Mobile phone charging method and system, terminal and storage medium

Abstract

The invention relates to a mobile phone charging method and system, a terminal and a storage medium, and the method comprises the steps: recognizing the model, the battery type and the charging demand of each device, and collecting the real-time battery state data of each device; based on the model, the battery type and the charging demand of each device and the real-time battery state data of the device, performing calculation through a pre-trained charging model to obtain a charging strategy of each device; monitoring the charging progress of each device, and adjusting the charging power of each device according to the charging strategy of each device and the thermal management requirement of the battery of each device in the charging process; and when the charging of each device is completed, triggering a charging power supply disconnection message, disconnecting the charging power supply of each device, and generating a charging completion notification to the user interface. The mobile phone charging method has the effect of improving the mobile phone charging efficiency.

Description

Technical Field

[0001] The present application relates to the technical field of power supply systems, and in particular to a mobile phone charging method, system, terminal and storage medium. Background Art

[0002] At present, with the development of science and technology, people use more and more electronic devices in their daily lives, such as mobile phones, tablets, etc. These devices need to be charged regularly. In public places, such as high-speed rail stations, users often need to charge multiple devices at the same time, but most charging cables on the market can only charge one device or cannot allocate appropriate charging power to each device, which brings inconvenience to users. At present, users usually need to use multiple charging cables or a multi-port charger to charge multiple devices, but these solutions cannot intelligently allocate appropriate charging power to each device.

[0003] The above-mentioned existing technical solutions have the following defects: the existing charging solutions cannot well meet the needs of users to quickly charge multiple devices at the same time in public places, and cannot intelligently adjust the charging power according to the specific needs of the devices, which may result in low charging efficiency and slow device charging speed, so there is room for improvement. Summary of the invention

[0004] In order to improve the efficiency of mobile phone charging, the present application provides a mobile phone charging method, system, terminal and storage medium.

[0005] The above-mentioned invention objective of the present application is achieved through the following technical solutions: A mobile phone charging method, the mobile phone charging method comprising: Identify the model, battery type and charging requirements of each device, and collect real-time battery status data of each device; Based on the model, battery type, charging requirement and real-time battery status data of each device, a charging strategy for each device is calculated by a pre-trained charging model; Monitoring the charging progress of each device, and adjusting the charging power of each device according to the charging strategy of each device and the thermal management requirements of the battery of each device during the charging process; When the charging of each device is completed, a charging power disconnection message is triggered to disconnect the charging power of each device, and a charging completion notification is generated to the user interface.

[0006] By adopting the above technical solution, by identifying the model, battery type and charging requirements of each device and collecting the real-time battery status data of each device, the specific charging requirements and current battery status of each device can be accurately understood, thereby improving the personalization and accuracy of the charging strategy; by calculating based on the model, battery type, charging requirements and real-time battery status data of each device through a pre-trained charging model, the charging strategy of each device can be obtained, and the charging process can be optimized using a machine learning algorithm, making the charging strategy more intelligent and adaptable, thereby improving the charging efficiency and extending the battery life; by monitoring the charging progress of each device, adjusting the charging power of each device according to the charging strategy of each device and the thermal management requirements of the battery of each device during the charging process, the charging power can be dynamically adjusted during the charging process to avoid overheating or overcharging problems, thereby improving charging safety and battery life; by triggering a charging power disconnection message when each device is charged, the charging power of each device is disconnected, and a charging completion notification is generated to the user interface, the charging power can be disconnected in time to prevent overcharging, and a charging completion notification is provided to the user, thereby improving the user experience and the safety of the charging process.

[0007] In a preferred example, the present application can be further configured as follows: the identifying the model, battery type and charging requirement of each device, and collecting the real-time battery status data of each device includes: Using sensors to identify the model and battery type of each of the devices; Using a communication protocol to exchange data with each device, determining the charging specifications and current requirements of each device, wherein the charging specifications and current requirements of each device are both charging requirements of each device; Real-time battery status data of each device is collected by monitoring battery voltage, current and temperature in real time.

[0008] By adopting the above technical solution, by using sensors to identify the model and battery type of each device, the hardware information of the device can be determined quickly and accurately, thereby improving the accuracy and efficiency of device identification; by utilizing communication protocols to exchange data with each device, the charging specifications and current requirements of each device can be determined, and the charging demand information of the device can be obtained in real time, thereby improving the accuracy and real-time nature of the charging strategy; by real-time monitoring of battery voltage, current and temperature to collect real-time battery status data of each device, the working status of the battery can be fully understood, thereby improving the safety of the charging process and the effectiveness of the charging strategy.

[0009] In a preferred example, the present application may be further configured as follows: before calculating the charging strategy of each device based on the model, battery type, charging requirement of each device and the real-time battery status data of the device through a pre-trained charging model, the mobile phone charging method further includes: Acquire historical charging data and device performance data of different devices, and preprocess the historical charging data and device performance data of the different devices to obtain a training set; Performing cluster analysis on the training set using a k-means algorithm to obtain a feature set of the training set; A charging model is constructed based on a long short-term memory network, the charging model is trained using the feature set of the training set, and the parameters of the trained charging model are optimized using a genetic algorithm to obtain the pre-trained charging model.

[0010] By adopting the above technical solution, by obtaining the historical charging data and device performance data of different devices and preprocessing the historical charging data and device performance data of different devices to obtain a training set, it is possible to provide rich data support for the charging model, thereby improving the training effect and accuracy of the charging model; by using the k-means algorithm to perform cluster analysis on the training set to obtain the feature set of the training set, it is possible to effectively extract the features of the data, thereby improving the generalization ability of the charging model; by constructing a charging model based on a long short-term memory network, using the feature set of the training set to train the charging model, and optimizing the parameters of the trained charging model through a genetic algorithm, it is possible to construct a high-precision charging model, thereby improving the intelligence and adaptability of the charging strategy.

[0011] In a preferred example, the present application can be further configured as follows: the charging strategy of each device is calculated based on the model, battery type, charging requirement and real-time battery status data of each device through a pre-trained charging model, and includes: Preprocessing the model, battery type, charging requirement and real-time battery status data of each device to obtain a preprocessed data set; Performing cluster analysis on the preprocessed data set using a k-means algorithm to obtain a feature data set of the preprocessed data; The characteristic data set is transmitted to the pre-trained charging model for comparison and analysis to obtain the charging strategy of each device.

[0012] By adopting the above technical solution, the model, battery type, charging requirements and real-time battery status data of each device are preprocessed to obtain a preprocessed data set, which can effectively clean and normalize the data, thereby improving the accuracy of data processing; by using the k-means algorithm to perform cluster analysis on the preprocessed data set, a feature data set of the preprocessed data is obtained, which can extract the key features of the data, thereby improving the analysis capability of the charging model; by transmitting the feature data set to a pre-trained charging model for comparison and analysis, the charging strategy of each device is obtained, and an optimized charging strategy can be obtained based on data features and model analysis, thereby improving charging efficiency and battery life.

[0013] In a preferred example, the present application may be further configured as follows: the monitoring of the charging progress of each device and adjusting the charging power of each device according to the charging strategy of each device and the thermal management requirements of the battery of each device during the charging process include: Using thermal sensors and temperature control algorithms to monitor the temperature of the batteries of each device in real time; Determining a battery charging curve for each device according to the temperature of the battery of each device; The charging power of each device is correspondingly adjusted according to the battery charging curve of each device and the charging strategy of each device.

[0014] By adopting the above technical scheme, by using thermal sensors and temperature control algorithms to monitor the temperature of the batteries of each device in real time, the temperature changes of the batteries can be grasped in real time, thereby improving the safety of the charging process; by determining the battery charging curve of each device according to the temperature of the battery of each device, the charging strategy can be adjusted according to the actual temperature of the battery, thereby improving the charging efficiency and the service life of the battery; by adjusting the charging power of each device according to the battery charging curve of each device and the charging strategy of each device, the charging power can be dynamically adjusted to avoid overheating or overcharging problems, thereby improving the safety of the charging process and the charging effect.

[0015] In a preferred example, the present application can be further configured as follows: the mobile phone charging method further includes: Recording the temperature change and power adjustment data of each device during the charging process, and feeding back the temperature change and power adjustment data of each device to the pre-trained charging model to optimize the parameters in the pre-trained charging model; The user interface is set to allow the user to view the charging status, estimated completion time and historical charging data of each device, and to allow the user to manually adjust the charging parameters through the user interface.

[0016] By adopting the above technical solution, by recording the temperature change and power adjustment data of each device during the charging process, and feeding back the temperature change and power adjustment data of each device to a pre-trained charging model, the parameters in the pre-trained charging model are optimized, and the parameters of the charging model can be continuously optimized, thereby improving the accuracy and adaptability of the charging strategy; by setting a user interface, the user is allowed to view the charging status, estimated completion time and historical charging data of each device, and the user is allowed to manually adjust the charging parameters through the user interface, which can provide a user-friendly interface for users to view and manage the charging process, thereby improving user experience and the flexibility of charging management.

[0017] The second object of the invention is achieved by the following technical solutions: A mobile phone charging method system, the mobile phone charging method system comprising: A data collection module is used to identify the model, battery type and charging requirements of each device, and collect real-time battery status data of each device; A calculation strategy module, configured to calculate the charging strategy of each device through a pre-trained charging model based on the model, battery type, charging requirement and real-time battery status data of each device; An adjustment module, configured to monitor the charging progress of each device and adjust the charging power of each device according to the charging strategy of each device and the thermal management requirements of the battery of each device during the charging process; The notification module is used to trigger a charging power disconnection message when the charging of each device is completed, disconnect the charging power of each device, and generate a charging completion notification to the user interface.

[0018] By adopting the above technical solution, by identifying the model, battery type and charging requirements of each device and collecting the real-time battery status data of each device, the specific charging requirements and current battery status of each device can be accurately understood, thereby improving the personalization and accuracy of the charging strategy; by calculating based on the model, battery type, charging requirements and real-time battery status data of each device through a pre-trained charging model, the charging strategy of each device can be obtained, and the charging process can be optimized using a machine learning algorithm, making the charging strategy more intelligent and adaptable, thereby improving the charging efficiency and extending the battery life; by monitoring the charging progress of each device, adjusting the charging power of each device according to the charging strategy of each device and the thermal management requirements of the battery of each device during the charging process, the charging power can be dynamically adjusted during the charging process to avoid overheating or overcharging problems, thereby improving charging safety and battery life; by triggering a charging power disconnection message when each device is charged, the charging power of each device is disconnected, and a charging completion notification is generated to the user interface, the charging power can be disconnected in time to prevent overcharging, and a charging completion notification is provided to the user, thereby improving the user experience and the safety of the charging process.

[0019] The third objective of the present application is achieved through the following technical solutions: A computer terminal comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the above-mentioned mobile phone charging method are implemented.

[0020] The fourth objective of the present application is achieved through the following technical solutions: A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned mobile phone charging method are implemented.

[0021] In summary, the present application includes at least one of the following beneficial technical effects: 1. By identifying the model, battery type and charging requirements of each device and collecting the real-time battery status data of each device, the specific charging requirements and current battery status of each device can be accurately understood, thereby improving the personalization and accuracy of the charging strategy; by calculating based on the model, battery type, charging requirements and real-time battery status data of each device through a pre-trained charging model, the charging strategy of each device can be obtained, and the charging process can be optimized using machine learning algorithms, making the charging strategy more intelligent and adaptable, thereby improving charging efficiency and extending battery life; 2. By monitoring the charging progress of each device and adjusting the charging power of each device according to the charging strategy of each device and the thermal management requirements of the battery of each device during the charging process, the charging power can be dynamically adjusted during the charging process to avoid overheating or overcharging problems, thereby improving charging safety and battery life; by triggering the charging power disconnection message when each device is fully charged, the charging power of each device is disconnected, and a charging completion notification is generated to the user interface, the charging power can be disconnected in time to prevent overcharging, and a charging completion notification can be provided to the user, thereby improving the user experience and the safety of the charging process. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 is a flow chart of a mobile phone charging method in one embodiment of the present application; Figure 2 This is a flowchart for implementing step S10 in a mobile phone charging method in one embodiment of the present application; Figure 3 This is a flowchart of implementing step S20 in a mobile phone charging method in an embodiment of the present application; Figure 4 This is a flowchart for implementing step S20 in a mobile phone charging method in one embodiment of the present application; Figure 5 This is a flowchart for implementing step S30 in a mobile phone charging method in one embodiment of the present application; Figure 6 This is a flowchart of a method for charging a mobile phone in one embodiment of the present application; Figure 7 This is a principle block diagram of a mobile phone charging method system in one embodiment of the present application; Figure 8 It is a schematic diagram of a terminal in an embodiment of the present application. DETAILED DESCRIPTION

[0023] The present application is further described in detail below in conjunction with the accompanying drawings.

[0024] In one embodiment, if Figure 1 As shown, the present application discloses a mobile phone charging method, which specifically includes the following steps: S10: Identify the model, battery type and charging requirements of each device, and collect real-time battery status data of each device.

[0025] Specifically, in order to provide customized charging strategies based on the models and battery types of different devices, the system first identifies the models, battery types and charging requirements of each electronic device connected to the charging station. This may be achieved through the device's identification code, built-in sensors or communication with the device. The system collects battery status data of each device in real time, including current power, battery health status, temperature and other information.

[0026] S20: Based on the model, battery type, charging requirements and real-time battery status data of each device, a charging strategy for each device is calculated through a pre-trained charging model.

[0027] Specifically, a pre-trained charging model is used. This model may be based on a machine learning algorithm and can calculate the optimal charging strategy based on the device model, battery type, charging requirements and real-time battery status data. Using the collected device model, battery type, charging requirements and real-time battery status data, the system calculates the optimal charging strategy for each device through a pre-trained charging model. The charging strategy may include charging rate, charging time, charging termination conditions, etc.

[0028] S30: monitoring the charging progress of each device, and adjusting the charging power of each device according to the charging strategy of each device and the thermal management requirements of the battery of each device during the charging process.

[0029] Specifically, the system continuously monitors the charging progress of each device to ensure that the charging process complies with the preset charging strategy. During the charging process, the system dynamically adjusts the charging power according to the battery thermal management requirements of the device. For example, if the battery temperature is too high, the system may reduce the charging speed to prevent overheating. The system dynamically adjusts the charging power of each device to ensure a safe and efficient charging process.

[0030] S40: When charging of each device is completed, a charging power disconnection message is triggered to disconnect the charging power of each device, and a charging completion notification is generated to the user interface.

[0031] Specifically, when the device is fully charged, the system automatically triggers a message to disconnect the charging power source to prevent the battery from overcharging. The system generates a charging completion notification and sends it to the user interface so that users know that their device is fully charged and can safely disconnect the power source and use the device.

[0032] By adopting the above technical solution, by identifying the model, battery type and charging requirements of each device and collecting the real-time battery status data of each device, the specific charging requirements and current battery status of each device can be accurately understood, thereby improving the personalization and accuracy of the charging strategy; by calculating based on the model, battery type, charging requirements and real-time battery status data of each device through a pre-trained charging model, the charging strategy of each device can be obtained, and the charging process can be optimized using a machine learning algorithm, making the charging strategy more intelligent and adaptable, thereby improving the charging efficiency and extending the battery life; by monitoring the charging progress of each device, adjusting the charging power of each device according to the charging strategy of each device and the thermal management requirements of the battery of each device during the charging process, the charging power can be dynamically adjusted during the charging process to avoid overheating or overcharging problems, thereby improving charging safety and battery life; by triggering a charging power disconnection message when each device is charged, the charging power of each device is disconnected, and a charging completion notification is generated to the user interface, the charging power can be disconnected in time to prevent overcharging, and a charging completion notification is provided to the user, thereby improving the user experience and the safety of the charging process.

[0033] In one embodiment, if Figure 2 As shown, in step S10, the model, battery type and charging requirement of each device are identified, and the real-time battery status data of each device is collected, which specifically includes: S11: Use sensors to identify the model and battery type of each device.

[0034] Specifically, the system is equipped with sensors that can detect the physical characteristics of electronic devices connected to the charging station. Through these sensors, the system can automatically identify the model and battery type of the device, such as detecting the device's shape, size, specific sensor tags or using wireless communication technology such as NFC.

[0035] S12: Using the communication protocol to exchange data with each device, determine the charging specifications and current requirements of each device, and the charging specifications and current requirements of each device are the charging requirements of each device.

[0036] Specifically, the system establishes a connection with the device through standard communication protocols such as USB PD, Bluetooth, Wi-Fi, etc. Through these protocols, the system can exchange data with the device and obtain the charging specifications and current requirements of the device. The charging specifications of the device may include maximum voltage, maximum current, and possible supported charging protocols. The current requirement of the device refers to the current required by the device battery under optimal safety and efficiency. This information is critical to ensuring that the device can be charged with the correct parameters.

[0037] S13: Collect real-time battery status data of each device by monitoring battery voltage, current and temperature in real time.

[0038] Specifically, the system monitors the battery's voltage, current, and temperature in real time, which are key indicators for assessing battery status and health. Voltage monitoring ensures that the battery receives the correct energy input. Current monitoring helps the system understand the battery's charging rate and ensures that the device's maximum current requirement is not exceeded. Temperature monitoring is to prevent the battery from overheating. Overheating during device charging may damage the battery or reduce charging efficiency.

[0039] In one embodiment, if Figure 3 As shown, before step S20, that is, based on the model, battery type, charging requirement and real-time battery status data of each device, a charging strategy for each device is calculated by a pre-trained charging model, and the mobile phone charging method further includes: S201: Acquire historical charging data and device performance data of different devices, and pre-process the historical charging data and device performance data of different devices to obtain a training set.

[0040] Specifically, in order to obtain data for training the model, the system needs to collect historical charging data and device performance data of different devices. These data may include charging time, charging amount, battery status, device usage pattern, and preprocess the collected data. The preprocessing steps include cleaning the data to remove erroneous or incomplete data, and adjusting the data to the same scale. The preprocessed data is used to form a training set for training the model.

[0041] S202: Perform cluster analysis on the training set using the k-means algorithm to obtain a feature set of the training set.

[0042] Specifically, the k-means algorithm is used to perform cluster analysis on the training set. k-means is an unsupervised learning algorithm that is used to group data points into k clusters so that the sum of the distances between each data point and the center of the cluster to which it belongs is minimized. Through cluster analysis, patterns and feature sets in the data can be identified, and finally the data after cluster analysis are integrated to obtain the feature set of the training set.

[0043] S203: constructing a charging model based on a long short-term memory network, training the charging model using a feature set of a training set, and optimizing parameters of the trained charging model using a genetic algorithm to obtain a pre-trained charging model.

[0044] Specifically, a charging model is constructed based on a long short-term memory network. The long short-term memory network is suitable for a recurrent neural network that processes time series data, and can capture the feature set of the training set to train the charging model. During the training process, the charging model learns the mapping relationship from input data to output data, adjusts internal parameters to minimize the prediction error, and uses a genetic algorithm to optimize the parameters of the trained charging model. The genetic algorithm optimizes the long-term dependencies in the solution data of the problem through selection, crossover and mutation operations in an iterative process, and is suitable for predicting changes in power during the charging process. After training and parameter optimization, a pre-trained charging model is obtained, which can predict the best charging strategy based on the historical charging data and performance data of the device.

[0045] In one embodiment, if Figure 4 As shown, in step S20, based on the model, battery type, charging requirement and real-time battery status data of each device, a charging strategy for each device is calculated through a pre-trained charging model, specifically including: S21: Preprocess the model, battery type, charging requirement and real-time battery status data of each device to obtain a preprocessed data set.

[0046] Specifically, first, the model, battery type, charging requirements and real-time battery status data of each device are collected, and these raw data are preprocessed for subsequent analysis and modeling, including cleaning, removing invalid or erroneous data, standardization, adjusting the data to the same magnitude, normalization, scaling the data to a specific range, such as between 0 and 1, removing noise, feature encoding and other steps, and finally the data is integrated to obtain the preprocessed data set.

[0047] S22: Perform cluster analysis on the preprocessed data set using a k-means algorithm to obtain a feature data set of the preprocessed data.

[0048] Specifically, the k-means algorithm is used to perform cluster analysis on the preprocessed data set. Cluster analysis is used to discover the inherent structure in the data and identify different charging modes or device categories. After the cluster analysis is completed, feature data sets are obtained. These data sets contain key features that can represent the original data set, such as specific modes of battery status, specific combinations of charging requirements, etc.

[0049] S23: The feature data set is transmitted to a pre-trained charging model for comparison and analysis to obtain a charging strategy for each device.

[0050] Specifically, the feature data set is transmitted to a pre-trained charging model, which is trained based on historical data and device characteristics and is used to predict or determine the charging strategy. In the charging model, the feature data set is used for comparative analysis. The model will evaluate and determine the best charging strategy based on the input feature data. Finally, the model outputs the charging strategy for each device. These strategies are recommendations on charging speed, charging time, charging cycle, etc., with the aim of maximizing charging efficiency, extending battery life, and ensuring the safety of the charging process.

[0051] In one embodiment, if Figure 5 As shown, in step S30, the charging progress of each device is monitored, and the charging power of each device is adjusted according to the charging strategy of each device and the thermal management requirements of the battery of each device during the charging process, specifically including: S31: Use thermal sensors and temperature control algorithms to monitor the temperature of each device battery in real time.

[0052] Specifically, because the battery may heat up during the charging process, excessively high temperatures may damage the battery or cause safety issues. Therefore, the system is equipped with thermal sensors to measure the temperature of the batteries of each device in real time, and improve the safety of the device while charging through real-time detection and feedback.

[0053] S32: Determine a battery charging curve for each device according to the temperature of the battery of each device.

[0054] Specifically, the system uses a temperature control algorithm to analyze and respond to changes in battery temperature. Based on the battery temperature, the system determines the charging curve for each device battery. The charging curve is a graph that describes the relationship between the charging rate and the battery state at different stages. The battery charging curve may vary with temperature. For example, at lower temperatures, the battery may require a slower charging rate to avoid damage.

[0055] S33: adjusting the charging power of each device according to the battery charging curve of each device and the charging strategy of each device.

[0056] Specifically, the system adjusts the charging power according to the battery charging curve of each device and the predefined charging strategy. The charging strategy may include parameters such as the maximum charging rate and the charging cut-off voltage. For example, if the battery is nearly full, the system may reduce the charging rate to avoid overcharging. If the battery temperature is high, the system may also reduce the charging rate to reduce heat generation. Finally, the system dynamically adjusts the charging power of each device based on the real-time monitored temperature and the predefined charging curve. This adjustment helps to optimize the battery charging efficiency, extend the battery life, and ensure the safety of the charging process.

[0057] In one embodiment, if Figure 6 As shown, the mobile phone charging method also includes: S51: Record the temperature change and power adjustment data of each device during the charging process, and feed back the temperature change and power adjustment data of each device to a pre-trained charging model to optimize the parameters in the pre-trained charging model.

[0058] Specifically, during the charging process, the system records the temperature changes of each device and the power adjustments made according to the charging strategy, and feeds the recorded temperature change and power adjustment data back to the pre-trained charging model. Through feedback, the system uses the collected data to optimize the parameters in the charging model, which may involve adjusting the model to better adapt to the actual charging behavior of the device, including temperature response and power requirements, so as to improve the model's prediction accuracy for future charging processes.

[0059] S52: Setting a user interface allows the user to view the charging status, estimated completion time, and historical charging data of each device, and allows the user to manually adjust charging parameters through the user interface.

[0060] Specifically, the system provides a user interface that allows users to interact with the charging system. Users can view the current charging status of each device through the interface, including the power percentage and battery status icon. Users can view the historical charging data of the device, including past charging cycles, charging rates, temperature changes and other information, which helps users understand the charging habits and battery health of the device. The user interface provides options that allow users to manually adjust charging parameters as needed, such as setting the maximum charging rate, adjusting the time to start or end charging, etc.

[0061] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0062] In one embodiment, a mobile phone charging method system is provided, and the mobile phone charging method system corresponds one-to-one to a mobile phone charging method in the above embodiment. Figure 7 As shown, the mobile phone charging method system includes a data collection module, a calculation strategy module, an adjustment module and a notification module. The detailed description of each functional module is as follows: The data collection module is used to identify the model, battery type and charging requirements of each device, and collect the real-time battery status data of each device; The calculation strategy module is used to calculate the charging strategy of each device based on the model, battery type, charging requirements and real-time battery status data of each device through a pre-trained charging model; An adjustment module is used to monitor the charging progress of each device and adjust the charging power of each device according to the charging strategy of each device and the thermal management requirements of the battery of each device during the charging process; The notification module is used to trigger a charging power disconnection message when each device is fully charged, disconnect the charging power of each device, and generate a charging completion notification to the user interface.

[0063] Optionally, the mobile phone charging method system further includes: A feedback module is used to record the temperature change and power adjustment data of each device during the charging process, and feed back the temperature change and power adjustment data of each device to a pre-trained charging model to optimize the parameters in the pre-trained charging model; The user interaction module is used to set the user interface, allowing the user to view the charging status, estimated completion time and historical charging data of each device, and allowing the user to manually adjust the charging parameters through the user interface.

[0064] A training set acquisition module is used to acquire historical charging data and device performance data of different devices, and pre-process the historical charging data and device performance data of the different devices to obtain a training set; A cluster analysis module, used to perform cluster analysis on the training set using a k-means algorithm to obtain a feature set of the training set; The model training module is used to construct a charging model based on a long short-term memory network, train the charging model using the feature set of the training set, and optimize the parameters of the trained charging model through a genetic algorithm to obtain the pre-trained charging model.

[0065] Optionally, the data collection module includes: A sensor detection submodule, used to use sensors to identify the model and battery type of each device; A demand determination submodule is used to exchange data with each device using a communication protocol to determine the charging specifications and current requirements of each device, where the charging specifications and current requirements of each device are both charging requirements of each device; The real-time monitoring submodule is used to collect the real-time battery status data of each device by real-time monitoring of the battery voltage, current and temperature.

[0066] Optionally, the calculation strategy module includes: A data preprocessing submodule, used to preprocess the model, battery type, charging requirement and real-time battery status data of each device to obtain a preprocessed data set; A data analysis submodule, used to perform cluster analysis on the preprocessed data set using a k-means algorithm to obtain a feature data set of the preprocessed data; The model comparison submodule is used to transmit the feature data set to the pre-trained charging model for comparison and analysis to obtain the charging strategy of each device.

[0067] Optionally, the adjustment module includes: A temperature detection submodule, used to monitor the temperature of the battery of each device in real time using a thermal sensor and a temperature control algorithm; A curve determination submodule, used to determine a battery charging curve of each device according to the temperature of the battery of each device; The power adjustment submodule is used to adjust the charging power of each device according to the battery charging curve of each device and the charging strategy of each device.

[0068] For the specific definition of a mobile phone charging method system, please refer to the definition of a mobile phone charging method above, which will not be repeated here. Each module in the above-mentioned mobile phone charging method system can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer terminal in the form of hardware, or can be stored in the memory of the computer terminal in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0069] In one embodiment, a computer terminal is provided. The computer terminal may be a server, and its internal structure diagram may be as follows: Figure 8 As shown. The computer terminal includes a processor, a memory, a network interface and a database connected through a system bus. Among them, the processor of the computer terminal is used to provide computing and control capabilities. The memory of the computer terminal includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer terminal is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a mobile phone charging method is implemented.

[0070] In one embodiment, a computer terminal is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the following steps when executing the computer program: Identify the model, battery type and charging requirements of each device, and collect real-time battery status data for each device; Based on the model, battery type, charging requirements and real-time battery status data of each device, the charging strategy of each device is calculated through the pre-trained charging model; Monitor the charging progress of each device and adjust the charging power of each device according to the charging strategy of each device and the thermal management requirements of the battery of each device during the charging process; When charging of each device is completed, a charging power disconnection message is triggered to disconnect the charging power of each device, and a charging completion notification is generated to the user interface.

[0071] In one embodiment, a computer readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented: Identify the model, battery type and charging requirements of each device, and collect real-time battery status data for each device; Based on the model, battery type, charging requirements and real-time battery status data of each device, the charging strategy of each device is calculated through the pre-trained charging model; Monitor the charging progress of each device and adjust the charging power of each device according to the charging strategy of each device and the thermal management requirements of the battery of each device during the charging process; When charging of each device is completed, a charging power disconnection message is triggered to disconnect the charging power of each device, and a charging completion notification is generated to the user interface.

[0072] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0073] Those skilled in the art will clearly understand that for the sake of convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the system can be divided into different functional units or modules to complete all or part of the functions described above.

[0074] The embodiments described above 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, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such 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, and should all be included in the protection scope of the present application.

Claims

1. A mobile phone charging method, characterized in that: The mobile phone charging method comprises: Identify the model, battery type and charging requirements of each device, and collect real-time battery status data of each device; Based on the model, battery type, charging requirement and real-time battery status data of each device, a charging strategy for each device is calculated by a pre-trained charging model; Monitoring the charging progress of each device, and adjusting the charging power of each device according to the charging strategy of each device and the thermal management requirements of the battery of each device during the charging process; When the charging of each device is completed, a charging power disconnection message is triggered to disconnect the charging power of each device, and a charging completion notification is generated to the user interface.

2. A mobile phone charging method according to claim 1, characterized in that: The identifying the model, battery type and charging requirement of each device and collecting the real-time battery status data of each device includes: Using sensors to identify the model and battery type of each of the devices; Using a communication protocol to exchange data with each device, determining the charging specifications and current requirements of each device, wherein the charging specifications and current requirements of each device are both charging requirements of each device; Real-time battery status data of each device is collected by monitoring battery voltage, current and temperature in real time.

3. A mobile phone charging method according to claim 1, characterized in that: Before calculating the charging strategy of each device based on the model, battery type, charging requirement and real-time battery status data of each device by using a pre-trained charging model, the mobile phone charging method further includes: Acquire historical charging data and device performance data of different devices, and preprocess the historical charging data and device performance data of the different devices to obtain a training set; Performing cluster analysis on the training set using a k-means algorithm to obtain a feature set of the training set; A charging model is constructed based on a long short-term memory network, the charging model is trained using the feature set of the training set, and the parameters of the trained charging model are optimized using a genetic algorithm to obtain the pre-trained charging model.

4. A mobile phone charging method according to claim 1, characterized in that: The charging strategy for each device is calculated based on the model, battery type, charging requirement and real-time battery status data of each device through a pre-trained charging model, and includes: Preprocessing the model, battery type, charging requirement and real-time battery status data of each device to obtain a preprocessed data set; Performing cluster analysis on the preprocessed data set using a k-means algorithm to obtain a feature data set of the preprocessed data; The characteristic data set is transmitted to the pre-trained charging model for comparison and analysis to obtain the charging strategy of each device.

5. A mobile phone charging method according to claim 1, characterized in that: The monitoring of the charging progress of each device and adjusting the charging power of each device according to the charging strategy of each device and the thermal management requirements of the battery of each device during the charging process include: Using thermal sensors and temperature control algorithms to monitor the temperature of the batteries of each device in real time; Determining a battery charging curve for each device according to the temperature of the battery of each device; The charging power of each device is correspondingly adjusted according to the battery charging curve of each device and the charging strategy of each device.

6. A mobile phone charging method according to claim 1, characterized in that: The mobile phone charging method further comprises: Recording the temperature change and power adjustment data of each device during the charging process, and feeding back the temperature change and power adjustment data of each device to the pre-trained charging model to optimize the parameters in the pre-trained charging model; The user interface is provided to allow the user to view the charging status, estimated completion time and historical charging data of each device, and to allow the user to manually adjust charging parameters through the user interface.

7. A mobile phone charging method system, characterized in that: The mobile phone charging method system comprises: A data collection module is used to identify the model, battery type and charging requirements of each device, and collect real-time battery status data of each device; A calculation strategy module, configured to calculate the charging strategy of each device through a pre-trained charging model based on the model, battery type, charging requirement and real-time battery status data of each device; An adjustment module, configured to monitor the charging progress of each device and adjust the charging power of each device according to the charging strategy of each device and the thermal management requirements of the battery of each device during the charging process; The notification module is used to trigger a charging power disconnection message when the charging of each device is completed, disconnect the charging power of each device, and generate a charging completion notification to the user interface.

8. A mobile phone charging method and system according to claim 7, characterized in that: The mobile phone charging method system also includes: A feedback module, used to record the temperature change and power adjustment data of each device during the charging process, and feed back the temperature change and power adjustment data of each device to the pre-trained charging model to optimize the parameters in the pre-trained charging model; The user interaction module is used to set the user interface, allowing the user to view the charging status, estimated completion time and historical charging data of each device, and allowing the user to manually adjust the charging parameters through the user interface.

9. A computer terminal comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the mobile phone charging method as described in any one of claims 1 to 6 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of a mobile phone charging method as claimed in any one of claims 1 to 6 are implemented.

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