CN101860623A - Method and system for indicating service time of intelligent phone battery by sensing system context - Google Patents
Method and system for indicating service time of intelligent phone battery by sensing system context Download PDFInfo
- Publication number
- CN101860623A CN101860623A CN201010199410A CN201010199410A CN101860623A CN 101860623 A CN101860623 A CN 101860623A CN 201010199410 A CN201010199410 A CN 201010199410A CN 201010199410 A CN201010199410 A CN 201010199410A CN 101860623 A CN101860623 A CN 101860623A
- Authority
- CN
- China
- Prior art keywords
- battery
- system context
- time
- context
- discharge rate
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Classifications
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
- Y02E60/10—Energy storage using batteries
Abstract
The invention discloses a method and a system for indicating the service time of an intelligent phone battery by sensing a system context, belonging to the field of the application software of an embedded type system. The method of the invention comprises the following steps of: collecting and analyzing the electric quantity and time of a battery and the context attribute information of the system; establishing a model for the context attribute of the system and the discharge rate of the battery, and calculating the discharge rate of the battery in various contexts of the system according to the model for the context attribute of the system and the discharge rate of the battery; predicting the rest service time of the battery according to the discharge rate of the battery, and predicting the prolonged or shortened variation of the rest service time of the battery according to the discharge rate of the battery when the user changes the context attribute value of the system. The invention can indicate the rest service time of the battery on an intelligent phone and change the influence of the context attribute value of the system to the rest service time of the battery, so that the indication function of the electric quantity of the intelligent phone battery is more humanized and the prediction of the rest service time of the battery is more accurate; in addition, the invention has good marketing prospect and application value.
Description
Technical field
The present invention relates to battery of mobile phone, be specifically related to a kind of intelligent mobile phone battery residue indicating means service time and system of sensory perceptual system situation, belong to embedded system application software field.
Background technology
Along with operational capability, memory capacity, the multimedia processing capability fast development of embedded system, the application that smart mobile phone can be supported is more and more, and function develops towards variation, personalized direction from strength to strength.Yet the development of battery technology is very slow, is difficult to satisfy the demand of smart mobile phone equipment to electric weight.The subject matter that smart mobile phone faces is service time and the user experience that limited battery capacity has limited equipment.In order to address this problem, people research and develop and cut down the consumption of energy, improve each class methods of battery service efficiency on the one hand; Be exactly by improving smart mobile phone and user's man-machine interaction method, improving user's experience on the other hand.
Smart mobile phone is typical interactive device, user and system are mainly reflected in two aspects alternately: 1) smart mobile phone is by display or audible device, to user's indication mechanism current operation conditions and battery behaviour in service, for example show battery electric quantity or remaining battery service time of current residual; 2) input equipment and the menu of user by smart mobile phone starts or stops application program, and the pattern of regulation and control using system for example starts different application, perhaps allows cell phone standby, thereby changes the service time of battery of mobile phone.
Therefore, from battery relevant angle service time, a kind of method that improves user experience is exactly the battery electric quantity deixis that can provide good on smart mobile phone, accurately and timely remaining battery information service time is offered the user with reasonable manner; Perhaps can indicate: if the user continues to carry out the remaining battery service time of (as making a phone call, see video, browsing page) under certain application-specific or the operational circumstances to the user; If the user adjusts operation or changes system context attribute (reduce brightness of display screen or reduce sound), remaining battery prolongation service time or the variable quantity that shortens.Thereby be convenient to the situation that the user understands battery of mobile phone exactly, regulate own mobile phone use pattern according to own expectation to remaining battery service time.
On the present existing mobile phone, great majority all are the percentage that shows remaining battery power with the bar graph of 5-7 lattice, the user can't obtain the mobile phone information of service time, also can't know and oneself carry out under certain operational circumstances, the accurate situation of mobile phone variation service time, inconvenient user makes rational use decision-making.
Summary of the invention
The objective of the invention is to make the smart mobile phone user can be in the remaining battery mode of service time, obtain information about battery of mobile phone, can also obtain the user changes under operation or the system context situation, the variable quantity of remaining battery service time (prolong or shorten) information is so that the user regulates use pattern to mobile phone according to own expectation to remaining battery service time.
In the method, introduce the notion of system context attribute, set up system context attribute and discharge rate of battery and remaining battery three's's service time relation.The set that system context described here refers to influence battery consumption in the cell phone system and can monitor the system unit state that obtains by software mode, described system context attribute refers to constitute the individual system unit status of set, and different system context attribute and values thereof are described different system context.Studies show that system context feature and battery of mobile phone consumption have substantial connection.Utilize the monitoring of real-time system context to obtain the Data Dynamic prediction battery of mobile phone method of service time, Billy is with discharge rate of battery historical statistical data forecast method, has higher accuracy and to the adaptability of varying duty.
Specifically, battery indicating means service time that the present invention is directed to smart mobile phone comprises the following steps:
A. monitor the battery electric quantity-temporal information of battery discharge procedure under the different system context, its implementation is:
A1. allow the mobile phone executive software, obtain stable system context context and system context attribute.
Described " software " refers to the program that can carry out in the mobile phone operating system, can be the operating system program carried out of application program or backstage arbitrarily.
The set that described system context refers to influence battery consumption in the cell phone system and can monitor the system unit state that obtains by software mode; Described system context attribute refers to constitute the individual system unit status of set, and different system context attribute and values thereof are described different system context.Described system context comprises: screen backlight brightness, and processor utilization, the wireless network on off state, processor is waited for the free time ratio of input and output, network transfer speeds, and other attributes.These attributes are used symbol brt respectively, cpu, and wifi, io, spd represents.
Described other attributes refer to the system unit state except that above-mentioned attribute, sensing switch state for example, GPS on off state etc.Consider in the smart mobile phone of different model to comprise different external components that use other attribute descriptions herein, processing mode is identical with above-mentioned attribute.
Tuple (brt, cpu, a wifi constituting by one group of property value of determining, io spd) describes a specific system context, is designated as contextm, m represents system context numbering stable in this sampling process, and span is (1≤m≤M, M are the system context sums).
Described " stablizing " refers to described system context property value and is constant or excursion less than some values (for example processor utilization fluctuation range＜0.01);
A2. under this system context, monitor battery electric quantity information and temporal information.Since a time point monitoring, read battery electric quantity c (numeric representation) with 0～100, operating system time t at that time, it is right to form battery electric quantity-time.Whenever the moment that the intelligent mobile phone battery electric weight changes, the data tuple that obtains is designated as (t
i, c
i) write in the file.Wherein i represent electric weight-time sequence to numbering, span is 0～100.
Described " operating system " provides any operation system of smart phone that this type of calls, for example Android, Symbian, Windows Mobile operating system.
Described " operating system time " has been relative time or the international Greenwich Mean Time since the system start-up returned of operating system;
A3. when reaching sampling number threshold value N, sampling number stops sampling.Initially be called the sampling time during this period of time from sampling to what finish.With (t
0, C
0), (t
1, C
1) ... (t
N, c
N) battery electric quantity-time of expression in the described sampling time, N was the sampling number threshold value to sequence.
Described " sampling number threshold value N " should guarantee that the sampled data that is obtained can characterize the discharge rate of battery feature of this system context state correspondence, can be according to the system context status adjustment, and default value is 100;
A4. according to the described method of A1-A3, change the property value of system context, obtain m (m=1,2 ... M) battery electric quantity-time data under the individual system context deposits file in to sequence.With respect to first system context, the situation of change has:
1) value of an attribute of change, for example value the screen intensity from 30 to 255; And keep other property values constant;
2) value of a plurality of attributes of change for example keeps screen intensity constant, changes processor utilization and transmitted data on network amount;
3) change any property value, all defined a new context
m
B. calculate the discharge rate of battery under every kind of system context, set up the regression model by system context property calculation cell phone system discharge rate of battery, its implementation is:
B1. from monitor data file, read a system context context
mCorresponding battery electric quantity-time is to sequence;
B2. utilize described battery electric quantity-time of method B1 to sequence, carry out linear fit and obtain the discharge rate curve, as shown in Figure 2.The discharge rate curve is designated as dr=a-k among the figure
m* t, wherein a is the charge value at the intersection point place of this curve and Y-axis; k
mBe the discharge curve slope absolute value, be called for short discharge rate, be described system context context
mUnder discharge rate of battery parameter (discharge rate: battery electric quantity variable quantity in the unit interval comprises such as using voltage, the electric quantity change amount that electric current, parameters such as power are represented).System context context
mAnd corresponding discharge rate parameter k
mBe saved in the file.
B3. from the described file of A4, obtain different system context and parameter thereof, repeat the described process of B1-B2, calculate shape as (k
m, brt
m, cpu
m, wifi
m, io
m, spd
m), (the data sequence group of 1≤m≤M).Described (brt
m, cpu
m, wifi
m, io
m, spd
m) be context
mThe expansion form;
B4. the data sequence group to obtaining with B3 is carried out multiple linear regression analysis, sets up the relational model between battery of mobile phone discharge rate and the system context attribute.Multiple linear regression model is expressed as:
A=cX+ ε (formula 1)
Wherein:
In described formula 1, A is the vector of n * 1, the discharge rate of battery value that the expression aforementioned calculation obtains, and the n value is the M described in the method A4; X is a n * p matrix, expression said system situation attribute brt, cpu, wifi, io, the value each time of spd; P is the number of the system context attribute selected for use according to model, and minimum value is 1, and maximum is not limit, and default value is 5; ε is the vector of n * 1 of mould, the expression random error; C is a p * 1 matrix, and expression regression model coefficient obtains by regression analysis, preserves hereof, is used for following method C and calculates discharge rate of battery;
C. in the mobile phone running, sampling system situation property value, according to the system context property value, the model coefficient that utilizes method B4 to obtain, the dynamic calculation discharge rate of battery, with discharge rate of battery prediction battery service time, its implementation is:
C1. at any time, obtain current battery electric quantity value C, and the system context property value (brt, cpu, wifi, io, spd);
C2. obtain the electric weight C that sign electric weight default in the operating system exhausts
0Default value is 2;
C3. from file, obtain the discharge rate of battery model coefficient, counting cell discharge rate parameter
Wherein, c
iBe
The described model coefficient of method B4, x
iBe system context attribute brt, cpu, wifi, io, the value of spd.
C4. utilize the discharge rate of battery k counting cell residue of said method acquisition that C3 calculates to be T=(C-C service time
0)/(k).Time with " hour: minute " or " minute " mode represents.
D. select according to the user, prediction is change system context property value under existing battery electric quantity situation, the variation of residue service time of battery of mobile phone.Implementation method is as follows:
D1. obtain current battery electric quantity C;
D2. obtain the system context property value that user expectation changes, calculate its corresponding discharge rate of battery
D3. predict that the pairing remaining battery of described k service time is T '=(C-C
0)/(k).Time with " hour: minute " or " minute " mode represents;
D4. calculate battery of mobile phone residue variation delta T=T '-T of service time;
E. upgrade designation data, and show.Comprise following several situation:
E1. regular update shows the remaining battery duration; " regularly " default time is 1 minute, can be configured to other times by the user.
E2. when the user activates new operation or change the system context property value, upgrade described remaining battery service time.
E3. working as the user selects to check under the current battery electric quantity that the remaining battery of the particular system situation property value correspondence that it is concerned about upgrades the described time during service time.
The present invention comprises in a kind of operation system of smart phone the indication remaining battery system of service time simultaneously, and this system is made up of following module:
1). user configuration module is used to allow the user that sampling threshold values, time indicating mode, default battery are set and uses up the time interval that charge value, regular update show, the information such as system context that expection changes.Wherein the system context information of expection change is the information that must import, and under the not specified situation of user, system only shows the remaining battery service time under the current system context; All the other configuration informations are alternate information, and under situation about not importing, system adopts default information.
2). monitoring modular is used for from computer system monitoring system situation attribute, battery electric quantity-temporal information;
3). MBM, be used to utilize system context property value, the battery electric quantity-time data of monitoring acquisition right, set up system context-discharge rate of battery model.The method that realizes is shown in method B;
4). memory module is used for the information such as model coefficient that the various configurations the user, data that monitoring modular obtains and MBM calculate and is saved in the file;
5). prediction module is used for battery service time of computing system situation attribute correspondence, and changes the system context attribute and cause the battery variation of service time.The method that realizes is shown in method C, D;
6). sampling module is used to read current system context attribute and battery electric quantity information;
7). display module, be used to show battery under current system context, perhaps change the system context property value and cause the battery variable quantity of service time.The method that realizes is shown in method E.
More than each intermodule relation as shown in Figure 5:
1). user configuration module receives the user and imports configuration information, and information such as sampling threshold are issued monitoring modular, and information such as display mode are sent to display module;
2). monitoring modular sends to memory module with Monitoring Data;
3). MBM obtains Monitoring Data from memory module, will calculate the gained model coefficient and send to memory module;
4). sampling module sends to prediction module with sampled data;
5). prediction module obtains model coefficient information from memory module, obtains sampled data from sampling module; To calculate
The gained result sends to display module;
6). display module according to user's configuration, is presented at the remaining battery service time that obtains on the screen.
Advantage of the present invention is can the perception user to use the current system context of mobile phone, do not rely on specific application program or user identity, can obtain higher forecasting accuracy, stable and astable load had better adaptability, provide the battery that meets user psychology demand indication information service time to the user, thereby help the user to make corresponding selection according to the expectation of oneself, make the battery deixis service time hommization more of smart mobile phone, more accurate, have good market prospects and using value.
Description of drawings
Fig. 1: the intelligent mobile phone battery indicating means service time flow chart of sensory perceptual system situation;
Fig. 2: intelligent mobile phone battery discharge rate curve and battery predictor formula service time;
Fig. 3: battery discharge curve and discharge rate thereof when system context is (255,0.2,0,0.008,0) on the HTC-G1 smart mobile phone;
Fig. 4: the battery discharge curve when quicksort programming system situation is (255,1,0,0,0) on the HTC-G1 smart mobile phone;
Fig. 5: the intelligent mobile phone battery of sensory perceptual system situation is indicated the software systems block diagram service time.
Embodiment
Below in conjunction with accompanying drawing, the present invention is further described by battery of mobile phone residue indicating device embodiment service time who is enclosed within on the HTC-G1 smart mobile phone.
Realize an application system as shown in Figure 5, this system comprises 7 modules of user's configuration, monitoring, storage, modeling, prediction, sampling, demonstration as shown in the figure.
Method flow diagram as shown in Figure 1.The inventive method comprises: a) at mobile phone in multiple systems situation following term of execution, monitoring battery electric quantity-time is to information; B) with the inventive method the data that obtain are done analysis, set up system context attribute-discharge rate of battery relational model, obtain model coefficient, preserve hereof; C) term of execution of cell phone software, monitoring battery electric quantity information and system context attribute information; D) according to the system context attribute-discharge rate of battery of discharge rate of battery relational model calculating under this system context state; With discharge rate prediction battery service time; When e) changing the system context property value according to the discharge rate predictive user, remaining battery prolongation service time or the variable quantity that shortens.F) upgrade designation data and demonstration.Concrete steps are as follows:
1. monitor the battery electric quantity-temporal information of battery discharge procedure under the different system context.The step of concrete monitoring is described below:
A) after mobile phone operating system starts, play the film of " before sunset " with VideoPlayer.It is 255 that screen display brightness is set, and closes WiFi, and sound level is 3.
B) after system is stable, obtain current system context context, comprise 5 system context attributes (brt, cpu, wifi, io, value spd) is (255,0.2,0,0.008,0).
C) since a time point sampling.The operating system time of first sampled point is 14:26:32, and as start time point 0, battery electric quantity numerical value is 99.Note (t
0, c
0) be (0,99).Whenever the moment that battery electric quantity changes, once sample, sample altogether 100 times.The data that obtain see Table 1 to sequence:
Table 1HTC-G1 intelligent mobile phone battery electric weight sampled data
D) value of change system context attribute, the method that can take includes but not limited to change screen intensity, carries out different application program such as calculator, quicksort, the ping supervisor, open WiFi etc., obtain 16 system context attribute values shown in table 2 the 1st row, monitoring battery electric quantity-time data is right.Limit by length, omit sampled data herein.
2. calculate the discharge rate of battery under every kind of system context, set up regression model by system context property calculation cell phone system discharge rate of battery.Its specific implementation method is:
A) utilize 1 described battery electric quantity-time of method B1 his-and-hers watches to sequence, carry out linear fit and obtain the discharge rate curve, as shown in Figure 3, obtain the discharge curve slope absolute value, shown in table 2 the 1st row the 2nd row.
| ??(brt，cpu，wifi，io，spd) | Discharge rate of battery |
| ??255，0.2，0，0.008，0 | ??0.56 |
| ??192，0.2，0，0，0.008，0 | ??0.47 |
| ??80，0.2，0，0，0.008，0 | ??0.41 |
| ??30，0.2，0，0，0.008，0 | ??0.35 |
| ??255，1，0，0，0 | ??0.61 |
| ??255，0.91，0，0，0 | ??0.58 |
| ??255，0.73，0，0，0 | ??0.55 |
| ??255，0.63，0，0，0 | ??0.52 |
| ??255，0.38，0，0，0 | ??0.50 |
| ??255，0.3，0，0，0 | ??0.48 |
| ??255，0.2，0，0，0 | ??0.47 |
| ??255，0.1，0，0，0 | ??0.46 |
| ??255，0.06，1，0，40 | ??0.69 |
| ??255，0.04，1，0，30 | ??0.67 |
| ??255，0.2，1，0.007，0 | ??0.51 |
| ??255，0.21，1，0.007，0 | ??0.52 |
Table 2 example system situation property value and corresponding discharge rate of battery thereof
B) to the d of above-mentioned steps 1) the battery discharge electric weight-time data sequence of each system context property value correspondence of obtaining of step, calculate discharge rate according to method B1, shown in the 2nd row of table 2, be saved in the file.
C) the data sequence group shown in the his-and-hers watches 2 is carried out multiple linear regression analysis, sets up the model between battery of mobile phone discharge rate and the system context attribute, and expression formula is as follows:
K=0.252+0.0008*brt+0.118*cpu+0.22*wifi+7.090*io+0.0017*s pd (formula 2)
3. in the mobile phone running, sampling system situation property value, according to the system context property value, the model coefficient that utilizes method B4 to obtain, the dynamic calculation discharge rate of battery is with discharge rate of battery prediction battery service time.Concrete steps are as follows:
A) on mobile phone, carry out a quicksort program (discharge curve as shown in Figure 4).When battery electric quantity is 80, obtains the system context property value and be (255,1,0,0,0).
B) utilize described system context property value and above-mentioned formula 2, calculating discharge rate of battery is 0.574, and prediction remaining battery service time is 136 minutes; Using the remaining battery service time based on the prediction of 20 minutes in the past discharge rate of battery of historical approach is 108 minutes.Actual measurement continues to carry out this program, and the battery up duration is 135 minutes.Therefore the error with this method is 1 minute (1%), and using based on the historical approach error is 27 minutes (15%);
4. select according to the user, prediction is change system context property value under existing battery electric quantity situation, the variable quantity of battery of mobile phone service time.The concrete steps of prediction are as follows:
A) the hypothesis user starts a VideoPlayer program displaying video, and screen intensity is 255, and when battery electric quantity was 60, sampling obtained the system context property value and is (255,0.183,0,0.007,0).
B) utilize described system context property value and above-mentioned formula 2, calculating discharge rate of battery is 0.527, and prediction remaining battery service time is 110 minutes; Using the remaining battery service time based on the prediction of the discharge rate of battery of historical approach is 104 minutes.
Actual measurement: if continue to carry out this program, the battery up duration is 112 minutes.Error with this method is 2 minutes (1.8%), and using the methodical error based on history is 8 minutes (7%);
C) change the variable quantity that screen intensity is remaining battery service time under 80 the situation if user's this moment wonders,, obtain the system context property value and be (80,0.183,0,0.007,0) then according to the screen intensity value of user's input.
D) utilize described system context property value and above-mentioned formula 2, calculating discharge rate of battery is 0.386, and prediction battery service time is 149 minutes.The variable quantity that obtains the remaining battery service time under the new system context that remaining battery service time under the existing system situation and user select is Δ T=149-110=39 minute.Actual measurement is 80 if the user has changed current screen intensity value really, and then the battery time used is 139 minutes, and the variable quantity of remaining battery service time is 27 minutes.The error of this method prediction is 39-27=12 minute (7%).If adopt based on historical approach prediction remaining battery service time still be 104 minutes, the variable quantity of remaining battery service time be Δ T '=104-104=0 then error be 27 minutes (20%).
5. renewal designation data, and show.Concrete steps are as follows:
A) every 1 minute update displayed battery service time.
B) when the user uses mobile phone, whenever the system context property value changes, promptly upgrade battery service time.
C) when the user selects to check certain system context property value of change, upgrade corresponding battery variable quantity service time.
In the present embodiment, utilizing this method predicated error scope is 1%～7%, if use the method prediction based on history, error range is 7%～20%.As seen this method can provide indication more accurately and effectively for the user, is convenient to the use pattern of user according to expectation adjusting oneself, improves user experience.
It should be noted that at last the purpose of publicizing and implementing example is to help further to understand the present invention, but it will be appreciated by those skilled in the art that: without departing from the spirit and scope of the invention and the appended claims, various substitutions and modifications all are possible.Therefore, the present invention should not be limited to the disclosed content of embodiment, and the scope of protection of present invention is as the criterion with the scope that claims define.
Claims (10)
1. the method for pilot cell service time in the operation system of smart phone is characterized in that, may further comprise the steps:
A. monitor the battery electric quantity-temporal information of battery discharge procedure under the different system context;
B. calculate the discharge rate of battery under every kind of system context, set up regression model by system context property calculation cell phone system discharge rate of battery;
C. in the mobile phone running, sampling system situation property value, according to the system context property value, the model coefficient that utilizes method B to obtain, the dynamic calculation discharge rate of battery is with discharge rate of battery prediction battery service time;
D. select according to the user, prediction changes system context property value, the variation of the service time of battery of mobile phone under existing battery electric quantity situation;
E. upgrade designation data, and be presented on the mobile phone screen.
2. the method for claim 1 is characterized in that, the battery time display format of prediction be " hour: minute " or " minute "; Described system context comprises at least for influencing the cell phone system energy consumption and can monitoring the situation that obtains by software mode: screen intensity, and processor utilization, the wireless network on off state, processor is waited for the free time ratio of input and output, network transfer speeds.These attributes are used symbol brt respectively, and cpu, wifi, io, spd represent, and a tuple that constitutes by one group of property value of determining (brt, cpu, wifi, io spd) describes a specific system context, is designated as context
m, m represents system context numbering stable in this sampling process, and span is 1≤m≤M, and M is the system context sum.
3. method as claimed in claim 2 is characterized in that, the implementation method of described steps A is:
A1. allow the mobile phone executive software, obtain stable system context context
1With the system context attribute;
A2. monitoring battery electric quantity information and temporal information under this system context since a time point monitoring, read battery electric quantity c, operating system time t at that time, it is right to form battery electric quantity-time, and whenever the moment that the intelligent mobile phone battery electric weight changes, the data tuple that obtains is designated as (t
i, c
i), write in the file;
A3. when reaching sampling number threshold value N, sampling number stops sampling, with (t
0, c
0), (t
1, c
1) ... (t
N, c
N) the ordered pair during battery electric quantity of expression in the described sampling time-time;
A4. according to the described method of A1-A3, change the property value of system context, obtain m (m=1,2 ... M) battery electric quantity-time data under the individual system context deposits file in to sequence.
4. method as claimed in claim 3 is characterized in that, the implementation method of described step B is:
B1. from monitor data file, read a system context context
mCorresponding battery electric quantity-time is to sequence;
B2. described battery electric quantity-time of method B1 to sequence, carry out linear fit and obtain the discharge rate curve, the absolute value of discharge curve slope is called for short discharge rate, is described system context context
mUnder the discharge rate of battery parameter, system context context
mAnd corresponding discharge rate parameter k
mBe saved in the file;
B3. from the described file of A4, obtain different system context and parameter thereof, repeat the described process of B1-B2, calculate shape as (k
m, brt
m, cpu
m, wifi
m, io
m, spd
m), (the data sequence group of 1≤m≤M);
B4. the data sequence group to obtaining with B3 is carried out multiple linear regression analysis, sets up the relational model between battery of mobile phone discharge rate and the system context attribute, and multiple linear regression model is expressed as: A=cX+ ε wherein
A is the vector of n * 1, the discharge rate of battery value that the expression aforementioned calculation obtains, and the n value is the M described in the method A4; X is a n * p matrix, expression said system situation attribute brt, cpu, wifi, io, the value each time of spd; P is the number of the system context attribute selected for use according to model, and minimum value is 1, and maximum is not limit, and default value is 5; ε is the vector of n * 1 of mould, the expression random error; C is a p * 1 matrix, and expression regression model coefficient obtains by regression analysis, preserves hereof, is used for the subsequent calculations discharge rate of battery.
5. method as claimed in claim 4 is characterized in that, the implementation method of described step C is:
C1. at any time, obtain current battery electric quantity value C, and the system context property value (brt, cpu, wifi, io, spd);
C2. obtain the electric weight C that sign electric weight default in the operating system exhausts
0
C3. from file, obtain the discharge rate of battery model coefficient, counting cell discharge rate parameter
Wherein, c
iBe the described model coefficient of method B4, x
iBe system context attribute brt, cpu, wifi, io, the value of spd;
C4. utilize the discharge rate of battery k counting cell of said method acquisition that C3 calculates to be T=(C-C service time
0)/(k).
6. method as claimed in claim 5 is characterized in that, the implementation method of described step D is:
D1. obtain current battery electric quantity C;
D2. obtain the system context property value that user expectation changes, calculate its corresponding discharge rate of battery
D3. predict that the pairing battery of described k service time is T '=(C-C
0)/(k);
D4. calculate the battery of mobile phone variation delta T=T ' of service time-T.
7. the method for claim 1 is characterized in that, described step e comprises following situation:
E1. regular update shows the remaining battery duration; The default time of " regularly " is 1 minute, can be configured to other times by the user;
E2. when the user activates new operation or change the system context attribute, upgrade described remaining battery service time;
E3. working as the user selects to check under the current battery electric quantity that the remaining battery of the particular system situation property value correspondence that it is concerned about upgrades the described time during service time.
8. the software systems of pilot cell service time in the operation system of smart phone is characterized in that this system comprises following module:
1) user configuration module, be used to allow the user that sampling threshold values, time indicating mode, default battery are set and use up the time interval that charge value, regular update show, the information such as system context that expection changes, wherein the system context information of expection change is the information that must import, under the not specified situation of user, system only shows the remaining battery service time under the current system context; All the other configuration informations are alternate information, and under situation about not importing, system adopts default information;
2) monitoring modular is used for from computer system monitoring system situation attribute, battery electric quantity-temporal information;
3) MBM is used to utilize system context property value, the battery electric quantity-time data of monitoring acquisition right, sets up system context-discharge rate of battery model, and the method for realization is shown in method B;
4) memory module is used for the information such as model coefficient that the various configurations the user, data that monitoring modular obtains and MBM calculate and is saved in the file;
5) prediction module is used for battery service time of computing system situation attribute correspondence, and changes the system context attribute and cause the battery variation of service time, and the method for realization is shown in method C, D;
6) sampling module is used to read current system context attribute and battery electric quantity information;
7) display module is used to show battery under current system context, perhaps changes the system context property value and causes the battery variable quantity of service time, and the method for realization is shown in method E.
9. software systems as claimed in claim 8 is characterized in that:
1) user configuration module receives the user and imports configuration information, and information such as sampling threshold are issued monitoring modular, and information such as display mode are sent to display module;
2) monitoring modular sends to memory module with Monitoring Data;
3) MBM obtains Monitoring Data from memory module, will calculate the gained model coefficient and send to memory module;
4) sampling module sends to prediction module with sampled data;
5) prediction module obtains model coefficient information from memory module, obtains sampled data from sampling module; To calculate the gained result and send to display module;
6) display module according to user's configuration, is presented at the remaining battery service time that obtains on the screen.
10. software systems as claimed in claim 8 is characterized in that, show on mobile phone that the form of battery up time is " hour: minute " or " minute ".
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN2010101994105A CN101860623B (en) | 2010-06-12 | 2010-06-12 | Method and system for indicating service time of intelligent phone battery by sensing system context |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN2010101994105A CN101860623B (en) | 2010-06-12 | 2010-06-12 | Method and system for indicating service time of intelligent phone battery by sensing system context |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN101860623A true CN101860623A (en) | 2010-10-13 |
| CN101860623B CN101860623B (en) | 2012-06-13 |
Family
ID=42946272
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN2010101994105A Active CN101860623B (en) | 2010-06-12 | 2010-06-12 | Method and system for indicating service time of intelligent phone battery by sensing system context |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN101860623B (en) |
Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102928783A (en) * | 2012-07-19 | 2013-02-13 | 北京金山安全软件有限公司 | Method and device for estimating available time of battery power and mobile equipment |
| CN102937830A (en) * | 2012-07-19 | 2013-02-20 | 北京金山安全软件有限公司 | Electric quantity management method and device of mobile equipment and mobile equipment |
| CN103024761A (en) * | 2011-09-26 | 2013-04-03 | 艾默生网络能源有限公司 | Establishing method for energy consumption model of base station, and energy consumption predicating method and device |
| CN103323786A (en) * | 2013-06-24 | 2013-09-25 | 贝壳网际（北京）安全技术有限公司 | Method, device and equipment for detecting service life of battery |
| CN104244396A (en) * | 2013-06-11 | 2014-12-24 | 国际商业机器公司 | Method and system for wirelessly syncing content |
| WO2015007197A1 (en) * | 2013-07-15 | 2015-01-22 | Tencent Technology (Shenzhen) Company Limited | A method, device, platform, and mobile terminal for recording and analyzing battery power of a mobile terminal |
| CN104991192A (en) * | 2015-05-28 | 2015-10-21 | 努比亚技术有限公司 | Battery power loss detecting method and device and mobile terminal |
| CN108769347A (en) * | 2018-04-16 | 2018-11-06 | Oppo广东移动通信有限公司 | Information cuing method based on backlight illumination and Related product |
| CN111613840A (en) * | 2019-02-25 | 2020-09-01 | 仁宝电脑工业股份有限公司 | Portable electronic device and battery power management method thereof |
| CN113253121A (en) * | 2021-07-07 | 2021-08-13 | 江苏和晖电动工具有限公司 | Battery monitoring method, monitoring system and electric tool |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20030069704A1 (en) * | 2001-10-09 | 2003-04-10 | Bean Heather N. | Use-adaptive fuel gauging for battery powered electronic devices |
| CN1622039A (en) * | 2003-11-24 | 2005-06-01 | 顺德市顺达电脑厂有限公司 | Power supply detecting arrangement for hand-held information processing device and method thereof |
| US20060135218A1 (en) * | 2004-12-02 | 2006-06-22 | Son Jin H | Method for indicating a battery charge level of a wireless headset |
| CN1838086A (en) * | 2005-03-24 | 2006-09-27 | 华硕电脑股份有限公司 | Notebook computer with battery indicator |
| CN101599805A (en) * | 2008-06-06 | 2009-12-09 | 中兴通讯股份有限公司 | A kind of real-time computing technique and device of CDMA mobile phone Overall Power Consumption |
-
2010
- 2010-06-12 CN CN2010101994105A patent/CN101860623B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20030069704A1 (en) * | 2001-10-09 | 2003-04-10 | Bean Heather N. | Use-adaptive fuel gauging for battery powered electronic devices |
| CN1622039A (en) * | 2003-11-24 | 2005-06-01 | 顺德市顺达电脑厂有限公司 | Power supply detecting arrangement for hand-held information processing device and method thereof |
| US20060135218A1 (en) * | 2004-12-02 | 2006-06-22 | Son Jin H | Method for indicating a battery charge level of a wireless headset |
| CN1838086A (en) * | 2005-03-24 | 2006-09-27 | 华硕电脑股份有限公司 | Notebook computer with battery indicator |
| CN101599805A (en) * | 2008-06-06 | 2009-12-09 | 中兴通讯股份有限公司 | A kind of real-time computing technique and device of CDMA mobile phone Overall Power Consumption |
Cited By (17)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103024761A (en) * | 2011-09-26 | 2013-04-03 | 艾默生网络能源有限公司 | Establishing method for energy consumption model of base station, and energy consumption predicating method and device |
| CN103024761B (en) * | 2011-09-26 | 2016-03-02 | 艾默生网络能源有限公司 | Base station energy consumption method for establishing model, energy consumption Forecasting Methodology and device |
| CN102937830A (en) * | 2012-07-19 | 2013-02-20 | 北京金山安全软件有限公司 | Electric quantity management method and device of mobile equipment and mobile equipment |
| CN102928783B (en) * | 2012-07-19 | 2014-09-03 | 北京金山安全软件有限公司 | Method and device for estimating available time of battery power and mobile equipment |
| CN102928783A (en) * | 2012-07-19 | 2013-02-13 | 北京金山安全软件有限公司 | Method and device for estimating available time of battery power and mobile equipment |
| CN102937830B (en) * | 2012-07-19 | 2016-02-17 | 北京金山安全软件有限公司 | Electric quantity management method and device of mobile equipment and mobile equipment |
| CN104244396A (en) * | 2013-06-11 | 2014-12-24 | 国际商业机器公司 | Method and system for wirelessly syncing content |
| CN103323786B (en) * | 2013-06-24 | 2016-04-20 | 贝壳网际（北京）安全技术有限公司 | Method, device and equipment for detecting service life of battery |
| CN103323786A (en) * | 2013-06-24 | 2013-09-25 | 贝壳网际（北京）安全技术有限公司 | Method, device and equipment for detecting service life of battery |
| WO2014206270A1 (en) * | 2013-06-24 | 2014-12-31 | 贝壳网际（北京）安全技术有限公司 | Method, apparatus, and device for detecting length of time of use of battery |
| WO2015007197A1 (en) * | 2013-07-15 | 2015-01-22 | Tencent Technology (Shenzhen) Company Limited | A method, device, platform, and mobile terminal for recording and analyzing battery power of a mobile terminal |
| CN104991192A (en) * | 2015-05-28 | 2015-10-21 | 努比亚技术有限公司 | Battery power loss detecting method and device and mobile terminal |
| CN108769347A (en) * | 2018-04-16 | 2018-11-06 | Oppo广东移动通信有限公司 | Information cuing method based on backlight illumination and Related product |
| CN108769347B (en) * | 2018-04-16 | 2020-05-08 | Oppo广东移动通信有限公司 | Information prompting method based on backlight brightness and related product |
| CN111613840A (en) * | 2019-02-25 | 2020-09-01 | 仁宝电脑工业股份有限公司 | Portable electronic device and battery power management method thereof |
| CN111613840B (en) * | 2019-02-25 | 2022-03-29 | 仁宝电脑工业股份有限公司 | Portable electronic device and battery power management method thereof |
| CN113253121A (en) * | 2021-07-07 | 2021-08-13 | 江苏和晖电动工具有限公司 | Battery monitoring method, monitoring system and electric tool |
Also Published As
| Publication number | Publication date |
|---|---|
| CN101860623B (en) | 2012-06-13 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN101860623B (en) | Method and system for indicating service time of intelligent phone battery by sensing system context | |
| CN101853166B (en) | Application-oriented method and system for indicating use time of intelligent mobile phone battery | |
| CN102402726B (en) | Method for predicting electric quantity of large-scale distribution network based on regional load analysis | |
| CN103310388B (en) | Based on the composite index of grid operation method of calculation of information source entropy | |
| CN103017290A (en) | Air conditioner electric energy control device and air conditioner electric energy management method | |
| CN103338461B (en) | Based on network plan method and the device of Traffic prediction | |
| CN101206246A (en) | Method for preestimating battery residual capacity | |
| CN111680841B (en) | Short-term load prediction method, system and terminal equipment based on principal component analysis | |
| CN104007806A (en) | Method and device for controlling CPU frequency in Android system and equipment | |
| Edwards et al. | Assessing the contribution of nightly rechargeable grid-scale storage to generation capacity adequacy | |
| CN106951990A (en) | Electric load intelligent Forecasting and device | |
| CN103297623A (en) | Telephone traffic predicting method and device | |
| CN113255973A (en) | Power load prediction method, power load prediction device, computer equipment and storage medium | |
| CN103853918A (en) | Cloud computing server dispatching method based on idle time prediction | |
| CN102509173A (en) | Markov chain based method for accurately forecasting power system loads | |
| CN110544130A (en) | flow transaction monitoring method and device, computer equipment and storage medium | |
| CN104504619A (en) | Temperature/ economic growth factor considered monthly total electricity consumption predication method | |
| CN103679289B (en) | Methods of electric load forecasting based on multiple regression extrapolation | |
| CN104699890A (en) | Modeling method of short-term forewarning model for heavy overload of distribution transformer of urban power network | |
| CN114372360A (en) | Method, terminal and storage medium for power load prediction | |
| CN103106248A (en) | Stage-discharge data assimilation flood informing method, device and flood informing system | |
| CN103278707B (en) | Obtain method and the terminal of terminal standby time | |
| CN113723684A (en) | Device and method for load prediction and energy management of enterprise power users | |
| CN102722754B (en) | Coking industry is based on the coal gas dynamic balancing dispatching method of expert system and system | |
| CN109741213A (en) | Zero energy consumption architectural energy consumption control method of one kind and system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant |