Charging behavior recognition methods, device, terminal device and storage medium
Technical field
The application belong to vehicle technology field more particularly to a kind of charging behavior recognition methods, device, terminal device and
Computer readable storage medium.
Background technique
With the continuous development of science and technology, the application of electric vehicle is also more and more extensive.
Currently, when car owner is charged using charging station for electric vehicle, after completing corresponding charging order and paying, charging pile
Respective socket be electrically energized, then, electric vehicle can be connected to charging pile by the adapter of electric vehicle, charging cable by car owner
Respective socket, to charge to electric vehicle.
In electric vehicle charging process, need to ensure the interests of car owner, to guarantee the charging experience of user.If charging
The case where occurring electric vehicle plug in the process to be pulled up by other people, changing another electric vehicle charging into, will lead to the benefit of charge user
Benefit is impaired, reduces the charging experience of user.And the charging behavior for being changed trains in charging process, there is presently no effective
Recognition methods.
Summary of the invention
In view of this, the embodiment of the present application provides a kind of charging behavior recognition methods, device, terminal device and computer
Readable storage medium storing program for executing whether there is the behavior changed trains to solve not identifying in the prior art in electric vehicle charging process, from
And the problem of reducing user's charging experience.
The first aspect of the embodiment of the present application provides a kind of charging behavior recognition methods, comprising:
The charge data for the electric vehicle that charging pile uploads is obtained, the charge data includes charging current data;
Judge whether the charging current data meets preset condition, the preset condition is in charging initial time and knot
There are the periods that electric current is continuously default value between the beam time, and advance to first in advance from the initial time of the period
If the charge data at moment and the finish time from the period are belonging respectively to the charge data of the second predetermined time backward
Different charge types;
When the charging current data meets the preset condition, the behavior for existing in charging process and being changed trains is determined.
With reference to first aspect, described to judge whether the charging current data meets in a kind of feasible implementation
Preset condition, comprising:
According to the charging current data, charging current curve is generated;
Charging curve samples pictures are converted by the charging current curve;
By charging curve samples pictures input neural network model trained in advance, the charging current curve is obtained
Corresponding charge mode;
When the charge mode is preset charged mode, the charging current data meets the preset condition, described
Preset charged mode is to be configured to two times upper non-conterminous fisrt feature or two times upper non-conterminous second spy substantially
The corresponding charge mode of the charging current curve of sign, the fisrt feature is the first stage in three stage charging system curve, described
Second feature is the second stage in three stage charging system curve;
When the charge mode is non-default charge mode, the charging current data does not meet the preset condition.
With reference to first aspect, in a kind of feasible implementation, the neural network model be include input layer, first
The neural network based on the sparse coding certainly of stacking of hidden layer, the second hidden layer, more classification layers and output layer;
The neural network model that charging curve samples pictures input is trained in advance, obtains the charging current
The corresponding charge mode of curve, comprising:
The charging curve samples pictures are obtained by the input layer;
The charging curve samples pictures are inputted into first hidden layer, so that first hidden layer is to the charging
Curve samples pictures carry out feature extraction operation, export the first current curve feature;
The first current curve feature is inputted into second hidden layer, so that second hidden layer is to described first
Current curve feature carries out feature extraction operation, exports the second current curve feature, the precision of the second current curve feature
Higher than the first current curve feature;
By the second current curve feature input layers of classifying, so that more classification layer identifications second electricity more
Flow curve feature obtains charge mode classification results according to the corresponding relationship of current curve feature and charge mode;
The charge mode classification results are inputted into the output layer, so that the output layer exports the charge mode.
With reference to first aspect, described to judge whether the charging current data meets in a kind of feasible implementation
Preset condition, comprising:
By charging current data input Random Forest model trained in advance, it is corresponding to obtain the charging current data
Charge mode;
When the charge mode is preset charged mode, the charging current data meets the preset condition, described
Preset charged mode is to be configured to two times upper non-conterminous fisrt feature or two times upper non-conterminous second spy substantially
The corresponding mode of the charging current curve of sign, the fisrt feature be three stage charging system curve in first stage, described second
Feature is the second stage in three stage charging system curve;
When the charge mode is non-default charge mode, the charging current data does not meet the preset condition.
With reference to first aspect, in a kind of feasible implementation, the Random Forest model be include n decision tree
Model, n are the positive integer greater than zero;
The Random Forest model that charge data input is trained in advance, it is corresponding to obtain the charging current data
Charge mode, comprising:
Classified by the n decision tree to the charging current data, obtains n classification results;
Determine final classification as a result, using the final classification result as the charging mould from the n classification results
Formula, the final classification result are the classification results that quantity is more than or equal to n/2 in the n classification results.
With reference to first aspect, in a kind of feasible implementation, judge whether the charging current data accords with described
Before conjunction preset condition, further includes:
According to the charging voltage data in the charge data, whether the charging station where judging the charging pile stands
The case where point spread of voltage;
When the charging station does not occur the case where website spread of voltage, it is into the subsequent judgement charging current data
No the step of meeting preset condition.
With reference to first aspect, in a kind of feasible implementation, exist in the determining charging process and changed trains
After behavior, further includes:
Generate prompt information;
The prompt information is presented to charge user by user terminal, to prompt in the charge user charging process
In the presence of the behavior of changing trains.
The second aspect of the embodiment of the present application provides a kind of charging behavior identification device, comprising:
Charge data obtains module, the charge data of the electric vehicle for obtaining charging pile upload, the charge data packet
Include charging current data;
Judgment module, for judging whether the charging current data meets preset condition, the preset condition is to fill
There are the periods that electric current is continuously default value between electrical initiation time and end time, and from when the starting of the period
Carve the charge data for advancing to the first predetermined time and finish time from the period filling to the second predetermined time backward
Electric data are belonging respectively to different charge types;
Identification module, for determining in charging process and existing when the charging current data meets the preset condition
The behavior changed trains.
In conjunction with second aspect, in a kind of feasible implementation, the judgment module includes:
Curve generation unit, for generating charging current curve according to the charging current data;
Conversion unit, for converting charging curve samples pictures for the charging current curve;
First charge mode recognition unit, for the neural network that charging curve samples pictures input is trained in advance
Model obtains the corresponding charge mode of the charging current curve;
First determination unit, for when the charge mode is preset charged mode, the charging current data to meet
The preset condition, the preset charged mode are to be configured to two times upper non-conterminous fisrt feature or two times substantially
The corresponding charge mode of charging current curve of upper non-conterminous second feature, the fisrt feature are in three stage charging system curve
First stage, the second feature be three stage charging system curve in second stage;
Second determination unit, for when the charge mode is non-default charge mode, the charging current data to be not
Meet the preset condition.
In conjunction with second aspect, in a kind of feasible implementation, the neural network model be include input layer, first
The neural network based on the sparse coding certainly of stacking of hidden layer, the second hidden layer, more classification layers and output layer;
The first charge mode recognition unit includes:
Subelement is obtained, for obtaining the charging curve samples pictures by the input layer;
Fisrt feature extracts subelement, for the charging curve samples pictures to be inputted first hidden layer, so that
First hidden layer carries out feature extraction operation to the charging curve samples pictures, exports the first current curve feature;
Second feature extracts subelement, for the first current curve feature to be inputted second hidden layer, so that
Second hidden layer carries out feature extraction operation to the first current curve feature, exports the second current curve feature, institute
The precision for stating the second current curve feature is higher than the first current curve feature;
First classification subelement, for the second current curve feature to be inputted the layers of classifying more, so that described more
Classification layer identifies that the second current curve feature is charged according to the corresponding relationship of current curve feature and charge mode
Pattern classification result;
Subelement is exported, for the charge mode classification results to be inputted the output layer, so that the output layer is defeated
The charge mode out.
In conjunction with second aspect, in a kind of feasible implementation, the judgment module includes:
Second charge mode recognition unit, for the random forest mould that charging current data input is trained in advance
Type obtains the corresponding charge mode of the charging current data;
Third determination unit, for when the charge mode is preset charged mode, the charging current data to meet
The preset condition, the preset charged mode are to be configured to two times upper non-conterminous fisrt feature or two times substantially
The corresponding mode of charging current curve of upper non-conterminous second feature, the fisrt feature are the in three stage charging system curve
One stage, the second feature are the second stage in three stage charging system curve;
4th determination unit, for when the charge mode is non-default charge mode, the charging current data to be not
Meet the preset condition.
In conjunction with second aspect, in a kind of feasible implementation, the Random Forest model be include n decision tree
Model, n are the positive integer greater than zero;
The second charge mode recognition unit includes:
Second classification subelement obtains n for classifying by the n decision tree to the charging current data
A classification results;
Subelement is chosen, for determining final classification as a result, by the final classification result from the n classification results
As the charge mode, the final classification result is the classification knot that quantity is more than or equal to n/2 in the n classification results
Fruit.
In conjunction with second aspect, in a kind of feasible implementation, further includes:
Website voltage judgment module, for judging the charging pile according to the charging voltage data in the charge data
Whether the charging station at place there is the case where website spread of voltage;
Into module, for not occurring the case where website spread of voltage when the charging station, into described in subsequent judgement
The step of whether charging current data meets preset condition.
In conjunction with second aspect, in a kind of feasible implementation, further includes:
Generation module, for generating prompt information;
Cue module, for the prompt information to be presented to charge user by user terminal, to prompt the charging
There is behavior of changing trains in user's charging process.
The third aspect of the embodiment of the present application provides a kind of terminal device, including memory, processor and is stored in institute
The computer program that can be run in memory and on the processor is stated, the processor executes real when the computer program
Now such as the step of any one of above-mentioned first aspect the method.
The fourth aspect of the embodiment of the present application provides a kind of computer readable storage medium, the computer-readable storage medium
Matter is stored with computer program, and the side as described in above-mentioned any one of first aspect is realized when the computer program is executed by processor
The step of method.
Existing beneficial effect is the embodiment of the present application compared with prior art:
The embodiment of the present application judges to fill in charging process with the presence or absence of the behavior changed trains by charging current data
There is intermediate a period of time electric current and be continuously default value in electric current, and electric current is continuously the charge data point before and after default value
Do not belong to different charging class process, then occur the behavior changed trains in charging process, to realize in charging process
The identification for the charging behavior changed trains improves user's charging experience.
Detailed description of the invention
It in order to more clearly explain the technical solutions in the embodiments of the present application, below will be to embodiment or description of the prior art
Needed in attached drawing be briefly described, it should be apparent that, the accompanying drawings in the following description is only some of the application
Embodiment for those of ordinary skill in the art without any creative labor, can also be according to these
Attached drawing obtains other attached drawings.
Fig. 1 is charging scenarios schematic diagram provided by the embodiments of the present application;
Fig. 2 is a kind of schematic process flow diagram of charging behavior recognition methods provided by the embodiments of the present application;
Fig. 3 is charging curve schematic diagram provided by the embodiments of the present application;
It is that the another of charging behavior recognition methods provided by the embodiments of the present application flows that Fig. 4, which is provided by the embodiments of the present application,
Journey schematic block diagram;
Fig. 5 is provided by the embodiments of the present application sparse from the neural network schematic diagram encoded based on stacking;
Fig. 6 is charge mode identification process schematic block diagram provided by the embodiments of the present application;
Fig. 7 is the current curve feature schematic diagram of the first hidden layer provided by the embodiments of the present application output;
Fig. 8 is the current curve feature schematic diagram of the second hidden layer provided by the embodiments of the present application output;
Fig. 9 is another schematic process flow diagram of charging behavior recognition methods provided by the embodiments of the present application;
Figure 10 is Random Forest model schematic diagram provided by the embodiments of the present application;
Figure 11 is the confusion matrix schematic diagram of Random Forest model provided by the embodiments of the present application;
Figure 12 is the structural schematic block diagram of charging behavior identification device provided by the embodiments of the present application;
Figure 13 is the schematic diagram of terminal device provided by the embodiments of the present application.
Specific embodiment
In being described below, for illustration and not for limitation, the tool of such as particular system structure, technology etc is proposed
Body details, so as to provide a thorough understanding of the present application embodiment.However, it will be clear to one skilled in the art that there is no these specific
The application also may be implemented in the other embodiments of details.In other situations, it omits to well-known system, device, electricity
The detailed description of road and method, so as not to obscure the description of the present application with unnecessary details.
Before introducing the specific technical solution of the embodiment of the present application, the embodiment of the present application may relate to first application
Explanation is introduced in scene.
Referring to charging scenarios schematic diagram shown in fig. 1, under the charging scenarios include charging station 1, electric vehicle to be charged 2,
Server 3, user terminal 4, interior charging station 1 includes at least one charging pile 11.User terminal can by carrier network with
Background server communication, charging station and charging pile can be communicated by the place network in charging station with background server, be used
Family terminal can be communicated by internet with charging pile.There is at least one socket on charging pile, charging car owner can be by electronic
Electric vehicle to be charged is connected to the socket of charging pile by vehicle adapter, charging cable and plug.When charging car owner passes through user's end
After charging order payment is completed at end, background server can control the corresponding socket of charging pile and be powered, and can treat charging electric
Motor-car charges.
Wherein, corresponding APP is installed, to realize and the corresponding industry such as backstage interaction, calculating, human-computer interaction in user terminal
Business function, which can be specially the intelligent terminals such as mobile phone, plate.Electric vehicle to be charged can be specially electrical salf-walking
Vehicle, battery-operated motor cycle or electric car etc..
Car owner charge by the two dimensional code on user terminal barcode scanning charging pile, user terminal get two-dimensional barcode information it
After jump to corresponding interface;On the surface, charging car owner can carry out the behaviour such as charge mode selection, charging amount of money input
Make;After determining charging order information, which can be uploaded to server by charging pile, server by with user terminal
Data interaction is carried out, after completing order payment, server is notified that charging pile, charging pile can then control respective socket energization,
Then, charging car owner can then start to charge.
During the charging process, charging pile can acquire the charge datas such as charging current, charging voltage, charge power, and will
The charge data is uploaded to server.Specifically, charging pile is after collecting the charge data of electric vehicle, to background server
Reporting equipment telemetering message, which may include the information such as charging current, voltage, charge power, so that backstage
Server can collect the charge data of each electric vehicle to charge on charging pile.After the completion of to be charged, background server
It can store when time relevant information of charging order, and the charge data of charge order and charging process is associated storage.
After server receives the charge data that charging pile reports, charging current curve, charging can be correspondingly drawn
Voltage curve, power curve etc..Then, server identifies that is reported fills according to data such as charging current curve, voltage curves
The corresponding charge mode of electric data.After identifying charge mode, can be determined according to charge mode in charging process whether
There are the either unsound charging behaviors of some unusual conditions can pass through in real time if identifying corresponding charging behavior
User terminal feeds back to user.
It is to be appreciated that above-mentioned mentioned application scenarios are only exemplary scene, do not cause to the embodiment of the present application
The restriction of concrete scene.
It, below will be to skill provided by the embodiments of the present application after having introduced the application scenarios that the embodiment of the present application may relate to
Art scheme describes in detail explanation.In order to illustrate technical solution described herein, carried out below by specific embodiment
Explanation.
Embodiment one
Fig. 2 is referred to, is a kind of schematic process flow diagram of charging behavior recognition methods provided by the embodiments of the present application, the party
Method may comprise steps of:
Step S201, the charge data for the electric vehicle that charging pile uploads is obtained, charge data includes charging current data.
It is to be appreciated that above-mentioned charge data generally comprises charging voltage data, charging current data and charge power number
According to.And in electric vehicle charging process, charging voltage and charge power are usually invariable, in some cases it may
It is based only on charging current data and carries out corresponding analysis identification, at this point, above-mentioned charge data can only include charging current data.And
In other cases, it needs to use charging current data and charging voltage data, at this point, above-mentioned charge data can only include
Charging voltage data and charging current data.
The charge data can be the data that charging pile uploads in real time, i.e., passes through power supply adaptor, charging cable in electric vehicle
During the socket of connection charging pile is charged, charge data collected is passed through the formal of telemetering message by charging pile
Background server is offered, background server parses the telemetering message, according to relevant information entrained by telemetering message, for example, filling
Electric unique ID of stake equipment etc., obtains the charge data that each charging pile reports.Certainly, which is also possible to history charging
Data, the history charge data are obtained by storing the electric vehicle charge data that charging pile uploads in real time.
Step S202, judge whether charging current data meets preset condition, when charging current data meets preset condition
When, S203 is entered step, conversely, entering step S204 when charging current data does not meet preset condition.
Wherein, preset condition be charge initial time and between the end time there are electric current be continuously default value when
Between section, and advance to the charge data of the first predetermined time and finish time from the period backward from the initial time of period
Charge data to the second predetermined time is belonging respectively to different charge types.
It is to be appreciated that above-mentioned default value can be zero, i.e., during terminating since charging to charging, when centre has one section
Between current value be continuously zero;The default value may be other non-zero values, which is typically less than steady section electricity
50% numerical value of flow valuve.
Above-mentioned first predetermined time and the second predetermined time can determine by actual charging current data, this is first pre-
If the moment be usually charge initial time, the second predetermined time is usually charging finishing time, at this point, from charging initial time to
The charge data being continuously between the initial time of the period of default value belongs to a charging process class, and pre- from being continuously
If the finish time of the period of numerical value to the charge data between charging finishing time belongs to another charging process class.Change sentence
It talks about, is continuously the charge data before and after the period of default value and belongs to two different charging processes, that is, be continuously default
Charging electric vehicle before and after the period of numerical value is different two electric vehicles.
In order to preferably introduce the form of expression for the charging current data for meeting preset condition, shown in Figure 3 fills
Electric curve synoptic diagram includes 9 width figures in Fig. 3, is in three rows, three column distribution, this 9 width figure is according to charging voltage data and charging
The charge graph that current data is accordingly drawn includes charging current curve and charging voltage curve in every width figure.
Wherein, the horizontal axis in every width figure indicates the time, and left side vertical pivot indicates that electric current, the right vertical pivot indicate voltage, every width figure
In it is constant near 220V, continue for some time directly be kept to afterwards zero curve be charging voltage curve, that is, one charge
In period, charging voltage keeps 220V constant, and fluctuates little.And another song in every width figure in addition to charging voltage curve
Line is charging current curve.
The charge graph shown in Fig. 3 the second row first row as it can be seen that near t=100, be down to suddenly by charging current value
Zero, and continue to t=200, it then skyrockets again to certain numerical value.At this point, default value is zero, it is continuously for zero period
Initial time between near t=200, to be continuously for zero period near t=100 is near t=100, to be continuously zero
Period finish time be t=200 near, and the first predetermined time be t=0, the second predetermined time be t=400.Hold very much
Easily find out, the charging curve for being continuously zero front and back is different two charging current curves.The first row first row in Fig. 3,
A line third column, the third line first row and the tertial charge graph of the third line and the second row first row described above
Charge graph is similar.And from the charge graph of the first row secondary series in Fig. 3 as it can be seen that its default value is not zero, and should
Default value is less than the 50% of steady section current value (1.2A or so), similar with the tertial curve graph of the second row in Fig. 3.
In the specific implementation, judging whether the detailed process of preset condition may include: first according to charging to charging current data
Current data identifies the corresponding charge mode of the current data, then determines whether the current data accords with according to charge mode
It closes and states preset condition.Wherein, the identification of charge mode can be carried out by Random Forest model, at this point, in advance train with
Current data, is then input in Random Forest model by machine forest model, and charge mode can be obtained;It can also be first by charging
Data conversion is converted to samples pictures at charging curve, then by charging curve, then utilizes neural network model trained in advance
Charge mode identification is carried out to the samples pictures, obtains recognition result.It is of course also possible to by being different from above two mode
Judgment mode realizes the judgement of charging current data.
The material type of each battery of electric vehicle, all kinds of component contents, battery capacity, residue SOC, cell degradation degree,
Charging adapter, generation producer etc. are different, lead to battery during the charging process and will appear the various electric current forms of expression,
There can not be identical two electric vehicles of current curve, therefore can be with charging current curve to the current charging shape of battery
State, the safety of battery, the degree of aging of battery, safety of user's charging behavior etc. identify and judge.Further
Charging Activity recognition is carried out in conjunction with practical charging scenarios and type of service.
In other words, when occurring the behavior changed trains during the charging process, in some cases, it can be possible to need to pull out
The charging plug of electric vehicle, plug again an other electric vehicle charging plug movement, in this process, charging current can
Zero or some numerical value can be dropped suddenly to, and continue for some time and (may be considered that pull out plug slotting to plugging the duration
Direct time interval), and being continuously the charging curve before and after the period of default value is to belong to different charge types.When
So, in other cases,
Step S203, there is the behavior changed trains in charging process.
Step S204, there is no the behaviors changed trains in charging process.
Refer to that the electric vehicle that will currently charge changes another electric vehicle charging into it is appreciated that changing trains.In view of filling
It is lower that a possibility that behavior changed trains occurs during the charging process in electric car master, therefore under normal circumstances it is considered that the behavior of changing trains is it
What its user executed.That is, the electric vehicle that other car owners are charging has been changed into the electric vehicle of oneself by some users, this
Sample, the user do not have to pay i.e. chargeable, but this stealing electricity behavioral implications legitimate interests of charges paid car owner.
The embodiment of the present application is realized in conjunction with practical charging scenarios and type of service to charging according to charging current data
In the process by the identification for the behavior of changing trains, and it can timely feedback after identifying this behavior and give charging car owner, to ensure
The interests of charging car owner provide charging experience.
It in some embodiments, can also include: that generation mentions after there is the behavior changed trains in determining charging process
Show information;It will be prompted to information by user terminal and be presented to charge user, changed trains with prompting to exist in charge user charging process
Behavior.Wherein, which refers to the information for behavior of having changed trains in characterization charging process, for example, the prompt information can be
" according to charging curve intellectual analysis, suspecting that charging is replaced by other Vehicular chargings on the way ".The prompt information can by mobile phone,
The display screen of the user terminals such as plate is presented to the user.
In addition, charging current is influenced by charging station voltage, in order to improve identification accuracy, charging station can be first judged
Whether website voltage is stable, then judges whether charging current data meets preset condition again.Therefore in some embodiments, upper
It states before judging whether charging current data meets preset condition, can also include: according to the charging voltage number in charge data
According to whether the charging station where judging charging pile the case where website spread of voltage occurs;When charging station does not occur website voltage
The step of whether charging current data meets preset condition judged into subsequent for unstable situation.
Wherein it is possible to set voltage and fluctuated between 200~240V be it is normal, can according to the collected charging voltage of institute
To judge whether website voltage is stable, if phenomena such as high pressure, under-voltage, oscillation occur.If website voltage stabilization,
Subsequent charging current judgment step can be entered.Certainly, in some other embodiment, can also not have to carry out website voltage
Judgement.
In the present embodiment, judge to fill in charging process with the presence or absence of the behavior changed trains by charging current data
There is intermediate a period of time electric current and be continuously default value in electric current, and electric current is continuously the charge data point before and after default value
Do not belong to different charging class process, then occur the behavior changed trains in charging process, to realize in charging process
The identification for the charging behavior changed trains improves user's charging experience.
Embodiment two
Fig. 4 is referred to, is another schematic process flow diagram of charging behavior recognition methods provided by the embodiments of the present application, it should
Method may comprise steps of:
Step S401, the charge data for the electric vehicle that charging pile uploads is obtained, charge data includes charging current data.
Step S402, according to charging current data, charging current curve is generated.
Specifically, according to data such as charging current, voltages, corresponding curve is drawn out in the coordinate system of setting.
It is to be appreciated that under normal circumstances, charge data mainly includes electric current, voltage and power, and the charging of electric vehicle
Power be usually it is constant, power curve battery status analysis identification on can play the role of very little, voltage identification mistake
Journey is relatively simple, can generally complete before electric current identification.Therefore, in some cases, it is only necessary to current curve or electricity
Flow curve and voltage curve.In addition, the material type of battery of electric vehicle, all kinds of component contents, battery capacity, residue SOC,
Cell degradation degree, charging adapter, manufacturer etc. are different, lead to battery during the charging process and will appear and is various
The electric current form of expression, it is impossible to which there are identical two electric vehicles of current curve, therefore can be with charging current curve to battery
Present charge state, the charge safety etc. of behavior of the safety of battery, user identifies and judges.In other words, exist
During charge mode identifies, the analysis identification that current curve carries out charge mode is depended on.
Step S403, charging curve samples pictures are converted by charging current curve.
Specifically, current curve is converted to the picture of certain pixel size (such as 128 × 128);To the picture pixels
Gray value is standardized, and obtains charging curve samples pictures.Wherein it is possible to pass through the standardized side of logarithm Logistic
Formula handles picture, so that the pixel value of picture is fallen between 0~1.
Step S404, the input of charging curve samples pictures is trained in advance neural network model, obtains charging current song
The corresponding charge mode of line.
It is to be appreciated that above-mentioned neural network model can be based on the sparse neural network from coding is stacked, which can
To specifically include input layer, two layers of hidden layer, more classification layers and output layer.The neural network model is advanced with including all
The current data of charge mode is trained, to obtain suitable network parameter.The neural network model can be from charging curve
Extract corresponding current curve feature in samples pictures, and identify the current curve feature, according to the current curve feature and
The corresponding relationship of charge mode obtains the corresponding charge mode of the charging curve.
The charging current curve of different electric vehicles be it is different, still, different charge datas but may include certain
A little identical features, different feature combinations may be constructed different charge modes.Wherein, current curve feature can refer to table
The feature of the curve of certain shapes is levied, i.e., with a certain section of character representation certain curve shape or the curve of certain function.For example, electric
Notch feature in flow curve feature, the corresponding one section of current curve in groove shapes of the notch feature, is embodied in electric current
After one is persistently slowly drop down to not for 0 numerical value, and then slowly rises to and differed with decline process starting position electric current
The position below 0.2A.
In another example charging current curve is usually three-stage, normal three stage charging system curve includes first stage,
Two-stage and phase III regard first stage, second stage, phase III corresponding curve as a current curve respectively
Feature, that is, first stage feature, second stage feature and phase III feature, wherein first stage character representation is normal
The curve of first segment in three stage charging system curve;Under continuing in the normal three stage charging system curve of second stage character representation
Section drops, and the time span declined is half an hour;Continue in the normal three stage charging system curve of phase III character representation
Charging duration is greater than 1 hour after descending branch, and current value is lower than one section of curve of 0.3A.
The rest may be inferred, shape and other characteristics for current curve, with the different curved section of different character representations.
In the present embodiment, charging current curve feature may include 14, be respectively as follows: groove, first stage, second stage, third
Stage, the first stage part oscillation, second stage part oscillation, the phase III part oscillation, it is convex, start electric current be less than 0.3A,
Intermediate is 0, single ladder, intermediate ladder, full oscillation, multiple lasting ladders.It certainly, can also be as needed in practical application
Increase or decrease the classification of current curve feature.
The sequential combination of different curvilinear characteristics can characterize different charge modes, i.e., wrapped according in charging current curve
The chronological order that the curvilinear characteristic contained and these curvilinear characteristics occur, corresponding different charge mode.In the present embodiment
In, charge mode may include 13, which may include: an only stage, only a two-stage, three stages
Entirely, one or three stages, only two or three stages, only more three stages, high current, vehicles of the same order same period fill simultaneously
More vehicles of electric, same order different periods charge respectively, the temperature control time is greater than the charging of 2 hours, full oscillation, stop suddenly
(extraneous factor) stops (non-extraneous factor) suddenly.Wherein, different charge modes are combined to obtain by different curvilinear characteristics.Example
Such as, " an only stage " corresponding charging current curve of charge mode only has " first stage " curvilinear characteristic, that is, charging at this time
Current curve only includes the first segment in normal three stage charging system curve.
After the picture of corresponding charging current curve is input to neural network model, neural network model can be extracted
Curvilinear characteristic determines the corresponding charge mode of the charging current curve according to extracted curvilinear characteristic.If for example, some
It include first stage, second stage and the phase III in normal three-stage curve in charging current curve, then by the charging
Curve picture is input to after neural network model, and neural network model can then extract " first stage " feature, " second-order
Section " feature and " phase III " feature, then according to " first stage " feature, " second stage feature " and " phase III "
The time of occurrence of feature determines the corresponding charge mode of the charging current curve, if the chronological order of three features is
" first stage " feature, " second stage " feature, " phase III " feature, then neural network model can determine the charging current
The corresponding charge mode of curve is " three stages are complete ".
In some embodiments, above-mentioned neural network model be include input layer, it is the first hidden layer, the second hidden layer, more
The neural network based on the sparse coding certainly of stacking of classification layer and output layer, which can be specifically as shown in Figure 5
Neural network specifically includes input layer Input L1, hidden layer Layer L2, hidden layer Layer L3, output layer Output
L4.Network parameter W, h, f therein can be determined by model training.Layers of classifying are not shown in Fig. 5 more.
At this point, charge mode identification process schematic block diagram shown in Figure 6, above-mentioned to input charging curve samples pictures
Trained neural network model, the detailed process for obtaining charge mode recognition result may include: in advance
Step S601, charging curve samples pictures are obtained by input layer.
Step S602, charging curve samples pictures are inputted into the first hidden layer, so that the first hidden layer is to charging curve sample
This picture carries out feature extraction operation, exports the first current curve feature.
Step S603, the first current curve feature is inputted into the second hidden layer, so that the second hidden layer is to the first electric current song
Line feature carries out feature extraction operation, exports the second current curve feature, and the precision of the second current curve feature is higher than the first electricity
Flow curve feature.
Step S604, by the second current curve feature input layers of classifying, so that more classification layer identifications more
The second current curve feature obtains charge mode classification knot according to the corresponding relationship of current curve feature and charge mode
Fruit.
Step S605, the charge mode classification results are inputted into the output layer, so that described in output layer output
Charge mode recognition result.
Specifically, after neural network model gets charging current curve picture, the first hidden layer can be according to picture
Data extract the feature of charging current curve, input of the output of the first hidden layer as the second hidden layer, the second hidden layer
Extraction further is carried out to the curvilinear characteristic inputted, obtains more accurate curvilinear characteristic, and the curvilinear characteristic is defeated
At most classification layer, layers of classifying carry out mode combinations classification according to curvilinear characteristic, then export classification results to output layer more out,
Obtain charge mode classification results.
It is to be appreciated that second current curve aspect ratio the first current curve feature is more accurate, the output of the first hidden layer
Current curve feature can be as shown in fig. 7, the current curve feature of the second hidden layer output can be as shown in Figure 8.Second hides
The precision that the effect of layer can be further improved curvilinear characteristic is extracted accordingly, it can be said that the quantity of hidden layer
The precision of curvilinear characteristic is higher, conversely, the quantity of hidden layer is fewer, curvilinear characteristic precision is lower.But hide the increasing of layer number
Can mostly will lead to certain features to be submerged, therefore, can according to actual needs, accuracy requirement etc. determine the number of hidden layer
Amount.
The corresponding relationship of above-mentioned current curve feature and charge mode refers to preset different charge mode and each
Corresponding relationship between curvilinear characteristic, different charge modes can be combined by different current curve features.In order to preferably
The relationship between current curve feature, current curve feature and charge mode is introduced, is illustrated below in conjunction with Tables 1 and 2.
1 charging current curve mark sheet of table
Upper table 1 be charging current curve mark sheet, for convenience, when this 14 features are described below, respectively with A~
N capitalization accordingly indicates.In table 1, each feature has corresponding feature description, indicatrix, and indicatrix refers to this
The expression of the corresponding curve shape of feature.It is appreciated that in a particular application, can also define as needed different from upper table 1
Shown in curvilinear characteristic.
Different features is combined available different charge mode.14 features in above-mentioned table 1 are carried out
Combination, available 13 kinds of small charge modes, 13 kinds of small charge modes can be divided into 4 kinds of big charge modes again, and 4
The big charge mode of kind is respectively to charge normal, abnormal charging, vibrate charging entirely and stop charging suddenly.Physical relationship is as follows
Shown in table 2.
2 charge mode table of table
Upper table 2 shows 13 kinds of small charge modes and the corresponding curvilinear characteristic of 13 kinds small charge mode combines, is corresponding
Big charge mode.Wherein, the feature of A~N in table 2 in charge mode composition refers to A~N feature shown in above-mentioned table 1.It can
To understand, charge mode shown in table 2 is only a kind of example, may include more or fewer charging moulds in concrete application
Formula classification.
It is to be appreciated that neural network model exports 13 kinds of small charge modes the result is that in above-mentioned table 2, and filled according to small
Power mode and the preset corresponding relationship of big charge mode, the corresponding big charge mode of available each small charge mode.
It is of course also possible to preset the corresponding relationship of small charge mode and big charge mode, neural network in neural network model
Model can also identify small charge mode and then export big charge mode according to small charge mode.That is, neural network model
Output result be also possible to the big charge mode of above-mentioned table 2.Certainly, output result can also simultaneously include small charge mode and
Big charge mode.
It is appreciated that above-mentioned neural network model can be what training in advance was completed, and the training of the neural network model
Process can specifically include: obtain training sample data collection, training sample data collection be include the corresponding electricity of all charge modes
The data set of flow curve samples pictures;Data preprocessing operation is carried out to training sample data collection;According to pretreated training
Sample data set is trained the neural network model pre-established.
It is appreciated that above-mentioned training sample data collection includes multiple pictures, it include all charge modes pair in the data set
The picture for the charging curve answered.
Wherein, picture can be converted to the picture of a standard by data preprocessing operation.For example, samples pictures are big
Small is 128 × 128 pixels, and above-mentioned third presetted pixel size is 8 × 8 pixels, and the first preset quantity is 1000, the second present count
Amount is 500,000, and third preset quantity is 30,000.Firstly, converting 128 × 128 for the current data of all charging curve modes
Then the picture of pixel size extracts the small of 1000 8 × 8 pixels at random from the picture of each 128 × 128 pixels
Picture, and these small pictures are divided into U1, U2 two major classes, the small picture in U1 include current curve and picture background, in U2
Small picture only includes picture background.Then, randomly selected out respectively from U1 data set and U2 data set 50 Wan Zhang little pictures and
3 Wan Zhang little pictures form 530,000 training samples, and press logarithm Logistic to the grey scale pixel value of this 530,000 training samples
Mode makees standardization, so that the grey scale pixel value of training sample is fallen between 0~1.By 530,000 after standardization
A training sample is denoted as X={ x1, x2..., xn, n=530000.
For example, when neural network model is neural network as shown in Figure 5, by training sample X={ x1,
x2..., xnBe input to after neural network, hidden layer L2Extracting obtained current curve feature is Λm={ λ21, λ22...,
λ2m, hidden layer L3To ΛmFeature extraction is carried out, Λ is obtainedk={ λ31, λ32..., λ3k, hidden layer L3By ΛkThe more classification of input
Layer, obtains classification results, then classification results are exported to output layer L4, obtain output result Y={ y1, y2..., yn}.Meanwhile
It can also obtain to obtain input layer L1With hidden layer L2Between parameter ω, hidden layer L2With hidden layer L3Between parameter h, it is hidden
Hide layer L3With output layer L4Between parameter f.
After training, obtained training result can be detected, when the training result of output and the charging of setting
When difference between mode is in acceptable accuracy rating, then corresponding network parameter can be determined, subsequently into identification rank
Section.
After identifying charge mode, charging behavior can be further identified according to charge mode.
Step S405, judge whether charge data is preset charged mode.When charge mode is preset charged mode, into
Enter step S406, conversely, entering step S407 when charge mode is non-default charge mode.
Step S406, charging current data meets preset condition, determines the behavior for existing in charging process and being changed trains.It is default
Charge mode is to be configured to two times upper non-conterminous fisrt feature or two times upper non-conterminous second feature substantially
The corresponding charge mode of charging current curve, fisrt feature are the first stage in three stage charging system curve, second feature three
Second stage in segmentation charging curve.
It is appreciated that above-mentioned preset charged mode can refer to the 9th kind of charge mode in above-mentioned table 2.The charge mode
Corresponding charging current curve is configured to two B features or two C features substantially, and identical feature not phase in time
It is adjacent.When analyzing by neural network model charge data, determine that the different periods of the same charging order there are more vehicle chargings,
It may be considered that being to have been changed vehicle in charging process, conversely, not changed trains then.
Step S407, charging current data does not meet preset condition, determines that there is no the behaviors changed trains in charging process.
It can be seen that charge data of the present embodiment based on electric vehicle, by based on the nerve net stacked from sparse coding
With the presence or absence of the charging behavior changed trains in network model identification charging process, user's charging experience is improved.
Embodiment three
Fig. 9 is referred to, is another schematic process flow diagram of charging behavior recognition methods provided by the embodiments of the present application, it should
Method may comprise steps of:
Step S901, the charge data for the electric vehicle that charging pile uploads is obtained, charge data includes charging current data.
Step S902, charge data input is trained in advance Random Forest model, obtains the corresponding charging of charge data
Mode.
It is to be appreciated that Random Forest model can be the model for including n decision tree, n is the positive integer greater than zero, the mould
Type can model in the Random Forest model schematic diagram of specific Figure 10, as shown in Figure 10 comprising decision tree Tree1,
Tree2 ... Tree (n-1), Tree (n), every decision tree carry out classification processing to corresponding random sample collection, obtain corresponding
Classification results Class1, Class1 ... type 2, type 3.At this point, the above-mentioned random forest mould that charge data input is trained in advance
Type, the detailed process for obtaining the charge mode of charge data may include: to be divided by n decision tree charging current data
Class obtains n classification results;Determine final classification as a result, by final classification result from n classification results by voting mechanism
As charge mode, final classification result is the classification results that quantity is more than or equal to n/2 in n classification results.Wherein, random
After each decision tree obtains classification results in forest, voting mechanism can use, quantity accounting is reached to 50% or more point
Class result is as final classification as a result, the classification results result is the corresponding charge mode recognition result of charge data.For example,
As shown in figure 3, being then " class by the final result of ballot when the quantity accounting of classification results " Class1 " reaches 50% or more
Type 1 ".
The output result of random forest is usually 13 kinds of small charge modes shown in table 2 in above-described embodiment two, and according to
Small charge mode and the preset corresponding relationship of big charge mode, the corresponding big charging mould of available each small charge mode
Formula.It is of course also possible to preset the corresponding relationship of small charge mode and big charge mode in Random Forest model, in this way with
Machine forest model is obtaining small charge mode and then is exporting big charge mode according to small charge mode, that is, Random Forest model
Output result be also possible to the big charge mode of above-mentioned table 2.Certainly, output result can also simultaneously include small charge mode and
Big charge mode.
After charging current data is input to Random Forest model, each decision tree in random forest is according to preparatory instruction
Experienced model parameter, two Tables 1 and 2, classifies to the charging current data based on the above embodiment, obtains classification knot
Fruit, each classification results are pattern classification of each decision tree to the charging current data, finally by each classification results of statistics
Quantity, using the classification results of 50% or more quantity accounting as the final output of model, which is
Charge mode recognition result.If for example, including normal three sections in the corresponding charging current curve of some charging current data
First stage, second stage and phase III in formula curve, after Random Forest model carries out Classification and Identification to the current data,
It can determine that the corresponding charge mode of the charging current data is " three stages are complete ".
Wherein, the model parameter of random forest is to be obtained by training in advance, and the training process of random forest is specific
It include: to obtain training sample set and corresponding charge mode label;According to training sample set and charge mode label, to random gloomy
Woods model is trained.
It is appreciated that constructing the pattern recognition model i.e. random forest mould of charge data based on random forest C4.5 algorithm
Type, random forest are a kind of learning algorithms for having supervision, and supervised learning algorithm needs to utilize the sample data area for having label
Model is gone on patrol, model is enable to reach desired effectiveness.In the training process, random forest has the selection put back to using random
Training sample set simultaneously constructs corresponding decision tree, and each decision tree randomly chooses feature again and classifies.Random forest obtains institute
There are the classification results of decision tree, by the result that selects frequency of occurrence most as final output.
Above-mentioned training sample set is the data set for including the corresponding charging current data of all charge modes, voltage data,
Corresponding charge mode label refers to that the corresponding charge mode of each charging current data, voltage data, the charge mode are behaved
Work calibration.As shown in Figure 10, in training process, random forest extracts one with randomly putting back to from whole training samples concentration
Divided data chooses n sample set as sample set altogether, obtains n decision tree, each decision tree m feature of random selection into
Row classification, each decision tree obtains corresponding classification results, then by counting to obtain training result to the end.
It include these three hyper parameters of the number of Characteristic Number, the number of decision tree and leaf in Random Forest model.Pass through
After enough training samples are trained model, the relevant parameter in model can be determined.It can be right after the completion of training
Whether model is tested, met the expected requirements with testing model effect.Referring to obscuring for Random Forest model shown in Figure 11
For matrix schematic diagram it is found that it is to test under the parameter setting of default Random Forest model, the classification for obtaining 32 classes is smart
Degree is 84.3%, from Figure 11 it can also be seen that when the classification of training data is more, due to having spy between each charge mode
The part for levying overlapping, can have a certain impact to precision, therefore 32 kinds of charge modes in Figure 11 can be merged into table 2 above
13 class charge modes, after merging, nicety of grading be can be improved to 87%.Certainly, in practical application, point of charge mode
Class can be set according to actual needs.
After identifying charge mode, charging behavior can further be identified according to the charge mode.
Step S903, judge whether charge mode is preset charged mode, when charge mode is preset charged mode, into
Enter step S904, conversely, entering step S905.
Step S904, charging current data meets preset condition, determines the behavior for existing in charging process and being changed trains.Its
In, preset charged mode is to be configured to two times upper non-conterminous fisrt feature or two times upper non-conterminous second substantially
The corresponding mode of the charging current curve of feature, fisrt feature are the first stage in three stage charging system curve, and second feature is
Second stage in three stage charging system curve.
It is appreciated that above-mentioned preset charged mode can refer to the 9th kind of charge mode in above-mentioned table 2.The charge mode
Corresponding charging current curve is configured to two B features or two C features substantially, and identical feature not phase in time
It is adjacent.When analyzing by neural network model charge data, determine that the different periods of the same charging order there are more vehicle chargings,
It may be considered that being to have been changed vehicle in charging process, conversely, not changed trains then.
Step S905, charging current data does not meet preset condition, determines that there is no the behaviors changed trains in charging process.
It can be seen that charge data of the present embodiment based on electric vehicle, identified in charging process by Random Forest model
With the presence or absence of the charging behavior changed trains, user's charging experience is improved.
It should be understood that the size of the serial number of each step is not meant that the order of the execution order in above-described embodiment, each process
Execution sequence should be determined by its function and internal logic, the implementation process without coping with the embodiment of the present application constitutes any limit
It is fixed.
Example IV
Referring to Figure 12, it is a kind of structural schematic block diagram of charging behavior identification device provided by the embodiments of the present application, it should
Device may include:
Charge data obtains module 121, the charge data of the electric vehicle for obtaining charging pile upload, and charge data includes
Charging current data;
Judgment module 122, for judging whether charging current data meets preset condition, preset condition is to originate in charging
There are the periods that electric current is continuously default value between time and end time, and advance to from the initial time of period
The charge data of one predetermined time and finish time from the period are belonging respectively to the charge data of the second predetermined time backward
Different charge types;
Identification module 123 is changed trains for when charging current data meets preset condition, determining to exist in charging process
Behavior.
In a kind of feasible implementation, above-mentioned judgment module includes:
Curve generation unit, for generating charging current curve according to charging current data;
Conversion unit, for converting charging curve samples pictures for charging current curve;
First charge mode recognition unit, for the neural network mould that the input of charging curve samples pictures is trained in advance
Type obtains the corresponding charge mode of charging current curve;
First determination unit is used for when charge mode is preset charged mode, and charging current data meets preset condition,
Preset charged mode is to be configured to two times upper non-conterminous fisrt feature or two times upper non-conterminous second spy substantially
The corresponding charge mode of the charging current curve of sign, fisrt feature are the first stage in three stage charging system curve, second feature
For the second stage in three stage charging system curve;
Second determination unit, for when charge mode is non-default charge mode, charging current data not to meet default
Condition.
In a kind of feasible implementation, neural network model be include input layer, the first hidden layer, second hide
The neural network based on the sparse coding certainly of stacking of layer, more classification layers and output layer;
Above-mentioned first charge mode recognition unit includes:
Subelement is obtained, for obtaining charging curve samples pictures by input layer;
Fisrt feature extracts subelement, for charging curve samples pictures to be inputted the first hidden layer, so that first hides
Layer carries out feature extraction operation to charging curve samples pictures, exports the first current curve feature;
Second feature extracts subelement, for the first current curve feature to be inputted the second hidden layer, so that second hides
Layer carries out feature extraction operation to the first current curve feature, exports the second current curve feature, the second current curve feature
Precision is higher than the first current curve feature;
First classification subelement, for the second current curve feature to be inputted layers of classifying more, so that mostly classification layer identification the
Two current curve features obtain charge mode classification results according to the corresponding relationship of current curve feature and charge mode;
Subelement is exported, is used for charge mode classification results input and output layer, so that output layer exports charge mode.
In a kind of feasible implementation, above-mentioned judgment module includes:
Second charge mode recognition unit is obtained for the Random Forest model that charging current data input is trained in advance
To the corresponding charge mode of charging current data;
Third determination unit is used for when charge mode is preset charged mode, and charging current data meets preset condition,
Preset charged mode is to be configured to two times upper non-conterminous fisrt feature or two times upper non-conterminous second spy substantially
The corresponding mode of the charging current curve of sign, fisrt feature are the first stage in three stage charging system curve, second feature three
Second stage in segmentation charging curve;
4th determination unit, for when charge mode is non-default charge mode, charging current data not to meet default
Condition.
In a kind of feasible implementation, Random Forest model is the model for including n decision tree, and n is greater than zero
Positive integer;
Above-mentioned second charge mode recognition unit includes:
Second classification subelement obtains n classification knot for classifying by n decision tree to charging current data
Fruit;
Subelement is chosen, for determining final classification from n classification results as a result, using final classification result as charging
Mode, final classification result are the classification results that quantity is more than or equal to n/2 in n classification results.
In a kind of feasible implementation, above-mentioned apparatus further include:
Website voltage judgment module, for according to the charging voltage data in charge data, judging filling where charging pile
Whether power station there is the case where website spread of voltage;
Into module, for not occurring the case where website spread of voltage when charging station, into subsequent judgement charging current
The step of whether data meet preset condition.
In a kind of feasible implementation, above-mentioned apparatus further include:
Generation module, for generating prompt information;
Cue module is presented to charge user for will be prompted to information by user terminal, to prompt charge user to charge
There is behavior of changing trains in the process.
The embodiment of the present application judges to fill in charging process with the presence or absence of the behavior changed trains by charging current data
There is intermediate a period of time electric current and be continuously default value in electric current, and electric current is continuously the charge data point before and after default value
Do not belong to different charging class process, then occur the behavior changed trains in charging process, to realize in charging process
The identification for the charging behavior changed trains improves user's charging experience.
Embodiment five
Figure 13 is the schematic diagram for the terminal device that one embodiment of the application provides.As shown in figure 13, the terminal of the embodiment
Equipment 13 includes: processor 130, memory 131 and is stored in the memory 131 and can be on the processor 130
The computer program 132 of operation.The processor 130 realizes above-mentioned each charging behavior when executing the computer program 132
Step in recognition methods embodiment, such as step S201 to S203 shown in Fig. 2.Alternatively, the processor 130 execute it is described
Realize each module or the function of unit in above-mentioned each Installation practice when computer program 132, for example, module 121 shown in Figure 12 to
123 function.
Illustratively, the computer program 132 can be divided into one or more modules or unit, it is one or
The multiple modules of person or unit are stored in the memory 131, and are executed by the processor 130, to complete the application.
One or more of modules or unit can be the series of computation machine program instruction section that can complete specific function, the instruction
Section is for describing implementation procedure of the computer program 132 in the terminal device 13.For example, the computer program
132 can be divided into charge data obtain module, judgment module and identification module, each module concrete function it is as follows:
Charge data obtains module, and the charge data of the electric vehicle for obtaining charging pile upload, charge data includes filling
Electric current data;Judgment module, for judging whether charging current data meets preset condition, preset condition is to originate in charging
There are the periods that electric current is continuously default value between time and end time, and advance to from the initial time of period
The charge data of one predetermined time and finish time from the period are belonging respectively to the charge data of the second predetermined time backward
Different charge types;Identification module is changed for when charging current data meets preset condition, determining to exist in charging process
The behavior of vehicle.
The terminal device 13 is server.The terminal device may include, but be not limited only to, processor 130, storage
Device 131.It will be understood by those skilled in the art that Figure 13 is only the example of terminal device 13, do not constitute to terminal device 13
Restriction, may include perhaps combining certain components or different components, such as institute than illustrating more or fewer components
Stating terminal device can also include input-output equipment, network access equipment, bus etc..
Alleged processor 130 can be central processing unit (Central Processing Unit, CPU), can also be
Other general processors, digital signal processor (Digital Signal Processor, DSP), specific integrated circuit
(Application Specific Integrated Circuit, ASIC), ready-made programmable gate array (Field-
Programmable Gate Array, FPGA) either other programmable logic device, discrete gate or transistor logic,
Discrete hardware components etc..General processor can be microprocessor or the processor is also possible to any conventional processor
Deng.
The memory 131 can be the internal storage unit of the terminal device 13, such as the hard disk of terminal device 13
Or memory.The memory 131 is also possible to the External memory equipment of the terminal device 13, such as on the terminal device 13
The plug-in type hard disk of outfit, intelligent memory card (Smart Media Card, SMC), secure digital (Secure Digital,
SD) block, flash card (Flash Card) etc..Further, the memory 131 can also both include the terminal device 13
Internal storage unit also includes External memory equipment.The memory 131 is for storing the computer program and the end
Other programs and data needed for end equipment.The memory 131, which can be also used for temporarily storing, have been exported or will
The data of output.
It is apparent to those skilled in the art that for convenience of description and succinctly, only with above-mentioned each function
Can unit, module division progress for example, in practical application, can according to need and by above-mentioned function distribution by different
Functional unit, module are completed, i.e., the internal structure of described device is divided into different functional unit or module, more than completing
The all or part of function of description.Each functional unit in embodiment, module can integrate in one processing unit, can also
To be that each unit physically exists alone, can also be integrated in one unit with two or more units, it is above-mentioned integrated
Unit both can take the form of hardware realization, can also realize in the form of software functional units.In addition, each function list
Member, the specific name of module are also only for convenience of distinguishing each other, the protection scope being not intended to limit this application.Above system
The specific work process of middle unit, module, can refer to corresponding processes in the foregoing method embodiment, and details are not described herein.
In the above-described embodiments, it all emphasizes particularly on different fields to the description of each embodiment, is not described in detail or remembers in some embodiment
The part of load may refer to the associated description of other embodiments.
Those of ordinary skill in the art may be aware that list described in conjunction with the examples disclosed in the embodiments of the present disclosure
Member and algorithm steps can be realized with the combination of electronic hardware or computer software and electronic hardware.These functions are actually
It is implemented in hardware or software, the specific application and design constraint depending on technical solution.Professional technician
Each specific application can be used different methods to achieve the described function, but this realization is it is not considered that exceed
Scope of the present application.
In embodiment provided herein, it should be understood that disclosed device, terminal device and method, it can be with
It realizes by another way.For example, device described above, terminal device embodiment are only schematical, for example, institute
The division of module or unit is stated, only a kind of logical function partition, there may be another division manner in actual implementation, such as
Multiple units or components can be combined or can be integrated into another system, or some features can be ignored or not executed.Separately
A bit, shown or discussed mutual coupling or direct-coupling or communication connection can be through some interfaces, device
Or the INDIRECT COUPLING or communication connection of unit, it can be electrical property, mechanical or other forms.
The unit as illustrated by the separation member may or may not be physically separated, aobvious as unit
The component shown may or may not be physical unit, it can and it is in one place, or may be distributed over multiple
In network unit.It can select some or all of unit therein according to the actual needs to realize the mesh of this embodiment scheme
's.
It, can also be in addition, each functional unit in each embodiment of the application can integrate in one processing unit
It is that each unit physically exists alone, can also be integrated in one unit with two or more units.Above-mentioned integrated list
Member both can take the form of hardware realization, can also realize in the form of software functional units.
If the integrated module or unit are realized in the form of SFU software functional unit and sell as independent product
Or it in use, can store in a computer readable storage medium.Based on this understanding, the application realizes above-mentioned reality
The all or part of the process in a method is applied, relevant hardware can also be instructed to complete by computer program, it is described
Computer program can be stored in a computer readable storage medium, the computer program when being executed by processor, it can be achieved that
The step of above-mentioned each embodiment of the method.Wherein, the computer program includes computer program code, the computer program
Code can be source code form, object identification code form, executable file or certain intermediate forms etc..Computer-readable Jie
Matter may include: can carry the computer program code any entity or device, recording medium, USB flash disk, mobile hard disk,
Magnetic disk, CD, computer storage, read-only memory (ROM, Read-Only Memory), random access memory (RAM,
Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium etc..It should be noted that described
The content that computer-readable medium includes can carry out increasing appropriate according to the requirement made laws in jurisdiction with patent practice
Subtract, such as does not include electric carrier signal and electricity according to legislation and patent practice, computer-readable medium in certain jurisdictions
Believe signal.
Embodiment described above is only to illustrate the technical solution of the application, rather than its limitations;Although referring to aforementioned reality
Example is applied the application is described in detail, those skilled in the art should understand that: it still can be to aforementioned each
Technical solution documented by embodiment is modified or equivalent replacement of some of the technical features;And these are modified
Or replacement, the spirit and scope of each embodiment technical solution of the application that it does not separate the essence of the corresponding technical solution should all
Comprising within the scope of protection of this application.