experience.

Abstract: The present invention relates to a method for identifying parameters of a 10 kV static load model based on similar daily load curves. In the present invention, an optimization model for identifying full-period parameters of the static load model is proposed based on a large number of daily load curves with response characteristic, a structure and constraints of the static load model, and two theoretical basic assumptions about loads. Full-period (including 96 moments) static voltage model parameters of 10 kV loads are given through optimization solution. A rule that active power and reactive power of the loads at each moment change with voltage is obtained. In addition, a change rule of load constituents is obtained. The method delivers good applicability, satisfies actual demands, and is suitable for large-scale static model analyses for 10 kV loads.

Abstract: Embodiments of the present disclosure disclose a channel change method and a related apparatus. The channel change method includes: receiving a live stream of a first channel, where the live stream of the first channel includes N media file sets; separately encoding the live stream of the first channel in different coding manners, to generate both a main stream and a sub stream; and generating a playlist of the first channel according to channel information of the first channel, coding information of the main stream, and coding information of the sub stream. The playlist instructs the client to perform channel change according to the main stream and the sub stream in response to that a client sends a channel change instruction.

Abstract: Embodiments of the present disclosure disclose example broadcast message management methods and a broadcast message management apparatus. One example method includes receiving a broadcast message sent by a target application program, determining a user experience assurance priority of the target application program, and determining a target broadcast queue corresponding to the target application program. The broadcast message is saved in the target broadcast queue, and, when a broadcast message scheduling request is received, the broadcast message stored in the target broadcast queue is scheduled according to a target broadcast message scheduling priority corresponding to the target broadcast queue.

Abstract: Embodiments of the present disclosure disclose a broadcast message management method, including: receiving a broadcast message sent by a target application program; determining a user experience assurance priority of the target application program, and determining a target broadcast queue corresponding to the target application program; saving the broadcast message in the target broadcast queue; and when a broadcast message scheduling request is received, scheduling, according to a target broadcast message scheduling priority corresponding to the target broadcast queue, the broadcast message stored in the target broadcast queue. The embodiments of the present disclosure further disclose a broadcast message management apparatus.

Abstract: This application provides a method for managing a resource in a computer system and a terminal device. The method includes: obtaining data, where the data includes application sequence feature data related to a current foreground application, and the data further includes at least one of the following real-time data: a system time of the computer system, current status data of the computer system, and current location data of the computer system; selecting, from a plurality of machine learning models based on at least one of the real-time data, a target machine learning model that matches the real-time data; inputting the obtained data into the target machine learning model to rank importance of a plurality of applications installed in the computer system; and performing resource management based on a result of the importance ranking.

Abstract: A method for managing a trigger on a terminal device, and a terminal device are provided. In response to an operation (S301) of setting a trigger by an application program (181), a trigger manager (235) determines a triggering policy of the trigger based on a use habit of a user, characteristic information of the trigger, and characteristic information of the application program. The triggering policy is accelerating triggering, normal triggering, or delaying triggering. Different triggering policies are executed for triggers having different characteristics, to dynamically change timings for the triggers to trigger scheduled tasks. In this way, a malicious trigger configured to keep an application alive or trigger a task that does not match a true will of the user is prevented to reduce resource consumption, and a trigger beneficial to a system or the user can be triggered earlier to improve user experience.