Power consumption optimization method and device, terminal equipment and computer readable storage medium

Abstract

This application discloses a power consumption optimization method, apparatus, terminal device, and computer-readable storage medium. The method includes: acquiring hardware power consumption data and system behavior data of an electronic device; identifying abnormal power consumption data in the hardware power consumption data; determining abnormal behavior data corresponding to the abnormal power consumption data in the system behavior data; determining a target strategy from a strategy library based on the abnormal behavior data; and optimizing the power consumption of the electronic device based on the target strategy. The method of this application can identify power consumption anomalies and further accurately locate the root causes of abnormal power consumption behavior, implementing targeted optimization strategies, thereby improving the accuracy and effectiveness of power consumption optimization and achieving a more refined and reasonable power consumption optimization effect for electronic devices.

Description

Technical Field

[0001] This application relates to the field of data processing technology, specifically to a power consumption optimization method, apparatus, terminal device, and computer-readable storage medium. Background Technology

[0002] As the functions of smart terminals and other electronic devices become increasingly sophisticated, the number of integrated hardware components and system services has significantly increased. During operation, electronic devices involve the collaborative work of multiple hardware units and exhibit complex and diverse system behaviors, leading to increasingly complex power consumption patterns. Existing power optimization techniques typically focus on single-dimensional control methods, often adjusting based on preset rules or empirical thresholds. In complex usage scenarios, it is difficult to accurately pinpoint the root cause of power consumption, resulting in insufficient targeting of power optimization strategies and a tendency for over-restriction or insignificant optimization effects. Summary of the Invention

[0003] This application provides a power consumption optimization method, apparatus, terminal device, and computer-readable storage medium, which can identify power consumption anomalies, further accurately locate the root cause of abnormal power consumption behavior, and implement targeted optimization strategies, thereby improving the accuracy and effectiveness of power consumption optimization and achieving a more refined and reasonable power consumption optimization effect for electronic devices.

[0004] The technical solution adopted by this invention to solve the problem is as follows:

[0005] On the one hand, this application provides a power consumption optimization method, including: Acquire hardware power consumption data and system behavior data of electronic devices; Identify abnormal power consumption data in hardware power consumption data; In the system behavior data, identify the abnormal behavior data corresponding to the abnormal power consumption data; The target policy is determined from the policy library based on abnormal behavior data, and the power consumption of electronic devices is optimized based on the target policy.

[0006] In some embodiments of this application, abnormal power consumption data in the hardware power consumption data is identified, including: Determine the current usage scenario of the electronic device; Based on the anomaly detection model corresponding to the current usage scenario, the hardware power consumption data is analyzed to identify abnormal power consumption data. The anomaly detection model is used to detect anomalies in hardware power consumption data based on the normal power consumption distribution data under the current usage scenario.

[0007] In some embodiments of this application, the current use case of the electronic device is determined, including: Obtain current application information and / or current system status data of electronic devices; Determine the current usage scenario based on current application information and / or current system status data.

[0008] In some embodiments of this application, the abnormal behavior data corresponding to abnormal power consumption data is determined from the system behavior data, including: Obtain the first correlation information; the first correlation information is used to characterize the correlation between hardware power consumption data and system behavior data; Based on the first association information, the abnormal behavior data corresponding to the abnormal power consumption data is determined in the system behavior data.

[0009] In some embodiments of this application, the first related information is determined based on the following methods: Acquire the first timestamp data of hardware power consumption data, and the second timestamp data of system behavior data; Align the timelines of the first timestamp data with those of the second timestamp data to determine the second association information between the first timestamp data and the second timestamp data. Based on the second related information, determine the first related information.

[0010] In some embodiments of this application, a target policy is determined from a policy library based on anomalous behavior data, including: Multiple candidate strategies are identified from the strategy library based on abnormal behavior data; Based on the current usage scenario of the electronic device and user preference information, the target strategy is determined from multiple candidate strategies.

[0011] In some embodiments of this application, the method further includes: Acquire power consumption change data and system performance data of electronic devices after power optimization; The strategies included in the strategy library are updated based on power consumption change data and system performance data.

[0012] Secondly, embodiments of the present invention also provide a power consumption optimization device, comprising: The acquisition module is used to acquire hardware power consumption data and system behavior data of electronic devices; The identification module is used to identify abnormal power consumption data in the hardware power consumption data; The determination module is used to determine the abnormal behavior data corresponding to the abnormal power consumption data in the system behavior data; The optimization module is used to determine the target policy from the policy library based on abnormal behavior data, and to optimize the power consumption of electronic devices based on the target policy.

[0013] Thirdly, this application also provides a terminal device, which includes: One or more processors; Memory; and One or more applications, wherein the applications are stored in memory and configured to be executed by a processor to implement the power optimization method of any of the first aspects.

[0014] Fourthly, embodiments of this application provide a computer-readable storage medium having a computer program stored thereon, the computer program being loaded by a processor to perform the steps in the power optimization method of any of the first aspects.

[0015] The beneficial effects of this invention are as follows: By acquiring hardware power consumption data and system behavior data of electronic devices, analyzing the hardware power consumption data to identify abnormal power consumption data, and further identifying abnormal behavior data corresponding to the abnormal power consumption data in the system behavior data, the cause of power consumption anomalies can be effectively located. Based on the identified abnormal behavior data, a target strategy matching the abnormal behavior is selected from a pre-established strategy library, and the power consumption of the electronic device is optimized using the target strategy. This can identify power consumption anomalies, further accurately locate the root cause of abnormal power consumption behavior, and implement targeted optimization strategies, thereby improving the accuracy and effectiveness of power consumption optimization and achieving a more refined and reasonable power consumption optimization effect for electronic devices. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a schematic diagram of a power optimization system provided in an embodiment of the present invention; Figure 2 This is a flowchart illustrating one embodiment of the power consumption optimization method provided in this invention. Figure 3 This is a schematic diagram of the voltage acquisition framework provided in an embodiment of the present invention; Figure 4 This is a schematic diagram of the current acquisition framework provided in an embodiment of the present invention; Figure 5 This is a schematic block diagram of the power consumption optimization device provided in the embodiments of the present invention; Figure 6 This is a schematic diagram of an embodiment of the terminal device provided in this invention. Detailed Implementation

[0018] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0019] In the description of this application, the terms "first," "second," "third," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined with "first," "second," "third," etc., may explicitly or implicitly include one or more of the stated features.

[0020] In this application, the term "exemplary" is used to mean "used as an example, illustration, or description." Any embodiment described as "exemplary" in this application is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is provided to enable any person skilled in the art to make and use this application. Details are set forth in the following description for purposes of explanation. It should be understood that those skilled in the art will recognize that this application can be made without using these specific details. In other instances, well-known structures and processes are not described in detail to avoid obscuring the description of this application with unnecessary detail. Therefore, this application is not intended to be limited to the embodiments shown, but is consistent with the broadest scope of the principles and features disclosed in this application.

[0021] It should be noted that since the method in this application embodiment is executed in a terminal device, the processing objects of each terminal device exist in the form of data or information, such as time, which is essentially time information. It can be understood that if size, quantity, position, etc. are mentioned in subsequent embodiments, they are all corresponding data that exist so that the terminal device can process them. Specific details will not be elaborated here.

[0022] This application provides a power consumption optimization method, apparatus, terminal device, and computer-readable storage medium, which will be described in detail below.

[0023] Please see Figure 1 , Figure 1 This is a schematic diagram of a power optimization system provided in an embodiment of this application. The power optimization system may include a terminal device 100, which integrates a power optimization device, such as... Figure 1 Terminal devices in the process.

[0024] In this embodiment, the terminal device 100 is mainly used to acquire hardware power consumption data and system behavior data of electronic devices; identify abnormal power consumption data in the hardware power consumption data; determine abnormal behavior data corresponding to the abnormal power consumption data in the system behavior data; determine target strategies from the strategy library based on the abnormal behavior data; and optimize the power consumption of electronic devices based on the target strategies. This can identify power consumption anomalies and further accurately locate the root cause of abnormal power consumption behavior, implement targeted optimization strategies, thereby improving the accuracy and effectiveness of power consumption optimization and achieving a more refined and reasonable power consumption optimization effect for electronic devices.

[0025] In this embodiment, the terminal device 100 can be an independent server, a server network, or a server cluster. For example, the terminal device 100 described in this embodiment includes, but is not limited to, a computer, a network host, a single network server, a set of multiple network servers, or a cloud server composed of multiple servers. The cloud server is composed of a large number of computers or network servers based on cloud computing.

[0026] It is understood that the terminal device 100 used in the embodiments of this application can be a device that includes both receiving and transmitting hardware, that is, a device having receiving and transmitting hardware capable of performing bidirectional communication on a bidirectional communication link. Such a device may include: cellular or other communication devices having a single-line display, a multi-line display, or a cellular or other communication device without a multi-line display. Specifically, the terminal device 100 may be a desktop terminal or a mobile terminal, and the terminal device 100 may also be one of a mobile phone, tablet computer, laptop computer, etc.

[0027] Those skilled in the art will understand that Figure 1 The application environment shown is merely one application scenario of the solution in this application and does not constitute a limitation on the application scenario of the solution in this application. Other application environments may include those that are more specific to this application. Figure 1 The number of more or fewer terminal devices shown, for example Figure 1 Only one terminal device is shown in the diagram. It is understood that the power optimization system may also include one or more other services, which are not specified here.

[0028] In addition, such as Figure 1 As shown, the power optimization system may also include a memory 200 for storing data, such as hardware power consumption data, abnormal power consumption data, system behavior data, abnormal behavior data, etc.

[0029] It should be noted that, Figure 1The schematic diagram of the power optimization system shown is merely an example. The power optimization system and scenario described in this application are for the purpose of more clearly illustrating the technical solutions of this application and do not constitute a limitation on the technical solutions provided in this application. As those skilled in the art will know, with the evolution of power optimization systems and the emergence of new business scenarios, the technical solutions provided in this application are also applicable to similar technical problems.

[0030] First, this application provides a power consumption optimization method. The power consumption optimization method is executed by a power consumption optimization device, which is applied to a terminal device. The power consumption optimization method includes: acquiring hardware power consumption data and system behavior data of an electronic device; identifying abnormal power consumption data in the hardware power consumption data; determining abnormal behavior data corresponding to the abnormal power consumption data in the system behavior data; determining a target strategy from a strategy library based on the abnormal behavior data; and optimizing the power consumption of the electronic device based on the target strategy.

[0031] like Figure 2 The diagram shown is a flowchart of an embodiment of the power consumption optimization method in this application. The power consumption optimization method may include the following steps S201 to S203, as detailed below: Step S201: Obtain hardware power consumption data and system behavior data of the electronic device.

[0032] In one specific embodiment, hardware power consumption data refers to data reflecting the power consumption of various hardware components in an electronic device during operation. These hardware components may include a screen, processor, camera, wireless communication module, etc. System behavior data refers to data characterizing the operating status and activities of the electronic device's operating system and its upper-layer software, and may include application running status, system service call status, network communication behavior, hardware wake-up behavior, etc.

[0033] This step obtains fundamental data reflecting the sources of power consumption in electronic devices from both the hardware and system behavior levels, providing a data foundation for subsequent power consumption anomaly analysis. In practice, a power consumption monitoring module can acquire power consumption data for each hardware component at different time periods, while system behavior data can be obtained through system logs, kernel events, or behavior monitoring interfaces. The power consumption monitoring module can be a newly added module in the electronic device's circuitry; specifically, it calculates the power consumed by a hardware component by acquiring its corresponding voltage and current.

[0034] Step S202: Identify abnormal power consumption data in the hardware power consumption data.

[0035] In one specific embodiment, abnormal power consumption data refers to power consumption data in hardware power consumption data that exhibits an abnormal increase, abnormal duration, or mismatch with the current operating state relative to the normal operating state or expected power consumption level.

[0036] This step can filter out key data that may indicate power consumption problems from a large amount of hardware power consumption data, thereby narrowing down the scope of subsequent analysis and improving the efficiency and focus of power consumption analysis. In practice, the acquired hardware power consumption data can be compared with preset power consumption thresholds, historical power consumption characteristics, or normal models. When the power consumption of a certain hardware component exceeds the reasonable range within a corresponding time period, the power consumption data is identified as abnormal power consumption data.

[0037] Step S203: In the system behavior data, determine the abnormal behavior data corresponding to the abnormal power consumption data.

[0038] In one specific embodiment, abnormal behavior data refers to system behavior data that is associated with abnormal power consumption data and may lead to the occurrence of such abnormal power consumption.

[0039] This step identifies the specific system behaviors corresponding to the identified abnormal power consumption phenomena, thereby tracing the cause of the power consumption anomalies. In practice, based on the timestamps corresponding to the abnormal power consumption data, system behaviors occurring within the same or adjacent time periods can be searched in the system behavior data. From these, behavioral data with a causal relationship to the abnormal power consumption can be filtered out, such as frequent background network communications, abnormal hardware wake-up operations, or unreasonable system service calls. This step transforms power consumption anomalies from simple numerical anomalies into interpretable system behavior anomalies, providing a basis for subsequent strategy selection.

[0040] Step S204: Determine the target policy from the policy library based on the abnormal behavior data, and optimize the power consumption of the electronic device based on the target policy.

[0041] In one specific embodiment, the policy library refers to a pre-stored set of power optimization policies formulated for different types of anomalous behavior. The target policy refers to a power optimization policy that matches the currently identified anomalous behavior data and can suppress or eliminate the anomalous behavior.

[0042] This step enables targeted optimization measures based on the specific root causes of abnormal power consumption, thus avoiding the use of a single, coarse-grained power control method. In practice, corresponding optimization strategies can be matched from the strategy library based on the type of abnormal behavior data. For example, a strategy to restrict background network activity can be selected for unnecessary frequent network transmissions, or a strategy to adjust the screen refresh rate can be selected for unreasonable high screen refresh rates. These target strategies are then applied to the operation control of the electronic device. This step achieves refined and scenario-based optimization of electronic device power consumption, improving the accuracy and effectiveness of power consumption optimization.

[0043] In one specific implementation, identifying abnormal power consumption data in the hardware power consumption data includes: determining the current usage scenario of the electronic device; analyzing the hardware power consumption data based on the anomaly detection model corresponding to the current usage scenario to determine abnormal power consumption data, wherein the anomaly detection model is used to detect anomalies in the hardware power consumption data based on the normal power consumption distribution data under the current usage scenario.

[0044] In this embodiment, the usage scenario refers to the overall usage state and purpose type of an electronic device within a specific time period. It characterizes the main functional activities performed by the electronic device during that time period. For example, the current usage scenario could be web browsing, game operation, navigation, video playback, or standby. The anomaly detection model is a model established for a specific usage scenario to characterize the normal distribution characteristics of hardware power consumption under that scenario. It is used to determine whether the collected hardware power consumption data deviates from this normal distribution. The normal power consumption distribution data refers to the statistical characteristic data of the power consumption levels of each hardware component when the electronic device is in normal operation under the corresponding usage scenario. The anomaly detection model can be a mathematical model, a machine learning model, or a neural network model; this embodiment does not impose specific limitations.

[0045] The purpose of this implementation is to address the significant differences in power consumption characteristics of electronic devices across various usage scenarios. If a uniform anomaly detection standard is used without distinguishing between usage scenarios, normal power consumption under high load conditions may be misjudged as abnormal, or actual power consumption anomalies may be missed under low load conditions, thus reducing the accuracy of power consumption anomaly identification. By introducing usage scenarios and corresponding anomaly detection models, the anomaly power consumption identification standard can be matched with the current operating state of the electronic device, making the judgment of anomaly power consumption more consistent with actual usage conditions, thereby improving the accuracy and reliability of anomaly power consumption identification.

[0046] In practice, the current usage scenario of the electronic device can be determined first, and it can be comprehensively judged whether it is a video playback scenario, a standby scenario, a gaming scenario, or a navigation scenario. Then, the anomaly detection model corresponding to the usage scenario is called to analyze the hardware power consumption data collected in the usage scenario, and the hardware power consumption data is compared with the normal power consumption distribution data in the usage scenario. When the hardware power consumption data deviates from the normal power consumption distribution and reaches the preset anomaly judgment condition, the corresponding hardware power consumption data is determined to be abnormal power consumption data.

[0047] In one specific implementation, determining the current usage scenario of the electronic device includes: acquiring the current application information and / or current system status data of the electronic device; and determining the current usage scenario based on the current application information and / or current system status data.

[0048] In this embodiment, current application information refers to information related to the application running on the electronic device at the current moment, including but not limited to the foreground application identifier, application type, application running status, and the system resources requested by the application. Current system status data refers to data used to characterize the current running status of the electronic device's operating system, including but not limited to processor load, screen display status, network connection status, and the start / stop status of each hardware module.

[0049] In practice, the current application information of the electronic device can be obtained through the system interface. For example, the application package name currently running in the foreground can be identified. At the same time, the current system status data reflecting the overall system operation status can be obtained, such as whether the screen is on, whether the network is active, and the processor load. Then, based on the obtained current application information and / or current system status data, the current operating status of the electronic device can be comprehensively judged according to the preset scenario judgment rules or logical relationships, thereby determining the current usage scenario of the electronic device.

[0050] In one specific implementation, determining the abnormal behavior data corresponding to the abnormal power consumption data in the system behavior data includes: obtaining first association information; the first association information is used to characterize the association relationship between hardware power consumption data and system behavior data; and determining the abnormal behavior data corresponding to the abnormal power consumption data in the system behavior data based on the first association information.

[0051] In this embodiment, the first association information refers to the information used to characterize the correspondence between hardware power consumption data and system behavior data, and is used to describe the temporal, object or logical association between specific hardware power consumption changes and system behavior occurrences.

[0052] In practice, the first association information can be obtained or constructed in advance, which specifically describes the correspondence between the power consumption of each hardware module and the specific system behavior, or the system behavior's call relationship to hardware resources. After identifying abnormal power consumption data, based on the first association information, system behavior data that matches the abnormal power consumption data in terms of association is selected from the system behavior data, thereby determining the selected system behavior data as the abnormal behavior data corresponding to the abnormal power consumption data.

[0053] The above approach can accurately identify behavioral data related to abnormal power consumption at the system behavior level, reduce the interference of irrelevant behaviors on the analysis results, and provide a more reliable basis for selecting power consumption optimization strategies based on abnormal behavior data, thereby improving the accuracy and effectiveness of the overall power consumption optimization scheme.

[0054] In one specific implementation, the first association information is determined based on the following method: acquiring first timestamp data of hardware power consumption data and second timestamp data of system behavior data; aligning the timelines of the first timestamp data and the second timestamp data to determine second association information between the first timestamp data and the second timestamp data; and determining the first association information based on the second association information.

[0055] In this embodiment, the first timestamp data refers to the time identifier information corresponding to the hardware power consumption data, used to characterize the specific time point or time interval in which each hardware power consumption data is generated. The second timestamp data refers to the time identifier information corresponding to the system behavior data, used to characterize the specific time point or time interval in which each system behavior occurs. Timeline alignment refers to mapping the first and second timestamp data to the same time base, so as to compare and analyze data from different sources in the time dimension. The second correlation information refers to the correlation result obtained after timeline alignment based on the time correspondence between the first and second timestamp data, used to characterize the temporal correlation between changes in hardware power consumption and the occurrence of system behavior.

[0056] In practice, the first timestamp data corresponding to the hardware power consumption data and the second timestamp data corresponding to the system behavior data can be obtained separately. Then, the first timestamp data and the second timestamp data are aligned with a unified time base to make the data from different sources comparable on the same time axis. Based on the aligned timeline, the correspondence between the hardware power consumption data and the system behavior data in the time dimension is determined, thereby forming the second association information. Finally, based on the second association information, the first association information that can reflect the association between the hardware power consumption data and the system behavior data is comprehensively determined.

[0057] In one specific implementation, determining the target policy from the policy library based on abnormal behavior data includes: determining multiple candidate policies from the policy library based on abnormal behavior data; and determining the target policy from the multiple candidate policies based on the current usage scenario of the electronic device and the user's preference information.

[0058] In this implementation, user preference information refers to user-defined preference data regarding performance, power consumption, and user experience, such as screen brightness preferences, notification frequency preferences, or performance mode selections. Since different abnormal behaviors may correspond to multiple feasible optimization strategies, directly selecting a single strategy may result in insufficient power consumption optimization or negatively impact user experience. By introducing candidate strategy screening and combining usage scenarios and user preferences to determine the target strategy, we can ensure power consumption optimization while also considering device usage needs and user experience.

[0059] In practice, multiple candidate strategies related to the behavior type can be matched in the strategy library based on the abnormal behavior data. These candidate strategies may include adjusting the execution frequency of background tasks, restricting the activation of non-critical hardware modules, reducing the screen refresh rate, or adjusting network transmission strategies. Subsequently, based on the current usage scenario of the electronic device, such as video playback, gaming, or standby mode, and user preference information, such as performance mode or power saving mode settings, the candidate strategies are comprehensively evaluated to select the strategy that best matches the current scenario and user preferences as the target strategy. Finally, the target strategy is applied to the operation control of the electronic device to optimize power consumption caused by abnormal behavior.

[0060] The above solutions can provide personalized and scenario-based power consumption optimization measures for different abnormal behaviors, making power consumption optimization both accurate and in line with user habits, thereby improving the overall energy efficiency and user experience of electronic devices.

[0061] In one specific implementation, the method further includes: acquiring power consumption change data and system performance data of the electronic device after power consumption optimization; and updating the strategies included in the strategy library based on the power consumption change data and system performance data.

[0062] In this embodiment, power consumption change data refers to the power consumption changes of each hardware component after the electronic device implements a power consumption optimization strategy, reflecting the actual impact of the optimization strategy on hardware power consumption. System performance data refers to the overall or key system performance data of the electronic device during the implementation of the power consumption optimization strategy, including but not limited to processor load, application response speed, frame rate performance, and network transmission efficiency.

[0063] Since electronic devices may exhibit different power consumption and performance characteristics under different usage environments and user behaviors, if the policy library is fixed, the preset policies may not be able to continuously adapt to actual operating conditions, thereby reducing the effectiveness of power optimization. Therefore, after power optimization is implemented, the optimization effect can be evaluated in real time to update the policies in the library and further improve the adaptability and accuracy of the policy library.

[0064] In practice, power consumption change data and system performance data of the electronic device after power consumption optimization using the target strategy can be obtained, demonstrating the actual effect of the strategy implementation. Subsequently, based on the power consumption change data and system performance data, the strategies in the strategy library are updated, including but not limited to adjusting strategy parameters, modifying strategy application conditions, or optimizing strategy priorities. This ensures that the strategies stored in the library can more accurately match the optimization needs under different abnormal behaviors and usage scenarios. Through this step, a closed-loop optimization mechanism is formed, allowing the strategy library to continuously self-correct and improve during actual use, thereby continuously improving the effectiveness of power consumption optimization and the overall energy efficiency of the electronic device, while ensuring that system performance is not affected by improper power consumption optimization.

[0065] like Figure 3 As shown, V_BUCK_CTRL is the signal controlling the voltage regulator (BUCK converter). Connected to VBUCK CTRL in the power management chip, it regulates and controls the device's input voltage. Voltage regulators are typically used to convert higher input voltages to lower, stable output voltages to power other components such as processors and displays. VPROC_IN is the measurement point for the CPU input voltage. It is connected to the VPROC power supply in the power management chip via a wire, ensuring a stable power supply to the CPU. VCORE_IN is the CPU core voltage input measurement point, connected to the VCORE in the power management chip, providing the necessary voltage to the CPU core. VMODEM_IN is the measurement point for the wireless communication module (usually a modem) voltage input, connected to the VMODEM in the power management chip, providing a stable voltage to the modem. Figure 3 This diagram illustrates the voltage measurement paths between different voltage input points (PMIC power, CPU power, modem power, etc.) and the power management chip (PMIC). Each voltage measurement point is connected to a power module within the PMIC and filtered by a capacitor. The purpose of measuring these voltage signals is to monitor the power input of various modules in electronic devices in real time, in order to calculate the power consumption of each hardware module (such as the CPU, modem, etc.).

[0066] like Figure 4As shown, a BUCK is a buck converter used to convert a higher input voltage to a lower output voltage to power modules such as processors and modems. BUCKs are typically connected to SOCs and modems to provide stable power to these high-power components. A DLDO is a low-dropout regulator used to provide precise voltage output to devices, especially suitable for applications where the power input and output voltages are close. DLDOs are typically connected to modules such as PMICs, USB, and SIMs to provide them with a stable low-voltage power supply. An ALDO is a low-dropout regulator specifically designed to provide stable power to audio modules. It is typically connected to modules such as Audio and RFFE to ensure audio signals operate in a low-power, high-fidelity environment. RFFE stands for Radio Frequency Front-End, used for the radio frequency section of wireless communication. An SLDO is a sensor low-dropout regulator primarily used to power sensors in devices (such as cameras and memory). SLDOs are typically connected to modules such as Cameras and SRAM (Static Random Access Memory) to ensure these modules receive a stable power supply when needed. Battery charging management chips can monitor the current flowing to hardware modules in real time to calculate the power consumption of each hardware module (such as CPU, modem, etc.).

[0067] To better implement the power consumption optimization method in the embodiments of this application, a power consumption optimization device is also provided in the embodiments of this application, such as... Figure 5 As shown, the power optimization device 500 includes: The acquisition module 510 is used to acquire hardware power consumption data and system behavior data of the electronic device; The identification module 520 is used to identify abnormal power consumption data in the hardware power consumption data; The determination module 530 is used to determine the abnormal behavior data corresponding to the abnormal power consumption data in the system behavior data; The optimization module 540 is used to determine the target policy from the policy library based on abnormal behavior data, and to optimize the power consumption of the electronic device based on the target policy.

[0068] In this embodiment, hardware power consumption data and system behavior data of an electronic device are acquired; abnormal power consumption data in the hardware power consumption data are identified; abnormal behavior data corresponding to the abnormal power consumption data is determined in the system behavior data; a target strategy is determined from the strategy library based on the abnormal behavior data, and power consumption of the electronic device is optimized based on the target strategy. This can identify power consumption anomalies and further accurately locate the root cause of abnormal power consumption behavior, implement targeted optimization strategies, thereby improving the accuracy and effectiveness of power consumption optimization and achieving a more refined and reasonable power consumption optimization effect for electronic devices.

[0069] In some embodiments of this application, the identification module 520 identifies abnormal power consumption data in the hardware power consumption data, including: Determine the current usage scenario of the electronic device; Based on the anomaly detection model corresponding to the current usage scenario, the hardware power consumption data is analyzed to identify abnormal power consumption data. The anomaly detection model is used to detect anomalies in hardware power consumption data based on the normal power consumption distribution data under the current usage scenario.

[0070] In some embodiments of this application, the identification module 520 determines the current usage scenario of the electronic device, including: Obtain current application information and / or current system status data of electronic devices; Determine the current usage scenario based on current application information and / or current system status data.

[0071] In some embodiments of this application, the determining module 530 determines the abnormal behavior data corresponding to the abnormal power consumption data in the system behavior data, including: Obtain the first correlation information; the first correlation information is used to characterize the correlation between hardware power consumption data and system behavior data; Based on the first association information, the abnormal behavior data corresponding to the abnormal power consumption data is determined in the system behavior data.

[0072] In some embodiments of this application, the power optimization device 500 determines the first association information based on the following method: Acquire the first timestamp data of hardware power consumption data, and the second timestamp data of system behavior data; Align the timelines of the first timestamp data with those of the second timestamp data to determine the second association information between the first timestamp data and the second timestamp data. Based on the second related information, determine the first related information.

[0073] In some embodiments of this application, the optimization module 540 determines the target policy from the policy library based on abnormal behavior data, including: Multiple candidate strategies are identified from the strategy library based on abnormal behavior data; Based on the current usage scenario of the electronic device and user preference information, the target strategy is determined from multiple candidate strategies.

[0074] In some embodiments of this application, the power optimization device 500 is further used for: Acquire power consumption change data and system performance data of electronic devices after power optimization; The strategies included in the strategy library are updated based on power consumption change data and system performance data.

[0075] This application embodiment also provides a terminal device that integrates any of the power consumption optimization devices provided in this application embodiment. The terminal device includes: One or more processors; Memory; and One or more applications, wherein the applications are stored in memory and configured to be executed by the processor as steps in the power optimization method of any of the embodiments described above.

[0076] This application also provides a terminal device that integrates any of the power consumption optimization devices provided in this application. For example... Figure 6 As shown, it illustrates a structural schematic diagram of the terminal device involved in the embodiments of this application. Specifically: The terminal device may include components such as a processor 601 with one or more processing cores, a memory 602 with one or more computer-readable storage media, a power supply 603, and an input unit 604. Those skilled in the art will understand that... Figure 6 The terminal device structure shown does not constitute a limitation on the terminal device and may include more or fewer components than shown, or combine certain components, or have different component arrangements. Wherein: The processor 601 is the control center of the terminal device. It connects various parts of the terminal device via various interfaces and lines. By running or executing software programs and / or modules stored in the memory 602, and by calling data stored in the memory 602, it performs various functions and processes data of the terminal device, thereby providing overall monitoring of the terminal device. Optionally, the processor 601 may include one or more processing cores; preferably, the processor 601 may integrate an application processor and a modem processor, wherein the application processor mainly handles the operating system, user interface, and applications, and the modem processor mainly handles wireless communication. It is understood that the modem processor may not be integrated into the processor 601.

[0077] The memory 602 can be used to store software programs and modules. The processor 601 executes various functional applications and data processing by running the software programs and modules stored in the memory 602. The memory 602 may mainly include a program storage area and a data storage area. The program storage area may store the operating system, application programs required for at least one function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the terminal device, etc. In addition, the memory 602 may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device. Accordingly, the memory 602 may also include a memory controller to provide the processor 601 with access to the memory 602.

[0078] The terminal device also includes a power supply 603 that supplies power to the various components. Preferably, the power supply 603 can be logically connected to the processor 601 through a power management system, thereby enabling functions such as charging, discharging, and power consumption management through the power management system. The power supply 603 may also include one or more DC or AC power supplies, recharging systems, power fault detection circuits, power converters or inverters, power status indicators, and other arbitrary components.

[0079] The terminal device may also include an input unit 604, which can be used to receive input digital or character information, and generate keyboard, mouse, joystick, optical or trackball signal inputs related to user settings and function control.

[0080] Although not shown, the terminal device may also include a display unit, etc., which will not be described in detail here. Specifically, in this embodiment, the processor 601 in the terminal device loads the executable files corresponding to the processes of one or more applications into the memory 602 according to the following instructions, and the processor 601 runs the applications stored in the memory 602 to realize various functions, as follows: Acquire hardware power consumption data and system behavior data of electronic devices; Identify abnormal power consumption data in hardware power consumption data; In the system behavior data, identify the abnormal behavior data corresponding to the abnormal power consumption data; The target policy is determined from the policy library based on abnormal behavior data, and the power consumption of electronic devices is optimized based on the target policy.

[0081] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be performed by instructions, or by instructions controlling related hardware. These instructions can be stored in a computer-readable storage medium and loaded and executed by a processor.

[0082] Therefore, embodiments of this application provide a computer-readable storage medium, which may include: read-only memory (ROM), random access memory (RAM), a magnetic disk, or an optical disk, etc. A computer program is stored thereon, and the computer program is loaded by a processor to execute the steps in any of the power optimization methods provided in embodiments of this application. For example, the computer program loaded by the processor can execute the following steps: Acquire hardware power consumption data and system behavior data of electronic devices; Identify abnormal power consumption data in hardware power consumption data; In the system behavior data, identify the abnormal behavior data corresponding to the abnormal power consumption data; The target policy is determined from the policy library based on abnormal behavior data, and the power consumption of electronic devices is optimized based on the target policy.

[0083] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the detailed descriptions of other embodiments above, which will not be repeated here.

[0084] In practice, each of the above units or structures can be implemented as an independent entity or can be arbitrarily combined to be implemented as the same or several entities. For the specific implementation of each of the above units or structures, please refer to the previous method embodiments, which will not be repeated here.

[0085] For details on the implementation of each of the above operations, please refer to the previous examples, which will not be repeated here.

[0086] The above provides a detailed description of a power consumption optimization method, apparatus, terminal device, and computer-readable storage medium provided in the embodiments of this application. Specific examples have been used to illustrate the principles and implementation methods of this application. The description of the above embodiments is only for the purpose of helping to understand the method and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A power consumption optimization method, characterized in that, include: Acquire hardware power consumption data and system behavior data of electronic devices; Identify abnormal power consumption data in the hardware power consumption data; From the system behavior data, determine the abnormal behavior data corresponding to the abnormal power consumption data; Based on the abnormal behavior data, a target policy is determined from the policy library, and the power consumption of the electronic device is optimized based on the target policy.

2. The power consumption optimization method according to claim 1, characterized in that, The identification of abnormal power consumption data in the hardware power consumption data includes: Determine the current usage scenario of the electronic device; Based on the anomaly detection model corresponding to the current usage scenario, the hardware power consumption data is analyzed to determine the abnormal power consumption data. The anomaly detection model is used to detect anomalies in the hardware power consumption data based on the normal power consumption distribution data under the current usage scenario.

3. The power consumption optimization method according to claim 2, characterized in that, Determining the current usage scenario of the electronic device includes: Obtain the current application information and / or current system status data of the electronic device; The current usage scenario is determined based on the current application information and / or the current system status data.

4. The power consumption optimization method according to claim 1, characterized in that, The step of determining the abnormal behavior data corresponding to the abnormal power consumption data in the system behavior data includes: Obtain first association information; the first association information is used to characterize the association relationship between the hardware power consumption data and the system behavior data; Based on the first association information, the abnormal behavior data corresponding to the abnormal power consumption data is determined from the system behavior data.

5. The power consumption optimization method according to claim 4, characterized in that, The first associated information is determined based on the following method: Acquire the first timestamp data of the hardware power consumption data and the second timestamp data of the system behavior data; Align the timelines of the first timestamp data and the second timestamp data to determine the second association information between the first timestamp data and the second timestamp data; The first association information is determined based on the second association information.

6. The power consumption optimization method according to claim 1, characterized in that, The step of determining the target policy from the policy library based on the abnormal behavior data includes: Based on the abnormal behavior data, multiple candidate strategies are determined from the strategy library; The target strategy is determined from the plurality of candidate strategies based on the current usage scenario of the electronic device and the user's preference information.

7. The power consumption optimization method according to claim 1, characterized in that, Also includes: Acquire power consumption change data and system performance data of the electronic device after power consumption optimization; The strategies included in the strategy library are updated based on the power consumption change data and the system performance data.

8. A power consumption optimization device, characterized in that, include: The acquisition module is used to acquire hardware power consumption data and system behavior data of electronic devices; The identification module is used to identify abnormal power consumption data in the hardware power consumption data; The determining module is used to determine the abnormal behavior data corresponding to the abnormal power consumption data from the system behavior data; An optimization module is used to determine a target policy from a policy library based on the abnormal behavior data, and to optimize the power consumption of the electronic device based on the target policy.

9. A terminal device, characterized in that, The terminal device includes: one or more processors, a memory, and one or more applications, wherein the one or more applications are stored in the memory and configured to be executed by the processor to implement the power optimization method of any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, It stores a computer program, which is loaded by a processor to perform the steps of the power optimization method according to any one of claims 1 to 7.

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