Battery endurance improving method based on Android operating system equipment

Abstract

The battery endurance improving method based on the Android operating system equipment comprises the following steps: 1, intelligent electric quantity monitoring and analysis: monitoring the electric quantity consumption condition of the equipment in real time, and performing intelligent analysis on the electric quantity consumption by combining factors such as the running state, the application use condition and the network environment of the equipment; 2, an intelligent power management strategy, which is used for intelligently adjusting the power management strategy of the equipment according to the electric quantity monitoring and analysis result; the intelligent dormancy and awakening mechanism is used for automatically adjusting the dormancy and awakening states of the equipment according to the actual use condition of the equipment; 4, intelligent charging management, which is used for optimizing the charging process of the equipment; and 5, learning and optimizing user behaviors. By optimizing the power management system of the Android equipment, more intelligent and automatic power management is realized, unnecessary power consumption is reduced, and the battery life of the equipment is improved, so that the service time of a user is prolonged, and the user experience is improved.

Description

Technical Field

[0001] The present invention relates to the field of mobile communication technology, and in particular to a method for improving battery life of an Android operating system device. Background Art

[0002] With the rapid development of mobile Internet, Android devices such as smartphones and tablets have become an indispensable part of people's daily lives. However, battery life has always been one of the main problems that plague Android device users. Although battery capacity is constantly increasing, the improvement of battery life is still limited due to hardware technology and physical size. At the same time, the diverse applications and rich functions of Android devices also lead to higher power consumption, making the battery life problem more prominent.

[0003] At present, there are some battery life improvement technologies on the market, such as low power mode and intelligent power management. These technologies can reduce power consumption and improve battery life to a certain extent. However, these technologies often have limitations. For example, low power mode may affect device performance and user experience, and intelligent power management requires users to manually set up and manage, which is not intelligent and automatic enough. Summary of the invention

[0004] In order to make up for the deficiencies of the prior art, the present invention provides a method for improving the battery life of an Android operating system device, which can intelligently identify and manage the power consumption of the device, reduce unnecessary power consumption, and improve the battery life of the device.

[0005] To achieve the above object, the present invention adopts the following technical solutions: A method for improving battery life of an Android operating system device, characterized by: The battery life improvement system used in the battery life improvement method includes the following functional modules: Intelligent power monitoring and analysis module, which is used to monitor the power consumption of the device in real time, and conduct intelligent analysis of power consumption based on factors such as the device's operating status, application usage, and network environment; Intelligent power management strategy module, used to intelligently adjust the power management strategy of the device based on the results of power monitoring and analysis; Intelligent sleep and wake-up mechanism module, used to automatically adjust the sleep and wake-up state of the device according to the actual use of the device; Intelligent charging management module, used to optimize the charging process of the device and reduce power loss and battery consumption during charging; The user behavior learning and optimization module is used to learn and analyze user behavior data through machine learning algorithms, identify users' power usage habits and preferences, and thus provide users with more personalized power management strategies.

[0006] The battery life improvement method comprises the following steps: Step 1: Intelligent power monitoring and analysis; Step 2: Intelligent power management strategy; Step 3: Intelligent sleep and wake-up mechanism; Step 4: Intelligent charging management; Step 5: User behavior learning and optimization.

[0007] The detailed steps are as follows: Step 1. Intelligent power monitoring and analysis Technical features: The built-in power monitoring module monitors the power consumption of the device in real time. At the same time, it conducts intelligent analysis of power consumption based on factors such as the device's operating status, application usage, and network environment, and identifies the main sources of power consumption and potential problems.

[0008] Implementation method: Use the power management interface and sensor information of the Android system to monitor the power consumption of the device in real time. Use machine learning algorithms to analyze and mine the power consumption data, identify the main applications and scenarios of power consumption, and the factors that may cause abnormal power consumption.

[0009] The sensors mainly include the battery temperature sensor, current sensor and voltage sensor that comes with the mobile phone.

[0010] The specific implementation steps are as follows: 1-1. Enable the power monitoring module to monitor the power consumption of the device in real time.

[0011] 1-2. Collect data such as device operating status, application usage, and network environment.

[0012] 1-3. Use machine learning algorithms to analyze and mine power consumption data to identify the main applications and scenarios of power consumption.

[0013] 1-4. Generate a power consumption report based on the analysis results and display it to the user.

[0014] Step 2. Intelligent power management strategy Technical features: Based on the results of power monitoring and analysis, the device's power management strategy is intelligently adjusted, such as adjusting screen brightness, closing unnecessary background applications, optimizing network connections, etc., to reduce power consumption.

[0015] The specific implementation steps are as follows: 2-1. Develop corresponding power management strategies based on the main sources of power consumption and potential problems.

[0016] 2-2. When it is detected that the screen brightness is too high and the battery is consumed too quickly, the screen brightness will be automatically adjusted to an appropriate level.

[0017] 2-3. When it is detected that too many background applications are consuming too much power, unnecessary background applications will be automatically closed.

[0018] 2-4. When it is detected that the network connection is unstable and causes increased power consumption, optimize the network connection strategy.

[0019] 2-5. Apply the formulated power management strategy to the device to implement intelligent power management.

[0020] Step 3. Intelligent sleep and wake-up mechanism Technical features: Through the intelligent sleep and wake-up mechanism, the sleep and wake-up state of the device is automatically adjusted according to the actual usage of the device to reduce unnecessary power consumption.

[0021] The specific implementation steps are as follows: 3-1. Use the sensor information and user behavior data of the Android system to determine the actual usage of the device.

[0022] 3-2. When the device is not used for a long time, it will automatically enter sleep mode to reduce power consumption.

[0023] 3-3. When the device is awakened, the device's operating status and power management strategy are automatically adjusted according to the user's actual needs.

[0024] 3-4. Monitor the sleep and wake-up status of the device in real time, and make adjustments and optimizations based on actual conditions.

[0025] Step 4. Intelligent charging management Technical features: Through intelligent charging management, the charging process of the device is optimized to reduce power loss and battery loss during charging.

[0026] The specific implementation steps are as follows: 4-1. Intelligently adjust charging current and voltage according to the device's power status and charging environment.

[0027] 4-2. When the battery power is low, use a larger charging current to charge quickly.

[0028] 4-3. When the battery is close to full charge, use a smaller charging current to protect the battery.

[0029] 4-4, Use the battery health monitoring function of the Android system to monitor the battery health status in real time.

[0030] 4-5. Adjust the charging strategy according to the battery health status, such as reducing the charging current or extending the charging time.

[0031] Step 5. User behavior learning and optimization Technical features: Through machine learning algorithms, the user's behavioral data is learned and analyzed to identify the user's power usage habits and preferences, thereby providing users with more personalized power management strategies.

[0032] The specific implementation steps are as follows: 5-1. Use the user behavior data interface of the Android system to collect user power usage behavior and preference data.

[0033] 5-2. Learn and analyze user behavior data through machine learning algorithms to identify users’ electricity usage patterns and preferences.

[0034] 5-3. Provide users with personalized power management strategies and suggestions based on their power usage patterns and preferences, including adjusting screen brightness, turning off unnecessary notifications, etc.

[0035] 5-4. Monitor users’ electricity usage behaviors and preference changes in real time, and make adjustments and optimizations based on actual conditions.

[0036] 5-5. Display personalized power management strategies and suggestions to users, and guide users to make corresponding settings and adjustments.

[0037] Beneficial effects of the present invention: By optimizing the power management system of Android devices, more intelligent and automated power management can be achieved, unnecessary power consumption can be reduced, and the battery life of the device can be improved, thereby extending the user's usage time and improving the user experience.

[0038] (1) Improving battery life: Through technical means such as intelligent power monitoring and analysis, intelligent power management strategies, and intelligent sleep and wake-up mechanisms, unnecessary power consumption can be reduced and the battery life of the device can be improved.

[0039] (2) Improving user experience: Through technical means such as intelligent charging management and user behavior learning and optimization, we provide users with more personalized power management strategies and suggestions to improve user experience.

[0040] (3) Reduce battery loss: Through technical means such as intelligent charging management, the charging process of the equipment can be optimized, the power loss and battery loss during the charging process can be reduced, and the battery life can be extended.

[0041] (4) Improved intelligence level: The technical solution of the present invention realizes more intelligent and automated power management, eliminating the need for manual settings and management by users, thereby improving the intelligence level of the equipment. DETAILED DESCRIPTION

[0042] The technical solution of the present invention is described in detail below in conjunction with specific embodiments.

[0043] The battery life improvement method based on the Android operating system device of the present invention and the battery life improvement system used therein include the following functional modules: Intelligent power monitoring and analysis module, which is used to monitor the power consumption of the device in real time, and conduct intelligent analysis of power consumption based on factors such as the device's operating status, application usage, and network environment; Intelligent power management strategy module, used to intelligently adjust the power management strategy of the device based on the results of power monitoring and analysis; Intelligent sleep and wake-up mechanism module, used to automatically adjust the sleep and wake-up state of the device according to the actual use of the device; Intelligent charging management module, used to optimize the charging process of the device and reduce power loss and battery consumption during charging; The user behavior learning and optimization module is used to learn and analyze user behavior data through machine learning algorithms, identify users' power usage habits and preferences, and thus provide users with more personalized power management strategies.

[0044] The battery life improvement method comprises the following steps: Step 1: Intelligent power monitoring and analysis; Step 2: Intelligent power management strategy; Step 3: Intelligent sleep and wake-up mechanism; Step 4: Intelligent charging management; Step 5: User behavior learning and optimization.

[0045] Step 1: Intelligent power monitoring and analysis Technical features: This step uses the built-in power monitoring module to monitor the power consumption of the device in real time, and combines factors such as the device's operating status, application usage, and network environment to perform intelligent analysis of power consumption and identify the main sources of power consumption and potential problems.

[0046] Implementation method: Use the power management interface and sensor information of the Android system to monitor the power consumption of the device in real time. Use machine learning algorithms to analyze and mine the power consumption data, identify the main applications and scenarios of power consumption, and the factors that may cause abnormal power consumption.

[0047] The sensors mainly include the battery temperature sensor, current sensor and voltage sensor that comes with the mobile phone.

[0048] Battery temperature sensor: used to monitor the temperature of the battery to ensure that the battery operates within a safe operating temperature range. High temperatures may damage the battery and accelerate its aging process, so monitoring battery temperature is critical to extending battery life and improving safety.

[0049] Current sensor: used to monitor the current consumption of the battery. By monitoring the current consumption, the device can understand which applications or services are consuming power and take appropriate measures to reduce unnecessary power consumption.

[0050] Voltage sensor: used to monitor the voltage level of the battery. The voltage level is one of the important indicators of battery status, which can help the device determine whether the battery needs to be charged or whether it needs to reduce power consumption to extend battery life.

[0051] The specific implementation steps are as follows: 1-1. Turn on the power monitoring module to monitor the power consumption of the device in real time.

[0052] 1-2. Collect data such as device operating status, application usage, and network environment.

[0053] 1-3. Use machine learning algorithms to analyze and mine power consumption data to identify the main applications and scenarios of power consumption.

[0054] 1-4. Generate a power consumption report based on the analysis results and display it to the user.

[0055] The machine learning algorithm in steps 1-3 may be linear regression.

[0056] Linear regression is a basic machine learning algorithm that attempts to build a linear model to predict a target value. In power prediction, linear regression is used to predict the power consumption of a device under different operating conditions, application usage, network environment, and other factors.

[0057] The basic formula for linear regression is: y = β0 + β1x1 + β2x2 + ... + βnxn Where: y is the target variable, which is the power consumption we want to predict.

[0058] β0 is the intercept term, which represents the expected value of the target variable y when all independent variables (x1, x2, ..., xn) are 0.

[0059] β1, β2, ..., βn are regression coefficients, indicating the influence of each variable on the target variable y.

[0060] x1, x2, ..., xn are independent variables, representing factors that may affect power consumption, such as the device's operating status, application usage, and network environment.

[0061] Step 2: Intelligent power management strategy Technical features: This step intelligently adjusts the device's power management strategy based on the results of power monitoring and analysis, such as adjusting screen brightness, closing unnecessary background applications, optimizing network connections, etc., to reduce power consumption.

[0062] The specific implementation steps are as follows: 2-1. Develop corresponding power management strategies based on the main sources of power consumption and potential problems.

[0063] 2-2. When it is detected that the screen brightness is too high and the battery is consumed too quickly, the screen brightness will be automatically adjusted to an appropriate level.

[0064] 2-3. When it is detected that too many background applications are consuming too much power, unnecessary background applications will be automatically closed.

[0065] 2-4. When it is detected that the network connection is unstable and causes increased power consumption, optimize the network connection strategy.

[0066] 2-5. Apply the formulated power management strategy to the device to implement intelligent power management.

[0067] Step 3: Intelligent sleep and wake-up mechanism Technical features: This step uses an intelligent sleep and wake-up mechanism to automatically adjust the sleep and wake-up states of the device according to the actual usage of the device to reduce unnecessary power consumption.

[0068] The specific implementation steps are as follows: 3-1. Use the sensor information and user behavior data of the Android system to determine the actual usage of the device.

[0069] 3-2. When the device is not used for a long time, it will automatically enter sleep mode to reduce power consumption.

[0070] 3-3. When the device is awakened, the device's operating status and power management strategy are automatically adjusted according to the user's actual needs.

[0071] 3-4. Monitor the sleep and wake-up status of the device in real time, and make adjustments and optimizations based on actual conditions.

[0072] Step 4: Intelligent charging management Technical features: This step optimizes the charging process of the device through intelligent charging management, reducing power loss and battery loss during the charging process.

[0073] The specific implementation steps are as follows: 4-1. Intelligently adjust charging current and voltage according to the device's power status and charging environment.

[0074] 4-2. When the battery power is low, use a larger charging current to charge quickly.

[0075] 4-3. When the battery is close to full charge, use a smaller charging current to protect the battery.

[0076] 4-4, Use the battery health monitoring function of the Android system to monitor the battery health status in real time.

[0077] 4-5. Adjust the charging strategy according to the battery health status, such as reducing the charging current or extending the charging time.

[0078] Step 5: User behavior learning and optimization Technical features: This step uses machine learning algorithms to learn and analyze user behavior data, identify users' power usage habits and preferences, and provide users with more personalized power management strategies.

[0079] The specific implementation steps are as follows: 5-1. Use the user behavior data interface of the Android system to collect user power usage behavior and preference data.

[0080] 5-2. Learn and analyze user behavior data through machine learning algorithms to identify users’ electricity usage patterns and preferences.

[0081] 5-3. Provide users with personalized power management strategies and suggestions based on their power usage patterns and preferences, including adjusting screen brightness, turning off unnecessary notifications, etc.

[0082] 5-4. Monitor users’ electricity usage behaviors and preference changes in real time, and make adjustments and optimizations based on actual conditions.

[0083] 5-5. Display personalized power management strategies and suggestions to users, and guide users to make corresponding settings and adjustments.

[0084] The machine learning algorithm in step 5-2 may be a support vector machine (SVM), a clustering algorithm (such as K-means), or the like.

[0085] When using the K-means clustering algorithm to identify user electricity usage patterns and preferences, the details are as follows: 1. Data Preparation Collect data: Collect data about power usage behavior from users' devices, such as daily power consumption, number of charging times, usage time distribution (e.g., daytime, nighttime), most frequently used application types, etc. Feature engineering: Convert raw data into feature vectors that can be used in clustering algorithms; for example, daily power consumption, number of charging times, etc. can be used as independent features; Data standardization: Since the dimensions and value ranges of different features may be different, the data needs to be standardized to ensure that the contribution of each feature in the clustering process is balanced.

[0086] 2. K-means clustering Select K value: K value represents the number of clusters to be formed, that is, the types of user power consumption patterns; this usually needs to be preset according to the characteristics of the data and business requirements or determined by other methods (such as the elbow method); Initialize cluster centers: randomly select K data points as initial cluster centers; Iterative update: calculate the distance of each data point to each cluster center and assign it to the nearest cluster center; Recalculate the center of each cluster (i.e. the mean of all points in the cluster); Repeat the above steps until the cluster center no longer changes significantly or the preset number of iterations is reached.

[0087] 3. Interpretation and application of results Clustering result analysis: Based on the clustering results, analyze the power usage patterns and preferences of users in each cluster; for example, one cluster may represent "heavy users" who use their devices for a long time and consume a lot of power every day; another cluster may represent "light users" who use their devices for a short time and consume less power; User profile construction: Based on the clustering results, an electricity consumption profile is constructed for each user, including their electricity consumption patterns, preferences, and possible energy-saving suggestions; Strategy formulation: Develop personalized energy-saving strategies or recommendation services based on the user's power usage patterns and preferences; for example, for heavy users, more efficient charging devices or energy-saving applications can be recommended; for light users, suggestions on how to better manage device power can be provided.

[0088] 4. Model evaluation and optimization Evaluation indicators: Use indicators such as Silhouette Coefficient and Calinski-Harabasz Index to evaluate the effect of clustering; Optimization strategy: If the clustering effect is not good, you can try to adjust the K value, use different distance measurement methods, add more features or perform feature selection, etc.

Claims

1. A method for improving battery life of an Android operating system device, characterized in that: The following steps are involved: Step 1: Intelligent power monitoring and analysis; Step 2: Intelligent power management strategy; Step 3: Intelligent sleep and wake-up mechanism; Step 4: Intelligent charging management; Step 5: User behavior learning and optimization.

2. The method for improving battery life of an Android operating system device according to claim 1, characterized in that: The specific implementation steps of step one are as follows: (1-1) Enable the power monitoring module to monitor the power consumption of the device in real time; (1-2) Collect data such as device operating status, application usage, and network environment; (1-3) Use machine learning algorithms to analyze and mine power consumption data to identify the main applications and scenarios of power consumption; (1-4) Based on the analysis results, generate a power consumption report and display it to the user.

3. The method for improving battery life of an Android operating system device according to claim 1, characterized in that: The specific implementation steps of step 2 are as follows: (2-1) Develop corresponding power management strategies based on the main sources of power consumption and potential problems; (2-2) When it is detected that the screen brightness is too high and the battery is consumed too quickly, the screen brightness is automatically adjusted to an appropriate level; (2-3) When it is detected that too many background applications are consuming too much power, unnecessary background applications will be automatically closed; (2-4) When it is detected that the network connection is unstable and causes increased power consumption, optimize the network connection strategy; (2-5) Apply the formulated power management strategy to the device to implement intelligent power management.

4. The method for improving battery life of an Android operating system device according to claim 1, characterized in that: The specific implementation steps of step three are as follows: (3-1) Use the sensor information and user behavior data of the Android system to determine the actual usage of the device; (3-2) When the device is not used for a long time, it will automatically enter sleep mode to reduce power consumption; (3-3) When the device is awakened, the device's operating status and power management strategy are automatically adjusted according to the user's actual needs; (3-4) Monitor the sleep and wake-up status of the device in real time, and make adjustments and optimizations based on actual conditions.

5. The method for improving battery life of an Android operating system device according to claim 1, characterized in that: The specific implementation steps of step 4 are as follows: (4-1) Intelligently adjust charging current and voltage according to the device’s power status and charging environment; (4-2) When the battery power is low, a larger charging current is used to charge quickly; (4-3) When the battery is close to full charge, use a smaller charging current to protect the battery; (4-4) Use the battery health monitoring function of the Android system to monitor the battery health status in real time; (4-5) Adjust the charging strategy according to the battery health status, including reducing the charging current or extending the charging time.

6. The method for improving battery life of an Android operating system device according to claim 1, characterized in that: The specific implementation steps of step five are as follows: (5-1) Using the user behavior data interface of the Android system, collect users’ electricity usage behavior and preference data; (5-2) Learning and analyzing user behavior data through machine learning algorithms to identify users’ electricity usage patterns and preferences; (5-3) Provide users with personalized power management strategies and recommendations based on their power usage patterns and preferences; Including adjusting screen brightness and turning off unnecessary notifications; (5-4) Monitor users’ electricity consumption behaviors and preference changes in real time, and make adjustments and optimizations based on actual conditions; (5-5) Display personalized power management strategies and recommendations to users, and guide users to make corresponding settings and adjustments.

7. The method for improving battery life of an Android operating system device according to claim 2, characterized in that: In step one, 1-1, the power management interface and sensor information of the Android system are used to monitor the power consumption of the device in real time; the sensors mainly refer to the battery temperature sensor, current sensor, and voltage sensor built into the mobile phone.

8. The method for improving battery life of an Android operating system device according to claim 2, characterized in that: In step 1, 1-3, the machine learning algorithm uses linear regression.

9. The method for improving battery life of an Android operating system device according to claim 6, characterized in that: In step five, 5-2, the machine learning algorithm uses the K-means clustering algorithm.

10. A battery life improving system used in the battery life improving method based on an Android operating system device as claimed in claim 1, characterized in that: Includes the following functional modules: Intelligent power monitoring and analysis module, which is used to monitor the power consumption of the device in real time, and conduct intelligent analysis of the power consumption based on the device's operating status, application usage, and network environment factors; Intelligent power management strategy module, used to intelligently adjust the power management strategy of the device based on the results of power monitoring and analysis; Intelligent sleep and wake-up mechanism module, used to automatically adjust the sleep and wake-up state of the device according to the actual use of the device; Intelligent charging management module, used to optimize the charging process of the device and reduce power loss and battery consumption during charging; The user behavior learning and optimization module is used to learn and analyze user behavior data through machine learning algorithms, identify users' power usage habits and preferences, and thus provide users with more personalized power management strategies.