CN109856544B

CN109856544B - Terminal electricity usage time analysis method, terminal and computer-readable storage medium

Info

Publication number: CN109856544B
Application number: CN201910069647.2A
Authority: CN
Inventors: 夏令君
Original assignee: Nubia Technology Co Ltd
Current assignee: Nubia Technology Co Ltd
Priority date: 2019-01-24
Filing date: 2019-01-24
Publication date: 2022-04-19
Anticipated expiration: 2039-01-24
Also published as: CN109856544A

Abstract

The invention discloses a method for analyzing the electricity consumption time of a terminal, the terminal and a computer readable storage medium, wherein the method comprises the following steps: establishing a neural network model for analyzing the service time of the residual electric quantity of the terminal; generating a value of a node in the neural network model according to a preset initial slope; modifying the value of the node according to the history record of the terminal used by the user; and after modification, analyzing the service time of the residual electric quantity of the terminal by using the neural network model. According to the technical scheme of the invention, the time of the user using the residual electric quantity of the terminal can be more accurately analyzed by using the neural network model.

Description

Terminal electricity usage time analysis method, terminal and computer-readable storage medium

Technical Field

The invention relates to the field of mobile terminals, in particular to a method for analyzing the electricity consumption time of a terminal, the terminal and a computer readable storage medium.

Background

In modern society, a mobile phone becomes an important tool indispensable to daily life of users, and life, work and entertainment all depend on participation of the mobile phone. The problem brought by the long-time and high-frequency use of the mobile phone by a user is that the use of the electric quantity of the mobile phone is increased, so that the analysis of the use time of the residual electric quantity of the mobile phone becomes a key problem, the use time of the residual electric quantity of the mobile phone is accurately analyzed, the reasonable arrangement of the use of the mobile phone by the user is facilitated, and the situation that the electric quantity of the mobile phone is insufficient when the user needs to use the mobile phone is avoided.

In the prior art, a neural network model is usually set, and a fixed average slope or a weighted average slope is used to predict the predicted usage time of the remaining power. The method has the problems that the calculation mode is fixed, the result is single, and accurate estimation is difficult to achieve for the analysis of the service time under the complex terminal service condition.

Therefore, a new technical solution is needed, which can more accurately analyze the service time of the remaining power of the terminal.

Disclosure of Invention

The invention mainly aims to provide a method for analyzing the power utilization time of a terminal, the terminal and a computer readable storage medium, aiming at accurately analyzing the utilization time of the residual power of the terminal.

In order to achieve the above object, the present invention provides a method for analyzing power consumption time of a terminal, including: establishing a neural network model for analyzing the service time of the residual electric quantity of the terminal; generating a value of a node in the neural network model according to a preset initial slope; modifying the value of the node according to the history record of the terminal used by the user; and after modification, analyzing the service time of the residual electric quantity of the terminal by using the neural network model.

In order to achieve the above object, the present invention provides a terminal, characterized in that the terminal comprises a processor, a memory, a communication bus; the communication bus is used for realizing connection communication between the processor and the memory; the processor is used for executing the terminal electricity consumption time analysis program stored in the memory so as to realize the steps of the method.

To achieve the above object, the present invention provides a computer-readable storage medium storing one or more programs, which are executable by one or more processors to implement the steps of the aforementioned method.

According to the technical scheme, the method for analyzing the electricity consumption time of the terminal, the terminal and the computer readable storage medium have the advantages that:

according to the technical scheme of the invention, after a neural network model for analyzing the electricity consumption time is established and the initial slope is used for generating the nodes in the neural network, the values of the nodes in the neural network model are corrected according to the historical data of the terminal actually used by the user, so that the actual condition of electricity consumption of the terminal used by the user can be truly reflected by the value of each node, and the neural network model based on the corrected nodes can more accurately analyze the time of the residual electricity consumption of the terminal used by the user according to the specific condition of the terminal used by the user.

Drawings

Fig. 1 is a schematic diagram of a hardware structure of a mobile terminal implementing various embodiments of the present invention;

FIG. 2 is a diagram of a wireless communication system for the mobile terminal shown in FIG. 1;

fig. 3 is a flowchart of a terminal power usage time analysis method according to an embodiment of the present invention;

fig. 4 is a flowchart of a terminal power usage time analysis method according to an embodiment of the present invention;

fig. 5 is a flowchart of a terminal power usage time analysis method according to an embodiment of the present invention;

fig. 6 is a block diagram of a terminal according to one embodiment of the present invention.

The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.

Detailed Description

It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

In the following description, suffixes such as "module", "part", or "unit" used to denote elements are used only for facilitating the explanation of the present invention, and have no peculiar meaning in itself. Thus, "module", "component" or "unit" may be used mixedly.

The terminal may be implemented in various forms. For example, the terminal described in the present invention may include a mobile terminal such as a mobile phone, a tablet computer, a notebook computer, a palmtop computer, a Personal Digital Assistant (PDA), a Portable Media Player (PMP), a navigation device, a wearable device, a smart band, a pedometer, and the like, and a fixed terminal such as a Digital TV, a desktop computer, and the like.

The following description will be given by way of example of a mobile terminal, and it will be understood by those skilled in the art that the construction according to the embodiment of the present invention can be applied to a fixed type terminal, in addition to elements particularly used for mobile purposes.

Referring to fig. 1, which is a schematic diagram of a hardware structure of a mobile terminal for implementing various embodiments of the present invention, the mobile terminal 100 may include: RF (Radio Frequency) unit 101, WiFi module 102, audio output unit 103, a/V (audio/video) input unit 104, sensor 105, display unit 106, user input unit 107, interface unit 108, memory 109, processor 110, and power supply 111. Those skilled in the art will appreciate that the mobile terminal architecture shown in fig. 1 is not intended to be limiting of mobile terminals, which may include more or fewer components than those shown, or some components may be combined, or a different arrangement of components.

The following describes each component of the mobile terminal in detail with reference to fig. 1:

the radio frequency unit 101 may be configured to receive and transmit signals during information transmission and reception or during a call, and specifically, receive downlink information of a base station and then process the downlink information to the processor 110; in addition, the uplink data is transmitted to the base station. Typically, radio frequency unit 101 includes, but is not limited to, an antenna, at least one amplifier, a transceiver, a coupler, a low noise amplifier, a duplexer, and the like. In addition, the radio frequency unit 101 can also communicate with a network and other devices through wireless communication. The wireless communication may use any communication standard or protocol, including but not limited to GSM (Global System for Mobile communications), GPRS (General Packet Radio Service), CDMA2000(Code Division Multiple Access 2000), WCDMA (Wideband Code Division Multiple Access), TD-SCDMA (Time Division-Synchronous Code Division Multiple Access), FDD-LTE (Frequency Division duplex Long Term Evolution), and TDD-LTE (Time Division duplex Long Term Evolution).

WiFi belongs to short-distance wireless transmission technology, and the mobile terminal can help a user to receive and send e-mails, browse webpages, access streaming media and the like through the WiFi module 102, and provides wireless broadband internet access for the user. Although fig. 1 shows the WiFi module 102, it is understood that it does not belong to the essential constitution of the mobile terminal, and may be omitted entirely as needed within the scope not changing the essence of the invention.

The audio output unit 103 may convert audio data received by the radio frequency unit 101 or the WiFi module 102 or stored in the memory 109 into an audio signal and output as sound when the mobile terminal 100 is in a call signal reception mode, a call mode, a recording mode, a voice recognition mode, a broadcast reception mode, or the like. Also, the audio output unit 103 may also provide audio output related to a unique function performed by the mobile terminal 100 (e.g., a call signal reception sound, a message reception sound, etc.). The audio output unit 103 may include a speaker, a buzzer, and the like.

The a/V input unit 104 is used to receive audio or video signals. The a/V input Unit 104 may include a Graphics Processing Unit (GPU) 1041 and a microphone 1042, the Graphics processor 1041 Processing image data of still pictures or video obtained by an image capturing device (e.g., a camera) in a video capturing mode or an image capturing mode. The processed image frames may be displayed on the display unit 106. The image frames processed by the graphic processor 1041 may be stored in the memory 109 (or other storage medium) or transmitted via the radio frequency unit 101 or the WiFi module 102. The microphone 1042 may receive sounds (audio data) via the microphone 1042 in a phone call mode, a recording mode, a voice recognition mode, or the like, and may be capable of processing such sounds into audio data. The processed audio (voice) data may be converted into a format output transmittable to a mobile communication base station via the radio frequency unit 101 in case of a phone call mode. The microphone 1042 may implement various types of noise cancellation (or suppression) algorithms to cancel (or suppress) noise or interference generated in the course of receiving and transmitting audio signals.

The mobile terminal 100 also includes at least one sensor 105, such as a light sensor, a motion sensor, and other sensors. Specifically, the light sensor includes an ambient light sensor that can adjust the brightness of the display panel 1061 according to the brightness of ambient light, and a proximity sensor that can turn off the display panel 1061 and/or a backlight when the mobile terminal 100 is moved to the ear. As one of the motion sensors, the accelerometer sensor can detect the magnitude of acceleration in each direction (generally, three axes), can detect the magnitude and direction of gravity when stationary, and can be used for applications of recognizing the posture of a mobile phone (such as horizontal and vertical screen switching, related games, magnetometer posture calibration), vibration recognition related functions (such as pedometer and tapping), and the like; as for other sensors such as a fingerprint sensor, a pressure sensor, an iris sensor, a molecular sensor, a gyroscope, a barometer, a hygrometer, a thermometer, and an infrared sensor, which can be configured on the mobile phone, further description is omitted here.

The display unit 106 is used to display information input by a user or information provided to the user. The Display unit 106 may include a Display panel 1061, and the Display panel 1061 may be configured in the form of a Liquid Crystal Display (LCD), an Organic Light-Emitting Diode (OLED), or the like.

The user input unit 107 may be used to receive input numeric or character information and generate key signal inputs related to user settings and function control of the mobile terminal. Specifically, the user input unit 107 may include a touch panel 1071 and other input devices 1072. The touch panel 1071, also referred to as a touch screen, may collect a touch operation performed by a user on or near the touch panel 1071 (e.g., an operation performed by the user on or near the touch panel 1071 using a finger, a stylus, or any other suitable object or accessory), and drive a corresponding connection device according to a predetermined program. The touch panel 1071 may include two parts of a touch detection device and a touch controller. The touch detection device detects the touch direction of a user, detects a signal brought by touch operation and transmits the signal to the touch controller; the touch controller receives touch information from the touch sensing device, converts the touch information into touch point coordinates, sends the touch point coordinates to the processor 110, and can receive and execute commands sent by the processor 110. In addition, the touch panel 1071 may be implemented in various types, such as a resistive type, a capacitive type, an infrared ray, and a surface acoustic wave. In addition to the touch panel 1071, the user input unit 107 may include other input devices 1072. In particular, other input devices 1072 may include, but are not limited to, one or more of a physical keyboard, function keys (e.g., volume control keys, switch keys, etc.), a trackball, a mouse, a joystick, and the like, and are not limited to these specific examples.

Further, the touch panel 1071 may cover the display panel 1061, and when the touch panel 1071 detects a touch operation thereon or nearby, the touch panel 1071 transmits the touch operation to the processor 110 to determine the type of the touch event, and then the processor 110 provides a corresponding visual output on the display panel 1061 according to the type of the touch event. Although the touch panel 1071 and the display panel 1061 are shown in fig. 1 as two separate components to implement the input and output functions of the mobile terminal, in some embodiments, the touch panel 1071 and the display panel 1061 may be integrated to implement the input and output functions of the mobile terminal, and is not limited herein.

The interface unit 108 serves as an interface through which at least one external device is connected to the mobile terminal 100. For example, the external device may include a wired or wireless headset port, an external power supply (or battery charger) port, a wired or wireless data port, a memory card port, a port for connecting a device having an identification module, an audio input/output (I/O) port, a video I/O port, an earphone port, and the like. The interface unit 108 may be used to receive input (e.g., data information, power, etc.) from external devices and transmit the received input to one or more elements within the mobile terminal 100 or may be used to transmit data between the mobile terminal 100 and external devices.

The memory 109 may be used to store software programs as well as various data. The memory 109 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data (such as audio data, a phonebook, etc.) created according to the use of the cellular phone, and the like. Further, the memory 109 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid state storage device.

The processor 110 is a control center of the mobile terminal, connects various parts of the entire mobile terminal using various interfaces and lines, and performs various functions of the mobile terminal and processes data by operating or executing software programs and/or modules stored in the memory 109 and calling data stored in the memory 109, thereby performing overall monitoring of the mobile terminal. Processor 110 may include one or more processing units; preferably, the processor 110 may integrate an application processor, which mainly handles operating systems, user interfaces, application programs, etc., and a modem processor, which mainly handles wireless communications. It will be appreciated that the modem processor described above may not be integrated into the processor 110.

The mobile terminal 100 may further include a power supply 111 (e.g., a battery) for supplying power to various components, and preferably, the power supply 111 may be logically connected to the processor 110 via a power management system, so as to manage charging, discharging, and power consumption management functions via the power management system.

Although not shown in fig. 1, the mobile terminal 100 may further include a bluetooth module or the like, which is not described in detail herein.

In order to facilitate understanding of the embodiments of the present invention, a communication network system on which the mobile terminal of the present invention is based is described below.

Referring to fig. 2, fig. 2 is an architecture diagram of a communication Network system according to an embodiment of the present invention, where the communication Network system is an LTE system of a universal mobile telecommunications technology, and the LTE system includes a UE (User Equipment) 201, an E-UTRAN (Evolved UMTS Terrestrial Radio Access Network) 202, an EPC (Evolved Packet Core) 203, and an IP service 204 of an operator, which are in communication connection in sequence.

Specifically, the UE201 may be the terminal 100 described above, and is not described herein again.

The E-UTRAN202 includes eNodeB2021 and other eNodeBs 2022, among others. Among them, the eNodeB2021 may be connected with other eNodeB2022 through backhaul (e.g., X2 interface), the eNodeB2021 is connected to the EPC203, and the eNodeB2021 may provide the UE201 access to the EPC 203.

The EPC203 may include an MME (Mobility Management Entity) 2031, an HSS (Home Subscriber Server) 2032, other MMEs 2033, an SGW (Serving gateway) 2034, a PGW (PDN gateway) 2035, and a PCRF (Policy and Charging Rules Function) 2036, and the like. The MME2031 is a control node that handles signaling between the UE201 and the EPC203, and provides bearer and connection management. HSS2032 is used to provide registers to manage functions such as home location register (not shown) and holds subscriber specific information about service characteristics, data rates, etc. All user data may be sent through SGW2034, PGW2035 may provide IP address assignment for UE201 and other functions, and PCRF2036 is a policy and charging control policy decision point for traffic data flow and IP bearer resources, which selects and provides available policy and charging control decisions for a policy and charging enforcement function (not shown).

The IP services 204 may include the internet, intranets, IMS (IP Multimedia Subsystem), or other IP services, among others.

Although the LTE system is described as an example, it should be understood by those skilled in the art that the present invention is not limited to the LTE system, but may also be applied to other wireless communication systems, such as GSM, CDMA2000, WCDMA, TD-SCDMA, and future new network systems.

Based on the above mobile terminal hardware structure and communication network system, the present invention provides various embodiments of the method.

As shown in fig. 3, an embodiment of the present invention provides a method for analyzing power usage time of a terminal, including:

step S310, establishing a neural network model for analyzing the service time of the residual electric quantity of the terminal.

In the present embodiment, Neural Networks (NN) are a complex network system formed by a large number of simple processing units (called neurons) widely connected to each other, which reflects many basic features of human brain functions and is a highly complex nonlinear dynamical learning system. In this embodiment, the specific form of the neural network model is not limited, and the neural network models in the prior art are all applicable to the technical solution of this embodiment.

Step S320, generating a value of a node in the neural network model according to a preset initial slope.

In the present embodiment, the specific value of the initial slope is not limited, and the number and the type of the nodes in the neural network model are not limited.

And step S330, modifying the node value according to the history record of the user using the terminal.

In the present embodiment, the data used in the history is not limited, and for example, an operation mode of the terminal, a current power consumption situation, or the like may be used. The modified node value according to the user use history reflects the habit of using the terminal by the user, so the neural network model can be analyzed according to the use habit of the user.

And step S340, analyzing the service time of the residual electric quantity of the terminal by using the neural network model after modification.

According to the technical scheme of the embodiment, after a neural network model for analyzing the electricity using time is established and the initial slope is used for generating the nodes in the neural network, the values of the nodes in the neural network model are corrected according to the historical data of the terminal actually used by the user, so that the actual condition of electricity consumption of the terminal used by the user can be truly reflected by the value of each node, and the neural network model based on the corrected nodes can more accurately analyze the time of the residual electricity of the terminal used by the user according to the specific condition of the terminal used by the user.

As shown in fig. 4, an embodiment of the present invention provides a method for analyzing power usage time of a terminal, including:

and step S410, acquiring factors influencing the electricity utilization efficiency in the terminal, analyzing the influence degree of the factors on the electricity utilization efficiency when the factors are multiple, and screening the factors according to the influence degree.

In this embodiment, because there are many factors included in the history of the user in the terminal, and some types of factors have no influence or have a small influence on the power consumption loss of the terminal, such factors are also excluded, and only the factors with the large influence are used to set the value types of each node in the neural network model, which helps to reduce the computational complexity of the neural network model and ensure the accuracy of the neural network model in analyzing the power usage time.

Step S420, the type of the value of the node is set according to the factor.

In this embodiment, since the performance of the terminal is always limited, it is necessary to reduce the computational complexity of the neural network model, and therefore, the influence degree of the power usage efficiency is screened according to a plurality of factors, the number of node values is reduced, and meanwhile, factors having a large influence on the power usage efficiency are retained, so that the accuracy of the neural network model is ensured.

In this embodiment, the factors include the current operating mode of the terminal, the application being turned on, and/or the real-time discharge power.

And step S430, establishing a neural network model for analyzing the service time of the residual electric quantity of the terminal.

Step S440, generating a value of a node in the neural network model according to a preset initial slope.

And step S450, detecting the current load of the terminal.

And step S460, when the load is lower than the preset standard, selecting the value of the corresponding type from the historical record as the value of the node according to the type of the value of the node.

In this embodiment, when the load of the terminal is high, if the value of each node of the neural network model is modified, other work execution of the terminal is necessarily affected, so that only the node value is selected to be modified under the condition of low load.

And step S470, analyzing the service time of the residual electric quantity of the terminal by using the neural network model after modification.

A specific example of the technical solution according to this embodiment is as follows: and establishing a BP (back propagation of errors) neural network model, continuously learning the use habits of the user, and predicting the residual use time according to the use habits of the user. The specific process comprises the following steps: step 1: and establishing a neural network mathematical model, and determining the number and types of the nodes. Step 2: and setting an initial slope, and generating each node value of the neural network according to the initial slope. And step 3: the user usage pattern, including the applications turned on and the time of usage, is recorded for each point in time, and the real-time discharge power of the battery in that pattern is recorded. And 4, step 4: and correcting the node values of the neural network according to the real use condition of the user. And 5: and predicting the available time of the residual capacity by using the generated neural network.

According to the technical scheme of the embodiment, the usable time of the residual electric quantity is predicted through the neural network model, so that the usable time of the residual electric quantity is more fit with the actual use habit of a user and is not a fixed mathematical formula.

As shown in fig. 5, an embodiment of the present invention provides a method for analyzing power usage time of a terminal, including:

step S510, setting the number of the node values according to the calculation performance of the terminal.

In this embodiment, since the number of the node values in the neural network model directly affects the computational complexity, in order to avoid too high load when the terminal analyzes the power usage time, the number of the node values is set reasonably in advance according to the computational performance of the terminal, thereby avoiding affecting other operations of the terminal.

Step S520, establishing a neural network model for analyzing the usage time of the remaining power of the terminal.

Step S530, generating a value of a node in the neural network model according to a preset initial slope.

And step S540, identifying the identity of the user according to the fingerprint of the user and/or the account information used by the user.

In this embodiment, the identity of the user currently using the terminal can be identified regardless of the fingerprint of the user or the account information of the user logging in the terminal system or the application.

And step S550, when the identity of the user meets the preset condition, selecting the value of the corresponding number from the historical record as the value of the node according to the number of the values of the node.

In the present embodiment, since there may be a plurality of persons using the terminal, and the use habits of the plurality of persons are different, it is not possible to modify the node values of the neural network model according to the history formed by the plurality of persons. In this embodiment, the identity of the user is first identified, and the neural network model may be set and the corresponding values may be modified only for a certain user, for example, the owner of the terminal.

And step S560, analyzing the service time of the residual electric quantity of the terminal by using the neural network model after modification.

As shown in fig. 6, a terminal is provided in one embodiment of the invention and includes a processor 610, a memory 620, a communication bus 630; the communication bus 630 is used for realizing connection communication between the processor 610 and the memory 620; the processor 610 is configured to execute the terminal power usage time analysis program stored in the memory 620 to implement the following steps:

and establishing a neural network model for analyzing the service time of the residual electric quantity of the terminal.

And generating the values of the nodes in the neural network model according to the preset initial slope.

And modifying the value of the node according to the history of the terminal used by the user.

And after modification, analyzing the service time of the residual electric quantity of the terminal by using a neural network model.

the method comprises the steps of obtaining factors influencing the electricity utilization efficiency in a terminal, analyzing the influence degree of the factors on the electricity utilization efficiency when the factors are multiple, and screening the factors according to the influence degree.

The type of the value of the node is set according to the factor.

And detecting the current load of the terminal.

And when the load is lower than the preset standard, selecting the value of the corresponding type from the historical records as the value of the node according to the type of the value of the node.

and setting the number of the values of the nodes according to the calculation performance of the terminal.

And identifying the identity of the user according to the fingerprint of the user and/or the account information used by the user.

And when the identity of the user meets the preset condition, selecting the value of the corresponding number from the historical record as the value of the node according to the number of the values of the node.

One embodiment of the present invention provides a computer-readable storage medium storing one or more programs, the one or more programs being executable by one or more processors to perform the steps of:

It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.

The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.

Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which is stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal (such as a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the method according to the embodiments of the present invention.

While the present invention has been described with reference to the embodiments shown in the drawings, the present invention is not limited to the embodiments, which are illustrative and not restrictive, and it will be apparent to those skilled in the art that various changes and modifications can be made therein without departing from the spirit and scope of the invention as defined in the appended claims.

Claims

1. A method for analyzing the electricity consumption time of a terminal is characterized by comprising the following steps:

establishing a neural network model for analyzing the service time of the residual electric quantity of the terminal;

generating a value of a node in the neural network model according to a preset initial slope;

modifying the value of the node according to the history record of the terminal used by the user;

and after modification, analyzing the service time of the residual electric quantity of the terminal by using the neural network model.

2. The method of claim 1, further comprising, prior to the generating values for nodes in the neural network model according to a preset initial slope:

obtaining factors influencing the electricity use efficiency in the terminal, and setting the type of the node value according to the factors;

the modifying the value of the node according to the history of the terminal used by the user comprises:

and selecting the value of the corresponding type from the historical record as the value of the node according to the type of the value of the node.

3. The method of claim 2, wherein the obtaining of factors affecting power usage efficiency in the terminal further comprises:

when the factors are multiple, analyzing the influence degree of the multiple factors on the electricity use efficiency, and screening the multiple factors according to the influence degree.

4. The method of claim 2,

the factors include the current operating mode of the terminal, the application being turned on and/or the real-time discharge power.

5. The method of claim 1, further comprising, prior to the generating values for nodes in the neural network model according to a preset initial slope:

setting the number of the values of the nodes according to the calculation performance of the terminal;

and selecting the value of the corresponding number from the historical record as the value of the node according to the number of the values of the node.

6. The method according to claim 1, before said modifying the value of the node according to the history of the use of the terminal by the user, further comprising:

and identifying the identity of the user, and executing the historical record according to the terminal used by the user when the identity of the user meets the preset condition, and modifying the value of the node.

7. The method of claim 6, wherein the identifying the identity of the user comprises:

8. The method of claim 1, further comprising, prior to modifying the value of the node based on a history of use of the terminal by the user:

and detecting the current load of the terminal, and modifying the value of the node according to the history record of the terminal used by the user when the load is lower than a preset standard.

9. A terminal, characterized in that the terminal comprises a processor, a memory, a communication bus; the communication bus is used for realizing connection communication between the processor and the memory; the processor is configured to execute a terminal power usage time analysis program stored in the memory to implement the steps of the method of any one of claims 1 to 8.

10. A computer readable storage medium, characterized in that the computer readable storage medium stores one or more programs which are executable by one or more processors to implement the steps of the method of any one of claims 1 to 8.