CN111899057B - Customer portrait data cluster analysis system based on edge cloud node data collection - Google Patents
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Abstract
The invention provides a customer portrait data cluster analysis system based on edge cloud node data collection, which comprises a plurality of edge data acquisition terminals of a plurality of financial network points distributed at different positions within a preset range; the plurality of edge data acquisition terminals are respectively provided with a wireless data acquisition module and a broadcasting module; the wireless data acquisition module is used for acquiring financial data information generated by the client within the preset range; the edge data acquisition terminal broadcasts the position information of the terminal by using the broadcasting module and acquires partial financial data information; the customer portrait data cluster analysis system also comprises a customer portrait data grouping module, wherein the customer portrait data grouping module collects the customer portrait data acquired by a plurality of edge data acquisition terminals of a plurality of financial network points at different positions within the preset range and groups the customer portrait data to obtain at least one stable customer portrait data group.
Description
Technical Field
The invention belongs to the technical field of big data processing, and particularly relates to a customer portrait data cluster analysis system based on edge cloud node data collection.
Background
Currently, market competition is increasingly strong, enterprises face serious challenges, and customer demands present increasingly obvious diversified and personalized characteristics, namely customers are no longer passive recipients of consistent products and services, but active selectors. Therefore, better understanding clients, analyzing the purchasing behavior and preference of the clients become the most urgent demands of enterprises, so as to aim at carrying out accurate sales and accurate service on different clients, further reduce the operating cost, improve the service capability, and make the core competitiveness of the enterprises take a new step.
The cloud computing platform is used for extracting various information of the clients into a unified data center, and the unified data center mainly comprises data such as personal detailed information of the clients, account information of the clients, asset information of the clients, transaction information of the clients, income-creating information of the clients and the like, and the data are subjected to deep analysis statistics to obtain client portraits, wherein the client portraits are obtained by labeling the clients, giving weights according to different labels, and classifying the clients through specific classification software and a clustering algorithm, so that personalized services are provided after the clients are classified in layers.
And collecting massive structured and unstructured data into a data warehouse, analyzing the data in real time by applying a data warehouse technology, providing all-dimensional information of clients for some financial institutions, and estimating the consumption habit of the clients by deep mining and analyzing the transaction behaviors and consumption information of the clients and accurately predicting the purchasing behavior of the clients, namely big data finance. Big data finance may provide technical support for financial institutions or financial services electronic commerce platforms in principle for customer promotion and customer appropriateness. The large data of the large data financial service platform is supported, and the aim is to provide financial services, and the core is how to quickly acquire valuable information from a large amount of data. Therefore, data integration, processing and analysis of big data are often based on cloud computing, and modeling is performed on the big data through a cloud computing platform to characterize customer portraits.
The patent application of China patent application No. CN201511025848.0 filed by China silver community of stock, inc. provides a method and a device for generating a consumer image of a cardholder, wherein the method comprises the following steps: the acquired cardholder consumption information comprises cardholder attribute information and bank card transaction information; data cleaning is carried out on the consumption information of the cardholder, so that effective consumption information of the cardholder to be analyzed is obtained; performing cluster analysis on the consumption information of the effective cardholders according to the attribute information of the cardholders to obtain an effective consumption information set aiming at each cardholder; determining a consumption type corresponding to each bank card transaction information aiming at a cardholder; dividing the effective consumption information set of the cardholder into effective consumption information subsets corresponding to different consumption types according to the consumption type corresponding to each bank card transaction information; and determining the consumption portrait of the cardholder according to the effective consumption information subset, so as to solve the problem that the analysis result is not comprehensive and accurate in the prior art.
The Chinese patent application with the application number of CN201811568454.3 provides a customer portrait construction method which comprises the following steps: acquiring a plurality of data information of a target object, wherein each data information comprises: a plurality of data dimensions, each data dimension comprising one or more sub-tags. And respectively calculating the information value IV value of each sub-label, and selecting the data dimension meeting the preset condition as the in-mould label according to the IV value of each sub-label. Calculating sub-label scores according to the in-mold labels, and respectively constructing a high-quality customer portrait and a low-quality customer portrait according to the sub-label scores. The method and the system can construct high-quality customer portraits and low-quality customer portraits according to the sub-label scores so as to further realize accurate business recommendation and service for target groups.
However, in the conventional customer representation generation method, the customer data used is not necessarily critical data; at the same time, individual customer portrayal analysis will increase data processing complexity, whereas the prior art does not take into account the data stability problem of packet data portrayal.
Disclosure of Invention
In order to solve the technical problems, the invention provides a customer portrait data cluster analysis system based on edge cloud node data collection, which comprises a plurality of edge data acquisition terminals of a plurality of financial network points distributed at different positions within a preset range; the plurality of edge data acquisition terminals are respectively provided with a wireless data acquisition module and a broadcasting module; the wireless data acquisition module is used for acquiring financial data information generated by the client within the preset range; the edge data acquisition terminal broadcasts the position information of the terminal by using the broadcasting module and acquires partial financial data information; the customer portrait data cluster analysis system also comprises a customer portrait data grouping module, wherein the customer portrait data grouping module collects the customer portrait data acquired by a plurality of edge data acquisition terminals of a plurality of financial network points at different positions within the preset range and groups the customer portrait data to obtain at least one stable customer portrait data group.
Specifically, the customer portrait data cluster analysis system based on the edge cloud node data collection comprises a plurality of edge data acquisition terminals of a plurality of financial network points distributed at different positions within a preset range;
The plurality of edge data acquisition terminals are respectively provided with a wireless data acquisition module and a broadcasting module;
The wireless data acquisition module is used for acquiring financial data information generated by the client within the preset range;
the edge data acquisition terminal broadcasts the position information of the terminal by using the broadcasting module and acquires partial financial data information;
The customer portrait data cluster analysis system also comprises a customer portrait data grouping module, wherein the customer portrait data grouping module collects customer portrait data acquired by a plurality of edge data acquisition terminals of a plurality of financial network points at different positions within the preset range and groups the customer portrait data to obtain at least one stable customer portrait data group;
The customer portrait data acquired by the plurality of edge data acquisition terminals comprises financial data information generated by the customers within the preset range and acquired by each edge data acquisition terminal by utilizing the wireless data acquisition module of the edge data acquisition terminal, and partial financial data information broadcast by other edge data acquisition terminals and acquired by each edge data acquisition terminal by utilizing the broadcasting module of the edge data acquisition terminal.
Further advantages of the invention will be further elaborated in the description section of the embodiments in connection with the drawings.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a block diagram of a customer representation data cluster analysis system for edge cloud node data collection in accordance with one embodiment of the invention
FIG. 2 is a schematic diagram of the system of FIG. 1 for financial data acquisition APP
FIG. 3 is a schematic diagram of the system of FIG. 1 for obtaining at least one stable customer representation data set
Fig. 4 is a specific implementation of the embodiment depicted in fig. 3.
Detailed Description
The invention will be further described with reference to the drawings and detailed description.
Referring to FIG. 1, a block diagram of a customer representation data cluster analysis system for edge cloud node data collection according to one embodiment of the invention is shown.
The customer portrait data cluster analysis system based on edge cloud node data collection shown in fig. 1 comprises a plurality of edge data acquisition terminals of a plurality of financial network points distributed at different positions within a preset range;
The plurality of edge data acquisition terminals are respectively provided with a wireless data acquisition module and a broadcasting module;
The wireless data acquisition module is used for acquiring financial data information generated by the client within the preset range;
the edge data acquisition terminal broadcasts the position information of the terminal by using the broadcasting module and acquires partial financial data information;
The customer portrait data cluster analysis system also comprises a customer portrait data grouping module, wherein the customer portrait data grouping module collects customer portrait data acquired by a plurality of edge data acquisition terminals of a plurality of financial network points at different positions within the preset range and groups the customer portrait data to obtain at least one stable customer portrait data group;
The customer portrait data acquired by the plurality of edge data acquisition terminals comprises financial data information generated by the customers within the preset range and acquired by each edge data acquisition terminal by utilizing the wireless data acquisition module of the edge data acquisition terminal, and partial financial data information broadcast by other edge data acquisition terminals and acquired by each edge data acquisition terminal by utilizing the broadcasting module of the edge data acquisition terminal.
As an illustrative example, the plurality of edge data collection terminals of the plurality of financial sites distributed at different locations within the predetermined range may be touchable input terminals distributed at halls of each big bank site, and the customer may log in to the input terminals for touch input, inquiry and other transactions.
In another aspect, the customer may use the mobile terminal to generate financial data most of the time, and in an embodiment, the financial data generated by the customer using the mobile terminal is acquired by an edge data acquisition terminal within a predetermined range of the customer, in a manner based on fig. 1, see fig. 2.
The wireless data acquisition module is used for acquiring financial data information generated by the client within the preset range, and specifically comprises the following steps:
The financial data information generated by the client comprises financial data information generated by the client logging in the mobile terminal within the preset range;
the mobile terminal is provided with a financial data acquisition APP, and the financial data information comprises client information generated after a user logs in the financial data acquisition APP.
The financial data information comprises generated customer information after a user logs in the financial data acquisition APP, and the financial data acquisition APP further comprises:
the financial data acquisition APP comprises an input environment detection component, wherein the input environment detection component is used for acquiring client login environment data after detecting that a client logs in the mobile terminal.
Of particular importance is that certain specific data, referred to herein as customer login context data, can be characteristic of the customer, corresponding to the touch terminal as well as the removable input terminal.
More specifically, as one of the findings of the present invention, the client login environment data includes a time start point at which a client logs in the mobile terminal, a time end point at which the client exits the mobile terminal, and an operation edit-action parameter between the time start point and the time end point; the operation editing action parameters comprise a return operation of a client, a current page exiting operation, a deleting operation and a page pause operation.
As a salient feature of the present invention, which is different from the prior art, the edge data collecting terminal broadcasts its own position information and acquired partial financial data information by using the broadcasting module, and specifically includes:
The part of financial data information is generated after a user logs in one of a plurality of edge data acquisition terminals of the plurality of financial network points.
In the prior art, although customer information is collected at a website setting terminal in a relatively common manner, the customer information is collected in an isolated static manner, and the above embodiment of the invention improves interactivity, so that customer portrait data acquired by the plurality of edge data collecting terminals comprises financial data information generated by customers in the preset range and acquired by each edge data collecting terminal by utilizing the wireless data acquiring module of the edge data collecting terminal, and part of financial data information broadcast by other edge data collecting terminals and acquired by utilizing the broadcasting module of the edge data collecting terminal.
See next figures 3-4.
The customer portrait grouping module gathers the customer portrait data acquired by a plurality of edge data acquisition terminals of a plurality of financial websites at different positions within the preset range, groups the customer portrait data to obtain at least one stable customer portrait data group, and specifically comprises the following steps:
establishing a customer portrait data matrix based on the customer portrait data;
determining at least one stability sub-order matrix of the customer representation data matrix;
And taking the stability sub-order matrix as the stable customer portrait data group.
Based on the customer portrait data, a customer portrait data matrix is established, which concretely comprises:
Carrying out quantization coding on the customer portrait data according to the attribute of the financial data information to obtain quantization coding values of the financial data information of different attributes of different customers;
And combining the quantized coded values of the financial data information with different attributes of different clients into the client portrait data matrix according to the quantized coded values of the financial data attributes-client IDs.
As an example, the customer portrait data acquired by the plurality of edge data acquisition terminals includes financial data related to customers, and specifically includes:
the financial data includes customer login data, customer query data, customer payment data, and customer login environment data.
The client login data comprises a client login ID, login terminal hardware parameters, login time, login places and the like;
the client query data comprises query keywords, query pages, confirmation results and the like which are input after the client logs in;
the customer payment data includes data related to transactions, payments after customer login, including payment, transfer, etc.
As an example, the customer portrait data matrix is specifically as follows:
where D ij is the vectorized representation of the ith financial data corresponding to the jth customer.
It should be noted that, depending on the purpose of the customer portrait and the type of the customer data, various vectorization representation methods may be adopted, including a binarization encoding method, a score normalization method, an expert scoring method, and a quantization method, which is not particularly limited in the present invention.
In the prior art, how to acquire customer portrait data, how to process the customer portrait data, and how to perform quantization coding based on the customer portrait data are described in detail. This is because the customer data itself is not recognizable by the computer, must be converted into a form or language recognizable by the machine through a certain machine coding or vectorization method,
For example, vectorizing the client login time may be:
[0:00-6:00 registration time, denoted as 001;
[6:00-8:00 registration time, denoted 002;
……
In this regard, a data cluster matrix matrixD of different customer data compositions may be established.
More client data matrixing methods and data vectorizing and encoding methods can be seen in the following technical literature:
Pan B,Wang X,Song E,et al.CAMSPF：Cloud-assisted mobile service provision framework supporting personalized user demands in pervasive computing environment[C]//Wireless Communications and Mobile Computing Conference.IEEE,2013：649-654.
ding Wei, wang Ti, liu Xinhai, etc. mobile phone user portraits and credit investigation [ J ]. Postal and electronic design technique 2016 (3) based on big data technique: 64-69
Danette Mc Gilvray,2008.Executing Data Quality Projects：Ten Steps to Quality Data and Trusted Information(TM),Morgan Kaufman.
Rodbard H W,Jellinger P S,Davidson J A,et al.Statement by an American Association of Clinical Endocrinologists/American College of Endocrinology consensus panel on type 2diabetes mellitus：an algorithm for glycemic control[J].Endocrine Practice Official Journal of the American College of Endocrinology&the American Association of Clinical Endocrinologists,2009,15(6)：540.
The determining at least one stability sub-order matrix of the customer portrait data matrix specifically comprises:
Sequentially judging whether the characteristic values of all the order submatrices corresponding to the customer portrait data matrix meet the preset conditions;
And selecting a sub-matrix with the order value larger than a preset value from all the order sub-matrices with the characteristic values meeting preset conditions as the stability sub-order matrix.
The customer portrait data also comprises the self position information of the edge data acquisition terminal corresponding to the generated partial financial data information.
In each stable customer portrait data group, the position information of the edge data acquisition terminal corresponding to all the partial financial data information is in a preset position range.
As a schematic illustration, the respective rank sub-matrices for the customer image data matrix may be represented as follows:
……
Wherein matrixD is the customer portrait data matrix, matrixD is its 2-order submatrix; matrixD4 is its 4 th order submatrix.
As an example, D ij is a vectorized representation of the ith financial data corresponding to the jth customer.
Obviously, for an m-order matrix in m dimensions, it includes m-order (the original matrix itself), m-1-order, m-3-order … …, and 2-order submatrices.
In the above embodiment, this may be performed by judging whether the absolute values of all the feature roots of the sub-matrix are smaller than 1, and if the absolute values of all the feature roots are smaller than 1, the sub-matrix is stable, satisfying a predetermined condition.
The submatrices with order values larger than a preset value in each order submatrix can be set according to the realization setting, the modeling accuracy of the customer portrait and the data processing amount.
For example, for matrixD dimensions 5 x5 as described above, the predetermined value may be 3, i.e., a4 th order submatrix and a5 th order submatrix are acquired (5 th order submatrix is matrixD itself).
Based on the existing customer data, how to obtain the customer portrait is also known in the art, and the present invention is not described herein, for example, see:
master paper: zhao Feihong analysis, research and application of binary K-means algorithm based on financial customer portraits [ D ]. University of Chinese academy of sciences (institute of engineering and information technology), 2016.
Master paper: wu Yitao design and implementation of a Hadoop-based image system for Jiangxi cigarette retailers [ D ]. Nanchang university, 2018.
Based on the customer portrait, sending a page adjustment message to a financial data acquisition APP on a mobile terminal of a customer group represented by a customer identifier corresponding to the stability sub-order matrix;
And when the clients of the client group log in the financial data acquisition APP, adjusting the page display mode of the financial data acquisition APP based on the page adjustment information.
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (4)
1. The customer portrait data cluster analysis system based on the edge cloud node data collection comprises a plurality of edge data acquisition terminals of a plurality of financial network points distributed at different positions within a preset range;
The method is characterized in that:
The plurality of edge data acquisition terminals are respectively provided with a wireless data acquisition module and a broadcasting module;
the wireless data acquisition module is used for acquiring financial data information generated by the client within the preset range; the financial data information generated by the client comprises financial data information generated by the client logging in the mobile terminal within the preset range; the mobile terminal is provided with a financial data acquisition APP, and the financial data information comprises generated client information after a user logs in the financial data acquisition APP; the financial data acquisition APP comprises an input environment detection component, wherein the input environment detection component is used for acquiring client login environment data after detecting that a client logs in the mobile terminal; the client login environment data comprise a time starting point of client login to the mobile terminal, a time ending point of exiting the mobile terminal and operation editing action parameters between the time starting point and the time ending point; the operation editing action parameters comprise a return operation of a client, a current page exiting operation, a deleting operation and a page pause operation;
the edge data acquisition terminal broadcasts the position information of the terminal by using the broadcasting module and acquires partial financial data information;
The customer portrait data cluster analysis system also comprises a customer portrait data grouping module, wherein the customer portrait data grouping module collects customer portrait data acquired by a plurality of edge data acquisition terminals of a plurality of financial network points at different positions within the preset range and groups the customer portrait data to obtain at least one stable customer portrait data group; the method specifically comprises the following steps:
Establishing a customer portrait data matrix based on the customer portrait data; the method comprises the following steps: carrying out quantization coding on the customer portrait data according to the attribute of the financial data information to obtain quantization coding values of the financial data information of different attributes of different customers; combining the quantized coded values of the financial data information with different attributes of different clients into the client portrait data matrix according to the quantized coded values of the financial data attributes-client IDs;
Determining at least one stability sub-order matrix of the customer representation data matrix; the method comprises the following steps: sequentially judging whether the characteristic values of all the order submatrices corresponding to the customer portrait data matrix meet the preset conditions; selecting a submatrix with an order value larger than a preset value from all the order submatrices with characteristic values meeting preset conditions as the stability submatrix; wherein the predetermined condition includes: judging whether the absolute values of all the characteristic roots of the submatrices are smaller than 1;
taking the stability sub-order matrix as the stable customer portrait data group;
Based on the customer portrait, sending a page adjustment message to a financial data acquisition APP on a mobile terminal of a customer group represented by a customer identifier corresponding to the stability sub-order matrix; when the clients of the client group log in the financial data acquisition APP, adjusting a page display mode of the financial data acquisition APP based on the page adjustment information;
The customer portrait data acquired by the plurality of edge data acquisition terminals comprises financial data information generated by the customers within the preset range and acquired by each edge data acquisition terminal by utilizing the wireless data acquisition module of the edge data acquisition terminal, and partial financial data information broadcast by other edge data acquisition terminals and acquired by each edge data acquisition terminal by utilizing the broadcasting module of the edge data acquisition terminal.
2. The customer representation data cluster analysis system based on edge cloud node data collection of claim 1, wherein:
The edge data acquisition terminal broadcasts the position information of the terminal and the acquired partial financial data information by using the broadcasting module, and specifically comprises the following steps:
The part of financial data information is generated after a user logs in one of a plurality of edge data acquisition terminals of the plurality of financial network points.
3. The customer representation data cluster analysis system based on edge cloud node data collection of claim 2, wherein:
The customer portrait data also comprises the self position information of the edge data acquisition terminal corresponding to the generated partial financial data information.
4. The customer representation data cluster analysis system based on edge cloud node data collection of claim 3, wherein:
in each stable customer portrait data group, the position information of the edge data acquisition terminal corresponding to all the partial financial data information is in a preset position range.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105931068A (en) * | 2015-12-30 | 2016-09-07 | ***股份有限公司 | Cardholder consumption figure generation method and device |
| CN106980663A (en) * | 2017-03-21 | 2017-07-25 | 上海星红桉数据科技有限公司 | Based on magnanimity across the user's portrait method for shielding behavioral data |
| CN111062757A (en) * | 2019-12-17 | 2020-04-24 | 山大地纬软件股份有限公司 | Information recommendation method and system based on multi-path optimization matching |
| CN111352962A (en) * | 2018-12-24 | 2020-06-30 | 网智天元科技集团股份有限公司 | Client portrait construction method and device |
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Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105931068A (en) * | 2015-12-30 | 2016-09-07 | ***股份有限公司 | Cardholder consumption figure generation method and device |
| CN106980663A (en) * | 2017-03-21 | 2017-07-25 | 上海星红桉数据科技有限公司 | Based on magnanimity across the user's portrait method for shielding behavioral data |
| CN111352962A (en) * | 2018-12-24 | 2020-06-30 | 网智天元科技集团股份有限公司 | Client portrait construction method and device |
| CN111062757A (en) * | 2019-12-17 | 2020-04-24 | 山大地纬软件股份有限公司 | Information recommendation method and system based on multi-path optimization matching |
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