Power adjustment method and related equipment

Abstract

The invention discloses a power adjustment method and related equipment, and relates to the technical field of data processing, and the method comprises the steps: obtaining first power corresponding to a local first module and second power corresponding to a local second module in response to a power adjustment instruction, and determining a target influence factor between the first power and the second power, and generating a target decision scheme based on the first power, the second power, the target influence factor and a preset decision generation model, and adjusting the first power and the second power based on the target decision scheme. It can be understood that the influence factors between the corresponding powers of the different modules are firstly determined, then the optimal target decision scheme is generated based on the influence factors and the preset decision generation model, and the powers of the different modules are adjusted based on the target decision scheme, so that the overall efficiency of the equipment is improved, and the user experience is improved.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a power adjustment method and related equipment. Background Art

[0002] In today's era of rapid technological advancement, electronic devices are rapidly developing towards high levels of integration and multifunctionality. The integration of numerous modules with diverse functions within a single device has become an unstoppable trend. For example, smartphones integrate numerous components with diverse functions, including communication modules, camera modules, processor modules, and sensor modules. Another example is power banks, which combine wireless charging modules with portable Wi-Fi modules for a more convenient, efficient, and intelligent user experience. This modular integration significantly enriches device functionality, reduces size, and improves portability and practicality, satisfying the demand for high-performance, multifunctional, and miniaturized electronic devices.

[0003] However, modular integrated devices face numerous challenges in heat dissipation and efficiency optimization. Heat dissipation in different modules not only affects their own power but also the temperature of other modules, which in turn affects the power of other modules, resulting in low overall device efficiency and a poor user experience. Ensuring efficient coordination among modules while ensuring good heat dissipation, 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 assist in understanding the technical solution of this application and does not constitute 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, aiming to solve the technical problem of how to improve the overall efficiency of the equipment.

[0006] To achieve the above objectives, the present application proposes a power adjustment method, which includes: In response to the power adjustment instruction, obtaining a first power corresponding to the local first module and a second power corresponding to the local second module, and determining a target impact factor between the first power and the second power; Generate a target decision plan based on the first power, the second power, the target influencing factor and a preset decision generation model; Based on the target decision scheme, the first power and the second power are adjusted.

[0007] In one embodiment, the step of determining a target impact factor between the first power and the second power further includes: Determining a first temperature corresponding to the first module, and determining a second temperature corresponding to the second module; determining a first influencing factor between the first temperature and the first power, determining a second influencing factor between the second temperature and the second power, and determining a third influencing factor between the first temperature and the second temperature; A target impact factor between the first power and the second power is determined based on the first impact factor, the second impact factor, and the third impact factor.

[0008] In one embodiment, the step of generating a target decision solution based on the first power, the second power, the target influencing factor, and a preset decision generation model further includes: Determine a first work task corresponding to the first module, and determine a second work task corresponding to the second module; Determining a first work scoring rule corresponding to the first work task and determining a second work scoring rule corresponding to the second work task based on a preset work scoring rule library; generating a prompt word based on the first power, the second power, the target impact factor, the first work scoring rule, and the second work scoring rule; Based on the prompt words and the preset decision generation model, a target decision plan is generated.

[0009] In one embodiment, the preset decision generation model includes a curve generation sub-model and a strategy generation sub-model, and the step of generating a target decision solution based on the prompt word and the preset decision generation model further includes: Based on the prompt words and the curve generation sub-model, generating a total score curve corresponding to the first module and the second module; Determining a maximum total score based on the total score curve; A target decision plan is generated based on the maximum total score and the strategy generation sub-model.

[0010] In one embodiment, before the step of using a preset work scoring rule base, the method further includes: Obtain user portraits and historical rating data; Determine the user's different preferences for different local modules based on the user profile and historical rating data; Based on the preferences, work scoring rules corresponding to each module are generated to construct a preset work scoring rule library.

[0011] In one embodiment, before the step of generating a target decision solution based on the first power, the second power, the target influencing factor, and a preset decision generation model, the step further includes: Acquire sample data, wherein the decision solution corresponding to the sample data is a first decision solution; Processing the sample data using the current decision generation model to obtain a second decision solution; Determining whether the first decision-making solution is consistent with the second decision-making solution; If there is inconsistency, adjust the parameters of the current decision generation model, and based on the adjusted decision generation model, return to the step of using the current decision generation model to process the sample data to obtain a second decision solution, until the first decision solution is consistent with the second decision solution to obtain a preset decision generation model.

[0012] In addition, to achieve the above-mentioned purpose, the present application also proposes a power adjustment device, which includes: an acquisition module, configured to acquire, 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, and determine a target impact factor between the first power and the second power; A generation module, configured to generate a target decision plan based on the first power, the second power, the target influencing factor, and a preset decision generation model; An adjustment module is used to adjust the first power and the second power based on the target decision scheme.

[0013] In one embodiment, the acquisition module further includes: a first determining unit, configured to determine a first temperature corresponding to the first module, and determine a second temperature corresponding to the second module; a second determining unit, configured to determine a first influencing factor between the first temperature and the first power, determine a second influencing factor between the second temperature and the second power, and determine a third influencing factor between the first temperature and the second temperature; The third determining unit is configured to determine a target impact factor between the first power and the second power based on the first impact factor, the second impact factor, and the third impact factor.

[0014] In one embodiment, the generating module further includes: a fourth determining unit, configured to determine a first work task corresponding to the first module, and determine a second work task corresponding to the second module; a fifth determining unit, configured to determine a first work scoring rule corresponding to the first work task and a second work scoring rule corresponding to the second work task based on a preset work scoring rule library; a first generating unit, configured to generate a prompt word based on the first power, the second power, the target impact factor, the first work scoring rule, and the second work scoring rule; The second generating unit is used to generate a target decision solution based on the prompt word and a preset decision generation model.

[0015] In one embodiment, the generating module further includes: a third generating unit, configured to generate a total score curve corresponding to the first module and the second module based on the prompt word and the curve generating sub-model; a sixth determining unit, configured to determine a maximum total score based on the total score curve; The fourth generating unit is used to generate a target decision-making plan based on the maximum total score and the strategy generation sub-model.

[0016] In one embodiment, the generating module further includes: A first acquisition unit is used to acquire user portraits and historical rating data; a seventh determining unit, configured to determine the user's different preferences for different local modules based on the user portrait and historical rating data; A construction unit is used to generate a work scoring rule corresponding to each module based on the preference to construct a preset work scoring rule library.

[0017] In one embodiment, the power adjustment device further includes a model training module, and the model training module further includes: A second acquisition unit is used to acquire sample data, wherein the decision solution corresponding to the sample data is the first decision solution; a data processing unit, configured to process the sample data using the current decision generation model to obtain a second decision solution; a judging unit, configured to judge whether the first decision-making scheme is consistent with the second decision-making scheme; The training unit is used to adjust the parameters of the current decision generation model if there is any inconsistency, and based on the adjusted decision generation model, return to the step of using the current decision generation model to process the sample data to obtain a second decision solution, until the first decision solution is consistent with the second decision solution to obtain a preset decision generation model.

[0018] In addition, to achieve the above-mentioned purpose, the present application also proposes a power adjustment device, which includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the power adjustment method described above.

[0019] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium. A computer program is stored on the storage medium, and when the computer program is executed by a processor, the steps of the power adjustment method described above are implemented.

[0020] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the steps of the power adjustment method described above are implemented.

[0021] One or more technical solutions proposed in this application have at least the following technical effects: The present application proposes a power adjustment method and related equipment, which relate to the field of data processing technology. Compared with the related art, in which the heat dissipation of different modules not only affects their own power, but also affects the temperature of other modules, thereby affecting the power of other modules, resulting in low overall efficiency of the device and poor user experience, in the present application, first, 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 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 the present application first determines the influence factor between the corresponding powers of different modules, and then generates an optimal target decision scheme (the target decision scheme corresponds to the maximum overall efficiency) based on the influence factor and the preset decision generation model. The power of different modules is adjusted based on the target decision scheme, thereby improving the overall efficiency of the device and improving the user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0023] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0024] Figure 1 A schematic diagram of a flow chart of a power adjustment method according to the present invention; Figure 2 A schematic diagram of a flow chart of the second embodiment of the power adjustment method of the present application; Figure 3A schematic diagram of a flow chart of the third embodiment of the power adjustment method of the present application; Figure 4 This is a schematic diagram of the module structure of the power adjustment device according to an embodiment of the present application; Figure 5 Schematic diagram of the device structure of the hardware operating environment involved in the power adjustment method in the embodiment of the present application.

[0025] 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 DESCRIPTION

[0026] It should be understood that the specific embodiments described herein are merely used to explain the technical solutions of the present application and are not intended to limit the present application.

[0027] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0028] The main solutions of the embodiments of this application are: In this embodiment, for ease of description, the following description is made with the power adjustment device as the execution entity.

[0029] Existing technologies for 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, and thus the power, of other modules, leading to low overall device efficiency and a poor user experience. Ensuring effective heat dissipation while ensuring efficient coordination among modules, improving overall device efficiency, and providing the best user experience has become a critical issue urgently needed in electronic device design.

[0030] The present application provides a solution, which enables: first, in response to a power adjustment instruction, obtaining the first power corresponding to the local first module and the second power corresponding to the local second module, and determining the target influence factor between the first power and the second power; then, based on the first power, the second power, the target influence factor and a preset decision generation model, generating a target decision scheme; finally, based on the target decision scheme, adjusting the first power and the second power. It can be understood that the present application first determines the influence factor between the powers corresponding to different modules, and then generates an optimal target decision scheme (the overall efficiency corresponding to the target decision scheme is the largest) 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 device and improving the user experience.

[0031] It should be noted that the execution subject of this embodiment may be a computing service device with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, mobile phone, etc., or an electronic device or power adjustment device capable of implementing the above functions. The power adjustment device is used as an example to illustrate this embodiment and the following embodiments.

[0032] Based on this, the embodiment of the present application provides a power adjustment method, referring to Figure 1 , Figure 1 This is a flowchart of the first embodiment of the power adjustment method of the present application.

[0033] In this embodiment, the power adjustment method includes steps S100 to S300: Step S100: In response to a power adjustment instruction, obtaining a first power corresponding to a local first module and a second power corresponding to a local second module, and determining a target impact factor between the first power and the second power; It should be noted that the execution subject of this embodiment may be a computing service device with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, mobile phone, etc., or an electronic device or power adjustment device capable of implementing the above functions. The power adjustment device is used as an example to illustrate this embodiment and the following embodiments.

[0034] It's important to note that a power adjustment command is an action-triggering instruction that instructs the system to perform power-related operations or adjustments. It can be issued by an internal controller or an external management unit to optimize power distribution, improve energy efficiency, or meet specific power requirements.

[0035] It should be noted that the "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 run independently and consume power.

[0036] Power refers to the amount of work completed per unit time or the rate at which energy is converted. In electronic systems, power is typically measured in watts (W). In this application, the power of each module refers to the amount of work completed per unit time.

[0037] The target impact factor (TIF) is a parameter that measures the mutual influence between the power of two modules. It can be a proportional factor, a difference, or other quantitative indicator that describes the degree to which a power change in the first module affects the power of the second module, or vice versa.

[0038] In this application, specific application scenarios may be: A power bank with both wireless charging and mobile Wi-Fi functions dissipates heat during operation. Wireless charging generates significant heat, and the heat dissipated by both modules affects their power (charging power and network throughput). Therefore, the power of the wireless charging and mobile Wi-Fi modules needs to be coordinated and controlled. By determining the target influencing factors, the operating power of the wireless charging and mobile Wi-Fi modules, as well as the power of the cooling system, can be optimized to provide the best overall user experience.

[0039] Specifically, the step of determining the target impact factor between the first power and the second power further includes steps S110 to S130: Step S110, determining a first temperature corresponding to the first module, and determining a second temperature corresponding to the second module; It should be noted that the first temperature and 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 and is typically measured in degrees Celsius (°C) or Fahrenheit (°F).

[0040] In this application, the system measures the current temperature of the first module through a built-in temperature sensor or an external temperature monitoring device. These sensors are usually installed in key locations of the module, such as the chip surface, near the heat sink, etc., to accurately obtain temperature data.

[0041] The system reads temperature data from the temperature sensor and stores it in memory or a database. This process may be automated and performed periodically by the system, or it may be performed dynamically based on certain trigger conditions (such as when the temperature exceeds a threshold).

[0042] Step S120, determining a first influencing factor between the first temperature and the first power, determining a second influencing factor between the second temperature and the second power, and determining a third influencing factor between the first temperature and the second temperature; It should be noted that the purpose of determining the first impact factor between the first temperature and the first power is to quantify the degree to which the temperature change of the first module affects the power change. This relationship can be determined through experiments, data analysis, or theoretical models. For example, if the power of the first module increases by 0.5W for every 1°C increase in the temperature of the first module, the first impact factor can be expressed as:

[0043] If the relationship is more complex, curve fitting or other mathematical models may be needed to determine the influencing factors.

[0044] It should be noted that the purpose of determining the third influencing factor between the first temperature and the second temperature is to quantify the degree of influence of the temperature change of the first module on the temperature change of the second module.

[0045] Step S130: Determine a target impact factor between the first power and the second power based on the first impact factor, the second impact factor, and the third impact factor.

[0046] 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, its power may increase (due to increased heat dissipation requirements or performance degradation).

[0047] It should be noted that the second impact 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, its power may decrease (because the protection mechanism is activated).

[0048] It should be noted that the third impact 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 increase as well (due to heat transfer).

[0049] 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.

[0050] It should be noted that these impact factors are usually combined through mathematical models or algorithms. For example, the following formula can be used to calculate the target impact factor: Target impact factor = f(first impact factor, second impact factor, third impact factor) Here, f is a function that can be a simple linear combination or a complex nonlinear model, depending on the actual physical characteristics and operating conditions of the system.

[0051] Assume: the first impact factor k1 (for every 1°C increase in the temperature of the first module, the power increases by 0.5W), the second impact factor k2 (for every 1°C increase in the temperature of the second module, the power decreases by 0.3W), and the third impact factor k3 (for every 1°C increase in the temperature of the first module, the temperature of the second module increases by 0.2°C).

[0052] If the temperature of the first module rises by 1°C, the power of the first module increases by 0.5W (determined by k1), and the temperature of the second module rises by 0.2°C (determined by k3). Since the temperature of the second module rises by 0.2°C, its power decreases by 0.3×0.2=0.06W (determined by k2).

[0053] Taking these influences into consideration, the target impact factor can be expressed as:

[0054] Therefore, for every 1W increase in the power of the first module, the power of the second module decreases by 0.12W.

[0055] In this embodiment, a target impact factor between the power of two modules is determined based on known impact factors. By comprehensively considering the impact of the first module's temperature change on its own power, the impact of the second module's temperature change on its own power, and the impact of the first module's temperature change on the second module's temperature, the degree of impact of the first module's power change on the second module's power change can be more accurately quantified. This helps optimize system performance, improve energy efficiency, and prevent failures.

[0056] Step S200, generating a target decision plan based on the first power, the second power, the target influencing factor and a preset decision generation model; It should be noted that a pre-designed decision generation model is used to generate decision solutions based on input parameters (such as power and impact factors). This model can be a simple rule engine, a complex machine learning model, or a simulation system based on a physical model.

[0057] The target decision plan is a decision plan generated based on input parameters and preset models, which is used to guide the operation or optimization of the system.

[0058] In this embodiment, a target decision solution is generated based on known power, target impact factors, and a pre-set decision generation model. By comprehensively considering these parameters, the model can generate a scientific and reasonable decision solution to guide system operation and optimization, improving system performance, reliability, and energy efficiency.

[0059] Specifically, the step of generating a target decision solution based on the first power, the second power, the target influencing factor, and a preset decision generation model further includes steps S210 to S240: Step S210: determining a first work task corresponding to the first module, and determining a second work task corresponding to the second module; It should be noted that a task refers to the specific task or function that a module needs to complete. Each module has its own specific tasks during device operation, which can include data processing, image acquisition, signal transmission, wireless charging, etc.

[0060] The "first task" here refers to the task that the first module needs to complete, and the "second task" refers to the task that the second module needs to complete. These tasks are determined based on the operating requirements of the device and the user's operating instructions.

[0061] During equipment operation, it is necessary to clearly define the tasks of each module in order to rationally allocate resources, optimize performance, and ensure the normal operation of the equipment. The process of determining tasks usually involves the following aspects: User needs analysis: Determine the tasks that the module needs to complete based on the user's operating instructions and usage scenarios. For example, when a user opens the camera application, the camera module needs to complete the image acquisition task.

[0062] 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, you may need to lower the task priority of some modules to save power.

[0063] Task allocation strategy: Based on the overall design and optimization goals of the equipment, tasks are allocated rationally. For example, to improve the overall efficiency of the equipment, some tasks may need to be allocated to modules with higher performance.

[0064] Step S220: determining a first work scoring rule corresponding to the first work task and determining a second work scoring rule corresponding to the second work task based on a preset work scoring rule library; A predefined work scoring rule base is a collection of predefined rules used to evaluate and quantify performance, efficiency, priority, and other metrics for different work tasks. This rule base typically includes a series of scoring criteria and weights that can be adjusted based on the specific application scenario and optimization goals.

[0065] The role of the rule base is to provide a unified evaluation framework so that the performance and importance of different tasks can be quantified and compared. This helps to rationally allocate resources in a multi-tasking environment and optimize system performance.

[0066] For example, in an intelligent device, the preset work scoring rule base may include the following: task response time (the faster the task is completed, the higher the score), task priority (system-defined high-priority tasks are scored higher than low-priority tasks), energy consumption (the lower the energy consumption of the task, the higher the score), and error rate (the lower the error rate of the task, the higher the score).

[0067] The first work scoring rule and the second work scoring rule are scoring rules for the first work task and the second work task respectively determined according to the preset work scoring rule library. These rules are used to evaluate the performance and importance of each task.

[0068] By determining the work scoring rules for each task, the performance and importance of each task can be quantified, thereby rationally allocating resources in a multi-task environment and optimizing system performance.

[0069] For example, for a graphics processing task of a processor module, the scoring rules may include task response time (weight 60%), energy consumption (weight 30%), and error rate (weight 10%).

[0070] For the high-definition video recording task of the camera module, the scoring rules may include task response time (weight 40%), video quality (weight 40%), and energy consumption (weight 20%).

[0071] In this embodiment, during system operation, the system determines scoring rules for each task based on a preset work scoring rule library. For example, for the graphics processing task of the processor module, the system scores 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 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. This process ensures that each module in the device can efficiently complete its task, thereby improving the overall performance of the device and the user experience.

[0072] Step S230: generating a prompt word based on the first power, the second power, the target impact factor, the first work scoring rule, and the second work scoring rule; It should be noted that the prompt word is a set of information generated based on the above-mentioned multiple factors (first power, second power, target influencing factor, first work scoring rule, second work scoring rule) and is used to guide the subsequent decision-making process.

[0073] The prompt word combines these various factors into a concise and clear input for the decision generation model. The prompt word can be a number, a vector, or a piece of text, depending on the design of the decision generation model.

[0074] Step S240: generating a target decision solution based on the prompt word and a preset decision generation model.

[0075] It should be noted that the target decision-making solution provides specific guidance for device operation, helping it to operate efficiently in a multi-tasking environment. For example, it may include adjusting module power allocation, optimizing task scheduling order, and adjusting heat dissipation strategies.

[0076] 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 solution based on the prompt word and the preset decision generation model further includes steps S241 to S243: Step S241, generating a total score curve corresponding to the first module and the second module based on the prompt word and the curve generation sub-model; It should be noted that the curve generation sub-model is a model specifically used to generate scoring curves. This model can generate one or more scoring curves based on the input prompt words. These curves reflect the performance scores of different modules under different operating conditions.

[0077] It can be understood that the curve generation sub-model transforms the complex multi-factor evaluation into an intuitive scoring curve through mathematical modeling and algorithms, which facilitates subsequent analysis and decision-making.

[0078] It's important to note that the overall score curve is generated by comprehensively considering the performance scores of both modules. This curve reflects the overall performance of the two modules under different operating conditions. The overall score curve provides an intuitive performance evaluation view, helping the system determine the optimal operating state.

[0079] Step S242, determining the maximum total score based on the total score curve; It should be noted that the maximum total score refers to the point on the total score curve where the score reaches its highest. This point represents the optimal overall performance of the two modules under the current conditions. Determining the maximum total score is to find the optimal operating state and thus generate the best decision-making solution.

[0080] Step S243: Generate a target decision plan based on the maximum total score and the strategy generation sub-model.

[0081] It's important to note that the strategy generation sub-model is specifically designed to generate decision solutions. This model generates an optimal decision solution based on the maximum total score input. By analyzing the maximum total score, the strategy generation sub-model generates a specific decision solution to guide device operation and resource allocation.

[0082] The target decision plan is the optimal decision plan generated based on the maximum total score. This plan aims to optimize overall device performance and enhance the user experience. It provides specific guidance for device operation, helping it operate efficiently in a multi-tasking environment. For example, it may include adjusting module power allocation, optimizing task scheduling order, and adjusting cooling strategies.

[0083] Step S300: Adjust the first power and the second power based on the target decision scheme.

[0084] The present application proposes a power adjustment method and related equipment, which relate to the field of data processing technology. Compared with the related art, in which the heat dissipation of different modules not only affects their own power, but also affects the temperature of other modules, thereby affecting the power of other modules, resulting in low overall efficiency of the device and poor user experience, in the present application, first, 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 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 the present application first determines the influence factor between the corresponding powers of different modules, and then generates an optimal target decision scheme (the target decision scheme corresponds to the maximum overall efficiency) based on the influence factor and the preset decision generation model. The power of different modules is adjusted based on the target decision scheme, thereby improving the overall efficiency of the device and improving the user experience.

[0085] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar contents as those in the above embodiment 1 can be referred to the above introduction and will not be described in detail later. Figure 2 , before the step based on the preset work scoring rule base, it also includes steps A1 to A3: Step A1: Obtain user profile and historical rating data; It should be noted that a user profile is a model that comprehensively describes user 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 preferred functional modules and operating styles). User profiles can help the system better understand user needs and behavior patterns, thereby providing more personalized and precise services.

[0086] It should be noted that historical rating data refers to user ratings for different modules or features during past use of a device or service. These ratings can be directly given by users (e.g., a 1 to 5-star rating) or automatically generated by the system based on user behavior and feedback. Historical rating data reflects user satisfaction and preferences for different modules or features and is an important basis for evaluating user preferences and optimizing services.

[0087] Step A2: determining the user's different preferences for different local modules based on the user profile and historical rating data; User preferences refer to a user's preference for different modules or features. By analyzing user profiles and historical ratings, we can determine user preferences for different 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 the performance and user experience of that module.

[0088] The analysis methods can be: user portrait analysis (preliminarily judging the user's possible preferences through the basic information and usage habits in the user portrait. For example, young users may prefer entertainment and social functions, while business users may prefer office and communication functions), historical rating data analysis (by analyzing the user's historical rating data for different modules, further confirming the user's preferences. For example, if users generally give high ratings to a certain module, it means that the user has a high preference for the module; conversely, if the rating is low, it means that the user has a low preference for the module).

[0089] Step A3: Based on the preferences, generate work scoring rules corresponding to each module to build a preset work scoring rule library.

[0090] It's important to note that work 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 module performance and importance. Work scoring rules help the system allocate resources appropriately in a multi-tasking environment, optimize task execution efficiency, and improve the overall user experience.

[0091] It is understandable that the preset work scoring rule library is a collection of multiple work scoring rules used to evaluate the performance and user experience of different modules.

[0092] It can be understood that the preset work scoring rule library provides a unified evaluation framework for the system, so that the system can dynamically adjust the scoring rules according to the user's preferences, thereby optimizing the operating status of the equipment.

[0093] Based on the first and second embodiments of the present application, in the third embodiment of the present application, the same or similar contents as those in the first and second embodiments can be referred to above and will not be described in detail. Figure 3 Before the step of generating a target decision solution based on the first power, the second power, the target influencing factor and a preset decision generation model, steps B1 to B4 are also included: Step B1, obtaining sample data, wherein the decision solution corresponding to the sample data is the first decision solution; It's important to note that sample data is the dataset used to train and validate decision-making models. This data typically includes various input conditions and corresponding outputs. Sample data provides realistic operational scenarios and known optimal decision solutions for training and validating model performance.

[0094] It should be noted that the first decision solution is the known optimal solution for the sample data. These solutions are typically derived through actual operations or expert experience and are considered optimal under the current conditions. The first decision solution serves as a benchmark for evaluating the accuracy of the output of the decision generation model.

[0095] It should be noted that the current decision generation model is the model that is being trained and optimized. This model generates decision solutions based on the input sample data. The purpose of the current decision generation model is to generate the optimal decision solution based on the input conditions.

[0096] Step B2, using the current decision generation model to process the sample data to obtain a second decision solution; It should be noted that the second decision solution is the decision solution generated by the current decision generation model based on the sample data. The second decision solution is used to compare with the first decision solution to evaluate the performance of the model.

[0097] Step B3, determining whether the first decision-making solution is consistent with the second decision-making solution; It should be noted that consistency judgment is to compare the first decision solution and the second decision solution to determine whether they are the same or sufficiently close. Consistency judgment is used to evaluate whether the performance of the current decision generation model meets expectations.

[0098] Step B4: If there is inconsistency, adjust the parameters of the current decision generation model, and based on the adjusted decision generation model, return to the step of using the current decision generation model to process the sample data to obtain a second decision solution, until the first decision solution is consistent with the second decision solution, and obtain the preset decision generation model.

[0099] It should be noted that adjusting model parameters refers to adjusting the parameters of the current decision-making model based on the consistency judgment results to improve model performance. Adjusting parameters can help the model better fit the sample data and generate more accurate decision solutions.

[0100] The return processing step involves reusing the adjusted model to process sample data after adjusting model parameters, generating a new second decision solution. Through multiple iterations, the model's performance is gradually optimized until the resulting decision solution is consistent with the first. Through this process, the system can gradually optimize the decision generation model to produce results consistent with the known optimal decision solution, thereby improving overall device performance and user experience.

[0101] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the power adjustment method of the present application. More simple transformations based on this technical concept are all within the scope of protection of the present application.

[0102] This application also provides a power adjustment device, please refer to Figure 4 , the power adjustment device includes: an acquisition module 10, configured to acquire, 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, and determine a target impact factor between the first power and the second power; A generating module 20, configured to generate a target decision solution based on the first power, the second power, the target influencing factor, and a preset decision generation model; The adjustment module 30 is configured to adjust the first power and the second power based on the target decision scheme.

[0103] In one embodiment, the acquisition module further includes: a first determining unit, configured to determine a first temperature corresponding to the first module, and determine a second temperature corresponding to the second module; a second determining unit, configured to determine a first influencing factor between the first temperature and the first power, determine a second influencing factor between the second temperature and the second power, and determine a third influencing factor between the first temperature and the second temperature; The third determining unit is configured to determine a target impact factor between the first power and the second power based on the first impact factor, the second impact factor, and the third impact factor.

[0104] In one embodiment, the generating module further includes: a fourth determining unit, configured to determine a first work task corresponding to the first module, and determine a second work task corresponding to the second module; a fifth determining unit, configured to determine a first work scoring rule corresponding to the first work task and a second work scoring rule corresponding to the second work task based on a preset work scoring rule library; a first generating unit, configured to generate a prompt word based on the first power, the second power, the target impact factor, the first work scoring rule, and the second work scoring rule; The second generating unit is used to generate a target decision solution based on the prompt word and a preset decision generation model.

[0105] In one embodiment, the generating module further includes: a third generating unit, configured to generate a total score curve corresponding to the first module and the second module based on the prompt word and the curve generating sub-model; a sixth determining unit, configured to determine a maximum total score based on the total score curve; The fourth generating unit is used to generate a target decision-making plan based on the maximum total score and the strategy generation sub-model.

[0106] In one embodiment, the generating module further includes: A first acquisition unit is used to acquire user portraits and historical rating data; a seventh determining unit, configured to determine the user's different preferences for different local modules based on the user portrait and historical rating data; A construction unit is used to generate a work scoring rule corresponding to each module based on the preference to construct a preset work scoring rule library.

[0107] In one embodiment, the power adjustment device further includes a model training module, and the model training module further includes: A second acquisition unit is used to acquire sample data, wherein the decision solution corresponding to the sample data is the first decision solution; a data processing unit, configured to process the sample data using the current decision generation model to obtain a second decision solution; a judging unit, configured to judge whether the first decision-making scheme is consistent with the second decision-making scheme; The training unit is used to adjust the parameters of the current decision generation model if there is any inconsistency, and based on the adjusted decision generation model, return to the step of using the current decision generation model to process the sample data to obtain a second decision solution, until the first decision solution is consistent with the second decision solution to obtain a preset decision generation model.

[0108] The power adjustment device provided in this application utilizes the power adjustment method described in the above-mentioned embodiments to 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 described in the above-mentioned embodiments. Other technical features of the power adjustment device are the same as those disclosed in the above-mentioned embodiments and are not further described here.

[0109] The present 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 that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the power adjustment method in the above-mentioned embodiment one.

[0110] Reference below Figure 5 , which shows a schematic diagram of the structure of a power adjustment device suitable for implementing the embodiments of the present application. The power adjustment device in the embodiments of the present application may include, but is not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), and in-vehicle terminals (e.g., in-vehicle navigation terminals), as well as fixed terminals such as digital TVs and desktop computers. Figure 5 The power adjustment device shown is merely an example and should not limit the functions and scope of use of the embodiments of the present application.

[0111] like Figure 5 As shown, the power adjustment device may include a processing device 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes based on programs stored in a read-only memory (ROM) 1002 or programs loaded from a storage device 1003 into a random access memory (RAM) 1004. RAM 1004 also stores various programs and data required for the operation of the power adjustment device. Processing device 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 may be connected to I / O interface 1006: input devices 1007, such as a touchscreen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; output devices 1008, such as a liquid crystal display (LCD), speaker, vibrator, etc.; storage device 1003, such as a magnetic tape or hard disk; and communication devices 1009. Communication device 1009 can allow the power adjustment device to communicate with other devices wirelessly or wired to exchange data. Although the figure shows a power adjustment device with various systems, it should be understood that it is not required to implement or have all of the systems shown. More or fewer systems can be implemented or have alternatively.

[0112] In particular, according to the embodiments disclosed in the present application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, the embodiments disclosed in the present application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program comprising program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0113] The power adjustment device provided in this application utilizes the power adjustment method in the above-mentioned embodiment to 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-mentioned embodiment. The other technical features of the power adjustment device are the same as those disclosed in the above-mentioned embodiment, and are not further described here.

[0114] 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 one or more embodiments or examples in a suitable manner.

[0115] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

[0116] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, a computer program) stored thereon, wherein the computer-readable program instructions are used to execute the power adjustment method in the above embodiment.

[0117] 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: an electrical connection with one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium that contains or stores 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.

[0118] The computer-readable storage medium may be included in the power adjustment device, or may exist independently without being assembled into the power adjustment device.

[0119] The computer-readable storage medium carries one or more programs. When the one or more programs are executed by the power adjustment device, the power adjustment device: In response to the power adjustment instruction, obtaining a first power corresponding to the local first module and a second power corresponding to the local second module, and determining a target impact factor between the first power and the second power; Generate a target decision plan based on the first power, the second power, the target influencing factor and a preset decision generation model; Based on the target decision scheme, the first power and the second power are adjusted.

[0120] Computer program code for performing the operations of the present application may be written in one or more programming languages, or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may 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 may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0121] The flow charts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present application. In this regard, each box in the flow chart or block diagram can represent a module, program segment or a part of code, and the module, program segment or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented by a dedicated hardware-based system that performs the specified function or operation, or can be implemented by a combination of dedicated hardware and computer instructions.

[0122] The modules described in the embodiments of the present application may be implemented in software or hardware, wherein the name of a module does not necessarily limit the unit itself.

[0123] The computer-readable storage medium provided in this application stores computer-readable program instructions (i.e., a computer program) for executing the aforementioned power adjustment method, thereby resolving the technical problem of power adjustment. Compared to the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the power adjustment method provided in the aforementioned embodiment, and are not further elaborated here.

[0124] The present application also provides a computer program product, comprising a computer program, which implements the steps of the power adjustment method described above when executed by a processor.

[0125] 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 those of the power adjustment method provided in the above embodiment, which will not be repeated here.

[0126] The above description is only part of the embodiments of the present application and does not limit the patent scope of the present application. All equivalent structural transformations made by using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect application in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A power adjustment method, characterized in that: The power adjustment method comprises: In response to the power adjustment instruction, obtaining a first power corresponding to the local first module and a second power corresponding to the local second module, and determining a target impact factor between the first power and the second power; Generate a target decision plan based on the first power, the second power, the target influencing factor and a preset decision generation model; Based on the target decision scheme, the first power and the second power are adjusted.

2. The power adjustment method according to claim 1, wherein: The step of determining a target impact factor between the first power and the second power further includes: Determining a first temperature corresponding to the first module, and determining a second temperature corresponding to the second module; determining a first influencing factor between the first temperature and the first power, determining a second influencing factor between the second temperature and the second power, and determining a third influencing factor between the first temperature and the second temperature; A target impact factor between the first power and the second power is determined based on the first impact factor, the second impact factor, and the third impact factor.

3. The power adjustment method according to claim 1, wherein: The step of generating a target decision solution based on the first power, the second power, the target influencing factor, and a preset decision generation model further includes: Determine a first work task corresponding to the first module, and determine a second work task corresponding to the second module; Determining a first work scoring rule corresponding to the first work task and determining a second work scoring rule corresponding to the second work task based on a preset work scoring rule library; generating a prompt word based on the first power, the second power, the target impact factor, the first work scoring rule, and the second work scoring rule; Based on the prompt words and the preset decision generation model, a target decision plan is generated.

4. The power adjustment method according to claim 3, wherein: The preset decision generation model includes a curve generation sub-model and a strategy generation sub-model. The step of generating a target decision solution based on the prompt word and the preset decision generation model further includes: Based on the prompt words and the curve generation sub-model, generating a total score curve corresponding to the first module and the second module; Determining a maximum total score based on the total score curve; A target decision plan is generated based on the maximum total score and the strategy generation sub-model.

5. The power adjustment method according to claim 3, wherein: Before the step of using the preset work scoring rule base, the method further includes: Obtain user portraits and historical rating data; Determine the user's different preferences for different local modules based on the user profile and historical rating data; Based on the preferences, work scoring rules corresponding to each module are generated to construct a preset work scoring rule library.

6. The power adjustment method according to claim 1, wherein: Before the step of generating a target decision solution based on the first power, the second power, the target influencing factor, and a preset decision generation model, the method further includes: Acquire sample data, wherein the decision solution corresponding to the sample data is a first decision solution; Processing the sample data using the current decision generation model to obtain a second decision solution; Determining whether the first decision-making solution is consistent with the second decision-making solution; If there is inconsistency, adjust the parameters of the current decision generation model, and based on the adjusted decision generation model, return to the step of using the current decision generation model to process the sample data to obtain a second decision solution, until the first decision solution is consistent with the second decision solution to obtain a preset decision generation model.

7. A power adjustment device, characterized in that: The power adjustment device comprises: an acquisition module, configured to acquire, 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, and determine a target impact factor between the first power and the second power; A generation module, configured to generate a target decision plan based on the first power, the second power, the target influencing factor, and a preset decision generation model; An adjustment module is used to adjust the first power and the second power based on the target decision scheme.

8. A power adjustment device, characterized in that: The device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the power adjustment method according to any one of claims 1 to 6.

9. 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, the steps of the power adjustment method according to any one of claims 1 to 6 are implemented.

10. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the steps of the power adjustment method according to any one of claims 1 to 6 are implemented.

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