CN111797848B - User classification method, device, equipment and storage medium - Google Patents
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Abstract
The application provides a user classification method, a device, equipment and a storage medium, wherein the method comprises the following steps: acquiring service data of a plurality of users; determining the group number of the users according to the service data of the plurality of users, wherein the group number of the users is used for classifying the plurality of users; and classifying the plurality of users according to the group number of the users, thereby realizing the classification of the users.
Description
Technical Field
The present application relates to the technical field of private network data analysis, and in particular, to a user classification method, apparatus, device, and storage medium.
Background
With the development of intelligent equipment, the daily life of users is more and more separated from intelligent terminal equipment, and the popularization of the terminal equipment greatly improves the quality of life of users. In order to improve the use activity rate of the user terminal, the situation of the user using the terminal needs to be analyzed so as to take relevant measures according to the situation of the user using the terminal, and further improve the use activity rate of the user using the terminal.
In the prior art, in order to analyze the situation that the terminal is used by the user, the service data of each user is generally obtained respectively, then the service data of each user is analyzed or ranked, and the situation that the terminal is used by each user is analyzed to obtain the rule of using the terminal by each user.
However, in the prior art, the terminal users are not classified by analyzing the situation of using the terminal for each user, and thus the rule of using the terminal for each user cannot be analyzed according to each user.
Disclosure of Invention
The application provides a user classification method, a device, equipment and a storage medium, so as to realize classification of users.
In a first aspect, the present application provides a user classification method, including:
and acquiring service data of a plurality of users.
And determining the group number of the users according to the service data of the plurality of users, wherein the group number of the users is used for classifying the plurality of users.
The plurality of users is classified according to the population number of users.
In the scheme, the classification of the users is realized by determining the group number of the users according to the service data of the users and classifying the users according to the group number of the users.
Optionally, before determining the group number of the users according to the service data of the plurality of users, the method further comprises:
preprocessing the service data of a plurality of users to obtain the preprocessed service data of the plurality of users.
In the scheme, the accuracy of determining the group number of the users according to the service data of the plurality of users is improved by preprocessing the service data of the plurality of users before determining the group number of the users according to the service data of the plurality of users.
Optionally, preprocessing service data of a plurality of users includes:
and carrying out normalization processing on the service data of a plurality of users.
Optionally, after classifying the plurality of users, the method further includes:
analyzing the business data of a plurality of users according to the types and the terminal characteristics of the users to obtain the analysis result of the business data of the users,
the terminal features include at least one of:
terminal type and terminal distribution area.
In the scheme, after classifying the plurality of users, the service data of the plurality of users are analyzed according to the types and the terminal characteristics of the users, so that the analysis of the terminal usage rule of each type of users is realized.
Optionally, the user classification method provided by the application further includes:
pushing the analysis result of the user service data.
In the scheme, the user service data analysis result is pushed, so that the visualization of the user service data analysis result is realized.
Optionally, the service data of the plurality of users includes at least one of:
the number of user calls, the user call duration, the user use flow and the number of user active days;
the user activity days are days when the user uses the terminal in a preset time period.
The user classification apparatus, device, storage medium and computer readable program product provided by the present application are described below, the content and effects of which may refer to the first aspect or the user classification method of the alternative manner of the first aspect.
In a second aspect, the present application provides a user classification apparatus, including:
and the acquisition module is used for acquiring the service data of a plurality of users.
And the determining module is used for determining the group number of the users according to the service data of the plurality of users, wherein the group number of the users is used for classifying the plurality of users.
And the processing module is used for classifying the plurality of users according to the population number of the users.
Optionally, the user classification device provided by the application further includes:
the preprocessing module is used for preprocessing the service data of the plurality of users to obtain the preprocessed service data of the plurality of users.
Optionally, the preprocessing module is specifically configured to:
and carrying out normalization processing on the service data of a plurality of users.
Optionally, the user classification device provided by the application further includes:
the analysis module is used for analyzing the service data of a plurality of users according to the types of the users and the terminal characteristics to obtain the analysis result of the service data of the users, wherein the terminal characteristics comprise at least one of the following:
terminal type and terminal distribution area.
Optionally, the user classification device provided by the application further includes:
and the pushing module is used for pushing the analysis result of the user service data.
Optionally, the service data of the plurality of users includes at least one of:
the number of user calls, the user call duration, the user use flow and the number of user active days; the user activity days are days when the user uses the terminal in a preset time period.
In a third aspect, an embodiment of the present application provides an apparatus, including:
a processor; a memory; a computer program; wherein a computer program is stored in a memory and configured to be executed by a processor, the computer program comprising instructions for performing the user classification method as provided in the first aspect or the alternative of the first aspect.
In a fourth aspect, embodiments of the present application provide a computer-readable storage medium storing a computer program that causes a server to execute the user classification method as provided in the first aspect or the alternative of the first aspect.
In a fifth aspect, embodiments of the present application provide a computer program product comprising: executable instructions for implementing a user classification method as provided in the first aspect or the alternative of the first aspect.
The user classification method, the device, the equipment and the storage medium provided by the application are characterized in that the service data of a plurality of users are obtained, then the group number of the users is determined according to the service data of the plurality of users, the group number of the users is used for classifying the plurality of users, and finally the plurality of users are classified according to the group number of the users. The classification of the users is realized by determining the group number of the users according to the service data of the users and then classifying the users according to the group number of the users.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions of the prior art, the drawings that are needed in the embodiments or the description of the prior art will be briefly described below, it will be obvious that the drawings in the following description are some embodiments of the present application, and that other drawings can be obtained according to these drawings without inventive effort to a person skilled in the art.
FIG. 1 is a flow chart of a user classification method according to an embodiment of the application;
FIG. 2 is an exemplary sum-of-squares line graph of cluster errors provided by an embodiment of the present application;
FIG. 3 is a flowchart illustrating a user classification method according to another embodiment of the present application;
FIG. 4 is a schematic diagram of analysis results of user service data according to an embodiment of the present application;
FIG. 5 is a schematic diagram of a user service data analysis result according to another embodiment of the present application;
FIG. 6 is a schematic diagram of a user classification device according to an embodiment of the application;
fig. 7 is a schematic structural diagram of a user classification device according to another embodiment of the present application;
fig. 8 is a schematic structural diagram of a user classification device according to another embodiment of the present application;
fig. 9 is a schematic structural diagram of a terminal device according to an embodiment of the present application.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present application more apparent, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments of the present application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
The terms "first," "second," "third," "fourth" and the like in the description and in the claims and in the above drawings, if any, are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the application described herein may be implemented, for example, in sequences other than those illustrated or otherwise 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.
With the development of intelligent equipment, the daily life of users is more and more separated from intelligent terminal equipment, and the popularization of the terminal equipment greatly improves the quality of life of users. In order to improve the use activity rate of the user terminal, the situation of the user using the terminal needs to be analyzed so as to take relevant measures according to the situation of the user using the terminal, and further improve the use activity rate of the user using the terminal. By analyzing the condition of using the terminal for each user, the terminal users are not classified, and therefore, the rule of using the terminal for each user cannot be analyzed according to each user. In order to solve the technical problems, the embodiment of the application provides a user classification method, a device, equipment and a storage medium.
In the following, an exemplary application scenario of an embodiment of the present application is described.
In the process of analyzing the business data of users, the business data of each user is often more unilateral and less discreet, but in general, in a certain type of user group, the behaviors or habits of using terminals are often highly similar, so that the business data of each type of user group is often more convinced to analyze, and the efficiency of taking relevant measures for each type of user group is higher than that of taking relevant measures for each type of user group. Based on the above, the embodiment of the application provides a user classification method, a device, equipment and a storage medium.
Fig. 1 is a flow chart of a user classification method according to an embodiment of the present application, where the method may be performed by a user classification device, and the device may be implemented by software and/or hardware, for example: the device may be part or all of a terminal device, where the terminal device may be a personal computer, a smart phone, a user terminal, a tablet computer, a wearable device, etc., and the user classification method is described below with the terminal device as an execution body, as shown in fig. 1, where the method in the embodiment of the present application may include:
step S101: and acquiring service data of a plurality of users.
The terminal device may obtain service data of a plurality of users, which may be obtained by a server, for example, a home subscriber server (Home Subscriber Server, HSS), or may also be obtained by a terminal device. In addition, the selection manner and the number of the plurality of users are not limited in the embodiment of the application, for example, the plurality of users for a certain area, such as a certain urban area, a certain street, etc., the plurality of users for each department, such as a public security department, etc., and the plurality of users for a certain group, such as students, white-collar, blue-collar, etc., may be adopted.
The service data of the plurality of users may include a terminal type, a video frequency, a video time length, a voice frequency, a voice time length, an address of the user, a calling frequency, a called frequency, a number of days from the last use, a group call establishment frequency, a group call robbery frequency, a video uploading frequency, and the like, and the embodiment of the present application does not limit a specific data type of the service data, and in a possible implementation manner, the service data of the plurality of users includes at least one of the following:
the number of user calls, the duration of user calls, the user usage flow, the number of days the user is active.
The user activity days are days when the user uses the terminal in a preset time period.
In order to improve accuracy of classification of users, when acquiring service data of a plurality of users, the service data of the users in the last period of time may be acquired, for example, service data of the last half year, service data of the last year, etc., which is not limited in the embodiment of the present application. For example, the number of user calls, the user passing time, the user usage flow and the user activity days of each user in the last year can be obtained; the number of days of active users is that the users use the terminal in a preset time period, for example: the preset time is the last month, if the user uses the terminal in the last 10 days of the month, the number of active days of the user is 10 days, wherein the preset time period can be one week, one quarter, half year or one year, and the like.
Step S102: and determining the group number of the users according to the service data of the plurality of users, wherein the group number of the users is used for classifying the plurality of users.
In order to improve the accuracy of determining the population number of the users according to the service data of the plurality of users, in one possible implementation, before determining the population number of the users according to the service data of the plurality of users, the method may further include:
preprocessing the service data of a plurality of users to obtain the preprocessed service data of the plurality of users.
In a possible implementation manner, the preprocessing of the service data of the plurality of users may be that the data cleaning is performed on the service data of the plurality of users, for example, the data cleaning is performed by filling in missing values, smoothing noise data, identifying or deleting outliers, and the like, and the preprocessing of the service data of the plurality of users is performed to achieve format standardization, abnormal data cleaning, error correction, repeated data cleaning, and the like of the service data of the users.
In a possible implementation manner, preprocessing service data of a plurality of users may further include: and carrying out normalization processing on the service data of a plurality of users.
The normalization processing may be performed on the service data of the plurality of users, for example, the normalization processing may be performed on the same type of data in the service data of the plurality of users, for example, the normalization processing may be performed on the number of user calls, the user call duration, the user usage flow, and the number of user active days of the plurality of users, where the number of user calls, the user call duration, the user usage flow, and the number of user active days are mapped in a range from 0 to 1, respectively.
The embodiment of the application does not limit how to determine the group number of the users according to the service data of the plurality of users. In one possible implementation, the population number of users may be determined by employing an elbow method based on business data of a plurality of users.
Specifically, in the process of determining the population number of the user through the elbow method, the population number of the preset population number interval can be calculated respectively, for example, the preset population number interval is 1 to 9, and then clustering can be performed through a clustering algorithm, for example, a K-means clustering algorithm. For example, taking service data of a plurality of users as samples, when the number k of clusters is 1, 2, … … and 9 respectively, that is, the number of groups of users is 1, 2, … … and 9 respectively, calculating the square sum of cluster errors of the service data of the plurality of users respectively, and finally determining the number of groups according to the square sum of cluster errors of each group number.
For example, fig. 2 is an exemplary cluster error square sum-of-squares line diagram provided in an embodiment of the present application, as shown in fig. 2, when the cluster number k is 1, the cluster error square sum of service data of a plurality of users is 2000, when the cluster number k is 2, the cluster error square sum of service data of a plurality of users is 800 … …, and so on, and as shown in fig. 2, as the cluster number k increases, the sample division is finer, the aggregation degree of each cluster increases gradually, and then the cluster error square sum of all samples naturally decreases gradually. When k is smaller than the actual cluster number, the aggregation degree of each cluster is greatly increased due to the increase of k, the dropping amplitude of the square sum of the cluster errors of all samples is large, when k reaches the actual cluster number, the aggregation degree obtained by increasing k is rapidly reduced, the dropping amplitude of the square sum of the cluster errors of all samples is rapidly reduced, and then the graph of the square sum of the cluster errors of all samples and the cluster number k is gradually flattened along with the continuous increase of the k value, namely the graph of the square sum of the cluster errors of all samples and the cluster number k is in the shape of an elbow, and the k value corresponding to the elbow is the actual cluster number of data. According to the above method, it can be seen that the clustering number k in fig. 2 is closest to the true clustering number when the value is 3. Thus, it is possible to determine when the number of user groups is 3. In another possible implementation manner, the number of clusters, and thus the number of user groups, may be determined according to other manners such as a contour coefficient method.
Step S103: the plurality of users is classified according to the population number of users.
After determining the population number of users, the plurality of users may be categorized, as embodiments of the application are not limited in this regard. To facilitate distinguishing each type of user, optionally, after categorizing the plurality of users, each type of user may also be identified. Illustratively, taking the classification of a plurality of users into 3 classes as an example, after classifying the users into 3 classes, each class of users may be identified by calculating an average value of service data of each class of users, for example: if the average value of the service data of the first type of users is within the first preset range, the service data of the first type of users can be identified as the first type of users, such as active users; if the average value of the service data of the second class user is within the second preset range, the second class user can be identified, for example, silence; if the average value of the service data of the third class of users is within the third preset range, the third class of users can be identified, for example, sleep, and the identification mode of each class of users in the embodiment of the application is not limited.
In the scheme, the classification of the users is realized by determining the group number of the users according to the service data of the users and classifying the users according to the group number of the users.
Optionally, fig. 3 is a flowchart of a user classification method according to another embodiment of the present application, where the method may be performed by a user classification device, and the device may be implemented by software and/or hardware, for example: the device may be part or all of a terminal device, where the terminal device may be a personal computer, a smart phone, a user terminal, a tablet pc, a wearable device, etc., and the user classification method is described below with the terminal device as an execution body, as shown in fig. 3, after step S103, the method in the embodiment of the present application may further include:
step S301: and analyzing the service data of a plurality of users according to the types and the terminal characteristics of the users to obtain a user service data analysis result.
After classifying the users, the service data of the users may be analyzed for each type of user to obtain a user service data analysis result, for example, for each type of user such as active, silent and sleep, one or more of a call duration, a use flow, a number of days from the last use, an active day, a number of callers, a number of group call establishment, a number of video calls, a number of voice calls, and a number of video uploads are respectively analyzed for each type of user, which is not limited by the embodiment of the present application.
The service data of the plurality of users are analyzed according to the types of the users and the characteristics of the terminals, and in a possible implementation manner, the service data of the plurality of users can be analyzed according to the types of the users and the types of the terminals.
An exemplary embodiment of the present application is shown in fig. 4, where the terminal type may be a model of a terminal device, for example, a model of a mobile phone, or may be a terminal type, for example, a vehicle terminal, a mobile phone terminal, a tablet computer, etc., and the embodiment of the present application does not limit the terminal type, and as shown in fig. 4, the user using the first type terminal, the second type terminal, the third type terminal, and the fourth type terminal may be analyzed by taking the type of the user as sleep, silence, and activity as examples, and the percentage of each type of user may be obtained for each terminal type through analysis.
The service data of a plurality of users are analyzed according to the types of the users and the characteristics of the terminals, and in a possible implementation manner, the service data of the plurality of users can be analyzed according to the types of the users and the distribution areas of the terminals.
For example, fig. 5 is a schematic diagram of a user service data analysis result provided by another embodiment of the present application, where a terminal distribution area may be a plurality of counties or regions of a certain city, and the embodiment of the present application is not limited thereto, where a first area, a second area, a third area, a fourth area and a fifth area respectively represent different areas of terminal distribution, and as shown in fig. 5, the types of users are sleep, silence and activity, and the users in the five areas are respectively analyzed, so that a proportion of each type of users in each terminal distribution area can be obtained.
Optionally, the service data of the plurality of users may be analyzed according to the types of the users and the terminal characteristics, and the service data of the plurality of users may be analyzed according to the types of the users, the types of the terminals and the terminal distribution areas. Specific features of the terminal features in the embodiment of the application are not limited.
In a possible implementation manner, the trend of the use time of the terminal may also be analyzed, for example, the number of times of calls, the use flow rate, the call duration, etc. of each class of users are respectively analyzed in each period of the day, which is not limited by the embodiment of the present application.
After step S301, optionally, as shown in fig. 3, the user classification method provided by the present application may further include:
step S302: pushing the analysis result of the user service data.
The terminal pushes the analysis result of the user service data, which may be pushed to the terminal display screen in the form of a report, which is not limited in the embodiment of the present application.
In the scheme, after classifying the plurality of users, the service data of the plurality of users are analyzed according to the types and the terminal characteristics of the users, so that the analysis of the terminal usage rule of each type of users is realized.
The user classification apparatus, device, storage medium, and computer-readable program product provided by the present application are described below, the contents and effects of which can be referred to the user classification method provided by the above examples.
An embodiment of the present application provides a user classification device, and fig. 6 is a schematic structural diagram of the user classification device according to an embodiment of the present application, where the device may be implemented in a software and/or hardware manner, for example: the device may be part or all of a terminal device, where the terminal device may be a personal computer, a smart phone, a user terminal, a tablet computer, a wearable device, etc., as shown in fig. 6, the user classification device provided by the embodiment of the present application may include:
an acquisition module 61, configured to acquire service data of a plurality of users.
Optionally, the service data of the plurality of users includes at least one of:
the number of user calls, the duration of user calls, the user usage flow, the number of days the user is active.
The user activity days are days when the user uses the terminal in a preset time period.
The determining module 62 is configured to determine a population number of users according to the service data of the plurality of users, where the population number of users is used to classify the plurality of users.
The processing module 63 is configured to classify the plurality of users according to the population number of the users.
Optionally, fig. 7 is a schematic structural diagram of a user classification device according to another embodiment of the present application, where the device may be implemented in software and/or hardware, for example: the device may be part or all of a terminal device, where the terminal device may be a personal computer, a smart phone, a user terminal, a tablet computer, a wearable device, etc., as shown in fig. 7, the user classification device provided by the embodiment of the present application may further include:
the preprocessing module 71 is configured to preprocess service data of a plurality of users to obtain preprocessed service data of a plurality of users.
Optionally, the preprocessing module 71 is specifically configured to:
and carrying out normalization processing on the service data of a plurality of users.
Optionally, fig. 8 is a schematic structural diagram of a user classification device according to another embodiment of the present application, where the device may be implemented in software and/or hardware, for example: the device may be part or all of a terminal device, where the terminal device may be a personal computer, a smart phone, a user terminal, a tablet computer, a wearable device, etc., as shown in fig. 8, the user classification device provided by the embodiment of the present application may further include:
and the analysis module 81 is used for analyzing the service data of a plurality of users according to the types and the terminal characteristics of the users to obtain the analysis result of the service data of the users.
The terminal features include at least one of:
terminal type and terminal distribution area.
Optionally, as shown in fig. 8, the user classification device provided by the present application may further include:
and the pushing module 82 is configured to push the analysis result of the user service data.
An embodiment of the present application provides an apparatus, and fig. 9 is a schematic structural diagram of a terminal apparatus provided in an embodiment of the present application, as shown in fig. 9, where the terminal apparatus provided in the embodiment of the present application includes:
a processor 91, a memory 92, a transceiver 93, and a computer program; the transceiver 93 realizes data transmission between the server and other devices, and a computer program is stored in the memory 92 and configured to be executed by the processor 91, and the computer program includes instructions for executing the above-mentioned user classification method, and the content and effects thereof refer to the method embodiment.
The embodiment of the application provides a computer readable storage medium, and the computer readable storage medium stores a computer program, and the computer program enables a server to execute the user classification method, and the content and effects of the user classification method refer to the embodiment of the method.
An embodiment of the present application provides a computer program product comprising: executable instructions for implementing the user classification method described above, the content and method of which refer to method embodiments.
Among them, computer-readable media include computer storage media and communication media including any medium that facilitates transfer of a computer program from one place to another. A storage media may be any available media that can be accessed by a general purpose or special purpose computer. An exemplary storage medium is coupled to the processor such the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium may be integral to the processor. The processor and the storage medium may reside in an ASIC. In addition, the ASIC may reside in a user device. The processor and the storage medium may reside as discrete components in a communication device.
Those of ordinary skill in the art will appreciate that: all or part of the steps for implementing the method embodiments described above may be performed by hardware associated with program instructions. The foregoing program may be stored in a computer readable storage medium. The program, when executed, performs steps including the method embodiments described above; and the aforementioned storage medium includes: various media that can store program code, such as ROM, RAM, magnetic or optical disks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present application, and not for limiting the same; although the application has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the application.
Claims (8)
1. A method of classifying users, comprising:
acquiring service data of a plurality of users; the service data of the user comprises at least one of the following: the method comprises the steps of user call times, user call time, user use flow and user activity days, wherein the user activity days are days when a user uses a terminal in a preset time period;
determining the group number of the users according to the service data of the plurality of users, wherein the group number of the users is used for classifying the plurality of users;
classifying the plurality of users according to the population number of the users;
after classifying the plurality of users, further comprising:
according to the types and terminal characteristics of the users, analyzing the service data of the users to obtain the service data analysis result of the users, wherein the types of the users comprise: active, silent and sleep, the analysis result comprising the percentage of each type of user for each terminal type;
the terminal features include at least one of:
terminal type and terminal distribution area, the terminal type includes: vehicle-mounted terminal, mobile phone terminal and tablet computer.
2. The method of claim 1, further comprising, prior to determining the number of groups of users from the traffic data of the plurality of users:
preprocessing the service data of the plurality of users to obtain the preprocessed service data of the plurality of users.
3. The method of claim 2, wherein preprocessing the traffic data of the plurality of users comprises:
and carrying out normalization processing on the service data of the plurality of users.
4. The method as recited in claim 1, further comprising:
pushing the analysis result of the user service data.
5. A user classification apparatus, comprising:
the acquisition module is used for acquiring service data of a plurality of users; the service data of the user comprises at least one of the following: the method comprises the steps of user call times, user call time, user use flow and user activity days, wherein the user activity days are days when a user uses a terminal in a preset time period;
the determining module is used for determining the group number of the users according to the service data of the plurality of users, wherein the group number of the users is used for classifying the plurality of users;
the processing module is used for classifying the plurality of users according to the population number of the users;
the analysis module is used for analyzing the service data of a plurality of users according to the types and the terminal characteristics of the users to obtain the analysis result of the service data of the users, wherein the types of the users comprise: active, silent and sleep, the analysis result comprising the percentage of each class of users for each terminal type, the terminal characteristics comprising at least one of the following: terminal type and terminal distribution area, the terminal type includes: vehicle-mounted terminal, mobile phone terminal and tablet computer.
6. The apparatus as recited in claim 5, further comprising:
and the preprocessing module is used for preprocessing the service data of the plurality of users to obtain the preprocessed service data of the plurality of users.
7. An electronic device, comprising:
a processor;
a memory; and
a computer program;
wherein the computer program is stored in the memory and configured to be executed by the processor, the computer program comprising instructions for performing the method of any of claims 1-4.
8. A computer readable storage medium, characterized in that the computer readable storage medium stores a computer program, which causes a server to perform the method of any one of claims 1-4.
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