CN110942350A - Data processing method, device, equipment and storage medium - Google Patents

Data processing method, device, equipment and storage medium Download PDF

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CN110942350A
CN110942350A CN201911192184.5A CN201911192184A CN110942350A CN 110942350 A CN110942350 A CN 110942350A CN 201911192184 A CN201911192184 A CN 201911192184A CN 110942350 A CN110942350 A CN 110942350A
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张静
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Bank of China Ltd
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Abstract

The invention provides a data processing method, a device, equipment and a storage medium, wherein user data of a user to be analyzed is obtained from a bank user database, the user data comprises user basic data and behavior data of the user using a mobile phone bank, the weighted similarity of the user to be analyzed is calculated according to the user basic data, the user to be analyzed is identified based on a clustering algorithm, different organization groups are determined, special function services used by the user to be analyzed in the organization groups are determined based on the behavior data of the user to be analyzed in the organization groups aiming at each organization group, a push message is sent to the user to be analyzed which does not use the special function services in the organization groups aiming at each organization group, and the push message comprises prompt information for prompting the opening of the special function services. By automatically pushing prompt information for activating the special function service to the user who does not use the special function service, the problems of low efficiency and high cost of offline manual popularization of bank workers are solved.

Description

Data processing method, device, equipment and storage medium
Technical Field
The present invention relates to the field of computer technologies, and in particular, to a data processing method, apparatus, device, and storage medium.
Background
With the progress of science and technology, the bank business is not only limited to be transacted online any more, but the related business can be transacted online through the APP of the mobile phone bank, so that the business transacting efficiency of the user is greatly improved. However, there is a problem that after a series of service functions are developed in a mobile banking system, manual popularization can be performed only on line in order to allow more users to understand and use the series of service functions.
The manual popularization is carried out under the line, the popularization efficiency is extremely low, the number of known customers is very small, and the popularization purpose is far from being achieved.
Disclosure of Invention
In view of this, embodiments of the present invention provide a data processing method, an apparatus, a device, and a storage medium, which solve the problems of low efficiency and high cost of offline manual promotion of bank workers by automatically pushing prompt information for activating a special function service to a user who does not use the special function service.
The embodiment of the invention provides the following technical scheme:
a first aspect of an embodiment of the present invention provides a data processing method, where the method includes:
acquiring user data of a user to be analyzed from a bank user database, wherein the user data comprises user basic data and behavior data of the user using a mobile phone bank;
calculating the weighted similarity of the users to be analyzed according to the user basic data, identifying the users to be analyzed based on a clustering algorithm, and determining different mechanism groups;
for each organization group, determining special function services used by the users to be analyzed in the organization group based on the behavior data of the users to be analyzed in the organization group;
and aiming at each mechanism group, sending a push message to a user to be analyzed who does not use the special function service in the mechanism group, wherein the push message comprises prompt information for prompting the opening of the special function service.
Preferably, the calculating the weighted similarity of the users to be analyzed according to the user basic data, identifying the users to be analyzed based on a clustering algorithm, and determining different organization groups includes:
for each user to be analyzed, carrying out abnormal value cleaning processing on the user basic data, or carrying out vacancy value filling processing on the user basic data, or carrying out synonym replacement processing on text information in the user basic data to obtain preprocessed user basic data;
performing weighted similarity calculation by using the preprocessed user basic data to obtain the weighted similarity of the user to be analyzed;
and determining the users to be analyzed and the organization groups which belong to the same organization group based on the clustering algorithm and the weighted similarity of all the users to be analyzed.
Preferably, the determining, for each of the organization groups, a special function service used by the user to be analyzed in the organization group based on the behavior data of the user to be analyzed in the organization group includes:
counting, for the user to be analyzed in each organization group, a first function service of which the use frequency is greater than a preset frequency within a first preset time period based on the behavior data;
counting a second function service of which the use frequency is greater than a preset frequency of the user to be analyzed in a second preset time period based on the behavior data for the user to be analyzed in each mechanism group, wherein the duration of the second preset time period is greater than that of the first preset time period;
and determining that the functional service in the union set is a special functional service used by the user to be analyzed in the organization group based on the union set of the first functional service and the second functional service.
Preferably, the determining, for each of the organization groups, a special function service used by the user to be analyzed in the organization group based on the behavior data of the user to be analyzed in the organization group includes:
counting a third functional service with the use frequency greater than a preset frequency of each user to be analyzed in a first preset time period based on the behavior data;
counting, for the user to be analyzed in each organization group, a first function service of which the use frequency is greater than a preset frequency within a first preset time period based on the behavior data;
counting a second function service of which the use frequency is greater than a preset frequency of the user to be analyzed in a second preset time period based on the behavior data for the user to be analyzed in each mechanism group, wherein the duration of the second preset time period is greater than that of the first preset time period;
acquiring a union set of the first functional service and the second functional service, and determining a fourth functional service in the union set;
and deleting the third functional service contained in the fourth functional service aiming at each organization group to obtain the special functional service used by the user to be analyzed in the organization group.
Preferably, for each of the organization groups, sending a push message to a user to be analyzed in the organization group who does not use the special function service includes:
and aiming at each organization group, sending a push message to the users to be analyzed, which do not use the special function service, in the organization group in a short message mode or a mobile banking message mode.
Preferably, the method further comprises the following steps:
and aiming at each type of the organization group, sending an opening message to a user to be analyzed who does not open the mobile phone banking application in the organization group, wherein the opening message comprises prompt information for prompting the opening of the mobile phone banking application and prompt information for prompting the opening of the special function service.
A second aspect of the embodiments of the present invention provides a data processing apparatus, including:
the system comprises an acquisition module, a database analysis module and a database analysis module, wherein the acquisition module is used for acquiring user data of a user to be analyzed from a bank user database, and the user data comprises user basic data and behavior data of the user using a mobile phone bank;
the mechanism group identification module is used for calculating the weighted similarity of the users to be analyzed according to the user basic data, identifying the users to be analyzed based on a clustering algorithm and determining different mechanism groups;
a special function service identification module, configured to determine, for each organization group, a special function service used by the user to be analyzed in the organization group based on behavior data of the user to be analyzed in the organization group;
and the pushing module is used for sending a pushing message to a user to be analyzed who does not use the special function service in the mechanism group aiming at each mechanism group, wherein the pushing message comprises prompt information for prompting the opening of the special function service.
Preferably, the organization group identification module includes:
the preprocessing unit is used for cleaning abnormal values of the user basic data, or filling vacancy values of the user basic data, or performing synonym replacement processing on text information in the user basic data to obtain preprocessed user basic data;
the weighted calculation unit is used for performing weighted similarity calculation by utilizing the preprocessed user basic data to obtain the weighted similarity of the user to be analyzed;
and the identification unit is used for determining the users to be analyzed and the organization groups which belong to the same organization group based on the clustering algorithm and the weighted similarity of all the users to be analyzed.
A third aspect of an embodiment of the present invention provides a computer storage medium, where the computer storage medium includes a stored program, and when the program runs, a device in which the computer storage medium is located is controlled to execute the data processing method provided in the first aspect of the embodiment of the present invention.
A fourth aspect of the embodiments of the present invention provides a data processing apparatus, including a processor and a memory, where the memory stores a program, and the processor is configured to execute the program, where the program executes to execute the data processing method provided in the first aspect of the embodiments of the present invention.
Compared with the prior art, the technical scheme provided by the invention has the following advantages:
the method comprises the steps of obtaining user data of a user to be analyzed from a bank user database, wherein the user data comprises user basic data and behavior data of the user using a mobile phone bank, calculating weighted similarity of the user to be analyzed according to the user basic data, identifying the user to be analyzed based on a clustering algorithm, determining different organization groups, determining special function services used by the user to be analyzed in the organization groups based on the behavior data of the user to be analyzed in the organization groups for each organization group, and sending push messages to the user to be analyzed, which does not use the special function services, in the organization groups for each organization group, wherein the push messages comprise prompt messages for prompting the opening of the special function services. By automatically pushing prompt information for activating the special function service to the user who does not use the special function service, the problems of low efficiency and high cost of offline manual popularization of bank workers are solved.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
Fig. 1 is a flowchart of a data processing method according to an embodiment of the present invention;
FIG. 2 is a flow chart of determining different organization groups according to an embodiment of the present invention;
FIG. 3 is another flow chart for determining a group of different organizations according to an embodiment of the present invention;
FIG. 4 is a flow chart of another method for determining different organization groups according to an embodiment of the present invention;
FIG. 5 is a flowchart illustrating a method for determining types of special function services used by users to be analyzed in an organization group according to an embodiment of the present invention;
FIG. 6 is a flowchart illustrating another method for determining types of special-function services used by users to be analyzed in an organization group according to an embodiment of the present invention;
FIG. 7 is a flow chart of another data processing method according to an embodiment of the present invention;
fig. 8 is a schematic structural diagram of a data processing apparatus according to an embodiment of the present invention;
fig. 9 is a schematic structural diagram of a data processing apparatus according to an embodiment of the present invention.
Detailed Description
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.
In this application, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
As can be seen from the background art, in the prior art, after a series of service functions are developed in a mobile banking system, manual popularization can only be performed on-line in order to enable more users to understand and use the series of function services. The manual popularization is carried out under the line, the popularization efficiency is extremely low, the number of known customers is very small, and the popularization purpose is far from being achieved.
Therefore, the invention provides a data processing method, a data processing device, data processing equipment and a data processing storage medium, and solves the problems of low efficiency and high cost of off-line manual promotion of bank workers by automatically pushing prompt information for activating special function services to users who do not use the special function services.
As shown in fig. 1, a flowchart of a data processing method according to an embodiment of the present invention is provided, where the method includes the following steps:
s101: and acquiring user data of the user to be analyzed from the bank user database.
In S101, the user data includes user basic data and behavior data of the user using the mobile banking.
In the specific implementation process of S101, user basic data of a user to be analyzed is obtained from a bank user database, where the user basic data includes, but is not limited to, occupation, age, and institution account opening bank of the user.
S102: and calculating the weighted similarity of the users to be analyzed according to the user basic data, identifying the users to be analyzed based on a clustering algorithm, and determining different mechanism groups.
In the process of implementing S102 specifically, after user basic data of the user is obtained, weighted similarity of the users to be analyzed is calculated according to the user basic data, and an organization group to which each user belongs is calculated according to the weighted similarity between each user in combination with a clustering algorithm.
It should be noted that the organization group includes, but is not limited to, schools, institutions, large-scale scientific and technical enterprises, and the like.
S103: and aiming at each organization group, determining the special function service used by the user to be analyzed in the organization group based on the behavior data of the user to be analyzed in the organization group.
In S103, the special function service refers to that a certain function service is developed on a mobile banking machine for a certain organization group to provide convenience for the organization group.
In the process of implementing S103 specifically, behavior data of each user to be analyzed who uses the mobile banking online for the same organization group is determined, and which special function services are used by the user to be analyzed in the organization group according to the behavior data.
S104: and aiming at each organization group, sending a push message to the user to be analyzed, which does not use the special function service, in the organization group.
In the process of implementing S104 specifically, after determining the special function services used by the users to be analyzed in the same organization group, since some users in the same organization group have not used the special function services, or have not used the special function services after a period of time, the determined special function services send push messages to the users to be analyzed who do not use the special function services.
For example: in a high school, a bank develops a special function service of a quick-recharge meal card on a mobile bank according to the requirement of students on going to a dining room to cook, part of students who act in an account opening act use the special function service, and the other part of students who act in the bank do not use the special function service but pay through WeChat, Payment treasure and the like. Therefore, the special function service needs to be pushed to another part of students who pay by using WeChat, Paibao and the like through the mobile phone.
It should be noted that the determined special function services send push messages to the users to be analyzed who do not use the special function services, and for each organization group, the push messages can be sent to the users to be analyzed who do not use the special function services in each organization group in a short message manner and a mobile banking message manner.
According to the data processing method disclosed by the embodiment of the invention, the user data of the user to be analyzed is obtained from the bank user database, the user data comprises user basic data and behavior data of the user using a mobile phone bank, the weighted similarity of the user to be analyzed is calculated according to the user basic data, the user to be analyzed is identified based on a clustering algorithm, different organization groups are determined, the special function service used by the user to be analyzed in the organization groups is determined based on the behavior data of the user to be analyzed in the organization groups aiming at each organization group, the push message is sent to the user to be analyzed which does not use the special function service in the organization groups aiming at each organization group, and the push message comprises prompt information for prompting the opening of the special function service. By automatically pushing prompt information for activating the special function service to the user who does not use the special function service, the problems of low efficiency and high cost of offline manual popularization of bank workers are solved.
In a first application scenario of the embodiment of the present invention, based on the data processing method disclosed in fig. 1 of the embodiment of the present invention, S102 shown in fig. 1: calculating the weighted similarity of the users to be analyzed according to the user basic data, identifying the users to be analyzed based on a clustering algorithm, and determining the specific implementation process of different organization groups, as shown in fig. 2, the method mainly comprises the following steps:
s201: and (4) carrying out abnormal value cleaning processing on the user basic data aiming at each user to be analyzed to obtain the preprocessed user basic data.
In the process of implementing S201 specifically, in order to enable the user basic data to be processed by the computer efficiently, it is necessary to perform preprocessing on the user basic data by using an abnormal value cleaning method, where the preprocessing process mainly processes missing values, abnormal values, and duplicate values of the user basic data, and the so-called cleaning is to perform operations such as discarding, padding, replacing, and deduplication on the data set, so as to achieve the purposes of removing an abnormality, correcting an error, and complementing a missing. The user profile to be preprocessed includes, but is not limited to, occupation, age, institution and place of account opening of the user.
It should be noted that, the method for preprocessing the data of the user basic data includes, but is not limited to, the method of cleaning the outliers.
S202: and performing weighted similarity calculation by utilizing the preprocessed user basic data to obtain the weighted similarity of the user to be analyzed.
In the process of implementing S202 specifically, the weighted similarity of each user to be analyzed is obtained by calculating the weighted similarity of the preprocessed user basic data, and then the next operation is performed according to the weighted similarity of each user to be analyzed. Specifically, how to calculate the weighted similarity of the user basic data, the calculation method can be implemented in the prior art, and is not described herein again.
S203: and determining the users to be analyzed and the organization groups which belong to the same organization group based on the clustering algorithm and the weighted similarity of all the users to be analyzed.
In S203, the clustering algorithm refers to clustering analysis, also called group analysis, which is a statistical analysis method for studying sample or index classification problems, and is also an important algorithm for data mining.
In the process of implementing S203 specifically, the weighted similarity of each user to be analyzed is calculated through a clustering algorithm, so that the relationship between each user to be analyzed can be analyzed, and thus, users belonging to the same organization group can be determined.
In a second application scenario of the embodiment of the present invention, in S102, the weighted similarity of the users to be analyzed is calculated with reference to the user basic data, the users to be analyzed are identified based on a clustering algorithm, and a specific implementation process of different organization groups is determined, as shown in fig. 3, the method mainly includes:
s301, carrying out vacancy filling processing on the user basic data to obtain the preprocessed user basic data.
In the process of implementing S301 specifically, in order to enable the user basic data to be better and efficiently processed by the computer, it is necessary to preprocess the user basic data by a null value filling method, where some text information is removed and added by the null value filling processing, because the text information portion of the preprocessed user basic data is different from that of the user basic data before preprocessing. Therefore, the text information in the preprocessed user basic data needs to be the standard.
It should be noted that, the method for preprocessing the user basic data includes, but is not limited to, the method of filling the blank value.
And S302, performing weighted similarity calculation by using the preprocessed user basic data to obtain the weighted similarity of the user to be analyzed.
And S303, determining the users to be analyzed and the organization groups which belong to the same organization group based on the clustering algorithm and the weighted similarity of all the users to be analyzed.
The execution principle of S302 and S303 is the same as that of S202 and S203, and will not be described herein again.
In a third application scenario of the embodiment of the present invention, in S102, the weighted similarity of the users to be analyzed is calculated with reference to the user basic data, the users to be analyzed are identified based on a clustering algorithm, and a specific implementation process of different organization groups is determined, as shown in fig. 4, the method mainly includes:
s401, carrying out synonym replacement processing on the text information in the user basic data to obtain the preprocessed user basic data.
In the process of specifically implementing S401, a synonym replacement method is used to perform synonym replacement processing on the text information in the user basic data. The text information includes, but is not limited to, occupation, institution and account opening position of the user. And carrying out synonym replacement processing on the text information by using a synonym replacement method. Through synonym replacement processing, the relationship among the users can be known more intuitively.
For example: the unit organization filled in the text information in the user basic data corresponding to the user A and the user B is 'Xian Jida', so that the 'Xian Jida' is replaced by 'Xian transportation university' through synonym replacement processing. Thus, the fact that the user A and the user B are both the Western Ann university can be intuitively and clearly known.
The method of performing synonym replacement processing on text information includes, but is not limited to, natural language processing technology.
S402, carrying out weighted similarity calculation by utilizing the preprocessed user basic data to obtain the weighted similarity of the user to be analyzed.
And S403, determining the users to be analyzed and the organization groups which belong to the same organization group based on the clustering algorithm and the weighted similarity of all the users to be analyzed.
The execution principle of S402 and S403 is the same as that of S202 and S203, and will not be described herein again.
According to the data processing method disclosed by the embodiment of the invention, the user data of the user to be analyzed is obtained from the bank user database, the user data comprises user basic data and behavior data of the user using a mobile phone bank, the weighted similarity of the user to be analyzed is calculated according to the user basic data, the user to be analyzed is identified based on a clustering algorithm, different organization groups are determined, the special function service used by the user to be analyzed in the organization groups is determined based on the behavior data of the user to be analyzed in the organization groups aiming at each organization group, the push message is sent to the user to be analyzed which does not use the special function service in the organization groups aiming at each organization group, and the push message comprises prompt information for prompting the opening of the special function service. By automatically pushing prompt information for activating the special function service to the user who does not use the special function service, the problems of low efficiency and high cost of offline manual popularization of bank workers are solved.
Based on the data processing method disclosed in fig. 1 in the above embodiment of the present invention, S103 shown in fig. 1: for each organization group, based on the behavior data of the users to be analyzed in the organization group, a specific implementation process of the special function service used by the users to be analyzed in the organization group is determined, as shown in fig. 5, which mainly includes:
s501: and counting the first functional services of which the use frequency is greater than the preset frequency of the users to be analyzed in a first preset time period based on the behavior data aiming at the users to be analyzed in each organization group.
In the process of implementing S501 specifically, according to the behavior data of each user to be analyzed on the mobile phone bank, even if the user does something with the mobile phone bank, the information that the frequency of using a certain function service on the mobile phone bank is greater than the function service using the preset frequency within the first preset time period is counted and obtained for each user to be analyzed.
For example: the method comprises the steps that 100 students exist in a determined student institution group, statistical analysis is carried out based on behavior data of the 100 students, the fact that functional service frequency of the 100 students using a mobile phone bank in a week is sequentially a charging meal card functional service, a charging telephone rate functional service and a shopping functional service from high to low is determined, and the using frequency of the charging meal card functional service and the charging telephone rate functional service is greater than a preset frequency, so that the first functional service, the using frequency of which is greater than the preset frequency in the week, of the student institution group can be determined to be the charging meal card functional service and the charging telephone rate functional service.
It should be noted that the number of times of using the functional service can be used as a basis for determining whether the frequency of using the functional service is greater than the preset frequency.
S502: and counting a second function service of which the use frequency is greater than the preset frequency of the user to be analyzed in a second preset time period based on the behavior data aiming at the user to be analyzed in each organization group.
In the process of specifically implementing S502, according to behavior data of users to be analyzed in each organization group on the mobile phone bank, statistics is performed to obtain information that the frequency of using the functional service on the mobile phone bank is greater than the frequency of using the functional service with the preset frequency for each user to be analyzed in a second preset time period, and according to the information, a second functional service with the top N use frequencies in all the functional services of the organization group is determined.
S503: and determining the functional service in the union set as the special functional service used by the user to be analyzed in the organization group based on the union set of the first functional service and the second functional service.
In the process of specifically implementing S503, the union set processing is performed on the first functional service and the second functional service, and the special functional service used by the user to be analyzed is determined.
For example: the first functional service comprises a recharging telephone fee functional service and a shopping functional service, the second functional service comprises a recharging telephone fee functional service and a recharging meal card functional service, and the special functional service used by the user to be analyzed is determined to comprise the recharging telephone fee functional service, the shopping functional service and the recharging meal card functional service through the union set processing. According to the data processing method disclosed by the embodiment of the invention, the user data of the user to be analyzed is obtained from the bank user database, the user data comprises user basic data and behavior data of the user using a mobile phone bank, the weighted similarity of the user to be analyzed is calculated according to the user basic data, the user to be analyzed is identified based on a clustering algorithm, different organization groups are determined, the special function service used by the user to be analyzed in the organization groups is determined based on the behavior data of the user to be analyzed in the organization groups aiming at each organization group, the push message is sent to the user to be analyzed which does not use the special function service in the organization groups aiming at each organization group, and the push message comprises prompt information for prompting the opening of the special function service. By automatically pushing prompt information for activating the special function service to the user who does not use the special function service, the problems of low efficiency and high cost of offline manual popularization of bank workers are solved.
Based on the data processing method disclosed in fig. 1 in the above embodiment of the present invention, S103 shown in fig. 1: for each organization group, based on the behavior data of the users to be analyzed in the organization group, another specific implementation process of the special function service used by the users to be analyzed in the organization group is determined, as shown in fig. 6, which mainly includes:
s601, counting a third function service with the use frequency greater than the preset frequency of each user to be analyzed in a first preset time period based on the behavior data.
S602, counting the first function service of the user to be analyzed, the use frequency of which is greater than the preset frequency, in a first preset time period based on the behavior data aiming at the user to be analyzed in each organization group.
And S603, counting a second function service of which the use frequency is greater than the preset frequency of the user to be analyzed in a second preset time period based on the behavior data aiming at the user to be analyzed in each mechanism group.
The execution principle of S602 to S603 is the same as that of S501 to S502 described above, and the description thereof is omitted here.
S604, acquiring a union set of the first functional service and the second functional service, and determining a fourth functional service in the union set.
And S605, deleting the third functional service contained in the fourth functional service aiming at each organization group to obtain the special functional service used by the user to be analyzed in the organization group.
In the process of specifically implementing S605, for the same organization group, after the deletion processing is performed on the third functional service included in the fourth functional service, the special functional service is finally obtained.
According to the data processing method disclosed by the embodiment of the invention, the user data of the user to be analyzed is obtained from the bank user database, the user data comprises user basic data and behavior data of the user using a mobile phone bank, the weighted similarity of the user to be analyzed is calculated according to the user basic data, the user to be analyzed is identified based on a clustering algorithm, different organization groups are determined, the special function service used by the user to be analyzed in the organization groups is determined based on the behavior data of the user to be analyzed in the organization groups aiming at each organization group, the push message is sent to the user to be analyzed which does not use the special function service in the organization groups aiming at each organization group, and the push message comprises prompt information for prompting the opening of the special function service. By automatically pushing prompt information for activating the special function service to the user who does not use the special function service, the problems of low efficiency and high cost of offline manual popularization of bank workers are solved.
As shown in fig. 7, a flowchart of another data processing method provided in an embodiment of the present invention is provided, where the method includes the following steps:
and S701, acquiring basic data of the user to be analyzed from the bank user database.
S702, calculating the weighted similarity of the users to be analyzed according to the user basic data, identifying the users to be analyzed based on a clustering algorithm, and determining different organization groups.
And S703, determining the special function service used by the user to be analyzed in the organization group according to the behavior data of the user to be analyzed in the organization group.
And S704, aiming at each organization group, sending a push message to the user to be analyzed, which does not use the special function service, in the organization group.
The execution principle of S701 to S704 is the same as that of S101 to S104, and is not described herein again.
S705, aiming at each type of organization group, sending an opening message to the user to be analyzed who does not open the mobile phone banking application in the organization group.
In the process of implementing S705 specifically, the message of activating the mobile banking is sent to the to-be-analyzed users of the same organization group who do not activate the mobile banking, and the prompt message of using the special function service can be better pushed to the to-be-analyzed users only by activating the mobile banking.
According to the data processing method disclosed by the embodiment of the invention, the user data of the user to be analyzed is obtained from the bank user database, the user data comprises user basic data and behavior data of the user using a mobile phone bank, the weighted similarity of the user to be analyzed is calculated according to the user basic data, the user to be analyzed is identified based on a clustering algorithm, different organization groups are determined, the special function service used by the user to be analyzed in the organization groups is determined based on the behavior data of the user to be analyzed in the organization groups aiming at each organization group, the push message is sent to the user to be analyzed which does not use the special function service in the organization groups aiming at each organization group, and the push message comprises prompt information for prompting the opening of the special function service. By automatically pushing prompt information for activating the special function service to the user who does not use the special function service, the problems of low efficiency and high cost of offline manual popularization of bank workers are solved.
Based on the data processing method disclosed in the embodiment of the present invention, the embodiment of the present invention further discloses a data processing apparatus correspondingly, as shown in fig. 8, a schematic structural diagram of the data processing apparatus is provided for the embodiment of the present invention, and the data processing apparatus mainly includes: the system comprises an acquisition module 80, an organization group identification module 81, a special function service identification module 82 and a push module 83.
The obtaining module 80 is configured to obtain user data of a user to be analyzed from a bank user database, where the user data includes user basic data and behavior data of the user using a mobile banking machine.
And the mechanism group identification module 81 is used for calculating the weighted similarity of the users to be analyzed according to the user basic data, identifying the users to be analyzed based on a clustering algorithm and determining different mechanism groups.
And the special function service identification module 82 is used for determining the special function service used by the user to be analyzed in the organization group according to the behavior data of the user to be analyzed in the organization group.
And the pushing module 83 is configured to send, for each organization group, a pushing message to a user to be analyzed who does not use the special function service in the organization group, where the pushing message includes a prompt message prompting to open the special function service.
An optional structure of the organization group identification module 81 in the embodiment of the present invention is: the institution group identification module 81 includes a preprocessing unit, a weight calculation unit, and an identification unit.
And the preprocessing unit is used for cleaning abnormal values of the user basic data, or filling vacancy values of the user basic data, or performing synonym replacement processing on text information in the user basic data to obtain preprocessed user basic data.
And the weighting calculation unit is used for performing weighting similarity calculation by using the user basic data to obtain the weighting similarity of the user to be analyzed.
And the identification unit is used for determining the users to be analyzed and the organization groups which belong to the same organization group based on the clustering algorithm and the weighted similarity of all the users to be analyzed.
An optional structure of the special function service identification module 82 in the embodiment of the present invention is: the special function service identification module 82 includes a first organization group processing unit and a first special function service identification unit.
The first mechanism group processing unit is used for counting a first function service of a user to be analyzed within a first preset time period, the use frequency of the first function service being greater than the preset frequency, for the user to be analyzed in each mechanism group, based on the behavior data, and counting a second function service of the user to be analyzed within a second preset time period, the use frequency of the second function service being greater than the preset frequency, for the user to be analyzed in each mechanism group, based on the behavior data, and the duration of the second preset time period is greater than the duration of the first preset time period.
And the first special function service identification unit is used for determining the function service in the union set as the special function service used by the user to be analyzed in the organization group based on the union set of the first function service and the second function service.
Another optional structure of the special function service identification module 82 in the embodiment of the present invention is: the special function service identification module 82 includes a user processing unit, an organization group processing unit, and a special function service identification unit.
And the user processing unit is used for counting the first function service of which the use frequency is greater than the preset frequency of each user to be analyzed in a first preset time period based on the behavior data.
And the second mechanism group processing unit is used for counting a first function service of which the use frequency of the user to be analyzed is greater than the preset frequency in a first preset time period based on the behavior data for the user to be analyzed in each mechanism group, counting a second function service of which the use frequency of the user to be analyzed is greater than the preset frequency in a second preset time period based on the behavior data for the user to be analyzed in each mechanism group, acquiring a union set of the first function service and the second function service, and determining a fourth function service in the union set, wherein the duration of the second preset time period is greater than the duration of the first preset time period.
And the second special function service identification unit is used for deleting the third function service contained in the fourth function service aiming at each mechanism group to obtain the special function service used by the user to be analyzed in the mechanism group.
An optional structure of the pushing module 83 in the embodiment of the present invention is: the push module 83 comprises a push message unit.
And the message pushing unit is used for sending a pushing message to the users to be analyzed who do not use the special function service in the organization groups in a short message mode and a mobile banking message mode aiming at each organization group.
According to the data processing device disclosed in the embodiment of the present invention, the user data of the user to be analyzed is obtained from the bank user database, the user data includes the user basic data and the behavior data of the user using the mobile phone bank, the weighted similarity of the user to be analyzed is calculated according to the user basic data, the user to be analyzed is identified based on the clustering algorithm, different organization groups are determined, for each organization group, the special function service used by the user to be analyzed in the organization group is determined based on the behavior data of the user to be analyzed in the organization group, for each organization group, the push message is sent to the user to be analyzed who does not use the special function service in the organization group, and the push message includes the prompt message for prompting the opening of the special function service. By automatically pushing prompt information for activating the special function service to the user who does not use the special function service, the problems of low efficiency and high cost of offline manual popularization of bank workers are solved.
Based on the data processing device disclosed in the above embodiment of the present invention, the data processing device further includes: and sending an opening message module.
And the opening message sending module is used for sending opening messages to users to be analyzed who do not open the mobile phone bank application in the organization group aiming at each type of organization group, wherein the opening messages comprise prompt messages for prompting the opening of the mobile phone bank application and prompt messages for prompting the opening of special function services.
According to the data processing device disclosed by the embodiment of the invention, the message for activating the mobile phone bank is sent to the users to be analyzed who do not activate the mobile phone bank in the same organization group, and the message for using the special function service can be better pushed to the users to be analyzed only by activating the mobile phone bank, so that more users activate the mobile phone bank, and then the message for pushing the special function service can be pushed to the users who activate the mobile phone bank, thereby solving the problem of low efficiency of offline manual popularization.
Optionally, an embodiment of the present invention further provides a computer storage medium, where the computer storage medium includes a stored program, and when the program runs, a device on which the computer storage medium is located is controlled to execute the data processing method in fig. 1 to 7 disclosed in the foregoing embodiment of the present invention.
Optionally, an embodiment of the present invention further provides a data processing apparatus 90, as shown in fig. 9, which shows a schematic structural diagram of the data processing apparatus 90 provided in the embodiment of the present invention, and mainly includes: memory 91, processor 92 and bus 93.
The memory 91 stores a program.
And a processor 92, configured to execute a program, where the program executes the data processing method disclosed in fig. 1 to 7 in the foregoing embodiments of the present invention.
The memory 91 and the processor 92 communicate with each other via a bus 93.
The method comprises the steps of obtaining user data of a user to be analyzed from a bank user database, wherein the user data comprises user basic data and behavior data of the user using a mobile phone bank, calculating weighted similarity of the user to be analyzed according to the user basic data, identifying the user to be analyzed based on a clustering algorithm, determining different organization groups, determining special function services used by the user to be analyzed in the organization groups based on the behavior data of the user to be analyzed in the organization groups for each organization group, and sending push messages to the user to be analyzed, which does not use the special function services, in the organization groups for each organization group, wherein the push messages comprise prompt messages for prompting the opening of the special function services. By automatically pushing prompt information for activating the special function service to the user who does not use the special function service, the problems of low efficiency and high cost of offline manual popularization of bank workers are solved.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, the system or system embodiments are substantially similar to the method embodiments and therefore are described in a relatively simple manner, and reference may be made to some of the descriptions of the method embodiments for related points. The above-described system and system embodiments are only illustrative, wherein the units described as separate parts may or may not be physically separate, and the 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 modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Those of skill would further appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative components and steps have been described above generally in terms of their functionality in order to clearly illustrate this interchangeability of hardware and software. 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 invention.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims (10)

1. A method of data processing, the method comprising:
acquiring user data of a user to be analyzed from a bank user database, wherein the user data comprises user basic data and behavior data of the user using a mobile phone bank;
calculating the weighted similarity of the users to be analyzed according to the user basic data, identifying the users to be analyzed based on a clustering algorithm, and determining different mechanism groups;
for each organization group, determining special function services used by the users to be analyzed in the organization group based on the behavior data of the users to be analyzed in the organization group;
and aiming at each mechanism group, sending a push message to a user to be analyzed who does not use the special function service in the mechanism group, wherein the push message comprises prompt information for prompting the opening of the special function service.
2. The method of claim 1, wherein calculating the weighted similarity of the users to be analyzed according to the user basic data, identifying the users to be analyzed based on a clustering algorithm, and determining different organization groups comprises:
for each user to be analyzed, carrying out abnormal value cleaning processing on the user basic data, or carrying out vacancy value filling processing on the user basic data, or carrying out synonym replacement processing on text information in the user basic data to obtain preprocessed user basic data;
performing weighted similarity calculation by using the preprocessed user basic data to obtain the weighted similarity of the user to be analyzed;
and determining the users to be analyzed and the organization groups which belong to the same organization group based on the clustering algorithm and the weighted similarity of all the users to be analyzed.
3. The method according to claim 1, wherein the determining, for each of the organization groups, the special function service used by the user to be analyzed in the organization group based on the behavior data of the user to be analyzed in the organization group comprises:
counting, for the user to be analyzed in each organization group, a first function service of which the use frequency is greater than a preset frequency within a first preset time period based on the behavior data;
counting a second function service of which the use frequency is greater than a preset frequency of the user to be analyzed in a second preset time period based on the behavior data for the user to be analyzed in each mechanism group, wherein the duration of the second preset time period is greater than that of the first preset time period;
and determining that the functional service in the union set is a special functional service used by the user to be analyzed in the organization group based on the union set of the first functional service and the second functional service.
4. The method according to claim 1, wherein the determining, for each of the organization groups, the special function service used by the user to be analyzed in the organization group based on the behavior data of the user to be analyzed in the organization group comprises:
counting a third functional service with the use frequency greater than a preset frequency of each user to be analyzed in a first preset time period based on the behavior data;
counting, for the user to be analyzed in each organization group, a first function service of which the use frequency is greater than a preset frequency within a first preset time period based on the behavior data;
counting a second function service of which the use frequency is greater than a preset frequency of the user to be analyzed in a second preset time period based on the behavior data for the user to be analyzed in each mechanism group, wherein the duration of the second preset time period is greater than that of the first preset time period;
acquiring a union set of the first functional service and the second functional service, and determining a fourth functional service in the union set;
and deleting the third functional service contained in the fourth functional service aiming at each organization group to obtain the special functional service used by the user to be analyzed in the organization group.
5. The method according to any one of claims 1 to 4, wherein sending, for each of the organization groups, a push message to a user to be analyzed in the organization group who does not use the special function service comprises:
and aiming at each organization group, sending a push message to the users to be analyzed, which do not use the special function service, in the organization group in a short message mode or a mobile banking message mode.
6. The method of any of claims 1 to 4, further comprising:
and aiming at each type of the organization group, sending an opening message to a user to be analyzed who does not open the mobile phone banking application in the organization group, wherein the opening message comprises prompt information for prompting the opening of the mobile phone banking application and prompt information for prompting the opening of the special function service.
7. A data processing apparatus, characterized in that the data processing apparatus comprises:
the system comprises an acquisition module, a database analysis module and a database analysis module, wherein the acquisition module is used for acquiring user data of a user to be analyzed from a bank user database, and the user data comprises user basic data and behavior data of the user using a mobile phone bank;
the mechanism group identification module is used for calculating the weighted similarity of the users to be analyzed according to the user basic data, identifying the users to be analyzed based on a clustering algorithm and determining different mechanism groups;
a special function service identification module, configured to determine, for each organization group, a special function service used by the user to be analyzed in the organization group based on behavior data of the user to be analyzed in the organization group;
and the pushing module is used for sending a pushing message to a user to be analyzed who does not use the special function service in the mechanism group aiming at each mechanism group, wherein the pushing message comprises prompt information for prompting the opening of the special function service.
8. The apparatus of claim 7, wherein the organization population identification module comprises:
the preprocessing unit is used for cleaning abnormal values of the user basic data, or filling vacancy values of the user basic data, or performing synonym replacement processing on text information in the user basic data to obtain preprocessed user basic data;
the weighted calculation unit is used for performing weighted similarity calculation by utilizing the preprocessed user basic data to obtain the weighted similarity of the user to be analyzed;
and the identification unit is used for determining the users to be analyzed and the organization groups which belong to the same organization group based on the clustering algorithm and the weighted similarity of all the users to be analyzed.
9. A computer storage medium, comprising a stored program, wherein the program, when executed, controls an apparatus in which the computer storage medium is located to perform the data processing method of any one of claims 1 to 6.
10. A data processing apparatus comprising a processor and a memory, the memory having a program stored therein, the processor being configured to execute the program, wherein the program when executed performs the data processing method of any one of claims 1 to 6.
CN201911192184.5A 2019-11-28 2019-11-28 Data processing method, device, equipment and storage medium Pending CN110942350A (en)

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