CN109948664A - Charge mode recognition methods, device, terminal device and storage medium - Google Patents
Charge mode recognition methods, device, terminal device and storage medium Download PDFInfo
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- 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
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
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- Y02T10/60—Other road transportation technologies with climate change mitigation effect
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- 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
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/60—Other road transportation technologies with climate change mitigation effect
- Y02T10/7072—Electromobility specific charging systems or methods for batteries, ultracapacitors, supercapacitors or double-layer capacitors
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- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
- Y02T90/10—Technologies relating to charging of electric vehicles
- Y02T90/12—Electric charging stations
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- 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
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
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Abstract
The embodiment of the present application is suitable for vehicle technology field, disclose a kind of charge mode recognition methods, device, terminal device and computer readable storage medium, the charge data that the embodiment of the present application passes through acquisition electric vehicle, by charge data input Random Forest model trained in advance, classified using the Random Forest model to charge data, the corresponding charge mode of the charge data is identified, to realize the identification and monitoring of electric vehicle charged state.
Description
Technical field
The application belong to vehicle technology field more particularly to a kind of charge mode recognition methods, device, terminal device and
Computer readable storage medium.
Background technique
With the continuous development of science and technology, electric vehicle using more and more extensive, the demand and problem of charging are also got over
Come more.
Electric vehicle during the charging process, battery, adapter, user charge behavior it is lack of standardization etc. due to, can exist big
The insecurity factor of amount, for example, battery inferior, adapter inferior, reequiping more high power battery, single order vehicles simultaneously privately
Charging, more vehicle time-sharing chargings of single order and charging are pulled out suddenly.In addition, also can inevitably go out in electric vehicle charging process
Existing various unusual conditions, for example, stopping charging suddenly in charging process, accessing other electrical equipments in charging process
The charging experience of user can be seriously affected Deng, these unusual conditions.Meanwhile user is unhealthy or unsafe charging behavior or
Unusual condition in person's charging process can all have a huge impact battery life, for example, if not allowing battery to enter for a long time
Trickle charge can make battery generate polarization phenomena, reduce battery life.And current electric vehicle is due to the factors such as price, electricity
Pond management system is not perfect, identifies, monitors the charged state of electric vehicle there is presently no effective method.
Summary of the invention
In view of this, the embodiment of the present application a kind of charge mode recognition methods, device, terminal device and computer are provided can
Storage medium is read, to solve the problems, such as not identifying in the prior art, monitor electric vehicle charged state.
The first aspect of the embodiment of the present application provides a kind of charge mode recognition methods, comprising:
The charge data of electric vehicle is obtained, the charge data includes charging current data and charging voltage data；
By charge data input Random Forest model trained in advance, the charge mode of the charge data is obtained.
With reference to first aspect, in a kind of feasible implementation, before the charge data for obtaining electric vehicle, also
Include:
Obtain training sample set and corresponding charge mode label；
According to the training sample set and the charge mode label, the Random Forest model is trained.
With reference to first aspect, in a kind of feasible implementation, the charge data for obtaining electric vehicle, comprising:
In electric vehicle charging process, the telemetering message that charging pile reports is received；
The telemetering message is parsed, the charge data is obtained.
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, obtains the charging mould of the charge data
Formula, comprising:
Classified by the n decision tree to the charging current data, obtains n classification results；
Final classification is determined from the n classification results by voting mechanism as a result, the final classification result is made
For the charge mode, the final classification result is 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, the charge data is inputted into training in advance described
Random Forest model, after obtaining the charge mode of the charge data, further includes:
According to the charge mode, the charging behavior of user is determined.
With reference to first aspect, in a kind of feasible implementation, described according to the charge mode, determine user's
After charging row is, further includes:
According to the charging behavior, corresponding charging advisory information and charge information are generated；
The charging advisory information and the charge information are presented to the user.
The second aspect of the embodiment of the present application provides a kind of charge mode identification device, comprising:
Module is obtained, for obtaining the charge data of electric vehicle, the charge data includes charging current data and charging
Voltage data；
Identification module obtains the charging number for the Random Forest model that charge data input is trained in advance
According to charge mode.
In conjunction with second aspect, in a kind of feasible implementation, further includes:
Training data obtains module, for obtaining training sample set and corresponding charge mode label；
Training module is used for according to the training sample set and the charge mode label, to the Random Forest model
It is trained.
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 identification module includes:
Taxon obtains n classification for classifying by the n decision tree to the charging current data
As a result；
Ballot unit, for determining final classification from the n classification results by voting mechanism as a result, by described in most
For whole classification results as the charge mode, the final classification result is that quantity is more than or equal to n/2 in the n classification results
Classification results.
In conjunction with second aspect, in a kind of feasible implementation, the acquisition module includes:
Message receiving unit, in electric vehicle charging process, receiving the telemetering message that charging pile reports；
Packet parsing unit obtains the charge data for parsing the telemetering message.
In conjunction with second aspect, in a kind of feasible implementation, further includes:
Charging behavior determining module, for determining the charging behavior of user according to the charge mode.
In conjunction with second aspect, in a kind of feasible implementation, further includes:
Generation module, for generating corresponding charging advisory information and charge information according to the charging behavior；
Module is presented, for the charging advisory information and the charge information to be presented to the user.
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 is random gloomy using this by the Random Forest model that charging current data input is trained in advance
Woods model classifies to charge data, identifies the corresponding charge mode of the charge data, to realize electric vehicle charging
The identification and monitoring of state.
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 the schematic process flow diagram of charge mode recognition methods provided by the embodiments of the present application；
Fig. 3 is Random Forest model schematic diagram provided by the embodiments of the present application；
Fig. 4 is another schematic process flow diagram of charge mode recognition methods provided by the embodiments of the present application；
Fig. 5 is the confusion matrix schematic diagram of Random Forest model provided by the embodiments of the present application；
Fig. 6 is the structural schematic block diagram of charge mode identification device provided by the embodiments of the present application；
Fig. 7 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
Vehicle adapter, charging cable are connected on the socket of charging pile.It is paid when charging car owner completes charging order by user terminal
Afterwards, background server can control the corresponding socket of charging pile and be powered, and can treat charging electric motor-car and charge.
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,
At this point, 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 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, if it find that some unusual conditions are either unsound
Charging behavior can feed back to user by user terminal in real time.
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 charge mode recognition methods provided by the embodiments of the present application, the party
Method may comprise steps of:
Step S201, the charge data for obtaining electric vehicle, including charging current data and charging voltage data.
It is appreciated that above-mentioned charge data can include but is not limited to charging current, charging voltage and electric vehicle charging
Power.Electric vehicle charge power can be identified by the power measurement chip on charging pile.
It is to be appreciated that the charge data can be the data that charging pile uploads in real time, that is, pass through power adaptation in electric vehicle
During the socket that device, charging cable connect charging pile is charged, charging pile in real time passes through charge data collected distant
The form for observing and predicting text is reported to background server, and background server parses the telemetering message, according to phase entrained by telemetering message
Information is closed, for example, unique ID of charging pile equipment etc., obtains the charge data that each charging pile reports.Certainly, the charge data
It can be the charge data of non real-time upload, at this point, the charge data can be history charge data, that is, the data are preparatory
The electric vehicle charge data for acquiring and storing.
Step S202, charge data input is trained in advance Random Forest model, obtains the charge mode of charge data.
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 Fig. 3, as shown in Figure 3 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 model that charge data input is trained in advance, is filled
The detailed process of the charge mode of electric data may include: to be classified by n decision tree to charging current data, obtain n
A classification results；Final classification is determined from n classification results by voting mechanism 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.Wherein, each in random forest
After decision tree obtains classification results, voting mechanism can use, the classification results that quantity accounting reaches 50% or more are made
For final classification as a result, the classification results result is the corresponding charge mode recognition result of charge data.For example, such as Fig. 3 institute
Show, is then " Class1 " by the final result of ballot when the quantity accounting of classification results " Class1 " reaches 50% or more.
The charging current data of different electric vehicles be it is different, still, different charge datas but may include certain
A little identical features, different features may be constructed different charge modes.And the feature of charge data can pass through electric current song
Line embodies.Current curve feature can refer to the feature of the curve of characterization certain shapes, i.e., certain with a certain section of character representation
The curve of curve shape or function.For example, the notch feature in current curve feature, corresponding one section of the notch feature is in groove type
The current curve of shape, be embodied in electric current be persistently slowly drop down to one for 0 numerical value after, and then slowly rise to
Decline process starting position electric current differs the position within 0.3A.
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
After descending branch charging duration be greater than 1 hour, and the charging current maximum value be less than or equal to 2A when, current value is lower than
0.4A, when the charging current maximum value is greater than 2A, current value is lower than one section of curve of 0.7A.
And so on, 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 17, 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 0.6A,
Centre is 0, single ladder, centre ladder, drops slow liter groove, multiple lasting ladder, persistently risings, a too short stage, electricity suddenly
Stream is 0.Certainly, in practical application, the classification of current curve feature can also be increased or decreased as needed.
The timing of different curvilinear characteristics, combination may be constructed different charge modes, that is, according to institute in charging current curve
The chronological order that the curvilinear characteristic for including and these curvilinear characteristics occur, corresponding different charge mode.In this implementation
In example, charge mode may include 12, be respectively as follows: that an only stage, an only two-stage, three stages are complete, only one or three ranks
Section, only two or three stages, only three stages, high current, more vehicles of the same order same period simultaneously charge, same order not
It charges respectively with more vehicles of period, the temperature control time is greater than the charging 2 of 2 hours, stops (extraneous factor) suddenly, stops suddenly
(non-extraneous factor).Wherein, different charge modes are combined to obtain by different curvilinear characteristics.For example, " an only stage " charges
The corresponding charging current curve of mode only has " first stage " curvilinear characteristic, that is, charging current curve at this time only includes normally
Three stage charging system curve in first segment.
After charging current data is input to Random Forest model, each decision tree in random forest is according to preparatory instruction
Experienced model parameter classifies to the charging current data, obtains classification results, and each classification results are each decision tree pair
The pattern classification of the charging current data, finally by the quantity for counting each classification results, by point of 50% or more quantity accounting
Final output of the class result as model, the final output are charge mode recognition result.If for example, some
It include first stage in normal three-stage curve, second stage and the in the corresponding charging current curve of charging current data
Three stages can determine that the charging current data was corresponding and fill after Random Forest model carries out Classification and Identification to the current data
Power mode is " three stages are complete ".
Different charge datas has different curvilinear characteristics, and different curvilinear characteristics corresponds to different charge modes.
The corresponding relationship between feature, feature and charge mode in order to preferably introduce charge data, below in conjunction with Tables 1 and 2
It is illustrated.
1 charging current curve mark sheet of table
Upper table 1 is that the mark sheet of charging current data when this 17 features are described below, uses A for convenience respectively
~Q capitalization accordingly indicates.In table 1, each feature has corresponding feature description, indicatrix, and indicatrix refers to
The expression of the corresponding curve shape of this feature.It is appreciated that in a particular application, can also define as needed different from upper table
Curvilinear characteristic shown in 1.
Different features is combined available different charge mode.17 features in above-mentioned table 1 are carried out
Combination, available 12 kinds of small charge modes, 12 kinds of small charge modes can be divided into 3 kinds of big charge modes again, and 3
The big charge mode of kind respectively charges normal, abnormal charging, stopping is charged suddenly.Physical relationship is as shown in table 2 below.
2 charge mode table of table
Upper table 2 shows 12 kinds of small charge modes and the combination of 12 kinds small charge mode corresponding feature, corresponding fills greatly
Power mode.Wherein, the feature of A~Q in table 2 in charge mode composition refers to A~Q feature shown in above-mentioned table 1.It can be with
Understand, charge mode shown in table 2 is only a kind of example, may include more or fewer charge modes in concrete application
Classification.
It is to be appreciated that the output result of random forest is usually 12 kinds of small charge modes 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 the corresponding relationship of small charge mode and big charge mode is preset in Random Forest model, it is random in this way
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 is also possible to the big charge mode of above-mentioned table 2.Certainly, output result can also include small charge mode and big simultaneously
Charge mode.
By identifying the corresponding charge mode of charge data, battery status, user's charging behavior etc. can be commented
Estimate, after obtaining assessment result, corresponding information can be fed back into user with the assessment result, and provide corresponding charging
It is recommended that improve user experience.
It is to be appreciated that the identification of charge mode is mainly based upon charging current data progress, the shape of charging voltage data
Formula is relatively simple, and the identification process in pattern-recognition is relatively simple, and the identification of voltage data is generally before recognition
It completes.But when the identification of practical progress charge mode, the input of random forest can be charging current data, charging voltage
Data export as charge mode recognition result.
As can be seen that the present embodiment utilizes this by the Random Forest model that charging current data input is trained in advance
Random Forest model classifies to charge data, identifies the corresponding charge mode of the charge data, to realize electronic
The identification and monitoring of vehicle charged state.
Embodiment two
Fig. 4 is referred to, is a kind of another process schematic block of charge mode recognition methods provided by the embodiments of the present application
Figure, this method may comprise steps of:
Step S401, training sample set and corresponding charge mode label are obtained.
Step S402, according to training sample set and charge mode label, Random Forest 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 3, random forest extracts one with randomly putting back to from whole training samples concentration in training process
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 Fig. 5
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 Fig. 5 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 Fig. 5 can be merged into table 2 above
12 class charge modes, after merging, nicety of grading be can be improved to 87%.Certainly, in practical application, the classification of charge mode
It can be set according to actual needs.
After Random Forest model training is completed, then new charge data can be input in Random Forest model and be carried out
The identification of charge mode.
Step S403, in electric vehicle charging process, the telemetering message that charging pile reports is received.
Specifically, in electric vehicle charging process, for example, car owner is charged after completing charging order by mobile phone,
When electric vehicle being connected and started to charge with charging pile using charging adapter, background server real-time reception charging pile is reported
Telemetering message, background server obtain current data, the voltage data, power data of electric vehicle by parsing the telemetering message
Deng.
Step S404, telemetering message is parsed, charge data is obtained.
Step S405, charge data input is trained in advance Random Forest model, obtains the charge mode of charge data.
Step S406, according to charge mode, the charging behavior of user is determined.
It is to be appreciated that above-mentioned charging behavior refers to the behavior made by user in electric vehicle charging process, for example, filling
In electric process, charging adapter by user's polybag and other items package, charging process it is private draw insert row give simultaneously multiple vehicles into
It changes trains suddenly in row electricity, charging process.Whether the charging behavior of user is healthy, safe, with battery life, the safety etc. that charges breath
Manner of breathing closes.
According to the specific charge mode of electric vehicle, the specific charging behavior of user can be determined.For example, above-mentioned when occurring
The 10th kind of charge mode in table 2, that is, when the temperature control time is greater than 2 hours charging-exception charge modes, under normal circumstances,
Adaptive charging can trigger adapter protection mechanism, actively break external charging power supply when the temperature is excessively high external, and cause to be adapted to
The excessively high reason of device temperature may be that ambient temperature is excessively high, it is also possible to which adapter is caught in the rain user in order to prevent, suitable
It is wrapped up caused by polybag and other items on orchestration.It therefore, then can be with when identifying the in above-mentioned table 2 the 10th kind of charge mode
Speculate that the behavior that adapter is wrapped up occurs during the charging process in user.
In another example that is, same more vehicles of order same period are simultaneously when identifying the in above-mentioned table 2 the 8th kind of charge mode
When charging-exception charge mode, since under the charging scenarios of charging station, the socket of a charging pile can only be electronic for one
Vehicle charging, and identify more vehicles at this time while charging, then show that user draws insert row by private, simultaneously for more vehicle chargings.
In another example when identifying the in above-mentioned table 2 the 9th kind of charge mode, that is, more vehicle difference of same order different periods
When charging-exception charge mode, since the corresponding charging curve classification of identical electric vehicle is the same, therefore before and after occurring
When two different charging curve classifications occurs in period, it may be considered that there is the behavior changed trains during the charging process in user.
It is to be appreciated that above-mentioned charging behavior be not limited to it is cited hereinabove, such as can also include charging process in dash forward
So pull out the behaviors such as charging plug.Different charge modes can be preset, charging scenarios correspond to different charging behaviors.?
It, can be based on the recognition result of charge mode, in conjunction with business scenario in order to improve user behavior recognition accuracy in practical application
Judged jointly with the history charge data of charge user.
Step S407, according to charging behavior, corresponding charging advisory information and charge information are generated.
Step S408, charging advisory information and charge information are presented to the user.
Specifically, after the charging row for identifying user is, for specification user charging behavior, it is hidden to eliminate charging safety
Suffer from, guarantee battery life, improve the safety of charging process, corresponding information can be timely feedbacked to user, and is directed to and fills
Electric behavior provides corresponding charging and suggests.
It is to be appreciated that above-mentioned charge information can refer to the letter for being characterized in and occurring which kind of charging behavior in current charging process
Breath, for example, the charge information can be specially " to detect because adapter temperature is excessively high when charging behavior is that adapter is wrapped up
And automatically power off, thus it is speculated that be that adapter is wrapped up ", in this way, user can learn that itself is currently deposited by the charge information in time
Charging behavior lack of standardization.
Above-mentioned charging advisory information can refer to that characterization is directed to the information of the corresponding counter-measure of corresponding charging behavior.Example
Such as, when charging behavior is that adapter is wrapped up, which can be specially " to guarantee charging safety and charging effect
Rate please guarantee the ventilation of adapter ".
For example, charging behavior is to have changed vehicle in charging process, the advisory information that charges and charge information are specially " to detect this
Secondary order and History Order are inconsistent, thus it is speculated that charging changes other Vehicular chargings either battery on the way and starts exception occur ".
It is appreciated that above-mentioned charging advisory information and charge information can be presented to especially by the interface of user terminal and fill
Electric user.The specific interface form of expression can be arbitrary, and be not limited thereto.
It is to be appreciated that the same or similar place of the present embodiment and above-described embodiment one, related introduction content refer to
Literary corresponding contents, details are not described herein.
As can be seen that the present embodiment utilizes this by the Random Forest model that charging current data input is trained in advance
Random Forest model classifies to charge data, identifies the corresponding charge mode of the charge data, to realize electronic
The identification and monitoring of vehicle charged state.And user's charging behavior is further identified according to charge mode, by relevant information
It timely feedbacks to user, to improve the safety and standardization in charging process, guarantees battery life, improve user's charge bulk
It tests.
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.
Embodiment three
Fig. 6 is referred to, is a kind of structural schematic block diagram of charge mode identification device provided by the embodiments of the present application, the dress
It sets and may include:
Module 61 is obtained, for obtaining the charge data of electric vehicle, charge data includes charging current data and charging electricity
Press data；
Identification module 62 obtains filling for charge data for the Random Forest model that charge data input is trained in advance
Power mode.
In a kind of feasible implementation, above-mentioned apparatus further include:
Training data obtains module, for obtaining training sample set and corresponding charge mode label；
Training module, for being trained to Random Forest model according to training sample set and charge mode label.
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 identification module includes:
Taxon obtains n classification results for classifying by n decision tree to charging current data；
Ballot unit, for determining final classification as a result, by final classification knot from n classification results by voting mechanism
For fruit as charge mode, final classification result is 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 acquisition module includes:
Message receiving unit, in electric vehicle charging process, receiving the telemetering message that charging pile reports；
Packet parsing unit obtains charge data for parsing telemetering message.
In a kind of feasible implementation, above-mentioned apparatus further include:
Charging behavior determining module, for determining the charging behavior of user according to charge mode.
In a kind of feasible implementation, above-mentioned apparatus further include:
Generation module, for generating corresponding charging advisory information and charge information according to charging behavior；
Module is presented, for that will charge advisory information and charge information is presented to the user.
It is to be appreciated that the charge mode identification device of the present embodiment and above-mentioned charge mode recognition methods correspond, tool
Body related introduction refers to corresponding contents above, and details are not described herein.
As can be seen that the present embodiment utilizes this by the Random Forest model that charging current data input is trained in advance
Random Forest model classifies to charge data, identifies the corresponding charge mode of the charge data, to realize electronic
The identification and monitoring of vehicle charged state.
Example IV
Fig. 7 is the schematic diagram for the terminal device that one embodiment of the application provides.As shown in fig. 7, the terminal of the embodiment is set
Standby 7 include: processor 70, memory 71 and are stored in the meter that can be run in the memory 71 and on the processor 70
Calculation machine program 72.The processor 70 realizes that above-mentioned each charge mode recognition methods is real when executing the computer program 72
Apply the step in example, such as step S201 to S202 shown in Fig. 2.Alternatively, the processor 70 executes the computer program
Each module or the function of unit in above-mentioned each Installation practice, such as the function of module 61 to 62 shown in Fig. 6 are realized when 72.
Illustratively, the computer program 72 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 71, and are executed by the processor 70, to complete the application.It is described
One or more modules or unit can be the series of computation machine program instruction section that can complete specific function, which uses
In implementation procedure of the description computer program 72 in the terminal device 7.For example, the computer program 72 can be with
It is divided into and obtains module, identification module, each module concrete function is as follows:
Module is obtained, for obtaining the charge data of electric vehicle, charge data includes charging current data and charging voltage
Data；Identification module obtains the charging mould of charge data for the Random Forest model that charge data input is trained in advance
Formula.
The terminal device is server, it may include but be not limited only to, processor 70, memory 71.Those skilled in the art
Member is appreciated that Fig. 7 is only the example of terminal device 7, does not constitute the restriction to terminal device 7, may include than diagram
More or fewer components perhaps combine certain components or different components, such as the terminal device can also include defeated
Enter output equipment, network access equipment, bus etc..
Alleged processor 70 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 71 can be the internal storage unit of the terminal device 7, for example, terminal device 7 hard disk or
Memory.The memory 71 is also possible to the External memory equipment of the terminal device 7, such as is equipped on the terminal device 7
Plug-in type hard disk, intelligent memory card (Smart Media Card, SMC), secure digital (Secure Digital, SD) card,
Flash card (Flash Card) etc..Further, the memory 71 can also both include the storage inside of the terminal device 7
Unit also includes External memory equipment.The memory 71 is for storing needed for the computer program and the terminal device
Other programs and data.The memory 71 can be also used for temporarily storing the data that has exported or will export.
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.
Claims (10)
1. a kind of charge mode recognition methods characterized by comprising
The charge data of electric vehicle is obtained, the charge data includes charging current data and charging voltage data；
By charge data input Random Forest model trained in advance, the charge mode of charge data is obtained.
2. charge mode recognition methods according to claim 1, which is characterized in that obtain electric vehicle charge data it
Before, further includes:
Obtain training sample set and corresponding charge mode label；
According to the training sample set and the charge mode label, the Random Forest model is trained.
3. charge mode recognition methods according to claim 1, which is characterized in that the charging number for obtaining electric vehicle
According to, comprising:
In electric vehicle charging process, the telemetering message that charging pile reports is received；
The telemetering message is parsed, the charge data is obtained.
4. charge mode recognition methods according to claim 1, which is characterized in that the Random Forest model be include n
The model of decision tree, n are the positive integer greater than zero；
By the Random Forest model of charge data input training in advance, the charge mode of the charge data is obtained,
Include:
Classified by the n decision tree to charging current data, obtains n classification results；
Final classification is determined from the n classification results by voting mechanism as a result, the final classification result is n classification
As a result middle quantity is more than or equal to the classification results of n/2, using final classification result as the charge mode.
5. charge mode recognition methods according to any one of claims 1 to 4, which is characterized in that by the charging number
According to the Random Forest model of input training in advance, after obtaining the charge mode of charge data, further includes:
According to the charge mode, the charging behavior of user is determined.
6. charge mode recognition methods according to claim 5, which is characterized in that according to the charge mode, determine
After the charging row of user is, further includes:
According to the charging behavior, corresponding charge information and charging advisory information are generated；
The charge information and charging advisory information are presented to the user.
7. a kind of charge mode identification device characterized by comprising
Module is obtained, for obtaining the charge data of electric vehicle, the charge data includes charging current data and charging voltage
Data；
Identification module obtains the charging of the charge data for the Random Forest model that charge data input is trained in advance
Mode.
8. charge mode identification device according to claim 7, which is characterized in that further include:
Training data obtains module, for obtaining training sample set and corresponding charge mode label；
Training module, for being trained to the Random Forest model according to the training sample set and charge mode label.
9. a kind of terminal device, which is characterized in that in the memory and can be in institute including memory, processor and storage
The computer program run on processor is stated, the processor realizes such as claim 1 to 6 times when executing the computer program
The step of one the method.
10. a kind of computer readable storage medium, which is characterized in that the computer-readable recording medium storage has computer journey
Sequence is realized when the computer program is executed by the processor such as the step of any one of claim 1 to 6 the method.
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| CN111209937B (en) * | 2019-12-27 | 2024-03-29 | 深圳智链物联科技有限公司 | Classification method and device for charging curve model and server |
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