A Bluetooth circuit board adaptive power consumption management method and system

Abstract

The present application relates to the technical field of circuit board power consumption monitoring, and discloses a method and system for adaptive power consumption management of a Bluetooth circuit board. The method comprises: setting power converters at different levels of a multi-layer circuit board and performing real-time power consumption monitoring to obtain a first power data set of a first power converter and a second power data set of a second power converter; extracting power features of the first power data set and the second power data set respectively to obtain a first power feature set and a second power feature set; creating global power consumption optimization parameters of the multi-layer circuit board according to the first power feature set and the second power feature set through a dual Q network and a PSO algorithm. The present application realizes adaptive power consumption management of a Bluetooth circuit board and improves the power consumption optimization effect of a multi-layer circuit board.

Description

Technical Field

[0001] The present application relates to the technical field of circuit board power consumption monitoring, and in particular to a method and system for adaptive power consumption management of a Bluetooth circuit board. Background Art

[0002] With the popularity of portable devices and wearable devices, how to effectively manage the energy consumption on the circuit board to extend the use time of the device and optimize the performance has become one of the main challenges faced by designers. As a wireless communication technology widely used in various portable devices, Bluetooth technology is particularly critical for the power consumption management of its circuit board, because it directly affects the operating efficiency of the device and the user experience.

[0003] Traditional Bluetooth circuit board power management relies on static power management strategies, which are usually determined at design time and are not adjusted according to the actual operating status of the device. This approach often cannot cope with dynamically changing usage environments and operating requirements in actual applications, resulting in serious waste of power or limited device performance. For example, when the device is in low load or standby state, the traditional management system may still supply power according to the high load state, resulting in unnecessary energy consumption. Summary of the invention

[0004] The present application provides a method and system for adaptive power consumption management of a Bluetooth circuit board, which implements adaptive power consumption management of a Bluetooth circuit board and improves the power consumption optimization effect of a multi-layer circuit board.

[0005] In a first aspect, the present application provides a method for adaptive power consumption management of a Bluetooth circuit board, the method comprising:

[0006] Power converters are arranged at different levels of the multi-layer circuit board and real-time power consumption monitoring is performed to obtain a first power data set of the first power converter and a second power data set of the second power converter;

[0007] Performing power feature extraction on the first power data set and the second power data set respectively to obtain a first power feature set and a second power feature set;

[0008] A global power consumption optimization parameter of the multi-layer circuit board is created according to the first power feature set and the second power feature set by using a dual Q network and a PSO algorithm.

[0009] In a second aspect, the present application provides a Bluetooth circuit board adaptive power consumption management system, the Bluetooth circuit board adaptive power consumption management system comprising:

[0010] A monitoring module, configured to set power converters at different levels of the multi-layer circuit board and perform real-time power consumption monitoring to obtain a first power data set of the first power converter and a second power data set of the second power converter;

[0011] An extraction module, configured to extract power features from the first power data set and the second power data set respectively to obtain a first power feature set and a second power feature set;

[0012] A creation module is used to create global power consumption optimization parameters of the multi-layer circuit board according to the first power feature set and the second power feature set through a dual Q network and a PSO algorithm.

[0013] The third aspect of the present application provides a computer device, comprising: a memory and at least one processor, wherein the memory stores instructions; the at least one processor calls the instructions in the memory so that the computer device executes the above-mentioned Bluetooth circuit board adaptive power consumption management method.

[0014] A fourth aspect of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores instructions, which, when executed on a computer, enable the computer to execute the above-mentioned Bluetooth circuit board adaptive power consumption management method.

[0015] In the technical solution provided by the present application, by setting power converters at different levels of a multi-layer circuit board and performing real-time monitoring, the power consumption data of each layer of the circuit board can be accurately obtained. This real-time monitoring ensures the maximum efficiency of energy utilization, enabling the device to dynamically adjust the power output according to the current operating state, thereby reducing ineffective energy consumption and extending the battery life of the device. The principal component analysis feature extraction technology is applied to effectively extract key features from the original current and voltage data. This in-depth data analysis method helps to more accurately understand the behavior of the circuit board under different working conditions and provides data support for subsequent power consumption optimization. Using the dual Q network and PSO algorithm, this method can not only create efficient global power consumption optimization parameters, but also dynamically adjust and optimize the power consumption configuration of the circuit board through the algorithm. This intelligent decision-making process significantly improves the energy efficiency of the circuit board and reduces the energy waste caused by fixed or outdated power consumption configuration. Combined with the calculation of reward feedback parameters and strategy updates, this method implements a self-learning power consumption management system. Through continuous learning and adjustment, the circuit board can continuously optimize its power consumption strategy to adapt to environmental changes and equipment requirements, and improve overall performance and efficiency. By comprehensively considering the power consumption characteristics of each layer of the circuit board and optimizing the execution strategy, this method can implement power consumption management at the level of the entire circuit board, thereby achieving more unified and coordinated energy management. This global perspective management not only improves energy efficiency, but also helps maintain the stability and reliability of the device, thereby achieving adaptive power consumption management of the Bluetooth circuit board and improving the power consumption optimization effect of multi-layer circuit boards. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0017] Figure 1 A schematic diagram of an embodiment of a method for adaptive power consumption management of a Bluetooth circuit board in an embodiment of the present application;

[0018] Figure 2 This is a schematic diagram of an embodiment of a Bluetooth circuit board adaptive power consumption management system in an embodiment of the present application. DETAILED DESCRIPTION

[0019] Embodiments of the present application provide a method and system for adaptive power consumption management of a Bluetooth circuit board. The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0020] For ease of understanding, the specific process of the embodiment of the present application is described below. Figure 1 , an embodiment of the method for adaptive power consumption management of a Bluetooth circuit board in the embodiment of the present application includes:

[0021] Step 101, setting power converters at different levels of a multi-layer circuit board and performing real-time power consumption monitoring to obtain a first power data set of a first power converter and a second power data set of a second power converter;

[0022] It is understandable that the execution subject of the present application can be a Bluetooth circuit board adaptive power consumption management system, or a terminal or a server, which is not limited here. The present application embodiment is described by taking a server as the execution subject as an example.

[0023] Specifically, a top circuit board, a middle circuit board and a bottom circuit board in a multi-layer circuit board are obtained, and the multi-layer circuit board is used for Bluetooth communication. In order to effectively manage the power requirements of these levels, a first power converter is set between the top circuit board and the middle circuit board, and a second power converter is set between the middle circuit board and the bottom circuit board, so that the power can be more accurately distributed on demand, and the energy efficiency of the entire system is optimized. Real-time current and voltage monitoring is performed. For the first power converter, the initial data of current and voltage are collected by a monitoring device to obtain the first initial current data and the first initial voltage data. The second initial current data and the second initial voltage data are obtained by monitoring the second power converter. In order to extract useful information from the initial data, the principal component analysis technology is used to process the first and second initial current and voltage data. Principal component analysis can effectively extract the main features from a large amount of data, reduce the complexity of the data while retaining the most critical information. Through analysis, a more representative data set is obtained, namely the first target current data and the first target voltage data, as well as the second target current data and the second target voltage data. The power data set is calculated based on the extracted target data. The first target current data and the first target voltage data are used to calculate the first power data set of the first power converter, which directly reflects the power consumption between the top layer and the middle layer circuit boards in actual operation. Similarly, the second target current data and the second target voltage data are used to calculate the second power data set to obtain the power consumption between the middle layer and the bottom layer circuit boards.

[0024] Feature extraction is performed on the initial current data and voltage data monitored by the first and second power converters. Key feature information is extracted from the original monitoring data, such as the peak value, valley value, average value and fluctuation rate of the current, and the peak value, valley value, average value and fluctuation rate of the voltage. By adopting data processing technology, such as signal processing algorithm, useful signals can be effectively separated from noise to ensure that the extracted features are representative and practical. Linear feature transformation is performed on the extracted current and voltage features to convert multidimensional data into main components to simplify subsequent processing and analysis. Linear feature transformation usually adopts principal component analysis method. By constructing the covariance matrix of the data, the components that can represent the maximum variance of the data are extracted. These components are the main eigenvectors of the data. After completing the linear feature transformation, the covariance matrices of the first and second currents and voltages are constructed respectively. The covariance matrix is ​​constructed to analyze the correlation between different features. The relationship between the features can provide important information on how to optimize and adjust the performance of the circuit board. By calculating the covariance of the eigenvectors, it is understood which features are related and which are independent. Feature correlation calculation is performed, and the correlation between the features is evaluated using the covariance matrix. By calculation, determine which features have a greater impact on the changes in current and voltage. Based on the feature correlation analysis, calculate the weight of each feature, which reflects the importance of each feature in circuit power management. Use the calculated feature weights to perform feature fusion on the initially extracted current and voltage features. By weighted averaging, integrate all features into a comprehensive current and voltage data to obtain the target current data and target voltage data.

[0025] Step 102: extract power features from the first power data set and the second power data set respectively to obtain a first power feature set and a second power feature set;

[0026] Specifically, the first and second power data sets are respectively subjected to curve fitting to obtain a first power curve and a second power curve describing the change of the power consumption of the circuit board over time. A mathematical model such as polynomial regression or a nonlinear model is used to ensure that the curve can reflect the actual power consumption data as accurately as possible. Feature extraction is performed based on the obtained power curve, and key power features such as peak value, average power consumption, fluctuation amplitude, etc. are identified from the simplified curve. Feature analysis is performed on the first power curve and the second power curve to obtain a first initial power feature and a second initial power feature, which reflect the specific situation of energy consumption management of each part of the circuit board. Feature classification is performed on multiple first initial power features to obtain multiple first high-frequency power features and multiple first low-frequency power features, and feature classification is performed on multiple second initial power features to obtain multiple second high-frequency power features and multiple second low-frequency power features. High-frequency features may be related to the instantaneous response and short-term changes of the circuit board, while low-frequency features may reflect longer-term energy consumption trends and patterns. Classification helps the system to perform more targeted management and optimization for different power consumption requirements and performance expectations. By calculating the weight of each feature, their importance in overall power consumption management is evaluated, and the degree of influence of the feature on the performance of the circuit board is evaluated. The high-frequency and low-frequency power features are integrated using weights to form a final first power feature set and a second power feature set.

[0027] The Pearson correlation coefficient is calculated for the first high-frequency power feature and the first low-frequency power feature to clarify the strength of the linear relationship between different features. The Pearson correlation coefficient provides a method to measure the degree of linear correlation between two variables, and its value is between -1 and 1, where 1 indicates a perfect positive correlation, -1 indicates a perfect negative correlation, and 0 means no linear correlation. Similarly, the Pearson correlation coefficient is calculated for the second set of high-frequency and low-frequency power features to obtain a second Pearson correlation coefficient. The weight value of each feature is set according to the obtained Pearson correlation coefficient. Features with high correlation receive higher weight values ​​because these features are more important in describing the power consumption behavior of the circuit board, ensuring that the construction of the feature set can truly reflect the actual performance of the circuit board under different working conditions. The high-frequency and low-frequency power features of the first and second groups are weighted using the set feature weights. Each feature will be adjusted according to its weight value to strengthen the role of those features that have a greater impact on the performance of the circuit board. The weighted high-frequency and low-frequency power features are merged to form a comprehensive first power feature set and a second power feature set. These feature sets include all the key power features that have been screened and weighted, ensuring that the power consumption optimization strategy of the circuit board is both accurate and adaptable, and can be dynamically adjusted according to actual needs to achieve optimal energy efficiency.

[0028] Step 103: Create global power consumption optimization parameters of the multi-layer circuit board according to the first power feature set and the second power feature set by using a dual Q network and a PSO algorithm.

[0029] Specifically, the first power feature set and the second power feature set are feature encoded respectively to obtain the first power feature vector and the second power feature vector, and the complex power data is simplified into a mathematical expression form that is easier to calculate and analyze. The power optimization execution strategy analysis is performed on the first power feature vector and the second power feature vector respectively through the dual Q network. The dual Q network is a reinforcement learning algorithm that can avoid overestimation through two independent learning paths while maintaining stability during the learning process. In this process, the dual Q network outputs the initial power optimization execution strategy of the first and second power converters by learning and simulating different power management strategies. These strategies are based on real-time data and pre-set goals to formulate the best power adjustment scheme. In order to optimize these strategies and ensure their effectiveness in practical applications, reward feedback parameter calculation is introduced. By evaluating the operation results of the first and second power converters, reward feedback parameters are calculated, which reflect the effect of each strategy execution and the effect of power saving. According to these feedback parameters, the dual Q network can update its initial power optimization execution strategy to make it more accurate and efficient. Through the PSO algorithm, the power consumption parameters of the multilayer circuit board are calculated according to the first target power optimization execution strategy and the second target power optimization execution strategy. The PSO algorithm simulates the search behavior of a flock of birds to find the optimal solution. It can effectively search in the global parameter space and quickly converge to the optimal power management strategy. Combining the first and second target power optimization execution strategies, the PSO algorithm calculates the global power optimization parameters of the multi-layer circuit board. These parameters take into account the independent needs of each layer of the circuit board and ensure the maximum energy efficiency of the entire system through global optimization.

[0030] Through the PSO algorithm, the first power converter is randomly initialized according to the first target power consumption optimization execution strategy to obtain the first random initial value set, and the second power converter is randomly initialized according to the second target power consumption optimization execution strategy to obtain the second random initial value set. The random initial value set provides the necessary diversity basis for particle swarm optimization, ensuring that the algorithm can explore a wide area of ​​the parameter space, thereby avoiding the local optimal solution and missing the better global solution. Through the reverse particle propagation algorithm, the first and second particle populations are constructed according to the random initial value set. The reverse particle propagation algorithm plays a role in optimizing the position and speed of particles, ensuring that the particle swarm can effectively migrate to the potential optimal solution area. Each particle represents a possible solution, and the entire particle population constitutes the search space for the solution. The first and second particle populations are divided into populations to form multiple sub-particle populations. The search process is refined and the search efficiency is improved, so that each sub-population can conduct a deeper exploration in a specific area of ​​the parameter space. Population division helps to finely manage resources, so that each sub-population can adaptively adjust the search strategy in different directions, so as to more effectively find the optimization parameters. The fitness of each sub-particle population is calculated to evaluate the effect of the solution represented by each particle on power consumption optimization. The fitness calculation is based on factors such as the degree of power consumption reduction, system stability, and performance efficiency to ensure that only the most suitable solution is selected as the final optimization strategy, resulting in the first particle fitness set and the second particle fitness set. Based on the fitness set, the power consumption optimization parameters of the first and second power converters are generated. These parameters are based on the optimal solution calculated previously, aiming to minimize the power consumption of the entire circuit board while maintaining the operating efficiency and stability of the system. The first power consumption optimization parameters and the second power consumption optimization parameters are globally optimized, and all optimized parameters are integrated to form the global power consumption optimization parameters of the multi-layer circuit board.

[0031] In the embodiment of the present application, by setting power converters at different levels of a multi-layer circuit board and performing real-time monitoring, the power consumption data of each layer of the circuit board can be accurately obtained. This real-time monitoring ensures the maximum efficiency of energy utilization, enabling the device to dynamically adjust the power output according to the current operating state, thereby reducing ineffective energy consumption and extending the battery life of the device. The principal component analysis feature extraction technology is applied to effectively extract key features from the original current and voltage data. This in-depth data analysis method helps to more accurately understand the behavior of the circuit board under different working conditions and provides data support for subsequent power consumption optimization. Using the dual Q network and PSO algorithm, this method can not only create efficient global power consumption optimization parameters, but also dynamically adjust and optimize the power consumption configuration of the circuit board through the algorithm. This intelligent decision-making process significantly improves the energy efficiency of the circuit board and reduces the energy waste caused by fixed or outdated power consumption configuration. Combined with the calculation of reward feedback parameters and strategy updates, this method implements a self-learning power consumption management system. Through continuous learning and adjustment, the circuit board can continuously optimize its power consumption strategy to adapt to environmental changes and equipment requirements, and improve overall performance and efficiency. By comprehensively considering the power consumption characteristics of each layer of the circuit board and optimizing the execution strategy, this method can implement power consumption management at the level of the entire circuit board, thereby achieving more unified and coordinated energy management. This global perspective management not only improves energy efficiency, but also helps maintain the stability and reliability of the device, thereby achieving adaptive power consumption management of the Bluetooth circuit board and improving the power consumption optimization effect of multi-layer circuit boards.

[0032] In a specific embodiment, the process of executing step 101 may specifically include the following steps:

[0033] (1) obtaining a top-layer circuit board, a middle-layer circuit board, and a bottom-layer circuit board in a multi-layer circuit board, where the multi-layer circuit board is used for Bluetooth communication;

[0034] (2) a first power converter is arranged between the top circuit board and the middle circuit board, and a second power converter is arranged between the middle circuit board and the bottom circuit board;

[0035] (3) monitoring the current and voltage of the first power converter to obtain first initial current data and first initial voltage data, and monitoring the current and voltage of the second power converter to obtain second initial current data and second initial voltage data;

[0036] (4) performing principal component feature analysis on the first initial current data and the first initial voltage data respectively to obtain first target current data and first target voltage data, and performing principal component feature analysis on the second initial current data and the second initial voltage data respectively to obtain second target current data and second target voltage data;

[0037] (5) Calculating a first power data set of the first power converter based on the first target current data and the first target voltage data, and calculating a second power data set of the second power converter based on the second target current data and the second target voltage data.

[0038] Specifically, a top circuit board, a middle circuit board and a bottom circuit board in a multilayer circuit board are obtained, and the multilayer circuit board is used for Bluetooth communication. The top circuit board is usually responsible for receiving and sending signals, while the middle and bottom circuit boards handle more data processing and power management functions. In order to effectively manage power distribution and consumption, a first and a second power converter are respectively set between the top circuit board and the middle circuit board and between the middle circuit board and the bottom circuit board. Each circuit board adjusts the power supply according to actual needs, thereby optimizing the power consumption of the entire system. The first power converter is monitored for current and voltage to obtain first initial current data and first initial voltage data, and the second power converter is monitored for current and voltage to obtain second initial current data and second initial voltage data. The first and second power converters are monitored to help the system detect any abnormality or signs of reduced efficiency in a timely manner. The initial data is subjected to principal component analysis to extract useful information and simplify the data processing process. Principal component analysis reduces the complexity of the data by identifying the main variables and patterns in the data, thereby more accurately identifying the key factors affecting the performance of the circuit board. For example, through principal component analysis, it may be found that certain specific current and voltage patterns are directly related to the degradation of system performance, and these findings can be used to adjust the working mode of the power converter to improve efficiency. After completing the principal component feature analysis, the first target current data and the first target voltage data, as well as the second target current data and the second target voltage data are obtained based on the analysis results. Using these data, the power data sets of the first and second power converters are calculated, which reflect the power consumption characteristics of each power converter under specific working conditions. For example, suppose that when the top-level circuit board is performing Bluetooth communication, its power consumption demand increases due to the increase in applications. After receiving the increased current and voltage demand signal, the first power converter adjusts according to the first target current and voltage data to meet the power consumption demand of the top layer without overloading. Similarly, if the middle-level circuit board processes more data, the second power converter will also adjust according to the second target current and voltage data to ensure that the power supply meets the demand and saves energy to the greatest extent.

[0039] In a specific embodiment, the execution step performs principal component feature analysis on the first initial current data and the first initial voltage data to obtain the first target current data and the first target voltage data, and performs principal component feature analysis on the second initial current data and the second initial voltage data to obtain the second target current data and the second target voltage data. The process may specifically include the following steps:

[0040] (1) performing feature extraction on the first initial current data and the first initial voltage data respectively to obtain a plurality of first initial current features and a plurality of first initial voltage features, and performing feature extraction on the second initial current data and the second initial voltage data respectively to obtain a plurality of second initial current features and a plurality of second initial voltage features;

[0041] (2) performing linear feature transformation on the plurality of first initial current features and the plurality of first initial voltage features respectively to obtain a first current linear feature set and a first voltage linear feature set, and performing linear feature transformation on the plurality of second initial current features and the plurality of second initial voltage features respectively to obtain a second current linear feature set and a second voltage linear feature set;

[0042] (3) constructing a corresponding first current covariance matrix and a first voltage covariance matrix based on the first current linear feature set and the first voltage linear feature set, and constructing a corresponding second current covariance matrix and a second voltage covariance matrix based on the second current linear feature set and the second voltage linear feature set;

[0043] (4) performing feature correlation calculation on the first current covariance matrix and the first voltage covariance matrix to obtain a first current feature correlation and a first voltage feature correlation, and performing feature correlation calculation on the second current covariance matrix and the second voltage covariance matrix to obtain a second current feature correlation and a second voltage feature correlation;

[0044] (5) calculating the first current principal component feature weight and the first voltage principal component feature weight corresponding to the first current linear feature set and the first voltage linear feature set respectively according to the first current feature correlation and the first voltage feature correlation, and calculating the second current principal component feature weight and the second voltage principal component feature weight corresponding to the second current linear feature set and the second voltage linear feature set respectively according to the second current feature correlation and the second voltage feature correlation;

[0045] (6) Feature fusion is performed on multiple first initial current features and multiple first initial voltage features according to the first current principal component feature weight and the first voltage principal component feature weight to obtain first target current data and first target voltage data, and feature fusion is performed on multiple second initial current features and multiple second initial voltage features according to the second current principal component feature weight and the second voltage principal component feature weight to obtain second target current data and second target voltage data.

[0046] Specifically, feature extraction is performed on the initial current and voltage data collected from the first power converter and the second power converter. The current and voltage values ​​of each converter are measured to capture the specific performance of the converter under the operating state, such as the peak value, average value, fluctuation and minimum value of the current and voltage. The initial features are subjected to linear feature transformation, usually using methods such as principal component analysis (PCA). The main features are extracted from multiple related original features to reduce the dimension of the data set while retaining the most important information. For example, if the original data shows that there is a high correlation between the current and voltage features, principal component analysis can help identify the key factors affecting the system performance, thereby simplifying the entire feature set. The covariance matrix of current and voltage is constructed. The covariance matrix helps understand the linear relationship between different features, that is, which features have a strong correlation with other features. The correlation of each feature is obtained by calculating the eigenvalues ​​and eigenvectors of the covariance matrix. The high or low correlation of the features represents the importance of each feature to the power consumption behavior of the circuit board. Features with high correlation will be given higher weights. According to the weights calculated based on the correlation of the features, all features are weighted and fused to form the final target current data and target voltage data. Fusion ensures that the most important features are highlighted, while less critical features are relatively reduced in influence. For example, if the principal component analysis shows that a specific current variation pattern of the first power converter is highly correlated with system performance degradation, then this feature will be given a higher weight in the final fusion.

[0047] In a specific embodiment, the process of executing step 102 may specifically include the following steps:

[0048] (1) performing curve fitting on the first power data set to obtain a first power curve, and performing curve fitting on the second power data set to obtain a second power curve;

[0049] (2) performing feature extraction on the first power curve to obtain a plurality of first initial power features, and performing feature extraction on the second power curve to obtain a plurality of second initial power features;

[0050] (3) performing feature classification on the plurality of first initial power features to obtain a plurality of first high-frequency power features and a plurality of first low-frequency power features, and performing feature classification on the plurality of second initial power features to obtain a plurality of second high-frequency power features and a plurality of second low-frequency power features;

[0051] (4) Performing feature weight calculation and feature set conversion on multiple first high-frequency power features and multiple first low-frequency power features to obtain a first power feature set, and performing feature weight calculation and feature set conversion on multiple second high-frequency power features and multiple second low-frequency power features to obtain a second power feature set.

[0052] Specifically, curve fitting is performed on the first and second power data sets. A mathematical model, such as a polynomial regression or nonlinear regression model, is used to approximate the actual observed data points. The purpose of curve fitting is to create a mathematical expression that can describe the trend of power data over time or other variables. For example, if the first power data set shows that power consumption gradually increases over time, curve fitting may reveal a linear growth or a more complex exponential growth pattern. Similarly, the fitting of the second power data set may reveal different patterns, depending on the specific power consumption behavior of different parts or different functional modules of the circuit board. Feature extraction is performed on the first power curve to obtain multiple first initial power features, and feature extraction is performed on the second power curve to obtain multiple second initial power features. Meaningful features such as peaks, valleys, averages, fluctuations, etc. are identified from the fitted curves. These features help to gain a deeper understanding of the power consumption dynamics of the circuit board. For example, the first power curve may show a sudden increase in power consumption under certain conditions, which can be extracted as a key feature; while the second power curve may reveal a periodic change in power consumption. After feature extraction, these features are classified, usually into high-frequency and low-frequency power features. High-frequency features focus on transient behaviors where power consumption changes rapidly, while low-frequency features focus on changing trends over longer time scales. Perform feature weight calculations and feature set conversions. Weights are assigned by analyzing the importance of each feature’s impact on overall power consumption. High-impact features will receive higher weights, reflecting their importance in power consumption optimization. For example, if a high-frequency current feature is closely related to system performance degradation, the weight of this feature in the first power feature set will be increased. Through weights, the initially extracted features are converted into a more comprehensive and influential power feature set, resulting in the first and second power feature sets, which guide the adjustment and optimization of the power converter, thereby achieving overall optimization of the circuit board power consumption.

[0053] In a specific embodiment, the execution step performs feature weight calculation and feature set conversion on multiple first high-frequency power features and multiple first low-frequency power features to obtain a first power feature set, and performs feature weight calculation and feature set conversion on multiple second high-frequency power features and multiple second low-frequency power features to obtain a second power feature set. The process may specifically include the following steps:

[0054] (1) calculating a Pearson correlation coefficient for a plurality of first high-frequency power features and a plurality of first low-frequency power features to obtain a first Pearson correlation coefficient, and calculating a Pearson correlation coefficient for a plurality of second high-frequency power features and a plurality of second low-frequency power features to obtain a second Pearson correlation coefficient;

[0055] (2) setting first feature weight values ​​of the plurality of first high-frequency power features and the plurality of first low-frequency power features according to the first Pearson correlation coefficient, and setting second feature weight values ​​of the plurality of second high-frequency power features and the plurality of second low-frequency power features according to the second Pearson correlation coefficient;

[0056] (3) performing feature weighting on the plurality of first high-frequency power features and the plurality of first low-frequency power features according to the first feature weight value to obtain a plurality of first high-frequency weighted features and a plurality of first low-frequency weighted features, and performing feature weighting on the plurality of second high-frequency power features and the plurality of second low-frequency power features according to the second feature weight value to obtain a plurality of second high-frequency weighted features and a plurality of second low-frequency weighted features;

[0057] (4) Generate a corresponding first power feature set based on the plurality of first high-frequency weighted features and the plurality of first low-frequency weighted features, and generate a corresponding second power feature set based on the plurality of second high-frequency weighted features and the plurality of second low-frequency weighted features.

[0058] Specifically, the Pearson correlation coefficient is calculated for the high-frequency and low-frequency power features in the first and second power data sets. The Pearson correlation coefficient is a statistical indicator that measures the strength of the linear relationship between two variables, and its value ranges from -1 to 1, where 1 indicates a perfect positive correlation, -1 indicates a perfect negative correlation, and 0 indicates no linear correlation. For example, for the first power data set, it is assumed that the rapid change of current (high-frequency feature) is observed to be highly correlated with the stability of voltage (low-frequency feature) in the high power consumption mode. This correlation may indicate that the stable voltage of the circuit in the high power consumption state is essential to maintain the performance of the device. Through the calculated Pearson correlation coefficient, a corresponding weight is set for each high-frequency and low-frequency power feature. These weights reflect the importance of each feature in the overall power consumption behavior of the circuit. For example, if a high-frequency current change feature is highly correlated with the degradation of circuit performance, it may be given a higher weight because such a feature helps predict and avoid potential performance problems. The first and second power features are weighted according to the set weights. By multiplying each feature by its corresponding weight, the features that have a greater impact on circuit performance are emphasized, while the impact of those less important features is reduced, and the weighted first and second high-frequency weighted features and low-frequency weighted features are obtained. The weighted features are combined into a comprehensive power feature set. All weighted features are integrated to form a comprehensive power feature set representing the first and second power converters. For example, the high-frequency weighted features and the low-frequency weighted features in the first power feature set are combined to provide a comprehensive view of how the performance of the first power converter is affected by various factors under different operating conditions. Similarly, the second power feature set is constructed in a similar manner to ensure that each power converter operates at optimal energy efficiency and performance.

[0059] In a specific embodiment, the process of executing step 103 may specifically include the following steps:

[0060] (1) performing feature encoding on the first power feature set and the second power feature set respectively to obtain a first power feature vector and a second power feature vector;

[0061] (2) performing power optimization execution strategy analysis on the first power characteristic vector and the second power characteristic vector respectively through a dual Q network to obtain a first initial power consumption optimization execution strategy for the first power converter and a second initial power consumption optimization execution strategy for the second power converter;

[0062] (3) calculating reward feedback parameters for the first power converter and the second power converter to obtain a first reward feedback parameter for the first power converter and a second reward feedback parameter for the second power converter;

[0063] (4) updating the first initial power consumption optimization execution strategy according to the first reward feedback parameter to obtain a first target power consumption optimization execution strategy, and updating the second initial power consumption optimization execution strategy according to the second reward feedback parameter to obtain a second target power consumption optimization execution strategy;

[0064] (5) Using the PSO algorithm, the power consumption parameters of the multi-layer circuit board are calculated according to the first target power consumption optimization execution strategy and the second target power consumption optimization execution strategy to obtain the global power consumption optimization parameters of the multi-layer circuit board.

[0065] Specifically, feature encoding is performed on the first power feature set and the second power feature set respectively to obtain a first power feature vector and a second power feature vector. For example, data such as peak power consumption, average power consumption, and volatility are converted into a series of numerical values ​​that can accurately describe the operating state of the power converter. If the first power converter exhibits a peak power consumption at high load, this information will be encoded into the first power feature vector. The encoded power feature vector is analyzed by a dual Q network to form an initial power consumption optimization execution strategy. The dual Q network is a reinforcement learning algorithm that uses two value function evaluators to reduce the deviation caused by overestimation, thereby providing more stable and accurate learning results. Each power feature vector is analyzed to predict the magnitude of the impact of different adjustment strategies on power consumption, and an initial power consumption optimization execution strategy for each converter is generated. In order to further improve the effectiveness of the strategy, reward feedback parameters are calculated. The performance of each strategy is evaluated by comparing the actual operating data with the strategy prediction results. For example, if the power consumption of the first power converter is significantly reduced and the stability is improved after implementing a certain strategy, the strategy will obtain a higher reward feedback parameter. These parameters directly affect the adjustment and optimization of subsequent strategies. The strategy is updated according to the reward feedback parameters. The dual Q network adjusts its value function evaluator based on the feedback obtained to optimize the original power optimization execution strategy. The strategy of each converter is gradually adjusted according to its actual performance to ensure higher efficiency and lower power consumption in future operations. Through the PSO algorithm, the power consumption parameters of the entire multi-layer circuit board are calculated globally according to the updated first and second target power optimization execution strategies. PSO is a swarm intelligence algorithm that simulates the search behavior of a flock of birds to find the optimal solution. In this process, the algorithm searches for the optimal parameter combination in the parameter space that minimizes the overall power consumption.

[0066] In a specific embodiment, the process of executing step 106 may specifically include the following steps:

[0067] (1) using a PSO algorithm, generating random initial values ​​for a first power converter according to a first target power consumption optimization execution strategy to obtain a first random initial value set, and generating random initial values ​​for a second power converter according to a second target power consumption optimization execution strategy to obtain a second random initial value set;

[0068] (2) constructing a particle population for the first random initial value set by using a reverse particle propagation algorithm to obtain a first particle population, and constructing a particle population for the second random initial value set by using a reverse particle propagation algorithm to obtain a second particle population;

[0069] (3) performing population segmentation on the first particle population to obtain a plurality of first sub-particle populations, and performing population segmentation on the second particle population to obtain a plurality of second sub-particle populations;

[0070] (4) performing fitness calculations on the plurality of first sub-particle populations respectively to obtain a first particle fitness set corresponding to the first particle population, and performing fitness calculations on the plurality of second sub-particle populations respectively to obtain a second particle fitness set corresponding to the second particle population;

[0071] (5) generating a first power consumption optimization parameter of the first power converter according to the first particle fitness set, and generating a second power consumption optimization parameter of the second power converter according to the second particle fitness set;

[0072] (6) Globally optimize the first power consumption optimization parameter and the second power consumption optimization parameter to obtain the global power consumption optimization parameter of the multi-layer circuit board.

[0073] Specifically, random initial values ​​are generated for the first and second power converters by the PSO algorithm. The PSO algorithm starts its search process with a set of random solutions, which are optimized by simulating the social behavior of bird flocks during the algorithm's iteration process. In this embodiment, each solution represents a set of possible power converter parameters, such as current limit, voltage setting, etc. For example, the first power converter may operate within a certain power range, and the set of random initial values ​​generated by the PSO algorithm will cover various possible settings within this range. The reverse particle propagation algorithm is used to construct a particle population for the randomly generated initial values. Each initial value is regarded as a particle, and the entire set forms a particle population. Reverse particle propagation is a process of adjusting the position and velocity of particles, and a better solution is found through repeated iterations of the algorithm. For example, if a parameter setting represented by a particle results in reduced power consumption while maintaining the same performance, then this setting will be pushed to a better direction in subsequent iterations. The particle population is divided to form multiple sub-particle populations, each of which focuses on searching different parts of the solution space. Population division increases the possibility of finding a global optimal solution and prevents the algorithm from focusing on a local optimal solution too early. Each sub-population performs an independent search, but shares information about the global optimal solution to guide the search process. Fitness calculations are performed on multiple first sub-particle populations to obtain first particle fitness sets corresponding to the first particle populations, and fitness calculations are performed on multiple second sub-particle populations to obtain second particle fitness sets corresponding to the second particle populations. Fitness calculations are usually based on the degree of power consumption reduction, improvement in system stability, and possible performance gains. These fitness values ​​provide a quantitative benefit assessment for each particle and its parameter settings to guide the final parameter selection. Based on the fitness set of each particle population, optimal power consumption parameters for the first and second power converters are generated. These parameters reflect the settings that performed best during the entire search process. These parameters are applied to global optimization, and all optimal solutions are integrated to form a comprehensive power consumption optimization solution for the multi-layer circuit board. Global optimization takes into account all relevant performance indicators and operating constraints to ensure that the circuit board can operate with the lowest power consumption under all operating conditions.

[0074] The above describes the adaptive power consumption management method of the Bluetooth circuit board in the embodiment of the present application. The following describes the adaptive power consumption management system of the Bluetooth circuit board in the embodiment of the present application. Figure 2 In the embodiment of the present application, an embodiment of the Bluetooth circuit board adaptive power consumption management system includes:

[0075] A monitoring module 201 is used to set power converters at different levels of the multi-layer circuit board and perform real-time power consumption monitoring to obtain a first power data set of the first power converter and a second power data set of the second power converter;

[0076] An extraction module 202 is used to extract power features from the first power data set and the second power data set respectively to obtain a first power feature set and a second power feature set;

[0077] The creation module 203 is used to create global power consumption optimization parameters of the multi-layer circuit board according to the first power feature set and the second power feature set by using a dual Q network and a PSO algorithm.

[0078] Through the synergy of the above components, by setting power converters at different levels of the multi-layer circuit board and performing real-time monitoring, the power consumption data of each layer of the circuit board can be accurately obtained. This real-time monitoring ensures the maximum efficiency of energy utilization, enabling the device to dynamically adjust the power output according to the current operating status, thereby reducing ineffective energy consumption and extending the battery life of the device. The principal component analysis feature extraction technology is applied to effectively extract key features from the original current and voltage data. This in-depth data analysis method helps to more accurately understand the behavior of the circuit board under different working conditions and provides data support for subsequent power consumption optimization. Using the dual Q network and PSO algorithm, this method can not only create efficient global power consumption optimization parameters, but also dynamically adjust and optimize the power consumption configuration of the circuit board through the algorithm. This intelligent decision-making process significantly improves the energy efficiency of the circuit board and reduces the energy waste caused by fixed or outdated power consumption configuration. Combined with the calculation of reward feedback parameters and strategy update, this method realizes a self-learning power management system. Through continuous learning and adjustment, the circuit board can continuously optimize its power consumption strategy to adapt to environmental changes and device requirements, improving overall performance and efficiency. By comprehensively considering the power consumption characteristics of each layer of the circuit board and optimizing the execution strategy, this method can implement power consumption management at the level of the entire circuit board, thereby achieving more unified and coordinated energy management. This global perspective management not only improves energy efficiency, but also helps maintain the stability and reliability of the device, thereby achieving adaptive power consumption management of the Bluetooth circuit board and improving the power consumption optimization effect of multi-layer circuit boards.

[0079] The present application also provides a computer device, which includes a memory and a processor. The memory stores computer-readable instructions. When the computer-readable instructions are executed by the processor, the processor executes the steps of the Bluetooth circuit board adaptive power consumption management method in the above-mentioned embodiments.

[0080] The present application also provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. Instructions are stored in the computer-readable storage medium. When the instructions are executed on a computer, the computer executes the steps of the Bluetooth circuit board adaptive power consumption management method.

[0081] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems, systems and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0082] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk and other media that can store program code.

[0083] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for adaptive power consumption management of a Bluetooth circuit board, characterized in that: The Bluetooth circuit board adaptive power consumption management method comprises: Power converters are arranged at different levels of a multi-layer circuit board and real-time power consumption monitoring is performed to obtain a first power data set of a first power converter and a second power data set of a second power converter; specifically comprising: obtaining a top circuit board, a middle circuit board and a bottom circuit board in the multi-layer circuit board, the multi-layer circuit board being used for Bluetooth communication; arranging a first power converter between the top circuit board and the middle circuit board, and at the same time, arranging a second power converter between the middle circuit board and the bottom circuit board; monitoring the current and voltage of the first power converter to obtain first initial current data and first initial voltage data, and monitoring the current and voltage of the second power converter to obtain second initial current data and second initial voltage data; performing principal component feature analysis on the first initial current data and the first initial voltage data respectively to obtain first target current data and first target voltage data, and performing principal component feature analysis on the second initial current data and the second initial voltage data respectively to obtain second target current data and second target voltage data; calculating the first power data set of the first power converter based on the first target current data and the first target voltage data, and calculating the second power data set of the second power converter based on the second target current data and the second target voltage data; Performing power feature extraction on the first power data set and the second power data set respectively to obtain a first power feature set and a second power feature set; The global power consumption optimization parameters of the multi-layer circuit board are created according to the first power feature set and the second power feature set by using a dual Q network and a PSO algorithm; specifically, the following steps are performed: feature encoding is performed on the first power feature set and the second power feature set respectively to obtain a first power feature vector and a second power feature vector; power optimization execution strategy analysis is performed on the first power feature vector and the second power feature vector respectively by using a dual Q network to obtain a first initial power consumption optimization execution strategy of the first power converter and a second initial power consumption optimization execution strategy of the second power converter; reward feedback parameter calculation is performed on the first power converter and the second power converter to obtain a first reward feedback parameter of the first power converter and a second reward feedback parameter of the second power converter; the first initial power consumption optimization execution strategy is updated according to the first reward feedback parameter to obtain a first target power consumption optimization execution strategy, and the second initial power consumption optimization execution strategy is updated according to the second reward feedback parameter to obtain a second target power consumption optimization execution strategy; power consumption parameters of the multi-layer circuit board are calculated according to the first target power consumption optimization execution strategy and the second target power consumption optimization execution strategy by using a PSO algorithm to obtain the global power consumption optimization parameters of the multi-layer circuit board.

2. The method for adaptive power consumption management of a Bluetooth circuit board according to claim 1, characterized in that: The performing principal component feature analysis on the first initial current data and the first initial voltage data to obtain first target current data and first target voltage data, and performing principal component feature analysis on the second initial current data and the second initial voltage data to obtain second target current data and second target voltage data, respectively, comprises: Performing feature extraction on the first initial current data and the first initial voltage data respectively to obtain a plurality of first initial current features and a plurality of first initial voltage features, and performing feature extraction on the second initial current data and the second initial voltage data respectively to obtain a plurality of second initial current features and a plurality of second initial voltage features; Performing linear feature transformation on the plurality of first initial current features and the plurality of first initial voltage features respectively to obtain a first current linear feature set and a first voltage linear feature set, and performing linear feature transformation on the plurality of second initial current features and the plurality of second initial voltage features respectively to obtain a second current linear feature set and a second voltage linear feature set; Constructing a corresponding first current covariance matrix and a first voltage covariance matrix based on the first current linear feature set and the first voltage linear feature set, and constructing a corresponding second current covariance matrix and a second voltage covariance matrix based on the second current linear feature set and the second voltage linear feature set; Performing feature correlation calculation on the first current covariance matrix and the first voltage covariance matrix to obtain a first current feature correlation and a first voltage feature correlation, and performing feature correlation calculation on the second current covariance matrix and the second voltage covariance matrix to obtain a second current feature correlation and a second voltage feature correlation; Calculate first current principal component feature weights and first voltage principal component feature weights corresponding to the first current linear feature set and the first voltage linear feature set respectively according to the first current feature correlation and the first voltage feature correlation, and calculate second current principal component feature weights and second voltage principal component feature weights corresponding to the second current linear feature set and the second voltage linear feature set respectively according to the second current feature correlation and the second voltage feature correlation; The multiple first initial current features and the multiple first initial voltage features are feature fused according to the first current principal component feature weight and the first voltage principal component feature weight to obtain first target current data and first target voltage data, and the multiple second initial current features and the multiple second initial voltage features are feature fused according to the second current principal component feature weight and the second voltage principal component feature weight to obtain second target current data and second target voltage data.

3. The method for adaptive power consumption management of a Bluetooth circuit board according to claim 1, characterized in that: The extracting power features from the first power data set and the second power data set respectively to obtain a first power feature set and a second power feature set includes: Performing curve fitting on the first power data set to obtain a first power curve, and performing curve fitting on the second power data set to obtain a second power curve; Performing feature extraction on the first power curve to obtain a plurality of first initial power features, and performing feature extraction on the second power curve to obtain a plurality of second initial power features; Feature classification is performed on the plurality of first initial power features to obtain a plurality of first high-frequency power features and a plurality of first low-frequency power features, and feature classification is performed on the plurality of second initial power features to obtain a plurality of second high-frequency power features and a plurality of second low-frequency power features; Feature weight calculation and feature set conversion are performed on the multiple first high-frequency power features and the multiple first low-frequency power features to obtain a first power feature set, and feature weight calculation and feature set conversion are performed on the multiple second high-frequency power features and the multiple second low-frequency power features to obtain a second power feature set.

4. The method for adaptive power consumption management of a Bluetooth circuit board according to claim 3, characterized in that: The step of performing feature weight calculation and feature set conversion on the plurality of first high-frequency power features and the plurality of first low-frequency power features to obtain a first power feature set, and performing feature weight calculation and feature set conversion on the plurality of second high-frequency power features and the plurality of second low-frequency power features to obtain a second power feature set includes: Calculating the Pearson correlation coefficient of the plurality of first high-frequency power features and the plurality of first low-frequency power features to obtain a first Pearson correlation coefficient, and calculating the Pearson correlation coefficient of the plurality of second high-frequency power features and the plurality of second low-frequency power features to obtain a second Pearson correlation coefficient; Setting first feature weight values ​​of the plurality of first high-frequency power features and the plurality of first low-frequency power features according to the first Pearson correlation coefficient, and setting second feature weight values ​​of the plurality of second high-frequency power features and the plurality of second low-frequency power features according to the second Pearson correlation coefficient; Performing feature weighting on the multiple first high-frequency power features and the multiple first low-frequency power features according to the first feature weight value to obtain multiple first high-frequency weighted features and multiple first low-frequency weighted features, and performing feature weighting on the multiple second high-frequency power features and the multiple second low-frequency power features according to the second feature weight value to obtain multiple second high-frequency weighted features and multiple second low-frequency weighted features; A corresponding first power feature set is generated according to the plurality of first high-frequency weighted features and the plurality of first low-frequency weighted features, and a corresponding second power feature set is generated according to the plurality of second high-frequency weighted features and the plurality of second low-frequency weighted features.

5. The method for adaptive power consumption management of a Bluetooth circuit board according to claim 1, characterized in that: The method of calculating the power consumption parameters of the multi-layer circuit board according to the first target power consumption optimization execution strategy and the second target power consumption optimization execution strategy by using the PSO algorithm to obtain the global power consumption optimization parameters of the multi-layer circuit board includes: By using the PSO algorithm, randomly generating initial values ​​for the first power converter according to the first target power consumption optimization execution strategy to obtain a first random initial value set, and randomly generating initial values ​​for the second power converter according to the second target power consumption optimization execution strategy to obtain a second random initial value set; Performing particle population construction on the first random initial value set by using a reverse particle propagation algorithm to obtain a first particle population, and performing particle population construction on the second random initial value set by using the reverse particle propagation algorithm to obtain a second particle population; Performing population segmentation on the first particle population to obtain a plurality of first sub-particle populations, and performing population segmentation on the second particle population to obtain a plurality of second sub-particle populations; Performing fitness calculations on the plurality of first sub-particle populations respectively to obtain a first particle fitness set corresponding to the first particle population, and performing fitness calculations on the plurality of second sub-particle populations respectively to obtain a second particle fitness set corresponding to the second particle population; generating a first power consumption optimization parameter of the first power converter according to the first particle fitness set, and generating a second power consumption optimization parameter of the second power converter according to the second particle fitness set; The first power consumption optimization parameter and the second power consumption optimization parameter are globally optimized to obtain a global power consumption optimization parameter of the multi-layer circuit board.

6. A Bluetooth circuit board adaptive power consumption management system, characterized in that: The Bluetooth circuit board adaptive power consumption management system comprises: A monitoring module, used to set power converters at different levels of a multi-layer circuit board and perform real-time power consumption monitoring to obtain a first power data set of a first power converter and a second power data set of a second power converter; specifically comprising: obtaining a top circuit board, a middle circuit board and a bottom circuit board in the multi-layer circuit board, the multi-layer circuit board being used for Bluetooth communication; setting a first power converter between the top circuit board and the middle circuit board, and at the same time, setting a second power converter between the middle circuit board and the bottom circuit board; performing current and voltage monitoring on the first power converter to obtain first initial current data and first initial voltage data, and performing current and voltage monitoring on the second power converter to obtain second initial current data and second initial voltage data; performing principal component feature analysis on the first initial current data and the first initial voltage data respectively to obtain first target current data and first target voltage data, and performing principal component feature analysis on the second initial current data and the second initial voltage data respectively to obtain second target current data and second target voltage data; calculating a first power data set of the first power converter based on the first target current data and the first target voltage data, and calculating a second power data set of the second power converter based on the second target current data and the second target voltage data; An extraction module, configured to extract power features from the first power data set and the second power data set respectively to obtain a first power feature set and a second power feature set; A creation module is used to create the global power consumption optimization parameters of the multi-layer circuit board according to the first power feature set and the second power feature set through a dual Q network and a PSO algorithm; specifically comprising: feature encoding the first power feature set and the second power feature set respectively to obtain a first power feature vector and a second power feature vector; performing power optimization execution strategy analysis on the first power feature vector and the second power feature vector respectively through a dual Q network to obtain a first initial power consumption optimization execution strategy of the first power converter and a second initial power consumption optimization execution strategy of the second power converter; performing reward feedback parameter calculation on the first power converter and the second power converter to obtain a first reward feedback parameter of the first power converter and a second reward feedback parameter of the second power converter; performing a strategy update on the first initial power consumption optimization execution strategy according to the first reward feedback parameter to obtain a first target power consumption optimization execution strategy, and performing a strategy update on the second initial power consumption optimization execution strategy according to the second reward feedback parameter to obtain a second target power consumption optimization execution strategy; performing power consumption parameter calculation on the multi-layer circuit board according to the first target power consumption optimization execution strategy and the second target power consumption optimization execution strategy through a PSO algorithm to obtain the global power consumption optimization parameters of the multi-layer circuit board.

7. A computer device, characterized in that: The computer device comprises: a memory and at least one processor, wherein instructions are stored in the memory; The at least one processor calls the instructions in the memory to enable the computer device to execute the Bluetooth circuit board adaptive power consumption management method according to any one of claims 1 to 5.

8. A computer-readable storage medium having instructions stored thereon, characterized in that: When the instructions are executed by the processor, the method for adaptive power consumption management of a Bluetooth circuit board as described in any one of claims 1 to 5 is implemented.

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