CN111899057A - Customer portrait data clustering analysis system based on edge cloud node data collection - Google Patents
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Abstract
The invention provides a customer portrait data clustering analysis system based on edge cloud node data collection, which comprises a plurality of edge data acquisition terminals of a plurality of financial websites distributed at different positions in a preset range; the plurality of edge data acquisition terminals are all provided with a wireless data acquisition module and a broadcast 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 edge data acquisition terminal and the acquired partial financial data information by using the broadcasting module; the customer portrait data clustering analysis system further comprises a customer portrait data grouping module, wherein the customer portrait grouping module summarizes customer portrait data acquired by a plurality of edge data acquisition terminals of a plurality of financial websites at different positions in 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 clustering analysis system based on edge cloud node data collection.
Background
At present, market competition is increasingly intense, enterprises face severe challenges, and customer demands present increasingly obvious characteristics of diversification and personalization, namely customers are no longer passive recipients of consistent products and services, but active selectors. Therefore, better understanding of customers and analysis of purchasing behaviors and preferences of customers become the most urgent requirements of enterprises, aiming at carrying out accurate sale and accurate service on different customers, further reducing the operating cost, improving the service capability and leading the core competitiveness of the enterprises to take a new step.
The cloud computing platform is used for extracting various information of customers into a unified data center, the data center mainly comprises data such as personal detailed information of the customers, account information of the customers, asset information of the customers, transaction information of the customers, income creating information of the customers and the like, deep analysis and statistics are carried out on the data to obtain customer figures, the customer figures label the customers, weights are given to the customer figures according to different labels, and the customers are classified through specific classification software and a clustering algorithm, so that the customers are classified hierarchically and provided with personalized services.
Massive structured and unstructured data are collected into a data warehouse, the data are analyzed in real time by applying a data warehouse technology, all-dimensional information of customers is provided for some financial institutions, the consumption habits of the customers are estimated by deeply mining and analyzing the transaction behaviors and consumption information of the customers, and the purchasing behaviors of the customers are accurately predicted, namely, the large-data finance is obtained. Big data finance can provide technical support for financial institutions or financial service e-commerce platforms in terms of customer promotion and customer appropriateness in principle. The big data of a big data financial service platform is based on the goal of providing financial services, and the core of the big data financial service platform 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 the big data are modeled by a cloud computing platform to depict a customer portrait.
The Chinese invention patent application with application number CN201511025848.0 filed by China Unionpay GmbH proposes a method and a device for generating a card holder consumption portrait, wherein the method comprises the following steps: acquiring consumption information of a cardholder, wherein the consumption information of the cardholder comprises cardholder attribute information and bank card transaction information; carrying out data cleaning on the consumption information of the card holder to obtain effective consumption information of the card holder to be analyzed; performing clustering analysis on the consumption information of the effective cardholders according to the attribute information of the cardholders to obtain an effective consumption information set for each cardholder; determining the consumption type corresponding to the transaction information of each bank card aiming at a cardholder; dividing the effective consumption information set of the card holder 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 application number CN201811568454.3 proposes a method for constructing a customer portrait, which comprises: acquiring a plurality of data information of a target object, wherein each data information comprises: a plurality of data dimensions, each data dimension including 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 a module entering label according to the IV value of each sub-label. And calculating sub-label scores according to the in-mold labels, and respectively constructing a high-quality customer portrait and a poor-quality customer portrait according to the sub-label scores. The method and the system realize the construction of the high-quality customer portrait and the poor-quality customer portrait according to the sub-label scores so as to further realize accurate service recommendation and service for the target group.
However, in the conventional client figure generation method, the client data used is not necessarily critical data; also, individual client representation analysis will increase data processing complexity, whereas the prior art does not consider the data stability issues of grouped data representations.
Disclosure of Invention
In order to solve the technical problem, the invention provides a customer portrait data clustering analysis system based on edge cloud node data collection, which comprises a plurality of edge data acquisition terminals of a plurality of financial websites distributed at different positions in a preset range; the plurality of edge data acquisition terminals are all provided with a wireless data acquisition module and a broadcast 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 edge data acquisition terminal and the acquired partial financial data information by using the broadcasting module; the customer portrait data clustering analysis system further comprises a customer portrait data grouping module, wherein the customer portrait grouping module summarizes customer portrait data acquired by a plurality of edge data acquisition terminals of a plurality of financial websites at different positions in 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 edge cloud node data collection provided by the invention comprises a plurality of edge data acquisition terminals of a plurality of financial websites distributed at different positions in a preset range;
the plurality of edge data acquisition terminals are all provided with a wireless data acquisition module and a broadcast 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 edge data acquisition terminal and the acquired partial financial data information by using the broadcasting module;
the customer portrait data clustering analysis system further comprises a customer portrait data grouping module, wherein the customer portrait grouping module summarizes customer portrait data acquired by a plurality of edge data acquisition terminals of a plurality of financial websites at different positions in 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 customer within the preset range and acquired by each edge data acquisition terminal through 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 through the broadcast module of the edge data acquisition terminal.
Further advantages of the invention will be apparent in the detailed description section in conjunction with the drawings attached hereto.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings 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 it is obvious for those skilled in the art to obtain other drawings without creative efforts.
FIG. 1 is a block diagram of a customer portrait data cluster analysis system for edge cloud node data collection according to an embodiment of the present invention
FIG. 2 is a schematic diagram of a financial data acquisition APP of the system of FIG. 1
FIG. 3 is a schematic diagram of the system of FIG. 1 obtaining at least one stable set of client representation data
Fig. 4 is a specific implementation of the embodiment shown in fig. 3.
Detailed Description
The invention is further described with reference to the following drawings and detailed description.
Referring to fig. 1, a block diagram of a customer figure data cluster analysis system for edge cloud node data collection according to an embodiment of the present invention is shown.
The customer portrait data clustering analysis system based on edge cloud node data collection in fig. 1 includes a plurality of edge data collection terminals of a plurality of financial websites distributed at different positions within a predetermined range;
the plurality of edge data acquisition terminals are all provided with a wireless data acquisition module and a broadcast 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 edge data acquisition terminal and the acquired partial financial data information by using the broadcasting module;
the customer portrait data clustering analysis system further comprises a customer portrait data grouping module, wherein the customer portrait grouping module summarizes customer portrait data acquired by a plurality of edge data acquisition terminals of a plurality of financial websites at different positions in 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 customer within the preset range and acquired by each edge data acquisition terminal through 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 through the broadcast module of the edge data acquisition terminal.
As an illustrative example, the plurality of edge data collecting terminals of the plurality of financial websites distributed at different positions within the predetermined range may be touch-enabled input terminals distributed in lobbies of various large banking websites, and the customer may log in the touch-enabled input terminals to perform touch input, query and other transactions.
In another aspect, the financial data generated by the customer using the mobile terminal is obtained by the edge data collecting terminal within the predetermined range of the customer in the embodiment, based on fig. 1, see fig. 2.
The wireless data acquisition module is used for acquiring financial data information generated by the customer in the preset range, and specifically comprises:
the financial data information generated by the client comprises financial data information generated by logging in a mobile terminal of the client in 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 includes the customer information that the user logs in the production behind the financial data collection APP, further includes:
financial data acquisition APP includes input environment detection subassembly, input environment detection subassembly is used for detecting the customer and logs in behind the mobile terminal, gathers customer login environmental data.
It is particularly important that certain data, corresponding to touch terminals as well as mobile input terminals, can be particularly characteristic of the customer, these critical data being referred to as customer login context data in the present invention.
More specifically, as one of the findings of the present invention, the client login environment data includes a time start point at which the client logs in the mobile terminal, a time end point at which the client logs out of 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, an operation of exiting a current page, a deletion operation and a page pause operation.
As a highlighted expression of the present invention 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 partial financial data information is financial data information generated after a user logs in one of a plurality of edge data acquisition terminals of the plurality of financial websites.
In the prior art, although it is common to collect customer information at a website setting terminal, the customer information is collected statically in isolation, and the above embodiment of the present invention improves the interactivity of the customer portrait data obtained by the plurality of edge data collection terminals, so that the customer portrait data obtained by each edge data collection terminal includes financial data information generated by the customer within the predetermined range, which is obtained by each edge data collection terminal through its own wireless data obtaining module, and partial financial data information broadcast by other edge data collection terminals, which is obtained by each edge data collection terminal through its own broadcasting module.
Reference is next made to fig. 3-4.
The customer portrait grouping module collects customer portrait data acquired by a plurality of edge data acquisition terminals of a plurality of financial websites at different positions in the preset range, groups the customer portrait data to obtain at least one stable customer portrait data group, and specifically comprises:
establishing a client portrait data matrix based on the client portrait data;
determining at least one stability sub-level matrix of the client representation data matrix;
and using the stable sub-rank matrix as the stable client representation data set.
Establishing a client portrait data matrix based on the client portrait data, specifically comprising:
carrying out quantization coding on the customer portrait data according to the attributes of the financial data information to obtain the quantization coding values of the financial data information with different attributes of different customers;
and combining the quantized coding values of the financial data information with different attributes of different customers into the customer portrait data matrix according to the quantized coding values of the financial data attributes and the customer ID.
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, a login place 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 comprises data related to transaction and payment after the customer logs in, and comprises payment, account transfer and the like.
As an example, the client representation data matrix is specified as follows:
wherein D isijA value is represented for vectorization of the ith financial data corresponding to the jth customer.
It should be noted that, according to the purpose of the client image and the type of the client data, various vectorization representation methods can be adopted, including a binarization encoding method, a score normalization method, an expert scoring method, and a quantization method, which is not particularly limited by the present invention.
In the prior art, how to acquire client image data, how to process client image data, and how to perform quantization coding based on client image data are thoroughly described. This is because the client data itself is not recognizable by the computer, must be converted into a machine-recognizable form or language by some machine coding or vectorization method,
for example, vectoring the client login time may be:
[0: 00-6: 00] login time, expressed as 001;
[6: 00-8: 00] login time, denoted 002;
……
and the like, a data clustering matrix matrixD composed of different customer data can be established.
For more client data matrixing methods and data vectorization and encoding methods, the following technical documents can be referred to:
Pan B,Wang X,Song E,et al.CAMSPF：Cloud-assisted mobile serviceprovision framework supporting personalized user demands in pervasivecomputing environment[C]//Wireless Communications and Mobile ComputingConference.IEEE,2013：649-654.
great data technology-based mobile phone user portrait and credit investigation [ J ] post and telecommunications design technology 2016 (3): 64-69
Danette Mc Gilvray,2008.Executing Data Quality Projects：Ten Steps toQuality Data and Trusted Information(TM),Morgan Kaufman.
Rodbard H W,Jellinger P S,Davidson J A,et al.Statement by an AmericanAssociation of Clinical Endocrinologists/American College of Endocrinologyconsensus panel on type 2diabetes mellitus：an algorithm for glycemic control[J].Endocrine Practice Official Journal of the American College ofEndocrinology&the American Association of Clinical Endocrinologists,2009,15(6)：540.
The determining at least one stable sub-level matrix of the client representation data matrix specifically includes:
sequentially judging whether the characteristic values of the sub-matrixes of each order corresponding to the customer image data matrix meet preset conditions or not;
and selecting a sub-matrix with the order value larger than a preset value from the various-order sub-matrices with the characteristic values meeting the preset conditions as the stable sub-order matrix.
The customer figure data further includes the own position information of the edge data collecting terminal corresponding to the generated partial financial data information.
In each stable customer figure data set, the position information of the edge data acquisition terminal corresponding to all the part of financial data information is in a preset position range.
As an illustrative illustration, each order sub-matrix corresponding to the client image data matrix may be represented as follows:
……
wherein, matrix is the customer portrait data matrix, and matrix 2 is the 2-order submatrix; matrixD4 is its 4 th order sub-matrix.
As an example, DijA value is represented for vectorization of the ith financial data corresponding to the jth customer.
It is clear that for an m-order matrix of m x m dimensions, it includes m-order (the original matrix itself), m-1 order, m-3 order … …, and 2 order sub-matrices.
In the above embodiment, the method may be performed by determining whether absolute values of all feature roots of the sub-matrix are smaller than 1, and if the absolute values of all feature roots are smaller than 1, the sub-matrix is stable and meets the predetermined condition.
And the submatrix with the order value larger than a preset value in each order submatrix, wherein the preset value can be implementation setting and is set according to the accuracy of customer portrait modeling and the data processing quantity.
For example, for the above-mentioned matrix xd with 5 × 5 dimensions, the predetermined value may be 3, that is, a 4-order sub-matrix and a 5-order sub-matrix are obtained (the 5-order sub-matrix is the matrix xd itself).
How to obtain a customer portrait based on the existing customer data is also a known method in the art, and the present invention is not described herein again, for example, see:
master thesis: zhao Feihong, a binary K-means algorithm analysis research and application based on financial customer figures [ D ]. university of Chinese academy of sciences (institute of engineering management and information technology), 2016.
Master thesis: wu Zhu, Jiangxi cigarette retail shop portrait system design and implementation based on Hadoop [ 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 appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (10)
1. A customer portrait data cluster analysis system based on edge cloud node data collection comprises a plurality of edge data acquisition terminals of a plurality of financial websites distributed at different positions in a preset range;
the method is characterized in that:
the plurality of edge data acquisition terminals are all provided with a wireless data acquisition module and a broadcast 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 edge data acquisition terminal and the acquired partial financial data information by using the broadcasting module;
the customer portrait data clustering analysis system further comprises a customer portrait data grouping module, wherein the customer portrait grouping module summarizes customer portrait data acquired by a plurality of edge data acquisition terminals of a plurality of financial websites at different positions in 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 customer within the preset range and acquired by each edge data acquisition terminal through 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 through the broadcast module of the edge data acquisition terminal.
2. The customer portrait data cluster analysis system based on edge cloud node data collection of claim 1, wherein:
the wireless data acquisition module is used for acquiring financial data information generated by the customer in the preset range, and specifically comprises:
the financial data information generated by the client comprises financial data information generated by logging in a mobile terminal of the client in 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.
3. The customer portrait data cluster analysis system based on edge cloud node data collection of claim 2, wherein:
the financial data information includes the customer information that the user logs in the production behind the financial data collection APP, further includes:
financial data acquisition APP includes input environment detection subassembly, input environment detection subassembly is used for detecting the customer and logs in behind the mobile terminal, gathers customer login environmental data.
4. The customer portrait data cluster analysis system based on edge cloud node data collection of claim 3, wherein:
the client login environment data comprises a time starting point of a client for logging in the mobile terminal, a time ending point of the client for logging out 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, an operation of exiting a current page, a deletion operation and a page pause operation.
5. The customer portrait 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 edge data acquisition terminal and the acquired partial financial data information by using the broadcasting module, and specifically comprises:
the partial financial data information is financial data information generated after a user logs in one of a plurality of edge data acquisition terminals of the plurality of financial websites.
6. The customer portrait data cluster analysis system based on edge cloud node data collection of claim 1, wherein:
the customer portrait grouping module collects customer portrait data acquired by a plurality of edge data acquisition terminals of a plurality of financial websites at different positions in the preset range, groups the customer portrait data to obtain at least one stable customer portrait data group, and specifically comprises:
establishing a client portrait data matrix based on the client portrait data;
determining at least one stability sub-level matrix of the client representation data matrix;
and using the stable sub-rank matrix as the stable client representation data set.
7. The customer portrait data cluster analysis system based on edge cloud node data collection of claim 6, wherein:
establishing a client portrait data matrix based on the client portrait data, specifically comprising:
carrying out quantization coding on the customer portrait data according to the attributes of the financial data information to obtain the quantization coding values of the financial data information with different attributes of different customers;
and combining the quantized coding values of the financial data information with different attributes of different customers into the customer portrait data matrix according to the quantized coding values of the financial data attributes and the customer ID.
8. The customer portrait data cluster analysis system based on edge cloud node data collection of claim 7, wherein:
the determining at least one stable sub-level matrix of the client representation data matrix specifically includes:
sequentially judging whether the characteristic values of the sub-matrixes of each order corresponding to the customer image data matrix meet preset conditions or not;
and selecting a sub-matrix with the order value larger than a preset value from the various-order sub-matrices with the characteristic values meeting the preset conditions as the stable sub-order matrix.
9. The customer portrait data cluster analysis system based on edge cloud node data collection of claim 5, wherein:
the customer figure data further includes the own position information of the edge data collecting terminal corresponding to the generated partial financial data information.
10. The customer portrait data cluster analysis system based on edge cloud node data collection of claim 9, wherein:
in each stable customer figure data set, the position information of the edge data acquisition terminal corresponding to all the part of financial data information is in a preset position range.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114528448A (en) * | 2022-02-25 | 2022-05-24 | 南京苏维博欣信息技术有限公司 | Accurate analytic system of portrait of global foreign trade customer |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105931068A (en) * | 2015-12-30 | 2016-09-07 | 中国银联股份有限公司 | 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 |
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105931068A (en) * | 2015-12-30 | 2016-09-07 | 中国银联股份有限公司 | 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 |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114528448A (en) * | 2022-02-25 | 2022-05-24 | 南京苏维博欣信息技术有限公司 | Accurate analytic system of portrait of global foreign trade customer |
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