CN111564877A - Method and device for charging management and control - Google Patents

Abstract

The present application provides a method and device for charging control. In the method, the electronic device obtains the first predicted charging duration corresponding to each basic prediction model according to a plurality of basic prediction models, and obtains a plurality of weight coefficients according to the weight model. The electronic device determines the second predicted charging duration according to the multiple weighting coefficients and the first predicted charging duration corresponding to each basic prediction model, and the electronic device controls the charging of the electronic device according to the second predicted charging duration, so as to prolong the life of the battery and improve the The battery life can help improve the user experience.

Description

Method and device for charging control

technical field

The present application relates to the field of charging, and more particularly to a method and apparatus for charging management in the field of charging.

Background technique

Different users have different charging habits when charging terminal devices, and different charging habits will affect battery life. For example, overcharging will reduce battery life and battery life, thereby affecting user experience.

In order to solve the above problems, it is necessary to predict the charging time of the user, and control the charging of the terminal device according to the predicted charging time, so as to prolong the battery life and improve the battery life. Therefore, how to predict the charging time is an urgent problem to be solved.

SUMMARY OF THE INVENTION

The embodiments of the present application provide a method and apparatus for charging control, which can manage and control the charging of a terminal device according to a predicted duration, which helps to prolong the life of the battery and improve the battery's endurance.

In a first aspect, a method for charging management and control is provided, the method can be executed by an electronic device, and the electronic device can be a device capable of supporting the electronic device to realize the functions required by the method, such as a chip system. The method includes: acquiring first charging data; inputting at least part of the charging data in the first charging data into a plurality of basic prediction models, and determining a first predicted charging duration corresponding to each basic prediction model; At least part of the charging data in the data is input into the weighting model to obtain multiple weighting coefficients; the second predicted charging duration is determined according to the multiple weighting coefficients and the first predicted charging duration corresponding to each basic prediction model, and the The second predicted charging duration is used to manage and control the charging of the electronic device.

In the above solution, the electronic device obtains the first predicted charging duration corresponding to each basic prediction model according to a plurality of basic prediction models, and the electronic device obtains a plurality of weight coefficients according to the weight model. The electronic device determines the second predicted charging duration according to the multiple weighting coefficients and the first predicted charging duration corresponding to each basic prediction model, and the electronic device controls the charging of the electronic device according to the second predicted charging duration, so as to prolong the life of the battery and improve the The battery life can help improve the user experience.

Wherein, at least two weight coefficients in the plurality of weight coefficients are the same, or at least two weight coefficients in the plurality of weight coefficients are different.

It can be understood that the charging data input into each basic prediction model may be different, or the charging data input to at least two basic prediction models may be the same.

It can also be understood that the charging data input to the weight model and the charging data input to the multiple base prediction models may be different.

In some possible implementations, different basic prediction models in the multiple basic prediction models correspond to different application scenarios.

In some possible implementations, determining the second predicted charging duration according to the multiple weighting coefficients and the first predicted charging duration corresponding to each of the basic prediction models includes: associating the multiple weighting coefficients with the each basic prediction model. The first predicted charging duration corresponding to each basic prediction model is weighted and calculated to obtain the second predicted charging duration.

In some possible implementations, the method further includes: acquiring a first actual charging duration of the electronic device corresponding to the first charging data; combining the first charging data, the first actual charging duration and the The second predicted charging duration is added as a sample to the first sample set; the weight model is updated according to the samples in the first sample set.

In this way, the samples in the first sample set can be updated in real time, thereby ensuring the accuracy of the weight model.

In some possible implementations, the updating the weight model according to the samples in the first sample set includes: determining a first pass rate of the samples in the first sample set; if the first pass rate If the rate is less than the preset value of the first pass rate, determine the number of qualified samples in the first sample set; if the number of qualified samples in the first sample set is greater than the preset value of the first sample number, according to Part of the qualified samples modifies the weight model.

In the above solution, when the electronic device determines that the samples in the first sample set do not meet the requirements of the first pass rate, it determines whether the qualified sample data in the first sample set meets the requirements of the preset value of the first sample quantity, if If the requirements are met, the weight model is revised according to some of the qualified samples, so that the accuracy of the revised weight model can be guaranteed.

The above-mentioned number of qualified samples used for revising the weight model needs to meet the preset quantity, so as to ensure the robustness requirement of the revised weight model.

In some possible implementations, the modifying the weight model according to the partial samples in the qualified samples includes: training according to the partial samples to obtain a first correction parameter, and modifying the weight model according to the first correction parameter Weight model, get the revised weight model.

In the above solution, the electronic device can determine the first correction parameter for correcting the first weight model, thereby ensuring the accuracy of the weight model.

In some possible implementations, the modifying the weight model according to the partial samples in the qualified samples includes: obtaining the first modified parameter according to the partial sample training, and applying the first modified parameter to the send to the cloud;

receiving a second correction parameter determined by the cloud according to the first correction parameter;

The weight model is modified according to the second modification parameter to obtain a modified weight model.

In the above solution, the complexity of the correction model of the electronic device can be simplified, and each electronic device sends the first correction parameter obtained by itself to the cloud. The first correction parameter is the correction parameter of the weight model, and there is no information about the user of the electronic device. , it can also protect the privacy of users, which is beneficial to improve security. At the same time, the second correction parameter determined by the cloud using big data can meet the requirements of robustness.

In some possible implementations, the method further includes: testing the stability of the revised weight model according to the remaining part of the qualified samples.

In some possible implementations, the inputting at least part of the charging data in the first charging data into multiple basic prediction models to obtain the first predicted charging duration corresponding to each basic prediction model, including:

inputting at least part of the charging data in the first charging data into the plurality of basic prediction models to obtain a third predicted charging duration corresponding to each basic prediction model;

The third predicted charging duration corresponding to each basic prediction model is adjusted by using the adjustment parameters of each basic prediction model, so as to obtain the first predicted charging duration corresponding to each basic prediction model.

In some possible implementations, the method further includes:

acquiring second charging data of the electronic device and a second actual charging duration corresponding to the second charging data;

inputting the second charging data into a first basic prediction model of the plurality of basic prediction models to obtain a fourth predicted charging duration;

adding the fourth predicted charging duration, the second charging data, and the second actual charging duration as samples to a second sample set;

The adjustment parameters corresponding to the first basic prediction model are determined according to the samples in the second sample set.

In some possible implementations, the determining the adjustment parameter corresponding to the first basic prediction model according to the samples in the second sample set includes:

determining a second pass rate for the samples in the second sample set;

If the second pass rate is less than a preset value of the second pass rate, determining the number of qualified samples in the second sample set;

If the number of qualified samples in the second sample set is greater than the preset value of the second number of samples, the adjustment parameters corresponding to the first basic prediction model are determined according to the qualified samples.

In some possible implementations, if the actual charging duration and the predicted charging duration corresponding to the qualified samples in the second sample set satisfy a linear relationship, the adjustment parameter corresponding to the first basic prediction model is the regression coefficient of the linear relationship and constants.

In some possible implementations, if the actual charging duration and the predicted charging duration corresponding to the qualified samples in the second sample set satisfy a nonlinear relationship, then some qualified samples can be used for nonlinear training to obtain the coefficients of the nonlinear relationship It is the adjustment parameter of the first basic prediction model.

In some possible implementations, the first charging data and the second charging data include at least one of the following: a type of a charger used to charge the electronic device, a manufacturer of a battery that produces the electronic device, The nominal capacity of the battery, the type of cells of the battery, the type of charging cable used to charge the electronic device, the number of cycles the battery has been charged, the nominal cycles the battery can be charged The number of times, the average internal resistance of the battery, the maximum internal resistance of the battery, the historical insertion time and historical extraction time of the charger, the charging cut-off time of the battery cells, and the battery cells of the battery. The starting and ending power levels, and the actual charging time for a preset time period per day within a preset number of days.

In a second aspect, a method for charging control is provided, including: acquiring first charging data; inputting at least part of the charging data in the first charging data into a first basic prediction model to obtain a third prediction duration; The third predicted charging duration is adjusted according to the adjustment parameter corresponding to the first basic prediction to obtain a first predicted charging duration, where the first predicted charging duration is used to manage and control the charging of the electronic device.

In the above solution, the electronic device can use the adjustment parameters corresponding to the first basic prediction model to adjust the third predicted charging duration obtained by the first basic prediction model, so as to obtain the first predicted charging duration for charging control. If the third predicted charging duration obtained by the first basic prediction model is inaccurate, adjustment parameters can be used to adjust, so that a possibly accurate first predicted charging duration can be obtained, and the accuracy of charging control can also be improved.

In some possible implementations, the method further includes: acquiring second charging data of the electronic device and a second actual charging duration corresponding to the second charging data; inputting the second charging data into the In the first basic prediction model, a fourth predicted charging duration is obtained; the fourth predicted charging duration, the second charging data and the second actual charging duration are added as samples to the second sample set; The samples in the second sample set determine the adjustment parameters corresponding to the first basic prediction model.

The adjustment parameters corresponding to the first basic prediction model in the above solution are obtained according to a plurality of actual samples, and therefore, can meet the actual adjustment requirements.

In some possible implementations, the determining the adjustment parameter corresponding to the first basic prediction model according to the samples in the second sample set includes:

determining a second pass rate for the samples in the second sample set;

If the second pass rate is less than a preset value of the second pass rate, determining the number of qualified samples in the second sample set;

If the number of qualified samples in the second sample set is greater than the preset value of the second number of samples, the adjustment parameters corresponding to the first basic prediction model are determined according to the qualified samples.

In some possible implementations, if the actual charging duration and the predicted charging duration corresponding to the qualified samples in the second sample set satisfy a linear relationship, the adjustment parameter corresponding to the first basic prediction model is the regression coefficient of the linear relationship and constants.

In some possible implementations, if the actual charging duration and the predicted charging duration corresponding to the qualified samples in the second sample set satisfy a nonlinear relationship, then some qualified samples can be used for nonlinear training to obtain the coefficients of the nonlinear relationship It is the adjustment parameter of the first basic prediction model.

In some possible implementations, the first charging data and the second charging data include at least one of the following: a type of a charger used to charge the electronic device, a manufacturer of a battery that produces the electronic device, The nominal capacity of the battery, the type of cells of the battery, the type of charging cable used to charge the electronic device, the number of cycles the battery has been charged, the nominal cycles the battery can be charged The number of times, the average internal resistance of the battery, the maximum internal resistance of the battery, the historical insertion time and historical extraction time of the charger, the charging cut-off time of the battery cells, and the battery cells of the battery. The starting and ending power levels, and the actual charging time for a preset time period per day within a preset number of days.

In a third aspect, an apparatus for charging management and control is provided, where the apparatus is configured to execute the method in the first aspect or any possible implementation manner of the first aspect. Specifically, the apparatus may include modules for performing the method in the first aspect or any possible implementation manner of the first aspect.

In a fourth aspect, an apparatus for charging management and control is provided, where the apparatus is configured to execute the method in the second aspect or any possible implementation manner of the second aspect. Specifically, the apparatus may comprise means for performing the method in the second aspect or any possible implementation manner of the second aspect.

A fifth aspect provides an apparatus for charging control, the apparatus includes a processor, the processor is coupled to a memory, the memory is used for storing computer programs or instructions, and the processor is used for executing the computer programs or instructions stored in the memory, so that the first The method in one aspect is executed.

For example, the processor is configured to execute a computer program or instructions stored in the memory to cause the apparatus to perform the method of the first aspect.

Optionally, the apparatus includes one or more processors.

Optionally, the apparatus may further include a memory coupled to the processor.

Optionally, the device may include one or more memories.

Alternatively, the memory may be integrated with the processor, or provided separately.

Optionally, the device may also include a transceiver.

In a sixth aspect, a device for charging control is provided, the device includes a processor, the processor is coupled to a memory, the memory is used for storing computer programs or instructions, and the processor is used for executing the computer programs or instructions stored in the memory, so that the first The method in the second aspect is performed.

For example, the processor is configured to execute a computer program or instructions stored in the memory to cause the apparatus to perform the method of the second aspect.

Optionally, the apparatus includes one or more processors.

Optionally, the apparatus may further include a memory coupled to the processor.

Optionally, the device may include one or more memories.

Alternatively, the memory may be integrated with the processor, or provided separately.

Optionally, the device may also include a transceiver.

In a seventh aspect, a computer-readable storage medium is provided, on which a computer program (which may also be referred to as instructions or codes) for implementing the method in the first aspect is stored.

For example, the computer program, when executed by a computer, causes the computer to perform the method of the first aspect.

In an eighth aspect, a computer-readable storage medium is provided, on which a computer program (which may also be referred to as instructions or codes) for implementing the method in the first aspect or the second aspect is stored.

For example, the computer program, when executed by a computer, causes the computer to perform the method of the second aspect.

In a ninth aspect, the present application provides a chip including a processor. The processor is adapted to read and execute the computer program stored in the memory to perform the method of the first aspect and any possible implementations thereof.

Optionally, the chip further includes a memory, and the memory and the processor are connected to the memory through a circuit or a wire.

Further optionally, the chip further includes a communication interface.

In a tenth aspect, the present application provides a chip system including a processor. The processor is adapted to read and execute the computer program stored in the memory to perform the method of the second aspect and any possible implementations thereof.

Optionally, the chip further includes a memory, and the memory and the processor are connected to the memory through a circuit or a wire.

Further optionally, the chip further includes a communication interface.

In an eleventh aspect, the present application provides a computer program product, the computer program product comprising a computer program (also referred to as instructions or codes), which, when executed by a computer, causes the computer to implement the first aspect. method.

In a twelfth aspect, the present application provides a computer program product, the computer program product comprising a computer program (also referred to as instructions or codes) that, when executed by a computer, causes the computer to implement the second aspect. method.

Description of drawings

FIG. 1 is a schematic diagram of a management and control strategy provided by an embodiment of the present application.

FIG. 2 is a schematic diagram of a system architecture provided by an embodiment of the present application.

FIG. 3 is a schematic diagram of training a basic prediction model provided by an embodiment of the present application.

FIG. 4 is a schematic diagram of a training weight model provided by an embodiment of the present application.

FIG. 5 is a schematic diagram of a method for charging control provided by an embodiment of the present application.

FIG. 6 is a schematic diagram of a modified weight model provided by an embodiment of the present application.

FIG. 7 is a schematic diagram of another modified weight model provided by an embodiment of the present application.

FIG. 8 is a schematic diagram of another method for charging management and control provided by an embodiment of the present application.

FIG. 9 is a schematic diagram of a method for obtaining adjustment parameters corresponding to a first basic prediction model provided by an embodiment of the present application.

FIG. 10 is a schematic block diagram of an apparatus for charging management and control provided by an embodiment of the present application.

FIG. 11 is a schematic block diagram of another apparatus for charging management and control provided by an embodiment of the present application.

FIG. 12 is a schematic block diagram of another apparatus for charging management and control provided by an embodiment of the present application.

Detailed ways

The technical solutions in the embodiments of the present application will be described below with reference to the accompanying drawings in the embodiments of the present application.

Different users have different charging habits when charging electronic devices. Different charging habits can affect the life of an electronic device's battery. For example, some users like to plug in the charger the night before and start charging until unplugging the charger the next morning, which will cause the battery to be overcharged. For example, some users like to charge during working hours during the day and unplug when fully charged, so that the battery life will not be affected. For example, some users like to use chargers with different powers to charge the same electronic device. Specifically, some users like to use low-power chargers to charge electronic devices that require high power, which will make the charging time too long, and some Most users like to use high-power chargers to charge electronic devices that require low-power, which will quickly make the battery saturated. If the charger is not unplugged in time, it will cause overcharging. For example, some users like to use electronic devices while charging, and the battery needs to be continuously charged and discharged, which shortens the service life of the battery.

In the above-mentioned user habits, if the battery is overcharged, the battery will be in a swollen state for a long time, resulting in reduced battery life and reduced battery life, thereby affecting user experience.

In order to prolong the life of the battery, the charging time can be predicted according to different user habits, and the charging of the electronic device can be controlled by using the predicted charging time. For example, for overcharging, t3 in Figure 1 is the predicted charging time according to the user's habits, and the electronic device can use the control strategy in Figure 1 to determine through t3: normal charging in the period of 0 to t1, and when it reaches 70% When the charging capacity is in the protection state from t1 to t2, the electronic equipment is not charged, or it is charged with a small current, and continues to charge after t2 until t3 reaches 100%. Different control strategies t1, t2 and t3 value is different. The management and control strategy in FIG. 1 is only an exemplary description, and should not cause any limitation to the present application. The use of the predicted charging duration to manage and control the charging of the electronic device in the embodiment of the present application may also be other management and control strategies, which are not limited in the embodiment of the present application.

It should be noted that, any electronic device that needs to be charged can be managed and controlled by using the method for charging control provided in the embodiment of the present application, for example, the charging of a terminal device can be managed and controlled.

The terminal devices mentioned in the embodiments of this application are user equipment (user equipment, UE), mobile station (mobile station, MS), mobile terminal (mobile terminal, MT), access terminal, subscriber unit, subscriber station, mobile station, mobile station, remote station, remote terminal, mobile device, user terminal, terminal, wireless communication device, user agent or user equipment, etc.

The terminal device may be a device that provides voice/data connectivity to the user, such as a handheld device with a wireless connection function, a vehicle-mounted device, and the like. At present, some examples of terminals are: mobile phone (mobile phone), tablet computer, notebook computer, palmtop computer, mobile internet device (mobile internet device, MID), wearable device, virtual reality (virtual reality, VR) device, augmented reality (augmented reality, AR) equipment, wireless terminals in industrial control, wireless terminals in self-driving, wireless terminals in remote medical surgery, and smart grids wireless terminal in transportation safety, wireless terminal in smart city, wireless terminal in smart home, cellular phone, cordless phone, session initiation protocol, SIP) telephones, wireless local loop (WLL) stations, personal digital assistants (PDAs), handheld devices with wireless communication capabilities, computing devices or other processing devices connected to wireless modems, in-vehicle devices , a wearable device, a terminal device in a 5G network, or a terminal device in a future evolved public land mobile network (public land mobile network, PLMN), etc., which are not limited in the embodiments of the present application.

As an example and not a limitation, in this embodiment of the present application, the terminal device may also be a wearable device. Wearable devices can also be called wearable smart devices, which are the general term for the intelligent design of daily wear and the development of wearable devices using wearable technology, such as glasses, gloves, watches, clothing and shoes. A wearable device is a portable device that is worn directly on the body or integrated into the user's clothing or accessories. Wearable device is not only a hardware device, but also realizes powerful functions through software support, data interaction, and cloud interaction. In a broad sense, wearable smart devices include full-featured, large-scale, complete or partial functions without relying on smart phones, such as smart watches or smart glasses, and only focus on a certain type of application function, which needs to cooperate with other devices such as smart phones. Use, such as various types of smart bracelets, smart jewelry, etc. for physical sign monitoring.

In addition, in the embodiment of the present application, the terminal device may also be a terminal device in an internet of things (Internet of things, IoT) system. IoT is an important part of the future development of information technology, and its main technical feature is that items are passed through communication technology. Connect with the network, so as to realize the intelligent network of human-machine interconnection and the interconnection of things. The terminal device of the present application may also be an on-board module, on-board module, on-board component, on-board chip or on-board unit built into the vehicle as one or more components or units. An on-board component, on-board chip or on-board unit may implement the method of the present application. Therefore, the embodiments of the present application can be applied to the Internet of Vehicles, such as vehicle to everything (V2X), long term evolution-vehicle (LTE-V), vehicle-to-vehicle (V2X) , V2V) and so on.

The following describes the system architecture of the embodiment of the present application with reference to FIG. 2 . As shown in FIG. 2 , in the embodiment of the present application, it includes: determining a model, using the determined model to obtain a predicted charging duration of the electronic device, and the predicted charging duration is used to control the charging of the electronic device, or the predicted charging duration can be used to control the charging of the electronic device. The model is corrected. Optionally, FIG. 2 may also not include a correction process, wherein the determination model includes a determination weight model and a plurality of basic prediction models. Optionally, the weight model may be preset or obtained by training or indicated by the cloud. Optionally, the multiple basic prediction models may also be preset or obtained by training or indicated by the cloud, which is not limited in this embodiment of the present application.

If the above multiple basic prediction models are obtained by cloud training and instructed to the electronic device or obtained by the electronic device training, the electronic device and the cloud can train to obtain multiple basic prediction models according to the steps shown in Figure 3. The training shown in Figure 3 The process can be performed by an electronic device or a cloud. The following description is only described by taking a basic prediction model (the first basic prediction model) obtained by training as an example. Specifically, the method 300 includes:

S310: Determine a training sample set for training the first basic prediction model.

Before S310, an initial basic prediction model may be preset, and the model parameters of the initial basic prediction model are preset initial values. The samples in the training sample set include: multiple sets of charging data, the actual charging duration corresponding to each set of charging data, and multiple predicted charging durations obtained by inputting each set of charging data into the initial basic prediction model. Among them, a set of charging data can be a piece of historical charging data of an electronic device. Inputting a specific piece of historical charging data into the initial basic prediction model can obtain a predicted charging time, and a specific historical charging data corresponds to the actual charging time of the electronic device. Charging time. In this way, a sample in the training sample set is a set of charging data, the predicted charging duration obtained by inputting the set of charging data into the initial basic prediction model, and the actual charging duration corresponding to the set of charging data.

One of the historical charging data may include at least one of the following charging parameters: the type of charger used to charge the electronic device, the manufacturer of the battery that produces the electronic device, the nominal capacity of the battery, the type of battery cells, the type of battery used for charging the electronic device The type of charging cable used to charge the electronic device, the number of times the battery has been charged, the nominal number of times the battery can be charged, the average internal resistance of the battery, the maximum internal resistance of the battery, the historical plug-in time and historical pull-out time of the charger, The charging cut-off time of the battery cells, the one-time historical starting power and the historical ending power of the battery cells, the actual charging time in a specific time period every day within a preset number of days, and the like. Certainly, a set of charging data may include other charging parameters, which are not limited in this embodiment of the present application.

Optionally, the samples in the training sample set may be samples marked with positive sample labels. Specifically, if the absolute value of the difference between the predicted charging duration output by a set of charging data input in the initial basic prediction model and the actual charging duration under the set of charging data is smaller than the preset value, the sample is a positive sample and can be used as training samples in the sample set.

Optionally, except for the samples in the training sample set that have positive sample labels, the predicted charging duration corresponding to the samples in the training sample set is greater than the preset value, so as to avoid the charging habit of the user unplugging the sample immediately after charging. The samples generated from the charging data affect the accuracy of the basic prediction model training.

S320, determine whether the number of samples in the training sample set reaches the preset number of training samples, if the number of samples in the training sample set reaches the preset number of training samples, execute S330, otherwise execute S310 to continue adding samples to the training sample set.

It should be noted that the preset number of training samples may be a fixed value specified in the protocol, or may be a variable value, which is not limited in this embodiment of the present application.

S330, a first basic prediction model is obtained by training according to the samples in the training sample set.

Specifically, S330 includes: inputting the samples in the sample set into the initial basic prediction model, the initial basic prediction model outputs a predicted charging duration, comparing the actual charging duration with the predicted charging duration, and repeatedly revising the coefficients of the initial basic prediction model according to the comparison results , when the accuracy of the predicted charging duration and the actual charging duration output by the modified basic prediction model is smaller than the preset value for a long time and lasts for a preset time period, the modified basic prediction model is the first basic prediction model. By analogy, other basic prediction models can be obtained.

Different basic prediction models can be used for different charging mode application scenarios. For example, some basic prediction models are for night charging mode, some basic prediction models are for daytime charging mode, some basic prediction models are for weekday charging mode, and some are for daytime charging mode. The underlying predictive model for rest day charging patterns. In different application scenarios, the input of the basic prediction model may be different or may be the same, which is not limited in this embodiment of the present application.

The basic prediction model may be a support vector machine (SVM) model, a decision tree (decision tree) model, a neural network (neural network) model, a bagging (Bagging) model or a boosting (boosting) model. Of course, the weight model may also be other models, which are not limited in this embodiment of the present application.

If the weight model is obtained by the training of the electronic device or the cloud is trained and instructed to the electronic device, the electronic device and the cloud can train to obtain the weight model according to the steps shown in FIG. 4 . Specifically, the method 400 shown in the map 4 includes:

S410: Determine a training sample set for training the weight model.

Before S410, an initial weight model may be preset, and the model parameters of the initial weight model are preset initial values. The samples in the training sample set include: multiple sets of charging data, the actual charging duration corresponding to each set of charging data, and multiple predicted charging durations obtained by inputting the initial weight model and multiple basic prediction models in each set of charging data (see method 500 for details). description of). Among them, a set of charging data can be a piece of historical charging data of an electronic device, input a specific historical charging data into the initial weight model to obtain multiple initial weight coefficients, and input a specific historical charging data into multiple basic prediction models to obtain Multiple predicted charging durations are weighted by multiple initial weight coefficients and multiple predicted charging durations to obtain a final predicted charging duration, and the electronic device corresponds to an actual charging duration under the condition of a specific historical charging data. In this way, a sample in the training sample set is a set of charging data, a final predicted charging duration obtained by inputting the set of charging data into the initial weight model and multiple basic prediction models, and the actual charging duration corresponding to the set of charging data.

For the description of a piece of historical charging data, reference may be made to the description of the method 300 .

Optionally, the samples in the training sample set may be samples marked with positive sample labels. Specifically, if the absolute value of the difference between a predicted charging duration obtained by inputting the initial weight model and multiple basic prediction models into a set of charging data and the actual charging duration under the set of charging data is smaller than the preset value, the sample It is a positive sample and can be used as a sample in the training sample set.

Optionally, except for the samples in the training sample set that have positive sample labels, the predicted charging duration corresponding to the samples in the training sample set is greater than the preset value, so as to avoid the charging habit of the user unplugging the sample immediately after charging. The samples generated from the charging data affect the accuracy of the basic prediction model training.

S420, determine whether the number of samples in the training sample set reaches the preset number of training samples, if the number of samples in the training sample set reaches the preset number of training samples, execute S430, otherwise execute S410 to continue adding samples to the training sample set.

S430, a weight model is obtained by training according to the samples in the training sample set.

Specifically, S430 includes: inputting a set of charging data corresponding to the samples in the training sample set into an initial weight model, the initial weight model outputs a plurality of weight coefficients, and inputting the set of charging data into a plurality of data obtained by training in method 300 For the multiple predicted charging durations output by the basic prediction model, the final predicted charging duration obtained by weighted calculation of multiple weighting coefficients and multiple predicted charging durations is compared with the actual charging duration, and the coefficients of the initial weighting model are repeatedly revised according to the comparison results until The weighted model is obtained by weighting a plurality of weight coefficients and the predicted charging duration output from the basic prediction model trained in the method 300 to obtain the final predicted charging duration and the accuracy of the actual charging duration being less than the preset value for a long time and for a certain period of time.

The weight model obtained in the above training process may be a support vector machine (SVM) model, a decision tree (decision tree) model, a neural network (neural network) model, a bagging (Bagging) model or a boosting (boosting) model. Of course, the weight model may also be other models, which are not limited in this embodiment of the present application.

In the above methods 300 and 400, each set of charging data may be charging data before preprocessing, or may be charging data after preprocessing. If each set of charging data is the charging data before preprocessing, in the process of training the model in S330 and S430, the charging data needs to be preprocessed, and the preprocessing includes denoising, quantization, normalization and other operations. at least one normalization treatment of . Of course, the charging data may not be preprocessed, and it is possible that the charging data itself meets the requirements of the training model, which is not limited in this embodiment of the present application.

For example, taking the noise point removal as an example, the predicted charging duration corresponding to each set of charging data in the training sample set in methods 300 and 400 needs to be greater than the preset duration. In other words, the valid samples in the training sample set are at least continuous The charging behavior of the preset duration avoids that the samples in the training sample set are samples generated by the user's charging habit of unplugging the battery immediately after charging, which affects the accuracy of the trained model.

Optionally, the number of samples in the training sample set in method 300 and method 400 may be the same or different, which is not limited in this embodiment of the present application. Optionally, the preset number of training samples in method 300 and method 400 may be the same or different, which is not limited in this embodiment of the present application. Optionally, the charging parameters included in the samples in the training sample set in method 300 and method 400 may be the same or different, which is not limited in this embodiment of the present application.

It should also be understood that the method 300 and the method 400 are two independent embodiments, and the basic prediction model required in the process of training the weight model in the method 400 may be obtained according to the method 300, or may be a plurality of preset basic predictions. The model may also be multiple basic prediction models indicated by the cloud, which is not limited in this embodiment of the present application.

The method 500 for charging management and control is described below with reference to the multiple basic prediction models obtained by the method 300 and the weight model obtained by the method 400, and the method 500 includes:

S510, the electronic device acquires first charging data.

It should be noted that the first charging data may be a set of charging data in the method 300 and the method 400 .

The first charging data can be discussed in the following cases.

Case 1, the electronic device has collected raw charging data, and the raw charging data satisfies the input of the weight model of S520 and the input of multiple basic prediction models of S530. In this case, the first charging data is the collected raw charging data. Accordingly, the electronic device saves the collected raw charging data.

In case 2, the electronic device has collected the original charging data, and the original charging data does not satisfy the input of the weight model of S520 and the input of multiple basic prediction models of S530. Charge data after processing. Correspondingly, the electronic device stores the pre-processed charging data.

Case 3: The electronic device collects raw charging data, and the raw charging data does not meet the input of the weight model of S520 and the input of multiple basic prediction models of S530. In this case, the electronic device can save the collected raw charging data, and the electronic The device may preprocess the collected raw charging data, and the preprocessed charging data obtained is the first charging data. Optionally, the electronic device may save the first charging data. In other words, in case three, the electronic device can only save the collected raw charging data, and when necessary, preprocess the collected raw charging data to obtain the first charging data, or the electronic device can save both the collected raw data and The preprocessed first charging data may be saved.

In the above-mentioned cases 2 and 3, the preprocessing of the original charging data includes at least one normalization process among operations such as denoising, quantization, and normalization.

For example, taking the noise removal point as an example, if the first charging data includes the historical insertion time and historical removal time of the charger, the difference between the historical removal time and the historical insertion time of the charger is greater than the preset duration, in other words, The first charging data is the charging data generated by the user's actual charging behavior that lasts for at least a preset period of time, to avoid that the first charging data is the charging data generated by the user's charging habit of unplugging immediately after charging, which affects the accuracy of prediction.

S520, the electronic device inputs at least part of the charging data in the first charging data into a plurality of basic prediction models, and determines the first predicted charging duration corresponding to each basic prediction model.

Optionally, S530 includes: inputting at least part of the charging data in the first charging data into a plurality of basic prediction models to obtain a plurality of third predicted charging durations, and each basic prediction model can output a third predicted charging duration , each basic prediction model has a corresponding adjustment parameter, and the third predicted charging duration output by each basic prediction model is adjusted by using the adjustment parameter corresponding to each basic prediction model, thereby obtaining the first predicted charging duration. The third predicted charging duration obtained by each basic prediction model may be the same or different. In other words, in this embodiment, the first predicted charging duration output by each basic prediction model is the predicted charging duration adjusted according to the adjustment parameter, so that the prediction accuracy can be higher.

S530, the electronic device inputs at least part of the charging data in the first charging data into the weighting model to obtain a plurality of weighting coefficients.

Optionally, at least two of the multiple weighting coefficients are the same, or at least two of the multiple weighting coefficients are different.

Specifically, the method 900 will describe how to obtain the corresponding adjustment parameters in the multiple basic prediction models, which will not be described in detail here in order to avoid repetition.

It should be noted that, at least part of the charging data input to the weight model in S530 and at least part of the charging data input to the multiple basic prediction models in S230 may be the same or different, which is not limited in this embodiment of the present application.

It should also be noted that, at least part of the charging data input into each basic prediction model in S520 may be the same or different, which is not limited in this embodiment of the present application.

It can be understood that there is no restriction on the sequence of S520 and S530, and S520 may be performed before, after or at the same time as S530, which is not limited in this embodiment of the present application.

S540, the electronic device determines a second predicted charging duration according to the multiple weight coefficients and the first predicted charging duration corresponding to each basic prediction model, where the second predicted charging duration is used to manage and control the charging of the electronic device.

Exemplarily, the second predicted charging duration may be t3 shown in FIG. 1 , so that the electronic device can be managed and controlled according to the control strategy shown in FIG. 1 .

Specifically, S540 includes: performing a weighted calculation on the first predicted charging duration corresponding to each basic prediction model with the multiple weighting coefficients, respectively, to obtain the second predicted charging duration. Exemplarily, the _L weighting coefficients output by _S530 are ω ₁ , _{ω 2} , . ..., K _L , then the second predicted charging duration is ω ₁ K ₁ +ω ₂ K ₂ +...+ω _L K _L , where L is a preset value.

In the process of predicting the charging duration in the embodiment of the present application, the electronic device obtains a plurality of weight coefficients according to the weight model. The electronic device obtains the first predicted charging duration corresponding to each basic prediction model according to the multiple basic prediction models, and determines the second predicted charging duration according to the multiple weight coefficients and the first predicted charging duration corresponding to each basic prediction model, and the electronic device determines the second predicted charging duration according to the multiple weight coefficients and the first predicted charging duration corresponding to each basic prediction model. The second predicting the charging time is to manage and control the charging of the electronic device, so as to prolong the battery life and improve the battery life, which helps to improve the user experience.

Optionally, the weight model in the above method 500 may be preset, for example, the weight model may be preset in the processor of the electronic device when the electronic device is shipped from the factory. Optionally, the weight model in the above method 500 may be obtained by training according to the method 400 . Regardless of whether the above-mentioned weight model is obtained by electronic equipment training or preset, in the process of predicting the charging duration, the weight model can be modified. Specifically, the method 500 may be executed multiple times to input at least part of the data in the multiple sets of charging data into multiple basic prediction models obtained by the method 300 and weight models obtained by the method 400, and output multiple second predicted charging durations, which will generate Multiple samples, each of which includes a set of charging data, a second predicted charging duration corresponding to the set of charging data, and an actual charging duration corresponding to the set of charging. The following describes the process of revising the weight model in two cases.

In the first case, the electronic device corrects the weight model according to the samples in the first sample set. As shown in Figure 6, the first case specifically includes:

S610, the electronic device determines a qualified sample among the multiple samples output by the method 500.

Specifically, if the absolute value of the difference between the first actual charging duration and the second predicted charging duration in a sample is less than the preset duration value, for example, the preset duration value is 60min, then the sample is a qualified sample, also referred to as Positive samples, otherwise unqualified samples, also known as negative samples.

Optionally, the qualified samples need to satisfy that the absolute value of the difference between the first actual charging duration and the second predicted charging duration is smaller than the preset duration value, and the second predicted charging duration of the qualified samples needs to be greater than the preset duration, In other words, the qualified samples are the actual charging behavior of the user that lasts for at least a preset period of time, avoiding some samples caused by the user's charging habit of unplugging immediately after charging, which affects the accuracy of the correction weight model.

S620, the electronic device determines whether the first pass rate under the current weight model of the sample is greater than or equal to the first pass rate preset value, for example, the first pass rate preset value is 95%, if it is greater than or equal to the first pass rate preset value Then go to S630, otherwise go to S640.

Among them, the first pass rate is the number of qualified samples/total number of samples.

It can be understood that, in S620, determining whether the pass rate of the samples reaches the first pass rate preset value can be replaced by determining whether the fail rates of the samples all reach the fail rate preset value, for example, the fail rate preset value is 5. %, if the preset value of the failure rate is reached, in S640, if the preset value of the failure rate is not reached, then execute S630.

S630, the electronic device does not correct the weight model.

S640, the electronic device determines whether the number of positive samples is greater than the preset value of the first number of samples, for example, the preset value of the first number of samples is 500, if it is greater than the preset value of the first number of samples, execute S640, if not less than or If it is equal to the preset value of the first number of samples, return to S610, and the method 500 can continue to input samples to S610.

S650, the electronic device uses some of the qualified samples to train the first correction parameter of the weight model, and uses the first correction parameter to correct the weight model to obtain the corrected weight model.

Exemplarily, if the number of qualified samples is 500, then the first correction parameter of the weight model can be trained by using 300 qualified samples.

Specifically, S650 includes: performing the process of method 500 on each set of charging data in the qualified samples, obtaining a second predicted charging duration corresponding to each set of charging data and the actual charging duration under each set of charging data, and comparing the second predicted charging duration For the charging duration and the actual charging duration, the first correction parameter of the weight model is repeatedly determined according to the comparison result.

Further, when the electronic device uses some of the qualified samples to train the first correction parameter of the weighting model, it can compare the actual charging duration under each set of charging data with the first predicted charging duration output by each basic prediction model, The first predicted charging duration output by the basic prediction model among the multiple weighting coefficients output after the first correction parameter modifies the weighting model is closer to the actual charging duration, and the higher the weighting coefficient of the corresponding basic prediction model is. For example, the actual charging time under the charging data 1 is 10 hours, the first predicted charging time output by the basic prediction model 1 is 9.5 hours, and the first predicted charging time output by the basic prediction model 2 is 12 hours, then the first correction After the parameters modify the weight model, the weight coefficient output by the weight model multiplied by the basic prediction model 1 is higher than the weight coefficient multiplied by the basic prediction model 2. For example, the weight coefficient multiplied by the basic prediction model 1 is 0.8, and the The weight factor for multiplying prediction model 2 is 0.2.

S660, the electronic device uses the remaining qualified samples in the qualified samples to test the qualified rate of the samples generated by the revised weighting model, and then judges the qualified rate according to S620. If the pass rate satisfies the preset value of the pass rate, the weight model is stable, and the weight model in the method 500 can be modified to the weight model obtained according to FIG. 6 .

For example, if the number of qualified samples is 500, 300 qualified samples can be used to train the first correction parameter of the weight model, and the remaining 200 qualified samples can be used to test the stability of the weight model modified according to the first correction parameter.

In the above method of revising the weight model in FIG. 6 , when the first pass rate is not satisfied, the electronic device can determine the first revision parameter for revising the first weight model, thereby ensuring the accuracy of the weight model.

In case 2, the electronic device obtains the first correction parameter according to the samples in the first sample set, and sends the first correction parameter to the cloud, and the cloud fits the second correction parameter according to the first correction parameter of the plurality of electronic devices. The specific steps are shown in Figure 7.

S710-S750, same as S610-S650.

After each electronic device in the plurality of electronic devices finishes executing S710-S750, the first correction parameter obtained by each electronic device is sent to the cloud.

S760, the cloud will receive multiple first correction parameters sent by multiple electronic devices, and at least some of the multiple first correction parameters may be the same or different. The correction parameter is fitted to generate a second correction parameter, and the second correction parameter generated by the fitting is sent to the electronic device.

S770, the electronic device corrects the weight model according to the second correction parameter to obtain the corrected weight model.

S780, the electronic device uses the remaining positive samples in the positive samples to test the pass rate of the samples generated by the revised weight model, and then judges the pass rate according to S720. If the pass rate satisfies the preset value of the pass rate, the weight model is stable. Optionally, the weight model in the method 500 may be modified to the weight model obtained according to FIG. 7 .

The above-mentioned method of correcting the weight model in FIG. 7 can simplify the complexity of the correction model of the electronic device. Each electronic device sends the first correction parameter obtained by itself to the cloud. The first correction parameter is the correction parameter of the weight model, without any The information of the user of the electronic device can also protect the privacy of the user, which is beneficial to improve the security. At the same time, the second correction parameter determined by the cloud using big data can meet the requirements of robustness.

It should be noted that, in the method of correcting the weight model in FIGS. 6 and 7 , different weight models correspond to different first correction parameters, and the first correction parameter or the second correction parameter is used to adjust the model parameters of the weight model.

The above-mentioned Fig. 3 is the process of training a plurality of basic prediction models, Fig. 4 is the process of training the weight model, Fig. 5 utilizes the model of Fig. 3 and Fig. The process of the weight model. Similarly, the prediction durations output by the multiple basic prediction models obtained in FIG. 3 can also be revised. The following describes the process of revising the prediction durations output by the multiple basic prediction models in conjunction with FIG. 8 . FIG. 8 can be an independent implementation. For example, it may also be an embodiment combined with the foregoing method, which is not limited in this embodiment of the present application.

FIG. 8 shows a method 800 for charging control according to an embodiment of the present application, including:

S810, the electronic device acquires first charging data.

The first charging data may be the charging parameters included in the first charging data of the aforementioned method 500 , and the value of the charging parameters included in the first charging data in the method 800 may be different from the charging parameters included in the first charging data in the method 500 . The values are the same or different, and are not described in detail here in order to avoid redundant description.

S820, the electronic device inputs at least part of the charging data in the first charging data into the first basic prediction model to obtain a third predicted charging duration.

Optionally, the first basic prediction model may be preset and stored in the electronic device, or trained by the cloud according to the method 300 and instructed to the electronic device, or trained by the electronic device itself according to the method 300, this application implements. The example does not limit this.

S830, the electronic device adjusts the third predicted charging duration according to the adjustment parameter corresponding to the first basic prediction model to obtain the first predicted charging duration.

In a possible implementation manner, the method 800 is an independent embodiment, and the first predicted charging duration obtained by the method 800 can be used to control the charging of the electronic device, since the third predicted charging output by the first basic prediction model The duration may be quite different from the actual charging duration. Therefore, the adjustment parameters corresponding to the first basic prediction model can be used to adjust the third predicted charging duration, and the adjusted first predicted charging duration can be used to control the charging of the electronic device, thereby improving the prediction. accuracy. In this case, the first charging data in the method 800 may be the same as or different from the first charging data in the previous embodiments, and similarly, the first predicted charging duration in the method 800 may be the same as the first predicted charging in the previous embodiments The durations are the same or different, which are not limited in this embodiment of the present application.

In another possible implementation manner, the embodiments of the method 800 and the method 500 may be combined, the method 800 may be used to obtain a first predicted charging duration output by the first basic prediction model, and the method 800 may be used to output multiple basic prediction models A first predicted charging duration corresponding to each basic prediction model is then used as the first predicted charging duration in S530 in method 500 .

Regardless of the above-mentioned implementation manner, the electronic device needs to obtain the adjustment parameters of each basic prediction model in the multiple basic prediction models. The following describes how to obtain the adjustment parameters corresponding to the first basic prediction model with reference to FIG. 9 . FIG. 9 Only the adjustment parameters corresponding to the first basic prediction model are obtained as an example for description. The adjustment parameters corresponding to other basic prediction models are similar to the adjustment parameters corresponding to the acquisition of the first basic prediction model. an enumeration.

The electronic device acquires the second charging data and the actual charging duration corresponding to the second charging data, the electronic device inputs the second charging data into the first basic prediction model, and outputs a fourth predicted charging duration, where the first basic prediction model can be is trained according to method 300. The electronic device adds the second charging data, the fourth predicted charging duration, and the second actual charging duration as one sample to the second sample set, and so on to determine the samples in the second sample set.

S910, the electronic device determines the qualified samples in the second sample set.

Specifically, if the absolute value of the difference between the second actual charging duration and the fourth predicted charging duration in a sample is less than the preset duration value, for example, the preset duration value is 60 minutes, then the sample is a qualified sample, also referred to as Positive samples, otherwise unqualified samples, also known as negative samples.

Optionally, the qualified samples need to satisfy that the absolute value of the difference between the second actual charging duration and the fourth predicted charging duration is less than the preset duration value, and the fourth predicted charging duration of the qualified samples needs to be greater than the preset value, In other words, a qualified sample is the actual charging behavior of the user that lasts for at least a preset period of time, avoiding some samples caused by the user's charging habit of unplugging the battery immediately after charging, which affects the accuracy.

S920, the electronic device determines whether the second pass rate of the sample is greater than or equal to the second pass rate preset value, if the second pass rate is greater than or equal to the second pass rate preset value, execute S930, otherwise execute S940.

Among them, the second pass rate is the number of qualified samples/total number of samples.

It can be understood that, in S920, determining whether the pass rate of the sample reaches the second pass rate preset value can be replaced by determining whether the fail rate of the sample reaches the fail rate preset value, for example, the fail rate preset value is 5%. , if the preset value of the unqualified rate is reached, go to S940, and if the preset value of the unqualified rate is not reached, go to S930.

S930, the electronic device does not update the adjustment parameters corresponding to the first basic prediction model.

It can be understood that the initial adjustment parameter may be a preset value or a value obtained according to historical experience data, which is not limited in this embodiment of the present application.

S940, the electronic device determines whether the number of qualified samples is greater than the second preset value of the number of samples, and if it is greater, executes S950, otherwise executes S910 to determine whether there are new qualified samples.

S950, the electronic device uses some qualified samples to train the adjustment parameters of the first basic prediction model.

In a possible implementation, if the second actual charging duration and the fourth predicted charging duration in the partially qualified samples satisfy a linear relationship, then the partially qualified samples are used for linear fitting to obtain the regression coefficient and constant of the linear relationship, namely Tuning parameters for the first base prediction model.

For example, the second actual charging duration is y, and the fourth predicted charging duration is x. If x and y satisfy y=kx+b, the value of the regression coefficient k and the value of the constant b are obtained in S950.

In another possible implementation, if the second actual charging duration and the fourth predicted charging duration in the partially qualified samples satisfy a nonlinear relationship, the partially qualified samples are used for nonlinear training to obtain the coefficients of the nonlinear relationship It is the adjustment parameter of the first basic prediction model.

S960, the electronic device updates the adjustment parameters of the first basic prediction model.

S970, the electronic device uses the remaining part of the qualified samples to test the stability of the updated adjustment parameters.

Exemplarily, if the second actual charging duration and the fourth predicted charging duration in the partially qualified samples satisfy a linear relationship, specifically, the second actual charging duration is y, and the fourth predicted charging duration is x, if x and y satisfy y=kx+b, after the above k and b satisfy the test threshold [0<k<M and b2<N], it means that the adjustment parameters k and b are stable, where M and N are preset values.

It should be noted that, in this embodiment of the present application, "greater than" can be replaced with "greater than or equal to", "less than or equal to" can be replaced with "less than", or "greater than or equal to" can be replaced with "greater than", "Less than" can be replaced with "less than or equal to". For example, if greater than A, perform step x, and less than or equal to A, perform step y, you can replace it with: greater than or equal to A, perform step x, and less than A, perform step y, in other words, when it is equal to A, you can perform step x and also perform Step y is not limited in this embodiment of the present application.

The various embodiments described herein may be independent solutions, or may be combined according to internal logic, and these solutions all fall within the protection scope of the present application.

It can be understood that, the methods and operations implemented by the electronic device in each of the above method embodiments may also be implemented by a component (eg, a chip or a circuit) that can be used in an electronic device.

The method embodiments provided by the present application are described above, and the device embodiments provided by the present application will be described below. It should be understood that the description of the apparatus embodiment corresponds to the description of the method embodiment. Therefore, for the content not described in detail, reference may be made to the above method embodiment, which is not repeated here for brevity.

Those skilled in the art should realize that the present application can be implemented in hardware or a combination of hardware and computer software with the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein. Whether a function is performed by hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Skilled artisans may use different methods to implement the described functions for each particular application, but such implementations should not be considered outside the scope of protection of this application.

In the embodiments of the present application, the electronic device may be divided into functional modules according to the foregoing method examples. For example, each functional module may be divided corresponding to each function, or two or more functions may be integrated into one processing module. The above-mentioned integrated modules can be implemented in the form of hardware, and can also be implemented in the form of software function modules. It should be noted that the division of modules in the embodiments of the present application is illustrative, and is only a logical function division, and other feasible division manners may be used in actual implementation. The following description will be given by taking as an example that each function module is divided corresponding to each function.

FIG. 10 is a schematic block diagram of an apparatus 1000 for charging management and control provided by an embodiment of the present application. The apparatus 1000 includes an acquisition unit 1010 and a processing unit 1020 . The acquisition unit 1010 can communicate with the outside. The acquiring unit 1010 may also be referred to as a communication interface or a communication unit, and the acquiring unit 1010 is configured to perform the operations related to acquiring or sending and receiving on the side of the electronic device in the embodiments of FIG. 5 to FIG. 7 or FIG. 9 above. The processing unit 1020 is configured to perform data processing, and the processing unit 1020 is configured to perform the processing-related operations on the electronic device side in the embodiments of FIG. 5 to FIG. 7 or FIG. 9 above.

an obtaining unit 1010, configured to obtain first charging data;

a processing unit 1020, configured to input at least part of the charging data in the first charging data into a plurality of basic prediction models, and determine a first predicted charging duration corresponding to each basic prediction model;

The processing unit 1020 is further configured to input at least part of the charging data in the first charging data into a weighting model to obtain a plurality of weighting coefficients;

The processing unit 1020 is further configured to determine a second predicted charging duration according to the multiple weight coefficients and the first predicted charging duration corresponding to each basic prediction model, where the second predicted charging duration is used for charging the electronic device. Charging is controlled.

As an optional embodiment, the obtaining unit 1010 is further configured to: obtain the first actual charging duration of the electronic device corresponding to the first charging data;

The processing unit 1020 is also used for:

adding the first charging data, the first actual charging duration and the second predicted charging duration as samples to a first sample set;

The weight model is updated according to the samples in the first sample set.

As an optional embodiment, the processing unit 1020 is specifically configured to:

determining a first pass rate of the samples in the first sample set;

If the first pass rate is less than a preset value of the first pass rate, determining the number of qualified samples in the first sample set;

If the number of qualified samples in the first sample set is greater than the preset value of the first number of samples, the weight model is modified according to some of the qualified samples.

As an optional embodiment, the processing unit 1020 is specifically configured to:

The first correction parameter is obtained according to the partial sample training, and the weight model is corrected according to the first correction parameter to obtain the corrected weight model; or the method includes:

Obtain the first correction parameter according to the partial sample training;

The apparatus 1000 further includes:

A transceiver unit 1010, configured to send the first correction parameter to the cloud, and receive the second correction parameter determined by the cloud according to the first correction parameter;

The processing unit 1020 is further configured to correct the weight model according to the second correction parameter to obtain a corrected weight model.

As an optional embodiment, the processing unit 1020 is further configured to: test the stability of the modified weight model according to the remaining part of the qualified samples.

As an optional embodiment, the processing unit 1020 is specifically configured to: input at least part of the charging data in the first charging data into the multiple basic prediction models, to obtain a third corresponding to each basic prediction model. Predict the charging time;

The third predicted charging duration corresponding to each basic prediction model is adjusted by using the adjustment parameters of each basic prediction model, so as to obtain the first predicted charging duration corresponding to each basic prediction model.

As an optional embodiment, the obtaining unit 1010 is further configured to: obtain second charging data of the electronic device and a second actual charging duration corresponding to the second charging data;

The processing unit 1020 is also used for:

inputting the second charging data into a first basic prediction model of the plurality of basic prediction models to obtain a fourth predicted charging duration;

adding the fourth predicted charging duration, the second charging data, and the second actual charging duration as samples to a second sample set;

The adjustment parameters corresponding to the first basic prediction model are determined according to the samples in the second sample set.

As an optional embodiment, the processing unit 1020 is specifically configured to:

determining a second pass rate for the samples in the second sample set;

If the second pass rate is less than a preset value of the second pass rate, determining the number of qualified samples in the second sample set;

If the number of qualified samples in the second sample set is greater than the preset value of the second number of samples, the adjustment parameters corresponding to the first basic prediction model are determined according to the qualified samples.

As an optional embodiment, if the actual charging duration and the predicted charging duration corresponding to the qualified samples in the second sample set satisfy a linear relationship, the adjustment parameter corresponding to the first basic prediction model is the regression coefficient of the linear relationship and constant.

As an optional embodiment, the first charging data and the second charging data include at least one of the following: the type of the charger used to charge the electronic device, the manufacturer of the battery that produces the electronic device, the the nominal capacity of the battery, the type of cells of the battery, the type of charging cable used to charge the electronic device, the number of cycles the battery has been charged, the nominal number of cycles the battery can be charged , the average internal resistance of the battery, the maximum internal resistance of the battery, the historical insertion time and historical extraction time of the charger, the charging cut-off time of the battery cells, the starting time of the battery cells The starting and ending power levels, and the actual charging time for a preset period of time each day within a preset number of days.

FIG. 11 is a schematic block diagram of another apparatus 1100 for charging management and control provided by an embodiment of the present application. The apparatus 1100 includes an acquisition unit 1110 and a processing unit 1120 . The acquisition unit 1110 can communicate with the outside. The acquiring unit 1110 may also be referred to as a communication interface or a communication unit, and the acquiring unit 1110 is configured to perform the operations related to acquiring or transceiving on the electronic device side in the embodiment of FIG. 8 or FIG. 9 above. The processing unit 1120 is configured to perform data processing, and the processing unit 1120 is configured to perform the processing-related operations on the electronic device side in the embodiment of FIG. 8 or FIG. 9 above.

Wherein, the obtaining unit 1110 is configured to obtain the first charging data. The processing unit 1120 is configured to: input at least part of the charging data in the first charging data into a first basic prediction model to obtain a third prediction duration; adjust the third prediction according to adjustment parameters corresponding to the first basic prediction For the charging duration, a first predicted charging duration is obtained, and the first predicted charging duration is used to manage and control the charging of the electronic device.

As an optional embodiment, the acquiring unit 1110 is further configured to: acquire second charging data of the device and a second actual charging duration corresponding to the second charging data. The processing unit 1120 is further configured to: input the second charging data into the first basic prediction model to obtain a fourth predicted charging duration; and combine the fourth predicted charging duration, the second charging data and the The second actual charging duration is added as a sample to the second sample set; the adjustment parameters corresponding to the first basic prediction model are determined according to the samples in the second sample set.

As an optional embodiment, the processing unit 1120 is specifically configured to:

determining a second pass rate for the samples in the second sample set;

If the second pass rate is less than a preset value of the second pass rate, determining the number of qualified samples in the second sample set;

If the number of qualified samples in the second sample set is greater than the preset value of the second number of samples, the adjustment parameters corresponding to the first basic prediction model are determined according to the qualified samples.

As an optional embodiment, if the actual charging duration and the predicted charging duration corresponding to the qualified samples in the second sample set satisfy a linear relationship, the adjustment parameter corresponding to the first basic prediction model is the regression coefficient of the linear relationship and constant.

As an optional embodiment, the first charging data and the second charging data include at least one of the following: a type of a charger used to charge the device, a manufacturer of a battery that produces the device, the battery the nominal capacity of the battery, the type of cells of the battery, the type of charging cable used to charge the device, the number of cycles the battery has been charged, the nominal number of cycles the battery can be charged, the The average internal resistance of the battery, the maximum internal resistance of the battery, the historical insertion time and historical extraction time of the charger, the charging cut-off time of the battery cells, the initial charge of the battery cells and Terminate power, the actual charging time for a preset period of time every day within a preset number of days.

FIG. 12 is a schematic structural diagram of an apparatus 1200 for charging management and control provided by an embodiment of the present application. The communication device 1200 includes: a processor 1210 , a memory 1220 , a communication interface 1230 , and a bus 1240 .

In a possible implementation manner, the processor 1210 in the apparatus 1200 shown in FIG. 12 may correspond to the processing unit 1020 in the apparatus 1000 in FIG. 10 . The communication interface 1230 in the apparatus 1200 shown in FIG. 12 may correspond to the obtaining unit 1010 in the apparatus 1000 in FIG. 10 .

In a possible implementation manner, the processor 1210 in the apparatus 1200 shown in FIG. 12 may correspond to the processing unit 1120 in the apparatus 1100 in FIG. 11 . The communication interface 1230 in the apparatus 1200 shown in FIG. 12 may correspond to the obtaining unit 1110 in the apparatus 1100 in FIG. 11 .

Wherein, the processor 1210 can be connected with the memory 1220 . The memory 1220 may be used to store the program codes and data. Therefore, the memory 1220 may be a storage unit inside the processor 1210 , or an external storage unit independent of the processor 1210 , or may include a storage unit inside the processor 1210 and an external storage unit independent of the processor 1210 . part.

Optionally, the apparatus 1200 may further include a bus 1240 . The memory 1220 and the communication interface 1230 may be connected to the processor 1210 through the bus 1240 . The bus 1240 may be a peripheral component interconnect (PCI) bus, an extended industry standard architecture (EISA) bus, or the like. The bus 1240 can be divided into an address bus, a data bus, a control bus, and the like. For ease of representation, only one line is shown in FIG. 12, but it does not mean that there is only one bus or one type of bus.

It should be understood that, in this embodiment of the present application, the processor 1210 may adopt a central processing unit (central processing unit, CPU). The processor may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), or other Programming logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. Alternatively, the processor 1210 uses one or more integrated circuits to execute related programs, so as to implement the technical solutions provided by the embodiments of the present application.

The memory 1220 , which may include read-only memory and random access memory, provides instructions and data to the processor 810 . A portion of processor 810 may also include non-volatile random access memory. For example, the processor 810 may also store device type information.

When the apparatus 1200 is running, the processor 1210 executes the computer-executable instructions in the memory 1220 to perform the operation steps of the above-described methods by the apparatus 1200 .

It should be understood that the device 1200 according to the embodiment of the present application may correspond to the device 1000 and the device 1100 in the embodiment of the present application, and the above-mentioned and other operations and/or functions of the respective units in the device 1000 and the device 1100 are for the purpose of implementing the method, respectively. For the sake of brevity, the corresponding process is not repeated here.

Optionally, in some embodiments, the embodiments of the present application further provide a computer-readable medium, where the computer-readable medium stores program codes, and when the computer program codes are executed on a computer, the computer executes the program code. The method of the above aspects.

Optionally, in some embodiments, the embodiments of the present application further provide a computer program product, the computer program product includes: computer program code, when the computer program code is executed on a computer, the computer program code enables the computer to execute the above-mentioned methods in various aspects.

In this embodiment of the present application, the terminal device or the network device includes a hardware layer, an operating system layer running on the hardware layer, and an application layer running on the operating system layer. The hardware layer may include hardware such as a central processing unit (central processing unit, CPU), a memory management unit (memory management unit, MMU), and memory (also referred to as main memory). The operating system of the operating system layer may be any one or more computer operating systems that implement business processing through processes, such as a Linux operating system, a Unix operating system, an Android operating system, an iOS operating system, or a Windows operating system. The application layer may include applications such as browsers, address books, word processing software, and instant messaging software.

The embodiments of the present application do not specifically limit the specific structure of the execution body of the methods provided by the embodiments of the present application, as long as the program in which the codes of the methods provided by the embodiments of the present application are recorded can be executed to execute the methods according to the embodiments of the present application. Just communicate. For example, the execution body of the method provided by the embodiment of the present application may be a terminal device or a network device, or a functional module in the terminal device or network device that can call a program and execute the program.

Various aspects or features of the present application may be implemented as methods, apparatus, or articles of manufacture using standard programming and/or engineering techniques. The term "article of manufacture" as used herein may encompass a computer program accessible from any computer-readable device, carrier or media. For example, computer-readable media may include, but are not limited to, magnetic storage devices (eg, hard disks, floppy disks, or magnetic tapes, etc.), optical disks (eg, compact discs (CDs), digital versatile discs (DVDs), etc. ), smart cards and flash memory devices (eg, erasable programmable read-only memory (EPROM), cards, stick or key drives, etc.).

Various storage media described herein may represent one or more devices and/or other machine-readable media for storing information. The term "machine-readable medium" may include, but is not limited to, wireless channels and various other media capable of storing, containing, and/or carrying instructions and/or data.

It should be understood that the processor mentioned in the embodiments of the present application may be a central processing unit (central processing unit, CPU), and may also be other general-purpose processors, digital signal processors (digital signal processors, DSP), application specific integrated circuits (application specific integrated circuits) integrated circuit, ASIC), off-the-shelf programmable gate array (field programmable gate array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, and the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.

It should also be understood that the memory mentioned in the embodiments of the present application may be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory may be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically programmable Erase programmable read-only memory (electrically EPROM, EEPROM) or flash memory. Volatile memory may be random access memory (RAM). For example, RAM can be used as an external cache. By way of example and not limitation, RAM may include the following forms: static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM) , double data rate synchronous dynamic random access memory (double data rate SDRAM, DDR SDRAM), enhanced synchronous dynamic random access memory (enhanced SDRAM, ESDRAM), synchronous link dynamic random access memory (synchlink DRAM, SLDRAM) and direct Memory bus random access memory (direct rambus RAM, DR RAM).

It should be noted that when the processor is a general-purpose processor, DSP, ASIC, FPGA or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components, the memory (storage module) can be integrated in the processor.

It should also be noted that the memory described herein is intended to include, but not be limited to, these and any other suitable types of memory.

Those of ordinary skill in the art can realize that the units and steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Skilled artisans may use different methods to implement the described functions for each particular application, but such implementations should not be considered outside the scope of protection of this application.

Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the above-described systems, devices and units may refer to the corresponding processes in the foregoing method embodiments, which will not be repeated here.

In the several embodiments provided in this application, it should be understood that the disclosed system, apparatus and method may be implemented in other manners. For example, the apparatus embodiments described above are only illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components may be combined or Can be integrated into another system, or some features can be ignored, or not implemented. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be through some interfaces, indirect coupling or communication connection of devices or units, which may be in electrical, mechanical or other forms.

The units described as separate components may or may not be physically separated, and components displayed as units may or may not be physical units, that is, may be located in one place, or may be distributed to multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution in this embodiment.

In addition, each functional unit in each embodiment of the present application may be integrated into one unit, or each unit may exist physically alone, or two or more units may be integrated into one unit.

The functions, if implemented in the form of software functional units and sold or used as independent products, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a computer software product, and the computer software product is stored in a storage In the medium, the computer software product includes several instructions, the instructions are used to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage medium may include, but is not limited to: U disk, removable hard disk, read-only memory (read-only memory, ROM), random access memory (random access memory, RAM), magnetic disk or optical disk, etc. medium of code.

Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the technical field to which this application belongs. The terms used herein in the specification of the application are for the purpose of describing specific embodiments only, and are not intended to limit the application.

The above are only specific embodiments of the present application, but the protection scope of the present application is not limited to this. should be covered within the scope of protection of this application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims (22)

1. A method for charging control, comprising: obtain the first charging data; inputting at least part of the charging data in the first charging data into a plurality of basic prediction models, and determining a first predicted charging duration corresponding to each basic prediction model; inputting at least part of the charging data in the first charging data into a weighting model to obtain a plurality of weighting coefficients; A second predicted charging duration is determined according to the multiple weighting coefficients and the first predicted charging duration corresponding to each basic prediction model, and the second predicted charging duration is used to manage and control the charging of the electronic device. 2. The method according to claim 1, wherein the method further comprises: acquiring the first actual charging duration of the electronic device corresponding to the first charging data; adding the first charging data, the first actual charging duration and the second predicted charging duration as samples to a first sample set; The weight model is updated according to the samples in the first sample set. 3. The method according to claim 2, wherein the updating the weight model according to the samples in the first sample set comprises: determining a first pass rate of the samples in the first sample set; If the first pass rate is less than a preset value of the first pass rate, determining the number of qualified samples in the first sample set; If the number of qualified samples in the first sample set is greater than the preset value of the first number of samples, the weight model is modified according to some of the qualified samples. 4. The method according to claim 3, wherein the modifying the weight model according to some samples in the qualified samples comprises: The first correction parameter is obtained according to the partial sample training, and the weight model is corrected according to the first correction parameter to obtain the corrected weight model; or the method includes: Obtain the first correction parameter according to the partial sample training, and send the first correction parameter to the cloud; receiving a second correction parameter determined by the cloud according to the first correction parameter; The weight model is modified according to the second modification parameter to obtain a modified weight model. 5. The method according to claim 4, wherein the method further comprises: The stability of the revised weight model is tested against the remaining part of the qualified samples. 6. The method according to any one of claims 1 to 5, characterized in that, inputting at least part of the charging data in the first charging data into a plurality of basic prediction models to obtain each basic prediction The first predicted charging duration corresponding to the model, including: inputting at least part of the charging data in the first charging data into the plurality of basic prediction models to obtain a third predicted charging duration corresponding to each basic prediction model; The third predicted charging duration corresponding to each basic prediction model is adjusted by using the adjustment parameters of each basic prediction model, so as to obtain the first predicted charging duration corresponding to each basic prediction model. 7. The method according to claim 6, wherein the method further comprises: acquiring second charging data of the electronic device and a second actual charging duration corresponding to the second charging data; inputting the second charging data into a first basic prediction model of the plurality of basic prediction models to obtain a fourth predicted charging duration; adding the fourth predicted charging duration, the second charging data, and the second actual charging duration as samples to a second sample set; The adjustment parameters corresponding to the first basic prediction model are determined according to the samples in the second sample set. 8. The method according to claim 7, wherein the determining the adjustment parameters corresponding to the first basic prediction model according to the samples in the second sample set, comprising: determining a second pass rate for the samples in the second sample set; If the second pass rate is less than a preset value of the second pass rate, determining the number of qualified samples in the second sample set; If the number of qualified samples in the second sample set is greater than the preset value of the second number of samples, the adjustment parameters corresponding to the first basic prediction model are determined according to the qualified samples. 9 . The method according to claim 8 , wherein if the actual charging duration and the predicted charging duration corresponding to the qualified samples in the second sample set satisfy a linear relationship, the adjustment parameter corresponding to the first basic prediction model is: 10 . Regression coefficients and constants for the linear relationship. 10. The method according to any one of claims 1 to 9, wherein the first charging data and the second charging data comprise at least one of the following: Type of charger used to charge the electronic device, manufacturer of the battery of the electronic device, nominal capacity of the battery, cell type of the battery, used to charge the electronic device The type of charging cable, the number of cycles the battery has been charged, the nominal number of cycles the battery can be charged, the average internal resistance of the battery, the maximum internal resistance of the battery, the historical insertion of the charger Time and historical pull-out time, charging cut-off time of the cells of the battery, starting and ending power of the cells of the battery, and actual charging duration in a preset time period every day within a preset number of days. 11. A device for charging control, characterized in that it comprises: an acquisition unit for acquiring the first charging data; a processing unit, configured to input at least part of the charging data in the first charging data into a plurality of basic prediction models, and determine the first predicted charging duration corresponding to each basic prediction model; The processing unit is further configured to input at least part of the charging data in the first charging data into the weighting model to obtain a plurality of weighting coefficients; The processing unit is further configured to determine a second predicted charging duration according to the multiple weighting coefficients and the first predicted charging duration corresponding to each of the basic prediction models, where the second predicted charging duration is used for charging the device. Charging is controlled. 12. The apparatus according to claim 11, wherein the acquiring unit is further configured to: acquiring the first actual charging duration of the device corresponding to the first charging data; The processing unit is also used to: adding the first charging data, the first actual charging duration and the second predicted charging duration as samples to a first sample set; The weight model is updated according to the samples in the first sample set. 13. The apparatus according to claim 12, wherein the processing unit is specifically configured to: determining a first pass rate of the samples in the first sample set; If the first pass rate is less than a preset value of the first pass rate, determining the number of qualified samples in the first sample set; If the number of qualified samples in the first sample set is greater than the preset value of the first number of samples, the weight model is modified according to some of the qualified samples. 14. The apparatus according to claim 13, wherein the processing unit is specifically configured to: The first correction parameter is obtained according to the partial sample training, and the weight model is corrected according to the first correction parameter to obtain the corrected weight model; or the method includes: Obtain the first correction parameter according to the partial sample training; The device also includes: a transceiver unit, configured to send the first correction parameter to the cloud, and receive the second correction parameter determined by the cloud according to the first correction parameter; The processing unit is further configured to correct the weight model according to the second correction parameter to obtain a corrected weight model. 15. The apparatus according to claim 14, wherein the processing unit is further configured to: The stability of the revised weight model is tested against the remaining part of the qualified samples. 16. The apparatus according to any one of claims 11 to 15, wherein the processing unit is specifically configured to: inputting at least part of the charging data in the first charging data into the plurality of basic prediction models to obtain a third predicted charging duration corresponding to each basic prediction model; The third predicted charging duration corresponding to each basic prediction model is adjusted by using the adjustment parameters of each basic prediction model, so as to obtain the first predicted charging duration corresponding to each basic prediction model. 17. The apparatus according to claim 16, wherein the acquiring unit is further configured to: acquiring second charging data of the device and a second actual charging duration corresponding to the second charging data; The processing unit is also used to: inputting the second charging data into a first basic prediction model of the plurality of basic prediction models to obtain a fourth predicted charging duration; adding the fourth predicted charging duration, the second charging data, and the second actual charging duration as samples to a second sample set; The adjustment parameters corresponding to the first basic prediction model are determined according to the samples in the second sample set. 18. The apparatus according to claim 17, wherein the processing unit is specifically configured to: determining a second pass rate for the samples in the second sample set; If the second pass rate is less than a preset value of the second pass rate, determining the number of qualified samples in the second sample set; If the number of qualified samples in the second sample set is greater than the preset value of the second number of samples, the adjustment parameters corresponding to the first basic prediction model are determined according to the qualified samples. 19 . The device according to claim 18 , wherein, if the actual charging duration and the predicted charging duration corresponding to the qualified samples in the second sample set satisfy a linear relationship, the adjustment parameter corresponding to the first basic prediction model is: 19 . Regression coefficients and constants for the linear relationship. 20. The device according to any one of claims 11 to 19, wherein the first charging data and the second charging data comprise at least one of the following: The type of charger used to charge the device, the manufacturer of the battery that produced the device, the nominal capacity of the battery, the type of cells of the battery, the charging cable used to charge the device type, the number of cycles the battery has been charged, the nominal number of cycles the battery can be charged, the average internal resistance of the battery, the maximum internal resistance of the battery, the charger's historical insertion time and history The pull-out time, the charging cut-off time of the cells of the battery, the starting power and the ending power of the cells of the battery, and the actual charging time in a preset time period every day within a preset number of days. 21. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program, when the computer program is executed, the method according to any one of claims 1 to 10 is implemented . 22. A chip comprising a processor connected to a memory for storing a computer program, the processor for executing the computer program stored in the memory, so that the chip performs as claimed in the claim The method of any one of claims 1 to 10.

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