CN109948664B

CN109948664B - Charging mode identification method and device, terminal equipment and storage medium

Info

Publication number: CN109948664B
Application number: CN201910151495.0A
Authority: CN
Inventors: 帅春燕; 刘晓波
Original assignee: Shenzhen Zhilian Iot Technology Co ltd
Current assignee: Shenzhen Zhilian Iot Technology Co ltd
Priority date: 2019-02-28
Filing date: 2019-02-28
Publication date: 2021-05-14
Anticipated expiration: 2039-02-28
Also published as: CN109948664A

Abstract

The embodiment of the application is suitable for the technical field of electric vehicles and discloses a charging mode identification method, a charging mode identification device, terminal equipment and a computer readable storage medium.

Description

Charging mode identification method and device, terminal equipment and storage medium

Technical Field

The application belongs to the technical field of electric vehicles, and particularly relates to a charging mode identification method and device, a terminal device and a computer readable storage medium.

Background

With the continuous development of science and technology, the application of electric vehicles is more and more extensive, and the demand and the problem of its charging are also more and more.

In the charging process of the electric vehicle, due to the reason that the charging behaviors of the battery, the adapter and the user are not standard, a large number of unsafe factors exist, such as poor-quality batteries, poor-quality adapters, privately modified high-power batteries, simultaneous charging of a plurality of vehicles in a single order, time-sharing charging of a plurality of vehicles in a single order, sudden unplugging of charging and the like. In addition, various abnormal conditions, such as sudden stop of charging during the charging process, access of other electric devices during the charging process, etc., may occur during the charging process of the electric vehicle, and these abnormal conditions may seriously affect the charging experience of the user. Meanwhile, unhealthy or unsafe charging behaviors of users or abnormal conditions in the charging process have great influence on the service life of the battery, for example, if the battery is not charged in a trickle charge for a long time, the battery is polarized, and the service life of the battery is reduced. However, the current electric vehicle has an imperfect battery management system due to factors such as price, and no effective method for identifying and monitoring the charging state of the electric vehicle exists at present.

Disclosure of Invention

In view of this, embodiments of the present application provide a charging mode identification method, an apparatus, a terminal device, and a computer-readable storage medium, so as to solve the problem in the prior art that the charging state of an electric vehicle cannot be identified and monitored.

A first aspect of an embodiment of the present application provides a charging mode identification method, including:

acquiring charging data of the electric vehicle, wherein the charging data comprises charging current data and charging voltage data;

and inputting the charging data into a pre-trained random forest model to obtain a charging mode of the charging data.

With reference to the first aspect, in a possible implementation manner, before the acquiring charging data of the electric vehicle, the method further includes:

acquiring a training sample set and a corresponding charging mode label;

and training the random forest model according to the training sample set and the charging mode label.

With reference to the first aspect, in one possible implementation manner, the acquiring charging data of an electric vehicle includes:

receiving a telemetering message reported by a charging pile in the charging process of the electric vehicle;

and analyzing the telemetering message to obtain the charging data.

With reference to the first aspect, in a possible implementation manner, the random forest model is a model including n decision trees, where n is a positive integer greater than zero;

the inputting the charging data into a pre-trained random forest model to obtain a charging mode of the charging data comprises the following steps:

classifying the charging current data through the n decision trees to obtain n classification results;

and determining a final classification result from the n classification results through a voting mechanism, and taking the final classification result as the charging mode, wherein the final classification result is a classification result of which the number is greater than or equal to n/2 in the n classification results.

With reference to the first aspect, in a possible implementation manner, after the inputting the charging data into a pre-trained random forest model to obtain a charging pattern of the charging data, the method further includes:

and determining the charging behavior of the user according to the charging mode.

With reference to the first aspect, in a possible implementation manner, after the determining, according to the charging mode, a charging behavior of the user, the method further includes:

generating corresponding charging suggestion information and charging information according to the charging behavior;

presenting the charging advice information and the charging information to the user.

A second aspect of the embodiments of the present application provides a charging pattern recognition apparatus, including:

the system comprises an acquisition module, a processing module and a control module, wherein the acquisition module is used for acquiring charging data of the electric vehicle, and the charging data comprises charging current data and charging voltage data;

and the recognition module is used for inputting the charging data into a pre-trained random forest model to obtain a charging mode of the charging data.

With reference to the second aspect, in one possible implementation manner, the method further includes:

the training data acquisition module is used for acquiring a training sample set and a corresponding charging mode label;

and the training module is used for training the random forest model according to the training sample set and the charging mode label.

With reference to the second aspect, in a possible implementation manner, the random forest model is a model including n decision trees, where n is a positive integer greater than zero;

the identification module comprises:

the classification unit is used for classifying the charging current data through the n decision trees to obtain n classification results;

and the voting unit is used for determining a final classification result from the n classification results through a voting mechanism, and taking the final classification result as the charging mode, wherein the final classification result is a classification result of which the number is greater than or equal to n/2 in the n classification results.

With reference to the second aspect, in one possible implementation manner, the obtaining module includes:

the message receiving unit is used for receiving the telemetering message reported by the charging pile in the charging process of the electric vehicle;

and the message analysis unit is used for analyzing the telemetering message to obtain the charging data.

and the charging behavior determining module is used for determining the charging behavior of the user according to the charging mode.

the generating module is used for generating corresponding charging suggestion information and charging information according to the charging behavior;

and the presenting module is used for presenting the charging suggestion information and the charging information to the user.

A third aspect of embodiments of the present application provides a terminal device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the steps of the method according to any one of the above first aspects when executing the computer program.

A fourth aspect of embodiments of the present application provides a computer-readable storage medium, in which a computer program is stored, which, when executed by a processor, performs the steps of the method according to any one of the above first aspects.

Compared with the prior art, the embodiment of the application has the advantages that:

according to the charging method and the charging device, the charging current data are input into the pre-trained random forest model, the charging data are classified by using the random forest model, and the charging mode corresponding to the charging data is recognized, so that the charging state of the electric vehicle is recognized and monitored.

Drawings

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

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

fig. 2 is a schematic block diagram of a flow of a charging mode identification method according to an embodiment of the present application;

fig. 3 is a schematic diagram of a random forest model provided in an embodiment of the present application;

fig. 4 is another schematic flow chart of a charging mode identification method according to an embodiment of the present disclosure;

fig. 5 is a schematic diagram of a confusion matrix of a random forest model provided in an embodiment of the present application;

fig. 6 is a block diagram schematically illustrating a structure of a charging pattern recognition apparatus according to an embodiment of the present disclosure;

fig. 7 is a schematic diagram of a terminal device according to an embodiment of the present application.

Detailed Description

In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the present application. It will be apparent, however, to one skilled in the art that the present application may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.

Before describing a specific technical solution of the embodiment of the present application, an application scenario that may be involved in the embodiment of the present application is described first.

Referring to fig. 1, a schematic view of a charging scenario is shown, in which a charging station 1, an electric vehicle 2 to be charged, a server 3, and a user terminal 4 are included, and at least one charging pile 11 is included in the charging station 1. User terminal can communicate with backend server through operator's network, and the charging station and fill electric pile can communicate with backend server through the place network in the charging station, and user terminal can communicate with the electric pile that fills through the internet. Fill and have at least one socket on the electric pile, the car owner that charges can be connected to on the socket that fills electric pile through electric vehicle adapter, charging wire. After the charging vehicle owner completes the payment of the charging order through the user terminal, the background server can control the corresponding socket of the charging pile to be electrified, and then the electric vehicle to be charged can be charged.

The user terminal is internally provided with a corresponding APP to realize corresponding service functions such as background interaction, calculation, man-machine interaction and the like, and can be specifically an intelligent terminal such as a mobile phone, a tablet and the like. The electric vehicle to be charged can be specifically an electric bicycle, an electric motorcycle, an electric automobile or the like.

A charging vehicle owner scans the two-dimensional code on the code charging pile through a user terminal, and the user terminal jumps to a corresponding interface after acquiring the two-dimensional code information; on the interface, the charging vehicle owner can perform operations such as charging mode selection, charging amount input and the like; after the charging order information is determined, the charging pile uploads the charging order to the server, the server performs data interaction with the user terminal, after order payment is completed, the server informs the charging pile, the charging pile can control the corresponding socket to be electrified, and at the moment, a charging vehicle owner can start charging.

In the charging process, charging data such as charging current, charging voltage and charging power can be collected by the charging pile, and the charging data is uploaded to the server. Specifically, after the charging pile collects charging data of the electric vehicle, the charging pile reports a device remote measurement message to a background server, wherein the device remote measurement message can include information such as charging current, voltage and charging power, so that the background server can collect the charging data of each electric vehicle charged in the charging pile.

After receiving the charging data reported by the charging pile, the server can correspondingly draw a charging current curve, a charging voltage curve, a power curve and the like. And then, the server identifies the charging mode corresponding to the reported charging data according to the charging current curve, the voltage curve and other data. After the charging mode is identified, if some abnormal conditions or unhealthy charging behaviors are found, the abnormal conditions or unhealthy charging behaviors can be fed back to the user through the user terminal in real time.

It should be noted that the above mentioned application scenarios are only exemplary scenarios and do not limit the specific scenarios in the embodiments of the present application.

After the application scenarios that may be related to the embodiments of the present application are introduced, detailed descriptions of the technical solutions provided in the embodiments of the present application will be provided below. In order to explain the technical solution described in the present application, the following description will be given by way of specific examples.

Example one

Referring to fig. 2, a schematic flow chart diagram of a charging mode identification method according to an embodiment of the present application is provided, where the method includes the following steps:

step S201, acquiring charging data of the electric vehicle, wherein the charging data includes charging current data and charging voltage data.

It is understood that the charging data may include, but is not limited to, charging current, charging voltage, and electric vehicle charging power. The charging power of the electric vehicle can be identified through a power metering chip on the charging pile.

It should be noted that the charging data may be data uploaded by the charging pile in real time, that is, in the process of charging the electric vehicle through the power adapter and the socket of the charging pile connected to the charging pile, the charging pile reports the collected charging data to the background server in real time in the form of a telemetry message, and the background server analyzes the telemetry message and obtains the charging data reported by each charging pile according to related information carried by the telemetry message, for example, the unique ID of the charging pile device. Of course, the charging data may also be charging data uploaded in non-real time, and at this time, the charging data may be historical charging data, that is, the data is electric vehicle charging data collected and stored in advance.

And S202, inputting the charging data into a pre-trained random forest model to obtain a charging mode of the charging data.

It should be noted that the random forest model may be a model including n decision trees, where n is a positive integer greater than zero, and the model may specifically be the model in the random forest model schematic diagram of fig. 3, as shown in fig. 3, the model includes decision trees Tree1, Tree2 … Tree (n-1), and Tree (n), and each decision Tree performs classification processing on a corresponding random sample set to obtain a corresponding classification result type 1, type 1 … type 2, and type 3. At this time, the specific process of inputting the charging data into the pre-trained random forest model to obtain the charging mode of the charging data may include: classifying the charging current data through n decision trees to obtain n classification results; and determining a final classification result from the n classification results through a voting mechanism, taking the final classification result as a charging mode, wherein the final classification result is a classification result of which the number is more than or equal to n/2 in the n classification results. After the classification result is obtained from each decision tree in the random forest, a voting mechanism can be used to take the classification result with the quantity ratio of more than 50% as a final classification result, and the classification result is the charging mode identification result corresponding to the charging data. For example, as shown in fig. 3, when the number of classification results "type 1" reaches 50% or more, the final result by voting is "type 1".

The charging current data of different electric vehicles are different, but different charging data may contain some same characteristics, and different characteristics may constitute different charging modes. And the charging data may be characterized by a current profile. The current curve characteristic may refer to a characteristic of a curve with a certain shape, that is, a curve with a characteristic representing a certain curve shape or function. For example, the notch feature in the current curve feature, which corresponds to a current curve in the shape of a notch, is characterized in that the current slowly decreases to a value different from 0 continuously, and then slowly increases to a position within 0.3A of the current difference from the starting position of the decreasing process.

For another example, the charging current curve is generally three-stage, the normal three-stage charging curve includes a first stage, a second stage and a third stage, and the curves corresponding to the first stage, the second stage and the third stage are respectively used as a current curve characteristic, that is, a first stage characteristic, a second stage characteristic and a third stage characteristic, wherein the first stage characteristic represents the curve of the first stage in the normal three-stage charging curve; the second stage characteristic represents a continuous descending stage in a normal three-stage charging curve, and the descending time length is half an hour; the third stage characteristic represents a section of the normal three-section charging curve in which the charging time length after the continuous descending section is more than 1 hour, the current value is lower than 0.4A when the maximum value of the charging current is less than or equal to 2A, and the current value is lower than 0.7A when the maximum value of the charging current is more than 2A.

By analogy, different curve segments are represented with different characteristics for the shape of the current curve and other characteristics. In this embodiment, the charging current curve characteristics may include 17, which are: the device comprises a groove, a first stage, a second stage, a third stage, a first stage part oscillation, a second stage part oscillation, a third stage part oscillation, a convex part, a starting current less than 0.6A, a middle part of 0, a single step, a middle step, a steep descending and slowly ascending groove, a plurality of continuous steps, a continuous ascending part and a short first stage, wherein the current is 0. Of course, in practical application, the categories of the current curve features can be increased or decreased according to needs.

The time sequences and combinations of different curve characteristics can form different charging modes, namely, the charging modes correspond to different charging modes according to the curve characteristics contained in the charging current curve and the time sequence of the appearance of the curve characteristics. In this embodiment, the charging modes may include 12 charging modes, which are respectively: the method comprises the following steps of only one stage, only two stages, all three stages, only one three stage, only two three stages, only three stages, high current, simultaneous charging of a plurality of vehicles in the same order in the same time period, separate charging of a plurality of vehicles in the same order in different time periods, charging for more than 2 hours under temperature control 2, sudden stop (external factors) and sudden stop (non-external factors). Wherein different charging modes are obtained by combining different curve characteristics. For example, the charging current curve corresponding to the "one-stage only" charging mode has only the characteristic of the "first-stage" curve, i.e., the charging current curve at this time only includes the first stage of the normal three-stage charging curve.

After charging current data are input into a random forest model, classifying the charging current data by each decision tree in the random forest according to pre-trained model parameters to obtain classification results, classifying the modes of the charging current data for each decision tree by each classification result, and finally taking the classification results of which the number accounts for more than 50% as the final output result of the model by counting the number of each classification result, wherein the final output result is the charging mode identification result. For example, if a charging current curve corresponding to a certain charging current data includes a first stage, a second stage, and a third stage in a normal three-stage curve, after the random forest model classifies and identifies the current data, it may be determined that a charging mode corresponding to the charging current data is "three-stage full".

Different charging data have different curve characteristics, and different curve characteristics correspond to different charging modes. In order to better describe the characteristics of the charging data, the correspondence between the characteristics and the charging mode, the following description will be made with reference to tables 1 and 2.

TABLE 1 CHARGING CURRENT PROGRAM

Table 1 above is a characteristic table of charging current data, and for convenience of description, the 17 characteristics are represented by capital letters a to Q, respectively. In table 1, each feature has a corresponding feature description and a feature curve, and the feature curve is a representation of a curve shape corresponding to the feature. It will be appreciated that in particular applications, other than the curve characteristics shown in table 1 above may also be defined as desired.

Different charging modes can be obtained by combining different characteristics. By combining the 17 characteristics in table 1, 12 small charging modes can be obtained, and the 12 small charging modes can be further divided into 3 large charging modes, wherein the 3 large charging modes are normal charging, abnormal charging and sudden stop charging respectively. The specific relationship is shown in table 2 below.

TABLE 2 charging mode table

Table 2 above shows 12 small charging modes, and 12 small charging modes corresponding to feature combinations and large charging modes. The characteristics of a to Q in the charge mode composition in table 2 refer to the characteristics of a to Q shown in table 1. It is understood that the charging mode shown in table 2 is merely an example, and that more or fewer charging mode categories may be included in a particular application.

It should be noted that the output result of the random forest is generally 12 small charging modes in table 2, and a large charging mode corresponding to each small charging mode can be obtained according to the preset corresponding relationship between the small charging mode and the large charging mode. Of course, the corresponding relationship between the small charging mode and the large charging mode may also be preset in the random forest model, so that after the small charging mode is obtained by the random forest model, the large charging mode is output according to the small charging mode, that is, the output result of the random forest model may also be the large charging mode in table 2. Of course, the output result may also include both the small charge mode and the large charge mode.

By identifying the charging mode corresponding to the charging data, the battery state, the user charging behavior and the like can be evaluated, after the evaluation result is obtained, corresponding information can be fed back to the user along with the evaluation result, and a corresponding charging suggestion is given, so that the user experience is improved.

It should be noted that the identification of the charging mode is mainly performed based on the charging current data, the form of the charging voltage data is relatively single, the identification process in the mode identification is relatively simple, and the identification of the voltage data can be generally completed before the identification. However, when the charging pattern is actually recognized, the input of the random forest may be charging current data and charging voltage data, and the output may be a charging pattern recognition result.

It can be seen that, in the embodiment, the charging current data is input into the pre-trained random forest model, the charging data is classified by using the random forest model, and the charging mode corresponding to the charging data is identified, so that the identification and monitoring of the charging state of the electric vehicle are realized.

Example two

Referring to fig. 4, another schematic flow chart of a charging pattern recognition method according to an embodiment of the present disclosure is shown, where the method includes the following steps:

step S401, a training sample set and a corresponding charging mode label are obtained.

And S402, training the random forest model according to the training sample set and the charging mode label.

It can be understood that a mode identification model of charging data, namely a random forest model, is constructed based on a random forest C4.5 algorithm, the random forest is a supervised learning algorithm, and the supervised learning algorithm needs to utilize a sample data area patrol model with a label to enable the model to achieve expected effectiveness. In the training process, the random forest adopts a random replaced selection training sample set and constructs corresponding decision trees, and each decision tree randomly selects features for classification. And obtaining classification results of all decision trees by the random forest, and selecting the result with the largest occurrence frequency as a final output result.

The training sample set is a data set including charging current data and voltage data corresponding to all charging modes, the corresponding charging mode label refers to the charging mode corresponding to each charging current data and voltage data, and the charging mode is manually calibrated. As shown in fig. 3, in the training process, the random forest randomly and repeatedly extracts a part of data from all training sample sets as a sample set, n sample sets are selected in total to obtain n decision trees, each decision tree randomly selects m features for classification, each decision tree obtains a corresponding classification result, and then a final training result is obtained through statistics.

The random forest model comprises three hyper-parameters of the number of characteristics, the number of decision trees and the number of leaves. After the model is trained by enough training samples, the corresponding parameters in the model can be determined. After training is completed, the model can be tested to check whether the effect of the model meets the expected requirement. As can be seen from the schematic diagram of the confusion matrix of the random forest model shown in fig. 5, the random forest model is tested under the default parameter setting, and the classification accuracy of 32 classes is obtained to be 84.3%, and as shown in fig. 5, when there are many classes of training data, because there is a part of feature overlap between each charging mode, there is a certain influence on the accuracy, so that the 32 charging modes in fig. 5 can be merged into the 12 classes of charging modes in table 2 above, and after merging, the classification accuracy can be improved to 87%. Of course, in practical applications, the classification of the charging mode can be set according to practical needs.

After the training of the random forest model is completed, new charging data can be input into the random forest model to identify the charging mode.

And S403, receiving the telemetering message reported by the charging pile in the charging process of the electric vehicle.

Specifically, in the charging process of the electric vehicle, for example, after a charging vehicle owner completes a charging order through a mobile phone, the electric vehicle and the charging pile are connected through the charging adapter and start charging, the background server receives a telemetry message reported by the charging pile in real time, and the background server obtains current data, voltage data, power data and the like of the electric vehicle by analyzing the telemetry message.

And S404, analyzing the telemetering message to obtain charging data.

And S405, inputting the charging data into a pre-trained random forest model to obtain a charging mode of the charging data.

Step S406, determining the charging behavior of the user according to the charging mode.

The charging behavior refers to a behavior performed by a user during the charging process of the electric vehicle, for example, during the charging process, the charging adapter is wrapped by the user with an article such as a plastic bag, during the charging process, the power is supplied to a plurality of vehicles simultaneously by private plug rows, during the charging process, and the vehicles are suddenly changed during the charging process. Whether the charging behavior of the user is healthy and safe is closely related to the service life of the battery, the charging safety and the like.

According to the specific charging mode of the electric vehicle, the specific charging behavior of the user can be determined. For example, when the 10 th charging mode in table 2, that is, the charging-abnormal charging mode with the temperature control time longer than 2 hours, occurs, in general, when the external temperature of the charging adapter is too high, the adapter protection mechanism is triggered, the external charging power supply is actively cut off, and the reason for the too high temperature of the adapter may be that the external environment temperature is too high, or the adapter is wrapped by a plastic bag or other articles in order to prevent the adapter from being drenched by rain. Therefore, when the 10 th charging mode in table 2 is identified, it can be presumed that the user has the behavior of the adapter being wrapped during the charging process.

For another example, when the 8 th charging mode in table 2, that is, the abnormal charging mode in which a plurality of vehicles are charged simultaneously in the same order and the same time period is identified, since the socket of one charging pile can only be used for charging one electric vehicle in the charging scene of the charging station, and the identification of the plurality of vehicles being charged simultaneously indicates that the user is charged simultaneously by the private patch panel.

For another example, when the 9 th charging mode in table 2, that is, the abnormal charging mode in which a plurality of vehicles are charged separately in different time periods of the same order is identified, since the charging curve categories corresponding to the same electric vehicle are the same, it can be considered that the user has a vehicle-changing behavior during the charging process when two different charging curve categories appear in the previous and subsequent time periods.

The charging behavior is not limited to the above-mentioned, and may include, for example, a sudden removal of a charging plug during charging. Different charging modes and charging scenes can be preset to correspond to different charging behaviors. In practical application, in order to improve the user behavior recognition accuracy, the common judgment can be performed by combining a service scene and the historical charging data of the charging user based on the recognition result of the charging mode.

Step 407, generating corresponding charging advice information and charging information according to the charging behavior.

And step S408, presenting the charging suggestion information and the charging information to the user.

Specifically, after the charging behavior of the user is identified, in order to standardize the charging behavior of the user, eliminate potential safety hazards of charging, guarantee the service life of the battery and improve the safety of the charging process, corresponding information can be fed back to the user in time, and a corresponding charging suggestion is given according to the charging behavior.

It should be noted that the charging information may refer to information indicating what charging behavior occurs in the current charging process, for example, when the charging behavior is that the adapter is wrapped, the charging information may specifically be "detecting that the adapter is automatically powered off due to an excessively high temperature, and supposing that the adapter is wrapped", so that the user may timely know the currently existing irregular charging behavior through the charging information.

The above-mentioned charge advice information may refer to information characterizing a countermeasure for the corresponding charging behavior. For example, when the charging behavior is that the adapter is wrapped, the charging advice information may be specifically "please ensure ventilation of the adapter in order to ensure charging safety and charging efficiency".

For example, the charging behavior is that the vehicle is changed during charging, and the charging advice information and the charging information are specifically "it is detected that the current order is inconsistent with the historical order, it is presumed that the vehicle is changed to another vehicle during charging or the battery starts to have an abnormality".

It is to be understood that the charging advice information and the charging information may be specifically presented to the charging user through an interface of the user terminal. The specific interface representation may be arbitrary and is not limited herein.

It should be noted that, for the same or similar parts in this embodiment as those in the first embodiment, please refer to the corresponding contents above, and the description thereof is omitted here.

It can be seen that, in the embodiment, the charging current data is input into the pre-trained random forest model, the charging data is classified by using the random forest model, and the charging mode corresponding to the charging data is identified, so that the identification and monitoring of the charging state of the electric vehicle are realized. And further identify the user charging behavior according to the charging mode, and feed back the related information to the user in time, so as to improve the safety and standardization in the charging process, guarantee the service life of the battery and improve the charging experience of the user.

It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

EXAMPLE III

Referring to fig. 6, a schematic block diagram of a charging pattern recognition apparatus according to an embodiment of the present disclosure is shown, where the apparatus may include:

the acquiring module 61 is configured to acquire charging data of the electric vehicle, where the charging data includes charging current data and charging voltage data;

and the recognition module 62 is configured to input the charging data into a pre-trained random forest model to obtain a charging mode of the charging data.

In a possible implementation, the apparatus further includes:

In one possible implementation, the random forest model is a model comprising n decision trees, n being a positive integer greater than zero; the above-mentioned identification module includes:

the classification unit is used for classifying the charging current data through n decision trees to obtain n classification results;

and the voting unit is used for determining a final classification result from the n classification results through a voting mechanism, taking the final classification result as a charging mode, and taking the final classification result as a classification result of which the quantity is more than or equal to n/2 in the n classification results.

In a possible implementation manner, the obtaining module includes:

and the message analysis unit is used for analyzing the telemetering message to obtain charging data.

In a possible implementation, the apparatus further includes:

It should be noted that the charging pattern recognition apparatus of the present embodiment corresponds to the charging pattern recognition method one to one, and for the specific description, please refer to the above corresponding contents, which are not described herein again.

Example four

Fig. 7 is a schematic diagram of a terminal device according to an embodiment of the present application. As shown in fig. 7, the terminal device 7 of this embodiment includes: a processor 70, a memory 71 and a computer program 72 stored in said memory 71 and executable on said processor 70. The processor 70, when executing the computer program 72, implements the steps in the above-described embodiments of the charging pattern recognition method, such as the steps S201 to S202 shown in fig. 2. Alternatively, the processor 70, when executing the computer program 72, implements the functions of each module or unit in each device embodiment described above, such as the functions of the modules 61 to 62 shown in fig. 6.

Illustratively, the computer program 72 may be partitioned into one or more modules or units that are stored in the memory 71 and executed by the processor 70 to accomplish the present application. The one or more modules or units may be a series of computer program instruction segments capable of performing specific functions, which are used to describe the execution process of the computer program 72 in the terminal device 7. For example, the computer program 72 may be divided into an acquisition module and an identification module, and the specific functions of each module are as follows:

the acquisition module is used for acquiring charging data of the electric vehicle, wherein the charging data comprises charging current data and charging voltage data; and the recognition module is used for inputting the charging data into a pre-trained random forest model to obtain a charging mode of the charging data.

The terminal device is a server, and may include, but is not limited to, a processor 70 and a memory 71. It will be appreciated by those skilled in the art that fig. 7 is merely an example of a terminal device 7 and does not constitute a limitation of the terminal device 7 and may comprise more or less components than shown, or some components may be combined, or different components, for example the terminal device may further comprise input output devices, network access devices, buses, etc.

The Processor 70 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.

The memory 71 may be an internal storage unit of the terminal device 7, such as a hard disk or a memory of the terminal device 7. The memory 71 may also be an external storage device of the terminal device 7, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, which are provided on the terminal device 7. Further, the memory 71 may also include both an internal storage unit and an external storage device of the terminal device 7. The memory 71 is used for storing the computer program and other programs and data required by the terminal device. The memory 71 may also be used to temporarily store data that has been output or is to be output.

It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-mentioned functions. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working processes of the units and modules in the system may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.

In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.

Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.

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

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

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

The integrated modules or units, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. Based on such understanding, all or part of the flow in the method of the embodiments described above can be realized by a computer program, which can be stored in a computer-readable storage medium and can realize the steps of the embodiments of the methods described above when the computer program is executed by a processor. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like. It should be noted that the computer readable medium may contain content that is subject to appropriate increase or decrease as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable media does not include electrical carrier signals and telecommunications signals as is required by legislation and patent practice.

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

Claims

1. A charging pattern recognition method, comprising:

inputting the charging data into a pre-trained random forest model to obtain a charging mode of the charging data;

the charging data is characterized by a charging current curve, and the charging current curve is characterized by comprising 17 charging current curves, which are respectively as follows: the device comprises a groove, a first stage, a second stage, a third stage, a first stage part oscillation, a second stage part oscillation, a third stage part oscillation, a convex part, a starting current less than 0.6A, a middle part of 0, a single step, a middle step, a steep descending and slowly ascending groove, a plurality of continuous steps, a continuous ascending part and a short first stage, wherein the current is 0;

combining different charging current curve characteristics to obtain different charging modes;

wherein the groove feature corresponds to a current curve in the shape of a groove;

the first stage characteristic represents a curve of a first stage in a normal three-stage charging curve;

the second-stage characteristic represents a continuous descending stage in a normal three-stage charging curve, and the descending time length is half an hour;

the third-stage characteristic represents a section of a normal three-section charging curve in which the charging time length after the continuous descending section is longer than 1 hour, the current value is lower than 0.4A when the maximum value of the charging current is less than or equal to 2A, and the current value is lower than 0.7A when the maximum value of the charging current is greater than 2A;

the first stage partial oscillation shows that the current has continuous obvious oscillation, the amplitude is more than or equal to 0.3A, and the current simultaneously occurs at the first stage position of the three-stage charging point curve;

the partial oscillation of the second stage shows that the current continuously and obviously oscillates, the amplitude is greater than or equal to 0.3A, and the partial oscillation of the second stage simultaneously occurs at the second stage position of the three-stage charging point curve;

the partial oscillation of the third stage shows that the current continuously and obviously oscillates, the amplitude is greater than or equal to 0.3A, and the partial oscillation of the third stage simultaneously occurs at the third stage position of the three-stage charging point curve;

the convex represents that the current curve has an obvious convex shape;

the single step represents that the current value drops sharply, the drop is more than 0.3A, the step length is more than one hour, and the dropped current value is more than or equal to 0.6A;

the middle step represents that a stable section is arranged between the two descending sections, the maximum current value is greater than or equal to 1.5A, and the first jump value is greater than or equal to 0.6A;

the image of the steep descending and slow ascending groove representing the current curve is a groove, the groove descends steeply in the descending stage, and rises slowly in the ascending stage.

2. The charging pattern recognition method of claim 1, further comprising, before acquiring charging data of the electric vehicle:

acquiring a training sample set and a corresponding charging mode label;

3. The charging mode identification method according to claim 1, wherein the acquiring charging data of the electric vehicle comprises:

and analyzing the telemetering message to obtain the charging data.

4. The charging pattern recognition method according to claim 1, wherein the random forest model is a model comprising n decision trees, n being a positive integer greater than zero;

inputting the charging data into the pre-trained random forest model to obtain a charging mode of the charging data, wherein the charging mode comprises the following steps:

and determining a final classification result from the n classification results through a voting mechanism, wherein the final classification result is a classification result of which the number is more than or equal to n/2 in the n classification results, and the final classification result is used as the charging mode.

5. The charging pattern recognition method according to any one of claims 1 to 4, wherein after inputting the charging data into the random forest model trained in advance to obtain a charging pattern of the charging data, the method further comprises:

6. The charging mode identification method of claim 5, after determining the charging behavior of the user according to the charging mode, further comprising:

generating corresponding charging information and charging suggestion information according to the charging behavior;

and presenting the charging information and the charging suggestion information to a user.

7. A charging pattern recognition apparatus, comprising:

the recognition module is used for inputting charging data into a pre-trained random forest model to obtain a charging mode of the charging data;

the convex represents that the current curve has an obvious convex shape;

8. The charging pattern recognition device according to claim 7, further comprising:

9. A terminal device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the steps of the method according to any one of claims 1 to 6 when executing the computer program.

10. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program which, when executed by the processor, implements the steps of the method according to any one of claims 1 to 6.