Parameter adjustment method and device, electronic equipment and storage medium

Abstract

The invention discloses a parameter adjustment method and device, electronic equipment and a storage medium, and belongs to the technical field of computers, the method comprises the steps that the value of a performance index item is acquired, the performance index item is an index associated with the operation performance of the electronic equipment, and the index value of the performance index item reflects the operation state of the electronic equipment; under the condition that the value of the performance index item is within a first abnormal range, determining a target system state parameter according to the abnormal context data and N abnormal system state parameters pre-associated with the performance index item; wherein the abnormal context data is data in the electronic equipment when the performance index item is abnormal, and the target system state parameter is a system state parameter causing the performance index abnormality in the N system state parameters; and adjusting the value of the target system state parameter.

Description

Technical Field

[0001] This application belongs to the field of computer technology, and specifically relates to a parameter adjustment method, device, electronic device and storage medium. Background Technology

[0002] The smooth operation of an operating system relies heavily on the dynamic adjustment of system state parameters using various tuning strategies. Specifically, the operating system senses various operational state values ​​of electronic devices in real time and selects tuning strategies based on these values ​​to adjust system state parameters, thereby ensuring the stable execution of various applications and system processes. Taking the Central Processing Unit (CPU) frequency as an example, when the operating system detects a high load on a currently running task, it will lower the CPU frequency according to the first tuning strategy to meet the task's computing power requirements. Alternatively, to allow the electronic device to run for extended periods, it will lower the CPU frequency according to the second tuning strategy to reduce heat generation and power consumption.

[0003] Optimizing the control of system state parameters is an urgent problem to be solved. Summary of the Invention

[0004] The purpose of this application is to provide a parameter adjustment method, apparatus, electronic device, storage medium, chip, and computer program product to optimize the control of system state parameters.

[0005] In a first aspect, embodiments of this application provide a parameter adjustment method, including: Obtain the value of the performance indicator item. The performance indicator item is an indicator related to the operating performance of the electronic device. The value of the performance indicator item reflects the operating status of the electronic device. If the value of a performance indicator is within the first abnormal range, the target system state parameter is determined based on the abnormal context data and N system state parameters pre-associated with the performance indicator. The abnormal context data is the data in the electronic device when the performance indicator is abnormal, and the target system state parameter is the system state parameter among the N system state parameters that caused the performance indicator to be abnormal, where N is an integer greater than or equal to 1. Adjust the values ​​of the target system state parameters.

[0006] Secondly, embodiments of this application provide a parameter adjustment device, including: The acquisition module is used to acquire the values ​​of performance indicators, which are metrics related to the operating performance of electronic devices. The values ​​of performance indicators reflect the operating status of electronic devices. The determination module is used to determine the target system state parameters based on the abnormal context data and N system state parameters pre-associated with the performance indicator when the value of the performance indicator is within the first abnormal range. The abnormal context data is the data in the electronic device when the performance indicator is abnormal, and the target system state parameters are the system state parameters among the N system state parameters that caused the performance indicator to be abnormal, where N is an integer greater than or equal to 1. The adjustment module is used to adjust the values ​​of the target system state parameters.

[0007] Thirdly, embodiments of this application provide an electronic device, which includes a processor, a memory, and a program or instructions stored in the memory and executable on the processor. When the program or instructions are executed by the processor, they implement the steps of the parameter adjustment method as shown in the first aspect.

[0008] Fourthly, embodiments of this application provide a readable storage medium on which a program or instructions are stored, and when the program or instructions are executed by a processor, the steps of the parameter adjustment method as described in the first aspect are implemented.

[0009] Fifthly, embodiments of this application provide a chip, which includes a processor and a display interface. The display interface and the processor are coupled, and the processor is used to run programs or instructions to implement the steps of the parameter adjustment method as shown in the first aspect.

[0010] In a sixth aspect, embodiments of this application provide a computer program product stored in a storage medium, which is executed by at least one processor to implement the steps of the parameter adjustment method as described in the first aspect.

[0011] In this embodiment, the value of a performance indicator can be obtained. This performance indicator is a metric associated with the operating performance of an electronic device, and its value reflects the operating state of the electronic device. When the value of the performance indicator falls within a first abnormal range, a target system state parameter is determined based on abnormal context data and N system state parameters pre-associated with the performance indicator. The abnormal context data consists of data from the electronic device when the performance indicator is abnormal, and the target system state parameter is the system state parameter among the N system state parameters that caused the performance indicator abnormality. The value of the target system state parameter is then adjusted. In this way, the root cause of parameter anomalies can be accurately located through three-dimensional data correlation analysis of the performance indicator value, abnormal context data, and the N system state parameters pre-associated with the performance indicator. This achieves a shift from passively executing fixed strategies to actively locating the root cause of anomalies, which is beneficial for dynamically optimizing system control. Attached Figure Description

[0012] Figure 1 Flowcharts of parameter adjustment methods provided for some embodiments of this application; Figure 2 A schematic diagram of the structure of a parameter adjustment device provided for some embodiments of this application; Figure 3 A schematic diagram of the structure of an electronic device is provided for some embodiments of this application; Figure 4 This is a schematic diagram of the hardware structure of an electronic device provided for some embodiments of this application. Detailed Implementation

[0013] The technical solutions of the embodiments of this application will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application are within the scope of protection of this application.

[0014] The terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such terms can be used interchangeably where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first," "second," etc., are generally of the same class and the number of objects is not limited; for example, a first object can be one or more. Furthermore, in the specification and claims, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0015] In related technologies, the logic for selecting adjustment strategies based on these state values ​​in the operating system is fixed. On the one hand, this may not cover the numerous usage scenarios in actual user operations, reducing the flexibility of adjusting system state parameters. On the other hand, when multiple adjustment strategies are in effect simultaneously, it can lead to conflicting settings of system state parameters. System state parameters are set in multiple logic segments, resulting in multi-threaded concurrency and overlapping scenarios, causing multiple adjustment strategies to conflict in setting system state parameters and resulting in abnormal system state parameter settings.

[0016] If certain critical system status parameters are set abnormally, it can affect the use and recycling of various operating system resources, leading to problems such as lag, overheating, and crashes in electronic devices. Current operating systems lack effective detection mechanisms to repair these issues, often requiring users to restart applications or the phone, significantly impacting the user experience.

[0017] To address the aforementioned technical problems, embodiments of this application provide a parameter adjustment method, apparatus, electronic device, and storage medium. The following description, in conjunction with the accompanying drawings, details... Figures 1 to 4 The parameter adjustment method provided in this application will be described in detail through specific embodiments and application scenarios.

[0018] The terminology used in the embodiments of this invention will be explained below.

[0019] Performance metrics are indicators related to the operating performance of electronic devices. The values ​​of performance metrics reflect the operating status of the electronic device and affect the user's use of the displayed data. In this embodiment, performance metrics include, but are not limited to, at least one of the following: frame drop rate, slow memory allocation metric, and memory pages.

[0020] Frame drop rate refers to the refresh rate of an electronic device's display at a fixed interval. For example, a 120Hz refresh rate means an interval of 8.33ms. If the application in the electronic device fails to render the content and transmit it to the operating system for display within the specified interval, or if the operating system takes longer than the interval to render the content, resulting in the content not being displayed on the screen in time, then that frame is dropped. The percentage of frames that are not displayed within the specified refresh interval per second represents the frame drop rate.

[0021] The slow memory allocation metric refers to the metrics involved in the process of scanning all memory pages to determine whether they can be reclaimed when an application requests memory if there is insufficient free memory in the operating system.

[0022] Memory pages, in the context of the Android operating system, are categorized into file pages and anonymous pages based on whether they have a file background. During memory page reclamation, the reclamation logic uses the swap piness parameter to determine the ratio of file pages to anonymous pages. The swap piness parameter ranges from 0 to 200; a lower value favors reclamation of anonymous pages, while a higher value favors reclamation of file pages. File pages and anonymous pages can be further categorized by activity level: active (filepage) and inactive (filepage); active (anon page) and inactive (anon page). During memory reclamation, file page-inactive and anonymous page-inactive pages are reclaimed.

[0023] The values ​​of performance metrics reflect the operating status of electronic devices, and these values ​​directly affect the displayed data displayed to the user. Specifically, the frame drop rate value can be the frame drop rate itself; the slow memory allocation metric value can be the number of slow memory allocations and the time taken; and the memory page value can be the distribution ratio of memory pages.

[0024] Anomaly context data can include performance metric values, scenario data, and device operating status data. Scenario data defines the conditions under which performance metric anomalies occur. Device operating status data includes the status values ​​of the device hardware and the operating system within the electronic device over a given time period; this data is used to analyze the causes of performance metric anomalies.

[0025] The target system state parameters are the system state parameters that cause abnormal performance indicators among N system state parameters.

[0026] It should be noted that the terminology used in the implementation section of this application is only used to explain the specific embodiments of this application and is not intended to limit this application.

[0027] The parameter adjustment method provided in this application can be executed by electronic devices such as mobile phones, tablets, laptops, PDAs, and wearable devices. Some embodiments of this application use electronic devices as the executing entity to illustrate the parameter adjustment method provided in this application.

[0028] The parameter adjustment method provided in this application can be applied to the dynamic control of various system state parameters by the operating system in electronic devices, thereby ensuring the stable execution of various applications and system processes.

[0029] The following is combined Figure 1 This application provides a detailed description of a parameter adjustment method according to an embodiment.

[0030] Figure 1 A flowchart of a parameter adjustment method provided for some embodiments of this application.

[0031] like Figure 1 As shown, the parameter adjustment method provided in this application embodiment can be applied to electronic devices. Based on this, the parameter adjustment method may include steps 110 to 130, as detailed below.

[0032] Step 110: Obtain the value of the performance indicator item. The performance indicator item is an indicator related to the operating performance of the electronic device, and the value of the performance indicator item reflects the operating status of the electronic device. Step 120: If the value of the performance indicator item is within the first abnormal range, determine the target system state parameter based on the abnormal context data and N system state parameters pre-associated with the performance indicator item. The abnormal context data is the data in the electronic device when the performance indicator item is abnormal, and the target system state parameter is the system state parameter among the N system state parameters that caused the performance indicator abnormality, where N is an integer greater than or equal to 1. Step 130: Adjust the value of the target system state parameter.

[0033] For example, taking the scenario of playing 4K video on an electronic device with an Android operating system as an example, the implementation process of steps 110 to 130 above will be described in detail. In this embodiment of the application, the performance indicator can be the frame drop rate, and its abnormality will directly cause the video picture to be stuttered in a way that is perceptible to the user. The preset normal range of the frame drop rate is ≤2% for ordinary video playback scenarios and ≤3% for 4K high-definition video playback scenarios. The first abnormal range is a frame drop rate ≥8% in any scenario and a duration ≥10 seconds.

[0034] Based on this, in step 110, the theoretical playback frame rate and the actual display frame rate can be calculated in real time by using the MediaCodec rendering callback function of the operating system application framework layer. Based on this, when a user plays a locally stored 4K high-definition video on an electronic device and simultaneously scrolls through the video comment section, if the frame rate collected within 25 consecutive seconds is 12%-15% and the duration exceeds 10 seconds, it meets the judgment condition of the first abnormal range and triggers the execution of the subsequent step 120.

[0035] Step 120 involves first obtaining the exception context data and N system status parameters that are pre-associated with the performance metrics.

[0036] The performance metrics can include a frame drop rate of 12%-15% for 25 seconds during 4K video playback. Scenario data can include a service scenario of local 4K video full-screen playback, with user interactions including concurrent video playback and comment section scrolling. Environmental scenarios can include a device battery level of 22%, indicating low power mode has been triggered, and two social media apps running in the background. Device operating status data can include a CPU load of 92% (CPU0-CPU3 cores at full load), a GPU utilization of 88%, and a battery temperature of 41°C.

[0037] Based on the impact mechanism of frame drop rate, N=3 core system state parameters are pre-associated in the operating system, such as the CPU temperature rise frequency throttling trigger threshold, the CPU frequency limit in low power mode, and the GPU rendering thread concurrency limit. The CPU temperature rise frequency throttling trigger threshold can be used to force the kernel to reduce the CPU frequency when the battery temperature reaches this threshold, preventing the device from overheating. When frame drop rate is abnormal, the actual effective value of the CPU temperature rise frequency throttling trigger threshold is 38 degrees Celsius. The low power mode CPU frequency limit can be used to limit the maximum operating frequency of the CPU after the device triggers low power mode, balancing power consumption and performance. When frame drop rate is abnormal, the low power mode CPU frequency limit can be 1.1GHz. The GPU rendering thread concurrency limit can be used to limit the number of rendering tasks that the GPU can process simultaneously, directly affecting frame rendering efficiency. When frame drop rate is abnormal, the GPU rendering thread concurrency limit can be 2.

[0038] Based on this, by combining scene data, device operating status data, and the conditions under which frame drop anomalies occur in the anomaly context data (i.e., in low power mode, 4K video playback, and concurrent scrolling in the comment section), and based on the hardware performance baseline of the electronic device and the application's operational requirements, the compliant variation range of each system status parameter can be derived. Based on this, the compliant variation range for the CPU temperature rise frequency limiting trigger threshold can be 43°C to 45°C, the compliant variation range for the CPU main frequency limit in low power mode is 1.6GHz to 1.8GHz, and the compliant variation range for the GPU rendering thread concurrency limit is 3 to 5. The actual effective value of each system status parameter at the time of the anomaly is compared one by one with the above compliant variation ranges. If the actual effective value of a parameter is not within the corresponding compliant variation range, then that parameter is determined to be the target system status parameter causing the frame drop anomaly. Therefore, the target system status parameters can include the CPU temperature rise frequency limiting trigger threshold, the CPU main frequency limit in low power mode, and the GPU rendering thread concurrency limit.

[0039] In step 130, the CPU temperature rise frequency limiting trigger threshold can be adjusted from 38° to 44°, the CPU frequency limit in low power mode can be adjusted from 1.1GHz to 1.7GHz, and the GPU rendering thread concurrency limit can be adjusted from 2 to 4.

[0040] It should be noted that the pre-associated relationships between performance metrics and system status parameters in this embodiment can be obtained through training with historical performance optimization data, ensuring a strong correlation between the parameters and the frame drop rate. Furthermore, this embodiment can read the actual effective values ​​of each system status parameter during abnormal periods via the kernel configuration interface.

[0041] Therefore, in this embodiment, the root cause of parameter anomalies can be accurately located through three-dimensional data correlation analysis of the values ​​of performance indicators, anomaly context data, and N system state parameters pre-associated with the performance indicators. This achieves a shift from passively executing fixed strategies to actively locating the root cause of anomalies. Specifically, the values ​​of performance indicators, such as frame drop rate, startup time, and memory usage, directly reflect the user-perceptible operating status, ensuring that anomaly judgment aligns with the actual user experience. Anomaly context data, such as scene data and device operating status data, can accurately define the conditions of anomalies, avoiding blind parameter adjustments that are detached from the actual usage scenario. Furthermore, by comparing the preset associated parameters with the actual effective values, the target system state parameters causing performance anomalies can be directly identified. This facilitates dynamic optimization of system control, resolves strategy conflicts and scenario adaptation issues, and the adjustment action only targets the value of the target system state parameter, ensuring that the adjusted value meets the performance requirements of the current scenario. This eliminates the need to enable multiple strategies and avoids interference from other irrelevant strategies, completely preventing conflicting adjustments to system state parameters and reducing the risk of system state parameters being set abnormally.

[0042] The steps described above are explained in detail below.

[0043] In step 110, in some embodiments of this application, various performance indicator items can be registered for monitoring. That is, positive and negative definitions are made for performance indicators in the operating system that affect the user's actual use of electronic devices, and monitoring events are registered to obtain the values ​​of various performance indicator items.

[0044] Specifically, if the value of the performance indicator is the frame drop rate, then an increase in the frame drop rate represents a negative trend, which will cause the screen to flicker and stutter; a decrease in the frame drop rate represents a positive trend, and the screen will be displayed more smoothly.

[0045] If the performance metrics are the number of slow memory allocations and the time taken, then an increase in the number of allocations and the time taken represents a negative trend, which can cause important processes to be blocked and become unsmooth; a decrease represents a positive trend, which means that there are no problems with memory allocation.

[0046] If the performance metric is the proportion of memory page distribution, a large difference between the proportions of file pages and anonymous pages indicates a negative trend, which will affect memory reclamation efficiency and cause important processes to be blocked; if the proportions are similar, it indicates a positive trend, meaning that there are no problems with the reclamation of various types of memory.

[0047] It should be noted that the process of collecting the values ​​of performance indicators in this application embodiment may include: using the Media Codec rendering callback function of the operating system application framework layer to count the theoretical playback frame rate and the actual display frame rate every 1 second in real time, and calculating the frame drop rate; at the same time, the kernel layer synchronously verifies the transmission status of the display frames through the Surface Flinger service to ensure data accuracy.

[0048] Regarding step 120, in some embodiments of this application, before performing step 120, the parameter adjustment method may further include the step of determining abnormal context data and N system state parameters pre-associated with performance metric items, which will be described in detail below.

[0049] In some embodiments of this application, before the step of determining the target system state parameters based on the abnormal context data and N system state parameters pre-associated with the performance metric, the parameter adjustment method may further include steps 1401 and 1402, as detailed below.

[0050] Step 1401: If the value of the performance indicator is within the first abnormal range, determine the time period during which the performance indicator is abnormal.

[0051] For example, using the above example, the preset normal range for frame drop rate is ≤2% for ordinary video playback scenarios and ≤3% for 4K high-definition video playback scenarios. The first abnormal range is a frame drop rate ≥8% in any scenario with a duration ≥10 seconds. Based on this, if a user is playing a locally stored 4K high-definition video on an electronic device and simultaneously scrolling through the video comment section, and the frame drop rate collected within 25 consecutive seconds is 12%-15% with a duration exceeding 10 seconds, then the frame drop rate value is determined to meet the criteria for the first abnormal range. In this case, the abnormal time period for the performance indicator can be determined as the time period corresponding to the duration of the frame drop rate of 12%-15% collected within 25 consecutive seconds with a duration exceeding 10 seconds, such as 10:00:00 to 10:00:15.

[0052] Step 1402: Generate exception context data based on the values ​​of performance indicators, scene data, and device operating status data within the time period.

[0053] In this embodiment, the scenario data includes a service scenario identifier within a time period, which is used to define the conditions under which performance indicator abnormalities occur; the device operating status data includes the status values ​​of the device hardware and the operating system within the electronic device within a time period, which is used to analyze the causes of performance indicator abnormalities.

[0054] It should be noted that, in this embodiment, the abnormal context data can be understood as a complete on-site data archive when a performance indicator anomaly occurs. The value of the performance indicator within this time period refers to the specific numerical representation of the performance indicator when the anomaly occurs. It is the core quantitative basis for measuring the severity of the anomaly, clearly indicating the specific degree to which the performance indicator deviates from the normal range, such as the specific percentage of frame drop rate, the proportion of various memory pages, etc. Scenario information can refer to the specific service scenario identifier when the performance indicator anomaly occurs, which may include the application involved, page components (activity, view), and specific user operations (swiping, clicking to switch, etc.), distinguishing between global anomalies and local scenario anomalies. Device operating status data can refer to the status values ​​of the device hardware and the operating system when the performance indicator anomaly occurs, serving as a key reference for analyzing the causes of the performance indicator anomaly. In some embodiments, it may also refer to system status parameters, such as CPU frequency, device temperature, remaining available memory, number of background applications, etc.

[0055] For example, if an abnormal frame drop rate occurs, the abnormal context data may include the specific percentage of the frame drop rate; the scenario of the frame drop, such as the application, activity, view, or operation, such as swiping or clicking to switch; and the system status, such as CPU frequency, CPU loading, temperature, battery level, and remaining available memory.

[0056] For example, the distribution ratio of memory pages, abnormal context data can include the size and proportion of different types of memory pages; abnormal scenarios, such as applications, activities, views, and operations such as swiping and clicking to switch; and system status, such as remaining available memory and the number of background applications.

[0057] Therefore, in this embodiment of the application, abnormal conditions can be accurately defined through abnormal context data such as scene data and device operating status data, avoiding blind parameter adjustments that are detached from the actual use scenario.

[0058] In some embodiments of this application, system state parameters are parameters in electronic devices that directly or indirectly affect performance indicators. The value of the system state parameter or the change of the system state parameter causes a change in the value. Based on this, before the step of determining the target system state parameter according to the abnormal context data and N system state parameters pre-associated with the performance indicator, the parameter adjustment method may also include steps 1403 and 1404, as shown below.

[0059] Step 1403: Based on the pre-defined correlation between the performance index items and the pre-defined system state parameters, determine the N system state parameters that are pre-associated with the performance index items.

[0060] For example, based on the impact mechanism of frame drop rate, N=3 core system state parameters are pre-associated in the operating system, such as the CPU temperature rise frequency limiting trigger threshold, the CPU main frequency limit in low power mode, and the GPU rendering thread concurrency limit.

[0061] Step 1404: Through the hooking mechanism, extract the actual effective values ​​of system status parameters during the operation of the electronic device's operating system within the time period when the performance indicators are abnormal.

[0062] For example, the actual effective values ​​of system status parameters can be recorded in real time through a hook mechanism. These parameters are the specific values ​​set during the actual operation of the operating system and the process of interface calls. For instance, when the frame rate is abnormal, the actual effective value of the CPU temperature rise frequency limiting trigger threshold is 38 degrees Celsius; when the frame rate is abnormal, the upper limit of the CPU frequency in low power mode can be 1.1 GHz; and when the frame rate is abnormal, the upper limit of the GPU rendering thread concurrency can be 2.

[0063] It should be noted that the Hook mechanism is a technology for intercepting and monitoring program execution flow. It involves pre-setting hook points in the predetermined execution chain of a program and / or operating system. When the program runs to this node, it will first trigger the pre-set custom logic, and then decide whether to continue executing the original flow, modify the input parameters and / or return values ​​of the original flow, or directly intercept the original flow, thereby achieving non-intrusive monitoring, data collection, and logic modification of program behavior.

[0064] Therefore, by establishing correlations, a baseline list of N pre-associated system state parameters with performance metrics can be defined to define the scope of investigation, ensuring that no system state parameters are overlooked. Furthermore, a hook mechanism provides actual evidence to verify whether parameters are abnormal. Even if a system state parameter is potentially abnormal and could affect performance metrics, it will not cause anomalies if its actual value does not deviate from a reasonable range. Based on this, the target system state parameter causing performance anomalies can be quickly identified by comparing pre-associated parameters with actual effective values. This allows for dynamic optimization of system control and resolution of policy conflicts and scenario adaptation issues.

[0065] Based on the above, in some embodiments of this application, the abnormal context data includes the value of the performance indicator, scene data, and device operating status data during the time period when the performance indicator is abnormal. Based on this, step 120 may specifically include steps 1201 to 1203, as shown below.

[0066] Step 1201: Based on the values ​​of the performance metrics and the scenario data, determine the conditions that cause the performance metrics to be abnormal.

[0067] For example, in the embodiments of this application, the data type collected during the abnormal frame drop rate period, such as the application foreground runtime period of 10:05-10:10, has a frame drop rate of ≥8% (normal threshold ≤2%), with a peak of 15% from 10:07-10:08.

[0068] In this application embodiment, the scene data collected during the abnormal frame drop rate period may include business scenarios, such as the application playing 4K high-definition video; user operations, such as simultaneously swiping the video comment section; and environmental scenarios, such as the mobile phone battery being ≤20% (triggering low power mode).

[0069] In this embodiment, the device operating status data collected during the abnormal frame drop rate period may include: CPU load: ≥90%; GPU utilization: ≥85%; Battery temperature: 42℃; System status: Low power frequency reduction strategy has been triggered.

[0070] Based on this, the occurrence conditions can be determined through the following steps: Scenario data shows that the anomaly only occurs in concurrent service scenarios involving 4K video playback and comment section scrolling, and is triggered only in low-power environments with battery level ≤ 20%. Furthermore, time-series data shows that the anomaly period completely overlaps with the low-power mode startup period; when there is no low power or no concurrent video and scrolling operations, the frame drop rate is ≤ 2%. Therefore, the occurrence conditions are defined as the foreground running scenario of concurrent 4K video playback and comment section scrolling in low-power mode. The triggering conditions for the frame drop rate anomaly include business conditions, namely, the dual-task concurrency of 4K video decoding and rendering and comment section UI scrolling rendering. The environmental condition is that the device battery level is ≤ 20%, triggering the low-power system policy. The indicator condition is a frame drop rate ≥ 8% and a duration ≥ 1 minute.

[0071] Step 1202: Based on the occurrence conditions and equipment operating status data, determine the compliant variation range of the value of each of the N system status parameters.

[0072] For example, based on the frame drop rate impact mechanism, N=3 system state parameters are associated in advance, such as CPU temperature rise frequency limit threshold, application foreground thread scheduling priority, and GPU rendering thread concurrency limit.

[0073] Based on this, the compliant variation range of the CPU temperature rise frequency limiting threshold can be determined by the following steps: if the current battery temperature of the device is 42℃, and the frequency limiting threshold is <42℃, the frequency will be directly triggered at the current temperature; while dual rendering tasks require sufficient computing power, the compliant threshold should be higher than the current temperature or equal to the default threshold of 45℃. Based on this, the compliant variation range of the CPU temperature rise frequency limiting threshold can be 42℃-45℃.

[0074] The compliant variation range of application foreground thread scheduling priority can be determined by the following steps: foreground video playback and UI swiping are core business threads, and the system specification requires that the priority of the core foreground threads be ≥16 to ensure CPU resource preemption capability. Based on this, the compliant variation range of application foreground thread scheduling priority is 16-18.

[0075] The compliant range of variation for the upper limit of GPU rendering thread concurrency can be determined by the following steps: dual rendering tasks require at least 3 concurrent threads, and the upper limit of 5 is the system default maximum value. This range can meet the requirements of concurrent rendering. Based on this, the compliant range of variation for the upper limit of GPU rendering thread concurrency can be 3-5.

[0076] Step 1203: In the event of an abnormal performance indicator, the system status parameter whose actual effective value is not within the corresponding compliance change range is identified as the target system status parameter.

[0077] For example, the actual effective values ​​of system status parameters when the acquired performance metrics are abnormal can be matched with the aforementioned determined compliance variation range, wherein the CPU temperature rise limiting threshold is lower than the compliance lower limit, the application foreground thread scheduling priority is lower than the compliance lower limit, and the GPU rendering thread concurrency limit is within the compliance range. Therefore, the CPU temperature rise limiting threshold and the application foreground thread scheduling priority can be determined as the target system status parameters.

[0078] Regarding step 130, in some embodiments of this application, before attempting to restore the abnormal value of the performance indicator, it is necessary to verify whether the preconditions have been met—that restoring the abnormal value of the performance indicator will not cause new performance anomalies, will not conflict with other system policies, and can effectively resolve the original performance indicator anomaly—by checking the values ​​of system status parameters directly associated with the abnormal value of the performance indicator, the current usage scenario conditions, and the status of associated service processes. Based on this, the parameter adjustment method may further include: Determine whether the values ​​of the target system state parameters meet the adjustment conditions; where the adjustment conditions are the conditions for eliminating the abnormality of performance index items without causing system resource conflicts or functional failures.

[0079] It should be noted that the adjustment conditions in the embodiments of this application may include conditions that satisfy the following: The original triggering factors have been eliminated. For example, the values ​​of target system state parameters that caused abnormal performance metrics, such as excessively high temperature or incomplete application memory reclamation, have been restored to the normal range.

[0080] There are no other conflicting strategies or scenario restrictions. For example, if there are no other effective strategies in the current operating system, such as scenario-based frequency limiting or application-specific configurations that need to retain the values ​​of the target system state parameters to avoid conflicts with other strategies after recovery.

[0081] The recovery operation will not affect the current service operation. For example, during the recovery of the target system status parameters, it will not cause new performance problems such as stuttering or crashing in core services that are currently running, such as video playback or application operation.

[0082] For example, if the frame drop rate is abnormal, the target system status parameter is the temperature rise frequency limiting parameter. The original anomaly was that the frequency limit was not removed even after the temperature decreased. In this case, the value of the target system status parameter to be verified can include the current device temperature; whether there are other scenario-based frequency limiting strategies, such as low-battery frequency limiting or high-load temporary frequency limiting. Specific criteria for meeting the recovery requirements can include: elimination of the original cause: the current device temperature has dropped below the normal threshold corresponding to the temperature rise frequency limiting parameter, such as the original frequency limiting trigger temperature being 38℃, and the current temperature dropping to 35℃, below the trigger threshold; no conflicting limits, meaning there are currently no other effective frequency limiting strategies, such as sufficient device battery power, and low-battery frequency limiting not being triggered; CPU load being at a moderate level, with no need for high-load temporary frequency limiting; and no impact on current business, meaning the current scenario, such as video playback or game operation, still requires normal CPU computing power. Removing the temperature-based frequency limiting can increase the frequency, resolve the frame drop problem, and will not cause the device to overheat due to the frequency increase. Thus, if the above conditions are met, it can be determined that the value of the target system status parameter meets the adjustment conditions. Similarly, if at least one of the above conditions is not met, it can be determined that the value of the target system status parameter does not meet the adjustment conditions.

[0083] Alternatively, if the memory page distribution ratio is abnormal, such as an abnormal application swap piness value, the target system status parameter can be the swap piness value of the application group. The original anomaly manifested as the inability to properly reclaim file pages, resulting in a swap piness value setting that deviates from the default value. In this case, the target system status parameter to be verified can include the application's current memory page reclamation status; and whether the application is running core services, such as foreground chat or file transfer. Specific criteria for meeting recovery requirements can include the elimination of the original cause, i.e., the application's current memory page reclamation has been completed, such as the operating system successfully reclamating the application's redundant file pages, and available memory returning to normal, eliminating the need to maintain an abnormal swap piness value. Conflict-free checks can verify that the current application is not executing any core business logic, such as file downloads or video calls that require a large number of file pages. Restoring the default swap piness value will not affect its normal operation or current business logic. After restoring the default swap piness value, subsequent memory page reclamation of the application can return to normal logic, preventing file pages from failing to be reclaimed again and not affecting the memory allocation of other applications. Therefore, if the above conditions are met, it can be determined that the values ​​of the target system's state parameters satisfy the adjustment conditions. Similarly, if at least one of the above conditions is not met, it can be determined that the values ​​of the target system's state parameters do not satisfy the adjustment conditions.

[0084] Based on this, step 130 may specifically include: If the values ​​of the target system state parameters are determined to meet the adjustment conditions, the values ​​of the target system state parameters are adjusted according to the adjustment range of the first parameter value corresponding to the adjustment conditions.

[0085] Therefore, the logic for determining the recovery requirement is directly linked to the original cause of the anomaly, ensuring that recovery is only performed after the root cause is eliminated, thus avoiding invalid operations.

[0086] In some embodiments of this application, after step 130, the parameter adjustment method may further include steps 1601 and 1602, or steps 1601 and 1603, as detailed below.

[0087] Step 1601: Monitor the values ​​of the performance indicators.

[0088] Step 1602: If the value of a performance indicator item is detected to have moved from the first abnormal range to the preset normal range, restore the value of the target system state parameter.

[0089] For example, taking frame drop rate as an example, if the abnormality is the temperature rise frequency limiting parameter, after adjusting the value of the temperature rise frequency limiting parameter, if the temperature has decreased and there is no other scene frequency limiting, and the frame drop rate returns to the preset normal range, then the temperature frequency limiting logic can be removed. That is, referring to the example in step 130 above, the temperature will be adjusted from 44° to the original 38°.

[0090] Taking an abnormal memory page distribution ratio as an example, after adjusting the memory page distribution ratio and detecting that the application has completed the current memory page reclamation, the swap piness value of the application group can be restored to the default value.

[0091] Step 1603: If the value of the performance indicator is still within the first abnormal range within a preset time period, adjust the value of the target system state parameter according to the second parameter value adjustment range corresponding to the adjustment condition.

[0092] For example, taking frame drop rate as an example, if the abnormality is the temperature rise frequency limiting parameter, and the frame drop rate does not return to the preset normal range after adjusting the value of the temperature rise frequency limiting parameter, then the CPU temperature rise frequency limiting trigger threshold can be adjusted from 44° to 45°.

[0093] Therefore, the adjustment action only targets the value of the system state parameter, ensuring that the adjusted value meets the performance requirements of the current scenario. It eliminates the need to enable multiple adjustment strategies, avoids interference from other irrelevant strategies, completely avoids conflicting settings of system state parameters by multiple adjustment strategies, and reduces the risk of abnormal settings of system state parameters.

[0094] The parameter adjustment method provided in the embodiments of this application will be described in detail below.

[0095] Step 201: Register listeners for various performance metrics. This involves defining positive and negative values ​​for performance metrics in the operating system that affect the actual user experience and registering listener events. In this embodiment, packet loss rate and memory page distribution ratio can be used as examples for illustration.

[0096] Step 102: If the value of a performance indicator changes negatively, step 103 is triggered to determine whether the value of the performance indicator is abnormal.

[0097] Step 103: Set a corresponding negative threshold for the performance indicator. If the negative threshold exceeds the corresponding threshold or there is a continuous deterioration trend, it is determined that the performance indicator is abnormal in this scenario, and proceed to step 104; if it is determined that there is no abnormality, return to step 101 to continue monitoring the changes of the performance indicator.

[0098] Step 104: After determining that an anomaly exists in a performance metric, obtain the anomaly context data. Then, step 105 can be executed.

[0099] For example, if an abnormal frame drop rate occurs, you can obtain: ① the specific percentage of the frame drop rate; ② the scenario of the frame drop, such as the application, activity, view, or operation such as swiping or clicking to switch; ③ the system status, such as CPU frequency, CPU loading, temperature, battery level, and remaining available memory.

[0100] For example, the memory page distribution ratio can provide information on: ① the size and proportion of different types of memory pages; ② abnormal scenarios such as applications, activities, views, and operations such as swiping and clicking to switch; and ③ system status such as remaining available memory and the number of background applications.

[0101] Step 105: Obtain N system status parameters pre-associated with the performance metrics, and extract the actual effective values ​​of the system status parameters during the operating system operation of the electronic device within the time period when the performance metrics are abnormal, using a hook mechanism. Then, step 106 can be executed.

[0102] For example, regarding frame drop rate, N system status parameters can include: temperature rise frequency limiting parameters, load frequency increasing parameters, and critical thread scheduling priorities. Values ​​extracted through the hook mechanism can include the frequency limiting point set by the frequency adjustment interface, the thread priority set by the thread priority setting interface, etc.

[0103] For example, the memory page distribution ratio, N system status parameters can include: swap piness value and inactive_ratio. Values ​​extracted through the hook mechanism can include swap piness values ​​for each application group.

[0104] Step 106: Determine the target system state parameters. Then you can proceed to step 107.

[0105] Regarding frame drop rate, first determine if the CPU frequency is limited. If the CPU frequency is limited, determine if the overall load of the electronic device is higher than the preset threshold. If the overall load of the electronic device is higher than the preset threshold, match the abnormal data with the opened parameter data and the interface settings to track suspicious points, and determine its CPU frequency as the target system status parameter.

[0106] If the CPU frequency is not limited or if the overall load of the electronic device is not higher than a preset threshold, proceed to the next step of determining whether the thread priority is lower than a preset threshold. If the thread priority is lower than the preset threshold, the thread priority is determined as the target system state parameter.

[0107] If the thread priority is not lower than the preset threshold, proceed to the next step to determine whether the platform load-related parameters are normal. If the platform load-related parameters are abnormal, then the platform load-related parameters are determined as the target system status parameters.

[0108] If the platform load-related parameters are normal, proceed to the next step to check other parameter checkers.

[0109] For abnormal memory page ratios, i.e., imbalanced ratios or a certain type of page failing to be reclaimed, if the ratio of pages in the active and inactive queues is abnormal (e.g., many active pages and few inactive pages), checking for an abnormal `inactive_ratio` might lead to no pages being reclaimed during memory page reclamation. If the ratio of file pages to anonymous pages is abnormal, and a certain type of page has had few successful reclamation attempts in recent memory page reclamation processes, it's necessary to match the system swap piness and application group swap piness values ​​to confirm whether abnormal parameter settings are causing the corresponding page to fail to trigger reclamation normally. In this case, target system status parameters can include an `inactive_ratio` within a preset range, abnormal matching systems and imbalanced page types, and abnormal single-application page distribution.

[0110] Step 107: Based on the target system state parameters obtained in Step 106, attempt to restore the values ​​of the target system state parameters. Specifically, first determine if the values ​​of the target system state parameters meet the restoration requirements. If they do, proceed to Step 108 to perform restoration; otherwise, proceed to Step 109 to monitor changes in relevant state values ​​and wait for the conditions to be met before performing restoration.

[0111] Taking frame drop rate as an example, if the abnormal parameter is the temperature rise frequency limiting parameter, and the frequency limit is not removed after the temperature decreases, step 106 will judge the related system status values ​​such as the current temperature and whether there is scene frequency limiting in the current scene. If the temperature has decreased and there is no other scene frequency limiting, the logic of removing the temperature frequency limiting will be removed.

[0112] Taking an abnormal memory page distribution ratio as an example, if the abnormality is that the file pages of the WeChat application cannot be properly reclaimed, after obtaining the abnormal application, confirm the swap piness value of the application group, and after WeChat completes this memory page reclamation, restore the swap piness value of the WeChat application group to the default value.

[0113] Step 108: Perform the parameter recovery operation and record the recovery operation. Confirm the effectiveness of the recovery operation based on the subsequent abnormal performance indicators.

[0114] Step 109: Monitor changes in relevant status values ​​and wait for the conditions to be met before re-triggering step 107 to perform the recovery operation.

[0115] Therefore, this application embodiment can monitor important performance indicators in the operating system. It can accurately locate the root cause of parameter anomalies through three-dimensional data correlation analysis of the performance indicator values, anomaly context data, and N pre-associated system state parameters. This achieves a shift from passively executing fixed strategies to actively locating the root cause of anomalies. Specifically, the values ​​of performance indicators, such as frame drop rate, startup time, and memory usage, directly reflect the user-perceptible operating status, ensuring that anomaly judgments align with the actual user experience. Anomaly context data, such as scene data and device operating status data, can accurately define the conditions of anomalies, avoiding blind parameter adjustments detached from actual usage scenarios. Furthermore, by comparing preset associated parameters with the actual effective values, the target system state parameter causing the performance anomaly can be directly identified. This facilitates dynamic optimization of system control, resolves strategy conflicts and scenario adaptation issues, and the adjustment action only targets the value of the target system state parameter, ensuring that the adjusted value meets the performance requirements of the current scenario. It eliminates the need to enable multiple adjustment strategies and avoids interference from other irrelevant strategies, completely preventing conflicting settings of system state parameters and reducing the risk of abnormal system state parameter settings.

[0116] The parameter adjustment method provided in this application can be executed by a parameter adjustment device. This application uses the parameter adjustment device performing the display as an example to illustrate the device of the parameter adjustment method provided in this application.

[0117] This application also provides a parameter adjustment device. (Specifically combined with...) Figure 2 Please provide a detailed explanation.

[0118] Figure 2 This is a schematic diagram of the structure of a parameter adjustment device provided for some embodiments of this application.

[0119] like Figure 2 As shown, the parameter adjustment device 20 can be applied to electronic devices, and the parameter adjustment device 20 specifically includes: The acquisition module 201 is used to acquire the value of the performance indicator item, which is an indicator related to the operating performance of the electronic device. The value of the performance indicator item reflects the operating status of the electronic device. The determination module 202 is used to determine the target system state parameters based on the abnormal context data and N system state parameters pre-associated with the performance indicator when the value of the performance indicator is within the first abnormal range; wherein, the abnormal context data is the data in the electronic device when the performance indicator is abnormal, and the target system state parameters are the system state parameters among the N system state parameters that cause the performance indicator to be abnormal, and N is an integer greater than or equal to 1. The adjustment module 203 is used to adjust the values ​​of the target system state parameters.

[0120] The parameter adjustment device 20 in the embodiments of this application will be described in detail below.

[0121] In some embodiments of this application, the determining module 202 is further configured to determine the time period of the abnormality of the performance indicator when the value of the performance indicator is within a first abnormal range; The parameter adjustment device 20 also includes a generation module, which is used to generate abnormal context data based on the values ​​of performance indicators, scene data and equipment operating status data within a time period; Among them, scenario data is used to define the conditions under which performance indicator anomalies occur; device operation status data includes the status values ​​of the device hardware and the operating system in the electronic device within a time period, and device operation status data is used to analyze the causes of performance indicator anomalies.

[0122] In some embodiments of this application, the determining module 202 is used to determine the conditions that cause the performance indicator to be abnormal based on the value of the performance indicator and the scene data, when the abnormal context data includes the value of the performance indicator, scene data and device operating status data during the time period when the performance indicator is abnormal. Based on the occurrence conditions and equipment operating status data, determine the compliant variation range of the value of each of the N system status parameters; In the event of an abnormal performance metric, the system status parameter whose actual effective value is outside the corresponding compliance change range will be identified as the target system status parameter.

[0123] In some embodiments of this application, the parameter adjustment device 20 further includes an extraction module, which is used to extract the actual effective value of the system status parameter during the operation of the electronic device's operating system during the period when the system status parameter is a parameter that directly or indirectly affects the performance index item in the electronic device, through a capture mechanism.

[0124] In some embodiments of this application, the determining module 202 is further configured to determine whether the value of the target system state parameter satisfies the adjustment conditions; The adjustment module 203 is used to adjust the value of the target system state parameter according to the adjustment range of the first parameter value corresponding to the adjustment condition, when it is determined that the value of the target system state parameter meets the adjustment condition; The adjustment condition is to eliminate the anomalies in the performance indicators without causing system resource conflicts or functional failures.

[0125] The parameter adjustment device in this application embodiment can be an electronic device or a component within an electronic device, such as an integrated circuit or a chip. The electronic device can be a terminal or other devices besides a terminal. For example, the electronic device can be a mobile phone, tablet computer, laptop computer, PDA, in-vehicle electronic device, mobile internet device (MID), augmented reality (AR) / virtual reality (VR) device, robot, wearable device, ultra-mobile personal computer (UMPC), netbook, or personal digital assistant (PDA), etc. It can also be a server, network attached storage (NAS), personal computer (PC), television (TV), ATM, or self-service machine, etc. This application embodiment does not specifically limit the device.

[0126] The parameter adjustment device in this application embodiment can be a device with an operating system. This operating system can be Android, iOS, or other possible operating systems; this application embodiment does not specifically limit it.

[0127] The device coordination apparatus provided in this application embodiment can achieve... Figure 1 The various processes implemented in the parameter adjustment method embodiments shown achieve the same technical effect, and will not be described again here to avoid repetition.

[0128] Based on this, the parameter adjustment device provided in this application embodiment can obtain the value of a performance indicator, which is an indicator related to the operating performance of an electronic device, and the value of the performance indicator reflects the operating status of the electronic device. When the value of the performance indicator is within a first abnormal range, a target system state parameter is determined based on abnormal context data and N system state parameters pre-associated with the performance indicator. The abnormal context data is the data in the electronic device when the performance indicator is abnormal, and the target system state parameter is the system state parameter among the N system state parameters that caused the performance indicator abnormality. The value of the target system state parameter is then adjusted. In this way, the root cause of parameter abnormalities can be accurately located through three-dimensional data correlation analysis of the performance indicator value, abnormal context data, and N system state parameters pre-associated with the performance indicator. This achieves a shift from passively executing fixed strategies to actively locating the root cause of abnormalities. Specifically, the values ​​of performance indicators such as frame drop rate, startup time, and memory usage rate can directly reflect the user-perceptible operating status, ensuring that abnormality judgments align with the actual user experience. Abnormal context data, such as scene data and device operating status data, can accurately define the conditions of the abnormality, avoiding blind parameter adjustments that are detached from the actual usage scenario. Furthermore, by comparing the preset associated parameters with the actual effective values, the target system state parameters that cause performance abnormalities can be directly identified. This is beneficial for dynamically optimizing system control, resolving policy conflicts and scenario adaptation issues, and the adjustment action only targets the value of the target system state parameter, ensuring that the adjusted value meets the performance requirements of the current scenario. There is no need to enable multiple adjustment policies, and no interference from other irrelevant policies will be introduced. This completely avoids conflicting settings of system state parameters by multiple adjustment policies and reduces the risk of system state parameters being set abnormally.

[0129] Optional, such as Figure 3 As shown, this application embodiment also provides an electronic device 30, including a processor 301 and a memory 302. The memory 302 stores a program or instructions that can run on the processor 301. When the program or instructions are executed by the processor 301, they implement the various steps of the above-described parameter adjustment method embodiment and can achieve the same technical effect. To avoid repetition, they will not be described again here.

[0130] It should be noted that the electronic devices in the embodiments of this application include the mobile electronic devices and non-mobile electronic devices described above.

[0131] Figure 4 This is a schematic diagram of the hardware structure of an electronic device provided for some embodiments of this application.

[0132] The electronic device 400 includes, but is not limited to, components such as: radio frequency unit 401, network module 402, audio output unit 403, input unit 404, sensor 405, display unit 406, user input unit 407, interface unit 408, memory 409, and processor 410.

[0133] Those skilled in the art will understand that the electronic device 400 may also include a power supply (such as a battery) for supplying power to various components. The power supply may be logically connected to the processor 410 through a power management system, thereby enabling functions such as managing charging, discharging, and power consumption through the power management system. Figure 4 The electronic device structure shown does not constitute a limitation on the electronic device. The electronic device may include more or fewer components than shown, or combine certain components, or have different component arrangements, which will not be elaborated here.

[0134] In this embodiment, processor 410 is used to acquire the value of a performance indicator, which is an indicator related to the operating performance of an electronic device, and the value of the performance indicator reflects the operating state of the electronic device. Processor 410 is used to determine a target system state parameter based on anomaly context data and N system state parameters pre-associated with the performance indicator when the value of the performance indicator falls within a first abnormal range; wherein the anomaly context data is the data in the electronic device when the performance indicator is abnormal, and the target system state parameter is the system state parameter among the N system state parameters that caused the performance indicator abnormality, where N is an integer greater than or equal to 1. Processor 410 is used to adjust the value of the target system state parameter.

[0135] Therefore, by using three-dimensional data correlation analysis of performance indicator values, anomaly context data, and N pre-associated system state parameters, the root cause of parameter anomalies can be accurately located. This achieves a shift from passively executing fixed strategies to actively locating the root cause of anomalies. Specifically, performance indicator values ​​such as frame drop rate, startup time, and memory usage directly reflect the user-perceptible operating status, ensuring that anomaly judgments align with the actual user experience. Anomaly context data, such as scene data and device operating status data, can accurately define the conditions of anomalies, avoiding blind parameter adjustments that are detached from the actual usage scenario. Furthermore, by comparing the preset associated parameters with the actual effective values, the target system state parameters causing performance anomalies can be directly identified. This facilitates dynamic optimization of system control, resolves policy conflicts and scenario adaptation issues, and the adjustment action only targets the value of the target system state parameter, ensuring that the adjusted value meets the performance requirements of the current scenario. This eliminates the need to enable multiple adjustment strategies and avoids interference from other irrelevant strategies, completely preventing conflicting settings of system state parameters by multiple adjustment strategies and reducing the risk of system state parameters being set abnormally.

[0136] The electronic device 400 is described in detail below.

[0137] In some embodiments of this application, processor 410 is configured to determine the time period during which a performance metric is abnormal when the value of the performance metric is within a first abnormal range. Based on the values ​​of performance indicators, scenario data, and device operating status data within a time period, generate exception context data; Among them, scenario data is used to define the conditions under which performance indicator anomalies occur; device operation status data includes the status values ​​of the device hardware and the operating system in the electronic device within a time period, and device operation status data is used to analyze the causes of performance indicator anomalies.

[0138] In some embodiments of this application, processor 410 is configured to determine the conditions that cause the performance indicator to be abnormal based on the value of the performance indicator and the scene data, when the abnormal context data includes the value of the performance indicator, scene data and device operating status data during the time period when the performance indicator is abnormal. Based on the occurrence conditions and equipment operating status data, determine the compliant variation range of the value of each of the N system status parameters; In the event of an abnormal performance metric, the system status parameter whose actual effective value is outside the corresponding compliance change range will be identified as the target system status parameter.

[0139] In some embodiments of this application, processor 410 is configured to extract, through a capture mechanism, the actual effective values ​​of system state parameters during the operation of the electronic device's operating system within a time period when performance indicators are abnormal.

[0140] In some embodiments of this application, processor 410 is used to determine whether the value of the target system state parameter satisfies the adjustment conditions; If the value of the target system state parameter is determined to meet the adjustment conditions, the value of the target system state parameter is adjusted according to the first value adjustment range corresponding to the adjustment conditions; The adjustment condition is to eliminate the anomalies in the performance indicators without causing system resource conflicts or functional failures.

[0141] It should be understood that the input unit 404 may include a graphics processing unit (GPU) 4041 and a microphone 4042. The GPU 4041 processes image information of still images or videos acquired by an image capture device (such as a camera) in video capture mode or image capture mode. The display unit 406 may include a display panel, which may be configured in the form of a liquid crystal display, an organic light-emitting diode, or the like. The user input unit 407 includes at least one of a touch panel 4071 and other input devices 4072. The touch panel 4071 is also called a touch screen. The touch panel 4071 may include a touch detection device and a touch display. Other input devices 4072 may include, but are not limited to, a physical keyboard, function keys (such as volume display buttons, power buttons, etc.), a trackball, a mouse, and a joystick, which will not be described in detail here.

[0142] The memory 409 can be used to store software programs and various information. The memory 409 may primarily include a first storage area for storing programs or instructions and a second storage area for storing information. The first storage area may store the operating system, application programs or instructions required for at least one function (such as sound playback, image playback, etc.). Furthermore, the memory 409 may include volatile memory or non-volatile memory, or both. The non-volatile memory may be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. Volatile memory can be random access memory (RAM), static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM), and direct memory bus RAM (DRRAM). The memory 409 in the embodiments of this application includes, but is not limited to, these and any other suitable types of memory.

[0143] Processor 410 may include one or more processing units; optionally, processor 410 integrates an application processor and a modem processor, wherein the application processor mainly handles operations involving the operating system, user interface, and applications, and the modem processor mainly handles wireless display signals, such as a baseband processor. It is understood that the aforementioned modem processor may also not be integrated into processor 410.

[0144] This application also provides a readable storage medium storing a program or instructions. When the program or instructions are executed by a processor, they implement the various processes of the above-described parameter adjustment method embodiments and achieve the same technical effect. To avoid repetition, they will not be described again here.

[0145] The processor is the processor in the electronic device described in the above embodiments. The readable storage medium includes computer-readable storage media, such as computer read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk.

[0146] In addition, this application embodiment provides another chip, which includes a processor and a display interface. The display interface and the processor are coupled. The processor is used to run programs or instructions to implement the various processes of the above parameter adjustment method embodiment and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0147] It should be understood that the chip mentioned in the embodiments of this application may also be referred to as a system-on-a-chip, system chip, chip system, or system-on-a-chip, etc.

[0148] This application provides a computer program product, which is stored in a storage medium and executed by at least one processor to implement the various processes of the parameter adjustment method embodiments described above, and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0149] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0150] Furthermore, it should be noted that the scope of the methods and apparatus in the embodiments of this application is not limited to performing functions in the order shown or discussed, but may also include performing functions substantially simultaneously or in the reverse order, depending on the functions involved. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. In addition, features described with reference to certain examples may be combined in other examples.

[0151] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a computer software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods of the various embodiments of this application.

[0152] The embodiments of this application have been described above with reference to the accompanying drawings. However, this application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of this application.

Claims

1. A parameter adjustment method, characterized in that, include: Obtain the value of a performance indicator item, which is an indicator related to the operating performance of an electronic device, and the value of the performance indicator item reflects the operating status of the electronic device; When the value of the performance indicator is within a first abnormal range, a target system state parameter is determined based on the abnormal context data and N system state parameters pre-associated with the performance indicator; wherein, the abnormal context data is the data in the electronic device when the performance indicator is abnormal, and the target system state parameter is the system state parameter among the N system state parameters that caused the performance indicator to be abnormal, and N is an integer greater than or equal to 1; The values ​​of the target system state parameters are adjusted.

2. The method according to claim 1, characterized in that, Before determining the target system state parameters based on the anomaly context data and N system state parameters pre-associated with the performance metric, the method further includes: If the value of the performance indicator is within the first abnormal range, determine the time period during which the performance indicator is abnormal; The abnormal context data is generated based on the values ​​of the performance indicators, scene data, and device operating status data within the time period. The scenario data is used to define the conditions under which the performance indicator abnormality occurs; the device operating status data includes the status values ​​of the device hardware and the operating system in the electronic device within the time period, and the device operating status data is used to analyze the causes of the performance indicator abnormality.

3. The method according to claim 1, characterized in that, The abnormal context data includes the value of the performance indicator, scene data, and device operating status data during the time period when the performance indicator is abnormal. The step of determining the target system state parameters based on the abnormal context data and N system state parameters pre-associated with the performance metric includes: Based on the value of the performance indicator and the scenario data, determine the conditions that cause the performance indicator to be abnormal; Based on the occurrence conditions and the equipment operating status data, determine the compliant variation range of the value of each of the N system status parameters; In the event of an abnormality in the performance indicator, the system status parameter whose actual effective value is outside the corresponding compliance change range is determined as the target system status parameter.

4. The method according to claim 3, characterized in that, The system state parameters are parameters in the electronic device that directly or indirectly affect the performance indicators; the method further includes: The capture mechanism extracts the actual effective values ​​of the system status parameters during the operation of the electronic device's operating system within the time period when the performance metric is abnormal.

5. The method according to claim 1, characterized in that, Before adjusting the values ​​of the target system state parameters, the method further includes: Determine whether the values ​​of the target system state parameters meet the adjustment conditions; The adjustment of the values ​​of the target system state parameters includes: If the value of the target system state parameter is determined to meet the adjustment condition, the value of the target system state parameter is adjusted according to the adjustment range of the first parameter value corresponding to the adjustment condition; The adjustment condition is the condition that eliminates the abnormality of the performance indicator item without causing system resource conflicts or functional failures.

6. A parameter adjustment device, characterized in that, include: The acquisition module is used to acquire the values ​​of performance indicator items, which are indicators related to the operating performance of electronic devices, and the values ​​of the performance indicator items reflect the operating status of the electronic devices. The determination module is configured to determine a target system state parameter based on anomaly context data and N system state parameters pre-associated with the performance indicator when the value of the performance indicator is within a first abnormal range; wherein, the anomaly context data is the data in the electronic device when the performance indicator is abnormal, and the target system state parameter is the system state parameter among the N system state parameters that caused the performance indicator to be abnormal, and N is an integer greater than or equal to 1; The adjustment module is used to adjust the values ​​of the target system state parameters.

7. The apparatus according to claim 6, characterized in that, The determining module is further configured to, when the value of the performance indicator is within a first abnormal range, determine the time period during which the performance indicator is abnormal; The parameter adjustment device further includes a generation module, used to generate the abnormal context data based on the values ​​of the performance index items, scene data, and equipment operating status data within the time period; The scenario data is used to define the conditions under which the performance indicator abnormality occurs; the device operating status data includes the status values ​​of the device hardware and the operating system in the electronic device within the time period, and the device operating status data is used to analyze the causes of the performance indicator abnormality.

8. The apparatus according to claim 6, characterized in that, The determining module is used to determine the conditions that cause the performance indicator to be abnormal, based on the value of the performance indicator and the scene data, when the abnormal context data includes the value of the performance indicator, scene data and device operating status data during the time period when the performance indicator is abnormal. Based on the occurrence conditions and the equipment operating status data, determine the compliant variation range of the value of each of the N system status parameters; In the event of an abnormality in the performance indicator, the system status parameter whose actual effective value is outside the corresponding compliance change range is determined as the target system status parameter.

9. The apparatus according to claim 8, characterized in that, The parameter adjustment device further includes an extraction module, which, when the system status parameter is a parameter in the electronic device that directly or indirectly affects the performance indicator, extracts the actual effective value of the system status parameter during the operation of the operating system of the electronic device during the period when the performance indicator is abnormal, through a capture mechanism.

10. The apparatus according to claim 6, characterized in that, The determining module is further configured to determine whether the value of the target system state parameter meets the adjustment conditions; The adjustment module is used to adjust the value of the target system state parameter according to the first parameter value adjustment range corresponding to the adjustment condition, when it is determined that the value of the target system state parameter meets the adjustment condition; The adjustment condition is the condition that eliminates the abnormality of the performance indicator item without causing system resource conflicts or functional failures.

11. An electronic device, characterized in that, include: A processor, a memory, and a program or instructions stored in the memory and executable on the processor, wherein the program or instructions, when executed by the processor, implement the steps of the parameter adjustment method as described in any one of claims 1-5.

12. A computer-readable storage medium, characterized in that, The readable storage medium stores a program or instructions that, when executed by a processor, implement the steps of the parameter adjustment method as described in any one of claims 1-5.

Images