CN112700271A - Big data image drawing method and system based on label model - Google Patents
Big data image drawing method and system based on label model Download PDFInfo
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- CN112700271A CN112700271A CN202011593878.2A CN202011593878A CN112700271A CN 112700271 A CN112700271 A CN 112700271A CN 202011593878 A CN202011593878 A CN 202011593878A CN 112700271 A CN112700271 A CN 112700271A
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Abstract
A big data image method and system based on label model comprises a data acquisition module, a data cleaning module, a data storage module, an analysis modeling module, a user image module, a loyalty analysis module and a classification module; the loyalty analysis module comprises an input unit, a query unit and an analysis unit; the big data portrait generation method comprises the following steps: s1, collecting user related data; s2, processing the data and storing the cleaned effective data; s3, generating a specific user portrait; s4, performing loyalty analysis on the user; and S5, performing loyalty classification on the users, and adding loyalty labels of the users aiming at specific enterprises. According to the invention, the problems of single form, incomplete data collection and difficulty in distinguishing truth in the traditional user understanding mode can be effectively avoided, users can be classified according to specific enterprises, loyalty labels are added, and the product recommendation accuracy is improved, so that the transaction rate is improved.
Description
Technical Field
The invention relates to the technical field of big data portrayal, in particular to a big data portrayal method and a big data portrayal system based on a label model.
Background
The user portrait, namely the tagging of user information, is a basic way for an enterprise to apply a big data technology by perfectly abstracting a user business overall view after the enterprise collects and analyzes data of main information such as social attributes, living habits, consumption behaviors and the like of consumers. The user portrait provides enough information foundation for enterprises, and can help the enterprises to quickly find more extensive feedback information such as accurate user groups and user requirements. The traditional user learning mode is mainly in the form of user research and interview, the form is single, data collection is incomplete, and true and false are difficult to distinguish, the existing big data image is only labeled to a customer, labels of different customers are often the same on many points in practice, the same company cannot distinguish similar users in loyalty, commodities and services recommended by user merchants of similar images are often similar, and the purpose of accurate pushing cannot be achieved, so that the accuracy of product recommendation is influenced, and the transaction rate is lower.
Disclosure of Invention
Objects of the invention
In order to solve the technical problems existing in the background technology, the invention provides a big data imaging method and system based on a label model, data is collected through a data collection module, the problems that the traditional user learning mode is single in form, incomplete in data collection and difficult in real and false distinguishing mainly through the forms of user research and interview can be effectively avoided, users can be classified according to specific enterprises, loyalty labels are added, the enterprises can conveniently push related information or products according to different users, for example, the latest products are pushed according to loyalty users, the products doing activities are pushed according to lost users, and the accuracy of product recommendation is improved, so that the transaction rate is improved.
(II) technical scheme
The invention provides a big data imaging method and a big data imaging system based on a label model, which comprise a data acquisition module, a data cleaning module, a data storage module, an analysis modeling module, a user imaging module, a loyalty analysis module and a classification module;
the data acquisition module is used for acquiring data from different sources; the data cleaning module is used for processing the data acquired by the data acquisition module; the data storage module is used for storing the cleaned effective data; the analysis modeling module is used for modeling the user portrait according to the cleaned data, generating a specific user portrait, and updating the user portrait at any time by combining the cleaned data, wherein the user portrait content comprises a static label and a dynamic label; the user portrait module is used for storing a produced user portrait, the user portrait module is connected with the analysis modeling module, and the user portrait module and the analysis modeling module synchronously update the user portrait;
the loyalty analysis module comprises an input unit, a query unit and an analysis unit, wherein the input unit is used for inputting user information and an enterprise name, the query unit is used for retrieving the consumption behavior of a user input to the enterprise and the search behavior of the user to the input enterprise in the data storage module, and the analysis unit is used for analyzing the loyalty of the user according to a preset loyalty rule;
the classification module is used for classifying the loyalty of the users according to the loyalty analysis result, defining different classes of users, adding loyalty labels aiming at the users of specific enterprises, and the definition method comprises the following steps: the potential users who do not have the consumption behavior of the enterprise but have searched and learned the enterprise, the lost users who have the consumption behavior of the enterprise but have not the consumption behavior of the enterprise in the last half of the year and more, and the loyal users who have the consumption behavior of the enterprise and still have the consumption behavior in the last half of the year.
Preferably, the big data portrait generation method includes the steps of: s1, collecting user related data from different sources; s2, processing the data collected by the data collection module and storing the cleaned effective data; s3, modeling the user portrait, generating a specific user portrait, and updating the user portrait at any time by combining the cleaned data; s4, analyzing the loyalty of the user according to the preset loyalty rule, wherein the loyalty refers to the loyalty of the user to a specific enterprise; and S5, classifying the loyalty of the users according to the loyalty analysis result, defining different classes of users, and adding loyalty labels of the users of specific enterprises.
Preferably, the data acquisition module is sourced from a core system of an enterprise, a marketing system of the enterprise, internet data and data acquired from third-party professional institutions, and the third-party professional institutions mainly refer to data purchased by data transaction centers in various regions.
Preferably, the cleaning method of the data cleaning module comprises missing value processing, repeated data processing and abnormal data processing.
Preferably, the static tag content includes name, gender, date of birth, academic calendar, occupation, wedding status, and various items with low variation frequency, and the variation of the dynamic tag is calculated by hour.
Preferably, a classification statistical unit is arranged in the classification module, the classification statistical unit outputs a user classification list, the user classification list comprises user portrait, enterprise information, consumption behavior, enterprise searching behavior and user classification conditions, and users of the same type are listed in the same table.
The technical scheme of the invention has the following beneficial technical effects: collecting user related data from different sources, processing the data collected by the data collection module, storing the cleaned effective data, modeling the user portrait, generating a specific user portrait, updating the user portrait at any time by combining the cleaned data, analyzing the loyalty of the user according to a preset loyalty rule, classifying the loyalty of the user to a specific enterprise according to the loyalty analysis result, defining different types of users, and adding loyalty tags for the users of the specific enterprise. Data are collected through the data acquisition module, the mode that can effectively avoid traditional understanding user is mainly through the form of user investigation and interview, the form is single, data collection is incomplete, the problem of true and false hard distinguishing, and can classify the user to specific enterprise, increase loyalty label, be convenient for the enterprise to carry out propelling movement relevant information or product to different users, for example to the newest product of loyalty user propelling movement, to losing the product that the user propelling movement was done etc. thereby improve the accuracy of product recommendation and improve the transaction rate.
Drawings
Fig. 1 is a schematic structural diagram of a big data imaging system based on a label model according to the present invention.
FIG. 2 is a flow chart of a big data portrait generation method according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail with reference to the accompanying drawings in conjunction with the following detailed description. It should be understood that the description is intended to be exemplary only, and is not intended to limit the scope of the present invention. Moreover, in the following description, descriptions of well-known structures and techniques are omitted so as to not unnecessarily obscure the concepts of the present invention.
As shown in fig. 1-2, the big data portrayal method and system based on the label model according to the present invention includes a data acquisition module, a data cleaning module, a data storage module, an analysis modeling module, a user portrayal module, a loyalty analysis module, and a classification module;
the data acquisition module is used for acquiring data from different sources; the data cleaning module is used for processing the data acquired by the data acquisition module; the data storage module is used for storing the cleaned effective data; the analysis modeling module is used for modeling the user portrait according to the cleaned data, generating a specific user portrait, and updating the user portrait at any time by combining the cleaned data, wherein the user portrait content comprises a static label and a dynamic label; the user portrait module is used for storing a produced user portrait, the user portrait module is connected with the analysis modeling module, and the user portrait module and the analysis modeling module synchronously update the user portrait;
the loyalty analysis module comprises an input unit, a query unit and an analysis unit, wherein the input unit is used for inputting user information and an enterprise name, the query unit is used for retrieving the consumption behavior of a user input to the enterprise and the search behavior of the user to the input enterprise in the data storage module, and the analysis unit is used for analyzing the loyalty of the user according to a preset loyalty rule;
the classification module is used for classifying the loyalty of the users according to the loyalty analysis result, defining different classes of users, adding loyalty labels aiming at the users of specific enterprises, and the definition method comprises the following steps: the potential users who do not have the consumption behavior of the enterprise but have searched and learned the enterprise, the lost users who have the consumption behavior of the enterprise but have not the consumption behavior of the enterprise in the last half of the year and more, and the loyal users who have the consumption behavior of the enterprise and still have the consumption behavior in the last half of the year. .
According to the invention, relevant data of users are collected from different sources, the data collected by a data collection module is processed, cleaned effective data is stored, user portrait modeling is carried out, a specific user portrait is generated, the user portrait is updated at any time by combining the cleaned data, the loyalty of the users is analyzed according to a preset loyalty rule, the loyalty refers to the loyalty of the users to a specific enterprise, the users are classified according to the loyalty analysis result, different types of users are defined, and loyalty labels aiming at the users of the specific enterprise are added. In the invention, the data acquisition module is used for acquiring data, so that the problems of single form, incomplete data acquisition and difficulty in distinguishing of users in the traditional user learning mode mainly through user investigation and interview can be effectively avoided, users can be classified according to specific enterprises, loyalty labels are added, the enterprises can push related information or products according to different users conveniently, for example, the latest products are pushed according to loyalty users, products doing activities are pushed according to lost users, and the accuracy of product recommendation is improved, so that the transaction rate is improved.
In an alternative embodiment, the large data representation generation method includes the steps of:
s1, collecting user related data from different sources;
s2, processing the data collected by the data collection module and storing the cleaned effective data;
s3, modeling the user portrait, generating a specific user portrait, and updating the user portrait at any time by combining the cleaned data;
s4, analyzing the loyalty of the user according to the preset loyalty rule, wherein the loyalty refers to the loyalty of the user to a specific enterprise;
and S5, performing loyalty classification on the users according to loyalty analysis results, defining different types of users, adding loyalty labels aiming at the users of specific enterprises, and enabling the enterprises to push different information and products to different users according to the loyalty classification, so that the product recommendation accuracy is improved, and the transaction rate is improved.
In an optional embodiment, the data acquisition module is sourced from a core system of an enterprise, a marketing system of the enterprise, internet data and data acquired from third-party professional institutions, wherein the third-party professional institutions mainly refer to data purchased by data transaction centers in various regions; and sufficient data are acquired through various channels, so that the user portrait can be accurately constructed.
In an optional embodiment, the cleaning method of the data cleaning module comprises missing value processing, repeated data processing and abnormal data processing; the missing value processing comprises deleting missing data, filling up the missing data and not processing the missing data, and the data acquisition is not easy and is not easy to delete generally.
In an alternative embodiment, the static tag content includes name, gender, date of birth, academic calendar, occupation, wedding status, and various items with low variation frequency, and the variation of the dynamic tag is calculated by hour; the static label changes the probability sole, can be used as the basic information label of the user, and the dynamic label refers to the characteristics and behaviors which change frequently and are very unstable, such as 'going to a swimming pool on a certain day' and 'going to a gymnasium on a certain day'.
In an optional embodiment, a classification statistical unit is arranged in the classification module, the classification statistical unit outputs a user classification list, the user classification list comprises user figures, enterprise information, consumption behaviors, enterprise searching behaviors and user classification conditions, and users of the same class are listed in the same table; the user classification list is convenient for enterprises to further know the users and is convenient for contacting with the users.
It is to be understood that the above-described embodiments of the present invention are merely illustrative of or explaining the principles of the invention and are not to be construed as limiting the invention. Therefore, any modification, equivalent replacement, improvement and the like made without departing from the spirit and scope of the present invention should be included in the protection scope of the present invention. Further, it is intended that the appended claims cover all such variations and modifications as fall within the scope and boundaries of the appended claims or the equivalents of such scope and boundaries.
Claims (6)
1. A big data image system based on a label model is characterized by comprising a data acquisition module, a data cleaning module, a data storage module, an analysis modeling module, a user image module, a loyalty analysis module and a classification module;
the data acquisition module is used for acquiring data from different sources; the data cleaning module is used for processing the data acquired by the data acquisition module; the data storage module is used for storing the cleaned effective data; the analysis modeling module is used for modeling the user portrait according to the cleaned data, generating a specific user portrait, and updating the user portrait at any time by combining the cleaned data, wherein the user portrait content comprises a static label and a dynamic label; the user portrait module is used for storing a produced user portrait, the user portrait module is connected with the analysis modeling module, and the user portrait module and the analysis modeling module synchronously update the user portrait;
the loyalty analysis module comprises an input unit, a query unit and an analysis unit, wherein the input unit is used for inputting user information and an enterprise name, the query unit is used for retrieving the consumption behavior of a user input to the enterprise and the search behavior of the user to the input enterprise in the data storage module, and the analysis unit is used for analyzing the loyalty of the user according to a preset loyalty rule;
the classification module is used for classifying the loyalty of the users according to the loyalty analysis result, defining different classes of users, adding loyalty labels aiming at the users of specific enterprises, and the definition method comprises the following steps: the potential users who do not have the consumption behavior of the enterprise but have searched and learned the enterprise, the lost users who have the consumption behavior of the enterprise but have not the consumption behavior of the enterprise in the last half of the year and more, and the loyal users who have the consumption behavior of the enterprise and still have the consumption behavior in the last half of the year.
2. The tag model-based big data representation system of claim 1, wherein the big data representation generation method comprises the steps of:
s1, collecting user related data from different sources;
s2, processing the data collected by the data collection module and storing the cleaned effective data;
s3, modeling the user portrait, generating a specific user portrait, and updating the user portrait at any time by combining the cleaned data;
s4, analyzing the loyalty of the user according to the preset loyalty rule, wherein the loyalty refers to the loyalty of the user to a specific enterprise;
and S5, classifying the loyalty of the users according to the loyalty analysis result, defining different classes of users, and adding loyalty labels of the users of specific enterprises.
3. The tag model-based big data imaging system according to claim 1, wherein the data acquisition module is derived from a core system of an enterprise, a marketing system of the enterprise, internet data, and data acquired from third-party professional organizations, and the third-party professional organizations mainly refer to data purchased by data trading centers in various regions.
4. The tag model-based big data imaging system according to claim 1, wherein the cleaning method of the data cleaning module comprises missing value processing, repeated data processing and abnormal data processing.
5. The tag model-based big data imaging system of claim 1, wherein the static tag contents include name, gender, date of birth, academic calendar, occupation, wedding status, and various items with low variation frequency, and the variation of the dynamic tag is calculated by hour.
6. The tag model-based big data image system of claim 1, wherein a classification statistical unit is disposed in the classification module, the classification statistical unit outputs a user classification list, the user classification list includes a user representation, enterprise information, consumption behavior, search enterprise behavior and user classification conditions, and users of the same type are listed in the same table.
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| CN113988070A (en) * | 2021-10-09 | 2022-01-28 | 广州快决测信息科技有限公司 | Investigation problem generation method and device, computer equipment and storage medium |
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