Power adjustment method and related device

Abstract

The application discloses a power adjustment method and related equipment, and relates to the technical field of data processing. The method comprises the following steps: in response to a power adjustment instruction, a first power corresponding to a local first module and a second power corresponding to a local second module are acquired, a target influence factor between the first power and the second power is determined, a target decision scheme is generated based on the first power, the second power, the target influence factor and a preset decision generation model, and the first power and the second power are adjusted based on the target decision scheme. It can be understood that the application first determines the influence factor between the powers corresponding to different modules, then generates an optimal target decision scheme based on the influence factor and the preset decision generation model, adjusts the powers of different modules based on the target decision scheme, and thus improves the overall efficiency of the equipment and improves the user experience.

Description

Technical Field

[0001] This application relates to the field of data processing technology, and in particular to power adjustment methods and related equipment. Background Technology

[0002] In today's era of rapid technological advancement, electronic devices are rapidly evolving towards high integration and multifunctionality. Integrating numerous modules with different functions into a single device has become an unstoppable trend. For example, smartphones integrate communication modules, camera modules, processor modules, sensor modules, and many other components with diverse functions; similarly, power banks highly integrate wireless charging modules and portable Wi-Fi modules to achieve a more convenient, efficient, and intelligent operating experience. This modular integration design concept greatly enriches the functionality of devices, reduces their size, and improves their portability and practicality, meeting people's demands for high-performance, multifunctional, and miniaturized electronic devices.

[0003] However, modular integrated devices face numerous challenges in terms of heat dissipation and efficiency optimization. The heat dissipation of different modules not only affects their own power but also the temperature of other modules, which in turn affects the power of those modules, resulting in low overall device efficiency and a poor user experience. How to achieve efficient collaborative work among modules while ensuring good heat dissipation, thereby improving overall device efficiency and providing the best user experience, has become a critical issue that urgently needs to be addressed in the field of electronic device design.

[0004] The above content is only used to help understand the technical solution of this application and does not represent an admission that the above content is prior art. Summary of the Invention

[0005] The main purpose of this application is to provide a power adjustment method and related equipment, which aims to solve the technical problem of how to improve the overall efficiency of the equipment.

[0006] To achieve the above objectives, this application proposes a power adjustment method, which includes:

[0007] In response to a power adjustment command, the system obtains the first power corresponding to the local first module and the second power corresponding to the local second module, and determines the target influence factor between the first power and the second power.

[0008] Based on the first power, the second power, the target influence factor, and the preset decision generation model, a target decision scheme is generated;

[0009] Based on the target decision scheme, the first power and the second power are adjusted.

[0010] In one embodiment, the step of determining the target influence factor between the first power and the second power further includes:

[0011] Determine the first temperature corresponding to the first module, and determine the second temperature corresponding to the second module;

[0012] Determine a first influence factor between the first temperature and the first power, determine a second influence factor between the second temperature and the second power, and determine a third influence factor between the first temperature and the second temperature;

[0013] Based on the first influence factor, the second influence factor, and the third influence factor, a target influence factor between the first power and the second power is determined.

[0014] In one embodiment, the step of generating a target decision scheme based on the first power, the second power, the target influence factor, and a preset decision generation model further includes:

[0015] Determine the first task corresponding to the first module, and determine the second task corresponding to the second module;

[0016] Based on a preset work scoring rule library, a first work scoring rule corresponding to the first work task is determined, and a second work scoring rule corresponding to the second work task is determined.

[0017] Based on the first power, the second power, the target impact factor, the first work scoring rule, and the second work scoring rule, generate prompt words;

[0018] Based on the prompt words and the preset decision generation model, a target decision scheme is generated.

[0019] In one embodiment, the preset decision generation model includes a curve generation sub-model and a strategy generation sub-model. The step of generating a target decision scheme based on the prompt words and the preset decision generation model further includes:

[0020] Based on the prompt words and curve generation sub-model, generate the total score curves corresponding to the first module and the second module;

[0021] Based on the overall score curve, determine the maximum total score;

[0022] Based on the maximum total score and the strategy generation sub-model, a target decision scheme is generated.

[0023] In one embodiment, prior to the step of basing the work score on a preset rule base, the method further includes:

[0024] Obtain user profiles and historical rating data;

[0025] Based on the user profile and historical rating data, we can determine the user's different preferences for different local modules;

[0026] Based on the preferences, job scoring rules are generated for each module to construct a preset job scoring rule library.

[0027] In one embodiment, before the step of generating a target decision scheme based on the first power, the second power, the target influence factor, and a preset decision generation model, the method further includes:

[0028] Obtain sample data, wherein the decision scheme corresponding to the sample data is the first decision scheme;

[0029] The sample data is processed using the current decision generation model to obtain a second decision scheme;

[0030] Determine whether the first decision option and the second decision option are consistent;

[0031] If there is a discrepancy, adjust the parameters of the current decision generation model, and based on the adjusted decision generation model, return to the step of processing the sample data using the current decision generation model to obtain a second decision scheme, until the first decision scheme and the second decision scheme are consistent, and obtain the preset decision generation model.

[0032] Furthermore, to achieve the above objectives, this application also proposes a power adjustment device, which includes:

[0033] The acquisition module is configured to, in response to a power adjustment command, acquire a first power corresponding to a local first module and a second power corresponding to a local second module, and determine a target influence factor between the first power and the second power;

[0034] The generation module is used to generate a target decision scheme based on the first power, the second power, the target influence factor and a preset decision generation model;

[0035] An adjustment module is used to adjust the first power and the second power based on the target decision scheme.

[0036] In one embodiment, the acquisition module further includes:

[0037] The first determining unit is used to determine the first temperature corresponding to the first module and to determine the second temperature corresponding to the second module;

[0038] The second determining unit is used to determine a first influence factor between the first temperature and the first power, a second influence factor between the second temperature and the second power, and a third influence factor between the first temperature and the second temperature.

[0039] The third determining unit is used to determine the target influence factor between the first power and the second power based on the first influence factor, the second influence factor and the third influence factor.

[0040] In one embodiment, the generation module further includes:

[0041] The fourth determining unit is used to determine the first working task corresponding to the first module and the second working task corresponding to the second module;

[0042] The fifth determining unit is used to determine the first work scoring rule corresponding to the first work task and the second work scoring rule corresponding to the second work task based on a preset work scoring rule library.

[0043] The first generation unit is used to generate prompt words based on the first power, the second power, the target impact factor, the first work scoring rule, and the second work scoring rule;

[0044] The second generation unit is used to generate a target decision scheme based on the prompt words and the preset decision generation model.

[0045] In one embodiment, the generation module further includes:

[0046] The third generation unit is used to generate a sub-model based on the prompt words and curves to generate the total score curves corresponding to the first module and the second module.

[0047] The sixth determining unit is used to determine the maximum total score based on the total score curve;

[0048] The fourth generation unit is used to generate a target decision scheme based on the maximum total score and the strategy generation sub-model.

[0049] In one embodiment, the generation module further includes:

[0050] The first acquisition unit is used to acquire user profiles and historical rating data;

[0051] The seventh determining unit is used to determine the user's different preferences for different local modules based on the user profile and historical rating data;

[0052] The construction unit is used to generate job scoring rules for each module based on the preferences, so as to build a preset job scoring rule library.

[0053] In one embodiment, the power adjustment device further includes a model training module, the model training module further including:

[0054] The second acquisition unit is used to acquire sample data, wherein the decision scheme corresponding to the sample data is the first decision scheme.

[0055] The data processing unit is used to process the sample data using the current decision generation model to obtain a second decision scheme;

[0056] A judgment unit is used to determine whether the first decision scheme and the second decision scheme are consistent;

[0057] The training unit is used to adjust the parameters of the current decision generation model if there is a discrepancy, and based on the adjusted decision generation model, return to the step of processing the sample data using the current decision generation model to obtain a second decision scheme, until the first decision scheme and the second decision scheme are consistent, thus obtaining a preset decision generation model.

[0058] In addition, to achieve the above objectives, this application also proposes a power adjustment device, the device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the power adjustment method as described above.

[0059] In addition, to achieve the above objectives, this application also proposes a storage medium, which is a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the steps of the power adjustment method described above.

[0060] In addition, to achieve the above objectives, this application also provides a computer program product, which includes a computer program that, when executed by a processor, implements the steps of the power adjustment method described above.

[0061] One or more technical solutions proposed in this application have at least the following technical effects:

[0062] This application proposes a power adjustment method and related equipment, relating to the field of data processing technology. Compared to related technologies where heat dissipation of different modules not only affects their own power but also the temperature of other modules, thus affecting their power and leading to low overall equipment efficiency and a poor user experience, this application first, in response to a power adjustment command, obtains the first power corresponding to a local first module and the second power corresponding to a local second module, and determines a target influence factor between the first and second powers. Then, based on the first power, the second power, the target influence factor, and a preset decision generation model, a target decision scheme is generated. Finally, based on the target decision scheme, the first and second powers are adjusted. It can be understood that this application first determines the influence factor between the powers of different modules, then generates an optimal target decision scheme (which maximizes overall efficiency) based on the influence factor and the preset decision generation model, and adjusts the power of different modules based on the target decision scheme, thereby improving the overall efficiency of the equipment and enhancing the user experience. Attached Figure Description

[0063] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0064] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0065] Figure 1 This is a flowchart illustrating an embodiment of the power adjustment method of this application.

[0066] Figure 2 This is a flowchart illustrating Embodiment 2 of the power adjustment method of this application;

[0067] Figure 3 This is a flowchart illustrating Embodiment 3 of the power adjustment method of this application;

[0068] Figure 4 This is a schematic diagram of the module structure of the power adjustment device according to an embodiment of this application;

[0069] Figure 5 This is a schematic diagram of the device structure of the hardware operating environment involved in the power adjustment method in the embodiments of this application.

[0070] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0071] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0072] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0073] The main solution in this application embodiment is:

[0074] In this embodiment, for ease of description, the power adjustment device will be used as the execution subject in the following description.

[0075] Due to current technology, modular integrated devices face numerous challenges in heat dissipation and efficiency optimization. The heat dissipation of different modules not only affects their own power but also the temperature of other modules, consequently impacting their power, leading to low overall device efficiency and a poor user experience. How to achieve efficient collaborative work among modules while ensuring good heat dissipation, thereby improving overall device efficiency and providing the best user experience, has become a critical issue urgently needing to be addressed in the field of electronic device design.

[0076] This application provides a solution in which: first, in response to a power adjustment command, a first power corresponding to a local first module and a second power corresponding to a local second module are obtained, and a target influence factor between the first power and the second power is determined; then, based on the first power, the second power, the target influence factor, and a preset decision generation model, a target decision scheme is generated; finally, based on the target decision scheme, the first power and the second power are adjusted. It can be understood that this application first determines the influence factor between the powers corresponding to different modules, then generates an optimal target decision scheme (which maximizes overall efficiency) based on the influence factor and a preset decision generation model, and adjusts the power of different modules based on the target decision scheme, thereby improving the overall efficiency of the device and enhancing the user experience.

[0077] It should be noted that the executing entity in this embodiment can be a computing service device with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, or mobile phone, or an electronic device or power adjustment device capable of performing the above functions. The following description uses a power adjustment device as an example to illustrate this embodiment and the subsequent embodiments.

[0078] Based on this, embodiments of this application provide a power adjustment method, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the power adjustment method of this application.

[0079] In this embodiment, the power adjustment method includes steps S100~S300:

[0080] Step S100: In response to the power adjustment command, obtain the first power corresponding to the local first module and the second power corresponding to the local second module, and determine the target influence factor between the first power and the second power;

[0081] It should be noted that the executing entity in this embodiment can be a computing service device with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, or mobile phone, or an electronic device or power adjustment device capable of performing the above functions. The following description uses a power adjustment device as an example to illustrate this embodiment and the subsequent embodiments.

[0082] It should be noted that a power adjustment command is a trigger command used to instruct the system to perform power-related operations or adjustments. It may be issued by an internal controller or an external management unit, with the purpose of optimizing power distribution, improving energy efficiency, or meeting specific power demands.

[0083] It should be noted that "local first module" and "local second module" refer to two different modules or components in the system, each with independent power characteristics. These modules may be hardware devices, software processes, or other units that can operate independently and consume power.

[0084] Power refers to the amount of work done per unit time or the rate of energy conversion. In electronic systems, power is usually measured in watts (W). In this application, the power of each module refers to the amount of work done by that module per unit time.

[0085] The target impact factor is a parameter that measures the mutual influence between the power of two modules. It can be a scaling factor, a difference, or other form of quantitative indicator used to describe the degree to which a change in the power of the first module affects the power of the second module, or vice versa.

[0086] In this application, specific application scenarios may include:

[0087] A power bank with wireless charging and mobile WiFi capabilities is described. Both the wireless charging module and the mobile WiFi module generate heat during operation, with wireless charging producing a significant amount of heat. Therefore, the heat dissipated when both modules are operating simultaneously affects their power output (charging power and network throughput). Consequently, the power output of the wireless charging module and the mobile WiFi module needs to be coordinated and controlled. By identifying target influencing factors, the operating power of the wireless charging module and the mobile WiFi module, along with the power of the cooling system, can be optimized to achieve the best overall user experience.

[0088] Specifically, the step of determining the target influence factor between the first power and the second power further includes steps S110 to S130:

[0089] Step S110: Determine the first temperature corresponding to the first module and determine the second temperature corresponding to the second module;

[0090] It should be noted that the first temperature and the second temperature refer to the current temperature values ​​of the first module and the second module, respectively. Temperature is a physical quantity that measures the thermal state of a module, usually expressed in degrees Celsius (°C) or degrees Fahrenheit (°F).

[0091] In this application, the system measures the current temperature of the first module using a built-in temperature sensor or an external temperature monitoring device. These sensors are typically installed in critical locations on the module, such as the chip surface or near the heat sink, to accurately acquire temperature data.

[0092] The system reads temperature data from a temperature sensor and stores it in memory or a database. This process may be automated, performed periodically by the system, or it may be dynamically executed based on certain triggering conditions, such as the temperature exceeding a threshold.

[0093] Step S120: Determine a first influence factor between the first temperature and the first power, determine a second influence factor between the second temperature and the second power, and determine a third influence factor between the first temperature and the second temperature;

[0094] It should be noted that the purpose of determining the first influence factor between the first temperature and the first power is to quantify the degree of influence of the temperature change of the first module on the power change. This relationship can be determined through experiments, data analysis, or theoretical models. For example, if the power increases by 0.5W for every 1°C increase in the temperature of the first module, then the first influence factor can be expressed as:

[0095]

[0096] If the relationship is more complex, it may be necessary to determine the influencing factors through curve fitting or other mathematical models.

[0097] It should be noted that the purpose of determining the third influencing factor between the first and second temperatures is to quantify the degree of influence of the temperature change of the first module on the temperature change of the second module.

[0098] Step S130: Based on the first influence factor, the second influence factor and the third influence factor, determine the target influence factor between the first power and the second power.

[0099] It should be noted that the first impact factor describes how the temperature change of the first module affects its own power change. For example, if the temperature of the first module increases, it may lead to an increase in its power (due to increased heat dissipation requirements or performance degradation).

[0100] It should be noted that the second influencing factor describes how the temperature change of the second module affects its own power change. For example, if the temperature of the second module increases, it may cause its power to decrease (because the protection mechanism is activated).

[0101] It should be noted that the third influencing factor describes how the temperature change of the first module affects the temperature change of the second module. For example, if the temperature of the first module increases, it may cause the temperature of the second module to also increase (due to heat transfer).

[0102] By comprehensively considering the above three influencing factors, the degree of influence of the power change of the first module on the power change of the second module is determined.

[0103] It should be noted that these influencing factors are usually synthesized using mathematical models or algorithms. For example, the target impact factor can be calculated using the following formula:

[0104] Target impact factor = f(first impact factor, second impact factor, third impact factor)

[0105] Here, f is a function, which can be a simple linear combination or a complex nonlinear model, depending on the actual physical characteristics and operating conditions of the system.

[0106] Assumptions: First influence factor k1 (power increases by 0.5W for every 1°C increase in temperature of the first module), second influence factor k2 (power decreases by 0.3W for every 1°C increase in temperature of the second module), third influence factor k3 (power increases by 0.2°C for every 1°C increase in temperature of the first module).

[0107] If the temperature of the first module increases by 1℃, then the power of the first module increases by 0.5W (determined by k1), and the temperature of the second module increases by 0.2℃ (determined by k3). Since the temperature of the second module increases by 0.2℃, its power decreases by 0.3 × 0.2 = 0.06W (determined by k2).

[0108] Taking all these influences into account, the target impact factor can be expressed as:

[0109]

[0110] Therefore, for every 1W increase in the power of the first module, the power of the second module decreases by 0.12W.

[0111] In this embodiment, the target influence factor between the power of the two modules is determined based on known influence factors. By comprehensively considering the impact of temperature changes in the first module on its own power, the impact of temperature changes in the second module on its own power, and the impact of temperature changes in the first module on the temperature of the second module, the degree of influence of power changes in the first module on power changes in the second module can be quantified more accurately. This helps to optimize system performance, improve energy efficiency, and prevent failures.

[0112] Step S200: Generate a target decision scheme based on the first power, the second power, the target influence factor, and the preset decision generation model;

[0113] It should be noted that the preset decision generation model is a pre-designed model used to generate decision schemes based on input parameters (such as power, influence factors, etc.). This model can be a simple rule engine, a complex machine learning model, or a simulation system based on a physics model.

[0114] A target decision scheme is a decision scheme generated based on input parameters and a preset model, used to guide the operation or optimization of the system.

[0115] In this embodiment, a target decision scheme is generated based on known power, target influence factors, and a preset decision generation model. By comprehensively considering these parameters, the model can generate a scientifically sound decision scheme to guide the operation and optimization of the system, thereby improving the system's performance, reliability, and energy efficiency.

[0116] Specifically, the step of generating a target decision scheme based on the first power, the second power, the target influence factor, and the preset decision generation model further includes steps S210 to S240:

[0117] Step S210: Determine the first task corresponding to the first module and determine the second task corresponding to the second module;

[0118] It should be noted that a work task refers to the specific task or function that a module needs to complete. Each module has its specific tasks during device operation, which may include data processing, image acquisition, signal transmission, wireless charging, etc.

[0119] Here, "first task" refers to the task that the first module needs to complete, and "second task" refers to the task that the second module needs to complete. These tasks are determined based on the equipment's operational requirements and the user's operating instructions.

[0120] During equipment operation, it is necessary to define the tasks of each module in order to allocate resources rationally, optimize performance, and ensure the normal operation of the equipment. The process of defining tasks typically involves the following aspects:

[0121] User requirements analysis: Based on user commands and usage scenarios, determine the tasks that the module needs to perform. For example, when a user opens the camera application, the camera module needs to perform the task of image acquisition.

[0122] System status monitoring: Dynamically adjust module tasks based on the device's current status (such as battery level, temperature, system load, etc.). For example, when the device's battery is low, it may be necessary to reduce the task priority of certain modules to save power.

[0123] Task allocation strategy: Allocate tasks reasonably based on the overall design and optimization goals of the equipment. For example, to improve the overall efficiency of the equipment, it may be necessary to assign certain tasks to higher-performance modules.

[0124] Step S220: Based on the preset work scoring rule library, determine the first work scoring rule corresponding to the first work task, and determine the second work scoring rule corresponding to the second work task;

[0125] A predefined task scoring rule base is a set of predefined rules used to evaluate and quantify the performance, efficiency, priority, and other metrics of different tasks. This rule base typically contains a series of scoring criteria and weights, which can be adjusted according to specific application scenarios and optimization goals.

[0126] The role of a rule base is to provide a unified evaluation framework that allows the performance and importance of different tasks to be quantified and compared. This helps to rationally allocate resources and optimize system performance in a multi-tasking environment.

[0127] For example, in a smart device, the preset task rating rule base may include the following: task response time (the faster the task is completed, the higher the rating), task priority (the higher the priority task defined by the system, the higher the rating), energy consumption (the lower the energy consumption of the task, the higher the rating), and error rate (the lower the error rate of the task, the higher the rating).

[0128] The first and second job scoring rules are scoring rules for the first and second job tasks, respectively, determined based on a pre-defined job scoring rule library. These rules are used to evaluate the performance and importance of each task.

[0129] By defining the work scoring rules for each task, the performance and importance of each task can be quantified, thereby enabling the rational allocation of resources and optimization of system performance in a multi-tasking environment.

[0130] For example, for graphics processing tasks of a processor module, the scoring rules may include task response time (weight 60%), energy consumption (weight 30%), and error rate (weight 10%).

[0131] For high-definition video recording tasks of camera modules, the scoring rules may include task response time (weight 40%), video quality (weight 40%), and energy consumption (weight 20%).

[0132] In this embodiment, during system operation, the system determines the scoring rules for each task based on a preset task scoring rule library. For example, for the graphics processing task of the processor module, the system scores it based on the weights of task response time, energy consumption, and error rate; for the high-definition video recording task of the camera module, the system scores it based on the weights of task response time, video quality, and energy consumption. In this way, the system can quantify the performance and importance of each task, thereby rationally allocating resources and optimizing the overall performance of the device. Through these steps, it can be ensured that each module in the device can efficiently complete its task, thereby improving the overall performance of the device and the user experience.

[0133] Step S230: Generate prompt words based on the first power, the second power, the target impact factor, the first work scoring rule, and the second work scoring rule;

[0134] It should be noted that the prompt words are a set of information generated based on the above-mentioned factors (first power, second power, target impact factor, first work scoring rule, and second work scoring rule), and are used to guide the subsequent decision-making process.

[0135] The prompt word combines the above factors to form a concise and clear input for the decision generation model. The prompt word can be a numerical value, a vector, or a piece of text, depending on the design of the decision generation model.

[0136] Step S240: Generate a target decision scheme based on the prompt words and the preset decision generation model.

[0137] It should be noted that the target decision-making scheme provides specific guidance for the operation of the equipment, helping it to operate efficiently in a multi-tasking environment. For example, it may include adjusting the power allocation of modules, optimizing the task scheduling sequence, and adjusting the heat dissipation strategy.

[0138] Specifically, the preset decision generation model includes a curve generation sub-model and a strategy generation sub-model. The step of generating a target decision scheme based on the prompt words and the preset decision generation model further includes steps S241 to S243:

[0139] Step S241: Based on the prompt words and curve generation sub-model, generate the total score curves corresponding to the first module and the second module;

[0140] It should be noted that the curve generation sub-model is a model specifically designed to generate rating curves. This model can generate one or more rating curves based on the input prompts, and these curves reflect the performance scores of different modules under different operating conditions.

[0141] Understandably, the curve generation sub-model uses mathematical modeling and algorithms to transform complex multi-factor assessments into intuitive scoring curves, facilitating subsequent analysis and decision-making.

[0142] It should be noted that the overall score curve is generated by comprehensively considering the performance scores of the first and second modules. This curve reflects the overall performance score of the two modules under different operating conditions. The overall score curve provides an intuitive view of performance evaluation, helping the system determine its optimal operating state.

[0143] Step S242: Determine the maximum total score based on the total score curve;

[0144] It should be noted that the maximum total score refers to the highest point on the total score curve. This point represents the optimal operating state for both modules under the current conditions. Determining the maximum total score is to find the optimal operating state, thereby generating the optimal decision-making solution.

[0145] Step S243: Generate a target decision scheme based on the maximum total score and the strategy generation sub-model.

[0146] It should be noted that the strategy generation sub-model is a model specifically designed to generate decision-making solutions. This model can generate an optimal decision solution based on the maximum total score input. By analyzing the maximum total score, the strategy generation sub-model generates specific decision solutions to guide equipment operation and resource allocation.

[0147] The target decision scheme is the optimal decision scheme generated based on the maximum total score. This scheme aims to optimize the overall performance of the device and improve the user experience. The target decision scheme provides specific guidance for device operation, helping the device operate efficiently in a multi-tasking environment. For example, it might include adjusting module power allocation, optimizing task scheduling order, and adjusting heat dissipation strategies.

[0148] Step S300: Based on the target decision scheme, adjust the first power and the second power.

[0149] This application proposes a power adjustment method and related equipment, relating to the field of data processing technology. Compared to related technologies where heat dissipation of different modules not only affects their own power but also the temperature of other modules, thus affecting their power and leading to low overall equipment efficiency and a poor user experience, this application first, in response to a power adjustment command, obtains the first power corresponding to a local first module and the second power corresponding to a local second module, and determines a target influence factor between the first and second powers. Then, based on the first power, the second power, the target influence factor, and a preset decision generation model, a target decision scheme is generated. Finally, based on the target decision scheme, the first and second powers are adjusted. It can be understood that this application first determines the influence factor between the powers of different modules, then generates an optimal target decision scheme (which maximizes overall efficiency) based on the influence factor and the preset decision generation model, and adjusts the power of different modules based on the target decision scheme, thereby improving the overall efficiency of the equipment and enhancing the user experience.

[0150] Based on the first embodiment of this application, in the second embodiment of this application, the content that is the same as or similar to that in Embodiment 1 above can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 2 Before the step based on the preset work scoring rule base, steps A1 to A3 are also included:

[0151] Step A1: Obtain user profiles and historical rating data;

[0152] It's important to note that a user profile is a comprehensive model describing a user's characteristics and behaviors. It typically includes basic user information (such as age, gender, and occupation), usage habits (such as frequently used functions and frequency of use), and preferences (such as favorite functional modules and operating styles). User profiles help systems better understand user needs and behavioral patterns, thereby providing more personalized and precise services.

[0153] It's important to note that historical rating data refers to users' rating records of different modules or functions when using the device or service in the past. These ratings can be directly given by users (such as ratings from 1 to 5 stars) or automatically generated by the system based on user behavior and feedback. Historical rating data reflects users' satisfaction and preferences for different modules or functions and is an important basis for evaluating user preferences and optimizing services.

[0154] Step A2: Based on the user profile and historical rating data, determine the user's different preferences for different local modules;

[0155] User preferences refer to a user's degree of liking for different modules or functions. By analyzing user profiles and historical rating data, it's possible to determine users' different preferences for various local modules. Identifying user preferences helps the system better meet user needs and provide more personalized services. For example, if a user prefers a particular module, the system can prioritize optimizing that module's performance and user experience.

[0156] The analysis methods can be: user profile analysis (using basic information and usage habits in user profiles to initially determine users' possible preferences. For example, younger users may prefer entertainment and social functions, while business users may prefer office and communication functions), and historical rating data analysis (further confirming user preferences by analyzing users' historical rating data for different modules. For example, if users generally give a module a high rating, it indicates that users have a high preference for that module; conversely, if the rating is low, it indicates that users have a low preference for that module).

[0157] Step A3: Based on the preferences, generate job scoring rules for each module to build a preset job scoring rule library.

[0158] It's important to note that the job performance scoring rules are a set of rules used to evaluate module performance and user experience. These rules are generated based on user preferences and are used to quantify the performance and importance of modules. Job performance scoring rules help the system allocate resources rationally in a multi-tasking environment, optimize task execution efficiency, and improve the overall user experience.

[0159] Understandably, the preset job scoring rule library is a collection of multiple job scoring rules used to evaluate the performance and user experience of different modules.

[0160] Understandably, the pre-set work scoring rule library provides the system with a unified evaluation framework, enabling the system to dynamically adjust the scoring rules according to user preferences, thereby optimizing the operating status of the equipment.

[0161] Based on the first and second embodiments of this application, in the third embodiment of this application, the content that is the same as or similar to that in embodiments one and two above can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 3 Before the step of generating the target decision scheme based on the first power, the second power, the target influence factor, and the preset decision generation model, steps B1 to B4 are further included:

[0162] Step B1: Obtain sample data, wherein the decision scheme corresponding to the sample data is the first decision scheme;

[0163] It's important to note that sample data is a dataset used to train and validate the decision generation model. This data typically includes various input conditions and corresponding output results. Sample data provides real-world operating scenarios and known optimal decision-making schemes for training and validating the model's performance.

[0164] It should be noted that the first decision scheme is the known optimal decision scheme corresponding to the sample data. These schemes are usually obtained through actual operation or expert experience and are considered to be the optimal decisions under the current conditions. The first decision scheme serves as a benchmark to evaluate the accuracy of the output results of the decision generation model.

[0165] It should be noted that the current decision generation model is a model that is being trained and optimized. This model generates decision schemes based on the input sample data. The goal of the current decision generation model is to generate the optimal decision scheme based on the input conditions.

[0166] Step B2: Process the sample data using the current decision generation model to obtain a second decision scheme;

[0167] It should be noted that the second decision option is generated by the current decision generation model based on the sample data. The second decision option is used to compare with the first decision option to evaluate the model's performance.

[0168] Step B3: Determine whether the first decision scheme and the second decision scheme are consistent;

[0169] It should be noted that consistency judgment is performed by comparing the first and second decision options to determine whether they are identical or sufficiently similar. Consistency judgment is used to evaluate whether the performance of the current decision generation model meets expectations.

[0170] Step B4: If there is no consistency, adjust the parameters of the current decision generation model. Based on the adjusted decision generation model, return to the step of using the current decision generation model to process the sample data and obtain the second decision scheme, until the first decision scheme and the second decision scheme are consistent, and obtain the preset decision generation model.

[0171] It should be noted that adjusting model parameters refers to modifying the parameters of the current decision generation model based on the consistency judgment results, in order to improve the model's performance. Adjusting parameters can help the model better fit the sample data and generate more accurate decision solutions.

[0172] The return processing step refers to reprocessing the sample data using the adjusted model after adjusting the model parameters to generate a new second decision scheme. Through multiple iterations, the model's performance is gradually optimized until the generated decision scheme is consistent with the first decision scheme. By following this process, the system can progressively optimize the decision generation model, enabling it to produce results consistent with the known optimal decision scheme, thereby improving the overall performance of the device and the user experience.

[0173] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the power adjustment method of this application. Any simple modifications based on this technical concept are within the protection scope of this application.

[0174] This application also provides a power adjustment device, please refer to... Figure 4 The power adjustment device includes:

[0175] The acquisition module 10 is configured to, in response to a power adjustment command, acquire a first power corresponding to a local first module and a second power corresponding to a local second module, and determine a target influence factor between the first power and the second power;

[0176] Generation module 20, the generation module is used to generate a target decision scheme based on the first power, the second power, the target influence factor and a preset decision generation model;

[0177] Adjustment module 30, the adjustment module is used to adjust the first power and the second power based on the target decision scheme.

[0178] In one embodiment, the acquisition module further includes:

[0179] The first determining unit is used to determine the first temperature corresponding to the first module and to determine the second temperature corresponding to the second module;

[0180] The second determining unit is used to determine a first influence factor between the first temperature and the first power, a second influence factor between the second temperature and the second power, and a third influence factor between the first temperature and the second temperature.

[0181] The third determining unit is used to determine the target influence factor between the first power and the second power based on the first influence factor, the second influence factor and the third influence factor.

[0182] In one embodiment, the generation module further includes:

[0183] The fourth determining unit is used to determine the first working task corresponding to the first module and the second working task corresponding to the second module;

[0184] The fifth determining unit is used to determine the first work scoring rule corresponding to the first work task and the second work scoring rule corresponding to the second work task based on a preset work scoring rule library.

[0185] The first generation unit is used to generate prompt words based on the first power, the second power, the target impact factor, the first work scoring rule, and the second work scoring rule;

[0186] The second generation unit is used to generate a target decision scheme based on the prompt words and the preset decision generation model.

[0187] In one embodiment, the generation module further includes:

[0188] The third generation unit is used to generate a sub-model based on the prompt words and curves to generate the total score curves corresponding to the first module and the second module.

[0189] The sixth determining unit is used to determine the maximum total score based on the total score curve;

[0190] The fourth generation unit is used to generate a target decision scheme based on the maximum total score and the strategy generation sub-model.

[0191] In one embodiment, the generation module further includes:

[0192] The first acquisition unit is used to acquire user profiles and historical rating data;

[0193] The seventh determining unit is used to determine the user's different preferences for different local modules based on the user profile and historical rating data;

[0194] The construction unit is used to generate job scoring rules for each module based on the preferences, so as to build a preset job scoring rule library.

[0195] In one embodiment, the power adjustment device further includes a model training module, the model training module further including:

[0196] The second acquisition unit is used to acquire sample data, wherein the decision scheme corresponding to the sample data is the first decision scheme.

[0197] The data processing unit is used to process the sample data using the current decision generation model to obtain a second decision scheme;

[0198] A judgment unit is used to determine whether the first decision scheme and the second decision scheme are consistent;

[0199] The training unit is used to adjust the parameters of the current decision generation model if there is a discrepancy, and based on the adjusted decision generation model, return to the step of processing the sample data using the current decision generation model to obtain a second decision scheme, until the first decision scheme and the second decision scheme are consistent, thus obtaining a preset decision generation model.

[0200] The power adjustment device provided in this application, employing the power adjustment method in the above embodiments, can solve the technical problem of power adjustment. Compared with the prior art, the beneficial effects of the power adjustment device provided in this application are the same as those of the power adjustment method provided in the above embodiments, and other technical features in the power adjustment device are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0201] This application provides a power adjustment device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, which are executed by the at least one processor to enable the at least one processor to perform the power adjustment method in Embodiment 1 above.

[0202] The following is for reference. Figure 5 The diagram illustrates a structural schematic suitable for implementing the power adjustment device in the embodiments of this application. The power adjustment device in the embodiments of this application may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 5 The power adjustment device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments of this application.

[0203] like Figure 5As shown, the power adjustment device may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.) that can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage device 1003 into a random access memory (RAM) 1004. The RAM 1004 also stores various programs and data required for the operation of the power adjustment device. The processing unit 1001, ROM 1002, and RAM 1004 are interconnected via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to the I / O interface 1006: input devices 1007 including, for example, a touchscreen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; storage devices 1003 including, for example, magnetic tape, hard disk, etc.; and communication devices 1009. Communication device 1009 allows the power conditioning device to communicate wirelessly or wiredly with other devices to exchange data. Although the figure shows power conditioning devices with various systems, it should be understood that implementation or possession of all the systems shown is not required. More or fewer systems may be implemented alternatively.

[0204] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from ROM 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0205] The power adjustment device provided in this application, employing the power adjustment method described in the above embodiments, can solve the technical problem. Compared with the prior art, the beneficial effects of the power adjustment device provided in this application are the same as those of the power adjustment method provided in the above embodiments, and other technical features of the power adjustment device are the same as those disclosed in the previous embodiment method, and will not be repeated here.

[0206] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0207] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0208] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, the computer-readable program instructions being used to perform the power adjustment method in the above embodiments.

[0209] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems or devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0210] The aforementioned computer-readable storage medium may be included in the power conditioning device; or it may exist independently and not assembled into the power conditioning device.

[0211] The aforementioned computer-readable storage medium carries one or more programs, which, when executed by the power adjustment device, cause the power adjustment device to:

[0212] In response to a power adjustment command, the system obtains the first power corresponding to the local first module and the second power corresponding to the local second module, and determines the target influence factor between the first power and the second power.

[0213] Based on the first power, the second power, the target influence factor, and the preset decision generation model, a target decision scheme is generated;

[0214] Based on the target decision scheme, the first power and the second power are adjusted.

[0215] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a Local Area Network (LAN) or a Wide Area Network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0216] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0217] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0218] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for performing the above-described power adjustment method, thereby solving the technical problem of power adjustment. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as the beneficial effects of the power adjustment method provided in the above embodiments, and will not be repeated here.

[0219] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the power adjustment method described above.

[0220] The computer program product provided in this application can solve the technical problem of power adjustment. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as the beneficial effects of the power adjustment method provided in the above embodiments, and will not be repeated here.

[0221] The above description is only a part of the embodiments of this application and does not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

Claims

1. A power adjustment method, characterized in that, The power adjustment method includes: In response to a power adjustment command, the system obtains the first power corresponding to the local first module and the second power corresponding to the local second module, and determines the target influence factor between the first power and the second power. Based on the first power, the second power, the target influence factor, and the preset decision generation model, a target decision scheme is generated; Based on the target decision scheme, the first power and the second power are adjusted; The step of determining the target influence factor between the first power and the second power further includes: Determine the first temperature corresponding to the first module, and determine the second temperature corresponding to the second module; Determine a first influence factor between the first temperature and the first power, determine a second influence factor between the second temperature and the second power, and determine a third influence factor between the first temperature and the second temperature; Based on the first influence factor, the second influence factor, and the third influence factor, a target influence factor between the first power and the second power is determined.

2. The power adjustment method as described in claim 1, characterized in that, The step of generating a target decision scheme based on the first power, the second power, the target influence factor, and a preset decision generation model further includes: Determine the first task corresponding to the first module, and determine the second task corresponding to the second module; Based on a preset work scoring rule library, a first work scoring rule corresponding to the first work task is determined, and a second work scoring rule corresponding to the second work task is determined. Based on the first power, the second power, the target impact factor, the first work scoring rule, and the second work scoring rule, generate prompt words; Based on the prompt words and the preset decision generation model, a target decision scheme is generated.

3. The power adjustment method as described in claim 2, characterized in that, The preset decision generation model includes a curve generation sub-model and a strategy generation sub-model. The step of generating a target decision scheme based on the prompt words and the preset decision generation model further includes: Based on the prompt words and curve generation sub-model, generate the total score curves corresponding to the first module and the second module; Based on the overall score curve, determine the maximum total score; Based on the maximum total score and the strategy generation sub-model, a target decision scheme is generated.

4. The power adjustment method as described in claim 2, characterized in that, Before the step based on the preset job scoring rule base, the following is also included: Obtain user profiles and historical rating data; Based on the user profile and historical rating data, we can determine the user's different preferences for different local modules; Based on the preferences, job scoring rules are generated for each module to construct a preset job scoring rule library.

5. The power adjustment method as described in claim 1, characterized in that, Before the step of generating the target decision scheme based on the first power, the second power, the target influence factor, and the preset decision generation model, the method further includes: Obtain sample data, wherein the decision scheme corresponding to the sample data is the first decision scheme; The sample data is processed using the current decision generation model to obtain a second decision scheme; Determine whether the first decision option and the second decision option are consistent; If there is a discrepancy, adjust the parameters of the current decision generation model, and based on the adjusted decision generation model, return to the step of processing the sample data using the current decision generation model to obtain a second decision scheme, until the first decision scheme and the second decision scheme are consistent, and obtain the preset decision generation model.

6. A power adjustment device, characterized in that, The power adjustment device includes: The acquisition module is configured to, in response to a power adjustment command, acquire a first power corresponding to a local first module and a second power corresponding to a local second module, and determine a target influence factor between the first power and the second power; The generation module is used to generate a target decision scheme based on the first power, the second power, the target influence factor and a preset decision generation model; An adjustment module is configured to adjust the first power and the second power based on the target decision scheme; The power adjustment device is also used to achieve: Determine the first temperature corresponding to the first module, and determine the second temperature corresponding to the second module; Determine a first influence factor between the first temperature and the first power, determine a second influence factor between the second temperature and the second power, and determine a third influence factor between the first temperature and the second temperature; Based on the first influence factor, the second influence factor, and the third influence factor, a target influence factor between the first power and the second power is determined.

7. A power adjustment device, characterized in that, The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the power adjustment method as described in any one of claims 1 to 5.

8. A storage medium, characterized in that, The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, it implements the steps of the power adjustment method as described in any one of claims 1 to 5.

9. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the steps of the power adjustment method as described in any one of claims 1 to 5.

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