CN112612934A - User charging behavior portrait processing method and device - Google Patents
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Abstract
The invention discloses a user charging behavior portrait processing method and device. Wherein, the method comprises the following steps: acquiring user charging behavior data; constructing a user charging behavior image, wherein the user charging behavior image comprises a plurality of dimensionalities of user charging behavior image labels; and clustering the user charging behavior data according to the user charging behavior portrait label to determine the user type. The invention solves the technical problems that the user charging behavior portrait depends on data such as user attributes, position characteristics, interest characteristics and the like, the statistical requirement is high, the workload is large and the cost is high in the related technology.
Description
Technical Field
The invention relates to the field of electric automobiles, in particular to a user charging behavior portrait processing method and device.
Background
As a novel vehicle, the electric automobile has incomparable advantages compared with the traditional automobile in the aspects of relieving energy crisis, promoting harmonious development of human beings and the environment and the like. The user portrait is used as an effective tool for sketching target users and connecting user appeal and design direction under the background of big data. The method is widely applied to various fields, and has certain advantages in the aspects of accurately identifying the user, dynamically tracking the behavior track of the user, evaluating the value of the user and the like. The user portrait construction is carried out to electric automobile charging user, and the electric automobile charging facility rational planning promotes accurate marketing of electric power and high-quality service level to guiding user's action of charging, optimizes distribution network planning and operation scheme, electric automobile charging pricing etc. and has the significance.
The conventional user charging behavior portrait depends on data such as user attributes, position characteristics and interest characteristics, has high statistical requirement, large workload and high cost, and may change data such as the user attributes and the position characteristics with time.
In view of the above problems, no effective solution has been proposed.
Disclosure of Invention
The embodiment of the invention provides a user charging behavior portrait processing method and device, and aims to at least solve the technical problems that in the related technology, a user charging behavior portrait depends on data such as user attributes, position characteristics and interest characteristics, the statistical requirement is high, the workload is large, and the cost is high.
According to an aspect of the embodiments of the present invention, there is provided a user charging behavior representation processing method, including: acquiring user charging behavior data; constructing a user charging behavior image, wherein the user charging behavior image comprises a plurality of dimensionalities of user charging behavior image labels; and clustering the user charging behavior data according to the user charging behavior image label to determine the user type.
Optionally, the obtaining the user charging behavior data includes: collecting charging transaction record data; preprocessing the charging transaction record data to obtain preprocessed charging transaction record data; and obtaining the user charging behavior data according to the preprocessed charging transaction record data.
Optionally, collecting the charging transaction record data comprises: and collecting the electricity utilization data of the private pile users and/or the transaction data of the public pile users.
Optionally, the user charging behavior data comprises at least one of: charging duration, charging equipment parameters, charging station address, charging station ID and power consumption data.
Optionally, constructing the user charging behavior image comprises: determining a user activity status label as the user charging behavior representation label of a first dimension, wherein the user activity status label comprises at least one of: the number of times of charging and the amount of charge; determining a user charging rule tag as the user charging behavior portrait tag of the second dimension, wherein the user charging rule tag includes at least one of: charging period, charging mobility; determining a user electricity rate sensitivity label as the user charging behavior portrait label of a third dimension, wherein the user electricity rate sensitivity label comprises at least one of: and finishing the charging electricity fee and the parking fee.
Optionally, the user type includes at least one of: highly active users, moderately active users, and low active users.
According to another aspect of the embodiments of the present invention, there is also provided a user charging behavior representation processing apparatus, including: the acquisition module is used for acquiring user charging behavior data; the system comprises a construction module, a display module and a display module, wherein the construction module is used for constructing a user charging behavior image, and the user charging behavior image comprises user charging behavior image labels with multiple dimensions; and the determining module is used for clustering the user charging behavior data according to the user charging behavior image label to determine the user type.
Optionally, the obtaining module includes: the acquisition unit is used for acquiring charging transaction record data; the first processing unit is used for preprocessing the charging transaction record data to obtain preprocessed charging transaction record data; and the second processing unit is used for obtaining the user charging behavior data according to the preprocessed charging transaction record data.
According to another aspect of the embodiment of the present invention, a computer-readable storage medium is further provided, where the computer-readable storage medium includes a stored program, and when the program runs, the apparatus where the computer-readable storage medium is located is controlled to execute any one of the above user charging behavior representation processing methods.
According to another aspect of the embodiments of the present invention, there is also provided a processor, configured to execute a program, where the program executes the method for processing the user charging behavior representation.
In the embodiment of the invention, the charging behavior data of the user is acquired; constructing a user charging behavior image, wherein the user charging behavior image comprises a plurality of dimensionalities of user charging behavior image labels; the user charging behavior data are clustered according to the user charging behavior portrait tags, the user type is determined, the user charging behavior data are clustered through the user charging behavior portrait tags of multiple dimensions in the user charging behavior portrait, the user type is determined, the purpose of mining the potential rules of user charging is achieved, the complexity and the cost of building the user charging behavior portrait are reduced, the technical effect of improving the working efficiency is achieved, and the technical problems that the user charging behavior portrait depends on data such as user attributes, position characteristics and interest characteristics in the related technology, the statistical requirement is high, the workload is large, and the cost is high are solved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the invention without limiting the invention. In the drawings:
fig. 1 is a flowchart of a user charging behavior image processing method according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a clustering result of a k-means clustering algorithm based on user charging behavior data according to an embodiment of the present invention;
FIG. 3 is a diagram of a user charging behavior representation processing apparatus according to an embodiment of the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Example 1
In accordance with an embodiment of the present invention, there is provided an embodiment of a user charging behavior image processing method, it being noted that the steps illustrated in the flowchart of the drawings may be performed in a computer system, such as a set of computer-executable instructions, and that while a logical order is illustrated in the flowchart, in some cases the steps illustrated or described may be performed in an order different than here.
Fig. 1 is a flowchart of a user charging behavior image processing method according to an embodiment of the present invention, and as shown in fig. 1, the user charging behavior image processing method includes the following steps:
step S102, acquiring user charging behavior data;
the user charging behavior data includes, but is not limited to, charging duration, charging device parameters, charging station address, charging station ID, electricity consumption data, user ID, charging time, charging amount, device longitude and latitude, and the like.
Step S104, constructing a user charging behavior image, wherein the user charging behavior image comprises a plurality of dimensionalities of user charging behavior image labels;
the multi-dimension user charging behavior image labels include, but are not limited to, user activity states, user charging rules, user electricity rate sensitivity and the like.
And step S106, clustering the user charging behavior data according to the user charging behavior portrait label, and determining the user type.
As an alternative embodiment, the clustering may adopt various clustering algorithms, for example, a k-means clustering algorithm, etc. The K-means clustering algorithm is a clustering analysis algorithm for iterative solution, and the steps are that data are divided into K groups in advance, K objects are randomly selected to serve as initial clustering centers, then the distance between each object and each seed clustering center is calculated, and each object is allocated to the nearest clustering center. The cluster centers and the objects assigned to them represent a cluster. The cluster center of a cluster is recalculated for each sample assigned based on the objects existing in the cluster. This process will be repeated until some termination condition is met. The termination condition may be that no (or minimum number) objects are reassigned to different clusters, no (or minimum number) cluster centers are changed again, and the sum of squared errors is locally minimal. The specific process is as follows:
(1) of the N data, K data (i.e., the last cluster is K classes) are randomly selected as the initial center of the cluster.
(2) And respectively calculating Euclidean distances from each data point to the K central points, and allocating the data point closest to the central point to the cluster.
(3) And recalculating the coordinate mean value of the K clusters of data, and taking the new mean value as the center of the cluster.
(4) And repeating the steps 2 and 3 until the coordinates of the cluster center are not transformed any more or the specified iteration times are reached, and forming final K clusters.
And according to the clustering result, combining the service and the user-defined rule for each type of output users to form a composite label, wherein the label is constructed on the basis of multiple indexes, so that the label can more accurately reflect the charging behavior characteristics of the users.
Fig. 2 is a schematic diagram of a clustering result of a k-means clustering algorithm based on user charging behavior data according to an embodiment of the present invention, as shown in fig. 2, the internal differentiation of users is severe, the user activity states, charging rules, and power rate sensitivities in the cluster 1 and the cluster 2 are low, and the users in the two clusters account for 73% of the total number of users, which indicates that most of the charging users of the electric vehicle are in low-medium activity at present; the user activity state, charging rule and electricity charge sensitivity in the cluster 3 are far higher than those of the users in the types 1 and 2, but the users in the type 3 only account for 27 percent of the total number of users.
And clustering analysis is carried out on the labels by adopting a k-means clustering algorithm, namely, the constructed charging behavior image of the user is analyzed, so that the hierarchy of the user can be finely divided, and the potential charging rule of the user can be deeply mined. Aiming at different types of users, the using preference and the using habit of the user can be found, so that a related charging welfare policy is formulated. For example, different marketing strategies are adopted according to the requirements of different types of electric vehicle users, different preferential services are provided, and personalized products are customized for each type of users. In addition, a k-means clustering algorithm is used for carrying out portrait analysis on electric vehicle users so as to identify charging behavior characteristics of the users, and data analysis results of the k-means clustering algorithm have certain guiding significance for guiding the charging behaviors of the users, planning charging facilities of the electric vehicles, improving the accurate marketing and high-quality service level of electric power and the like.
Through the steps, the charging behavior data of the user can be acquired; constructing a user charging behavior image, wherein the user charging behavior image comprises a plurality of dimensionalities of user charging behavior image labels; the user charging behavior portrayal method based on the user charging behavior portrayal comprises the steps of clustering user charging behavior data according to user charging behavior portrayal labels, determining user types, clustering the user charging behavior data through the user charging behavior portrayal labels with multiple dimensions in the user charging behavior portrayal, determining the user types, achieving the purpose of mining potential rules of user charging, achieving the technical effects of reducing complexity and cost of building the user charging behavior portrayal and improving working efficiency, and further solving the technical problems that the user charging behavior portrayal depends on data such as user attributes, position characteristics and interest characteristics in the related technology, and is high in statistical requirement, large in workload and high in cost.
Optionally, the obtaining the user charging behavior data includes: collecting charging transaction record data; preprocessing the charging transaction record data to obtain preprocessed charging transaction record data; and obtaining user charging behavior data according to the preprocessed charging transaction record data.
As an optional embodiment, the preprocessing the charging transaction record data includes removing missing and abnormal data in the data, and performing normalization preprocessing on the processed data. By preprocessing the charging transaction record data, the influence of dimension can be eliminated, so that the features extracted from all samples can be compared under the same dimension to obtain data suitable for an algorithm model.
Optionally, collecting the charging transaction record data comprises: and collecting the electricity utilization data of the private pile users and/or the transaction data of the public pile users.
As an alternative embodiment, the charging transaction record data includes, but is not limited to, user charging behavior data recorded by the car networking system, and specifically, may be user charging behavior data recorded by the car networking system within a certain period of time, for example, 7 months to 10 months in 2020, private post user electricity consumption data recorded by the car networking system, and/or public post user transaction data.
Optionally, the user charging behavior data includes at least one of: charging duration, charging equipment parameters, charging station address, charging station ID and power consumption data.
As an alternative embodiment, for the user charging transaction record, the charging time length of each time of the user is obtained by subtracting the charging start time from the user charging end time.
As an alternative embodiment, the internet of vehicles transaction data is associated with the charging equipment asset table, and the related equipment data such as the type of the charging equipment, the power of the equipment and the like selected by the user during charging are calculated.
As an alternative embodiment, the address of the charging station and the ID of the charging station selected by the user during charging are obtained by associating the Internet of vehicles transaction data with the asset table of the charging station.
As an alternative embodiment, for the electricity consumption data of the user, since the time of updating the electric energy representation number in the acquired date data may vary every day, for convenience of uniform calculation, the acquired time is rounded to become the integral point data.
Optionally, constructing the user charging behavior image comprises: determining a user activity status label as a user charging behavior portrait label of a first dimension, wherein the user activity status label comprises at least one of: the number of times of charging and the amount of charge; determining a user charging rule label as a second-dimension user charging behavior portrait label, wherein the user charging rule label comprises at least one of the following: charging period, charging mobility; determining a user electricity rate sensitivity label as a third-dimension user charging behavior portrait label, wherein the user electricity rate sensitivity label comprises at least one of the following: and finishing the charging electricity fee and the parking fee.
As an alternative embodiment, in order to research the charging behavior portrait of the electric automobile user, a user charging behavior portrait label is constructed. The label system can be constructed from 3 dimensions of the active state, the charging rule and the electric charge sensitivity of a user on the basis of collecting charging behavior data of the user. The active state comprises charging times and charging electric quantity; the user charging rule comprises a charging period (daily charging time period) and charging mobility (charging numbers of different charging stations); the electricity rate sensitivity includes a charge completion rate and a parking rate.
Optionally, the user type includes at least one of: highly active users, moderately active users, and low active users.
As an optional embodiment, taking an electric vehicle as an example, the user charging behavior portrait label includes 3 sub-labels of an active state, a charging rule and an electric charge sensitivity, and the charging behavior portrait label of the electric vehicle user is clustered integrally to realize classification of the electric vehicle user. The electric vehicle users can be classified into high-activity users, medium-activity users and low-activity users according to the clustered charging behavior data of the users.
Example 2
According to another aspect of the embodiments of the present invention, there is also provided a user charging behavior representation processing apparatus, and fig. 3 is a schematic diagram of the user charging behavior representation processing apparatus according to the embodiments of the present invention, as shown in fig. 3, the user charging behavior representation processing apparatus includes: an acquisition module 32, a construction module 34, and a determination module 36. The user charging behavior image processing apparatus will be described in detail below.
An obtaining module 32, configured to obtain user charging behavior data; a constructing module 34, connected to the acquiring module 32, configured to construct a user charging behavior image, where the user charging behavior image includes multiple-dimensional user charging behavior image tags; and the determining module 36 is connected to the constructing module 34 and is used for clustering the user charging behavior data according to the user charging behavior portrait label to determine the user type.
It should be noted that the above modules may be implemented by software or hardware, for example, for the latter, the following may be implemented: the modules can be located in the same processor; alternatively, the modules may be located in different processors in any combination.
It should be noted here that the above-mentioned obtaining module 32, building module 34 and determining module 36 correspond to steps S102 to S106 in embodiment 1, and the above-mentioned modules are the same as the examples and application scenarios realized by the corresponding steps, but are not limited to what is disclosed in embodiment 1 above. It should be noted that the modules described above as part of an apparatus may be implemented in a computer system such as a set of computer-executable instructions.
In the embodiment of the invention, the user charging behavior image processing device can cluster the user charging behavior data through the user charging behavior image labels with multiple dimensions in the user charging behavior image, so as to determine the user type and achieve the purpose of mining the potential rules of user charging, thereby achieving the technical effects of reducing the complexity and cost for constructing the user charging behavior image and improving the working efficiency, and further solving the technical problems that the user charging behavior image depends on data such as user attributes, position characteristics, interest characteristics and the like in the related technology, and the statistics requirement is high, the workload is large and the cost is high.
Optionally, the obtaining module 32 includes: the acquisition unit is used for acquiring charging transaction record data; the first processing unit is used for preprocessing the charging transaction record data to obtain preprocessed charging transaction record data; and the second processing unit is used for obtaining user charging behavior data according to the preprocessed charging transaction record data.
Optionally, the collecting unit includes: and the acquisition subunit is used for acquiring the electricity utilization data of the private pile users and/or the transaction data of the public pile users.
Optionally, the user charging behavior data includes at least one of: charging duration, charging equipment parameters, charging station address, charging station ID and power consumption data.
Optionally, the building module 34 includes: a first determining unit, configured to determine a user activity status label as a user charging behavior image label of a first dimension, where the user activity status label includes at least one of: the number of times of charging and the amount of charge; a second determining unit, configured to determine a user charging rule tag as a second-dimension user charging behavior portrait tag, where the user charging rule tag includes at least one of: charging period, charging mobility; a third determining unit, configured to determine a user electricity rate sensitivity label as a third-dimension user charging behavior portrait label, where the user electricity rate sensitivity label includes at least one of: and finishing the charging electricity fee and the parking fee.
Optionally, the user type includes at least one of: highly active users, moderately active users, and low active users.
Example 3
According to another aspect of the embodiments of the present invention, there is also provided a computer-readable storage medium, where the computer-readable storage medium includes a stored program, and when the program runs, the apparatus where the computer-readable storage medium is located is controlled to execute any one of the above methods for processing a user charging behavior representation.
Optionally, in this embodiment, the computer-readable storage medium may be located in any one of a group of computer terminals in a computer network or in any one of a group of mobile terminals, and the computer-readable storage medium includes a stored program.
Optionally, the program when executed controls an apparatus in which the computer-readable storage medium is located to perform the following functions: acquiring user charging behavior data; constructing a user charging behavior image, wherein the user charging behavior image comprises a plurality of dimensionalities of user charging behavior image labels; and clustering the user charging behavior data according to the user charging behavior portrait label to determine the user type.
Example 4
According to another aspect of the embodiments of the present invention, there is also provided a processor, configured to execute a program, where the program executes the user charging behavior representation processing method according to any one of the above.
The embodiment of the invention provides equipment, which comprises a processor, a memory and a program which is stored on the memory and can run on the processor, wherein the processor executes the program and realizes the following steps: acquiring user charging behavior data; constructing a user charging behavior image, wherein the user charging behavior image comprises a plurality of dimensionalities of user charging behavior image labels; and clustering the user charging behavior data according to the user charging behavior portrait label to determine the user type.
An embodiment of the present invention further provides a computer program product, which, when executed on a data processing apparatus, is adapted to execute a program that initializes the following method steps: acquiring user charging behavior data; constructing a user charging behavior image, wherein the user charging behavior image comprises a plurality of dimensionalities of user charging behavior image labels; and clustering the user charging behavior data according to the user charging behavior portrait label to determine the user type.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
In the above embodiments of the present invention, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the embodiments provided in the present application, it should be understood that the disclosed technology can be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units may be a logical division, and in actual implementation, there may be another division, for example, multiple units or components may be combined or 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, units or modules, and may be in an electrical 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 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 invention 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 unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.
Claims (10)
1. A user charging behavior image processing method is characterized by comprising the following steps:
acquiring user charging behavior data;
constructing a user charging behavior image, wherein the user charging behavior image comprises a plurality of dimensionalities of user charging behavior image labels;
and clustering the user charging behavior data according to the user charging behavior image label to determine the user type.
2. The method of claim 1, wherein obtaining user charging behavior data comprises:
collecting charging transaction record data;
preprocessing the charging transaction record data to obtain preprocessed charging transaction record data;
and obtaining the user charging behavior data according to the preprocessed charging transaction record data.
3. The method of claim 1, wherein collecting charging transaction record data comprises:
and collecting the electricity utilization data of the private pile users and/or the transaction data of the public pile users.
4. The method of claim 2, wherein the user charging behavior data comprises at least one of: charging duration, charging equipment parameters, charging station address, charging station ID and power consumption data.
5. The method of claim 1, wherein constructing a user charging behavior picture comprises:
determining a user activity status label as the user charging behavior representation label of a first dimension, wherein the user activity status label comprises at least one of: the number of times of charging and the amount of charge;
determining a user charging rule tag as the user charging behavior portrait tag of the second dimension, wherein the user charging rule tag includes at least one of: charging period, charging mobility;
determining a user electricity rate sensitivity label as the user charging behavior portrait label of a third dimension, wherein the user electricity rate sensitivity label comprises at least one of: and finishing the charging electricity fee and the parking fee.
6. The method according to any of claims 1 to 5, wherein the user type comprises at least one of: highly active users, moderately active users, and low active users.
7. A user charging behavior representation processing device is characterized by comprising:
the acquisition module is used for acquiring user charging behavior data;
the system comprises a construction module, a display module and a display module, wherein the construction module is used for constructing a user charging behavior image, and the user charging behavior image comprises user charging behavior image labels with multiple dimensions;
and the determining module is used for clustering the user charging behavior data according to the user charging behavior image label to determine the user type.
8. The apparatus of claim 7, wherein the obtaining module comprises:
the acquisition unit is used for acquiring charging transaction record data;
the first processing unit is used for preprocessing the charging transaction record data to obtain preprocessed charging transaction record data;
and the second processing unit is used for obtaining the user charging behavior data according to the preprocessed charging transaction record data.
9. A computer-readable storage medium, comprising a stored program, wherein when the program runs, the computer-readable storage medium controls a device to execute the user charging behavior representation processing method according to any one of claims 1 to 6.
10. A processor, configured to execute a program, wherein the program executes to execute the user charging behavior representation processing method according to any one of claims 1 to 6.
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| CN106909686A (en) * | 2017-03-06 | 2017-06-30 | 吉林省盛创科技有限公司 | A kind of man-machine interaction builds user's portrait cluster calculation method |
| CN111461761A (en) * | 2020-02-29 | 2020-07-28 | 国网江苏省电力有限公司苏州供电分公司 | Resident user portrait method based on multi-dimensional fine-grained behavior data |
| CN111959330A (en) * | 2020-08-28 | 2020-11-20 | 华北电力大学（保定） | User DR scheme customization method based on user charging and traveling habits |
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| CN106909686A (en) * | 2017-03-06 | 2017-06-30 | 吉林省盛创科技有限公司 | A kind of man-machine interaction builds user's portrait cluster calculation method |
| CN111461761A (en) * | 2020-02-29 | 2020-07-28 | 国网江苏省电力有限公司苏州供电分公司 | Resident user portrait method based on multi-dimensional fine-grained behavior data |
| CN111959330A (en) * | 2020-08-28 | 2020-11-20 | 华北电力大学（保定） | User DR scheme customization method based on user charging and traveling habits |
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| CN113407827A (en) * | 2021-06-11 | 2021-09-17 | 广州三七极创网络科技有限公司 | Information recommendation method, device, equipment and medium based on user value classification |
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