CN112053011A

CN112053011A - Power supply optimization management method and device, electronic equipment and storage medium

Info

Publication number: CN112053011A
Application number: CN202011094702.2A
Authority: CN
Inventors: 杜玮
Original assignee: Tencent Technology Shenzhen Co Ltd
Current assignee: Tencent Technology Shenzhen Co Ltd
Priority date: 2020-10-14
Filing date: 2020-10-14
Publication date: 2020-12-08

Abstract

The invention discloses a power supply optimization management method, a device, electronic equipment and a storage medium, wherein the method comprises the following steps: obtaining historical electric quantity information of the battery, and classifying and regulating the historical electric quantity information of the battery according to a preset rule to obtain time interval classification and different electric consumption information with different fine granularities; establishing electric quantity consumption maps of different fine-grained time intervals according to the time interval classification of different fine-grained and different electric quantity consumption information; acquiring the current residual electric quantity of a battery of the electronic equipment, and estimating the available time of the current residual electric quantity of the battery of the electronic equipment according to electric quantity consumption maps of different fine-grained time intervals; and outputting the current residual capacity and the available time of the battery of the electronic equipment. According to the invention, the prediction accuracy can be improved through the electric quantity consumption maps of different fine-grained time intervals; in addition, through obtaining the historical electric quantity information of the battery, the electric consumption of the running program does not need to be collected, the system compatibility is good, and the method can be widely applied to the technical field of electronics.

Description

Power supply optimization management method and device, electronic equipment and storage medium

Technical Field

The invention relates to the technical field of electronics, in particular to a power supply optimization management method, a power supply optimization management device, electronic equipment and a storage medium.

Background

With the rapid development of communication and internet technologies, electronic devices such as mobile phones, tablet computers, electric vehicles, smart home terminals and the like are widely used, and batteries are necessary components for the operation of various electronic devices. The available time of the current remaining capacity of the battery refers to the available time of the battery from the current capacity to 0% capacity calculated by the electronic device when the battery is in a power utilization state.

In the conventional technology, when the available time of the current remaining power of the battery is calculated, the future power consumption of the battery is estimated by generally collecting the historical power consumption of a plurality of programs in the electronic equipment and then presetting the power consumption of the programs, so that the remaining time of the battery is determined. In this regard, the related art desires to estimate the future power consumption of the battery by collecting the power consumption of more programs, but in some electronic devices, such as mobile phones running the iOS system, the iOS system does not provide an interface for obtaining the power consumption of other applications, and because of the limitation of the system, it is impossible to know which applications are currently running, so that the power consumption of more programs cannot be collected, and thus the technology cannot be applied in practice.

Disclosure of Invention

In view of this, embodiments of the present invention provide a power optimization management method, device, electronic device, and storage medium with high accuracy and good system compatibility.

The first aspect of the present invention provides a power optimization management method, including:

acquiring historical electric quantity information of a battery in electronic equipment, wherein the historical electric quantity information comprises historical time information that the electronic equipment has operated and electric consumption information in the historical time;

classifying and regulating the historical electric quantity information of the battery according to a preset rule to obtain time interval classifications with different fine granularities and different electric consumption information corresponding to the time interval classifications with different fine granularities;

establishing electric quantity consumption maps of different fine-grained time intervals according to the time interval classifications of different fine granularities and different electric quantity consumption information corresponding to the time interval classifications of different fine granularities;

acquiring the current residual electric quantity of the battery of the electronic equipment, and estimating the available time of the current residual electric quantity of the battery of the electronic equipment according to the electric quantity consumption maps of different fine-grained time intervals;

and outputting the current residual capacity of the battery of the electronic equipment and the available time.

A second aspect of the present invention provides a power optimization management apparatus, including:

the device comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is used for acquiring historical electric quantity information of a battery in the electronic equipment, and the historical electric quantity information comprises historical time information that the electronic equipment has operated and power consumption information in the historical time;

the classification and normalization processing module is used for classifying and normalizing the historical electric quantity information of the battery according to a preset rule to obtain time interval classifications with different fine granularities and different electric power consumption information corresponding to the time interval classifications with different fine granularities;

the map creation module is used for creating electric quantity consumption maps of different fine-grained time intervals according to the time interval classifications of different fine granularities and different electric quantity consumption information corresponding to the time interval classifications of different fine granularities;

the estimation module is used for acquiring the current residual electric quantity of the battery of the electronic equipment and estimating the available time of the current residual electric quantity of the battery of the electronic equipment according to the electric quantity consumption maps of the different fine-grained time intervals;

and the output module is used for outputting the current residual capacity of the battery of the electronic equipment and the available time.

A third aspect of the invention provides an electronic device comprising a processor and a memory;

the memory is used for storing programs;

the processor executes the program to implement the method of the first aspect of the invention.

A fourth aspect of the invention provides a computer readable storage medium storing a program for execution by a processor to perform the method according to the first aspect of the invention.

According to the embodiment of the invention, the historical electric quantity information of the battery in the electronic equipment is obtained, the historical electric quantity information is classified and normalized according to different fine-grained time intervals, then electric quantity consumption maps of different fine-grained time intervals are created, the available time of the current residual electric quantity of the battery of the electronic equipment is estimated according to the electric quantity consumption maps of the different fine-grained time intervals, the estimation accuracy can be improved through the electric quantity consumption maps of the different fine-grained time intervals, and finally, the current residual electric quantity of the battery of the electronic equipment and the available time are output, so that the power management of the electronic equipment can be optimized; in addition, the invention acquires the historical time information of the running of the electronic equipment and the power consumption information in the historical time, and does not need to collect the actual power consumption of each running program in the electronic equipment, thereby having good system compatibility.

Drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.

Fig. 1 is a schematic view of an application scenario provided in an embodiment of the present invention;

fig. 2 is a schematic flowchart of a power optimization management method according to an embodiment of the present invention;

fig. 3 is a schematic diagram of an electric quantity obtaining process according to an embodiment of the present invention;

fig. 4 is a flowchart of steps of creating an electricity consumption map for different fine-grained time intervals according to an embodiment of the present invention;

fig. 5 is a schematic diagram of an electric energy consumption map according to an embodiment of the present invention;

fig. 6 is a schematic flowchart of another power optimization management method according to an embodiment of the present invention;

fig. 7 is a schematic flowchart of another power optimization management method according to an embodiment of the present invention;

fig. 8 is a schematic flow chart illustrating the abnormal data identification and cleaning process performed on the historical electric quantity information of the battery after the classification and normalization process according to the embodiment of the present invention;

fig. 9 is a flowchart illustrating a complete implementation of a power optimization management method according to an embodiment of the present invention;

FIG. 10 is a diagram illustrating a power consumption map for a legal work day, according to an embodiment of the present invention;

FIG. 11 is a flow chart illustrating exception data handling according to an embodiment of the present invention;

fig. 12 is a logic block diagram of a power optimization management apparatus according to an embodiment of the present invention;

fig. 13 is a logic block diagram of an electronic device according to an embodiment of the present invention.

Detailed Description

In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.

The embodiments of the present invention will be further explained with reference to the drawings.

Fig. 1 is a schematic view of an application scenario provided in an embodiment of the present invention, in which at least one electronic device 100 and at least one server 103 may be included, the electronic device 100 is equipped with a battery, and the electronic device 100 and the server 103 may implement a communication connection, where the communication connection may be a Wireless connection or a wired connection, the Wireless connection may include, but is not limited to, a Wireless Fidelity (WIFI) connection, a data connection, a bluetooth connection, an infrared connection, and the wired connection may include, but is not limited to, a Universal Serial Bus (USB) connection.

The embodiment of the invention performs power supply optimization management on the electronic equipment 100. The battery on the electronic device 100 may be any type of battery, such as a lithium battery, a nickel cadmium battery, or other batteries. The battery can be used for providing power for various electronic devices, such as an aircraft, an automobile, a terminal device, a wearable device and the like, and the electronic device can be a device equipped with the battery, and the terminal device can be a smart phone, a Personal Digital Assistant (PDA), a wearable device, a pocket pc (Personal Digital Assistant), a tablet computer and the like.

The server 103 in the embodiment of the present invention may be a server, or a server cluster composed of a plurality of servers, or a cloud computing service center, and the server 103 may be configured to obtain data information on the internet, such as historical electric quantity information of a battery of the electronic device 100, and a preset network loss ratio and a preset positioning service loss ratio. The server 103 may receive the communication data of the electronic device 100 and perform corresponding data processing, for example, execute the power optimization management method according to the embodiment of the present invention. Specifically, the server 103 may obtain the available time of the current remaining power of the battery after executing the power optimization management method, then send the available time of the current remaining power of the battery to the electronic device 100 through data communication, and finally perform power management on the electronic device 100 according to the available time of the current remaining power of the battery, or display the available time of the current remaining power of the battery, for example, the display information 101 "100% of the battery power, and the battery is expected to be available for 20 hours and 0 minute" in fig. 1.

Referring to fig. 1, taking a smart phone as an example of an electronic device, after determining an available time of a current remaining power of a battery by a power optimization management method, an embodiment of the present invention displays a content "battery power 100%, and the battery is expected to be available for 20 hours and 0 minutes" of a prompt message 101 on an interface of the smart phone, and optionally, the embodiment of the present invention may further provide a function of referring to a historical charging record, for example, a reference button 102 of "charging record" in fig. 1.

It is understood that the foregoing embodiments illustrate that the power optimization management method may be executed at the server 103, and the power optimization management method may also be executed by any suitable type of chip or controller with certain logic operation capability, which can implement the function of power optimization management, such as a main control chip (e.g., MCU) of a battery and electronic devices such as an aircraft, an automobile, a terminal device, and a wearable device equipped with a battery.

In the embodiment of the invention, a server is taken as an execution main body as an example, and when the power supply optimization management method is executed, historical electric quantity information of a battery in electronic equipment is obtained firstly, wherein the historical electric quantity information comprises historical time information of the running of the electronic equipment and electric consumption information in historical time; classifying and regulating the historical electric quantity information of the battery according to a preset rule to obtain different time interval classifications and different electric consumption information corresponding to the different time interval classifications; establishing electric quantity consumption maps of different fine-grained time intervals according to different time interval classifications and different electric quantity consumption information corresponding to the different time interval classifications; then, acquiring the current residual electric quantity of the battery of the electronic equipment, and estimating the available time of the current residual electric quantity of the battery of the electronic equipment according to electric quantity consumption maps of different fine-grained time intervals; and finally, outputting the current residual capacity and the available time of the battery of the electronic equipment to manage the power supply of the electronic equipment or enable the electronic equipment to display the available time of the current residual capacity of the battery.

Fig. 2 is a schematic flow chart of a power optimization management method according to an embodiment of the present invention. The execution main body of the power optimization management method can be a device equipped with a battery, such as an electronic device like a smart phone, a notebook computer, a tablet computer or a Personal Digital Assistant (PDA), and can also be a server end with a function of realizing power optimization management. Referring to fig. 2, taking a server as an execution subject, the power optimization management method according to the embodiment of the present invention includes steps S210 to S250:

s210, obtaining historical electric quantity information of a battery in the electronic equipment, wherein the historical electric quantity information comprises historical time information that the electronic equipment has operated and power consumption amount information in the historical time.

Specifically, the historical electric quantity information refers to electric quantity information of the battery at each time in historical dates, and the historical electric quantity information of the electronic equipment can be acquired through the internet or recorded in real time through the database.

The embodiment of the invention does not need to acquire the power consumption of each program in the electronic equipment by acquiring the power consumption of the battery, so that the embodiment of the invention can be widely applied to various types of operating systems, such as iOS, Android, Windows, Linux and the like, and has good system compatibility.

S220, classifying and regulating the historical electric quantity information of the battery according to a preset rule to obtain time interval classifications with different fine granularities and different electric consumption information corresponding to the time interval classifications with different fine granularities;

specifically, the preset rules for classifying the time intervals with different fine granularities in the embodiment of the present invention may be classified according to the legal working day and the legal holiday. It is understood that different fine-grained classifications of embodiments of the present invention may also be individually customized date types, such as determining monday through friday as one time interval, and saturday through sunday as another time interval; or, different fine-grained classifications can be determined to be in other combination forms according to the equipment use habits and the schedule planning of the user. The embodiment of the invention does not limit the time intervals of different fine-grained classifications, and those skilled in the art can understand that when the power supply optimization management method is executed in a relevant application scene, different time intervals can be adjusted according to actual needs. The embodiment of the invention aims to obtain the time interval classification with different fine granularities and different power consumption information corresponding to the time interval classification with different fine granularities so as to improve the accuracy of estimating the available time.

S230, creating electric quantity consumption maps of different fine-grained time intervals according to the time interval classifications of different fine granularities and different electric quantity consumption information corresponding to the time interval classifications of different fine granularities;

specifically, the embodiment of the invention creates the electric quantity consumption maps of different fine-grained time intervals, and the electric quantity consumption maps are more beneficial to data classification storage and data classification identification. Through the classification of different fine-grained time intervals, different date types can be determined, and then electric quantity consumption maps under different types of dates are obtained, so that the available time of the battery on the corresponding date can be estimated according to the electric quantity consumption maps under different types of dates, and the result is more accurate. The following is illustrated by a specific application scenario: for example, a user may be on a weekday from 6:00 begin using the smartphone, at 14:00 begin charging the smartphone, at 16: 00 stop charging and use to 24: 00, the battery power information of the user in the same fine-grained time interval on the working day can be recorded as shown in the following table 1:

TABLE 1

The table 1 shows that the electric quantity information of the battery in the same time interval is basically similar in the same type of date, so that after enough electric quantity information data in a certain time interval are collected, the average value of the electric consumption of the battery in the current time interval can be determined by calculating the average value of the electric consumption in the time interval. For example, in table 1 above, the user has 6: 00-9: the average power consumption of 00 is: [ (99-92) + (99-91) + (98-90) ]/3 ═ 7.67, a power consumption map was finally constructed, which was used to represent battery power consumption data for the battery during the working day.

It can be understood that for other types of dates, the method can be adopted to collect the electric quantity information of the corresponding dates, and then different electric quantity consumption maps are constructed and obtained.

S240, obtaining the current residual electric quantity of the battery of the electronic equipment, and estimating the available time of the current residual electric quantity of the battery of the electronic equipment according to the electric quantity consumption maps of different fine-grained time intervals.

Specifically, in the embodiment of the present invention, the estimated loss ratio is determined according to the current remaining capacity of the battery and the type of the current date, for example, assuming that the current time is 8 a.m. sunday: 00, the current remaining capacity of the battery is 8 in the morning on sunday: the battery power at 00 hours is classified according to the sunday time intervals determined in step S220, a power consumption map corresponding to the time interval classification is obtained, and the available time of the current remaining power of the battery of the electronic device is estimated according to the power consumption map.

Specifically, after the current remaining power of the battery is determined, the power consumption of the battery in a future period of time can be calculated by combining the power consumption map corresponding to the current time interval, and then the available time of the current remaining power of the battery is determined.

And S250, outputting the current residual capacity and the available time of the battery of the electronic equipment.

Specifically, after determining the available time of the current remaining capacity of the battery, the embodiment of the present invention may display the remaining capacity of the battery in the electronic device, for example, the display information 101 "battery capacity 100% and battery expected available 20 hours and 0 minutes" in fig. 1.

It can be understood that, according to the available time of the current remaining power of the battery, the application program running on the electronic device can be shut down, so as to save the power consumption of the relevant application program, and further prolong the operating time of the electronic device. According to the available time of the residual electric quantity of the battery, the electronic equipment can also send out a charging prompt so as to prevent the electronic equipment from stopping working due to the exhaustion of the battery. The embodiment of the invention can execute corresponding power management according to the application scene and the actual electric quantity information of the electronic equipment, and is not limited herein.

In some embodiments, the step S210 includes: when the battery is in an uncharged state, recording the real-time residual electric quantity information of the battery as historical electric quantity information.

Specifically, the embodiment of the invention can record the remaining power information of the battery through an application program running on the electronic device, and the application program can acquire the battery power of the electronic device in real time in the wake-up state. In addition, because the power consumption of the battery in the charging state cannot accurately reflect the power consumption condition of the battery, the embodiment of the invention acquires the electric quantity information of the battery in the non-charging state, reduces the error of data, uses more accurate historical electric quantity information to determine the electric quantity consumption map in the corresponding time interval, and further improves the accuracy of the available time of the current residual electric quantity of the battery through the electric quantity consumption map.

It should be understood that, in addition to recording the power information of the battery through the application program, the power information of the battery may also be recorded through an operating system installed on the electronic device, and is not limited herein.

Fig. 3 is a schematic diagram of an electric quantity obtaining process according to an embodiment of the present invention, where the electric quantity obtaining process is implemented by an application program on an electronic device, and as shown in fig. 3, when the application program is in an awake state and a battery is in a non-charging state, the electric quantity information of the battery starts to be recorded, and when the battery enters a charging state, the current recording is marked to be ended, and electric quantity data collection in a time interval is completed; and when the battery finishes charging, restarting to record the battery electric quantity information until the battery enters the charging state again or the application program is in the dormant state.

It is understood that the wake-up state of the application program may be a background wake-up or a foreground wake-up, which is not limited herein. For example, in some embodiments, the application program may start recording the battery level information by clicking a wake-up button of the application program; in other embodiments, a default startup time of the application program may be set, and when the default startup time is reached, the background automatically wakes up the application program so that the application program starts to record the battery level information.

Fig. 4 is a flowchart of steps of creating an electricity consumption map of different fine-grained time intervals according to an embodiment of the present invention, as shown in fig. 4, in some embodiments, the step S230 includes steps S410 to S440:

s410, determining a plurality of time intervals from any day of historical time, wherein the time intervals at least comprise starting time and ending time;

specifically, the embodiment of the invention processes the daily electric quantity information in the historical electric quantity information. For example, referring to table 1, for the collected power information in workday 1, it includes 6: 00-24: and 00, electric quantity information of a plurality of time intervals. At 6 in table 1: 00-14:00 this time interval is taken as an example, and in this time interval, the starting time is 6:00, end time 14:00, electric quantity data at other time points are collected in the middle of the time interval, such as 9: 00 and 12: 00. it is understood that any selected time interval has a corresponding start time and end time, and the time length of the time interval can be determined according to the start time and the end time, for example, 6:00-14: the time duration of this time interval 00 is 8 hours.

S420, determining power consumption information of the time interval according to the residual power of the time interval at the starting time and the residual power of the time interval at the ending time;

specifically, the remaining power at the starting time and the ending time is obtained according to the starting time and the ending time in the time interval determined in step S410, and then the remaining power at the starting time is subtracted from the remaining power at the ending time to calculate the battery power consumption in the time interval. For example, 6 of workday 1 of table 1: 00-14: the power consumption between 00 is 99-60 ═ 39.

S430, determining the average power consumption of each time interval according to the average value of the power consumption information of each time interval;

specifically, in the embodiment of the present invention, the power consumption amount of any time interval in any day in the historical power amount information can be determined through steps S410 and S420, and therefore, it can be understood that the historical average power consumption amount of the time interval can be calculated according to the power consumption amounts of the batteries in the same time interval of a plurality of same type dates in the historical power amount information. For example, for the weekday type illustrated in the above step S410, 6:00-14:00 this time interval has a battery power consumption of 39 hours, and according to the data in table 1, on the same type of date, weekday 2 and weekday 3 are 6:00-14:00 the power consumption in this time interval is 38 and 37 respectively, and is adjusted to 6:00-14:00 the power consumption in this time interval can be calculated as 6:00-14: the average power consumption in the time interval of 00 is (39+38+37)/3 ═ 38. In the embodiment of the present invention, the calculation formula of the average power consumption in a time interval is as follows: a (t) ([ a (t1) + a (t2) + … + a (tn)) ]/n, wherein t represents the identifier of the time interval, a (t) represents the average power consumption, and a (tn) represents the power consumption of the nth date in the time interval of t.

It can be understood that the dates corresponding to the same type in the historical power amount information are not limited to the 3 days shown in table 1, and the calculated average power consumption amount in the time interval is more accurate as the number of days of the same type increases.

And S440, creating electric quantity consumption maps of different fine-grained time intervals according to the average electric quantity consumption of each time interval.

Specifically, according to the above steps S410-S430, the average power consumption of any type of date in one time interval can be determined, so that the average power consumption of each time interval in each type can be calculated according to the power information provided by the historical power information on a plurality of types of dates, and the corresponding power consumption map can be determined by combining all the calculated average power consumptions with the average remaining power at each time in the historical power information.

For example, fig. 5 is a schematic diagram of a power consumption map according to an embodiment of the present invention, as shown in fig. 5, in a holiday type day, a value of a vertical coordinate in the power consumption map is determined according to an average remaining power at each time, each time is taken as a value of a horizontal coordinate in the power consumption map, a slope of a straight line between any two times is determined according to the average power consumption calculated in step S430, and finally the power consumption map shown in fig. 5 is constructed. In this electricity consumption map, 00: 12 the corresponding electric quantity is 99; 10: the electric quantity corresponding to 00 is 90; 11: the electric quantity corresponding to 00 is 70; 12: the electric quantity corresponding to 00 is 60; 13: the electric quantity corresponding to 00 is 55; 13: 30 is 55; 14: the electric quantity corresponding to 00 is 55; 15: 30 corresponds to an electrical quantity of 52. According to the embodiment of the invention, through constructing the electric quantity consumption map, a user can conveniently look up and analyze data, and accurate estimation of the available time of the battery can be provided based on the data.

In some embodiments, the step S240 of obtaining the current remaining power of the battery of the electronic device and estimating the available time of the current remaining power of the battery of the electronic device according to the power consumption maps of different fine-grained time intervals includes steps S241 to S242:

referring to fig. 7, S241, establishing a remaining power consumption evaluation model of the electronic device according to the power consumption maps of different fine-grained time intervals;

referring to fig. 6, in S242, after the current remaining power of the battery of the electronic device is obtained, the available time of the current remaining power of the battery of the electronic device is estimated by calling the remaining power consumption estimation model.

Specifically, after the electric quantity consumption maps of different fine-grained time intervals are created in step S440, a remaining electric quantity power consumption evaluation model of the electronic device is established, where the model includes each electric quantity consumption map of the electronic device in different time intervals, according to the model, as long as the current remaining electric quantity of the battery of the electronic device is obtained, then, in combination with the current time, the consumption of the battery in a future period of time can be estimated according to the corresponding electric quantity consumption map, and further, the available time of the current remaining electric quantity of the battery can be determined.

In some embodiments, the step S241 establishes a remaining power consumption evaluation model of the electronic device according to the power consumption maps of different fine-grained time intervals, including steps S2411-S2412:

referring to fig. 6, S2411, obtaining the remaining power at each time in the historical time from the power consumption map, and determining an average remaining power at each time;

specifically, when determining the remaining usable time of the battery of the electronic device, the power consumption of the battery after the current time needs to be calculated to estimate the usable time of the battery. Therefore, the embodiment of the invention is based on the structureAnd acquiring the residual electric quantity of each moment in the historical date corresponding to the current date from the electric quantity consumption map. For example, the current time is 16 days of the week: 00, the embodiment of the present invention needs to estimate the battery of the current electronic device on 16: loss ratio after 00. Based on this, the embodiment of the present invention obtains the collected remaining power at each time in the historical sunday from the power consumption map, for example, 16: 30. 17: 00. 17: 30. 18: 00 … …, recording the time of the current electronic equipment as t, and recording the time corresponding to the period of time as t₁、t₂、t₃……t_nAnd the residual capacity corresponding to each time is recorded as a₁、a₂、a₃……a_n。

It should be noted that, in the embodiment of the present invention, the remaining power amounts corresponding to the respective times of the multiple historical sundays are obtained, and then the average remaining power amount corresponding to each time is calculated.

The estimated loss proportion refers to the power consumption proportion of the battery in a period of time in the future. According to the embodiment of the invention, the power consumption proportion of the battery in each future time interval is estimated according to the acquired historical electric quantity information, so that parameters are provided for the subsequent process of determining the available time of the current residual electric quantity of the battery of the electronic equipment. Since the time interval classifications with different fine granularities and the different power consumption information corresponding to the time interval classifications with different fine granularities are obtained in step S220, different estimated loss ratios can be respectively determined by the power consumption data in different time intervals. The embodiment of the invention can determine the power consumption proportion of the battery in a period of time in the future according to the estimated loss proportion under different types of dates (such as legal working days and legal holidays), thereby improving the estimation accuracy.

S2412, establishing a residual electricity consumption evaluation model of the electronic equipment according to the average residual electricity at each moment, wherein the residual electricity consumption evaluation model comprises an estimated loss proportion corresponding to each moment.

Specifically, according to the average remaining power at each time, the estimated loss ratio can be calculated by combining the time difference between the times. The residual electricity consumption evaluation model is obtained by collecting the estimated loss proportion of each moment, the model can estimate the available time of the battery at any moment, and the estimation accuracy is high because each moment has the corresponding estimated loss proportion.

In some embodiments, the step S2412 is to establish a remaining power consumption evaluation model of the electronic device according to the average remaining power at each time, as shown in fig. 6, in the power optimization management method, the step S2412 includes S610-S640:

s610, acquiring time values of any two moments, and determining the time difference between the two moments;

specifically, after the current remaining capacity of the battery is determined, the remaining usable time during which the current remaining capacity of the battery can be continuously used can be determined by calculating the estimated loss proportion of the battery in a future period of time. Therefore, the purpose of steps S610-S630 of the embodiment of the present invention is to determine the estimated loss ratio of the battery in any time interval, so as to determine the estimated loss ratio of the battery in the future time interval at any time, and further determine the remaining available time.

In the embodiment of the present invention, assuming that the current time of the battery to be estimated is t, the time recorded in a time interval in the future of the time includes t₁，t₂，t₃，…，t_nIt can be understood that for a battery to be evaluated for remaining usable time, an estimated loss ratio of the battery in a future period of time needs to be determined, and the corresponding remaining usable time can be calculated by combining the current remaining capacity of the battery. In the embodiment of the present invention, a future time interval of the time refers to a time interval between the time as a starting time and a time when the latest electric quantity collection in the future is finished. Optionally, if the current time is the charging timeThen, the charging end time corresponding to the latest time in the future at the current time is used as the starting time. For example, the current time is 6:00, in the historical electric quantity information, if 6: the time corresponding to 00 is not the charging time, then 6:00 as the starting time, correspondingly, in the historical electric quantity information, if 14:00 is the charging time, then 6:00-14:00 is determined as a time interval, and thus, in combination with the data of the historical power information of table 1, in the embodiment of the present invention, n is 3, t is 6:00, and t is 3₁＝6:00，t₂＝9：00，t₃＝12：00，t₄The time difference between the respective moments is calculated as: t is t₂-t₁＝3，t₃-t₂＝3，t₄-t₃2. Optionally, in some embodiments, if the historical electric quantity information does not record the 9: 00 and 12:00, then 14:00 and 6: time difference between 00. In other embodiments, the time difference between two non-adjacent time instants may also be calculated, for example, t may be calculated₃-t₁＝6，t₄-t₁8. For another example, if the current time 6: when 00 is a charging time in the historical electric quantity information, comparing the charging time with the charging time of 6:00 the closest end of charge time as the starting time, e.g. the battery is at 6: 00-8: 00 is charging, and in the historical charge information in table 1, no 7: 00, so distance 7: 00 nearest 9: 00 this moment is taken as the starting moment, i.e. t₁＝9:00，t₂＝12：00，t₃14: 00. embodiments of the present invention therefore consist in calculating the time difference between any two times in a future period of time based on the current time.

S620, obtaining average residual electric quantity corresponding to the two moments, and determining the electric quantity difference of the two moments;

specifically, after the time difference between any two time instants is calculated in step S610, the embodiment of the present invention calculates the electric quantity difference of the average remaining electric quantity corresponding to the two time instants. For example, n is 3, t is 6:00, t₁＝6:00，t₂＝9：00，t₃＝12：00，t₄14:00, according to the works recorded in table 1Electric quantity information of working day 1-working day 3, 6:00 is 98.67 on average; 9: 00 is an average remaining capacity of 91; 12:00 is 81.67 on average; 14: the average remaining capacity of 00 is 61. Recording the average residual capacity corresponding to each moment as a_nI.e. a₁＝98.67；a₂＝91；a₃＝81.67；a₄61. At this time, the power difference between the average remaining powers at any two times is: a is₁-a₂＝7.67，a₂-a₃＝9.33，a₃-a₄＝20.67。

S630, determining a pre-estimated loss ratio according to the ratio of the time difference to the electric quantity difference;

specifically, according to the time difference and the electric quantity difference calculated in step S610 and step S620, the embodiment of the present invention can calculate the estimated loss ratio of the battery in a future period of time at the current time, where the calculation formula of the estimated loss ratio is:

wherein, a_nRepresenting the average residual capacity corresponding to each moment; t is t_nRepresenting the nth time recorded in a future time interval.

And S640, determining a residual electric quantity power consumption evaluation model according to the estimated loss proportion corresponding to each moment.

After the estimated loss proportion of each moment is determined, the residual electric quantity evaluation model comprising the estimated loss proportion corresponding to each moment is collected, the available time of the battery at any moment can be estimated based on the model, and the accuracy is high.

In some embodiments, after the current remaining power of the battery of the electronic device is obtained in step S242, the available time of the current remaining power of the battery of the electronic device is estimated by invoking the remaining power consumption estimation model, as shown in fig. 7, including steps S710-S750:

s710, acquiring the longest service time of the battery in a full-charge state and the current residual charge of the battery;

specifically, the longest usage duration of the battery in the full charge state obtained by the present invention refers to a corresponding expected usage duration of the battery in the state of 100%, where the longest usage duration may be determined from original parameters of a battery supplier, and the longest usage duration may also be determined from an average value of usage durations of the batteries in a specific usage process.

S720, acquiring a preset network loss proportion and a preset positioning service loss proportion;

the network loss ratio refers to an electric quantity loss ratio of a network communication module in the electronic device, for example, a WiFi module, a 2G module, a 3G module, a 4G module, a 5G module, a bluetooth module, and the like in a smart phone, and these network communication modules are ubiquitous in the electronic device and keep running continuously, so when the remaining usable time of a battery of the electronic device is calculated, the network loss ratio of the network communication module is used as a necessary power consumption part in the embodiment of the present invention. In addition, the loss ratio of the Positioning service refers to a power loss ratio of a Positioning module in the electronic device, such as a Global Positioning System (GPS) in a smart phone, a glonass Satellite NAVIGATION System (Global NAVIGATION SATELLITE SYSTEM, GLONASS), a Galileo Satellite NAVIGATION System (Galileo), a BeiDou Satellite NAVIGATION System (BDS), and the like, which are ubiquitous in the electronic device and keep running continuously, so when calculating the remaining available time of the battery of the electronic device, the embodiment of the present invention also takes the network loss ratio of the Positioning module as a necessary power consumption part.

S730, obtaining the estimated loss proportion of the battery by calling a residual electric quantity power consumption evaluation model;

in the embodiment of the present invention, the estimated loss ratio of the battery at the time is obtained by using the remaining power consumption estimation model established in step S241 and combining the current time of the battery.

S740, determining the sum of the estimated loss proportion, the network loss proportion and the positioning service loss proportion as a battery loss proportion;

specifically, the embodiment of the present invention changes the necessary power consumption portion determined in step S720 to: and the network loss proportion and the positioning service loss proportion are combined with the estimated loss proportion determined in the residual power consumption evaluation model, and the battery loss proportion is obtained in a gathering manner, namely the battery loss proportion is the network loss proportion, the positioning service loss proportion and the estimated loss proportion.

And S750, determining the available time of the current residual capacity of the battery according to the longest service time, the current residual capacity and the battery loss ratio.

Specifically, the formula for calculating the remaining usable time of the battery in the embodiment of the present invention is as follows: the remaining usable time of the battery is the longest usage time length × the current remaining power ratio × [1- (network loss ratio + location service loss ratio + estimated loss ratio) ].

In summary, the embodiment of the present invention can accurately calculate the remaining usable time of the battery by determining the estimated loss ratio of the battery and combining the necessary network loss ratio and the necessary location service loss ratio. Meanwhile, the battery historical electric quantity information is obtained, the electric consumption of the running program does not need to be collected, the method and the device can be widely applied to electronic equipment carrying various operating systems such as iOS, Android, Windows and Linux, and the system compatibility is good.

In some embodiments, before the step of creating the power consumption maps for different fine-grained time intervals, the method further comprises:

and S231, performing abnormal data identification and cleaning treatment on the historical electric quantity information of the batteries subjected to classification and arrangement treatment.

In the embodiment of the invention, more accurate time interval classification and power consumption information are obtained through the step S231.

Specifically, as shown in fig. 8, step S231 includes S810-S820:

s810, acquiring difference value tolerance according to the historical electric quantity information, and identifying abnormal electric quantity values in the historical electric quantity information according to the difference value tolerance;

specifically, the tolerance of the difference value refers to an acceptable degree of abnormal data in the process of collecting historical electric quantity information; if the tolerance is higher, the acceptable degree of the abnormal data is higher; a smaller tolerance indicates a smaller acceptable level of outlier data. According to the embodiment of the invention, the tolerance of the difference value is obtained, the acceptable degree of the abnormal data is further determined, and finally the abnormal electric quantity value in the historical electric quantity information is identified through the tolerance of the difference value.

S820, cleaning the abnormal electric quantity value, wherein the cleaning includes deleting the abnormal electric quantity value or storing the abnormal electric quantity value to an abnormal value database.

Specifically, for the abnormal electric quantity value determined in step S810, the embodiment of the present invention may discard the abnormal electric quantity value, so as to avoid error influence on data analysis of the normal electric quantity value, and improve the accuracy of processing and analyzing the normal electric quantity value. In other embodiments, for example, the date type of holiday, the frequency of use of the electronic device by the user is uncertain, so that the power consumption situation of the battery on holiday may fluctuate greatly, and at this time, the abnormal electric quantity value may be stored in the abnormal value database, so that the data of the date of the specific type may be processed and analyzed by the abnormal value database.

In some embodiments, step S810 includes S811-S813:

s811, acquiring the power consumption to be recorded and historical average power consumption corresponding to the power consumption to be recorded;

the power consumption to be recorded refers to the power consumption in any time interval in the historical power consumption information, wherein an abnormal power value possibly exists; the historical average power consumption amount refers to an average power consumption amount in the above-described time interval in which the abnormal power amount value may exist in the historical power amount information. For example, the power consumption amount to be recorded is a power consumption amount between 6:00 and 13:00, and the historical average power consumption amount refers to an average value of power consumption amounts between 6:00 and 13:00 on all the same type dates in the historical power amount information.

The calculation formula of the historical average power consumption of the embodiment of the invention is as follows:

the historical average power consumption amount a (t) ([ a (t1) + a (t2) + … + a (tn) ]/n, where a (tn)) represents the power consumption amount on the nth same type date in the historical power information.

S812, determining a difference value between the power consumption to be recorded and the historical average power consumption according to the power consumption to be recorded and the historical average power consumption;

specifically, the difference between the power consumption amount to be recorded and the historical average power consumption amount in step S811 is calculated, and it can be understood that the larger the difference between the power consumption amount to be recorded and the historical average power consumption amount is, the higher the possibility that the power amount value at the time corresponding to the power consumption amount to be recorded is the abnormal power amount value is; if the difference value between the power consumption to be recorded and the historical average power consumption is smaller, the higher the possibility that the power value at the moment corresponding to the power consumption to be recorded is the normal power value is.

And S813, when the difference value is larger than the tolerance of the difference value, determining the electric quantity value at the corresponding moment as an abnormal electric quantity value according to the electric quantity to be recorded.

Specifically, the embodiment of the present invention compares the difference value determined in step S812 with the tolerance of the difference value in step S811, and when the difference value is greater than the tolerance of the difference value, it indicates that the abnormal degree of the to-be-recorded power consumption exceeds the acceptable degree of the to-be-recorded power consumption to the abnormal data in the embodiment of the present invention, so that the power value corresponding to the to-be-recorded power consumption is determined as the abnormal power value. For example, in a piece of data recorded in the historical electric quantity information, the electric quantity value of 6:00 is 80, the electric quantity value of 12:00 is 20, the electric quantity value between 6:00 and 12:00 of the day is 60, the electric quantity value between 6:00 and 12:00 of all the same type dates in the historical electric quantity information is 20, the tolerance of the current difference value is 10, and 60 to 20 is greater than 10, so that the electric quantity values corresponding to the time points of 6:00 and 12:00 are marked as abnormal electric quantity values.

In some embodiments, step S812 includes:

s8121, acquiring the power consumption to be recorded and historical average power consumption corresponding to the power consumption to be recorded, and determining a difference value between the power consumption to be recorded and the historical average power consumption;

and S8122, determining the ratio of the difference value to the historical average power consumption as a difference value.

Specifically, the calculation formula of the difference value in the embodiment of the present invention is:

the difference value is | power consumption to be recorded-historical average power consumption |/historical average power consumption.

In some embodiments, the power optimization management method further includes step S232:

and S232, dynamically updating the tolerance of the difference value through the abnormal value database.

The embodiment of the invention can preset the value of the tolerance of the difference value and can also dynamically update the value of the tolerance of the difference value according to the historical electric quantity information. By the method of the embodiment of the invention, the tolerance of the difference value is dynamically updated through the historical electric quantity information, the use rule change of the battery can be adapted, for example, when more and more abnormal electric quantity values appear in the historical electric quantity information of the battery, the use rule of the battery is possible to change, the tolerance of the difference value is increased at the moment, so that the acceptable degree of the abnormal electric quantity values which are possible to appear is improved, the use rule of the battery is further updated through the possible abnormal electric quantity values, the electric quantity consumption map of the battery is re-determined, and the accurate calculation of the estimated loss proportion of the battery is ensured.

In some embodiments, as shown in FIG. 8, step S232 includes steps S2321-S2323:

s2321, the recorded days in the historical electric quantity information and the abnormal data days in the abnormal value database are obtained;

s2322, determining a negative correlation coefficient according to the recorded days, and determining a positive correlation coefficient according to the abnormal data days; wherein the negative correlation coefficient is used for determining the influence of the recorded days on the tolerance of the difference value; the positive correlation coefficient is used for determining the influence of abnormal data days on tolerance of the difference value;

s2323, tolerance initial values are obtained, and tolerance of the difference values is calculated according to the tolerance initial values, the negative correlation coefficients and the positive correlation coefficients.

Specifically, the calculation formula of the tolerance of the difference value in the embodiment of the present invention is as follows:

tolerance of difference value 50% -MAX (1, (recorded days/30 days)). 0.5+ MAX (1, (abnormal data days/30 days)). 0.5, where MAX (a, B) indicates the larger of a and B as a function.

In an embodiment of the invention, the above formula "-MAX (1, (recorded days/30 days)). 0.5" represents a negative correlation coefficient, the value of which is negative; the above expression "MAX (1, (number of abnormal data days/30 days)). 0.5" represents a positive correlation coefficient, and the value of the positive correlation coefficient is positive. The initial tolerance value of the embodiment of the invention is 50%. It is understood that the initial tolerance value is preset to be 50%, and in other embodiments, the value of the initial tolerance value may be changed according to an actual application scenario, and the present invention is not limited herein.

It can be understood that, by the method for calculating the tolerance of the difference value according to the embodiment of the present invention, it can be obtained that: when more and more data are recorded by the user, the recorded days in the calculation formula are increased, and the calculation result corresponding to the whole formula is reduced, so that the tolerance of the difference value is reduced, and abnormal data are more easily discarded; when the number of abnormal data days is more and more, the number of abnormal data days in the calculation formula is increased, the calculation result corresponding to the whole formula is increased, and further the tolerance of the difference value is increased. Therefore, the dynamic calculation of the tolerance of the difference value in the embodiment of the invention can adapt to the change of the use habit of the battery, and further can more accurately feed back the remaining available time of the battery, so that a user can more accurately plan the battery consumption of the user, and meanwhile, the battery health is kept, and the battery is prevented from being aged too fast.

Fig. 9 is a complete implementation flowchart of the power optimization management method according to the embodiment of the present invention, and the following detailed description of the power optimization management method according to the embodiment of the present invention takes classification rules of legal working days and legal holidays as preset rules for classifying the historical electric quantity information of the battery, as shown in fig. 9, includes the following steps S910 to S960:

and S910, acquiring historical electric quantity information.

Specifically, the method for acquiring the historical electric quantity information according to the embodiment of the invention can be realized by calling an application program in the electronic device. The application program may be an application program that obtains battery power information, such as a housekeeping cell phone, and when the application program is in an awake state, the application program can obtain the battery power information through the function of the application program. It is understood that the application program may be woken in a foreground manner by the user (e.g., clicking an application program start button on the interactive interface) or in a background manner actively performed by the application program (e.g., periodically starting the application program). In addition, in order to accurately grasp the usage rule of the battery, the application program of the embodiment of the invention acquires the electric quantity information of the battery in the non-charging state, because the specific power consumption condition of the battery cannot be accurately expressed in the charging state. Referring to fig. 3, the application program according to the embodiment of the present invention records the power information when the battery is in the non-charging state in the wake-up state.

And S920, classifying and regularizing the historical electric quantity information of the battery according to a preset rule to obtain time interval classifications with different fine granularities and different electric consumption information corresponding to the time interval classifications with different fine granularities.

Specifically, the embodiment of the invention takes classification rules of legal working days and legal holidays as preset rules for classifying historical electric quantity information of the battery to obtain different electric consumption information of the legal working days and the legal holidays.

By way of example, assume that a user regularly uses a smartphone on a legal day: 6, getting up and stopping charging of the smart phone; the smart phone is in an uncharged state between 6 th and 14 th points; after 14 points later, the smartphone is charged, and then the battery power information recorded by the application program in this example is 6:00-14:00, and the recorded information of three legal working days is summarized in table 2 below:

TABLE 2

	Electric quantity information of workday 1	Electric quantity information of working day 2	Electric quantity information of working day 3
				6：00	99	99	97
9：00	90	92	88
				9：30	85	82	83
10：00	80	79	77
				11：00	78	77	77
12：00	75	75	77
				13：00	60	70	66
13：30	55	60	62
				14：00	40	50	44

Based on the data provided in table 2, the power consumption map shown in fig. 10 is created in the embodiment of the present invention, and fig. 10 is a schematic diagram of the power consumption map provided in the embodiment of the present invention in the legal working day; wherein, the first folding line 1001 represents the power information of working day 1, the second folding line 1002 represents the power information of working day 2, and the third folding line 1003 represents the power information of working day 3. As can be seen from the power information of the three working days shown in fig. 10, in the power information of the same type of dates, the similarity of the power consumption processes is high, and the power consumption of each same type of date in the same time interval is basically close, so the power consumption of the time interval can be estimated according to the average value of the power consumption of a plurality of same type of dates in the time interval, and the calculation formula of the average power consumption is as follows: a (t) ([ a (t1) + a (t2) + … + a (tn)) ]/n, wherein t represents the identifier of the time interval, a (t) represents the average power consumption, and a (tn) represents the power consumption of the nth date in the time interval of t. For example, the average power consumption in the time interval 6:00-10:00 in table 2 above is: a (6:00-10:00) [ (99-80) + (99-79) + (97-77) ]/3 ═ 19.33.

And S930, judging whether the acquired historical electric quantity information is abnormal data.

Specifically, when determining whether the historical electric quantity information is abnormal data, the embodiment of the present invention may implement the following steps as shown in fig. 11:

s1110, acquiring to-be-recorded electric quantity information;

s1120, judging whether the electric quantity information to be recorded is used for constructing an electric quantity consumption map;

when the to-be-recorded electric quantity information is not used for constructing an electric quantity consumption map, executing step S1131, and storing the to-be-recorded electric quantity information into the electric quantity consumption map;

when the to-be-recorded electric quantity information is already used for constructing an electric quantity consumption map, executing step S1130, and judging whether the to-be-recorded electric quantity information belongs to the electric quantity information of the working day;

when the to-be-recorded electric quantity information does not belong to the electric quantity information of the working day, executing the step S1141, and storing the to-be-recorded electric quantity information into an electric quantity consumption map;

when the to-be-recorded electric quantity information belongs to the working day electric quantity information, executing step S1140, and determining whether the difference between the to-be-recorded electric quantity information and the historical electric quantity information is too large;

because the possibility of the change of the battery power consumption on holidays is high, the tolerance of the possible difference value is higher than that of a working day, and therefore, for the power information which does not belong to the working day, the power information to be recorded is stored in the power consumption map.

If the difference between the to-be-recorded power information and the historical power information is too large, go to step 1150, discard the to-be-recorded power information;

if the difference between the to-be-recorded power information and the historical power information is not large, step 1151 is performed to store the to-be-recorded power information in the power consumption map.

Specifically, when the step S1140 is executed, the embodiment of the present invention can be implemented by using the methods of the foregoing steps S250 and S260.

S931, discarding or storing the abnormal data into an abnormal value database;

s940, judging whether the acquired historical electric quantity information is enough to establish an electric quantity consumption map, if so, executing a step S950, and using the acquired historical electric quantity information to establish the electric quantity consumption map; otherwise, the process returns to step S910 to continue to acquire the historical power information.

And S960, estimating the available time of the current residual electric quantity of the battery according to the data of the electric quantity consumption map.

Specifically, when the available time of the current residual capacity of the battery is estimated, the estimated loss proportion can be determined by calling the residual capacity power consumption estimation model, and then the available time of the current residual capacity of the battery can be estimated by estimating the loss proportion.

The power supply optimization management method can accurately calculate the available time of the current residual electric quantity of the battery, can optimize the power supply management of the electronic equipment through the more accurate available time of the current residual electric quantity of the battery, does not need to collect the power consumption of an operation program by acquiring the historical electric quantity information of the battery, and has good system compatibility. In addition, the abnormal data in the historical electric quantity information is processed through the tolerance of the difference value, the electric quantity consumption map of the battery can be dynamically updated, the tolerance of the difference value can be adjusted in time, and the calculation accuracy of the available time of the current residual electric quantity of the battery is further improved.

Fig. 12 is a logic block diagram of a power optimization management apparatus according to an embodiment of the present invention, where the apparatus 1200 may be applied to an electronic device with a battery to implement the steps in the power optimization management method. As shown in fig. 12, the power optimization management apparatus 1200 may include:

an obtaining module 1201, configured to obtain historical power information of a battery in an electronic device, where the historical power information includes historical time information that the electronic device has been operated and power consumption amount information in the historical time;

a classification and normalization processing module 1202, configured to perform classification and normalization processing on the historical electric quantity information of the battery according to a preset rule, so as to obtain time interval classifications of different fine granularities and different electric consumption information corresponding to the time interval classifications of different fine granularities;

the map creating module 1203 is configured to create an electric quantity consumption map of different fine-grained time intervals according to the time interval classifications of different fine-grained and different electric quantity consumption information corresponding to the time interval classifications of different fine-grained;

the estimation module 1204 is configured to obtain a current remaining power of the battery of the electronic device, and estimate available time of the current remaining power of the battery of the electronic device according to the power consumption maps of the different fine-grained time intervals;

an output module 1205, configured to output the current remaining battery capacity of the electronic device and the available time.

The embodiment of the invention also provides electronic equipment, which comprises a processor and a memory;

a memory for storing a program;

and the processor is used for executing programs to execute the power supply optimization management method of the embodiment of the invention. The electronic equipment of the embodiment of the invention can realize the function of the power supply optimization management device. The electronic device may be a device with a battery, such as an aircraft, an automobile, a terminal device, a wearable device, and the like, and the terminal device may be a smart phone, a Personal Digital Assistant (PDA), a wearable device, a pocket pc (pocket pc), a tablet pc, and the like, and the electronic device is described below with reference to the drawings, and with reference to fig. 13, an embodiment of the present invention takes the terminal device as a smart phone as an example:

fig. 13 is a block diagram illustrating a partial structure of a smartphone related to a terminal device provided in an embodiment of the present invention. Referring to fig. 13, the smart phone includes: a Radio Frequency (RF) circuit 1310, a memory 1320, an input unit 1330, a display unit 1340, a sensor 1350, an audio circuit 1360, a wireless fidelity (WiFi) module 1370, a processor 1380, and a power supply 1390. The power supply 1390 may be a battery as mentioned in the embodiments of the present invention. Those skilled in the art will appreciate that the smartphone configuration shown in fig. 13 is not limiting and may include more or fewer components than shown, or some components in combination, or a different arrangement of components.

In the embodiment of the present invention, the processor 1380 included in the terminal device has the following functions:

acquiring historical electric quantity information of a battery in the electronic equipment, wherein the historical electric quantity information comprises historical time information that the electronic equipment has operated and electric consumption information in the historical time;

classifying and regulating historical electric quantity information of the battery according to a preset rule to obtain time interval classifications with different fine granularities and different electric consumption information corresponding to the time interval classifications with different fine granularities;

acquiring the current residual electric quantity of a battery of the electronic equipment, and estimating the available time of the current residual electric quantity of the battery of the electronic equipment according to electric quantity consumption maps of different fine-grained time intervals;

and outputting the current residual capacity and the available time of the battery of the electronic equipment.

The embodiment of the present invention further provides a computer-readable storage medium, where a program is stored in the computer-readable storage medium, and the program is executed by a processor to implement the power optimization management method according to the foregoing embodiments.

Embodiments of the present invention also provide a computer program product or computer program comprising computer instructions stored in a computer readable storage medium. The computer instructions may be read by a processor of the computer device from a computer-readable storage medium, and the computer instructions executed by the processor cause the computer device to perform the power optimization management method.

The terms "first," "second," "third," "fourth," and the like in the description of the application and the above-described figures, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the application described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

It should be understood that in the present application, "at least one" means one or more, "a plurality" means two or more. "and/or" for describing an association relationship of associated objects, indicating that there may be three relationships, e.g., "a and/or B" may indicate: only A, only B and both A and B are present, wherein A and B may be singular or plural. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship. "at least one of the following" or similar expressions refer to any combination of these items, including any combination of single item(s) or plural items. For example, at least one (one) of a, b, or c, may represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", wherein a, b, c may be single or plural.

In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.

The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.

In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.

The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be substantially implemented or contributed to by the prior art, or all or part of the technical solution may be embodied in a software product, which is stored in a storage medium and includes multiple instructions for causing a computer device (which may be a personal computer, a server, or a network device) to perform all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: various media capable of storing programs, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.

The above embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions in the embodiments of the present application.

Claims

1. A power supply optimization management method is characterized by comprising the following steps:

2. The method according to claim 1, wherein the creating of the power consumption maps of different fine-grained time intervals according to the different fine-grained time interval classifications and different power consumption information corresponding to the different fine-grained time interval classifications comprises:

determining a plurality of time intervals from any day of the historical time, wherein the time intervals at least comprise starting time and ending time;

determining power consumption information of the time interval according to the residual power of the time interval at the starting time and the residual power of the time interval at the ending time;

determining the average power consumption of each time interval according to the average value of the power consumption information of each time interval;

and creating electric quantity consumption maps of different fine-grained time intervals according to the average electric quantity consumption of each time interval.

3. The method according to claim 1 or 2, wherein the obtaining of the current remaining power of the battery of the electronic device and the estimating of the available time of the current remaining power of the battery of the electronic device according to the power consumption maps of the different fine-grained time intervals comprise:

establishing a residual electric quantity and power consumption evaluation model of the electronic equipment according to the electric quantity consumption maps of the different fine-grained time intervals;

and after the current residual capacity of the battery of the electronic equipment is obtained, the available time of the current residual capacity of the battery of the electronic equipment is estimated by calling the residual capacity power consumption estimation model.

4. The method for power supply optimization management according to claim 3, wherein the establishing a remaining power consumption evaluation model of the electronic device according to the created power consumption maps of different fine-grained time intervals comprises:

acquiring the residual capacity of each moment in the historical time from the electric quantity consumption map, and determining the average residual capacity of each moment;

and establishing a residual electricity consumption evaluation model of the electronic equipment according to the average residual electricity at each moment, wherein the residual electricity consumption evaluation model comprises an estimated loss proportion corresponding to each moment.

5. The method according to claim 4, wherein the establishing a power consumption estimation model of the remaining power of the electronic device according to the average remaining power at each time comprises:

acquiring time values of any two moments and determining the time difference between the two moments;

acquiring average residual electric quantity corresponding to the two moments, and determining the electric quantity difference between the two moments;

determining a pre-estimated loss ratio according to the ratio of the time difference to the electric quantity difference;

and determining a residual electric quantity power consumption evaluation model according to the estimated loss proportion corresponding to each moment.

6. The power supply optimization management method according to claim 3, wherein estimating an available time of the current remaining power of the battery of the electronic device by calling the remaining power consumption evaluation model after the current remaining power of the battery of the electronic device is obtained comprises:

acquiring the longest service time of the battery in a full-charge state and the current remaining charge of the battery;

acquiring a preset network loss proportion and a preset positioning service loss proportion;

acquiring the estimated loss proportion of the battery by calling the residual electric quantity power consumption evaluation model;

determining the sum of the estimated loss proportion, the network loss proportion and the positioning service loss proportion as a battery loss proportion;

and determining the available time of the current residual electric quantity of the battery according to the longest service time, the current residual electric quantity and the battery loss proportion.

7. The power optimization management method according to claim 1 or 2, wherein before the step of creating power consumption maps of different fine-grained time intervals, the method further comprises:

and performing abnormal data identification and cleaning treatment on the historical electric quantity information of the batteries after the classification and the arrangement treatment.

8. The power supply optimization management method according to claim 7, wherein the performing of abnormal data identification and cleaning on the historical electric quantity information of the classified and regularized batteries comprises:

acquiring difference value tolerance according to the historical electric quantity information, and identifying abnormal electric quantity values in the historical electric quantity information according to the difference value tolerance;

and performing cleaning processing on the abnormal electric quantity value, wherein the cleaning processing comprises deleting the abnormal electric quantity value or storing the abnormal electric quantity value to an abnormal value database.

9. The power supply optimization management method according to claim 8, wherein the identifying the abnormal electric quantity value in the historical electric quantity information according to the difference value tolerance includes:

acquiring power consumption to be recorded and historical average power consumption corresponding to the power consumption to be recorded;

determining a difference value between the power consumption to be recorded and the historical average power consumption according to the power consumption to be recorded and the historical average power consumption;

and when the difference value is greater than the difference value tolerance, determining the electric quantity value at the corresponding moment as an abnormal electric quantity value according to the electric quantity to be recorded.

10. The power optimization management method according to claim 9, wherein the determining a difference value between the power consumption amount to be recorded and the historical average power consumption amount according to the power consumption amount to be recorded and the historical average power consumption amount includes:

acquiring the power consumption to be recorded and historical average power consumption corresponding to the power consumption to be recorded, and determining a difference value between the power consumption to be recorded and the historical average power consumption;

determining a ratio between the difference and the historical average power consumption as a difference value.

11. The power optimization management method according to any one of claims 8-10, further comprising:

dynamically updating the tolerance of the difference value through the abnormal value database.

12. The power optimization management method according to claim 11, wherein the dynamically updating the tolerance for the difference value through the outlier database comprises:

acquiring the recorded days in the historical electric quantity information and the abnormal data days in the abnormal value database;

determining a negative correlation coefficient according to the recorded days, and determining a positive correlation coefficient according to the abnormal data days; wherein the negative correlation coefficient is used to determine the effect of the recorded days on the tolerance of the discrepancy value; the positive correlation coefficient is used for determining the influence of the abnormal data days on the tolerance of the difference value;

and acquiring an initial tolerance value, and calculating the tolerance of the difference value according to the initial tolerance value, the negative correlation coefficient and the positive correlation coefficient.

13. A power optimization management apparatus, comprising:

14. An electronic device comprising a processor and a memory;

the memory is used for storing programs;

the processor executing the program realizes the method according to any one of claims 1-12.

15. A computer-readable storage medium, characterized in that the storage medium stores a program, which is executed by a processor to implement the method according to any one of claims 1-12.