Notebook computer endurance optimization method combined with AI technology

Abstract

The invention discloses a notebook computer endurance optimization method combined with an AI technology, and relates to the technical field related to data processing, the method comprises the following steps: obtaining a user endurance optimization target, the user endurance optimization target comprising target endurance time and endurance constraint conditions; obtaining the residual electric quantity of the notebook computer of the user; according to the residual electric quantity and the target endurance time of the notebook computer of the user, obtaining an endurance target threshold value; positioning optimization target parameters according to the endurance constraint condition; and taking the endurance target threshold as an optimization target, and performing strategy optimization on the optimization target parameter according to the endurance constraint condition to obtain an endurance control parameter strategy. According to the method and the device, the technical problem that dynamic endurance optimization cannot be carried out according to personalized requirements of the user due to the fact that the use scene and the actual operation state of the user are not fully considered in the prior art is solved, and the technical effect of intelligently prolonging the endurance time of the battery of the notebook computer on the premise of meeting the specific requirements of the user is achieved.

Description

Technical Field

[0001] The present application relates to the technical field related to data processing, and in particular to a method for optimizing the battery life of a laptop computer in combination with AI technology. Background Art

[0002] With the continuous improvement of the performance of modern laptops, especially the enhancement of processor and graphics chip performance, laptops have been widely used in many fields such as work, entertainment, and education. However, the battery life of laptops has always been an important issue for users during use, especially in the absence of an external power supply. How to effectively extend the battery life has become the key to optimizing the user experience. Traditional laptop battery life optimization methods achieve the best battery life by dynamically adjusting hardware resources (such as processors, memory, graphics cards, etc.) and combining software-level power consumption management. To a certain extent, they solve the problem of laptop battery life. However, they rely on fixed optimization strategies or power management modes based on static settings, and usually fail to fully consider the user's usage scenarios, needs, and the actual operating status of the device. They cannot perform personalized battery life optimization based on the specific needs of different users, thus affecting the battery life of laptops.

[0003] At present, the technologies related to laptop battery life optimization fail to fully consider the user's usage scenarios and actual operating status, which leads to the technical problem of being unable to dynamically optimize the battery life according to the user's personalized needs. Summary of the invention

[0004] This application solves the technical problem in the prior art of failing to fully consider the user's usage scenarios and actual operating status, which leads to the inability to dynamically optimize the battery life according to the user's personalized needs, by providing a laptop computer battery life optimization method combined with AI technology. This achieves the technical effect of intelligently extending the battery life of the laptop computer while meeting the user's specific needs.

[0005] The present application provides a method for optimizing the battery life of a laptop computer in combination with AI technology, including: obtaining a user's battery life optimization target, the user's battery life optimization target including a target battery life time and a battery life constraint condition, the battery life constraint condition being a software or hardware that must be used in setting the user's battery life; obtaining the remaining power of the user's laptop computer; obtaining a battery life target threshold value based on the remaining power of the user's laptop computer and the target battery life time; locating optimization target parameters based on the battery life constraint condition; taking the battery life target threshold value as the optimization target, and performing strategy optimization on the optimization target parameters based on the battery life constraint condition to obtain a battery life control parameter strategy.

[0006] In a possible implementation, the method for optimizing the battery life of a laptop computer combined with AI technology further performs the following processing: obtaining the charging time and charging constraints set by the user, wherein the charging constraints are application systems that cannot be closed during charging; obtaining charging access power parameters through a charging interface; and optimizing charging parameters based on the charging time and charging access power parameters set by the user and in combination with the charging constraints, with the goal of maximizing the charging amount, to obtain a charging optimization control strategy.

[0007] In a possible implementation, the laptop battery life optimization method combined with AI technology also performs the following processing: obtaining the health status information and charge and discharge monitoring log of the laptop battery, fitting the charging power endurance characteristics according to the health status information and charge and discharge monitoring log, and the charging power endurance characteristics have a discharge attenuation rate; obtaining the current power after charging, and fitting and projecting the current power using the charging power endurance characteristics to obtain a mapping attenuation rate performance of the current power; according to the standard power attenuation rate, performing standard share division on the mapping attenuation rate performance of the current power to obtain the remaining power of the user's laptop.

[0008] In a possible implementation, the laptop battery life optimization method combined with AI technology also performs the following processing: calculating the average power consumption based on the remaining power of the user's laptop computer and the target battery life time, the average power consumption being the maximum power consumption per hour; performing power consumption calculation based on the average power consumption to obtain the average power consumption, the average power consumption being the maximum power per hour; performing a proportion calculation based on the average power consumption and the standard power consumption average, and configuring a segmentation time granularity based on the proportion relationship, the segmentation time granularity including a single level or multiple levels; performing grid segmentation of the average power consumption based on the segmentation time granularity, and configuring a battery life target threshold for each segmented grid, the battery life target threshold having a grid time granularity identifier.

[0009] In a possible implementation, the laptop battery life optimization method combined with AI technology also performs the following processing: performing constrained power consumption analysis according to the battery life constraint conditions to obtain constrained power consumption characteristics; establishing an energy-saving adjustment relationship for the optimization target parameters to construct a target evaluation function; establishing an alignment constraint relationship between the battery life target threshold and the constrained power consumption characteristics to obtain a battery life target power consumption value; based on the target evaluation function, with the goal of minimizing the approximate difference of the battery life target power consumption value, optimizing the target parameter control to obtain the battery life control parameter strategy.

[0010] In a possible implementation, the laptop battery life optimization method combined with AI technology also performs the following processing: granularity clustering of the battery life target thresholds according to time granularity identifiers, and continuous splicing according to the timing relationship to construct timing battery life target thresholds for each granularity; establishing timing characteristics of constrained power consumption based on the constrained power consumption characteristics; and establishing an alignment constraint relationship between the timing battery life target thresholds of each granularity and the constrained power consumption characteristics according to the time correspondence.

[0011] In a possible implementation, the laptop battery life optimization method combined with AI technology further performs the following processing: establishing an initial solution space based on historical battery life optimization samples of the optimization target parameters; According to the target evaluation function, an excellent candidate solution set is searched from the initial solution space, and the excellent candidate solution set is an initial solution whose target evaluation result reaches the target ranking threshold; a search center solution and a search direction solution are randomly obtained from the excellent candidate solution set; the search center solution is used as the search center, and the search direction solution is used as the optimization direction. Based on the target evaluation function, an iterative search and update is performed with the minimum approach difference of the endurance target power consumption value as the goal, so as to obtain the endurance control parameter strategy, wherein the expression of the target evaluation function is: , The target power consumption value for battery life. To optimize the total power consumption of the target parameters, is the i-th optimization target parameter, and n is the number of optimization target parameters.

[0012] In a possible implementation, the laptop battery life optimization method combined with AI technology also performs the following processing: when there are multiple user battery life optimization targets, obtain a preset penalty coefficient for each user battery life optimization target, and the preset penalty coefficient is proportional to the influence of the battery life optimization target; according to the correspondence between the preset penalty coefficient and the user battery life optimization target, construct a penalty evaluation function, add the penalty evaluation function to the target evaluation function, and perform optimization target parameter control optimization.

[0013] Through the laptop battery life optimization method combined with AI technology proposed in this application, the user's battery life optimization target is obtained, and the user's battery life optimization target includes the target battery life time and battery life constraints; the remaining power of the user's laptop is obtained; according to the remaining power of the user's laptop and the target battery life time, the battery life target threshold is obtained; according to the battery life constraints, the optimization target parameters are located; with the battery life target threshold as the optimization target, the optimization target parameters are optimized according to the battery life constraints, and the battery life control parameter strategy is obtained. The technical problem that the existing technology fails to fully consider the user's usage scenarios and actual operating status, which leads to the inability to dynamically optimize the battery life according to the user's personalized needs, is solved, and the technical effect of intelligently extending the battery life of laptops on the premise of meeting the specific needs of users is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solution of the embodiment of the present invention, the accompanying drawings of the embodiment of the present invention will be briefly introduced below. A flow chart is used in the present application to illustrate the operations performed by the system according to the embodiment of the present application. It should be understood that the preceding or following operations are not necessarily performed accurately in order. On the contrary, various steps can be processed in reverse order or simultaneously as needed. At the same time, other operations can also be added to these processes, or one or more operations can be removed from these processes.

[0015] Figure 1 A flowchart of a method for optimizing the battery life of a laptop computer combined with AI technology provided in an embodiment of the present application; Figure 2 A schematic diagram of a process for obtaining the remaining power in a method for optimizing the battery life of a laptop computer combined with AI technology provided in an embodiment of the present application. DETAILED DESCRIPTION

[0016] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0017] In order to make the objectives, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings. The described embodiments should not be regarded as limiting the present application. All other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of this application.

[0018] In the following description, reference is made to "some embodiments", which describe a subset of all possible embodiments, but it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict, and the terms "first\second" involved are merely to distinguish similar objects and do not represent a specific ordering of objects. The terms "including" and "having" and any variations are intended to cover non-exclusive inclusions, for example, a process, method, product, or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those generally understood by technicians in the technical field of this application. The terms used herein are for the purpose of describing the embodiments of the present application only.

[0019] The present application embodiment provides a method for optimizing the battery life of a laptop computer by combining AI technology, such as Figure 1 As shown, the method includes: Step S100, obtaining a user's battery life optimization target, wherein the user's battery life optimization target includes a target battery life time and a battery life constraint condition, wherein the battery life constraint condition is software or hardware that must be used in the user's battery life.

[0020] Preferably, obtaining the user's battery life optimization goal refers to collecting the user's specific demand goals for laptop computer battery life optimization by some means (which may be user input or automatic recognition), wherein the user's battery life optimization goal includes target battery life time and battery life constraints. Specifically, the target battery life time refers to the time the user hopes the laptop computer can continue to work with the current remaining battery power. For example, the user may hope that the computer can be used for at least 3 hours with 20% remaining power, or the expected battery life in different usage scenarios (for example, 8 hours in office mode and 4 hours in video playback mode). The setting of the target battery life time is closely related to the user's specific needs and usage scenarios, such as whether working while going out or performing high-load tasks.

[0021] Preferably, the battery life constraints refer to the key software or hardware that must be continuously running when the user sets the battery life target, that is, the key functions or components that must remain running, which cannot be ignored or over-adjusted during the battery life optimization process. These constraints are usually explicitly specified by the user, requiring that during the battery optimization process, the performance of certain specific functions cannot be turned off or reduced. They may include software that must be used. For example, the user may need to continue using a certain professional software (such as design software, video conferencing and office software, programming IDE or development tools, etc.) in battery mode. Even if these software consumes more power, they cannot be completely turned off or their performance reduced; they also include hardware that must be used. For example, the user needs certain hardware components in the laptop (such as Wi-Fi modules, Bluetooth modules, USB and other external devices, specific sensors, etc.) to continue working. The operation of these hardware will affect the battery life, but they cannot be turned off due to battery life optimization.

[0022] Preferably, assume that a user wants to extend the battery life of a laptop to 4 hours. Within this target battery life, the user still needs to use certain high-power consumption software (such as video editing software) while keeping Wi-Fi and Bluetooth turned on to facilitate remote work or video conferencing. When optimizing the battery life, the battery life constraints of these software and hardware will be taken into account to ensure that they continue to run while extending the battery life through other means (such as adjusting the processor frequency, reducing the display brightness, etc.).

[0023] Furthermore, step S100 also includes step S110, obtaining the charging time and charging constraints set by the user, wherein the charging constraints are application systems that cannot be shut down during charging; step S120, obtaining charging access power parameters through the charging interface; and step S130, based on the charging time and charging access power parameters set by the user, combined with the charging constraints, optimizing charging parameters with the goal of maximizing the charging amount, to obtain a charging optimization control strategy.

[0024] Preferably, charging optimization is performed based on the charging time and charging constraints set by the user to maximize the charging amount, and the charging access power parameters and the user's constraints are taken into consideration. Specifically, the charging time and charging constraints set by the user are obtained. The user-set charging time refers to the charging time preset by the user, that is, the user hopes that the device will be fully charged or charged within a certain time. For example, the user hopes that the laptop will be fully charged one hour before going out. The charging constraints are some specific restrictions that need to be considered during the charging process. Usually, the user needs to ensure that certain important applications or functions can still be used during charging. For example, the user may require that certain key applications (such as video playback, important work software, etc.) must remain running during the charging process; the charging access power parameters, including charging power, voltage, and current, are obtained through the sensor or power management chip of the charging interface, reflecting the power status provided by the charger to the device during the charging process.

[0025] Preferably, the charging optimization parameters are optimized based on the charging time and charging access power parameters set by the user, combined with the charging constraints, with the goal of maximizing the charging amount, that is, within the charging time set by the user, as much electrical energy as possible is input into the battery, while balancing the charging time, charging power and constraints to ensure that the charging amount is maximized in the shortest time, and using optimization algorithms (such as gradient descent, genetic algorithms, etc.) to adjust multiple parameters in the charging process to find the best charging strategy, such as optimizing the charging speed by adjusting the charging power, adjusting the power consumption of the device to avoid affecting the normal use of the device during charging, and maximizing the charging amount within a given time by combining these parameters, and finally obtaining a charging optimization control strategy, including charging power control, adjusting the output power of the charger to ensure the maximization of the charging amount; device power consumption management, if the device has multiple applications running, the charging optimization strategy may adjust the power consumption of these applications or allocate resources to the applications; balance between charging time and power, optimize the time arrangement of the charging process, shorten the charging time as much as possible, and ensure that the battery is charged to its maximum capacity.

[0026] Step S200, obtaining the remaining power of the user's laptop computer.

[0027] Preferably, obtaining the remaining power of the user's laptop computer refers to real-time monitoring and obtaining the current remaining battery power of the laptop computer by some means (usually hardware sensors and the battery management module of the operating system), which is usually presented in the form of a percentage, indicating the proportion of the current remaining battery power to the total battery capacity. Specifically, the operating system (such as Windows, macOS, etc.) will periodically obtain the real-time status information of the battery from the hardware battery management module and pass it to the user interface. The user can see a battery icon in the system tray or menu bar, and the icon will display the percentage of remaining power. Calculating and monitoring the battery power of the laptop computer is achieved through a battery control chip (usually an intelligent management chip embedded in the battery). The battery control chip can measure parameters such as the battery voltage, current and temperature to calculate the remaining power of the battery. For example, if the maximum capacity of the battery is 50Wh and the current battery power is 25Wh, the remaining power is 50%; in some advanced application scenarios, software (such as battery optimization tools or power management software) calls the API provided by the operating system to read the battery status in real time to help determine the current battery health, remaining power, and predicted remaining usage time (for example, 2 hours remaining).

[0028] Further, such as Figure 2 As shown, step S200 also includes step S210, obtaining the health status information and charge and discharge monitoring log of the laptop computer battery, fitting the charging power endurance characteristics according to the health status information and the charge and discharge monitoring log, and the charging power endurance characteristics have a discharge attenuation rate; step S220, obtaining the current power after charging is completed, fitting and projecting the current power using the charging power endurance characteristics, and obtaining the mapping attenuation rate performance of the current power; step S230, according to the standard power attenuation rate, performing standard share division on the mapping attenuation rate performance of the current power, and obtaining the remaining power of the user's laptop computer.

[0029] Preferably, the remaining power of the laptop computer is calculated by using fitting and projection methods through analysis of the battery health status information and the charge and discharge monitoring log, and the battery attenuation and the fact that some batteries are charged with false power and lose power quickly are taken into consideration, so as to finally provide a more accurate remaining power value. Specifically, the health status information and charge and discharge monitoring log of the laptop computer battery are obtained, wherein the battery health status information refers to the degree of decay of the current state of the battery relative to its initial state, and generally includes the capacity decay of the battery, that is, the ratio of the current maximum available capacity of the battery to the maximum capacity of a new battery, the number of cycles of the battery, that is, the number of times the battery has been charged and discharged, and the battery performance will generally decline after exceeding a certain number of times, and the internal resistance, that is, the resistance inside the battery may It increases with the use time, affecting the battery charging and discharging efficiency; the charge and discharge monitoring log records the battery's charging status, discharge status, voltage change and other data at different time points, which helps to understand the battery's behavior in actual use (for example, charging rate, discharge rate, operating temperature, etc.), and is used to analyze changes in battery performance; according to the health status information and the charge and discharge monitoring log, the charging capacity endurance characteristics with a discharge decay rate are fitted, that is, by analyzing the battery's historical charge and discharge records, the battery's usage characteristics after charging are fitted, especially its endurance performance at a specific capacity. The discharge decay rate refers to the relationship between the battery's capacity and time during the discharge process. As the battery ages, the discharge rate may gradually increase, that is, the rate at which the battery capacity decreases during discharge will become faster.

[0030] Preferably, the current power after charging is completed is obtained, that is, the remaining power of the battery after charging is completed, which usually refers to the charging power in the current state of the battery (such as the actual power when the battery is displayed as 100%). The current power is mapped according to the fitted charging power endurance characteristics, that is, the current power is further "projected" using information such as the discharge decay rate to predict the current power consumption rate in the future. Fitting projection refers to mapping the decay characteristics of the current power through a mathematical model (such as the fitted discharge decay rate), so as to calculate the power decay behavior in the future discharge process, that is, obtaining the mapped decay rate performance of the current power, indicating the actual consumption speed and decay trend of the current power, reflecting that under the existing battery health state, the discharge rate of the current power increases with the increase of battery usage time, indicating that the efficiency of the battery is reduced and the discharge process is faster.

[0031] Preferably, the mapped decay rate performance of the current power is divided into standard shares according to the standard power decay rate, wherein the standard power decay rate refers to the normal battery decay rate defined according to standards such as battery health and discharge rules, and the standard share division refers to comparing the mapped decay rate of the current power with the standard decay rate, analyzing the decay performance of the battery in the current state, and dividing it into different "shares" or levels. For example, the decay rate of the current power of the battery may be divided into several levels (such as low, medium, and high), each level representing a different health status and battery life, thereby obtaining the remaining power of the user's laptop computer, that is, the actual power after the standardized power decay rate division, taking into account factors such as the battery's health status, power decay characteristics, and charging and discharging history, and being able to more accurately predict the actual available time of the battery, which is more accurate than the remaining power percentage, and significantly improves the prediction accuracy of battery life.

[0032] Step S300, obtaining a battery life target threshold value according to the remaining power of the user's laptop computer and the target battery life time.

[0033] Preferably, a battery life target threshold is determined by calculating and analyzing the user's current battery remaining power and the user's desired target battery life, indicating the power consumption limit that needs to be reached to ensure that the user's target battery life is achieved. For example, if the total capacity of the battery is 50Wh and the remaining power is 25Wh, the remaining power is 50%, and the target battery life is 5 hours. The battery consumption rate is calculated based on the remaining power and the target battery life, that is, 5Wh of power needs to be consumed per hour, and the battery life target threshold is 5Wh of power consumed per hour to ensure that the battery can support 5 hours of use. The operation of the laptop is optimized according to the battery life target threshold (such as adjusting the CPU frequency, display brightness, etc.) to ensure that the usage time is extended as much as possible with limited battery power.

[0034] Furthermore, step S300 also includes step S310, calculating the average power consumption according to the remaining power of the user's laptop computer and the target battery life, the average power consumption being the maximum power consumption per hour; step S320, performing power consumption calculation according to the average power consumption to obtain the average power consumption, the average power consumption being the maximum power per hour; step S330, performing a proportion calculation according to the average power consumption and the standard power consumption average, and configuring a segmentation time granularity based on the proportion relationship, the segmentation time granularity including a single level or multiple levels; step S340, performing grid segmentation on the average power consumption according to the segmentation time granularity, configuring the battery life target threshold of each segmented grid, the battery life target threshold having a grid time granularity identifier.

[0035] Preferably, the average power consumption, that is, the maximum power consumption per hour, is calculated based on the remaining power of the user's laptop computer and the target battery life, indicating the power consumption of the laptop computer per hour under the target battery life. The average power consumption is then converted into power consumption per unit time, that is, the product of the average power consumption and the battery voltage. The average power consumption reflects the maximum power consumed per hour. The average power consumption and the standard power consumption average are used to perform a ratio calculation to obtain a ratio value, indicating the change of the current power consumption relative to the standard power consumption, wherein the standard power consumption average is the average power consumption during normal operation given according to the laptop computer brand, specifications, historical data, etc., and the standard power consumption average is the average power consumption during normal operation given according to the laptop computer brand, specifications, historical data, etc. Reflect the normal power consumption of laptop computers in standard usage scenarios; configure the segmentation time granularity based on the proportion relationship, adjust the granularity of the battery life according to the actual power consumption level of the device, and use different time periods to optimize the use and management of the battery. The granularity takes into account the use relationship of the power. If the power is sufficient, the granularity can be larger. If the power is insufficient, the granularity is smaller to ensure that the target battery life can be achieved. Among them, the segmentation time granularity includes single or multi-level, that is, it can be a fixed granularity segmentation or a dynamic multi-stage multi-level granularity. Different time granularities help to manage battery life more flexibly, especially in the case of large changes in power consumption.

[0036] Preferably, the average power consumption is grid-segmented according to the segmentation time granularity. Specifically, the time is divided into multiple small blocks, each small block represents an independent time period, and each time period has a battery life target threshold, so that the battery life distribution can be controlled more finely, ensuring that the power consumption of each small time period can be within the set range, thereby optimizing the overall battery life, and then configuring the battery life target threshold of each segmented grid. The battery life target threshold refers to the maximum power consumption allowed in each segmented grid, which represents the maximum available power of the battery in the time period. For example, if the time granularity is 1 hour, the maximum power consumption within this hour is calculated to ensure that this limit will not be exceeded within the time period. The battery life target threshold has a grid time granularity identifier, which means that each grid (i.e., each time period) is associated with a specific time granularity (for example, 1 hour, 30 minutes, 15 minutes, etc.), and the granularity will mark and limit the power consumption of each time period during calculation, which helps to dynamically adjust the battery management strategy to ensure that the battery consumption can be managed according to the set target threshold in each time period.

[0037] Step S400: locating and optimizing target parameters according to the endurance constraint conditions.

[0038] Preferably, during the battery life optimization process, the system needs to determine which specific hardware or software parameters need to be optimized based on the battery life constraints set by the user (such as specific software or hardware that must be run) in order to achieve the purpose of extending the battery life, wherein the optimization target parameters refer to the variables that can be adjusted, controlled or optimized during the battery life optimization process, and the battery life constraints include the hardware or software functions that must be kept running or given priority set by the user. When optimizing battery life, the power consumption of these key functions cannot be reduced or limited. According to the battery life constraints, those key functions that must remain active are retained, which hardware or software parameters need to be optimized are identified, and other adjustable parameters are determined, such as processor frequency (adjusting the CPU operating frequency, reducing the processor load, and reducing power consumption), display brightness (reducing screen brightness to reduce battery consumption), hard disk and storage device operating modes (extending battery life by reducing hard disk read and write operations or reducing storage device power consumption), etc., to achieve the purpose of extending battery life and ensure that battery consumption is optimized without affecting the core needs of users.

[0039] Step S500, taking the cruising range target threshold as the optimization target, performing strategy optimization on the optimization target parameters according to the cruising range constraint condition, and obtaining a cruising range control parameter strategy.

[0040] Preferably, the battery life target threshold is used as the optimization target, and the optimization target parameters are strategically optimized according to the battery life constraints, that is, the various parameters of the laptop are adjusted and optimized to achieve the best performance of battery life, while ensuring that the battery life constraints set by the user are not violated during the optimization process. Among them, strategy optimization is an optimization algorithm that finds an optimal control strategy by repeatedly trying, adjusting, calculating and comparing different optimization schemes to make the battery consumption rate as low as possible while ensuring that key functions can operate normally. It involves adjusting and optimizing the optimization target parameters to meet the battery life target threshold and the user's battery life constraints. Specifically, based on the battery life target threshold, the maximum amount of electricity that can be consumed per hour is determined. , keep the hourly power consumption below this threshold, gradually adjust and optimize the target parameters, such as CPU frequency, screen brightness, etc., to ensure that the adjustment of each parameter can minimize power consumption while ensuring that the constraints are met, such as reducing the CPU load of unimportant applications, but not closing essential applications; then continuously adjust these parameters through optimization algorithms (such as linear programming, genetic algorithms, etc.), perform multiple calculations and simulations, and gradually optimize to ensure that the battery consumption rate does not exceed the target threshold while meeting all constraints. Finally, a set of control strategies, namely the battery life control parameter strategy, is obtained, which can maintain the operation of the hardware and software required by users without exceeding the battery life target threshold, thereby maximizing battery life.

[0041] Furthermore, step S500 also includes step S510, performing constrained power consumption analysis according to the endurance constraint condition to obtain a constrained power consumption characteristic; step S520, establishing an energy-saving adjustment relationship of the optimization target parameter and constructing a target evaluation function; step S530, establishing an alignment constraint relationship between the endurance target threshold and the constrained power consumption characteristic to obtain an endurance target power consumption value; step S540, based on the target evaluation function, with the goal of minimizing the approximate difference of the endurance target power consumption value, optimizing the target parameter control to obtain the endurance control parameter strategy.

[0042] Preferably, in charging and battery life optimization, the power consumption of the device is analyzed and evaluated according to the battery life constraints. For example, the user sets certain applications that cannot be closed, and the power consumed by the operation of these applications is calculated, and the battery life optimization is performed on this basis, so as to obtain the constrained power consumption characteristics, that is, the power consumption characteristics required by the laptop under given constraints, such as the power consumed by certain applications, or the minimum power consumption of the device that cannot be adjusted during operation, and establish an energy-saving adjustment relationship for the optimization target parameters, that is, establish a relationship between the key parameters affecting the battery life of the laptop (including the screen brightness, processor frequency, etc. of the device) and the energy-saving effect. For example, lowering the screen brightness or reducing the CPU frequency can usually significantly reduce power consumption, thereby extending the battery life of the device, so as to construct a target evaluation function to reflect the power consumption level and battery life performance under different parameter adjustment combinations.

[0043] Preferably, establishing an alignment constraint relationship between the battery life target threshold and the constrained power consumption characteristics refers to aligning the power consumption requirements under the constraints with the target battery life. For example, if the user sets certain applications that cannot be closed, and the actual power consumption of the device increases due to these applications, it is necessary to find a balance between the actual power consumption characteristics and the target battery life. By comparing the target battery life threshold with the power consumption characteristics under the constraints, the battery life target power consumption value is calculated, that is, the power consumption level that the device is expected to achieve while meeting the battery life target and the constraints set by the user. In other words, it is the power consumption value required under the constraints to ensure that the device can work within the target battery life. Then, according to the target evaluation function, with the minimum approach difference of the battery life target power consumption value as the goal (making the actual power consumption of the device as close as possible to the battery life target power consumption value set by the user, thereby minimizing the error between the actual power consumption and the target power consumption), the target parameter control optimization is performed, that is, the parameters of the device are adjusted to repeatedly calculate and evaluate different parameter combinations to ensure that the difference between the actual power consumption and the target power consumption is minimized until the optimal battery life control parameter strategy is determined, such as adjusting the screen brightness of the device, closing unnecessary applications, adjusting the processor frequency, etc., to ensure that the device achieves the longest battery life while meeting the battery life target and the constraints set by the user.

[0044] Furthermore, step S530 also includes step S531, clustering the endurance target thresholds according to the time granularity identifiers, and continuously splicing them according to the timing relationship to construct the timing endurance target thresholds of each granularity; step S532, establishing the timing characteristics of the constrained power consumption according to the constrained power consumption characteristics; step S533, establishing the alignment constraint relationship between the timing endurance target thresholds of each granularity and the constrained power consumption characteristics according to the time correspondence.

[0045] Preferably, the battery life target thresholds are clustered according to the time granularity identifier, that is, the battery life target thresholds are clustered according to different time periods. For example, the battery life target within each hour may be different. In different time periods, the power consumption and battery life target of the device may be quite different. Then, the battery life target thresholds of different time periods are spliced ​​in chronological order to form a time series battery life target threshold, that is, a battery life target sequence divided by time granularity. By merging and connecting the target power consumption values ​​of each time period, it is convenient to accurately manage and optimize the battery life of the device. Then, according to the constrained power consumption characteristics, the time series characteristics of the constrained power consumption are established, that is, the constrained power consumption characteristics are established. The changing pattern of the characteristics in the time dimension is established. For example, the power consumption of the device may be low at the beginning of charging, but as time goes by, the power consumption may increase due to the activation of high-load applications. By establishing the timing characteristics of constrained power consumption, the specific pattern of the change of device power consumption during the charging process can be obtained; then the time granularity and power consumption change are associated to ensure that the battery life target in each time period matches the power consumption characteristics, and an alignment constraint relationship between the timing battery life target threshold and the constrained power consumption characteristics is established, that is, the battery life target threshold and the constrained power consumption characteristics at different time granularities are aligned to ensure their consistency in the time dimension and to meet the battery life requirements of the device.

[0046] Further, step S540 also includes step S541, establishing an initial solution space based on the historical endurance optimization samples of the optimization target parameter; step S542, searching for an excellent candidate solution set from the initial solution space according to the target evaluation function, wherein the excellent candidate solution set is an initial solution whose target evaluation result reaches the target ranking threshold; step S543, randomly obtaining a search center solution and a search direction solution from the excellent candidate solution set; step S544, taking the search center solution as the search center and the search direction solution as the optimization direction, performing iterative search and updating based on the target evaluation function with the minimum approximate difference of the endurance target power consumption value as the goal, and obtaining the endurance control parameter strategy, wherein the expression of the target evaluation function is: , The target power consumption value for battery life. To optimize the total power consumption of the target parameters, is the i-th optimization target parameter, and n is the number of optimization target parameters.

[0047] Preferably, an initial solution space, i.e., a set of all possible combinations of optimization target parameters, is established based on historical battery life optimization samples of the user's laptop computer (optimization data in different usage scenarios, such as the actual battery life of the computer under different parameter settings). An excellent candidate solution set is searched from the initial solution space according to the target evaluation function. Specifically, the optimization effect is evaluated by calculating the gap between the battery life target power consumption value and the actual power consumption value using the target evaluation function. Multiple candidate best solutions are then selected from the initial solution space as the excellent candidate solution set. Then, one is randomly selected from the excellent candidate solution set, i.e., the search center solution, as the starting point of the optimization search. Then, one is randomly selected as the search direction solution to provide possible directions for iterative optimization.

[0048] Preferably, the search center solution is used as the current best solution, and the search direction solution is used as a possible optimization direction. An iterative search is performed in the solution space. Through multiple iterations, the parameters of the solution are adjusted according to the target evaluation function until the best strategy for minimizing the difference between the endurance target power consumption value and the actual power consumption value is found, that is, the search center solution and the search direction solution are updated according to the feedback of the target evaluation function. Through repeated searches and updates, the optimal solution is gradually approached, that is, the actual power consumption value is as close to the target power consumption value as possible, thereby maximizing the endurance effect, obtaining the endurance control parameter strategy, and helping the device achieve the longest endurance performance. The expression of the target evaluation function is: , The target power consumption value for battery life. To optimize the total power consumption of the target parameters, is the i-th optimization target parameter, and n is the number of optimization target parameters.

[0049] Furthermore, step S544 also includes step S544a, when there are multiple user battery life optimization targets, obtaining a preset penalty coefficient for each user battery life optimization target, wherein the preset penalty coefficient is proportional to the influence of the battery life optimization target; and step S544b, constructing a penalty evaluation function according to the correspondence between the preset penalty coefficient and the user battery life optimization target, adding the penalty evaluation function to the target evaluation function, and performing optimization target parameter control optimization.

[0050] Preferably, in some cases, the user may have multiple goals, and these goals may conflict with each other. For example, one goal may be to extend the battery life of the device, while another goal may be to optimize device performance or keep certain applications running. A penalty coefficient is assigned to each goal, where the penalty coefficient reflects the degree of influence of each goal on the optimization process. Goals with greater influence will be assigned higher penalty coefficients, while goals with less influence will be assigned lower penalty coefficients. The preset penalty coefficient is proportional to the influence of the battery life optimization goal, that is, the size of the penalty coefficient is proportional to the importance and priority of the goal in battery life optimization. According to the preset penalty coefficient and the user's battery life optimization goal, The penalty evaluation function is constructed based on the correspondence between the optimization results of each target and the corresponding penalty coefficient, which is used to measure the degree of penalty when each target fails to meet the expectation in multi-target optimization. Finally, the penalty evaluation function is added to the target evaluation function so that the target evaluation function can consider the weights and penalties of different targets when evaluating the optimization effect, and then optimize the target parameters, that is, search for the optimal combination of parameters to minimize the result of the target evaluation function. At the same time, the influence of the penalty coefficient is considered to ensure that multiple targets can be reasonably balanced in the overall optimization, so that the device can achieve the best endurance performance while meeting all requirements.

[0051] The above specific implementation manner does not constitute a limitation to the protection scope of the present application. It should be understood by those skilled in the art that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present application should be included in the protection scope of the present application. In some cases, the actions or steps recorded in the present application can be performed in an order different from that in the embodiment and can still achieve the desired results. In addition, the process depicted in the accompanying drawings does not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. A laptop battery life optimization method combining AI technology, characterized in that: The method comprises: Obtaining a user's battery life optimization goal, wherein the user's battery life optimization goal includes a target battery life time and a battery life constraint condition, wherein the battery life constraint condition is software or hardware that must be used in the user's battery life; Get the remaining power of the user's laptop; Obtaining a battery life target threshold value according to the remaining power of the user's laptop computer and the target battery life time; According to the endurance constraint conditions, locate and optimize target parameters; The cruising range target threshold is used as the optimization target, and the optimization target parameters are optimized according to the cruising range constraint condition to obtain the cruising range control parameter strategy.

2. The method for optimizing the battery life of a laptop computer in combination with AI technology as claimed in claim 1, characterized in that: The method further comprises: Obtaining the charging time and charging constraint conditions set by the user, wherein the charging constraint conditions are application systems that cannot be shut down during charging; Obtain charging access power parameters through the charging interface; According to the charging time and charging access power parameters set by the user, combined with the charging constraint conditions, charging optimization parameters are optimized with the goal of maximizing the charging amount to obtain a charging optimization control strategy.

3. The method for optimizing the battery life of a laptop computer in combination with AI technology as claimed in claim 2, characterized in that: The obtaining of the remaining power of the user's laptop computer includes: Obtaining health status information and charge and discharge monitoring logs of a laptop computer battery, and fitting a charging power endurance characteristic according to the health status information and the charge and discharge monitoring logs, wherein the charging power endurance characteristic has a discharge attenuation rate; Obtaining the current power after charging is completed, fitting and projecting the current power using the charging power endurance characteristics, and obtaining a mapping attenuation rate performance of the current power; According to the standard power decay rate, the mapped decay rate representation of the current power is divided into standard shares to obtain the remaining power of the user's laptop computer.

4. The method for optimizing the battery life of a laptop computer in combination with AI technology as claimed in claim 1, characterized in that: Obtaining a battery life target threshold according to the remaining power of the user's laptop computer and the target battery life time includes: Calculate average power consumption according to the remaining power of the user's laptop computer and the target battery life, where the average power consumption is the maximum power consumption per hour; Calculate the power consumption according to the average power consumption to obtain the average power consumption, where the average power consumption is the maximum power per hour; Calculate the proportion of the average power consumption to the standard power consumption, and configure the segmentation time granularity based on the proportion relationship, wherein the segmentation time granularity includes a single level or multiple levels; The average power consumption is divided into grids according to the division time granularity, and a battery life target threshold of each division grid is configured, wherein the battery life target threshold has a grid time granularity identifier.

5. The method for optimizing the battery life of a laptop computer in combination with AI technology as claimed in claim 4, characterized in that: Taking the cruising range target threshold as the optimization target, performing strategy optimization on the optimization target parameter according to the cruising range constraint condition to obtain the cruising range control parameter strategy, including: Performing power consumption analysis according to the endurance constraint condition to obtain power consumption characteristics; Establishing energy-saving adjustment relationships of the optimization target parameters and constructing target evaluation functions; Establishing an alignment constraint relationship between the endurance target threshold and the constrained power consumption feature to obtain an endurance target power consumption value; Based on the target evaluation function, the target parameter control is optimized with the goal of minimizing the approximate difference of the endurance target power consumption value, so as to obtain the endurance control parameter strategy.

6. The method for optimizing the battery life of a laptop computer in combination with AI technology as claimed in claim 5, characterized in that: Establishing an alignment constraint relationship between the endurance target threshold and the constrained power consumption feature includes: Granularly clustering the endurance target thresholds according to the time granularity identifiers, and continuously splicing them according to the time sequence relationship to construct the time sequence endurance target thresholds of each granularity; Establishing a timing feature of constraining power consumption according to the constrained power consumption feature; According to the time correspondence, an alignment constraint relationship between the timing endurance target threshold of each granularity and the constrained power consumption feature is established.

7. The method for optimizing the battery life of a laptop computer in combination with AI technology as claimed in claim 5, characterized in that: Based on the target evaluation function, the target parameter control is optimized with the goal of minimizing the approximate difference of the endurance target power consumption value, and the endurance control parameter strategy is obtained, including: Establishing an initial solution space based on historical endurance optimization samples of the optimization target parameters; Searching for an excellent candidate solution set from the initial solution space according to the target evaluation function, wherein the excellent candidate solution set is an initial solution whose target evaluation result reaches a target ranking threshold; Randomly obtain a search center solution and a search direction solution from the excellent candidate solution set; The search center solution is used as the search center, the search direction solution is used as the optimization direction, and an iterative search and update is performed based on the target evaluation function with the minimum approach difference of the endurance target power consumption value as the goal to obtain the endurance control parameter strategy, wherein the expression of the target evaluation function is: , The target power consumption value for battery life. To optimize the total power consumption of the target parameters, is the i-th optimization target parameter, and n is the number of optimization target parameters.

8. The method for optimizing the battery life of a laptop computer in combination with AI technology as claimed in claim 7, characterized in that: Also includes: When there are multiple battery life optimization targets for the user, a preset penalty coefficient for each battery life optimization target of the user is obtained, where the preset penalty coefficient is proportional to the influence of the battery life optimization target; According to the correspondence between the preset penalty coefficient and the user endurance optimization target, a penalty evaluation function is constructed, and the penalty evaluation function is added to the target evaluation function to perform optimization target parameter control and optimization.

Images