CN109934271B

CN109934271B - Charging behavior identification method and device, terminal equipment and storage medium

Info

Publication number: CN109934271B
Application number: CN201910151373.1A
Authority: CN
Inventors: 卢露
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: 2021-03-02
Anticipated expiration: 2039-02-28
Also published as: CN109934271A

Abstract

The embodiment of the application is suitable for the technical field of electric vehicles and discloses a charging behavior identification method, a charging behavior identification device, terminal equipment and a computer readable storage medium, wherein the method comprises the following steps: acquiring charging data of the electric vehicle uploaded by a charging pile, wherein the charging data comprises charging current data; judging whether the charging current data meet a preset condition, wherein the preset condition is that a time period with current continuously being a preset value exists between the starting time and the ending time of charging, and the charging data from the starting time of the time period to the first preset time and the charging data from the ending time of the time period to the second preset time belong to different charging classes respectively; and when the charging current data meet the preset conditions, determining that the vehicle is changed in the charging process. The charging behavior of whether being traded the car can be discerned in the charging process to this application embodiment.

Description

Charging behavior 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 behavior 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 the electric vehicle is more and more extensive.

At present, when the car owner used the charging station to charge for the electric motor car, after accomplishing corresponding order payment of charging, the corresponding socket that fills electric pile will circular telegram, then, the car owner can be connected to the corresponding socket that fills electric pile with the electric motor car through the adapter of electric motor car, charging wire to charge to the electric motor car.

In the charging process of the electric vehicle, the benefits of a vehicle owner need to be guaranteed so as to guarantee the charging experience of a user. If the plug of the electric vehicle is unplugged by other people in the charging process, the plug of the electric vehicle is replaced by another electric vehicle for charging, so that the benefit of a charging user is damaged, and the charging experience of the user is reduced. For the charging behavior of the vehicle to be changed in the charging process, no effective identification method exists at present.

Disclosure of Invention

In view of this, embodiments of the present application provide a charging behavior identification method and apparatus, a terminal device, and a computer-readable storage medium, so as to solve the problem that in the prior art, it is not possible to identify whether a behavior of being changed exists in a charging process of an electric vehicle, thereby reducing charging experience of a user.

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

acquiring charging data of the electric vehicle uploaded by a charging pile, wherein the charging data comprises charging current data;

judging whether the charging current data meet a preset condition or not, wherein the preset condition is that a time period with current continuously being a preset value exists between the starting time and the ending time of charging, and the charging data from the starting time of the time period to the first preset time and the charging data from the ending time of the time period to the second preset time belong to different charging classes respectively;

and when the charging current data accord with the preset conditions, determining that the vehicle is changed in the charging process.

With reference to the first aspect, in a possible implementation manner, the determining whether the charging current data meets a preset condition includes:

generating a charging current curve according to the charging current data;

converting the charging current curve into a charging curve sample picture;

inputting the charging curve sample picture into a pre-trained neural network model to obtain a charging mode corresponding to the charging current curve;

when the charging mode is a preset charging mode, the charging current data meet the preset condition, the preset charging mode is a charging mode corresponding to a charging current curve which is basically formed into two first characteristics which are not adjacent in time or two second characteristics which are not adjacent in time, the first characteristics are a first stage in a three-stage charging curve, and the second characteristics are a second stage in the three-stage charging curve;

and when the charging mode is a non-preset charging mode, the charging current data does not accord with the preset condition.

With reference to the first aspect, in one possible implementation manner, the neural network model is a stacked sparse self-coding-based neural network including an input layer, a first hidden layer, a second hidden layer, a multi-classification layer, and an output layer;

inputting the charging curve sample picture into a pre-trained neural network model to obtain a charging mode corresponding to the charging current curve, wherein the charging mode comprises the following steps:

acquiring the charging curve sample picture through the input layer;

inputting the charging curve sample picture into the first hidden layer, so that the first hidden layer performs feature extraction operation on the charging curve sample picture and outputs a first current curve feature;

inputting the first current curve characteristic into the second hidden layer, so that the second hidden layer performs characteristic extraction operation on the first current curve characteristic and outputs a second current curve characteristic, wherein the accuracy of the second current curve characteristic is higher than that of the first current curve characteristic;

inputting the second current curve characteristics into the multi-classification layer so that the multi-classification layer can identify the second current curve characteristics, and obtaining a charging mode classification result according to the corresponding relation between the current curve characteristics and the charging mode;

and inputting the charging mode classification result into the output layer so as to enable the output layer to output the charging mode.

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

when the charging mode is a preset charging mode, the charging current data meet the preset condition, the preset charging mode is a mode corresponding to a charging current curve which is basically formed into two first characteristics which are not adjacent in time or two second characteristics which are not adjacent in time, the first characteristics are a first stage in a three-stage charging curve, and the second characteristics are a second stage in the three-stage charging curve;

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;

inputting the charging data into a pre-trained random forest model to obtain a charging mode corresponding to the charging current data, wherein the charging mode 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, 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, before the determining whether the charging current data meets a preset condition, the method further includes:

judging whether the station voltage of the charging station where the charging pile is located is unstable or not according to charging voltage data in the charging data;

and when the station voltage is not unstable, the charging station enters a subsequent step of judging whether the charging current data meets the preset conditions.

With reference to the first aspect, in a possible implementation manner, after the determining that there is the act of being changed during the charging process, the method further includes:

generating prompt information;

and presenting the prompt information to a charging user through a user terminal so as to prompt the charging user that a vehicle-changing behavior exists in the charging process.

A second aspect of an embodiment of the present application provides a charging behavior identification apparatus, including:

the charging data acquisition module is used for acquiring charging data of the electric vehicle uploaded by the charging pile, wherein the charging data comprises charging current data;

the judging module is used for judging whether the charging current data meet a preset condition, the preset condition is that a time period with current continuously being a preset value exists between the starting time and the ending time of charging, and the charging data from the starting time of the time period to the first preset time and the charging data from the ending time of the time period to the second preset time belong to different charging classes respectively;

and the identification module is used for determining that the vehicle is changed in the charging process when the charging current data accords with the preset condition.

With reference to the second aspect, in a possible implementation manner, the determining module includes:

the curve generating unit is used for generating a charging current curve according to the charging current data;

the conversion unit is used for converting the charging current curve into a charging curve sample picture;

the first charging mode identification unit is used for inputting the charging curve sample picture into a pre-trained neural network model to obtain a charging mode corresponding to the charging current curve;

a first determining unit, configured to, when the charging mode is a preset charging mode, determine that the charging current data meets the preset condition, where the preset charging mode is a charging mode corresponding to a charging current curve that is basically configured as two first characteristics that are not adjacent in time or two second characteristics that are not adjacent in time, the first characteristics are a first stage in a three-stage charging curve, and the second characteristics are a second stage in the three-stage charging curve;

and the second determining unit is used for determining that the charging current data does not accord with the preset condition when the charging mode is a non-preset charging mode.

With reference to the second aspect, in one possible implementation manner, the neural network model is a stacked sparse self-coding-based neural network including an input layer, a first hidden layer, a second hidden layer, a multi-classification layer, and an output layer;

the first charging pattern recognition unit includes:

an obtaining subunit, configured to obtain the charging curve sample picture through the input layer;

a first feature extraction subunit, configured to input the charging curve sample picture into the first hidden layer, so that the first hidden layer performs a feature extraction operation on the charging curve sample picture, and outputs a first current curve feature;

a second feature extraction subunit, configured to input the first current curve feature into the second hidden layer, so that the second hidden layer performs a feature extraction operation on the first current curve feature and outputs a second current curve feature, where accuracy of the second current curve feature is higher than that of the first current curve feature;

the first classification subunit is used for inputting the second current curve characteristics into the multi-classification layer so as to enable the multi-classification layer to identify the second current curve characteristics, and obtaining a charging mode classification result according to the corresponding relation between the current curve characteristics and the charging mode;

and the output subunit is used for inputting the charging mode classification result into the output layer so as to enable the output layer to output the charging mode.

the second charging mode recognition unit is used for inputting the charging current data into a pre-trained random forest model to obtain a charging mode corresponding to the charging current data;

a third determining unit, configured to, when the charging mode is a preset charging mode, determine that the charging current data meets the preset condition, where the preset charging mode is a mode corresponding to a charging current curve that is basically configured as two first features that are not adjacent in time or two second features that are not adjacent in time, where the first feature is a first stage in a three-stage charging curve, and the second feature is a second stage in the three-stage charging curve;

and the fourth determining unit is used for determining that the charging current data does not accord with the preset condition when the charging mode is a non-preset charging mode.

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 second charging pattern recognition unit includes:

the second classification subunit is configured to classify the charging current data through the n decision trees to obtain n classification results;

and the selecting subunit is used for determining a final classification result from the n classification results, and taking the final classification result as the 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.

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

the station voltage judging module is used for judging whether the station voltage of the charging station where the charging pile is located is unstable or not according to the charging voltage data in the charging data;

and the entering module is used for entering the subsequent step of judging whether the charging current data meet the preset conditions or not when the station voltage of the charging station is not unstable.

the generating module is used for generating prompt information;

and the prompting module is used for presenting the prompting information to a charging user through a user terminal so as to prompt the charging user that a vehicle-changing behavior exists in the charging process.

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 embodiment of the application, whether the action of being changed is existed in the charging process is judged through the charging current data, namely, the current continuously reaches the preset value in the middle of the charging current, and the charging data before and after the current continuously reaches the preset value respectively belong to different charging processes, so that the action of being changed is appeared in the charging process, the identification of the charging action of being changed in the charging process is realized, and the charging experience of a user is improved.

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 behavior identification method according to an embodiment of the present disclosure;

fig. 3 is a schematic view of a charging curve provided in an embodiment of the present application;

fig. 4 is a schematic block diagram of another flow chart of a charging behavior identification method provided in an embodiment of the present application;

FIG. 5 is a schematic diagram of a stacked sparse self-encoding based neural network provided by an embodiment of the present application;

fig. 6 is a schematic block diagram of a charging mode identification process provided in an embodiment of the present application;

FIG. 7 is a schematic diagram illustrating a current curve characteristic of an output of a first hidden layer according to an embodiment of the present disclosure;

FIG. 8 is a graph illustrating current curve characteristics of a second hidden layer output according to an embodiment of the present disclosure;

fig. 9 is a schematic block diagram of another flow chart of a charging behavior identification method according to an embodiment of the present application;

fig. 10 is a schematic diagram of a random forest model provided in an embodiment of the present application;

fig. 11 is a schematic diagram of a confusion matrix of a random forest model according to an embodiment of the present application;

fig. 12 is a block diagram schematically illustrating a structure of a charging behavior identification apparatus according to an embodiment of the present application;

fig. 13 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 the socket that fills electric pile through electric motor car adapter, charging wire and plug with waiting to charge the electric motor car. 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 controls the corresponding socket to be electrified, and then 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 charging is completed, the background server stores the relevant information of the current charging order and stores the charging order and the charging data in the charging process in a correlation manner.

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, whether some abnormal conditions or unhealthy charging behaviors exist in the charging process can be determined according to the charging mode, and if the corresponding charging behaviors are identified, the charging behaviors can be fed back to a 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 block diagram of a flow of a charging behavior identification method provided in an embodiment of the present application may include the following steps:

step S201, charging data of the electric vehicle uploaded by the charging pile are obtained, and the charging data comprise charging current data.

It should be noted that the charging data generally includes charging voltage data, charging current data, and charging power data. In the charging process of the electric vehicle, the charging voltage and the charging power are generally constant, and in some cases, the charging voltage and the charging power can be analyzed and identified only based on the charging current data, and at the moment, the charging data can only include the charging current data. In other cases, the charging current data and the charging voltage data are needed, and the charging data may include only the charging voltage data and the charging current data.

The charging data can be data uploaded by the charging pile in real time, namely, in the process that the electric vehicle is charged through a power adapter and a socket of the charging pile connected with a charging wire, the charging pile reports the collected charging data to the background server in a telemetering message form, the background server analyzes the telemetering message, and the charging data reported by each charging pile is obtained according to related information carried by the telemetering message, such as the unique ID of the charging pile equipment. Of course, the charging data may also be historical charging data, and the historical charging data is obtained by storing charging data of the electric vehicle uploaded by the charging pile in real time.

Step S202, determining whether the charging current data meets a preset condition, and when the charging current data meets the preset condition, entering step S203, otherwise, when the charging current data does not meet the preset condition, entering step S204.

The preset condition is that a time period exists between the charging starting time and the charging ending time, the current of the time period continuously reaches a preset value, and the charging data from the starting time of the time period to the first preset time and the charging data from the ending time of the time period to the second preset time belong to different charging types respectively.

It should be noted that the preset value may be zero, that is, the current value continues to be zero for a period of time from the beginning of charging to the end of charging; the predetermined value may also be other non-zero values, typically less than 50% of the plateau current value.

The first preset time and the second preset time may be determined by actual charging current data, where the first preset time is generally a charging start time, and the second preset time is generally a charging end time, where charging data from the charging start time to the start time of a time period lasting a preset value belongs to one charging process class, and charging data from the end time of the time period lasting the preset value to the charging end time belongs to another charging process class. In other words, the charging data before and after the time period lasting the preset value belong to two different charging processes, that is, the charged electric vehicles before and after the time period lasting the preset value are two different electric vehicles.

To better describe the representation of the charging current data meeting the predetermined condition, please refer to the schematic diagram of the charging curve shown in fig. 3, wherein fig. 3 includes 9 graphs, which are distributed in three rows and three columns, and the 9 graphs are charging curve graphs respectively plotted according to the charging voltage data and the charging current data, and each graph includes a charging current curve and a charging voltage curve.

In each graph, the horizontal axis represents time, the left vertical axis represents current, the right vertical axis represents voltage, the graph is constant near 220V, and the curve which is directly reduced to zero after a period of time is a charging voltage curve, that is, in one charging period, the charging voltage keeps 220V unchanged and the fluctuation is not large. And the other curve in each figure except the charging voltage curve is the charging current curve.

As can be seen from the charging graph shown in the first column on the second row of fig. 3, the charging current value steeply decreases to zero in the vicinity of t ═ 100, continues until t ═ 200, and then steeply increases to a certain value. In this case, the preset value is zero, the time period of the zero duration is between t and about 100 and t and about 200, the starting time of the time period of the zero duration is about 100, the ending time of the time period of the zero duration is about 200, the first preset time is 0, and the second preset time is 400. It is readily seen that the charging curves before and after the sustained zero are two different charging current curves. The charging profiles of the first row and first column, the first row and third column, the third row and first column, and the third row and third column in fig. 3 are similar to the charging profile of the second row and first column described above. As can be seen from the charging curve diagram of the second row and the second column in fig. 3, the predetermined value is not zero, and is less than 50% of the plateau current value (about 1.2A), which is similar to the curve diagram of the third column in the second row and the third column in fig. 3.

In a specific implementation, the specific process of determining whether the charging current data is in the preset condition may include: firstly, according to the charging current data, a charging mode corresponding to the current data is identified, and then whether the current data meets the preset condition or not is determined according to the charging mode. The charging mode can be identified through a random forest model, at the moment, the random forest model is trained in advance, and then current data are input into the random forest model, so that the charging mode can be obtained; or the charging data can be converted into a charging curve, then the charging curve is converted into a sample picture, and then the sample picture is subjected to charging mode recognition by utilizing a pre-trained neural network model to obtain a recognition result. Of course, the determination of the charging current data may be implemented by a determination method different from the above-described two methods.

The material type, various component contents, battery capacity, residual SOC, battery aging degree, charging adapter, generation manufacturer and the like of each electric vehicle battery are different, so that various current expression forms can appear in the battery in the charging process, two electric vehicles with completely identical current curves cannot exist, and the current charging state of the battery, the safety of the battery, the aging degree of the battery, the safety of charging behaviors of users and the like can be identified and judged by the charging current curves. And further combining the actual charging scene and the service type to identify the charging behavior.

In other words, when a car is changed during charging, in some cases, it may be necessary to unplug a charging plug of an electric vehicle and then plug a charging plug of another electric vehicle, during which the charging current may suddenly drop to zero or a certain value and continue for a certain period of time (the duration may be considered as a time interval from unplugging to plugging), and charging curves around the time period which continues to be a preset value belong to different charging classes. Of course, in other cases,

step S203, a behavior of being changed exists in the charging process.

And step S204, no behavior of the vehicle to be changed exists in the charging process.

It is understood that the vehicle change refers to the change of a currently charged electric vehicle to another electric vehicle for charging. Considering that the possibility of the car changing behavior of the charger owner in the charging process is low, the car changing behavior can be generally considered to be performed by other users. That is, some users exchange the electric vehicle being charged by other vehicle owners with their own electric vehicle, so that the user can charge without paying, but the fraudulent use of electricity affects the legitimate interests of the paid vehicle owners.

According to the charging current data, the recognition of the car-changing behavior in the charging process is realized by combining the actual charging scene and the service type, and the recognition can be fed back to a charging car owner in time after the recognition of the behavior, so that the benefits of the charging car owner are guaranteed, and the charging experience is provided.

In some embodiments, after determining that there is a behavior of being switched during the charging process, the method may further include: generating prompt information; and presenting the prompt information to the charging user through the user terminal so as to prompt the charging user that a vehicle-changing behavior exists in the charging process. The prompt information is information representing a vehicle-change behavior in the charging process, and for example, the prompt information may be "the vehicle is suspected to be changed to other vehicles in the charging process according to intelligent analysis of a charging curve". The prompt message can be presented to the user through a display screen of the user terminal such as a mobile phone, a tablet and the like.

In addition, the charging current is influenced by the voltage of the charging station, and in order to improve the identification accuracy, whether the station voltage of the charging station is stable or not can be judged firstly, and then whether the charging current data meet the preset conditions or not can be judged. Therefore, in some embodiments, before the determining whether the charging current data meets the predetermined condition, the method may further include: judging whether the station voltage of a charging station in which the charging pile is positioned is unstable or not according to charging voltage data in the charging data; and when the station voltage is not unstable, the charging station enters a subsequent step of judging whether the charging current data meets the preset conditions.

Wherein, can set for the voltage to fluctuate for normal between 200 ~ 240V, according to the charging voltage who gathers, can judge whether the station voltage is stable, whether phenomenons such as high pressure, undervoltage, voltage shock appear. If the station voltage is stable, the subsequent charging current judgment step can be carried out. Of course, in other embodiments, the site voltage determination may not be performed.

In this embodiment, whether the action of being changed is existed in the charging process is judged through the charging current data, namely, when the charging current exists for a period of time, the current continues to be the preset value, and the charging data before and after the current continues to be the preset value respectively belong to different charging processes, the action of being changed occurs in the charging process, so that the identification of the charging action of being changed in the charging process is realized, and the charging experience of a user is improved.

Example two

Referring to fig. 4, another schematic flow chart of a charging behavior identification method provided in an embodiment of the present application is shown, where the method may include the following steps:

step S401, charging data of the electric vehicle uploaded by the charging pile are obtained, and the charging data comprise charging current data.

Step S402, generating a charging current curve according to the charging current data.

Specifically, according to data such as charging current, voltage, and the like, a corresponding curve is drawn in a set coordinate system.

It should be noted that, in general, the charging data mainly includes current, voltage and power, while the charging power of the electric vehicle is generally not changed, the power curve plays a small role in analyzing and identifying the battery state, the voltage identification process is simpler, and the charging data can be generally completed before the current identification. Thus, in some cases, only a current curve, or a current curve and a voltage curve, is required. In addition, the material type, the content of various components, the battery capacity, the remaining SOC, the battery aging degree, the charging adapter, manufacturers and the like of the electric vehicle battery are different, so that various current expression forms can appear in the battery in the charging process, and two electric vehicles with completely identical current curves cannot exist, so that the current charging state of the battery, the safety of a user charging behavior and the like can be identified and judged by the charging current curve. In other words, during the charging pattern recognition, the current curve is mainly relied upon for the analytical recognition of the charging pattern.

Step S403, converting the charging current curve into a charging curve sample picture.

Specifically, the current curve is converted into a picture of a certain pixel size (e.g., 128 × 128); and carrying out standardization processing on the pixel gray value of the picture to obtain a charging curve sample picture. The picture can be processed in a logarithmic Logistic standardization mode, so that the pixel value of the picture falls between 0 and 1.

And S404, inputting the charging curve sample picture into a pre-trained neural network model to obtain a charging mode corresponding to the charging current curve.

It should be noted that the neural network model may be a stacked sparse self-coding-based neural network, and the model may specifically include an input layer, two hidden layers, a multi-classification layer, and an output layer. The neural network model is trained in advance with current data including all charging modes to obtain appropriate network parameters. The neural network model can extract corresponding current curve characteristics from the charging curve sample picture, identify the current curve characteristics, and obtain the charging mode corresponding to the charging curve according to the corresponding relation between the current curve characteristics and the charging mode.

The charging current curves of different electric vehicles are different, but different charging data can contain some same characteristics, and different characteristic combinations can form different charging modes. The current curve characteristic may refer to a characteristic of a curve representing a certain shape, that is, a curve representing a certain curve shape or a certain function by the characteristic. For example, the notch feature in the current curve feature, which corresponds to a current curve in the shape of a notch, is embodied in that the current slowly decreases to a value other than 0, and then slowly increases to a position where the current differs from the current at the start of the decreasing process by less than 0.2A.

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 curve in which the charging time period is more than 1 hour after the continuous descending stage in the normal three-stage charging curve, and the current value is less than 0.3A.

And so on, different curve segments are represented with different characteristics for the shape and other characteristics of the current curve. In this embodiment, the charging current curve characteristics may include 14, 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, an initial current less than 0.3A, a middle part of 0, a single step, a middle step, a full oscillation and a plurality of continuous steps. Of course, in practical application, the categories of the current curve features can be increased or decreased according to needs.

The time sequence combination of different curve characteristics can represent different charging modes, namely, the time sequence combination corresponds to different charging modes according to the curve characteristics contained in the charging current curve and the time sequence of the curve characteristics. In this embodiment, the charging modes may include 13, and the 13 charging modes may include: 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 with the temperature control time of more than 2 hours, full oscillation, 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 the picture of the corresponding charging current curve is input to the neural network model, the neural network model can extract curve characteristics, and the charging mode corresponding to the charging current curve is determined according to the extracted curve characteristics. For example, if a certain charging current curve includes a first stage, a second stage, and a third stage in a normal three-stage curve, after the charging curve picture is input to the neural network model, the neural network model may extract a "first stage" feature, a "second stage" feature, and a "third stage" feature, and then determine a charging mode corresponding to the charging current curve according to occurrence times of the "first stage" feature, the "second stage" feature, and the "third stage" feature, and if the time sequence of the three features is the "first stage" feature, the "second stage" feature, and the "third stage" feature, the neural network model may determine that the charging mode corresponding to the charging current curve is "full three stages".

In some embodiments, the neural network model is a stacked sparse self-coding-based neural network including an Input layer, a first hidden layer, a second hidden layer, a multi-classification layer, and an output layer, and the neural network may be specifically a neural network as shown in fig. 5, which specifically includes an Input layer Input L₁A hidden Layer L₂A hidden Layer L₃Output layer Output L₄. The network parameters W, h and f can be determined through model training. The multiple classification layers are not shown in fig. 5.

At this time, referring to the schematic block diagram of the charging pattern recognition process shown in fig. 6, the specific process of inputting the charging curve sample picture into the pre-trained neural network model to obtain the charging pattern recognition result may include:

step S601, a charging curve sample picture is obtained through an input layer.

Step S602, inputting the charging curve sample picture into the first hidden layer, so that the first hidden layer performs a feature extraction operation on the charging curve sample picture, and outputs a first current curve feature.

Step S603, inputting the first current curve feature into the second hidden layer, so that the second hidden layer performs feature extraction operation on the first current curve feature, and outputs a second current curve feature, where accuracy of the second current curve feature is higher than that of the first current curve feature.

Step S604, inputting the second current curve characteristics into the multi-classification layer so that the multi-classification layer can identify the second current curve characteristics, and obtaining a charging mode classification result according to the corresponding relation between the current curve characteristics and the charging mode.

Step S605, inputting the charging mode classification result into the output layer, so that the output layer outputs the charging mode identification result.

Specifically, after the neural network model acquires a charging current curve picture, the first hidden layer can extract the characteristics of the charging current curve according to picture data, the output of the first hidden layer is used as the input of the second hidden layer, the second hidden layer further extracts the input curve characteristics to obtain more accurate curve characteristics, the curve characteristics are output to multiple classification layers, the multiple classification layers perform mode combination classification according to the curve characteristics, and then the classification results are output to the output layer to obtain the charging mode classification results.

It should be noted that the second current curve characteristic is more accurate than the first current curve characteristic, the current curve characteristic of the output of the first hidden layer can be shown in fig. 7, and the current curve characteristic of the output of the second hidden layer can be shown in fig. 8. The second hidden layer functions to further improve the accuracy of the curve feature, so that the greater the number of hidden layers, the higher the accuracy of the extracted curve feature is, and conversely, the smaller the number of hidden layers, the lower the accuracy of the curve feature is. However, an increase in the number of hidden layers may cause certain features to be overwhelmed, and thus, the number of hidden layers may be determined according to actual needs, accuracy requirements, and the like.

The correspondence between the current curve characteristics and the charging modes refers to the correspondence between different preset charging modes and the respective curve characteristics, and the different charging modes can be combined by different current curve characteristics. To better describe the current profile characteristics, the relationship between the current profile 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 charging current curve characteristic table, and for convenience of description, the 14 characteristics are represented by capital letters a to N, 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 14 characteristics in table 1, 13 small charging modes can be obtained, and the 13 small charging modes can be divided into 4 large charging modes, wherein the 4 large charging modes are normal charging, abnormal charging, full-oscillation charging and sudden stop charging respectively. The specific relationship is shown in table 2 below.

TABLE 2 charging mode table

Table 2 above shows 13 small charging modes, and the corresponding curve characteristic combinations and the corresponding large charging modes of the 13 small charging modes. The characteristics a to N in the charge mode composition in table 2 mean the characteristics a to N 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 neural network model is 13 kinds of small charging modes in table 2, and a large charging mode corresponding to each small charging mode can be obtained according to a preset correspondence 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 be preset in the neural network model, and the neural network model may also output the large charging mode according to the small charging mode after recognizing the small charging mode. That is, the output result of the neural network model may be the large charging pattern of table 2 described above. Of course, the output result may also include both the small charge mode and the large charge mode.

It is to be understood that the neural network model may be trained in advance, and the training process of the neural network model may specifically include: acquiring a training sample data set, wherein the training sample data set is a data set comprising current curve sample pictures corresponding to all charging modes; carrying out data preprocessing operation on the training sample data set; and training the pre-established neural network model according to the pre-processed training sample data set.

It is to be understood that the training sample data set includes a plurality of pictures, and the data set includes pictures of charging curves corresponding to all charging modes.

Wherein the data preprocessing operation can convert the picture into a standard picture. For example, the sample picture size is 128 × 128 pixels, the third predetermined pixel size is 8 × 8 pixels, the first predetermined number is 1000, the second predetermined number is 50 ten thousand, and the third predetermined number is 3 ten thousand. Firstly, converting the current data of all charging curve modes into pictures with the size of 128 × 128 pixels, then randomly extracting 1000 small pictures with 8 × 8 pixels from each picture with 128 × 128 pixels, and dividing the small pictures into two categories of U1 and U2, wherein the small pictures in U1 contain a current curve and a picture background, and the small pictures in U2 only contain the picture background. Then, respectively and randomly extracting 50 ten thousand small pictures and 3 ten thousand small pictures from the U1 data set and the U2 data set to form 53 ten thousand training samples, and standardizing the pixel gray values of the 53 ten thousand training samples according to a logarithmic Logistic mode to enable the pixel gray values of the training samples to fall between 0 and 1. The 53 ten thousand training samples after normalization are recorded as X ═ X₁，x₂，…，x_n}，n＝530000。

For example, when the neural network model is a neural network as shown in fig. 5, the training sample X ═ { X ═ X₁，x₂，…，x_nAfter the input to the neural network, the hidden layer L₂The extracted current curve is characterized by lambda_m＝{λ₂₁，λ₂₂，…，λ_2m}, hidden layer L₃To a_mCarrying out characteristic extraction to obtain Lambda_k＝{λ₃₁，λ₃₂，…，λ_3k}, hidden layer L₃Will be Λ_kInputting the multi-classification layer to obtain a classification result, and outputting the classification result to an output layer L₄Obtaining output result Y ═ Y₁，y₂，…，y_n}. At the same time, an input layer L can also be obtained₁And a hidden layer L₂Parameter ω between, hidden layer L₂And a hidden layer L₃Parameter h in between, hidden layer L₃And an output layer L₄Parameter f in between.

After training, the obtained training result can be detected, when the difference between the output training result and the set charging mode is within an acceptable precision range, the corresponding network parameter can be determined, and then the identification phase is entered.

After the charging pattern is identified, the charging behavior may be further identified according to the charging pattern.

Step S405, determining whether the charging data is in a preset charging mode. When the charging mode is the preset charging mode, the process proceeds to step S406, otherwise, when the charging mode is the non-preset charging mode, the process proceeds to step S407.

And step S406, the charging current data accords with preset conditions, and the fact that a vehicle is changed in the charging process is determined. The preset charging mode is a charging mode corresponding to a charging current curve which is basically formed into two first characteristics which are not adjacent in time or two second characteristics which are not adjacent in time, the first characteristic is a first stage in a three-stage charging curve, and the second characteristic is a second stage in the three-stage charging curve.

It is understood that the preset charging mode may refer to the 9 th charging mode in table 2. The charging current curve corresponding to the charging mode is basically formed into two B characteristics or two C characteristics, and the same characteristics are not adjacent in time. When the charging data is analyzed through the neural network model, and a plurality of vehicles are charged in different time periods of the same charging order, the vehicle is considered to be changed in the charging process, otherwise, the vehicle is not changed.

And step S407, determining that the vehicle is not changed in the charging process when the charging current data does not meet the preset condition.

It can be seen that, in the embodiment, based on the charging data of the electric vehicle, whether the charging behavior of the vehicle to be changed exists in the charging process is identified through the neural network model based on the stacked self-sparse codes, and the charging experience of a user is improved.

EXAMPLE III

Referring to fig. 9, a schematic block diagram of another flow chart of a charging behavior identification method provided in an embodiment of the present application may include the following steps:

step S901, acquiring charging data of the electric vehicle uploaded by the charging pile, where the charging data includes charging current data.

And S902, inputting the charging data into a pre-trained random forest model to obtain a charging mode corresponding to 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. 10, as shown in fig. 10, 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 output results of the random forest are generally 13 small charging modes shown in table 2 in the above second embodiment, 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.

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 based on the tables 1 and 2 of the second embodiment to obtain classification results, classifying the modes of the charging current data for each decision tree by each classification result, and finally counting the number of each classification result to take the classification result of which the number accounts for more than 50% as a final output result of the model, wherein the final output result is a 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".

The model parameters of the random forest are obtained through pre-training, and the training process of the random forest specifically comprises the following steps: 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.

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. 10, 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 the 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. 11, in the default parameter setting, the random forest model is tested, and the classification accuracy of 32 classes is obtained to be 84.3%, and as can be seen from fig. 11, 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 32 charging modes in fig. 11 can be merged into 13 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.

Step S903, determining whether the charging mode is a preset charging mode, and if the charging mode is the preset charging mode, going to step S904, otherwise, going to step S905.

And step S904, the charging current data accords with preset conditions, and the fact that a vehicle is changed in the charging process is determined. The preset charging mode is a mode corresponding to a charging current curve basically comprising two first characteristics which are not adjacent in time or two second characteristics which are not adjacent in time, the first characteristic is a first stage in a three-stage charging curve, and the second characteristic is a second stage in the three-stage charging curve.

And step S905, determining that the charging current data does not meet the preset condition and that no vehicle-changed behavior exists in the charging process.

It can be seen that, this embodiment is based on the charging data of electric motor car, through the charging action that whether random forest model discernment charging process exists the car of being traded, improves user's experience of charging.

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 four

Referring to fig. 12, a schematic block diagram of a charging behavior recognition apparatus according to an embodiment of the present disclosure is shown, where the apparatus may include:

the charging data acquisition module 121 is configured to acquire charging data of the electric vehicle uploaded by the charging pile, where the charging data includes charging current data;

the judging module 122 is configured to judge whether the charging current data meets a preset condition, where the preset condition is that a time period exists between a charging start time and a charging end time, where a current continues to be a preset value, and charging data from the start time of the time period to a first preset time and charging data from the end time of the time period to a second preset time belong to different charging classes respectively;

and the identification module 123 is configured to determine that a vehicle-changed behavior exists in the charging process when the charging current data meets the preset condition.

In a possible implementation manner, the determining module includes:

the first charging mode identification unit is used for inputting a charging curve sample picture into a pre-trained neural network model to obtain a charging mode corresponding to a charging current curve;

the charging system comprises a first determining unit, a second determining unit and a charging unit, wherein the first determining unit is used for determining charging current data according with a preset condition when a charging mode is a preset charging mode, the preset charging mode is a charging mode corresponding to a charging current curve which is basically formed into two first characteristics which are not adjacent in time or two second characteristics which are not adjacent in time, the first characteristics are a first stage in a three-stage charging curve, and the second characteristics are a second stage in the three-stage charging curve;

In one possible implementation, the neural network model is a stacked sparse self-coding-based neural network comprising an input layer, a first hidden layer, a second hidden layer, a multi-classification layer, and an output layer;

the first charging pattern recognition unit includes:

the acquisition subunit is used for acquiring a charging curve sample picture through the input layer;

the first characteristic extraction subunit is used for inputting the charging curve sample picture into the first hidden layer so as to enable the first hidden layer to carry out characteristic extraction operation on the charging curve sample picture and output a first current curve characteristic;

the second characteristic extraction subunit is used for inputting the first current curve characteristic into the second hidden layer so as to enable the second hidden layer to carry out characteristic extraction operation on the first current curve characteristic and output a second current curve characteristic, and the precision of the second current curve characteristic is higher than that of the first current curve characteristic;

In a possible implementation manner, the determining module includes:

a third determining unit, configured to determine that the charging current data meets a preset condition when the charging mode is a preset charging mode, where the preset charging mode is a mode corresponding to a charging current curve that is basically configured as two first characteristics that are not adjacent in time or two second characteristics that are not adjacent in time, the first characteristic is a first stage in a three-stage charging curve, and the second characteristic is a second stage in the three-stage charging curve;

In one possible implementation, the random forest model is a model comprising n decision trees, n being a positive integer greater than zero;

the second charging pattern recognition unit includes:

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

and the selecting subunit is used for determining a final classification result from the n classification results, taking the final classification result as a charging mode, and taking the final classification result as a classification result of which the number is more than or equal to n/2 in the n classification results.

In a possible implementation, the apparatus further includes:

the generating module is used for generating prompt information;

and the prompting module is used for presenting the prompting information to the charging user through the user terminal so as to prompt the charging user that a vehicle-changing behavior exists in the charging process.

EXAMPLE five

Fig. 13 is a schematic diagram of a terminal device according to an embodiment of the present application. As shown in fig. 13, the terminal device 13 of this embodiment includes: a processor 130, a memory 131 and a computer program 132 stored in the memory 131 and executable on the processor 130. The processor 130 implements the steps in the above-mentioned various charging behavior identification method embodiments, such as the steps S201 to S203 shown in fig. 2, when executing the computer program 132. Alternatively, the processor 130 implements the functions of the modules or units in the above device embodiments, such as the functions of the modules 121 to 123 shown in fig. 12, when executing the computer program 132.

Illustratively, the computer program 132 may be partitioned into one or more modules or units that are stored in the memory 131 and executed by the processor 130 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 132 in the terminal device 13. For example, the computer program 132 may be divided into a charging data acquiring module, a determining module and an identifying module, and each module specifically functions as follows:

the charging data acquisition module is used for acquiring charging data of the electric vehicle uploaded by the charging pile, and the charging data comprises charging current data; the judging module is used for judging whether the charging current data meet a preset condition, the preset condition is that a time period with current continuously being a preset value exists between the charging starting time and the charging ending time, and the charging data from the starting time of the time period to the first preset time and the charging data from the ending time of the time period to the second preset time belong to different charging classes respectively; and the identification module is used for determining that the vehicle is changed in the charging process when the charging current data meets the preset conditions.

The terminal device 13 is a server. The terminal device may include, but is not limited to, a processor 130, a memory 131. Those skilled in the art will appreciate that fig. 13 is merely an example of a terminal device 13 and does not constitute a limitation of terminal device 13 and may include more or fewer components than shown, or some components may be combined, or different components, for example, the terminal device may also include input output devices, network access devices, buses, etc.

The Processor 130 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf 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 storage 131 may be an internal storage unit of the terminal device 13, such as a hard disk or a memory of the terminal device 13. The memory 131 may also be an external storage device of the terminal device 13, 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 13. Further, the memory 131 may also include both an internal storage unit and an external storage device of the terminal device 13. The memory 131 is used for storing the computer program and other programs and data required by the terminal device. The memory 131 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 behavior recognition method, comprising:

judging whether the charging current data meet a preset condition, wherein the preset condition is that a time period with current continuously being a preset value exists between charging starting time and charging ending time, the charging data from the starting time of the time period to a first preset time and the charging data from the ending time of the time period to a second preset time belong to different charging classes respectively, the preset value is zero or less than 50% of a current value of a stable section, the first preset time is the charging starting time, and the second preset time is the charging ending time;

2. The charging behavior recognition method according to claim 1, wherein the determining whether the charging current data meets a preset condition comprises:

generating a charging current curve according to the charging current data;

converting the charging current curve into a charging curve sample picture;

3. The charging behavior identification method according to claim 2, wherein the neural network model is a stacked sparse self-coding-based neural network comprising an input layer, a first hidden layer, a second hidden layer, a multi-classification layer, and an output layer;

acquiring the charging curve sample picture through the input layer;

4. The charging behavior recognition method according to claim 1, wherein the determining whether the charging current data meets a preset condition comprises:

5. A charging behaviour identification method according to claim 4, characterised in that said random forest model is a model comprising n decision trees, n being a positive integer greater than zero;

6. The charging behavior identification method according to any one of claims 1 to 5, further comprising, before the determining whether the charging current data meets a preset condition:

7. The charging behavior identification method according to any one of claims 1 to 5, further comprising, after the determining that there is a behavior of being switched during charging:

generating prompt information;

8. A charging behavior recognition apparatus, comprising:

the judging module is used for judging whether the charging current data meets a preset condition, the preset condition is that a time period with current continuously being a preset value exists between charging starting time and charging ending time, the charging data from the starting time of the time period to a first preset time and the charging data from the ending time of the time period to a second preset time belong to different charging classes respectively, the preset value is zero or less than 50% of a current value of a stable section, the first preset time is the charging starting time, and the second preset time is the charging ending time;

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 7 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 a processor, implements the steps of the method according to any one of claims 1 to 7.