Power consumption data analysis method and system of notebook computer

Abstract

The invention relates to the field of data analysis, and discloses a power consumption data analysis method and system of a notebook computer, which are used for solving the problem that the influence of different power supply activities on total power consumption is neglected when power supply management is carried out. The method comprises the following steps: acquiring predicted use time of a user through manual input of the user and a machine learning algorithm, monitoring the power consumption state of each hardware component of the notebook computer in real time, calculating predicted endurance time of current equipment according to the monitored hardware power consumption state and use habit data of the user, and collecting related data of the computer. And the endurance adjustment factor is calculated according to the related data, the actual endurance time is calculated according to the adjustment factor, and the dynamic power supply adjustment is performed according to the actual endurance time and the predicted use time, so that the accuracy of battery condition detection is effectively improved, and the reasonability of power supply management is improved.

Description

Technical Field

[0001] The present invention relates to the field of data analysis, and more specifically to a method and system for analyzing power consumption data of a notebook computer. Background Art

[0002] As laptops continue to become thinner and lighter, the space for power supply installation is limited, which greatly limits the battery life of laptops. Currently, power management systems are becoming increasingly important. By monitoring the power consumption status of laptops and optimizing power usage strategies based on power consumption data, it is expected that longer battery life can be provided with the same power capacity. For example, power consumption can be reduced by lowering the CPU frequency or shutting down some unnecessary hardware components.

[0003] At present, the existing power management system mainly focuses on the overall power consumption of laptops, ignoring the impact of different target power activities on the overall power consumption. In addition, during the use of laptops, external factors may affect the performance of the computer. If the past evaluation standards are still used, inaccurate test results and unreasonable power management will occur.

[0004] In view of the above problems, the present invention proposes a solution. Summary of the invention

[0005] In order to overcome the above-mentioned defects of the prior art, the present invention provides a method and system for analyzing power consumption data of a notebook computer to solve the problems existing in the above-mentioned background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] The power consumption data analysis method of a laptop computer includes the following steps:

[0008] Step 1: Collecting the user's usage habit data, including the usage frequency of all applications, the duration of a single use, the number of applications opened simultaneously during use, and the usage period;

[0009] Step 2: Obtain the user's estimated usage time, which is manually input by the user. If the user does not input it, use a machine learning algorithm to analyze the user's usage habits based on the user's usage habit data, generate a usage habit model, and predict the usage time by using the usage habit model;

[0010] Step 3: monitor the power consumption status of each hardware component of the laptop in real time, wherein the hardware components include but are not limited to CPU, GPU, memory, display and other peripheral devices;

[0011] Step 4: Calculate the estimated battery life of the current device based on the monitored hardware power consumption status and the user's usage habit data;

[0012] Collecting computer-related data, including battery-related data, temperature data, and user usage data, including the total time the user has used the device and the number of programs running simultaneously since the start of this time, and calculating the battery life adjustment factor based on the relevant data;

[0013] The actual endurance time is calculated based on the adjustment factor, and the calculation formula is BF 实际 =RA×T ct , where BF 实际 It is expressed as the actual endurance time, RA is expressed as the endurance adjustment factor, T ct Indicates the estimated battery life;

[0014] Step 5: Dynamically adjust the power supply based on the actual battery life and the estimated usage time.

[0015] Preferably, the step of collecting user usage habit data is:

[0016] Design a background process to continuously monitor and record user operations and mark specific timestamps and related information;

[0017] Each time a user launches an application, the name of the application, the launch time, and the number of times the application is launched are recorded; and each time a user closes an application, the closing time is recorded. Based on the launch time and closing time of the application, the total duration of the use is calculated and recorded;

[0018] Record the user's application startup situation at different time periods of the day, including the specific time point and corresponding usage duration, and classify the data by time period;

[0019] All collected data is stored in date and time order.

[0020] Preferably, the steps of using a machine learning algorithm to analyze the user's usage habits, generating a usage habit model, and predicting the usage time by using the habit model are:

[0021] Use the mean imputation method to fill missing values ​​in the data, standardize the data to eliminate the scale effect of different features, and construct a feature vector for each time point;

[0022] Use the support vector regression algorithm to build a prediction model and divide the data set into a training set and a test set;

[0023] Use the training set data to train the selected model, use cross-validation technology to evaluate the performance of the model, and obtain the final usage habit model;

[0024] The estimated usage time is predicted using the final usage habit model.

[0025] Preferably, the step of evaluating the performance of the model using cross-validation technology is:

[0026] Divide the dataset into k non-overlapping subsets, called folds;

[0027] Each time, one fold is selected from the k folds as the validation set, and the remaining k-1 folds are used as the training set. The model is trained on the k-1 folds, and the model performance is evaluated on the validation set, and the error between the predicted result and the actual result is recorded.

[0028] For each fold, calculate the mean squared error, mean absolute error, and root mean squared error;

[0029] Calculate the standard deviation of the performance metrics across all folds, compare the cross-validation results of different models, and select the model with the smallest standard deviation.

[0030] Preferably, the battery life adjustment factor calculation step is:

[0031] Obtaining battery-related data of the laptop computer, and calculating the battery aging degree based on the battery-related data;

[0032] Obtain temperature data of the laptop when it is in use, and calculate the heat dissipation efficiency based on the temperature data;

[0033] Obtain the total time the user has used the computer and the maximum number of programs running simultaneously after the computer is turned on. Calculate the usage degree based on the total time the computer has been used and the maximum number of programs running simultaneously. The calculation formula is US = DU × NUM P , where US represents the degree of use, DU represents the total time of using the computer, and NUM P Expressed as the maximum number of programs running simultaneously;

[0034] The battery life adjustment factor is obtained by comprehensive evaluation of battery aging, heat dissipation efficiency and usage level. The calculation formula is: Among them, RA represents the battery life adjustment factor, AG represents the battery aging, DH represents the heat dissipation efficiency, US represents the degree of use, and a1, a2, and a3 represent the weight coefficients of battery aging, heat dissipation efficiency, and degree of use.

[0035] Preferably, the step of calculating the battery aging degree according to the battery related data is:

[0036] Extracting the charge and discharge times from a battery report of a battery management system of the laptop computer, wherein the charge and discharge times are the total number of times the battery undergoes complete charge and discharge cycles;

[0037] Obtain the current internal resistance of the battery from the data provided by the battery management system;

[0038] Collect the new battery internal resistance of the battery, and calculate the internal resistance change rate based on the current internal resistance and the new battery internal resistance. The calculation formula is: Where ir is the internal resistance change rate, R new Represents the internal resistance of a new battery, R battery Expressed as current internal resistance;

[0039] The internal resistance change rate and the number of charge and discharge times are normalized, and the battery aging degree is calculated based on the normalized internal resistance change rate and the number of charge and discharge times. The calculation formula is: Where AG represents the battery aging degree, ir represents the internal resistance change rate, NUM c Expressed as the number of charge and discharge cycles.

[0040] Preferably, the step of calculating the heat dissipation efficiency according to the temperature data is:

[0041] Record the temperature before the computer is used as the initial temperature, and read the real-time temperature data of the current CPU and GPU of the laptop through the temperature sensor;

[0042] Measure the total power consumption of the laptop in real time;

[0043] The temperature rise value is obtained by calculating the difference between the real-time temperature data and the initial temperature. The heat dissipation efficiency is calculated based on the total power consumption and the temperature rise value. The calculation formula is: Where DH is the heat dissipation efficiency, P total It is expressed as total power consumption and ΔW is expressed as temperature rise value.

[0044] Preferably, the step of dynamically adjusting the power supply according to the actual battery life and the expected usage time is:

[0045] Compare the actual battery life with the estimated usage time. If the actual battery life is longer than the estimated usage time, no adjustment will be made. If the actual battery life is shorter than the estimated usage time, the probability of calling each program in the computer will be analyzed, and the program with the smallest probability of calling will be terminated first. The evaluation will continue until the actual battery life is longer than the estimated usage time.

[0046] The calling probability of each program in the analyzed computer is to obtain the number of times each program is started on the computer during the detection period through the system log, add up the number of times each program is started to obtain the total number of times the program is started, and calculate the ratio of the number of times each program is started to the total number of times the program is started to obtain the calling probability.

[0047] Preferably, the power consumption data analysis system of a laptop computer comprises:

[0048] User usage habit collection module, used to collect the user's daily laptop usage habit data;

[0049] The estimated usage time acquisition module is used to provide an interface for the user to manually input the estimated usage time, and the user enters the estimated usage time by himself; if the user does not enter the estimated usage time, the user's usage habits are analyzed using a machine learning algorithm based on the user's habit data, a usage habit model is generated, and the usage time is predicted by using the usage habit model;

[0050] A power consumption monitoring module is used to monitor the power consumption status of laptop computer components in real time, including CPU, GPU, memory and display screen, and generate current power consumption data;

[0051] The battery life calculation module is used to calculate the actual battery life based on the current power consumption data;

[0052] Intelligent process management module, used to dynamically adjust power supply according to actual battery life and estimated usage time.

[0053] Technical effects and advantages of the present invention:

[0054] Collect user usage habit data, obtain the user's estimated usage time through manual input and machine learning algorithms, monitor the power consumption status of each hardware component of the laptop in real time, calculate the estimated battery life of the current device based on the monitored hardware power consumption status and user usage habit data, collect relevant computer data, and calculate the battery life adjustment factor based on the relevant data, calculate the actual battery life based on the adjustment factor, and dynamically adjust the power supply based on the actual battery life and the estimated usage time, which effectively improves the accuracy of battery condition detection and improves the rationality of power management. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 It is the overall flow chart of the present invention. DETAILED DESCRIPTION

[0056] The technical solution of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the present invention. In addition, the forms of the various structures recorded in the following embodiments are merely illustrative, and the power consumption data analysis method and system of a laptop computer involved in the present invention are not limited to the various structures recorded in the following embodiments. All other implementations obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0057] The present invention provides a method for analyzing power consumption data of a notebook computer, comprising the following steps:

[0058] Step 1: Collecting the user's usage habit data, including the usage frequency of all applications, the duration of a single use, the number of applications opened simultaneously during use, and the usage period;

[0059] In this embodiment, it should be specifically explained that the steps of collecting the user's usage habit data are:

[0060] Design a background process to continuously monitor and record user operations. Ensure that the process can run seamlessly without affecting the user's normal operations, record the launch, shutdown, foreground and background switching of each application, and mark the specific timestamp and related information;

[0061] Every time a user launches an application, the name and launch time of the application and the number of times the application is launched are recorded to calculate the frequency of use;

[0062] Each time a user closes an application, the closing time is recorded. Based on the application startup time and closing time, the total duration of the use is calculated and recorded.

[0063] When users use the application, all running programs are recorded at each time point, and the number of programs opened at the same time in a specific time period is regularly counted and associated with the currently used application;

[0064] Record the user's application launches at different times of the day, including the specific time and corresponding usage duration, and classify the data by time period (such as morning, afternoon, evening, etc.) to analyze usage habits in different time periods;

[0065] Store all collected data in date and time order to ensure data integrity and consistency. Regularly organize the collected data and remove abnormal or incomplete records to prepare for subsequent analysis.

[0066] Step 2: Obtain the user's estimated usage time, which can be manually input by the user. If the user does not input it, analyze the user's usage habits using a machine learning algorithm based on the user's usage habit data, generate a usage habit model, and predict the usage time by using the usage habit model;

[0067] The machine learning algorithm is a type of computational method that automatically makes predictions, classifications, or decisions by analyzing and learning patterns and regularities in data. Unlike traditional rule-based programming, machine learning algorithms do not require explicit programming of all instructions, but instead "train" from large amounts of data, self-adjust model parameters, and improve performance on new data.

[0068] In this embodiment, it should be specifically explained that the steps of using a machine learning algorithm to analyze the user's usage habits, generate a usage habit model, and predict the usage time by using the habit model are as follows:

[0069] Fill or delete missing values. Common methods include mean filling or interpolation, standardizing the data to eliminate the scale effect of different features. The calculation formula is Where X norm It represents the standardized data, X represents the original data, μ represents the data mean, and σ represents the data standard deviation;

[0070] For each time point, construct a feature vector whose expression is X i =[F i (t),D i (t),N i (t),T d (t)], where X i It is represented as the feature vector of the ith application at time t, F i (t) represents the usage frequency of the i-th application at time t, D i (t) represents the single usage duration of the i-th application at time t, N i (t) is the number of programs opened simultaneously by the i-th application at time t, T d (t) represents the usage period of the i-th application at time t, such as morning, afternoon, evening, etc.;

[0071] Use the support vector regression algorithm to build a prediction model and divide the data set into a training set and a test set. The commonly used split ratio is 70% training set and 30% test set;

[0072] The support vector regression is a regression analysis method based on support vector machines, which is used to predict continuous values. It seeks a regression function that is as smooth as possible in a high-dimensional feature space, so that the error between the predicted value and the true value does not exceed the set tolerance, and has strong robustness to noise and outliers in the training data. This goal is achieved by minimizing the complexity of the regression function and the synthetic loss function of the training sample error, thereby improving the prediction accuracy and preventing overfitting.

[0073] Use the training set data to train the selected model, use cross-validation technology to evaluate the performance of the model, ensure the generalization ability of the model, and obtain the final usage habit model;

[0074] The cross-validation is a technique for evaluating the performance of machine learning models by dividing the dataset into multiple non-overlapping subsets (called folds) for model training and validation. Specifically, cross-validation divides the dataset into k folds, using k-1 folds as training sets each time and the remaining fold as validation set. The model will repeat training and validation multiple times, each time selecting a different fold as the validation set. Ultimately, by calculating the average of the performance indicators (such as mean square error, accuracy, etc.) of the k-times validation, cross-validation can provide a reliable estimate of the model's generalization ability on unseen data, effectively reducing the evaluation error caused by the randomness of data partitioning.

[0075] The estimated usage time is predicted using the final usage habit model.

[0076] In this embodiment, it should be specifically explained that the steps of using the cross-validation technique to evaluate the performance of the model are:

[0077] Divide the dataset into k non-overlapping subsets (called folds), which are usually chosen to be 5 or 10, but other values ​​can be chosen depending on the size and characteristics of the dataset;

[0078] Each time, one fold is selected from the k folds as the validation set, and the remaining k-1 folds are used as the training set. The model is trained on the k-1 folds, and the model performance is evaluated on the validation set, and the error between the predicted result and the actual result is recorded.

[0079] For each fold, the mean squared error is calculated, which is expressed as Where MSE stands for mean square error, Y i is the actual value, is the predicted value, n is the number of samples; for each fold, the mean absolute error is calculated, and its expression is Where MAE is expressed as mean absolute error; for each fold, the root mean square error is calculated, and its expression is Where RMSE stands for root mean square error;

[0080] Calculate the standard deviation of the performance metrics across all folds, compare the cross-validation results of different models, and select the model with the smallest standard deviation.

[0081] Step 3: monitor the power consumption status of each hardware component of the laptop in real time, wherein the hardware components include but are not limited to CPU, GPU, memory, display and other peripheral devices;

[0082] Step 4: Calculate the estimated battery life of the current device based on the monitored hardware power consumption status and the user's usage habit data;

[0083] Collecting computer-related data, including battery-related data, temperature data, and user usage data, including the total time the user has used the device and the number of programs running simultaneously since the start of this time, and calculating the battery life adjustment factor based on the relevant data;

[0084] The actual endurance time is calculated based on the adjustment factor, and the calculation formula is BF 实际 =RA×T ct , where BF 实际 It is expressed as the actual endurance time, RA is expressed as the endurance adjustment factor, T ct Indicates the estimated battery life;

[0085] Battery performance is greatly affected by temperature. High temperatures accelerate battery aging and reduce its effective capacity, while low temperatures may affect battery discharge efficiency. By taking temperature data into account to adjust the estimated battery life, you can more accurately reflect the actual performance of the battery under different environmental conditions; the length of time the device is used and the number of programs running simultaneously will directly affect power consumption. For example, long-term high-load use will increase power consumption and reduce battery life. The battery life adjustment factor can correct the estimated battery life based on these usage conditions to more accurately reflect actual usage; by calculating the battery life adjustment factor, you can monitor the health status and performance changes of the battery. The power consumption and environmental factors (such as temperature) in actual use often do not match the theoretical values. The battery life adjustment factor helps to reduce this error and make the battery life prediction closer to reality.

[0086] In this embodiment, it should be specifically explained that the steps for calculating the estimated battery life of the current device are:

[0087] Monitor the real-time power consumption of the device, including the power consumption of each hardware component, such as CPU, GPU, screen, and hard disk, and add up the power consumption of each component to calculate the total power consumption;

[0088] Get the remaining battery energy and calculate the expected battery life based on the total power consumption and the remaining battery energy. The calculation formula is: Where T ct Indicates the estimated endurance time, E b Represented as the remaining battery energy, P total Expressed as total power consumption.

[0089] In this embodiment, it should be specifically explained that the steps for calculating the battery life adjustment factor are:

[0090] Obtaining battery data of the laptop computer, and calculating the battery aging degree based on the battery data;

[0091] Obtain temperature data of the laptop when it is in use, and calculate the heat dissipation efficiency based on the temperature data;

[0092] Obtain the total time the user has used the computer and the maximum number of programs running simultaneously after the computer is turned on. Calculate the usage degree based on the total time the computer has been used and the maximum number of programs running simultaneously. The calculation formula is US = DU × NUM P , where US represents the degree of use, DU represents the total time of using the computer, and NUM P Expressed as the maximum number of programs that can run simultaneously.

[0093] The battery life adjustment factor is obtained by comprehensive evaluation of battery aging, heat dissipation efficiency and usage level. The calculation formula is: RA is the battery life adjustment factor, AG is the battery aging degree. As the battery ages, the actual battery life will decrease accordingly. Specifically, a higher battery aging degree will result in a higher battery life adjustment factor, which will shorten the expected battery life of the device. This means that as the battery aging degree increases, the system will adjust the predicted battery life to reflect the decrease in battery capacity and the decrease in energy efficiency. DH is the heat dissipation efficiency. As the heat dissipation efficiency increases, the device's battery life adjustment factor will decrease, thereby reducing the impact of external factors on the expected battery life. When the device can dissipate heat more effectively, the overall energy consumption is optimized, especially reducing the additional energy consumption and possible performance limitations caused by overheating. The improvement in heat dissipation efficiency reduces the situation where the internal temperature of the device is too high, thereby reducing the power consumption demand of the cooling system and reducing the energy waste caused by the processor reducing the frequency due to the increase in temperature, ultimately extending the battery life. US is the usage level. As the intensity of computer use increases, the expected battery life will be significantly shortened. When users run more applications, perform more complex calculations, or use the device in higher performance mode, the computer's energy consumption will increase significantly, resulting in faster battery power consumption. Therefore, the battery life adjustment factor will increase accordingly, reflecting the actual battery life reduction. This means that high-intensity usage scenarios, such as multitasking or running high-performance applications, will lead to a reduction in device battery life. a1, a2, and a3 represent the weight coefficients of battery aging, heat dissipation efficiency, and usage. The specific values ​​of a1, a2, and a3 are determined by professionals based on actual conditions, and a1+a2+a3=1. For example, a1, a2, and a3 can be 0.4, 0.4, and 0.2.

[0094] In this embodiment, it should be specifically explained that the step of calculating the battery aging degree according to the battery-related data is as follows:

[0095] Extracting the charge and discharge times from the battery report of the battery management system of the laptop computer, wherein the charge and discharge times are the total number of times the battery undergoes a complete charge and discharge cycle, and are recorded as the charge and discharge times;

[0096] Use the data provided by the battery management system to obtain the current internal resistance of the battery, which reflects the power loss of the battery during charging and discharging;

[0097] The new battery internal resistance of the battery is collected, which means the initial internal resistance of the battery when it leaves the factory. The internal resistance change rate is calculated based on the current internal resistance and the new battery internal resistance. The calculation formula is: Where ir is the internal resistance change rate, R new Represents the internal resistance of a new battery, R battery Expressed as current internal resistance;

[0098] The internal resistance change rate and the number of charge and discharge times are normalized, and the battery aging degree is calculated based on the normalized internal resistance change rate and the number of charge and discharge times. The calculation formula is: Where AG represents the battery aging degree, ir represents the internal resistance change rate, NUM c Expressed as the number of charge and discharge cycles.

[0099] In this embodiment, it should be specifically explained that the step of calculating the heat dissipation efficiency according to the temperature data is:

[0100] Read the real-time temperature data of the laptop's key components (such as the CPU and GPU) through sensors. This data can usually be obtained through temperature monitoring tools or APIs within the system. Record the temperature before the start of heat dissipation as the initial temperature.

[0101] Measure the total real-time power consumption of the laptop, usually including the power consumption of components such as the CPU and GPU, which can be obtained through the power management system or dedicated software;

[0102] The temperature rise value is obtained by calculating the difference between the real-time temperature data and the initial temperature. The heat dissipation efficiency is calculated based on the total power consumption and the temperature rise value. The calculation formula is: Where DH is the heat dissipation efficiency, P total It is expressed as total power consumption and ΔW is expressed as temperature rise value.

[0103] Step 5: Dynamically adjust the power supply based on the actual battery life and the estimated usage time;

[0104] In this embodiment, it should be specifically explained that the steps of dynamically adjusting the power supply according to the actual battery life and the expected usage time are:

[0105] The actual battery life is compared with the estimated usage time. If the actual battery life is longer than the estimated usage time, no adjustment is made. If the actual battery life is less than the estimated usage time, the probability of calling each program in the computer is analyzed, and the program with the smallest probability of calling is terminated first, and the evaluation is continued until the actual battery life is greater than the estimated usage time.

[0106] The calling probability of each program in the analyzed computer is to obtain the number of times each program is started on the computer during the detection period through the system log, add up the number of times each program is started to obtain the total number of times the program is started, and calculate the ratio of the number of times each program is started to the total number of times the program is started to obtain the calling probability. The detection period can be changed according to the situation and can be one month or two months.

[0107] In this embodiment, it is necessary to specifically explain that the power consumption data analysis system of a notebook computer includes:

[0108] A user usage habit collection module is used to collect the user's daily laptop usage habit data, which includes the usage frequency of each application, the single usage duration, the number of programs opened simultaneously during use, and the usage period, etc.;

[0109] The estimated usage time acquisition module is used to provide an interface for users to manually input the estimated usage time. Users can enter the estimated duration of the current usage cycle according to their needs. If the user does not enter the estimated usage time, the user's usage habits are analyzed using a machine learning algorithm based on the user's habit data, a usage habit model is generated, and the usage time is predicted by using the habit model;

[0110] A power consumption monitoring module is used to monitor the power consumption status of laptop computer components in real time, including CPU, GPU, memory and display screen, and generate current power consumption data;

[0111] The battery life calculation module is used to calculate the estimated battery life that can be supported by the remaining power according to the current power consumption data;

[0112] Intelligent process management module, used to dynamically adjust power supply according to actual battery life and estimated usage time.

[0113] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

[0114] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for analyzing power consumption data of a notebook computer, characterized in that: The following steps are involved: Step 1: Collecting the user's usage habit data, including the usage frequency of all applications, the duration of a single use, the number of applications opened simultaneously during use, and the usage period; Step 2: Obtain the user's estimated usage time, which is manually input by the user. If the user does not input it, use a machine learning algorithm to analyze the user's usage habits based on the user's usage habit data, generate a usage habit model, and predict the usage time by using the usage habit model; Step 3: monitor the power consumption status of each hardware component of the laptop in real time, wherein the hardware components include but are not limited to CPU, GPU, memory, display and other peripheral devices; Step 4: Calculate the estimated battery life of the current device based on the monitored hardware power consumption status and the user's usage habit data; Collecting computer-related data, including battery-related data, temperature data, and user usage data, including the total time the user has used the device and the number of programs running simultaneously since the start of this time, and calculating the battery life adjustment factor based on the relevant data; The actual endurance time is calculated based on the adjustment factor, and the calculation formula is BF 实际 =RA×T ct , where BF 实际 It is expressed as the actual endurance time, RA is expressed as the endurance adjustment factor, T ct Indicates the estimated battery life; Step 5: Dynamically adjust the power supply based on the actual battery life and the estimated usage time.

2. The method for analyzing power consumption data of a notebook computer according to claim 1, characterized in that: The steps of collecting user usage habit data are as follows: Design a background process to continuously monitor and record user operations and mark specific timestamps and related information; Every time a user launches an application, the name of the application, the launch time, and the number of times the application is launched are recorded; Each time a user closes an application, the closing time is recorded. Based on the application startup time and closing time, the total duration of the use is calculated and recorded. Record the user's application startup situation at different time periods of the day, including the specific time point and corresponding usage duration, and classify the data by time period; All collected data is stored in date and time order.

3. The method for analyzing power consumption data of a notebook computer according to claim 1, characterized in that: The steps of using a machine learning algorithm to analyze the user's usage habits, generate a usage habit model, and predict the usage time by using the habit model are as follows: Use the mean imputation method to fill missing values ​​in the data, standardize the data to eliminate the scale effect of different features, and construct a feature vector for each time point; Use the support vector regression algorithm to build a prediction model and divide the data set into a training set and a test set; Use the training set data to train the selected model, use cross-validation technology to evaluate the performance of the model, and obtain the final usage habit model; The estimated usage time is predicted using the final usage habit model.

4. The method for analyzing power consumption data of a notebook computer according to claim 3, characterized in that: The steps for evaluating the performance of the model using cross-validation techniques are: Divide the dataset into k non-overlapping subsets, called folds; Each time, one fold is selected from the k folds as the validation set, and the remaining k-1 folds are used as the training set. The model is trained on the k-1 folds, and the model performance is evaluated on the validation set, and the error between the predicted result and the actual result is recorded. For each fold, calculate the mean squared error, mean absolute error, and root mean squared error; Calculate the standard deviation of the performance metrics across all folds, compare the cross-validation results of different models, and select the model with the smallest standard deviation.

5. The method for analyzing power consumption data of a notebook computer according to claim 1, characterized in that: The calculation steps of the endurance adjustment factor are as follows: Obtaining battery-related data of the laptop computer, and calculating the battery aging degree based on the battery-related data; Obtain temperature data of the laptop when it is in use, and calculate the heat dissipation efficiency based on the temperature data; Obtain the total time the user has used the computer and the maximum number of programs running simultaneously after the computer is turned on. Calculate the usage level based on the total time the computer has been used and the maximum number of programs running simultaneously. The calculation formula is US = DU × NUM P , where US represents the degree of use, DU represents the total time of using the computer, and NUM P Expressed as the maximum number of programs running simultaneously; The battery life adjustment factor is obtained by comprehensive evaluation of battery aging, heat dissipation efficiency and usage level. The calculation formula is: Among them, RA represents the battery life adjustment factor, AG represents the battery aging, DH represents the heat dissipation efficiency, US represents the degree of use, and a1, a2, and a3 represent the weight coefficients of battery aging, heat dissipation efficiency, and degree of use.

6. The method for analyzing power consumption data of a notebook computer according to claim 5, characterized in that: The steps of calculating the battery aging degree according to the battery related data are as follows: Extracting the charge and discharge times from a battery report of a battery management system of the laptop computer, wherein the charge and discharge times are the total number of times the battery undergoes complete charge and discharge cycles; Obtain the current internal resistance of the battery from the data provided by the battery management system; Collect the new battery internal resistance of the battery, and calculate the internal resistance change rate based on the current internal resistance and the new battery internal resistance. The calculation formula is: Where ir is the internal resistance change rate, R new Represents the internal resistance of a new battery, R battery Expressed as current internal resistance; The internal resistance change rate and the number of charge and discharge times are normalized, and the battery aging degree is calculated based on the normalized internal resistance change rate and the number of charge and discharge times. The calculation formula is: Where AG represents the battery aging degree, ir represents the internal resistance change rate, NUM c Expressed as the number of charge and discharge cycles.

7. The method for analyzing power consumption data of a notebook computer according to claim 5, characterized in that: The steps of calculating the heat dissipation efficiency according to the temperature data are as follows: Record the temperature before the computer is used as the initial temperature, and read the real-time temperature data of the current CPU and GPU of the laptop through the temperature sensor; Measure the total power consumption of a laptop in real time; The temperature rise value is obtained by calculating the difference between the real-time temperature data and the initial temperature. The heat dissipation efficiency is calculated based on the total power consumption and the temperature rise value. The calculation formula is: Where DH is the heat dissipation efficiency, P total It is expressed as total power consumption and ΔW is expressed as temperature rise value.

8. The method for analyzing power consumption data of a notebook computer according to claim 1, characterized in that: The steps of dynamically adjusting the power supply according to the actual battery life and the expected usage time are as follows: Compare the actual battery life with the estimated usage time. If the actual battery life is longer than the estimated usage time, no adjustment will be made. If the actual battery life is shorter than the estimated usage time, the probability of calling each program in the computer will be analyzed, and the program with the smallest probability of calling will be terminated first. The evaluation will continue until the actual battery life is longer than the estimated usage time. The calling probability of each program in the analyzed computer is to obtain the number of times each program is started on the computer during the detection period through the system log, add up the number of times each program is started to obtain the total number of times the program is started, and calculate the ratio of the number of times each program is started to the total number of times the program is started to obtain the calling probability.

9. A power consumption data analysis system for a notebook computer, used to implement the power consumption data analysis method for a notebook computer as claimed in claims 1 to 8, characterized in that: The system comprises: User usage habit collection module, used to collect the user's daily laptop usage habit data; The estimated usage time acquisition module is used to provide an interface for the user to manually input the estimated usage time, and the user enters the estimated usage time by himself; if the user does not enter the estimated usage time, the user's usage habits are analyzed using a machine learning algorithm based on the user's habit data, a usage habit model is generated, and the usage time is predicted by using the usage habit model; A power consumption monitoring module is used to monitor the power consumption status of laptop computer components in real time, including CPU, GPU, memory and display screen, and generate current power consumption data; The battery life calculation module is used to calculate the actual battery life based on the current power consumption data; Intelligent process management module, used to dynamically adjust power supply according to actual battery life and estimated usage time.

Images