CN118801543A - Method for identifying the cause of abnormal charging, electronic device, chip system, storage medium and program product - Google Patents

Abstract

The embodiment of the application provides a charging abnormality cause identification method, electronic equipment, a chip system, a storage medium and a program product, which are applied to the technical field of terminals, can automatically carry out charging abnormality identification in response to user trigger, are beneficial to the convenience and the rapidness of the user for processing such problems, and are beneficial to the improvement of user experience. The method comprises the following steps: displaying a first interface under the condition that the electronic equipment is being charged, wherein the first interface comprises a first control, and when the first control is triggered, the electronic equipment executes a charging abnormality cause identification flow; and responding to the triggering operation of the first control, displaying a second interface, wherein the second interface comprises a recognition result, and the recognition result comprises a charging abnormality reason and a solution proposal.

Description

Method for identifying abnormal charging causes, electronic equipment, chip system, storage medium and program product

Technical Field

The present application relates to the field of terminal technology, and in particular to a method for identifying the cause of abnormal charging, an electronic device, a chip system, a storage medium and a program product.

Background Art

An electronic device may be equipped with a battery. When the battery of the electronic device is low on power, the battery needs to be charged. In some implementations, the electronic device may be charged by a wired charger or a wireless charger. During the charging process of the electronic device, the power of the battery usually increases at a certain rate. The user may observe this phenomenon through the charging animation set in the user interface (UI), the digital display of the power set above the display screen, or the display of the proportion of the charged amount in the battery capacity.

However, in some implementations, when charging an electronic device, the user may find that the power of the electronic device increases slowly or does not increase at all. Usually, this situation requires professional maintenance personnel to use special testing instruments to detect the cause of such abnormal charging phenomenon and then give repair suggestions.

However, this processing method is inefficient and provides a poor user experience.

Summary of the invention

The embodiments of the present application provide a method for identifying the cause of abnormal charging, an electronic device, a chip system, a storage medium and a program product, which are applied to the field of terminal technology and can automatically identify abnormal charging in response to a user trigger, thereby facilitating users to handle such problems quickly and conveniently and improving user experience.

In a first aspect, an embodiment of the present application proposes a method for identifying the cause of charging abnormality, which is applied to an electronic device, comprising: when the electronic device is charging, displaying a first interface, the first interface including a first control, when the first control is triggered, the electronic device executes a process for identifying the cause of charging abnormality; in response to the triggering operation of the first control, displaying a second interface, the second interface including an identification result, and the identification result including the cause of the charging abnormality and a solution suggestion.

In this way, when users find that their electronic devices are charging slowly or stop charging, for example, they can trigger the electronic devices to automatically identify the causes of the abnormal charging and output identification results including the causes of the abnormal charging and solutions. In some simple scenarios, users do not need to seek help from professional repair personnel, which helps users deal with such problems quickly and conveniently and improves user experience.

In combination with the first aspect, in some implementations of the first aspect, the second interface also includes a second control, and the second control is used to select whether to execute a process corresponding to the solution suggestion.

The second control may include, for example, control 601 and control 602 shown in FIG. 6 . The user may trigger control 601 to instruct the electronic device to execute a process corresponding to the solution suggestion, or the user may trigger control 602 to abandon the process. This application does not limit this.

The setting of the second control in the embodiment of the present application provides the user with the option of executing or giving up the abnormal charging cause and solution suggested by the electronic device with one click. When the user needs to execute the process corresponding to the solution suggestion, the user does not need to perform other complex operations, which is convenient and fast, and is conducive to quickly solving the problem.

In combination with the first aspect, in some implementations of the first aspect, the first interface is a lock screen interface, a notification bar interface, or a settings application interface of the electronic device. In this way, regardless of whether the user charges with the screen locked or unlocked, the user can quickly find the first control that triggers the electronic device to execute the charging abnormality cause identification process, so as to further trigger the process, which is conducive to improving the user experience.

In combination with the first aspect, in certain implementations of the first aspect, before displaying the second desktop, the method also includes: obtaining charging-related data of the electronic device within a preset time period; uploading the charging-related data to a cloud server; receiving the recognition result from the cloud server, the recognition result is obtained by the cloud server based on the first charging data looking up a preset mapping relationship, the preset mapping relationship includes a mapping relationship between each data in the charging-related data and the corresponding recognition result, the charging-related data includes first charging data, the first charging data does not meet a threshold condition set for the first charging data in a preset condition, and the preset condition is obtained by the cloud server analyzing the charging-related data of a group of users using a decision tree.

In this way, when a user finds that the electronic device is charging abnormally, the abnormal cause identification process can be triggered to enable the electronic device to detect the cause of the abnormal charging by itself, without the need for the user to analyze it by themselves. In some simple scenarios, there is no need to seek help from outlets, which is conducive to improving user experience.

In combination with the first aspect, in certain implementations of the first aspect, the device type information of the electronic device is uploaded to a cloud server; the preset condition is determined by the cloud server based on the device type information, and the cloud server maintains preset conditions corresponding to different device type information.

Optionally, the device type information may include a brand name, a series name, a model of the device, a device serial number (SN), etc., which is not limited in this application.

In other words, the cloud server can maintain different preset conditions for different device types. This will help reduce the misjudgment of the cause of abnormal charging due to the use of unified preset conditions for judgments based on different device types and different hardware and software capabilities, and will help improve the accuracy of identifying the cause of abnormal charging and improve user experience.

In combination with the first aspect, in certain implementations of the first aspect, the charging-related data includes one or more of the following: data related to hardware temperature, data related to system temperature, data related to foreground application power consumption, data related to background application power consumption, data related to the charger, data related to charging settings, data related to charging current, data related to the battery and power level, or data related to screen power consumption.

In combination with the first aspect, in some implementations of the first aspect, the preset mapping relationship includes one or more of the following: when the first charging data includes data related to the power consumption of the background application, in the identification result corresponding to the first charging data, the cause of the charging abnormality includes that the background application consumes too much power, and the solution suggestion includes clearing the background application; when the first charging data includes data related to the power consumption of the foreground application, in the identification result corresponding to the first charging data, the cause of the charging abnormality includes that the foreground application consumes too much power, and the solution suggestion includes closing the foreground application; when the first charging data includes data related to hardware temperature and/or data related to system temperature, in the identification result corresponding to the first charging data, the cause of the charging abnormality includes that the charging temperature is too high, and the solution suggestion includes clearing the background application; The solution suggestion includes waiting for the electronic device to cool down before charging; when the first charging data includes data related to screen power consumption, in the identification result corresponding to the first charging data, the cause of the charging abnormality includes excessive screen power consumption, and the solution suggestion includes turning off the screen of the electronic device; when the first charging data includes data related to the charger, in the identification result corresponding to the first charging data, the cause of the charging abnormality includes an incompatible charger, and the solution suggestion includes replacing the charger; or, when the first charging data includes data related to charging settings and/or data related to charging current, in the identification result corresponding to the first charging data, the cause of the charging abnormality includes charging current not meeting requirements, and the solution suggestion includes replacing the charger or sending the charger to a network point for repair.

In a second aspect, the present application also provides a method for identifying the cause of charging abnormality, which is applied to a cloud server, and the method includes: receiving charging-related data from an electronic device; based on preset conditions, determining whether each data in the charging-related data satisfies the corresponding threshold conditions, wherein the preset conditions are obtained by the cloud server analyzing the charging-related data of a group of users using a decision tree; when it is determined that the first charging data does not satisfy the threshold conditions set for the first charging data in the preset conditions, searching for a preset mapping relationship based on the first charging data to obtain an identification result corresponding to the first charging data, the charging-related data including the first charging data, and the preset mapping relationship including a mapping relationship between each data in the charging-related data and the corresponding identification results; and sending the identification result to the electronic device.

In combination with the second aspect, in certain implementations of the second aspect, the method further includes: obtaining charging-related data reported by a group of users, the charging-related data of the group of users including normal charging data and abnormal charging data; building a decision tree using the charging-related data of the group of users; and using the charging-related data and its corresponding node threshold conditions in the decision tree, in which the sample size of the node satisfies a first threshold and/or the proportion of the abnormal charging data of the node in the sample size satisfies a second threshold, as preset conditions.

In combination with the second aspect, in certain implementations of the second aspect, the method also includes: receiving device type information from an electronic device; based on preset conditions, before determining whether each data in the charging-related data satisfies the respective corresponding threshold conditions, the method also includes: determining the preset conditions corresponding to the device type information based on the device type information, the cloud server maintaining preset conditions corresponding to different device type information, each preset condition being obtained by analyzing the charging-related data of a group of users with the same device type information using a decision tree.

In a third aspect, the present application also provides a method for identifying the cause of abnormal charging, which is applied to a system including an electronic device and a cloud server, and the method includes: when the electronic device is charging, the electronic device responds to a user operation, obtains charging-related data of the electronic device within a preset time period, and uploads the charging-related data to the cloud server; the cloud server receives the charging-related data, and based on preset conditions, determines whether each data in the charging-related data satisfies the corresponding threshold conditions, wherein the preset conditions are obtained by the cloud server analyzing the charging-related data of a group of users using a decision tree; when the cloud server determines that the first charging data does not meet the threshold conditions set for the first charging data in the preset conditions, the cloud server searches for a preset mapping relationship based on the first charging data to obtain an identification result corresponding to the first charging data, wherein the charging-related data includes the first charging data, and the preset mapping relationship includes a mapping relationship between each data in the charging-related data and the corresponding identification results; the cloud server sends the identification result to the electronic device; the electronic device receives and displays the identification result, and the identification result includes the cause of the abnormal charging and a solution suggestion.

In combination with the third aspect, in certain implementations of the third aspect, before the electronic device uploads the charging-related data to the cloud server, the method also includes: the cloud server obtains the charging-related data reported by the group users, the charging-related data of the group users including normal charging data and abnormal charging data; the cloud server uses the charging-related data of the group users to build a decision tree; and the charging-related data and its corresponding node threshold conditions in the decision tree, in which the sample size of the node meets the first threshold and/or the proportion of the abnormal charging data of the node in the sample size meets the second threshold, are used as preset conditions.

Optionally, the algorithm for constructing the decision tree may include a classification and regression tree (CART) algorithm, an iterative dichotomiser 3 (ID3) algorithm or a C4.5 algorithm.

In combination with the third aspect, in certain implementations of the third aspect, the method further includes: the electronic device also uploads the device type information of the electronic device to the cloud server; before the cloud server determines whether each data in the charging-related data satisfies the respective corresponding threshold conditions based on preset conditions, the method further includes: the cloud server receives the device type information of the electronic device, and determines the preset conditions corresponding to the device type information based on the device type information, the cloud server maintains preset conditions corresponding to different device type information, and each preset condition is obtained by analyzing the charging-related data of a group of users with the same device type information using a decision tree.

In combination with the third aspect, in certain implementations of the third aspect, the charging-related data includes one or more of the following: data related to hardware temperature, data related to system temperature, data related to foreground application power consumption, data related to background application power consumption, data related to the charger, data related to charging settings, data related to charging current, or data related to screen power consumption.

In a fourth aspect, an embodiment of the present application provides a device for identifying a charging abnormality cause, which may be an electronic device, or a chip or chip system in an electronic device. The device for identifying a charging abnormality cause may include a display unit and a processing unit. When the device for identifying a charging abnormality cause is an electronic device, the display unit may be a display screen. The display unit is used to perform the display step so that the electronic device implements a method for identifying a charging abnormality cause described in the first aspect or any possible implementation of the first aspect. When the device for identifying a charging abnormality cause is an electronic device, the processing unit may be a processor. The device for identifying a charging abnormality cause may also include a storage unit, which may be a memory. The storage unit is used to store instructions, and the processing unit executes the instructions stored in the storage unit so that the electronic device implements a method for identifying a charging abnormality cause described in the first aspect or any possible implementation of the first aspect. When the device for identifying a charging abnormality cause is a chip or chip system in an electronic device, the processing unit may be a processor. The processing unit executes the instructions stored in the storage unit so that the electronic device implements a method for identifying a charging abnormality cause described in the first aspect or any possible implementation of the first aspect. The storage unit may be a storage unit within the chip (eg, a register, a cache, etc.), or a storage unit within the electronic device that is located outside the chip (eg, a read-only memory, a random access memory, etc.).

In a fifth aspect, an embodiment of the present application provides an electronic device, comprising one or more processors and a memory, wherein the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code comprises computer instructions, and the one or more processors call the computer instructions so that the electronic device executes the method described in the first aspect or any possible implementation of the first aspect, as well as the method executed by the electronic device in the third aspect or any possible implementation of the third aspect.

In the sixth aspect, an embodiment of the present application provides a server, comprising one or more processors and a memory, the memory being coupled to the one or more processors, the memory being used to store computer program code, the computer program code comprising computer instructions, and the one or more processors calling the computer instructions so that the server executes the method described in the second aspect or any possible implementation of the second aspect, as well as the method executed by the cloud server in the third aspect or any possible implementation of the third aspect.

In the seventh aspect, an embodiment of the present application provides a system for identifying the cause of abnormal charging, including an electronic device and a cloud server, the electronic device being used to execute the method described in the first aspect or any possible implementation of the first aspect, and the method executed by the electronic device in the third aspect or any possible implementation of the third aspect, and the cloud server being used to execute the method described in the second aspect or any possible implementation of the second aspect, and the method executed by the cloud server in the third aspect or any possible implementation of the third aspect.

In an eighth aspect, an embodiment of the present application provides a computer-readable storage medium, the computer-readable storage medium comprising a computer program or instructions, which, when the computer program or instructions are run on a computer, enables the computer to execute the method described in the first aspect or any possible implementation of the first aspect, the method described in the second aspect or any possible implementation of the second aspect, or the method described in the third aspect or any possible implementation of the third aspect.

In the ninth aspect, an embodiment of the present application provides a computer program product, which includes computer program code. When the computer program code runs on a computer, it enables the computer to execute the method described in the first aspect or any possible implementation of the first aspect, the method described in the second aspect or any possible implementation of the second aspect, or the method described in the third aspect or any possible implementation of the third aspect.

In the tenth aspect, the present application provides a chip or a chip system, which includes one or more processors and a communication interface, wherein the communication interface and the one or more processors are interconnected by lines, and the one or more processors are used to run computer programs or instructions to execute the method described in the first aspect or any possible implementation of the first aspect, the method described in the second aspect or any possible implementation of the second aspect, or the method described in the third aspect or any possible implementation of the third aspect. The communication interface in the chip can be an input/output interface, a pin or a circuit, etc.

In a possible implementation, the chip or chip system described above in the present application further includes at least one memory, in which instructions are stored. The memory may be a storage unit inside the chip, such as a register, a cache, etc., or a storage unit of the chip (such as a read-only memory, a random access memory, etc.).

It should be understood that the second to tenth aspects of the present application correspond to the technical solutions of the first aspect of the present application, and the beneficial effects achieved by each aspect and the corresponding feasible implementation methods are similar and will not be repeated here.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG1 is a schematic diagram of an application scenario provided by an embodiment of the present application;

FIG2 is an exemplary block diagram of a software structure of an electronic device provided in an embodiment of the present application;

FIG3 is a schematic flow chart of a method for identifying the cause of abnormal charging provided in an embodiment of the present application;

FIG4 is an example diagram of an interface of an electronic device provided in an embodiment of the present application;

FIG5 is an example diagram of an interface of another electronic device provided in an embodiment of the present application;

FIG6 is an example diagram of an interface of another electronic device provided in an embodiment of the present application;

FIG7 is an exemplary flow chart of obtaining a condition for determining a charging abnormality cause according to an embodiment of the present application;

FIG8 is a decision tree provided in an embodiment of the present application;

FIG9 is an exemplary block diagram of the hardware structure of an electronic device provided in an embodiment of the present application;

FIG. 10 is an exemplary block diagram of a chip structure provided in an embodiment of the present application.

DETAILED DESCRIPTION

In order to clearly describe the technical solutions of the embodiments of the present application, some terms and technologies involved in the embodiments of the present application are briefly introduced below:

1. Decision tree: A decision tree is a model used for decision analysis and machine learning. It performs classification or regression by splitting data into smaller subsets.

The process of building a decision tree usually includes the following steps: ① Select the best feature: select the feature that can best segment the data according to a certain standard (such as information gain, Gini index, etc.); ② Split the data: split the data into subsets according to the selected features; ③ Recursively build subtrees: repeat the above steps for each subset until the stopping condition is met (such as reaching the maximum depth or the number of samples in the leaf node is less than a certain threshold).

The node in the decision tree is the basic unit that constitutes the decision tree. The node type may not include the root node (starting node), internal node and leaf node (terminal node), and the leaf node is no longer further divided.

In the embodiment of the present application, a decision tree is constructed using a charging process data set reported by a group of users to obtain a charging abnormality cause judgment condition based on the charging process data set reported by the group of users.

2. Other terms

In the embodiments of the present application, words such as "first" and "second" are used to distinguish the same or similar items with substantially the same functions and effects. For example, the first chip and the second chip are only used to distinguish different chips, and their order is not limited. Those skilled in the art can understand that words such as "first" and "second" do not limit the quantity and execution order, and words such as "first" and "second" do not necessarily limit them to be different.

It should be noted that in the embodiments of the present application, words such as "exemplary" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary" or "for example" in the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a specific way.

In the embodiments of the present application, "at least one" refers to one or more, and "more than one" refers to two or more. "And/or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and/or B can represent: A exists alone, A and B exist at the same time, and B exists alone, where A and B can be singular or plural. The character "/" generally indicates that the objects associated before and after are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can represent: a, b, c, a-b, a--c, b-c, or a-b-c, where a, b, c can be single or multiple.

3. Electronic equipment

The electronic device of the embodiment of the present application may include a rechargeable handheld device, a vehicle-mounted device, etc. For example, some electronic devices are: mobile phones, tablet computers, PDAs, laptop computers, mobile internet devices (MIDs), wearable devices, virtual reality (VR) devices, augmented reality (AR) devices, wireless terminals in industrial control, wireless terminals in self driving, wireless terminals in remote medical surgery, wireless terminals in smart grids, wireless terminals in transportation safety, wireless terminals in smart cities, wireless terminals in smart homes, cellular phones, cordless phones, session initiation protocol (SIP) phones, 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, vehicle-mounted devices, wearable devices, electronic devices in 5G networks or future evolved public land mobile communication networks (public landmobile networks). The electronic devices in the network (PLMN) and the like are not limited to this in the embodiments of the present application.

As an example but not limitation, in the embodiments of the present application, the electronic device may also be a wearable device. Wearable devices may also be referred to as wearable smart devices, which are a general term for wearable devices that are intelligently designed and developed using wearable technology for daily wear, 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 clothes or accessories. Wearable devices are not only hardware devices, but also powerful functions achieved through software support, data interaction, and cloud interaction. Broadly speaking, wearable smart devices include full-featured, large-sized, and fully or partially independent of smartphones, such as smart watches or smart glasses, as well as devices that only focus on a certain type of application function and need to be used in conjunction with other devices such as smartphones, such as various types of smart bracelets and smart jewelry for vital sign monitoring.

In addition, in the embodiments of the present application, the electronic device may also be an electronic device in an Internet of Things (IoT) system. IoT is an important part of the future development of information technology. Its main technical feature is to connect objects to the network through communication technology, thereby realizing an intelligent network that interconnects people and machines and things.

The electronic device in the embodiments of the present application may also be referred to as: electronic device, user equipment (UE), mobile station (MS), mobile terminal (MT), access terminal, user unit, user station, mobile station, mobile station, remote station, remote terminal, mobile device, user terminal, terminal, wireless communication equipment, user agent or user device, etc.

Devices usually require batteries to power them, and when the batteries are low on power, they need to be charged to ensure normal use of the electronic device.

FIG1 exemplarily shows a schematic diagram of an electronic device charging scenario provided by an embodiment of the present application. As shown in FIG1 , when the electronic device needs to be charged, the user can connect the charging interface of the charging cable 101 (e.g., the Type-C male connector shown in FIG1 ) to the charging interface of the electronic device (e.g., the Type-C female connector shown in FIG1 ), and connect the charging cable 101 to the power adapter 102 and then connect the power adapter to the power supply, so that the electronic device can obtain current input from the power supply. When the electronic device is connected to the power supply, the electronic device can display the display interface 100 shown in FIG1 . When the user wants to check the power, the power status of the electronic device can be observed through the charging animation 103 set by the electronic device, the digital display 104 of the power set above the display screen, or the display 105 of the proportion of the charged amount in the battery capacity. When the power is fully charged to 100%, the user can unplug the charging cable in time.

In some possible implementations, during the process of charging an electronic device, the user may find that the power of the electronic device increases slowly or does not increase at all. After spending a long time, the user still cannot find the cause. Therefore, the user has to take the electronic device to a professional maintenance site. Professional maintenance personnel use special testing instruments to detect the cause of such abnormal charging phenomenon and then give suggestions for solving the problem. This processing method requires users to invest more time and energy, is inefficient, and has a poor user experience.

In view of this, the present application proposes a method for identifying the cause of abnormal charging, an electronic device, a chip system, a storage medium and a program product. When the electronic device is charging, the process can be started in response to the user triggering the first control set on the first interface of the electronic device for triggering the electronic device to execute the abnormal charging cause identification process, and then the identification result including the abnormal charging cause and solution suggestions are displayed on the second interface. In this way, when the user finds abnormal charging phenomena such as slow charging or stopping charging of the electronic device, the electronic device can be triggered to automatically detect the cause of the abnormal charging, and output the abnormal charging cause and solution suggestions. In some scenarios, the user may not need to seek help from professional repair personnel, which is conducive to users to deal with such problems conveniently and quickly, and is conducive to improving user experience.

Optionally, the charging method and charging interface shown in FIG. 1 are merely exemplary, and the electronic device may also be charged wirelessly, and the charging interface may also be designed in other forms, which is not specifically limited in this application.

In order to facilitate the understanding of the present application, the electronic device involved in the present application is first introduced below. In the embodiment of the present application, the electronic 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 includes hardware such as a central processing unit (CPU), a memory management unit (MMU), and a memory (also called main memory). The operating system can 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 includes applications such as a browser, an address book, a word processing software, and an instant messaging software.

The electronic device involved in the embodiments of the present application may have a software system that adopts a layered architecture, an event-driven architecture, a micro-core architecture, a micro-service architecture, or a cloud architecture. Below, the Android system with a layered architecture is taken as an example to illustrate the software structure of the electronic device.

FIG2 is an exemplary block diagram of the software structure of an electronic device provided in an embodiment of the present application. The layered architecture divides the software into several layers, each layer has a clear role and division of labor. The layers communicate with each other through software interfaces. In some embodiments, the Android system is divided into four layers, namely, the application layer, the application framework layer, the hardware abstraction layer, and the kernel layer from top to bottom.

The application layer may include a series of application packages. The application layer may include at least a system desktop application. In the embodiment of the present application, the system desktop application may be used to display the power level.

The application framework layer provides an application programming interface (API) and a programming framework for the applications of the application layer. The application framework layer includes some predefined functions. For example, it may include at least a battery management service (BatteryManagerService) and a view system. In an embodiment of the present application, the battery management service can be used to receive a notification from the hardware abstraction layer when the charging state changes, update the battery status information of the system, and output a buffered image (buffer) after the power is updated to the view system.

The hardware abstraction layer may include a battery access interface and a display synthesis service (surfaceflinger), etc. The application layer and the application framework layer may access the battery through the battery access interface, and the battery, charging interface and other hardware may pass battery charge related information to the upper layer through the battery access interface. In the embodiment of the present application, the battery access interface may obtain the charging strategy based on the charging status notification from the battery interface driver of the kernel layer, and feed the charging strategy back to the charging interface driver; the display synthesis service may obtain the image buffer through the image system to synthesize the interface UI, and display it on the display screen through the display driver.

The kernel layer is the layer between hardware and software. The kernel layer at least includes the charging interface driver, display driver, etc.

The electronic device also includes a hardware layer. In the embodiment of the present application, the hardware layer may include a charging port, a battery, a display screen, etc., but the present application does not limit this.

The following is an example of the workflow of electronic device software and hardware in conjunction with the charging scenario.

When the charging interface is connected to a power source, the charging interface passes the charging status notification to the battery access interface of the hardware abstraction layer through the charging interface driver. The battery access interface communicates with the charging interface on one side of the charging line, identifies the charging protocol, and determines the type of charger and the maximum current and voltage that can be provided. This charging strategy is fed back through the charging interface driver so that the charging interface can charge based on this charging strategy. Furthermore, the battery access interface also feeds back the power-on status information to the battery management service of the application framework layer. The battery management service updates the battery status information of the system and passes the image buffer after the power change to the view system in order to display the synthesis service synthesis interface UI, and display the charging progress and power information on the system desktop application. In this way, users can view the changes in power during the charging process through the display screen.

In conjunction with FIG3 , a charging abnormality cause identification method 300 provided in an embodiment of the present application is described in detail from the perspective of interaction between an electronic device and a cloud server. The electronic device involved in the method 300 may have a software architecture as shown in FIG2 , and a hardware structure as shown in FIG9 .

As a possible scenario, the electronic device provided in the embodiment of the present application is also provided with a "Charging Abnormality Detection" button (such as the control 401 shown in Figure 4) in the charging interface. When the user observes that the battery power increases slowly or does not increase, the user can click this button to trigger the process of identifying the cause of the abnormal charging of the electronic device.

It should be understood that the name of the "Charging Abnormality Detection" button is only an example, and this application does not limit this name. Any button that can achieve the above function, regardless of its name, is within the protection scope of this application.

Optionally, when the electronic device is in a locked screen state, the "Charging Abnormality Detection" button may be displayed on the display screen, such as shown in FIG. 4. When the electronic device is in an unlocked state, the user may call out the notification bar (or status bar, whose interface may be shown in FIG. 5 b) by pulling down in any interface (such as the interface shown in FIG. 5 a). During the charging process, the "Charging Abnormality Detection" button may be resident in the notification bar, such as shown in the control 501 in FIG. 5 b. When the user observes that the battery power increases slowly or does not increase, the user may call out the notification bar by pulling down and click the "Charging Abnormality Detection" button in the notification bar to trigger the electronic device to perform charging abnormality detection.

In another possible scenario, when the electronic device is connected to a power source for charging, it can determine whether the charging is abnormal based on the increase in power. If a charging abnormality is detected, a "Charging Abnormality Detection" button will pop up at the top of the screen or anywhere else for the user to choose whether to perform a charging abnormality detection.

When the user clicks the “Charging Abnormality Detection” button, the electronic device executes the corresponding steps in method 300 .

The method 300 includes the following steps:

S301 . The electronic device obtains charging process data of the electronic device in time period 1 in response to a user operation.

S302, the electronic device transmits basic information of the electronic device and the charging process data to the cloud server, wherein the basic information of the electronic device includes the model 1 of the electronic device; correspondingly, the cloud server receives the basic information of the electronic device and the charging process data.

S303. The cloud server obtains a judgment condition corresponding to the model 1 based on the model 1 of the electronic device.

S304. The cloud server determines, based on the judgment condition, whether there is at least one data in the charging process data that does not satisfy the threshold value set for the data in the judgment condition, wherein the judgment condition is obtained through a decision tree based on the charging process data reported by the group of users.

S305: When there is at least one data in the charging process data that does not satisfy the threshold value set for the data in the judgment condition, obtain the detection result corresponding to the data.

S306: The cloud server outputs the detection result corresponding to the data to the electronic device. Correspondingly, the electronic device receives the detection result.

S307. The electronic device displays the detection result, which includes the cause of the abnormal charging and a suggested solution.

Optionally, the interface displaying the detection result may be, for example, as shown in FIG. 6 , and the user may operate control 601 to select whether to execute the suggestion.

In one possible implementation, the cloud server maintains the abnormal charging cause judgment conditions corresponding to each model of electronic device. Since different electronic devices may have different hardware and software capabilities, the charging rates and charging process data may differ. Different judgment conditions correspond to different models, which can achieve personalized adaptation for different models of electronic devices, which is conducive to improving the accurate judgment of the cause of abnormal charging and also conducive to providing accurate solution suggestions.

Below, taking the electronic device of model 1 as an example, the cloud server obtains the charging abnormality cause judgment condition for the electronic device of model 1 in detail, and this process can be shown in Figure 7. This process can be executed before the above method 300.

As shown in FIG7 , the cloud server obtains a group user charging process data set of an electronic device whose device model is model 1. The data set is the charging process data obtained by each user in the group user in time period 1. The charging process data includes a power growth rate (or a charging starting power). Based on the power growth rate, identification is performed, and user data whose power growth rate in time period 1 is less than a preset value 1 is determined as abnormal charging data. Furthermore, the difference between the abnormal charging data and the normal charging data in the data set is analyzed through a decision tree model, and the abnormal charging cause judgment condition corresponding to the electronic device of this model is obtained by constructing a decision tree.

Optionally, time period 1 may be 10 minutes, and this application does not limit the specific duration of time period 1.

Optionally, the charging process data may include all or part of the parameters shown in Table 1 below.

Table 1 Charging process data table

Optionally, the corresponding abnormal causes (or characteristic categories) and solution suggestions of the above-mentioned partial charging process data (or characteristic data) may be as shown in Table 2.

Table 2 Correspondence between charging process data, abnormal causes, and solution suggestions

In a possible implementation, a decision tree may be constructed using a charging process data set of a group of users of an electronic device of model 1, which includes a certain amount of normal charging data and a certain amount of abnormal charging data. Exemplarily, the decision tree algorithm may calculate the information gain corresponding to each type of data in the charging process data set, and use the data with the largest information gain as the node for segmentation until there is no data that meets the segmentation condition. This algorithm may be called an iterative dichotomiser 3 (ID3) algorithm.

Exemplarily, FIG8 illustrates an exemplary process of splitting nodes of a decision tree.

As shown in FIG8 , the data set of charging process data collected by the cloud server and reported by group users may include 8,327,548 pieces of normal charging data and 672,452 pieces of abnormal charging data. In these 9 million samples (the charging process data of one user is called one sample), the decision tree calculates the information gain of the multiple charging process data included in each of the 9 million samples according to the above-mentioned ID3 algorithm. For example, the data with the largest information gain is "the charging start power displayed on the user interface" and the data is segmented with a value of 4.5. After segmentation, the samples in which the charging start power displayed on the user interface is less than or equal to 4.5 include 96,298 pieces of normal charging data and 235,418 pieces of abnormal charging data, that is, the proportion of abnormal charging data in the samples in which the charging start power displayed on the user interface is less than or equal to 4.5 is about 71%, and the samples in which the charging start power displayed on the user interface is greater than 4.5 include 8,231,250 pieces of normal charging data and 437,034 pieces of abnormal charging data, that is, the proportion of abnormal charging data in the samples in which the charging start power displayed on the user interface is greater than 4.5 is about 5%.

Furthermore, for samples whose initial power level of the detection process displayed on the user interface is less than or equal to 4.5 and samples whose initial power level of the detection process displayed on the user interface is greater than 4.5, the information gain of the charging process data corresponding to each sample is calculated. Exemplarily, as shown in FIG6 , in the samples in which the charging starting power displayed on the user interface is less than or equal to 4.5, the data with the largest information gain may be, for example, “the proportion of the screen-on time in the detection process to the total detection time” and the data is segmented based on the value of 97.5. The samples in which the proportion of the screen-on time in the detection process to the total detection time is greater than 97.5 include 32,967 normal charging data and 234,079 abnormal charging data, that is, the proportion of abnormal charging data in the samples in which the proportion of the screen-on time in the detection process to the total detection time is greater than 97.5 is approximately 87%; and the samples in which the proportion of the screen-on time in the detection process to the total detection time is less than or equal to 97.5 include 63,331 normal charging data and 1,339 abnormal charging data, that is, the proportion of abnormal charging data in the samples in which the proportion of the screen-on time in the detection process to the total detection time is greater than 97.5 is approximately 2%.

Among the samples with a charging starting power greater than 4.5 displayed on the user interface, the data with the largest information gain may be, for example, the "power at the end of detection" and the data is segmented based on the value of 80.5. The samples with a power less than or equal to 80.5 at the end of detection include 129,413 normal charging data and 345,311 abnormal charging data, that is, the proportion of abnormal charging data in the samples with a power less than or equal to 80.5 at the end of detection is approximately 73%; and the samples with a power greater than 80.5 at the end of detection include 6,937,107 normal charging data and 91,723 abnormal charging data, that is, the proportion of abnormal charging data in the samples with a power greater than 80.5 at the end of detection is approximately 1%.

Furthermore, for samples in which the proportion of screen-on time during the detection process to the total detection time is greater than 97.5, samples in which the proportion of screen-on time during the detection process to the total detection time is less than or equal to 97.5, samples in which the battery level is less than or equal to 80.5 at the end of the detection, and samples in which the battery level is greater than 80.5 at the end of the detection, the information gain of the data corresponding to each sample can be calculated, and the nodes can be further split according to the data with the largest information gain. The partial splitting process can be shown in Figure 6, which will not be repeated here.

In a possible implementation, the number of data indicating normal charging is 0 and/or the number of data indicating abnormal charging is 0 can be used as a condition for ending the node splitting.

In a possible implementation, the node splitting condition that the proportion of abnormal charging data at the splitting node is greater than a threshold value 1 may be used as a judgment condition for whether the cause of the abnormal charging is the data category corresponding to the node.

Optionally, when threshold 1 is 50%, in combination with Table 2, whether the ratio of the screen-on time during the detection process to the total detection time (son) is greater than 97.5 can be used as a judgment condition for whether the cause of the charging abnormality is charging with the screen on; whether the charging start power (suisoc) displayed on the user interface is less than or equal to 4.5, whether the power at the start of the detection (selectric) is greater than 80.5, or whether the power at the end of the detection (felectric) is less than or equal to 80.5 can be used as a judgment condition for whether the cause of the charging abnormality is the influence of the charging current; whether the maximum system temperature (system_h) during the detection process is greater than 37.5 can be used as a judgment condition for whether the cause of the charging abnormality is too high a charging temperature; whether the power consumption (app_energy) of the application with the largest power consumption among the applications running in the background during the detection process is greater than 12317.5 can be used as a judgment condition for whether the cause of the charging abnormality is power consumption of the background application. This application does not specifically limit the value of threshold 1.

It should be understood that the detection process described above may refer to the period 1, and the period 1 may be, for example, 10 minutes.

In a possible implementation, the charging process data reported by the electronic device may be all the data shown in Table 1.

In another possible implementation, the charging process data reported by the electronic device may be data related to the judgment conditions screened by the cloud server, which is helpful to save the signaling overhead and energy consumption of the electronic device.

Exemplarily, in some implementations, the correspondence between the conditions for judging the cause of abnormal charging, the cause of abnormal charging and the suggested solution may include one or more of the following: judging whether the proportion of the screen-on time in the detection process to the total detection time is greater than 97.5, if so, the cause of the abnormal charging is charging with the screen on, and the suggested solution may be to turn off the screen; judging whether the maximum system temperature in the detection process is greater than 37.5, if so, the cause of the abnormal charging is that the charging temperature is too high, and the suggested solution may be to suspend charging and charge the electronic device after it cools down; judging whether the power consumption of the application with the highest power consumption among the applications running in the background during the detection process is greater than 12317.5, if so, the cause of the abnormal charging is the power consumption of the background application, and the suggested solution may be to clean up the background application process; judging whether the starting charging power displayed on the user interface is less than or equal to 4.5, whether the power at the start of the detection is greater than 80.5, whether the power at the end of the detection is less than or equal to 80.5, and whether the current peak during the detection process is less than 80.5, if any of the items is yes, the cause of the abnormal charging is the influence of the charging current, and the suggested solution may be to replace the charger, replace the power supply or go to the outlet for maintenance.

In a possible implementation, after S307, the electronic device can automatically handle the charging abnormality based on the solution suggestion corresponding to the abnormality. For example, if the abnormal charging reason is charging with the screen on, the electronic device can automatically turn off the screen or respond to the user's selection to turn off the screen. If the abnormal charging reason is power consumption of background applications, the electronic device can automatically clean up the background application process or respond to the user's selection (for example, the user triggers the control 601 in Figure 6) to execute the process of cleaning up the background application. In this way, the electronic device has a higher degree of automation, which is conducive to improving the user experience.

In one possible implementation, the cloud server can update the judgment conditions based on the latest charging process data set reported by the group users. That is to say, the corresponding charging abnormality judgment conditions for each model of electronic device are not fixed, but can be continuously updated.

It is worth noting that the steps executed by the cloud server in the above method 300 can also be executed by the electronic device. In this case, the corresponding relationship between the charging abnormality cause judgment condition, the charging abnormality cause and the solution suggestion can be maintained by the electronic device itself. After the cloud server updates the content of this corresponding relationship, it can send it to the electronic device through system update or parameter update. This application does not limit this update method.

Next, another method for identifying the cause of abnormal charging provided by an embodiment of the present application is described in detail. The method can be applied to an electronic device, the software structure of the electronic device can be as shown in FIG. 2 , and the hardware structure can be as shown in FIG. 9 .

The method includes: when the electronic device is charging, displaying a first interface, the first interface includes a first control, when the first control is triggered, the electronic device executes a charging abnormality cause identification process; in response to the triggering operation of the first control, displaying a second interface, the second interface includes an identification result, and the identification result includes the charging abnormality cause and a solution suggestion.

Optionally, the first interface may be, for example, as shown in Figure 4, the first control may be, for example, control 401 in Figure 4, the first interface may also be as shown in b in Figure 5, the first control may be, for example, control 501 shown in b in Figure 5, the first interface and the first control in the first interface may also be in other forms, which are not limited in the present application.

Optionally, the second interface may be as shown in FIG. 6 , for example, but the present application does not limit this.

In the embodiments of the present application, when a user discovers that an electronic device is charging slowly or stops charging, for example, the electronic device can be triggered to automatically detect the cause of the abnormal charging and output the cause and solution suggestions. In some scenarios, the user may not need to seek help from professional repair personnel, which is beneficial for the user to deal with such problems conveniently and quickly, and is beneficial for improving the user experience.

Furthermore, the setting of the first control is intended to trigger the charging abnormality cause identification process through the user's active selection after the user discovers the charging abnormality, and then provide the identification result. If the electronic device actively identifies whether the charging is abnormal and displays the identification result after connecting to the charger, the user may not feel that the charging is abnormal but the cause of the charging abnormality is displayed, which may cause confusion to the user and affect the user's experience of using the device. Therefore, the embodiment of the present application sets the first control for the user to trigger, and performs identification and provides identification results after the user has already considered the charging abnormality, which is conducive to improving the user experience and satisfaction with the device.

It should be understood that the charging-related data described here may have the same meaning as the charging process data described above, the identification result described here may have the same meaning as the detection result described above, the preset condition described here may have the same meaning as the judgment condition described above, and the first data described here may have the same meaning as at least one data described above, and the explanation will not be repeated later.

As an optional embodiment, the second interface also includes a second control, and the second control is used to select whether to execute the process corresponding to the solution suggestion. The second control may include, for example, control 601 and control 602 shown in FIG. 6, and the user may trigger control 601 to instruct the electronic device to execute the process corresponding to the solution suggestion, or the user may trigger control 602 to abandon the process, and this application does not limit this.

The setting of the second control in the embodiment of the present application provides the user with the option of executing or giving up with one click, with respect to the cause of abnormal charging and the solution suggestion identified by the electronic device. When the user needs to execute the process corresponding to the solution suggestion, the user does not need to perform other operations, which is convenient and quick, and is conducive to quickly solving the problem.

As an optional embodiment, the first interface is a lock screen interface of the electronic device, such as that shown in FIG. 4 , a notification bar interface, such as that shown in b in FIG. 5 , or an application settings interface.

As an optional embodiment, before displaying the second desktop, the method also includes: obtaining charging-related data of the electronic device within a preset time period; uploading the charging-related data to a cloud server; receiving an identification result from the cloud server, the identification result being obtained by the cloud server searching for a preset mapping relationship based on the first charging data, the preset mapping relationship including a mapping relationship between each data in the charging-related data and each corresponding identification result, the charging-related data including first charging data, the first charging data not satisfying a threshold condition set for the first charging data in a preset condition, the preset condition being obtained by the cloud server analyzing the charging-related data of a group of users using a decision tree.

The preset time period may be, for example, a time period after the first control is triggered. The preset time period may be similar to the above time period 1, and the duration of the preset time period may be 10 minutes.

In this way, when a user finds that the electronic device is charging abnormally, the abnormal cause identification process can be triggered to enable the electronic device to detect the cause of the abnormal charging by itself, without the need for the user to analyze it by themselves. In some simple scenarios, there is no need to seek help from outlets, which is conducive to improving user experience.

As an optional embodiment, after the abnormal charging cause identification process is triggered, the electronic device can also upload the device type information of the electronic device to the cloud server; the preset condition is determined by the cloud server based on the device type information, and the cloud server maintains preset conditions corresponding to different device type information.

Optionally, the device type information may include a brand name, a series name, a model of the device, a device serial number (SN), etc., which is not limited in this application.

As an optional embodiment, the charging-related data includes one or more of the following: data related to hardware temperature, data related to system temperature, data related to foreground application power consumption, data related to background application power consumption, data related to the charger, data related to charging settings, data related to charging current, battery and power related data, or data related to screen power consumption.

Specifically, the data related to the hardware temperature may include the data corresponding to the serial numbers 1 to 5 and 45 shown in Table 1 above, the data related to the system temperature may include the data corresponding to the serial number 13 in Table 1, the data related to the foreground application power consumption may be the data corresponding to the serial numbers 6 to 9, the data related to the background application power consumption may be the data corresponding to the serial numbers 10 to 12 in Table 1, and the data related to the charger may be the data corresponding to the serial numbers 22, 30, 33, 35, 36, 38, The data corresponding to serial number 42 and the data related to the charging settings may include the data corresponding to serial numbers 18 to 21 and 43 in Table 1, the data related to the charging current may be the data corresponding to serial numbers 16, 2, 8 and 31 in Table 1, the data related to the battery and power may be the data corresponding to serial numbers 14, 15, 17, 23, 29, 32, 37, 39, 41 and 44, and the data related to the screen power consumption may be serial numbers 26 and 27.

As an optional embodiment, the preset mapping relationship includes one or more of the following: when the first charging data includes data related to the power consumption of background applications, in the identification result corresponding to the first charging data, the cause of the charging abnormality includes that the background application consumes too much power, and the solution suggestion includes cleaning up the background application; when the first charging data includes data related to the power consumption of foreground applications, in the identification result corresponding to the first charging data, the cause of the charging abnormality includes that the foreground application consumes too much power, and the solution suggestion includes closing the foreground application; when the first charging data includes data related to hardware temperature and/or data related to system temperature, in the identification result corresponding to the first charging data, the cause of the charging abnormality includes that the charging temperature is too high, and the solution suggestion includes Including waiting for the electronic device to cool down before charging; when the first charging data includes data related to screen power consumption, in the identification result corresponding to the first charging data, the cause of the charging abnormality includes excessive screen power consumption, and the solution suggestion includes turning off the screen of the electronic device; when the first charging data includes data related to the charger, in the identification result corresponding to the first charging data, the cause of the charging abnormality includes incompatible charger, and the solution suggestion includes replacing the charger; or, when the first charging data includes data related to charging settings and/or data related to charging current, in the identification result corresponding to the first charging data, the cause of the charging abnormality includes charging current not meeting requirements, and the solution suggestion includes replacing the charger or sending it to a network point for repair.

In some implementations, the preset mapping relationship may be summarized by R&D personnel, or may be obtained by a cloud server based on a big data analysis of charging anomalies based on historical feedback from group users. This application does not limit the method for obtaining the preset mapping relationship.

Exemplarily, Table 2 shows the recognition results corresponding to some specific charging-related data.

An embodiment of the present application also provides a method for identifying the cause of abnormal charging. The system includes an electronic device and a cloud server. The software architecture of the electronic device can be as shown in FIG. 2 , and the hardware structure can be as shown in FIG. 9 .

The method includes: when the electronic device is being charged, the electronic device responds to user operations, obtains charging-related data of the electronic device within a preset time period, and uploads the charging-related data to a cloud server; the cloud server receives the charging-related data, and based on preset conditions, determines whether each data in the charging-related data meets the corresponding threshold conditions, the preset conditions are obtained by the cloud server using a decision tree to analyze the charging-related data of a group of users; when the cloud server determines that the first charging data does not meet the threshold conditions set for the first charging data in the preset conditions, the cloud server searches for a preset mapping relationship based on the first charging data to obtain an identification result corresponding to the first charging data, the charging-related data includes the first charging data, and the preset mapping relationship includes a mapping relationship between each data in the charging-related data and the corresponding identification results; the cloud server sends the identification result to the electronic device; the electronic device receives and displays the identification result, the identification result includes the cause of the abnormal charging and a solution suggestion.

Optionally, the user operation may refer to the user triggering the first control described above.

As an optional embodiment, before the electronic device uploads charging-related data to the cloud server, the method also includes: the cloud server obtains charging-related data reported by a group of users, wherein the charging-related data of the group of users includes normal charging data and abnormal charging data; the cloud server constructs a decision tree using the charging-related data of the group of users; and the charging-related data and its corresponding node threshold conditions in the decision tree, in which the sample size of the node satisfies a first threshold and/or the proportion of abnormal charging data of the node in the sample size satisfies a second threshold, are used as preset conditions.

It should be understood that the total sample size may refer to the number of group users who report group user charging-related data, and each user may report one or more charging-related data. The sample size of a node may refer to the number of group users who meet the conditions of the node. The first threshold value may be understood as the ratio of the node sample size to the total sample size. Using this first threshold value as a screening condition for the preset condition can ensure that the selected node has a certain degree of representativeness, which is conducive to improving the accuracy of the results obtained based on the preset conditions.

Optionally, the first threshold may be, for example, 50% of the total sample volume, and the second threshold may have the same meaning as the above threshold 1, but the present application does not limit the specific values of the first threshold and the second threshold.

Exemplarily, the specific method for constructing a decision tree provided in the embodiments of the present application can refer to FIG. 8 and the text description related to FIG. 8 , which will not be repeated here.

As an optional embodiment, the electronic device also uploads the device type information of the electronic device to the cloud server; before the cloud server determines whether each data in the charging-related data satisfies the corresponding threshold conditions based on the preset conditions, the method also includes: the cloud server obtains the device type information, and determines the preset conditions corresponding to the device type information based on the device type information, the cloud server maintains preset conditions corresponding to different device type information, and each preset condition is obtained by analyzing the charging-related data of a group of users with the same device type information using a decision tree.

In other words, the cloud server maintains different preset conditions for different device types. This helps reduce the misjudgment of the cause of abnormal charging due to the use of unified preset conditions for judgment due to different device types and different hardware and software capabilities, and helps improve the accuracy of identifying the cause of abnormal charging and improve user experience.

It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in the embodiments of the present application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with the relevant laws, regulations and standards of the relevant countries and regions, and provide corresponding operation entrances for users to choose to authorize or refuse.

The above is a description of the method for identifying the cause of abnormal charging in the embodiment of the present application. The following is a description of the device for executing the above method provided in the embodiment of the present application. Those skilled in the art can understand that the method and the device can be combined and referenced with each other, and the relevant device provided in the embodiment of the present application can execute the steps in the above method.

Fig. 9 shows a schematic diagram of the hardware structure of an electronic device 900 provided in an embodiment of the present application. As shown in Fig. 9, the electronic device may include a processor 910, a memory 920, a display screen 930, a sensor module 940, a universal serial bus (USB) interface 950, a charging management module 960, a power management module 961, a battery 962, etc.

The processor 910 may include one or more processing units, for example, the processor 910 may include a central processing unit (CPU), an application processor (AP), a modem processor, a graphics processing unit (GPU), an image signal processor (ISP), a controller, a video codec, a digital signal processor (DSP), a baseband processor, and/or a neural-network processing unit (NPU), etc. Different processing units may be independent devices or integrated into one or more processors.

The memory 920 may be used to store computer executable program codes, where the executable program codes include instructions, such as program codes corresponding to the methods in the embodiments of the present application.

The display screen 930 is used to display images, videos, etc. The display screen 930 includes a display panel. In some embodiments, the electronic device may include 1 or Q display screens 930, where Q is a positive integer greater than 1. The electronic device implements the display function through a GPU, a display screen 930, and an application processor. In the embodiment of the present application, the display screen 930 can be used to display the first control, the second control, and the recognition result.

It should be understood that in order to implement the functions described in the above embodiments, the electronic device may include hardware structures and/or software modules corresponding to the execution of each function. It should be easily appreciated by those skilled in the art that, in combination with the method steps of each example described in the embodiments disclosed herein, the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in the form of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

The embodiment of the present application can divide the functional modules of the device implementing the method according to the above method example. For example, each functional module can be divided according to each function, or two or more functions can be integrated into one processing module. The integrated module can be implemented in the form of hardware or in the form of software functional modules. It should be noted that the division of modules in the embodiment of the present application is schematic and is only a logical function division. There may be other division methods in actual implementation.

The method for identifying the cause of abnormal charging provided in the embodiment of the present application can be applied to electronic devices that can be charged. The software structure of the electronic device can be as shown in Figure 2, and the hardware structure can be as shown in Figure 9. The specific form of the electronic device can refer to the above description and will not be repeated here.

10 exemplarily shows a schematic diagram of the structure of a chip 1000 provided in an embodiment. The chip 1000 includes one or more (including two) processors 1001 , a communication line 1002 , a communication interface 1003 and a memory 1004 .

In some implementations, the memory 1004 stores the following elements: executable modules or data structures, or a subset thereof, or an extended set thereof.

The method described in the above embodiment of the present application can be applied to the processor 1001, or implemented by the processor 1001. The processor 1001 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by an integrated logic circuit of hardware in the processor 1001 or an instruction in the form of software. The above processor 1001 can be a general processor (for example, a microprocessor or a conventional processor), a digital signal processor (digital signal processing, DSP), an application specific integrated circuit (application specific integrated circuit, ASIC), a field-programmable gate array (field-programmable gate array, FPGA) or other programmable logic devices, discrete gates, transistor logic devices or discrete hardware components. The processor 1001 can implement or execute the methods, steps and logic block diagrams related to each processing disclosed in the embodiment of the present application.

The steps of the method disclosed in the embodiment of the present application can be directly embodied as being executed by a hardware decoding processor, or can be executed by a combination of hardware and software modules in the decoding processor. Among them, the software module can be located in a mature storage medium in the field such as a random access memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable read only memory (EEPROM). The storage medium is located in the memory 1004, and the processor 1001 reads the information in the memory 1004 and completes the steps of the above method in combination with its hardware.

The processor 1001 , the memory 1004 , and the communication interface 1003 may communicate with each other via the communication line 1002 .

In the above embodiments, the instructions stored in the memory for execution by the processor may be implemented in the form of a computer program product, wherein the computer program product may be pre-written in the memory, or may be downloaded and installed in the memory in the form of software.

In the embodiment of the present application, the chip 1000 may also be a chip system, such as a system on chip (SOC), which is not limited in the present application.

An embodiment of the present application provides an electronic device, which includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory stores computer program code, and the computer program code includes computer instructions; the one or more processors call the computer instructions to enable the electronic device to execute the method in the above embodiment.

The embodiment of the present application provides a chip or a chip system. The chip or chip system is applied to an electronic device, and the chip or chip system includes one or more processors, and the one or more processors are used to call computer instructions to enable the electronic device to execute the method in the above embodiment. Its implementation principle and technical effect are similar to those of the above-mentioned related embodiments, and will not be repeated here.

The embodiment of the present application also provides a computer-readable storage medium. The computer-readable storage medium includes computer instructions. When the computer instructions are run on an electronic device, the electronic device executes the method in the above embodiment. The method described in the above embodiment can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. If implemented in software, the function can be stored as one or more instructions or codes on a computer-readable medium or transmitted on a computer-readable medium. Computer-readable media can include computer storage media and communication media, and can also include any medium that can transfer a computer program from one place to another. The storage medium can be any target medium that can be accessed by a computer.

In one possible implementation, a computer readable medium may include RAM, ROM, compact disc read-only memory (CD-ROM) or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium that is intended to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer. Moreover, any connection is appropriately referred to as a computer readable medium. For example, if the software is transmitted from a website, server or other remote source using a coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL) or wireless technology (such as infrared, radio and microwave), the coaxial cable, fiber optic cable, twisted pair, DSL or wireless technology such as infrared, radio and microwave are included in the definition of medium. Disks and optical disks as used herein include optical disks, laser disks, optical disks, digital versatile discs (DVD), floppy disks and Blu-ray discs, where disks usually reproduce data magnetically, while optical disks reproduce data optically using lasers. Combinations of the above should also be included in the scope of computer readable media.

An embodiment of the present application provides a computer program product, which includes a computer program code. When the computer program code runs on an electronic device, the electronic device executes the method in the above embodiment.

The embodiments of the present application are described with reference to the flowcharts and/or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and/or box in the flowchart and/or block diagram, as well as the combination of the processes and/or boxes in the flowchart and/or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processing unit of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable device to generate a machine, so that the instructions executed by the processing unit of the computer or other programmable data processing device generate a device for implementing the functions specified in one process or multiple processes in the flowchart and/or one box or multiple boxes in the block diagram.

The above specific implementation methods further illustrate the purpose, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above are only specific implementation methods of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made on the basis of the technical solutions of the present invention should be included in the scope of protection of the present invention.

Claims (15)

1. A charging abnormality cause identification method applied to an electronic device, characterized by comprising:

Displaying a first interface under the condition that the electronic equipment is being charged, wherein the first interface comprises a first control, and when the first control is triggered, the electronic equipment executes a charging abnormality cause identification flow;

And responding to the triggering operation of the first control, displaying a second interface, wherein the second interface comprises a recognition result, and the recognition result comprises a charging abnormality reason and a solution proposal.

2. The method of claim 1, wherein the second interface further comprises a second control for selecting whether to execute a flow corresponding to the solution suggestion.

3. The method of claim 1, wherein the first interface is a lock screen interface, a notification bar interface, or a setup application interface of the electronic device.

4. A method according to any one of claims 1 to 3, wherein prior to displaying the second desktop, the method further comprises:

acquiring charging related data of the electronic equipment within a preset time period;

uploading the charging related data to a cloud server;

The identification result from the cloud server is received, the identification result is obtained by the cloud server searching for a preset mapping relation based on first charging data, the preset mapping relation comprises a mapping relation between each piece of charging related data and the corresponding identification result, the charging related data comprises first charging data, the first charging data does not meet a threshold condition set for the first charging data in preset conditions, and the preset conditions are obtained by the cloud server analyzing the charging related data of the group users by utilizing a decision tree.

5. The method according to claim 4, wherein the method further comprises:

uploading the equipment type information of the electronic equipment to a cloud server; the preset conditions are determined by the cloud server based on the equipment type information, and the cloud server maintains preset conditions corresponding to different equipment type information.

6. The method of claim 5, wherein the charging-related data comprises one or more of: data relating to hardware temperature, data relating to system temperature, data relating to foreground application power consumption, data relating to background application power consumption, data relating to a charger, data relating to a charging setting, data relating to a charging current, or data relating to screen power consumption.

7. The method of claim 6, wherein the preset mapping relationship comprises one or more of:

In the case that the first charging data includes data related to power consumption of the background application, in the identification result corresponding to the first charging data, the cause of the abnormal charging includes that the power consumption of the background application is too high, and the solution proposal includes that the background application is cleaned;

In the case that the first charging data includes data related to power consumption of the foreground application, in the identification result corresponding to the first charging data, the cause of the charging abnormality includes that power consumption of the foreground application is excessively high, and the solution proposal includes that the foreground application is closed;

In the case that the first charging data comprises data related to hardware temperature and/or data related to system temperature, in the identification result corresponding to the first charging data, the reason for the abnormal charging comprises that the charging temperature is too high, and the proposal of the solution comprises that the electronic equipment is charged after waiting for cooling;

In the case that the first charging data includes data related to screen electricity consumption, in the identification result corresponding to the first charging data, the cause of the charging abnormality includes that screen electricity consumption is excessively high, and the solution proposal includes that the screen of the electronic device is extinguished;

In the case where the first charging data includes data related to a charger, in the identification result corresponding to the first charging data, the cause of the charging abnormality includes a charger discomfort, and the solution proposal includes replacement of the charger; or alternatively

In the case that the first charging data includes data related to charging settings and/or data related to charging current, in the identification result corresponding to the first charging data, the cause of the charging abnormality includes that the charging current does not meet the requirement, and the solution proposal includes replacement of a charger or overhaul of a network.

8. The method for identifying the reason of the abnormal charging is characterized by being applied to a system comprising electronic equipment and a cloud server, and comprises the following steps:

Under the condition that the electronic equipment is being charged, the electronic equipment responds to user operation, acquires charging related data of the electronic equipment within a preset time period, and uploads the charging related data to a cloud server;

The cloud server receives the charging related data and judges whether each data in the charging related data meets a corresponding threshold condition or not based on preset conditions, wherein the preset conditions are obtained by analyzing the charging related data of the group users by utilizing a decision tree;

The cloud server searches a preset mapping relation based on the first charging data to obtain an identification result corresponding to the first charging data under the condition that the first charging data does not meet a threshold condition set for the first charging data in preset conditions, wherein the charging related data comprises the first charging data, and the preset mapping relation comprises a mapping relation between each data in the charging related data and the corresponding identification result;

The cloud server sends the identification result to the electronic equipment;

the electronic equipment receives and displays the identification result, wherein the identification result comprises a charging abnormality reason and a solution proposal.

9. The method of claim 8, wherein prior to the electronic device uploading the charging-related data to a cloud server, the method further comprises:

the cloud server acquires charging related data reported by group users, wherein the charging related data of the group users comprises charging normal data and charging abnormal data;

the cloud server builds a decision tree by using charging related data of group users;

And the cloud server takes the charging related data and the corresponding node threshold conditions of which the sample size of the nodes meets a first threshold and/or the proportion of the charging abnormal data of the nodes to the sample size meets a second threshold in the decision tree as preset conditions.

10. The method according to claim 8 or 9, characterized in that the method further comprises:

the electronic equipment also uploads equipment type information of the electronic equipment to a cloud server;

Before the cloud server judges whether each data in the charging related data meets the corresponding threshold condition or not based on the preset condition, the method further comprises:

The cloud server receives the equipment type information of the electronic equipment, and based on the preset conditions corresponding to the equipment type information, which are determined by the equipment type information, the cloud server maintains the preset conditions corresponding to different equipment type information, and each preset condition is obtained by analyzing charging related data of group users with the same equipment type information by utilizing a decision tree.

11. The method of claim 8, wherein the charging-related data comprises one or more of: data relating to hardware temperature, data relating to system temperature, data relating to foreground application power consumption, data relating to background application power consumption, data relating to a charger, data relating to a charging setting, data relating to a charging current, or data relating to screen power consumption.

12. An electronic device, the electronic device comprising: one or more processors and memory;

the memory being coupled to the one or more processors, the memory being for storing computer program code comprising computer instructions that are invoked by the one or more processors to cause the electronic device to perform the method of any one of claims 1 to 7 or to perform the steps performed by the electronic device in the method of any one of claims 8 to 11.

13. A chip system for application to an electronic device, the chip system comprising one or more processors to invoke computer instructions to cause the electronic device to perform the method of any of claims 1 to 7 or to perform the steps performed by the electronic device in the method of any of claims 8 to 11.

14. A computer readable storage medium comprising computer instructions which, when run on an electronic device, cause the electronic device to perform the method of any one of claims 1 to 7 or to perform the steps performed by the electronic device in the method of any one of claims 8 to 11.

15. A computer program product, characterized in that it comprises computer program code which, when run on an electronic device, causes the electronic device to perform the method according to any one of claims 1 to 7 or to perform the steps performed by the electronic device in the method according to any one of claims 8 to 11.