System resource configuration method and electronic equipment

Abstract

The invention provides a system resource configuration method. The method comprises the following steps: acquiring operation data of electronic equipment; performing generation processing on the operation data by utilizing the target processing model to obtain a configuration instruction for configuring system resources of the electronic equipment in the current operation time period and the first operation time period; configuring system resources of the electronic equipment at a target moment based on the configuration instruction so as to respectively meet the system resource requirements of the current operation time period and the first operation time period; wherein the first operation time period is a time period later than the current operation time period, and the current operation time period and the first operation time period have the same or different system resource configuration moments.

Description

Technical Field

[0001] The present disclosure relates to the field of electronic technology, and more particularly, to a system resource configuration method and an electronic device. Background Art

[0002] As computing device performance requirements and functional complexity continue to increase, efficient management of system resources has become a key challenge to ensuring device endurance and smooth operation. Existing resource scheduling solutions suffer from significant flaws: First, strategies such as frequency reduction and speed limiting, employed to reduce energy consumption, often result in delayed user response and struggle to adapt to sudden task demands. Second, resource allocation mechanisms based on static rules or simple heuristic algorithms are prone to resource idleness and contention in multitasking environments, and lack the ability to predict nonlinear load changes. Summary of the Invention

[0003] In view of this, the present disclosure provides a system resource configuration method and an electronic device.

[0004] One aspect of the present disclosure provides a system resource configuration method, including: acquiring operating data of an electronic device; generating and processing the operating data using a target processing model to obtain configuration instructions for configuring system resources of the electronic device in a current operating period and a first operating period; configuring the system resources of the electronic device at a target moment based on the configuration instructions to meet the system resource requirements of the current operating period and the first operating period, respectively; wherein the first operating period is a time period later than the current operating period, and the moments for configuring system resources for the current operating period and the first operating period are the same or different.

[0005] According to an embodiment of the present disclosure, generating and processing the operating data using a target processing model includes at least one of the following: obtaining attribute information of the operating data, and calling a target processing model that matches the attribute information from an artificial intelligence processing model deployed on an electronic device to generate and process the operating data; using a target intelligent agent to identify and process the operating data, and based on the identification processing result, calling a corresponding target processing model from an artificial intelligence processing model deployed on an electronic device to generate and process the operating data or the identification processing result.

[0006] According to an embodiment of the present disclosure, generating and processing the operation data using the target processing model also includes: obtaining target interaction data of the target user acting on the electronic device, generating and processing the operation data and the target interaction data using the target processing model, and obtaining configuration instructions.

[0007] According to an embodiment of the present disclosure, a target processing model is used to generate and process operation data and target interaction data, including: identifying the user intention represented by the target interaction data; predicting the system resource requirements of the current operation period and the first operation period based on the user intention and the operation data, so as to generate configuration instructions based on the system resource requirements.

[0008] According to an embodiment of the present disclosure, system resources of an electronic device are configured at a target moment based on a configuration instruction, including: obtaining type information and / or configuration tags of system resources required by the electronic device in the current operating period and the first operating period; determining the usage time of the system resources required for each time period based on the type information and / or configuration tags; configuring the system resources at the usage time; or, configuring the system resources at a corresponding time before the usage time based on the type information and / or configuration tags.

[0009] According to an embodiment of the present disclosure, the system resources of an electronic device are configured at a target moment based on a configuration instruction, including: obtaining the correspondence between the running processes and / or threads of the electronic device and the required system resources and the usage moments corresponding to each system resource based on the configuration instruction; monitoring the running process information and / or running thread information of the electronic device; completing the configuration of the system resources at the moment when the electronic device runs the target process and / or target thread; or, completing the configuration of the system resources at the first moment before the electronic device runs the target process and / or target thread, the first moment being determined based on the usage moments corresponding to each system resource.

[0010] According to an embodiment of the present disclosure, it also includes: reconfiguring the system resources of the electronic device based on target reference data, the target reference data including evaluation data of the electronic device in the current operating period and / or the first operating period, operating environment change data of the electronic device, operating task data of the electronic device, or at least one of user behavior data of the target user.

[0011] According to an embodiment of the present disclosure, the system resources of the electronic device are reconfigured, including at least one of the following: adjusting the priority of the running thread of the electronic device, the core configuration parameters of the processor, the memory configuration parameters, the power consumption parameters, the network parameters, and the display parameters based on evaluation data; adjusting the priority of the running thread of the electronic device, the core configuration parameters of the processor, the memory configuration parameters, the power consumption parameters, the network parameters, and the display parameters based on operating environment change data; adjusting the priority of the running thread of the electronic device, the core configuration parameters of the processor, the memory configuration parameters, the power consumption parameters, the network parameters, and the display parameters based on user behavior data.

[0012] According to an embodiment of the present disclosure, the target processing model is used to generate and process the operating data, including: using the target processing model to predict multiple operating time periods of the operating process and / or thread of the electronic device based on the operating data, and the system resource requirements corresponding to at least some of the operating time periods; wherein the operating time period can represent the time window corresponding to the system resources required by the process and / or thread, and different time window lengths can represent different system resource requirements.

[0013] Another aspect of the present disclosure provides a system resource configuration device, including: a first acquisition module for acquiring operating data of an electronic device; a first processing module for generating and processing the operating data using a target processing model to obtain configuration instructions for configuring system resources of the electronic device in a current operating period and a first operating period; and a first configuration module for configuring the system resources of the electronic device at a target moment based on the configuration instructions to meet the system resource requirements of the current operating period and the first operating period, respectively; wherein the first operating period is a time period later than the current operating period, and the moments for configuring system resources for the current operating period and the first operating period are the same or different.

[0014] Another aspect of the present disclosure provides an electronic device comprising: at least one processor and at least one processing model capable of running on the processor, wherein the processing model can be called by a target application to execute at least part of the system resource configuration method of any of the aforementioned embodiments.

[0015] Another aspect of the present disclosure provides a computer-readable storage medium storing computer instructions, wherein the computer instructions are used to enable a computer to execute the system resource configuration method according to any one of the aforementioned embodiments.

[0016] Another aspect of the present disclosure provides a computer program product, including a computer program / instruction, characterized in that when the computer program / instruction is executed by a processor, the operation of the system resource configuration method of any of the aforementioned embodiments is implemented. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The above and other objects, features and advantages of the present disclosure will become more apparent through the following description of the embodiments of the present disclosure with reference to the accompanying drawings, in which:

[0018] Figure 1 Schematically shows a flow chart of a system resource configuration method according to an embodiment of the present disclosure;

[0019] Figure 2 Another flow chart of the system resource configuration method according to an embodiment of the present disclosure is schematically shown;

[0020] Figure 3Schematically shows a flow chart of generating processing using a target processing model in a system resource configuration method according to an embodiment of the present disclosure;

[0021] Figure 4 A flowchart of resource configuration in a system resource configuration method according to an embodiment of the present disclosure is schematically shown;

[0022] Figure 5 Schematically shows another flow chart of resource configuration in the system resource configuration method according to an embodiment of the present disclosure;

[0023] Figure 6 Schematically shows a flow chart of a training target processing model in a system resource configuration method according to an embodiment of the present disclosure;

[0024] Figure 7 The following schematically shows the overall process framework diagram of the system resource configuration method according to an embodiment of the present disclosure;

[0025] Figure 8 A system architecture diagram schematically illustrates a system resource configuration method according to an embodiment of the present disclosure;

[0026] Figure 9 A block diagram schematically illustrates a system resource configuration apparatus according to an embodiment of the present disclosure; and

[0027] Figure 10 A block diagram of an electronic device suitable for implementing the above-described method according to an embodiment of the present disclosure is schematically shown. DETAILED DESCRIPTION

[0028] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are merely exemplary and are not intended to limit the scope of the present disclosure. In the detailed description below, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of the embodiments of the present disclosure. However, it is apparent that one or more embodiments may also be implemented without these specific details. In addition, in the following description, descriptions of well-known structures and technologies are omitted to avoid unnecessary confusion of the concepts of the present disclosure.

[0029] The terms used herein are only for describing specific embodiments and are not intended to limit the present disclosure. The terms "comprise," "include," etc. used herein indicate the presence of features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0030] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those skilled in the art unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification and should not be interpreted in an idealized or overly rigid manner.

[0031] When expressions such as "at least one of A, B, and C, etc." are used, they should generally be interpreted in accordance with the meaning commonly understood by those skilled in the art (for example, "a system having at least one of A, B, and C" should include but is not limited to a system having A alone, B alone, C alone, A and B, A and C, B and C, and / or A, B, C, etc.).

[0032] In the embodiments of this disclosure, the collection, updating, analysis, processing, use, transmission, provision, disclosure, and storage of all data involved (including, but not limited to, user personal information) comply with relevant laws and regulations, are used for legitimate purposes, and do not violate public order and good morals. In particular, necessary measures have been taken to prevent unauthorized access to user personal information data and to safeguard the security of user personal information, network security, and national security.

[0033] An embodiment of the present disclosure provides a system resource configuration method, including: acquiring operating data of an electronic device; generating and processing the operating data using a target processing model to obtain configuration instructions for configuring system resources of the electronic device in a current operating period and a first operating period; configuring the system resources of the electronic device at a target moment based on the configuration instructions to meet the system resource requirements of the current operating period and the first operating period, respectively; wherein the first operating period is a time period later than the current operating period, and the moments for configuring system resources for the current operating period and the first operating period are the same or different.

[0034] Figure 1 The flowchart of the system resource configuration method according to the embodiment of the present disclosure is schematically shown.

[0035] like Figure 1 As shown, the system resource configuration method may at least include operations S110 to S130.

[0036] In operation S110, operating data of the electronic device is obtained. Operating data may be all relevant data generated by the electronic device during operation, including but not limited to operating application data, operating environment data, executed task data, operating log data, or operating instruction data. Operating data can be obtained by monitoring various indicators and status information of the device and collecting all data related to the current operation and environment of the device. For example, information such as CPU utilization, memory usage, network traffic, and I / O operation frequency can be collected in real time through system monitoring tools as part of the operating data.

[0037] In operation S120, the operating data is generated and processed using the target processing model to obtain configuration instructions for configuring the system resources of the electronic device for the current operating period and the first operating period. The target processing model can be a specific algorithm or machine learning model for analyzing and processing operating data. Generating and processing the operating data using the target processing model refers to analyzing and processing the collected operating data using the specific algorithm or machine learning model to generate instructions that can guide system resource configuration. The instructions can directly control the status of various hardware resources or software resources to ensure that resources are reasonably allocated during different operating periods.

[0038] In operation S130, the system resources of the electronic device are configured at the target time based on the configuration instructions to meet the system resource requirements of the current operating period and the first operating period, respectively. Configuring the system resources of the electronic device at the target time based on the configuration instructions means adjusting the allocation of system resources at a specific time point or time period based on the generated configuration instructions to ensure that the resource requirements of different operating periods are met. The configuration time can be determined based on the operating data or the target reference data and can be performed at the same time or at different times.

[0039] For example, when running a conferencing application during the current runtime, CPU and memory allocation can be adjusted based on configuration instructions, while camera and display parameters can be pre-configured. At the same configuration moment, resources required for the first runtime period can be pre-loaded without affecting the resources required for the current runtime period. At different times, dynamic adjustments can be made based on predicted user behavior or device status, such as invoking AI functions or printing meeting minutes at the end of a meeting.

[0040] The first operating period is a time period later than the current operating period. The time at which system resources are configured for the current and first operating periods may be the same or different. This can mean that resource allocation can be performed at the same or different times, depending on the urgency of the task and resource requirements. The same time can mean pre-loading resources required for the first operating period without affecting the resources required for the current operating period. Different times can be dynamically adjusted based on predicted user behavior or device status.

[0041] For example, while a conference application is running during the current runtime, resources needed for subsequent presentations can be configured simultaneously. Alternatively, after the meeting, resources can be configured for AI function invocation and report generation during the first runtime. By anticipating potential user needs, such as AI functions for meeting summaries or printing meeting minutes, different resources can be deployed to complete tasks at different times before or after the meeting.

[0042] In conjunction with the above description, a specific example is a user engaging in a video conference on a consumer-grade device (such as a smartphone or tablet). During the current runtime, by monitoring user interaction data (such as video switching and screen touches) and the device's physical status (such as battery level), resource allocation is adjusted in real time to ensure smooth operation of the conferencing application. Furthermore, it is predicted that the user may need to present themselves during the first runtime, and camera and display parameters are configured in advance to avoid lags during resource switching.

[0043] In conjunction with the above description, a specific example can be used in a server or cloud computing environment where multiple background tasks (such as 3D rendering and database queries) are running simultaneously. By monitoring the historical data and real-time resource consumption of these processes, a machine learning model can be used to predict future resource requirements. During the current runtime, CPU cores and memory blocks are dynamically allocated to each background task to optimize task execution efficiency. During the first runtime, the resource allocation strategy for each background task is adjusted based on feedback data to improve overall resource utilization.

[0044] According to the disclosed embodiments, this system resource configuration method can effectively improve resource utilization efficiency in electronic devices, reduce lag, optimize user experience, achieve dynamic energy conservation, and extend device battery life. Through intelligent task classification and priority analysis, machine learning-driven resource demand prediction, dynamic load regulation, and resource allocation, this method achieves efficient resource allocation and utilization, adapting to changing load environments.

[0045] Based on the foregoing embodiment, operation S120 may include at least one of operations S210 to S220.

[0046] In operation S210, attribute information of the operational data is obtained, and a target processing model matching the attribute information is invoked from the artificial intelligence processing model deployed in the electronic device to generate and process the operational data. The attribute information may include the type of operational data, such as the type of running application, task type, instruction content and type (e.g., voice, text, or gesture image), and whether it is log data. By analyzing the characteristics of the operational data, the category or type to which it belongs is determined, and the target processing model that matches the category or type is invoked.

[0047] In operation S220, the target agent is used to identify and process the operating data, and based on the identification processing result, the corresponding target processing model is called from the artificial intelligence processing model deployed by the electronic device to generate and process the operating data or the identification processing result. The identification processing result can be the result data obtained by pre-processing the operating data, or it can be the result of scene judgment after scene judgment or usage demand judgment, or the result of classification or statistics of resource information matched by the operating data, etc. Using the target agent to identify and process the operating data means analyzing the content and status of the operating data through the agent to generate an identification result for selecting a suitable processing model. Based on the identification processing result, the corresponding target processing model is called from the deployed artificial intelligence processing model for further processing.

[0048] For example, a user is using the voice assistant function on a smartphone. The system analyzes the attributes of the voice command (such as command type and content) and invokes target processing models related to voice processing to optimize speech recognition and response speed. For example, the target agent performs recognition processing on operational data to obtain recognition results. Based on the recognition results, the agent determines that the user's intention is to query weather information and invokes the corresponding processing model to generate resource allocation instructions to allocate more network bandwidth and ensure fast access to weather data.

[0049] For example, in a server environment, multiple background tasks are performed simultaneously. By analyzing task attributes (such as task type and data format), the target processing model related to data processing is invoked to improve data processing efficiency. For example, the target agent processes the running data and identifies a task requiring high I / O performance. Based on this identification, the target agent invokes the I / O optimization model to allocate more I / O resources to the task, ensuring smooth execution.

[0050] Figure 2 Another flowchart of the system resource configuration method according to an embodiment of the present disclosure is schematically shown.

[0051] like Figure 2 As shown, based on the above embodiment, operation S120 may further include operation S310.

[0052] In operation S310, target interaction data of the target user acting on the electronic device is obtained, and the operation data and target interaction data are generated and processed using the target processing model to obtain configuration instructions. The target interaction data can be interaction information generated by the user when using the electronic device, which can represent the user's intention or needs. The target interaction data can refer to interaction information that can reflect the user's operating behavior and / or emotional state. The interaction information may include but is not limited to behavioral information such as the pressure or frequency of the user's touch operation, the pitch or speed of the voice command, the rate of mouse clicks, or the speed of keyboard input.

[0053] Obtaining target user interaction data on electronic devices involves monitoring and recording user behavior to collect data related to user interaction. This data is then combined with the acquired operational data and analyzed using a target processing model to generate more precise configuration instructions. For example, the target interaction data can be used to identify the user's current emotional state and, based on this emotional state, predict subsequent resource demand priorities, thereby determining the optimal time and plan for resource allocation.

[0054] For example, when a user is playing a game, the target processing model can identify the user's current state of tension or anxiety by analyzing the user's interaction data (such as rapid and forceful screen clicks and suddenly increased voice volume). The target processing model can then combine the current game's operating data (such as high GPU load) to predict that the user is about to enter a critical battle scene, thereby prioritizing the allocation of more CPU and GPU resources to the game application to prevent lag and ensure a smooth experience.

[0055] For example, a user is participating in an online video conference and receives interaction data, such as quick and ineffective mouse clicks and heavy keyboard strokes. Based on this interaction data, the target processing model determines that the user may be experiencing an irritable state due to network latency or unresponsive applications. Combined with the running data of the conference application, the model generates configuration instructions to prioritize network resources and attempt to free up memory resources for some non-core applications, prioritizing the smoothness of the conference application and alleviating the user's negative emotions.

[0056] For example, consider a user engaging in in-depth reading on an e-reader app. By monitoring user interaction data, such as extended periods of touch-free operation, smooth page-turning gestures, and low device movement frequency, the target processing model can determine that the user is in a focused, immersive reading state. Based on this state, the target processing model can combine the app's operating data to generate configuration instructions, proactively lowering the screen refresh rate to save power and suspending non-essential background synchronization tasks and message notifications, thereby creating a distraction-free reading environment for the user and extending device battery life.

[0057] According to the embodiments of the present disclosure, target interaction data that can reflect the user's emotional state is combined with the operation data of the electronic device for processing, so that the electronic device can understand the user's immediate status and potential needs at a deeper level, thereby achieving more accurate and humane resource allocation, reducing the freeze caused by insufficient resources, and thus optimizing the user experience. It can also predict the priority of resource needs and preload or release resources at the appropriate time, further improving resource utilization efficiency.

[0058] Figure 3 The flowchart of generating processing by using a target processing model in the system resource configuration method according to an embodiment of the present disclosure is schematically shown.

[0059] like Figure 3 As shown, based on the above embodiment, operation S310 may include operations S410 to S420.

[0060] In operation S410, the user intent represented by the target interaction data is identified. The user intent can be a specific function that the user wants to perform, a service to obtain, or an emotional state that the user may enter in the current or future period, which is inferred based on the user's interactive behavior. Specifically, identifying the user intent represented by the target interaction data means inferring the ultimate purpose of the user's current operation or the next possible action by analyzing one or a series of continuous user interaction data (such as the semantics of voice commands, the click sequence within the application, and the content of the input text). For example, when a user enters "cinema" in a map application and clicks search, the interaction data is text input and click behavior, so the identified user intent is "find and navigate to a nearby cinema."

[0061] In operation S420, the system resource requirements for the current operating period and the first operating period are predicted based on the user intention and the operating data, so as to generate configuration instructions based on the system resource requirements. The system resource requirements can be the specific type and quantity of hardware or software resources required to be allocated to meet the execution of tasks under specific intentions, such as the number of CPU cores, memory size, network bandwidth, or calling permissions for specific APIs. Specifically, the identified user intention is used as a key predictive factor to adjust and optimize the resource demand judgment based only on the current operating data. User intention can provide direction for predicting future resource usage trends, so that the priority or configuration time of resource calls can be judged more accurately.

[0062] For example, continuing with the previous example, we know the map app is running based solely on runtime data. However, by incorporating the user intent of "navigation," we can predict that during the first runtime period (i.e., after navigation begins), the GPS module, speaker, and screen will need to be used frequently and continuously. Therefore, configuration instructions are generated to preemptively increase the power state of the relevant hardware and ensure its resource supply, rather than passively responding when navigation freezes.

[0063] For another example, a user opens a large spreadsheet in office software and starts entering function formulas. The running data shows that the spreadsheet application takes up a large amount of memory. The user's interaction data is "entering a formula", and then it is identified that the user's intention is to "perform data analysis and calculations." Based on this intention, it is predicted that a high-performance CPU is required to perform calculations in the current running period, and network resources may be required in the first running period to query external data or save to the cloud. Therefore, the generated configuration instructions can instruct to adjust the CPU scheduling mode to performance priority and pre-establish a network connection with the cloud service to ensure smooth calculation and saving processes.

[0064] For example, a user might say "I'm ready to watch a movie" in a smart home control app. Runtime data indicates the app itself has a low load, and the user's interaction data is based on voice commands. The system identifies the user's intent as "creating a viewing environment." Based on this intent, it predicts that multiple device resources will need to be linked and controlled during the current and first runtime periods. The generated configuration commands might include dimming smart lights, closing smart curtains, turning on the TV and audio system, and pre-allocating sufficient network bandwidth and decoder resources for the video playback app.

[0065] According to the embodiments of the present disclosure, by introducing the recognition and prediction of user intentions, the system resource configuration is upgraded from a passive response mode based on the current load to an active prediction mode based on future needs. This can accurately foresee the resources required for the user's next operation and preemptively configure them, thereby significantly reducing delays and freezes during task switching or startup, and achieving seamless connection of the operation process.

[0066] Figure 4 The flowchart of resource configuration in the system resource configuration method according to an embodiment of the present disclosure is schematically shown.

[0067] like Figure 4 As shown, based on the above embodiment, operation S130 may include operations S510 to S530 or S540.

[0068] In operation S510, type information and / or configuration tags of system resources required by the electronic device during the current operating period and the first operating period are obtained. Type information refers to the specific category of the required system resources, such as CPU, GPU, memory, network module, sensor, etc. Configuration tags refer to metadata attached to the type information, which may include but are not limited to the priority of the system resource call, the estimated time required to complete the resource configuration, or a Boolean flag indicating whether pre-configuration is required. Obtaining type information and / or configuration tags of system resources means, after determining which resources are required, further classifying and tagging these resources to provide a decision basis for subsequent precise timing configuration. For example, for a 3D game application that is about to be launched, the GPU resource it requires has type information of "GPU" and its configuration tag may be marked as {Priority: High, Time Required for Configuration: 200ms, Pre-configuration: Yes}; while the storage IO resource required to read archived files may have a tag of {Priority: High, Time Required for Configuration: 5ms, Pre-configuration: No}.

[0069] In operation S520, the usage time of the system resources required for each time period is determined based on the type information and / or configuration tags. The usage time can be the point in time when the system resources need to be actually used by the upper-level application or task. Determining the usage time based on the type information and / or configuration tags can be based on the application's operating logic or the user's intention to predict at which specific time point in the future each marked system resource will be called. For example, when the user clicks the "Start Game" button, the system determines that this moment is the "usage time" of the GPU and storage IO resources.

[0070] In operation S530, the system resources are configured at the time of use. Configuring system resources at the time of use means that the allocation or status switching of resources is immediately executed at the moment when the application or task needs the resource. This method is suitable for system resources with extremely low or negligible configuration delay. For resources with "Advance Configuration" as No or "Configuration Time Required" as Very Short in the configuration tag, the configuration instruction will be triggered when the predicted "use time" arrives to achieve instant allocation. Continuing with the above example, for storage IO resources, because the time required for their configuration is only 5ms, the system will synchronize the configuration of the IO channel at the "use time" when the user clicks "Start Game", and the user will hardly be aware of it.

[0071] Alternatively, in operation S540, system resources are configured at a corresponding time before the actual time of use based on the type information and / or configuration tag. The corresponding time refers to an earlier time point calculated based on the "time of use" and the "configuration required duration." For example, the corresponding time = time of use - configuration required duration. Configuration is performed at a corresponding time before the actual time of use based on the type information and / or configuration tag. This proactively considers the physical time required for certain hardware resources to transition from dormancy to activation. By querying the "configuration required duration" in the configuration tag, the system calculates how far in advance configuration instructions should be executed to ensure that the resource is available when the actual "time of use" arrives. Continuing with the previous example, for a GPU resource, its configuration tag indicates a 200ms configuration duration. Therefore, GPU initialization, rendering pipeline establishment, and other configuration operations will begin at the "corresponding time," 200ms before the "time of use" when the user clicks "Start Game." This "corresponding time" may have already begun when the user enters the game's main menu.

[0072] For example, a user is using the camera app to take a photo. The current runtime is the viewfinder preview. The first runtime is the photo processing and saving after the shutter button is pressed. The required resource type information and configuration tags are obtained: camera sensor (configuration time: 50ms, requires pre-configuration), image signal processor (ISP) (configuration time: 5ms, no pre-configuration required), flash (configuration time: 100ms, requires pre-configuration), and memory (configuration time: 1ms, no pre-configuration required). When the user opens the camera app, the "use moment" is determined to be the moment the user presses the shutter button. Therefore, operation S540 is immediately executed to preheat and charge the camera sensor and flash. When the user presses the shutter button (use moment), operation S530 is executed again, instantly invoking the ISP and memory to complete processing and saving, thus achieving a zero-latency shutter.

[0073] For example, a user is using a voice assistant to set up navigation on the car's computer. The current runtime is voice recognition, and the first runtime is the start of route planning and navigation. Resource type information and configuration tags are obtained: microphone array (configuration time: 2ms, no pre-configuration required), network module (configuration time: 300ms, pre-configuration required), and GPS module (configuration time: 500ms, pre-configuration required). When the user utters the wake-up word, the microphone array is instantly configured (operation S530) to receive the command. After recognizing the user intent of "navigating to the company," route planning is determined to be at the "time of use." Therefore, operation S540 is immediately executed to pre-start and warm up the network and GPS modules simultaneously with voice recognition. When voice recognition is completed and the route planning "time of use" begins, both the network and GPS modules are ready, enabling immediate data requests and positioning, significantly reducing user wait time.

[0074] According to the disclosed embodiments, by attaching configuration tags to different types of system resources and distinguishing between configuration at the "time of use" and pre-configuration at the "corresponding time," this method implements a refined resource scheduling strategy that considers physical latency. This strategy effectively hides time-consuming hardware initialization processes and combines parallel preprocessing with immediate invocation, eliminating the user-perceived waits and pauses during application startup or function switching, resulting in a smooth and responsive interactive experience and significantly improving the overall operational efficiency and responsiveness of electronic devices.

[0075] Figure 5 Another flowchart of resource configuration in the system resource configuration method according to an embodiment of the present disclosure is schematically shown.

[0076] like Figure 5 As shown, based on the above embodiment, operation S130 may include operations S610 to S630 or S640.

[0077] In operation S610, based on the configuration instructions, the correspondence between the running processes and / or threads of the electronic device and the required system resources, as well as the corresponding usage time of each system resource, is obtained. A correspondence is a clear mapping that binds one or more specific running processes or threads to one or more system resources necessary for their execution. Obtaining the correspondence between the running processes and / or threads and the required system resources means that the system parses the configuration instructions and generates a "resource requirement list" that details which process (e.g., game.exe) or which thread (e.g., the rendering thread of game.exe) requires which system resource (e.g., GPU) and when it is expected to be used. For example, for a video playback application, the system can establish the following correspondence: the decoding thread of the player main process -> hardware video decoder resources, usage time: T1; the network thread of the player main process -> high-priority network bandwidth resources, usage time: T0.

[0078] In operation S620, the running process information and / or running thread information of the electronic device is monitored. The running process information and / or running thread information may refer to the status of the process / thread maintained by the operating system kernel, such as "running", "ready", "waiting", "created" or "terminated". Monitoring the running process information and / or running thread information may be a system scheduler or a dedicated monitoring service that continuously tracks the execution status of the target process or thread so as to use its state change as an event to trigger resource allocation. For example, the status of the decoding thread of a video player is continuously monitored. When the thread changes from "waiting" (waiting for data) to "ready" (data has arrived, ready to run), the monitoring service will capture this state change.

[0079] In operation S630, the configuration of system resources is completed at the moment when the electronic device runs the target process and / or target thread. The moment when the electronic device runs the target process and / or target thread refers to the moment when the operating system scheduler actually allocates the execution right of the CPU to the target process or thread. This operation refers to an "instant" or "synchronous" resource configuration mode. When it is monitored that the target thread is awakened by the scheduler and starts to execute on the CPU, the instantaneous configurable resources specified in its corresponding relationship are immediately allocated to it. For example, when the input processing thread of a text editor is awakened to process keyboard input, the memory buffer resources are configured for it at the same moment when the thread obtains the CPU execution right.

[0080] Alternatively, in operation S640, system resource configuration is completed at a first moment before the electronic device runs the target process and / or target thread. The first moment is determined based on the corresponding usage moment of each system resource. The first moment is a calculated time point that precedes the actual execution of the target process / thread. This time point is intended to ensure that resources with configuration delays are ready when the target process / thread needs them. Operation S640 represents a "prefetch" or "asynchronous" resource configuration mode. When the target process or thread is monitored to be scheduled (for example, its status changes to "Ready" and its priority is high), its corresponding relationship is searched. If a configuration delay is found for the required resources, the resource configuration is immediately initiated, rather than waiting for the thread to actually run. Continuing with the aforementioned video playback example, when the decoding thread is monitored to change to the "Ready" state due to data arrival, it is predicted that the thread will be scheduled. At this point, operation S640 is immediately executed to power on and initialize the hardware video decoder, as this process takes a certain amount of time. This ensures that the hardware decoder is already in a usable state when the decoding thread actually obtains CPU execution rights.

[0081] For example, a user launches a large-scale 3D game. In operation S610, a correspondence between the game rendering thread and GPU resources is established. In operation S620, the game rendering thread is monitored to be created and enter the "ready" queue. Because it takes time for the GPU to wake up from low-power mode, operation S640 is executed to increase the GPU frequency and power supply at the first moment before the rendering thread is actually selected by the scheduler to run. Finally, when the rendering thread starts executing the first instruction, the GPU resources have been configured and can be put into use immediately, thus avoiding screen freezes when the game starts.

[0082] For example, a user clicks to record a short video in a social networking app. The user's click event wakes up the app's camera data acquisition thread. This thread corresponds to the camera sensor and microphone resources. Because these resources are configured with minimal latency, operation S630 is executed. The camera and microphone are enabled and the data streams are configured synchronously with the moment the camera data acquisition thread obtains CPU execution power, ensuring that recording begins immediately without delay.

[0083] According to the disclosed embodiments, resource configuration triggers are deeply bound to the execution status of specific processes or threads, enabling more refined, event-driven, and real-time resource management. This ensures that resources are only configured when the software entity that truly needs them is about to run, thereby improving the accuracy of resource allocation and reducing resource usage and energy waste caused by incorrect predictions. By monitoring process / thread status to perform synchronous or asynchronous configuration, complex multi-tasking scenarios can be more effectively addressed, ensuring the responsiveness and smoothness of critical tasks.

[0084] Based on the above embodiment, the system resource configuration method may further include operation S710.

[0085] In operation S710, system resources of the electronic device are reconfigured based on target reference data, where the target reference data includes evaluation data of the electronic device in the current operating period and / or the first operating period, operating environment change data of the electronic device, operating task data of the electronic device, or at least one of user behavior data of the target user.

[0086] Target reference data refers to various types of real-time information used to evaluate the effectiveness of the initial resource configuration and trigger dynamic adjustments after it takes effect. Evaluation data can be quantitative scores (such as fluency scores and lag rates) or qualitative conclusions (such as "video call quality is good") generated by the system performance monitor; operating environment change data can refer to changes in the device's physical or network environment, such as reduced battery life, the network signal switching from Wi-Fi to cellular data, or dimming of ambient light; running task data can refer to changes in the execution status of the current task, such as a background download task nearing completion or a game entering the actual play phase from the loading phase; and user behavior data can refer to the user's ongoing interactive behavior after the configuration takes effect, such as quickly swiping the screen, switching application windows, or satisfaction or dissatisfaction with the current experience, as indicated by the user's facial expressions and tone of voice perceived by the camera or microphone.

[0087] Reconfiguring electronic device system resources based on target reference data involves establishing a continuous monitoring and feedback loop after completing a prediction-based resource configuration. By continuously acquiring at least one of the aforementioned types of target reference data, the system dynamically and in real time verifies that the current resource allocation strategy remains optimal. If the monitored target reference data indicates a mismatch in the existing configuration (e.g., user impatience or battery exhaustion), the original configuration instructions are immediately modified, overwritten, or supplemented to adapt to the new situation.

[0088] For example, standard decoder resources are initially configured for video playback. Subsequently, the performance monitor generates evaluation data indicating that the decoding frame rate of the current video stream is consistently lower than the playback frame rate, which is known as "stuttering." Based on this evaluation data, a reconfiguration is performed to allocate a higher-performance CPU core to the decoding process to eliminate the stuttering.

[0089] For another example, the corresponding network and codec resources have been configured for the user's video call task. During the call, the user walks from a quiet room to a noisy street. At this time, data on the change in the operating environment is obtained, that is, the ambient noise picked up by the microphone has increased sharply. Based on this data, a reconfiguration will be performed. On the basis of the original configuration, an additional AI noise reduction processing unit will be loaded and enabled, and computing power resources will be allocated to it to filter out background noise and ensure call quality. At the same time, if the battery level is detected to be less than 20%, another reconfiguration may be performed to appropriately reduce the video resolution to save power.

[0090] For example, a news app has been configured with a lower screen refresh rate and CPU frequency to save energy. While reading, the user encounters interesting content and quickly swipes the screen to find related comments. This user behavior data is obtained and interpreted as the user's desire for a smoother browsing experience. Based on this user behavior data, a reconfiguration is performed to immediately increase the screen refresh rate to the maximum and temporarily increase the CPU frequency to speed up UI rendering. When the user's swiping behavior stops and returns to a normal reading rhythm, the system can be reconfigured again to return system resources to energy-saving mode.

[0091] According to the disclosed embodiments, by introducing a reconfiguration mechanism based on target reference data, this method adds a dynamic, real-time closed-loop feedback correction capability to the initial predicted configuration. This eliminates the need for a one-time, static resource configuration and instead enables an intelligent process that continuously and adaptively optimizes based on actual operational performance, environmental changes, and real-world user feedback. This significantly improves the accuracy and robustness of system resource configuration, effectively addresses prediction deviations and various unexpected complex scenarios, and ensures that electronic devices provide the best balance of performance and user experience in all situations.

[0092] Based on the foregoing embodiment, operation S710 may further include at least one of operations S810 to S830.

[0093] At operation S810, at least one of the priority of the electronic device's running threads, processor core configuration parameters, memory configuration parameters, power consumption parameters, network parameters, and display parameters is adjusted based on the evaluation data. Specifically, processor core configuration parameters may refer to the CPU core frequency, scheduling policy, or the allocation of large and small cores; power consumption parameters may refer to the device's overall power limit or the power status of specific components; network parameters may refer to bandwidth allocation or connection protocol selection; and display parameters may refer to the screen's refresh rate or brightness. Adjustment based on evaluation data refers to automatically selecting and modifying one or more specific underlying system parameters based on pre-set rules or models, using a quantitative performance evaluation (such as frame rate) or a qualitative user experience conclusion (such as "stuttering") as input.

[0094] For example, when the evaluation data generated by the performance monitor is "the game frame rate is lower than 30FPS and rated as lag", the core configuration parameters of the processor can be automatically adjusted, such as switching two energy-efficiency cores (small cores) to one performance core (large core) to process the main thread of the game to improve the frame rate.

[0095] In operation S820, at least one of the priority of the running thread, the core configuration parameters of the processor, the memory configuration parameters, the power consumption parameters, the network parameters, and the display parameters of the electronic device is adjusted based on the operating environment change data. The explanations of the running thread priority, the core configuration parameters of the processor, the memory configuration parameters, the power consumption parameters, the network parameters, and the display parameters have been described above and will not be repeated here. Adjustment based on operating environment change data means that the system uses external environmental information (such as light intensity and network type) obtained from sensors or system services as a trigger condition and adjusts the system parameters most relevant to the environmental change.

[0096] For example, when the operating environment change data reported by the ambient light sensor shows that the ambient brightness drops from 500 lux to 10 lux (that is, the user moves from outdoors to indoors), the display parameters can be automatically adjusted to reduce the screen brightness from 80% to 30%, and the power consumption parameters may be adjusted at the same time to enter a more power-saving mode.

[0097] In operation S830, at least one of the priority of the running thread of the electronic device, the core configuration parameters of the processor, the memory configuration parameters, the power consumption parameters, the network parameters, and the display parameters is adjusted based on the user behavior data. Adjustment based on user behavior data means taking the user's direct operation or indirectly represented emotional state as an implicit instruction, and inferring the user's demand for current performance based on this, so as to proactively or responsively adjust the relevant system parameters. For example, when the user behavior data is monitored as continuously and rapidly sliding the screen at a speed higher than a threshold in a news application, it is inferred that the user's intention is to browse quickly rather than read carefully. To this end, the priority of the running thread will be adjusted, the priority of the UI rendering thread will be increased, and the core configuration parameters of the processor will be adjusted to instantly increase the CPU frequency to ensure the smoothness of the sliding process.

[0098] For example, a user is in the middle of a video call. The initial configuration is complete. During the call, the user switches the phone from a Wi-Fi environment to a cellular network environment, obtains data on changes in the operating environment, and executes operation S820 to adjust network parameters and enable a more aggressive packet loss retransmission strategy. Subsequently, the camera captures user behavior data showing the user frowning frequently, and it is inferred that the user is dissatisfied with the image quality. Operation S830 is executed to adjust the processor's core configuration parameters and allocate more computing power to the video encoding thread. Finally, the evaluation data shows that the MOS (mean opinion score) of the video call is lower than the preset threshold. Operation S810 is executed to adjust the network parameters again and request a higher network service quality level to comprehensively improve the call experience.

[0099] For example, a user is playing a game on a laptop computer that is not connected to a power source. Data on operating environment changes is obtained, indicating that the battery charge is below 20%. Operation S820 is executed to adjust power consumption parameters, limiting the overall TDP (thermal design power) to a lower level. Display parameters are also adjusted, reducing the screen refresh rate to 60Hz. At this point, evaluation data shows that the game frame rate is stable and within a playable range. However, the user then enters a crucial boss battle, and their user behavior data indicates extremely high-frequency keyboard and mouse operations. Based on this behavior, operation S830 is executed, determining that the user requires extreme performance. Power consumption parameters are temporarily adjusted, the TDP limit is lifted, and the processor's core configuration parameters are adjusted, overclocking all performance cores to ensure a smooth experience during critical moments. Energy-saving configurations are restored after the user's operation frequency decreases.

[0100] Based on the above embodiment, generating and processing the operating data using the target processing model may include operation S910.

[0101] In operation S910, a target processing model is used to predict multiple runtime periods of the running processes and / or threads of the electronic device based on the running data, as well as the system resource requirements corresponding to at least some of the runtime periods. The runtime period can represent the time window corresponding to the system resources required by the process and / or thread, and different time window lengths can represent different system resource requirements. The runtime period can be a time interval abstracted from the logical function stage of the process or thread (for example, the game loading stage, the game running stage) that requires specific system resources. A runtime period is defined by its starting time, duration (i.e., the time window length), and the type and amount of system resources required. For example, a short time window may correspond to a sudden IO read demand, while a longer time window may correspond to a continuous GPU rendering demand.

[0102] Specifically, using the target processing model to predict runtime periods and their corresponding system resource requirements based on operational data means the system doesn't just predict the next step of a task. Instead, it uses a pre-trained model to generate a timeline or state diagram of the task's future execution path based on operational data such as the current application state and historical operating patterns. For each or several key runtime periods along this path, the model estimates the type and quantity of resources, such as CPU, memory, and network, required, as well as the duration of the required time window.

[0103] For example, when a user imports a 4K video clip into video editing software, the target processing model can predict multiple subsequent runtime periods based on the clip's resolution, length, and other runtime data. The first period corresponds to "decoding and thumbnail generation," characterized by high CPU and I / O resource requirements within a medium-length window. The second period corresponds to "user editing and adding special effects," characterized by high GPU and memory resource requirements within multiple discrete, short time windows. The third period corresponds to "final video rendering and export," characterized by sustained high CPU resource requirements within a very long time window.

[0104] According to the embodiments of the present disclosure, by predicting the system resource requirements of a process or thread in multiple future runtime periods and characterizing them in the form of time windows, resource prediction is upgraded from single-point, discrete event prediction to continuous, temporal insight into the overall picture of task resource consumption. By identifying different resource demand patterns corresponding to different time window lengths, more forward-looking and global resource planning can be performed. For example, power consumption and heat dissipation assessments can be performed in advance for an upcoming, long-term, high-load runtime period, or aggregate optimization can be performed for multiple intensive, short-time window resource requests, thereby achieving more refined resource scheduling, effectively avoiding resource conflicts and performance bottlenecks, and significantly improving the overall operating efficiency and stability of the system while ensuring the end-to-end smoothness of complex tasks.

[0105] According to an embodiment of the present disclosure, the target processing model is constructed based on a nonlinear regression algorithm. A nonlinear regression algorithm may refer to a type of machine learning or statistical model that can establish a nonlinear relationship between input variables and output variables. In an embodiment of the present disclosure, the input variables may include operational data and / or target interaction data, and the output variable may be a predicted value for future system resource requirements. For example, a nonlinear regression algorithm may include, but is not limited to, random forest regression, support vector regression, or gradient boosting decision trees.

[0106] The target processing model is constructed based on a nonlinear regression algorithm, which can refer to the selection and use of nonlinear models such as random forest regression to process operational and interaction data characterized by dramatic instantaneous fluctuations and non-fixed behavioral patterns. Unlike traditional time series models, which are suitable for continuous and highly regular sequence data, the nonlinear regression algorithm used in the embodiments of the present disclosure can more effectively capture the sudden nature of user operations and the non-periodic changes in system load. Thus, even in the absence of obvious historical patterns, it is still possible to learn from the current multidimensional data features and infer future resource requirements.

[0107] For example, when users browse information streaming apps, their behaviors (such as swiping quickly, dwelling on a video, and clicking on a comment) are highly random and non-periodic. A target processing model built on the random forest regression algorithm can use current scrolling speed, dwell time, CPU load, network type, and other features as input to predict the probability that a user will click and play a high-definition video within the next second, as well as the network bandwidth and decoder resources required for this action. By combining the judgment results of multiple decision trees, this model can effectively handle the complex nonlinear relationships between these features, and its prediction accuracy is significantly better than time series models that attempt to identify fixed temporal patterns.

[0108] Figure 6 The flowchart of the training target processing model in the system resource configuration method according to an embodiment of the present disclosure is schematically shown.

[0109] like Figure 6 As shown, based on the above embodiment, the training process of the target processing model may include operations S1010 to S1040.

[0110] In operation S1010 , at least one first parameter of an initial model is determined, each first parameter having at least one preset value.

[0111] In operation S1020, preset value combinations of the first parameter are enumerated.

[0112] In operation S1030, in the case of each preset value combination, the initial model is trained and the second parameter of the target processing model is adjusted to obtain a plurality of intermediate models, wherein the first parameter is different from the second parameter.

[0113] In operation S1040 , the intermediate model with the best performance among the intermediate models is verified based on the verification set and is used as the target processing model.

[0114] First parameters can refer to model hyperparameters—parameters set before the learning process begins that control the learning process itself, rather than being acquired through training. Second parameters can refer to internal model parameters, such as weights or biases, that are determined by data learning during training. A preset value combination refers to a specific set of hyperparameter configurations formed by permuting and combining all preset values ​​for each first parameter. An intermediate model refers to a model instance trained using a specific preset value combination.

[0115] First, a set of hyperparameters (first parameters) with a significant impact on performance are selected for the target processing model, and a discrete list of candidate values ​​(preset values) is defined for each hyperparameter. Then, by enumerating all possible combinations of candidate values, a hyperparameter "grid" is formed. Next, for each combination point in the grid, a complete model is trained using that combination's hyperparameter configuration. During training, the model autonomously learns its internal parameters (second parameters), resulting in an "intermediate model." Finally, all trained intermediate models are evaluated on an independent validation set. The intermediate model with the best performance (e.g., the lowest prediction error) is selected as the target processing model for final deployment.

[0116] Assume that the target processing model uses the random forest regression algorithm. In operation S1010, two first parameters (hyperparameters) are determined: the number of decision trees (n_estimators) and the maximum depth of the decision tree (max_depth). Preset values ​​are set for each: n_estimators = [100, 200, 300] and max_depth = [10, 20]. In operation S1020, a total of 3 * 2 = 6 preset value combinations are enumerated: (100, 10), (100, 20), (200, 10), (200, 20), (300, 10), and (300, 20). In operation S1030, the system uses these six combinations to train six different random forest models, generating six intermediate models. In operation S1040, these six intermediate models are tested on the same validation set, and their mean squared errors are calculated. If the intermediate model corresponding to the combination (200, 20) is found to have the smallest mean square error, then this model will be selected as the final target processing model.

[0117] Figure 7 The overall process framework diagram in the system resource configuration method according to an embodiment of the present disclosure is schematically shown.

[0118] In combination with one or more of the above embodiments, the overall process framework is as follows: Figure 7 As shown in the figure, the MonitorDriver can be used to monitor the underlying operating data of electronic devices in real time, such as processor usage, memory access frequency, and network throughput. The MonitorDriver sends the monitored raw data to the "Various Resource Usage Calculation" module. The "Various Resource Usage Calculation" module processes, aggregates, and quantifies the raw data to obtain structured operating data, such as average CPU utilization and peak memory usage within a specific time window.

[0119] On the one hand, structured operational data can be sent to the "Data Archiving" module for storage, forming a training dataset containing a large number of historical samples. On the other hand, after model deployment, this data can serve as real-time input to the model. In one embodiment of the present disclosure, the target processing model can be constructed using the nonlinear regression algorithm "Random Forest". This model is composed of multiple decision trees and can effectively handle the nonlinear and highly volatile characteristics of operational data.

[0120] To improve the model's predictive accuracy, a "grid search" approach can be used to systematically optimize the model's hyperparameters. As shown in the figure, multiple preset values ​​can be set for different model hyperparameters (for example, C and gamma in the example), and all combinations of these values ​​can be enumerated. For each combination, an intermediate model is trained using the training dataset, and the intermediate model with the best performance on the validation set is selected as the final random forest model for deployment.

[0121] The trained random forest model can receive real-time output data from the "Various Resource Usage Calculation" module and predict future system resource requirements. The model's predictions are then exported to the "AppProfile database" to update or generate performance profiles for different applications in different scenarios. Furthermore, these predictions ultimately guide the "Resource Scheduling" module to generate and execute precise system resource allocation instructions, thereby dynamically optimizing system performance and improving user experience.

[0122] Figure 8 The system architecture diagram of the system resource configuration method according to an embodiment of the present disclosure is schematically shown.

[0123] like Figure 8 As shown, the system architecture can include three core functional modules: a task priority analysis module, a prediction and caching module, and a dynamic load regulation module.

[0124] Specifically, the task priority analysis module determines the relative importance of the current task in real time. This module receives a variety of input data, such as user interaction data (such as clicks and swipes), window information (such as the status of foreground and background windows), and network communication data (such as data transmission and reception status). This data is comprehensively analyzed and quantified by a data statistics unit, generating an output task priority. This task priority is then passed to the dynamic load control module as an important basis for resource scheduling.

[0125] The prediction and caching module is used to predict future resource needs based on historical and current behavior. At its core, this module is a task behavior learning unit that learns and models the execution behavior of one or more tasks. The learning results guide the user behavior prediction unit to predict the user's next likely action. Furthermore, these learning results, along with historical data, are used in the data caching unit to pre-load potentially needed data or resources into the cache, reducing latency for subsequent access.

[0126] The dynamic load control module is used to implement specific, refined resource allocation. This module first uses a system resource monitoring unit to monitor the load of key hardware components, such as the central processing unit (CPU), graphics processing unit (GPU), memory (Mem), and input / output (I / O), in real time. This monitoring data, along with the task priorities from the task priority analysis module, is fed into a dynamic threshold adjustment unit. Based on this input, this unit dynamically adjusts resource allocation policies and thresholds and generates specific control instructions. These instructions are distributed to multiple lower-level control units to achieve refined management of system resources. For example, the window style control unit adjusts window display effects or animations. The screen brightness control unit adjusts screen brightness based on task requirements or the environment. The network access control unit manages network bandwidth and connection priorities for different tasks. The window refresh rate control unit dynamically adjusts the screen refresh rate to balance smoothness and power consumption. The WinJob unit can refer to a specific task scheduling or process management unit, which adjusts the execution priority or resource quota of a process.

[0127] Figure 9 A block diagram schematically shows a system resource configuration apparatus according to an embodiment of the present disclosure.

[0128] like Figure 9 As shown, the system resource configuration device 1100 may include a first acquisition module 1110 , a first generation module 1120 , and a first configuration module 1130 .

[0129] The first acquisition module 1110 is used to acquire the operation data of the electronic device. In some embodiments, the first acquisition module 1110 can be used to perform operation S110 in the above-mentioned system resource configuration method, which will not be described in detail here.

[0130] The first generation module 1120 is used to generate and process the operation data using the target processing model to obtain configuration instructions for configuring the system resources of the electronic device in the current operation period and the first operation period. In some embodiments, the first generation module 1120 can be used to perform operation S120 in the above-mentioned system resource configuration method, which is not described in detail here.

[0131] The first configuration module 1130 is configured to configure the system resources of the electronic device at a target time based on the configuration instruction to meet the system resource requirements of the current operating period and the first operating period, respectively, wherein the first operating period is a time period later than the current operating period, and the time for configuring the system resources for the current operating period and the first operating period is the same or different. In some embodiments, the first configuration module 1130 can be used to perform operation S130 in the above-mentioned system resource configuration method, which is not further described here.

[0132] According to the embodiments of the present invention, any number of modules, sub-modules, units, and sub-units, or at least part of the functions of any number of them, can be implemented in one module. According to the embodiments of the present invention, any one or more of the modules, sub-modules, units, and sub-units can be split into multiple modules for implementation. According to the embodiments of the present invention, any one or more of the modules, sub-modules, units, and sub-units can be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on a chip, a system on a substrate, a system on a package, an application-specific integrated circuit (ASIC), or can be implemented by hardware or firmware in any other reasonable way of integrating or packaging the circuit, or can be implemented in any one of the three implementation methods of software, hardware, and firmware, or in any appropriate combination of any of them. Alternatively, according to the embodiments of the present invention, one or more of the modules, sub-modules, units, and sub-units can be at least partially implemented as a computer program module, which can perform the corresponding functions when the computer program module is executed.

[0133] For example, any multiple of the first acquisition module 1110, the first generation module 1120, and the first configuration module 1130 can be combined into a single module / unit / sub-unit, or any one of these modules / units / sub-units can be split into multiple modules / units / sub-units. Alternatively, at least part of the functionality of one or more of these modules / units / sub-units can be combined with at least part of the functionality of other modules / units / sub-units and implemented in a single module / unit / sub-unit. According to an embodiment of the present disclosure, at least one of the first acquisition module 1110, the first generation module 1120, and the first configuration module 1130 can be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on a chip, a system on a substrate, a system on a package, an application-specific integrated circuit (ASIC), or can be implemented in hardware or firmware through any other reasonable means of circuit integration or packaging, or can be implemented in any one of software, hardware, and firmware, or any appropriate combination of any of these. Alternatively, at least one of the first acquisition module 1110 , the first generation module 1120 , and the first configuration module 1130 may be at least partially implemented as a computer program module, and when the computer program module is executed, the corresponding function may be executed.

[0134] It should be noted that the data processing system part in the embodiments of the present disclosure corresponds to the data processing method part in the embodiments of the present disclosure. The description of the data processing system part specifically refers to the data processing method part and will not be repeated here.

[0135] Figure 10 A block diagram of an electronic device suitable for implementing the above-described method according to an embodiment of the present disclosure is schematically shown. Figure 10 The electronic device shown is only an example and should not limit the functions and scope of use of the embodiments of the present disclosure.

[0136] like Figure 10 As shown, the electronic device 1200 according to an embodiment of the present disclosure includes a processor 1201, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1202 or a program loaded from a storage portion 1208 into a random access memory (RAM) 1203. The processor 1201 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or a related chipset and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 1201 may also include onboard memory for caching purposes. The processor 1201 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of the present disclosure.

[0137] Various programs and data required for the operation of the electronic device 1200 are stored in the RAM 1203. The processor 1201, the ROM 1202, and the RAM 1203 are connected to each other via a bus 1204. The processor 1201 performs various operations of the method flow according to the embodiment of the present disclosure by executing the programs in the ROM 1202 and / or the RAM 1203. Specifically, the program may include at least one processing model. The processor 1201 is capable of loading and running the at least one processing model. The processing model can exist as an independent system service, a background daemon process, or in the form of an application program interface (API) library, so that it can be called by a target application (such as a game, office software, etc.). When the processing model is called by the target application, the processor 1201 executes the processing model so that it performs the aforementioned method, for example: obtaining the operating data of the electronic device; generating and processing the operating data using a target processing model to obtain configuration instructions for configuring the system resources of the electronic device in the current operating period and the first operating period; and configuring the system resources of the electronic device at the target time based on the configuration instructions to meet the system resource requirements of the current operating period and the first operating period respectively. It should be noted that the program (including the processing model) can also be stored in one or more memories other than ROM 1202 and RAM 1203. The processor 1201 can also perform various operations of the method flow according to the embodiment of the present disclosure by executing programs stored in one or more memories.

[0138] According to an embodiment of the present disclosure, electronic device 1200 may further include an input / output (I / O) interface 1205, which is also connected to bus 1204. Electronic device 1200 may also include one or more of the following components connected to I / O interface 1205: an input section 1206 including a keyboard, mouse, etc.; an output section 1207 including devices such as a cathode ray tube (CRT), liquid crystal display (LCD), and speakers; a storage section 1208 including a hard disk; and a communication section 1209 including a network interface card such as a LAN card or modem. Communication section 1209 performs communication processing via a network such as the Internet. A drive 1210 is also connected to I / O interface 1205 as needed. Removable media 1211, such as a magnetic disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed in drive 1210 as needed, so that computer programs read from the removable media can be installed into storage section 1208 as needed.

[0139] According to an embodiment of the present disclosure, the method flow according to an embodiment of the present disclosure can be implemented as a computer software program (for example, the processing model described above). For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a computer-readable storage medium, and the computer program contains program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 1209, and / or installed from the removable medium 1211. When the computer program is executed by the processor 1201, the above-mentioned functions defined in the system of the embodiment of the present disclosure are performed. According to an embodiment of the present disclosure, the systems, devices, means, modules, units, etc. described above can be implemented by computer program modules.

[0140] The present disclosure also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments, or may exist independently and not be incorporated into the device / apparatus / system. The computer-readable storage medium carries one or more programs, and when executed, implements the method according to the embodiments of the present disclosure.

[0141] According to embodiments of the present disclosure, a computer-readable storage medium may be a non-volatile computer-readable storage medium. Examples include, but are not limited to, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0142] For example, according to an embodiment of the present disclosure, the computer-readable storage medium may include the ROM 1202 and / or the RAM 1203 described above and / or one or more memories other than the ROM 1202 and the RAM 1203 .

[0143] An embodiment of the present disclosure also includes a computer program product, which includes a computer program, and the computer program contains program code for executing the method provided by the embodiment of the present disclosure. When the computer program product runs on an electronic device, the program code is used to enable the electronic device to implement the control method provided by the embodiment of the present disclosure.

[0144] When the computer program is executed by the processor 1201, the above functions defined in the system / device of the embodiment of the present disclosure are performed. According to the embodiment of the present disclosure, the system, device, module, unit, etc. described above can be implemented by a computer program module.

[0145] In one embodiment, the computer program may be based on a tangible storage medium such as an optical storage device or a magnetic storage device. In another embodiment, the computer program may also be transmitted and distributed in the form of a signal on a network medium, and downloaded and installed through the communication part 1209, and / or installed from a removable medium 1211. The program code contained in the computer program may be transmitted using any appropriate network medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above. According to an embodiment of the present disclosure, the program code for executing the computer program provided by the embodiment of the present disclosure may be written in any combination of one or more programming languages. Specifically, these computer programs may be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include but are not limited to languages ​​such as Java, C++, Python, "C" or similar programming languages. The program code may be executed entirely on the user's computing device, partially on the user's device, partially on a remote computing device, or entirely on a remote computing device or server. Where a remote computing device is involved, the remote computing device may be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., through the Internet using an Internet service provider).

[0146] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of the systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or part of the code, which contains one or more executable instructions for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions marked in the boxes can also occur in an order different from that marked in the accompanying drawings. For example, two boxes shown in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, as well as the combination of boxes in the block diagram or flowchart, can be implemented using a dedicated hardware-based system that performs the specified functions or operations, or can be implemented using a combination of dedicated hardware and computer instructions. It will be understood by those skilled in the art that the features described in the various embodiments of the present disclosure can be combined and / or combined in various ways, even if such combinations or combinations are not explicitly described in the present disclosure. In particular, without departing from the spirit and teachings of the present disclosure, the features described in the various embodiments of the present disclosure may be combined and / or coupled in various ways, and all of these combinations and / or couplings fall within the scope of the present disclosure.

[0147] The above describes the embodiments of the present disclosure. However, these embodiments are for illustrative purposes only and are not intended to limit the scope of the present disclosure. Although each embodiment has been described separately above, this does not mean that the measures in each embodiment cannot be advantageously used in combination. Without departing from the scope of the present disclosure, those skilled in the art may make various substitutions and modifications, which should all fall within the scope of the present disclosure.

Claims

1. A system resource configuration method, comprising: Obtaining operating data of electronic equipment; Generate and process the operating data using a target processing model to obtain configuration instructions for configuring system resources of the electronic device in a current operating period and a first operating period; configuring the system resources of the electronic device at a target time based on the configuration instruction to meet the system resource requirements of the current operating period and the first operating period respectively; The first operating period is a period later than the current operating period, and the time when the system resources are configured in the current operating period and the first operating period are the same or different.

2. The method according to claim 1, wherein Generating and processing the operating data using the target processing model includes at least one of the following: Obtaining attribute information of the operating data, and calling a target processing model that matches the attribute information from an artificial intelligence processing model deployed by the electronic device to generate and process the operating data; The target intelligent agent is used to identify and process the operating data, and based on the identification processing result, the corresponding target processing model is called from the artificial intelligence processing model deployed by the electronic device to generate and process the operating data or the identification processing result.

3. The method according to claim 1 or 2, wherein: Generating and processing the operation data using the target processing model further includes: Target interaction data of a target user acting on the electronic device is obtained, and the operation data and the target interaction data are generated and processed using a target processing model to obtain the configuration instruction.

4. The method according to claim 3, wherein: Generating and processing the operation data and the target interaction data using a target processing model includes: Identifying user intent represented by the target interaction data; System resource requirements for the current operating period and the first operating period are predicted based on the user intention and the operating data, so as to generate the configuration instruction based on the system resource requirements.

5. The method according to claim 1, wherein configuring the system resources of the electronic device at the target time based on the configuration instruction comprises: Obtaining type information and / or configuration tags of system resources required by the electronic device in the current operating period and the first operating period; Determining the usage time of the system resources required for each time period based on the type information and / or the configuration tag; configuring the system resources at the time of use; or, The system resource is configured based on the type information and / or the configuration tag at a corresponding time before the use time.

6. The method according to claim 1, wherein configuring the system resources of the electronic device at the target time based on the configuration instruction comprises: Obtaining, based on the configuration instructions, a correspondence between running processes and / or threads of the electronic device and required system resources, as well as usage times corresponding to each system resource; Monitoring the running process information and / or running thread information of the electronic device; completing the configuration of the system resources when the electronic device runs the target process and / or target thread; or, The configuration of the system resources is completed at a first moment before the electronic device runs the target process and / or target thread, and the first moment is determined based on the usage moment corresponding to each system resource.

7. The method according to claim 1, further comprising: The system resources of the electronic device are reconfigured based on target reference data, wherein the target reference data includes evaluation data of the electronic device in the current operating period and / or the first operating period, operating environment change data of the electronic device, operating task data of the electronic device, or at least one of user behavior data of the target user.

8. The method according to claim 7, wherein: Reconfiguring system resources of the electronic device includes at least one of the following: Adjusting at least one of the priority of a running thread of the electronic device, a core configuration parameter of a processor, a memory configuration parameter, a power consumption parameter, a network parameter, and a display parameter based on the evaluation data; Adjusting at least one of the priority of a running thread of the electronic device, a core configuration parameter of a processor, a memory configuration parameter, a power consumption parameter, a network parameter, and a display parameter based on the running environment change data; At least one of the priority of the running thread of the electronic device, the core configuration parameters of the processor, the memory configuration parameters, the power consumption parameters, the network parameters, and the display parameters is determined based on the user behavior data.

9. The method according to claim 1, wherein the generating and processing of the operation data using the target processing model comprises: Predicting, using a target processing model based on the operating data, multiple operating time periods of the operating processes and / or threads of the electronic device, and system resource requirements corresponding to at least some of the operating time periods; The runtime period can represent a time window corresponding to the system resources required by the process and / or thread, and different time window lengths can represent different system resource requirements.

10. An electronic device comprising at least one processor and at least one processing model capable of running on the processor, wherein the processing model can be called by a target application to perform at least one of the following: obtaining operation data of the electronic device; Generate and process the operating data using a target processing model to obtain configuration instructions for configuring system resources of the electronic device in a current operating period and a first operating period; configuring the system resources of the electronic device at a target time based on the configuration instruction to meet the system resource requirements of the current operating period and the first operating period respectively; The first operating period is a period later than the current operating period, and the time when the system resources are configured in the current operating period and the first operating period are the same or different.

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