CN111858560B - Financial data automatic testing and monitoring system based on data warehouse - Google Patents
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Abstract
The invention provides a financial data automatic testing and monitoring system based on a data warehouse. The automatic test system comprises a financial data input module, a financial data preprocessing module, a financial data storage bin, a financial data test engine and a financial data separation bin; the financial data storage bin comprises a financial data quantization coding module which carries out quantization coding on financial data preprocessed by a financial data preprocessing module to obtain a quantization coding vector of the financial data, and the quantization coding vector is stored in the financial data storage bin; and based on the financial data similarity and stability test result of the financial data test engine, separating the financial data and storing the financial data into the financial data separation bin. The automatic monitoring system monitors whether the financial data separation bin in the financial data automatic testing system based on the data warehouse exists data or not; if so, a customer representation is generated based on the data.
Description
Technical Field
The invention belongs to the technical field of big data processing, and particularly relates to a financial data automatic testing and monitoring system based on a data warehouse.
Background
The data warehouse, english name dataware house is abbreviated DW. As the name suggests, a data warehouse is a large collection of data stores created for enterprise analytical reporting and decision support purposes, screening and integrating diverse business data. It provides certain BI capabilities for the enterprise, directs business process improvement, monitors time cost, quality, and control.
Data warehouse is a new database technology which is developed more rapidly in the information field in recent years. The data warehouse can help enterprises fully utilize the existing data resources, convert data which cannot be spoken into readable information, and dig more favorable connotations for the enterprises from the data, and finally help the enterprises create value. Data warehouse technology is also receiving increasing attention from businesses.
The method is characterized in that massive structured and unstructured data are assembled into a data warehouse, the data are analyzed in real time by applying the data warehouse technology, full-dimension information of clients is provided for some financial institutions, the consumption habits of the clients are deduced by deep mining and analysis of the transaction behaviors and the consumption information of the clients, and the purchasing behaviors of the clients are accurately predicted.
The Chinese patent application with the application number of CN201510771611.0 provides a client information management method, which comprises the following steps: collecting customer information; establishing a non-relational column storage database according to the collected client information; wherein the non-relational column storage database is a distributed database with an annular closed structure; the non-relational column store database includes at least one node that has no master-slave score. The member data management method and system provided by the invention carry out data management, and the data safety is ensured because of the distributed non-center; because the non-relational column storage database is adopted, the method has the characteristics of high availability, can provide good read-write performance and improves the response speed of data query; the elastic and extensible characteristics of the non-relational column storage database are utilized, and the requirements of data volume increase and structure adjustment and update of the original data table can be met.
An electronic banking data processing system and method proposed in China patent application publication No. CN104766240A, wherein the system comprises: the data acquisition module is used for acquiring service data of each electronic channel system; the data processing module is used for establishing various data models according to the business data of each electronic channel system; the application area report display module is used for constructing an application area in the database, wherein the application area comprises a database table SCHEMA which automatically operates in batches; carrying out statistics processing and report display on various data by utilizing various data models in the application area; the analysis area module is used for constructing an analysis area in the database, wherein the analysis area comprises SCHEMA which is opened to the service; synchronizing various data of the SCHEMA in the analysis area with various data of the SCHEMA in the application area; and providing a front-end report development interface for carrying out statistical processing on various data by utilizing various data models in the analysis area. The invention can provide timely, comprehensive and flexible data support for the service.
However, in the financial big data age, not all user-generated financial data is valuable. The financial institution needs to obtain representative customer financial data and stable customer financial data to obtain accurate customer portraits, thereby realizing group type personalized service. However, the prior art does not give an effective solution for this.
Disclosure of Invention
In order to solve the technical problems, the invention provides a financial data automatic testing and monitoring system based on a data warehouse. The automatic test system comprises a financial data input module, a financial data preprocessing module, a financial data storage bin, a financial data test engine and a financial data separation bin; the financial data storage bin comprises a financial data quantization coding module which carries out quantization coding on financial data preprocessed by a financial data preprocessing module to obtain a quantization coding vector of the financial data, and the quantization coding vector is stored in the financial data storage bin; and based on the financial data similarity and stability test result of the financial data test engine, separating the financial data and storing the financial data into the financial data separation bin. The automatic monitoring system monitors whether the financial data separation bin in the financial data automatic testing system based on the data warehouse exists data or not; if so, a customer representation is generated based on the data.
According to the technical scheme, after the financial data is automatically tested, stable representative data can be obtained; then, the monitoring system can obtain accurate customer portraits based on the data, and then group the customers to provide personalized APP page display services.
In particular, in a first aspect of the present invention, there is provided a data warehouse-based automated financial data testing system comprising a financial data input module, a financial data preprocessing module, a financial data storage bin, a financial data testing engine, and a financial data separation bin;
the financial data input module is used for inputting financial data generated by a user based on a payment event operated by a mobile terminal, wherein the financial data comprises environment change parameters of the user operating the mobile terminal and customer transaction data related to the payment event;
the financial data preprocessing module is used for preprocessing the financial data input by the financial data input module, and the preprocessing comprises data cleaning and data filtering;
the financial data storage bin comprises a financial data quantization coding module, wherein the financial data quantization coding module is used for performing quantization coding on the financial data preprocessed by the financial data preprocessing module to obtain a quantization coding vector of the financial data, and the quantization coding vector is stored in the financial data storage bin;
as one of the inventive aspects of the present invention, the financial data test engine is configured to perform a financial data similarity and stability test for calculating similarity between quantized coded vectors of different ones of the financial data; the stability test is used for determining the stability of a quantization coding matrix composed of quantization coding vectors of different financial data;
and based on the financial data similarity and stability test result of the financial data test engine, separating the financial data and storing the financial data into the financial data separation bin.
The technical solution of the present invention is particularly focused on user operation data of a mobile terminal, for which the financial data include environmental change parameters of the user operating the mobile terminal and customer transaction data related to the payment event, and specifically include:
the mobile terminal is provided with financial data APP, an input environment detection assembly is arranged in the financial data APP, and the input environment detection assembly is used for collecting environment change parameters after detecting that a client logs in the mobile terminal;
the environment change parameters comprise a time starting point of a client logging in the mobile terminal, a time ending point of logging out of 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.
It should be noted that the above parameters selected by the present invention are obtained by researching and researching various factors which fully consider that the mobile terminal finance APP influences the operation habit of the user, and are closely related to the subsequent user portrait generation.
The mobile terminal is provided with financial data APP, and the client transaction data comprise login data of a client logging in the financial data APP, client inquiry data, client payment data and client login environment data.
As a further advantage of the present invention, based on the results of the similarity and stability tests of the financial data test engine, the financial data is separated and stored in the financial data separation bin, which specifically includes:
and if the similarity calculation results of the new quantized coded vector and the existing quantized coded vector stored in the financial data storage bin are both that the similarity is larger than a preset threshold value or the stability test result is stable, separating and storing the new quantized coded vector from the existing quantized coded vector stored in the financial data storage bin into the financial data separation bin.
In a second aspect of the invention, an automated financial data monitoring system is provided, the automated financial data monitoring system being coupled to the automated financial data testing system based on a data warehouse as described above.
More specifically, the automated monitoring system includes a customer representation generation interface; the automatic monitoring system monitors whether the financial data separation bin in the financial data automatic testing system based on the data warehouse exists data or not; if so, a customer representation is generated based on the data and displayed on the customer representation generation interface.
Preferably, the automated monitoring information further includes a feedback information generating module, which generates feedback information based on the customer portrait, and sends the feedback information to the financial data APP of the mobile terminal;
and when a customer logs in the financial data APP, adjusting a page display mode of the financial data APP based on the feedback information.
As another preferable mode, based on the customer portrait, sending a page adjustment message to financial data APP on a mobile terminal of a user corresponding to the customer portrait;
and when the client logs in the financial data APP, adjusting the page display mode of the financial data APP based on the page adjustment information.
By adopting the technical scheme of the invention, the financial institution can obtain representative customer financial data and stable customer financial data, thereby generating accurate customer portrait to realize group type personalized service.
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 an automated testing system for financial data based on a data warehouse in accordance with one embodiment of the present invention
FIG. 2 is a schematic diagram of the source and details of financial data obtained by the system of FIG. 1
FIG. 3 is a block diagram of an automated financial data monitoring system according to one embodiment of the invention
FIG. 4 is a schematic diagram of a customer representation in accordance with the present invention.
Detailed Description
The invention will be further described with reference to the drawings and detailed description.
Referring to FIG. 1, a block diagram of an automated data warehouse-based testing system for financial data in accordance with one embodiment of the present invention is shown.
The automated testing system of FIG. 1 includes a financial data input module, a financial data preprocessing module, a financial data storage bin, a financial data testing engine, and a financial data separation bin.
Specifically, the financial data input module is used for inputting financial data generated by a user based on a payment event operated by a mobile terminal, wherein the financial data comprises environment change parameters of the user operating the mobile terminal and customer transaction data related to the payment event;
the financial data preprocessing module is used for preprocessing the financial data input by the financial data input module, and the preprocessing comprises data cleaning and data filtering;
the financial data storage bin comprises a financial data quantization coding module, wherein the financial data quantization coding module is used for performing quantization coding on the financial data preprocessed by the financial data preprocessing module to obtain a quantization coding vector of the financial data, and the quantization coding vector is stored in the financial data storage bin;
the financial data test engine is used for executing financial data similarity and stability tests, and the financial data similarity tests are used for calculating similarity between quantized coded vectors of different financial data; the stability test is used for determining the stability of a quantization coding matrix composed of quantization coding vectors of different financial data;
and based on the financial data similarity and stability test result of the financial data test engine, separating the financial data and storing the financial data into the financial data separation bin.
It should be noted that in the embodiment shown in fig. 1, various quantization encoding methods may be used, 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.
This is because the financial 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] login time, denoted as 001;
[6:00-8:00] login time, denoted 002;
……
in this manner, different quantized encoded vectors of different financial data may be obtained.
Of course, it should be further noted that the above-described exemplary encoding scheme is merely illustrative, and does not represent the vector of 001/002 … … in each of the examples of the present invention.
For more quantization coding methods, see 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 ]. Post and telecommunications 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.
See fig. 2, based on fig. 1.
The financial data comprises environmental change parameters of a user operating the mobile terminal and customer transaction data related to the payment event, and specifically comprises:
the mobile terminal is provided with financial data APP, an input environment detection assembly is arranged in the financial data APP, and the input environment detection assembly is used for collecting environment change parameters after detecting that a client logs in the mobile terminal;
the environment change parameters comprise a time starting point of a client logging in the mobile terminal, a time ending point of logging out of 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 financial data comprises environmental change parameters of a user operating the mobile terminal and customer transaction data related to the payment event, and specifically comprises:
the mobile terminal is provided with financial data APP, and the client transaction data comprise login data of a client logging in the financial data APP, client inquiry data, client payment data and client login environment data.
In the above embodiment, the financial data similarity test is used to calculate the similarity between quantized coded vectors of different financial data, and specifically includes:
after the financial data newly generated by a payment event operated by a user based on a mobile terminal is preprocessed by the financial data preprocessing module, a new quantized coded vector is formed by the financial data quantized coding module;
and carrying out similarity calculation on the new quantized coded vector and the existing quantized coded vector stored in the financial data storage bin.
More specifically, the stability test is used for determining the stability of a quantization coding matrix composed of quantization coding vectors of different financial data, and specifically includes:
when the existing quantized coded vectors stored in the financial data storage bin form an N-order matrix, determining the N-order matrix as the quantized coded matrix;
and judging the stability of the quantized coding matrix.
And judging the stability of the quantization coding matrix, which specifically comprises:
calculating a characteristic root symbol of the quantization coding matrix;
and if the characteristic root symbols of the quantized coding matrixes are the same, stabilizing the quantized coding matrixes.
Based on the financial data similarity and stability test result of the financial data test engine, the financial data is stored in the financial data separation bin after being separated, and the method specifically comprises the following steps:
and if the similarity calculation results of the new quantized coded vector and the existing quantized coded vector stored in the financial data storage bin are both that the similarity is larger than a preset threshold value or the stability test result is stable, separating and storing the new quantized coded vector from the existing quantized coded vector stored in the financial data storage bin into the financial data separation bin.
Reference is next made to fig. 3.
Fig. 3 illustrates a financial data automation monitoring system coupled to the data warehouse-based financial data automation test system of fig. 1.
Wherein the automated monitoring system comprises a customer representation generation interface; the automatic monitoring system monitors whether the financial data separation bin in the financial data automatic testing system based on the data warehouse exists data or not; if so, a customer representation is generated based on the data and displayed on the customer representation generation interface.
The automatic monitoring information further comprises a feedback information generation module, wherein the feedback information generation module generates feedback information based on the customer portrait and sends the feedback information to financial data APP of the mobile terminal;
and when a customer logs in the financial data APP, adjusting a page display mode of the financial data APP based on the feedback information.
Based on the customer portrait, sending a page adjustment message to financial data APP on a mobile terminal of a user corresponding to the customer portrait;
and when the client logs in the financial data APP, adjusting the page display mode of the financial data APP based on the page adjustment information.
An illustrative example of a customer representation is shown in FIG. 4.
Customer portraits, also known as user portraits, were first proposed in the work The Inmates Are Running the Asy-lum-Why High Tech Products Drive Us Crazy and How to Restore the Sanity by the parent Alan Cooper of the interactive design in 1998, which defines a user portrait as a "virtual representation based on the user's real data". User portrayal, also called user role, is an effective tool for portraying the direction of a target user, contact user complaints and the related direction.
Customer portrayal is an identification of customers that is used to determine how to treat the customers-what price they accept, what products they like, how much effort is needed to hold or win the customers. Say a client: men, 31 years old, married, and over 1 ten thousand in income, loved food, group-buying daycare, like red wine to match with cigarettes. Such a string of descriptions is a typical case of a user representation. If described in terms of a sentence, namely: user information is tagged.
The core work of the customer portrait is to label the user, and the label provides a convenient way for the computer to process the information related to the person in a programmed way, and even to 'understand' the person through an algorithm and a model. Therefore, the data mining technology based on the customer portrait can be more convenient for people to understand and is easy for computer processing, so that the practicability of mining conclusions is greatly improved.
The customer representation is the basis for building many data mining projects, and the fields of the customer representation determine the representation of the customer in the data, as well as the effectiveness and information content of the data mining model. The periodic summary of transactions constitutes a large portion of the fields of the customer portrayal index hierarchy.
Based on the existing customer financial 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 it is based on the two-part K-means algorithm of financial customer portrait to analyze research and application [ D ]. University of China academy of sciences (engineering management and information technology), 2016.
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.
It can be seen that the technical scheme of the invention not only obtains the client data, but also obtains the client login environment data for generating the client data, and after the client login environment data is obtained, the automatic execution test and monitoring process is carried out, and the financial institution can obtain representative client financial data and stable client financial data, thereby generating accurate client portrait to realize the personalized service of the group.
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 (8)
1. An automatic financial data testing system based on a data warehouse comprises a financial data input module, a financial data preprocessing module, a financial data storage bin, a financial data testing engine and a financial data separation bin;
the method is characterized in that:
the financial data input module is used for inputting financial data generated by a user based on a payment event operated by a mobile terminal, wherein the financial data comprises environment change parameters of the user operating the mobile terminal and customer transaction data related to the payment event; the environment change parameters comprise a time starting point of a client logging in the mobile terminal, a time ending point of logging out of 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 financial data preprocessing module is used for preprocessing the financial data input by the financial data input module, and the preprocessing comprises data cleaning and data filtering;
the financial data storage bin comprises a financial data quantization coding module, wherein the financial data quantization coding module is used for performing quantization coding on the financial data preprocessed by the financial data preprocessing module to obtain a quantization coding vector of the financial data, and the quantization coding vector is stored in the financial data storage bin;
the financial data test engine is used for executing financial data similarity and stability tests, and the financial data similarity tests are used for calculating similarity between quantized coded vectors of different financial data; the stability test is used for determining the stability of a quantization coding matrix composed of quantization coding vectors of different financial data;
based on the financial data similarity and stability test result of the financial data test engine, separating financial data and storing the financial data into the financial data separation bin;
the stability test is used for determining the stability of a quantization coding matrix composed of quantization coding vectors of different financial data, and specifically comprises the following steps: when the existing quantized coded vectors stored in the financial data storage bin form an N-order matrix, determining the N-order matrix as the quantized coded matrix; calculating a characteristic root symbol of the quantization coding matrix; and if the characteristic root symbols of the quantized coding matrixes are the same, stabilizing the quantized coding matrixes.
2. A data warehouse-based financial data automated testing system as claimed in claim 1, wherein:
the financial data comprises environmental change parameters of a user operating the mobile terminal and customer transaction data related to the payment event, and specifically comprises:
the mobile terminal is provided with financial data APP, the financial data APP is internally provided with an input environment detection component, and the input environment detection component is used for collecting environment change parameters after detecting that a client logs in the mobile terminal.
3. A data warehouse-based financial data automated testing system as claimed in claim 1, wherein:
the financial data comprises environmental change parameters of a user operating the mobile terminal and customer transaction data related to the payment event, and specifically comprises:
the mobile terminal is provided with financial data APP, and the client transaction data comprise login data of a client logging in the financial data APP, client inquiry data, client payment data and client login environment data.
4. A data warehouse-based financial data automated testing system as claimed in claim 1, wherein:
based on the financial data similarity and stability test result of the financial data test engine, the financial data is stored in the financial data separation bin after being separated, and the method specifically comprises the following steps: and if the similarity calculation results of the new quantized coded vector and the existing quantized coded vector stored in the financial data storage bin are similarity larger than a preset threshold value or the test result of the stability test is stable, separating and storing the new quantized coded vector from the existing quantized coded vector stored in the financial data storage bin into the financial data separation bin.
5. A data warehouse-based financial data automated testing system as claimed in claim 1 or 4, wherein:
the financial data similarity test is used for calculating similarity between quantized coded vectors of different financial data, and specifically comprises the following steps: after the financial data newly generated by a payment event operated by a user based on a mobile terminal is preprocessed by the financial data preprocessing module, a new quantized coded vector is formed by the financial data quantized coding module;
and carrying out similarity calculation on the new quantized coded vector and the existing quantized coded vector stored in the financial data storage bin.
6. A financial data automation monitoring system connected to the data warehouse-based financial data automation test system of any one of claims 1-5, wherein:
the automated monitoring system includes a customer representation generation interface;
the automatic monitoring system monitors whether the financial data separation bin in the financial data automatic testing system based on the data warehouse exists data or not;
if so, a customer representation is generated based on the data and displayed on the customer representation generation interface.
7. A financial data automation monitoring system in accordance with claim 6 wherein: the automatic monitoring system further comprises a feedback information generation module, wherein the feedback information generation module generates feedback information based on the customer portrait and sends the feedback information to financial data APP of the mobile terminal;
and when a customer logs in the financial data APP, adjusting a page display mode of the financial data APP based on the feedback information.
8. A financial data automation monitoring system in accordance with claim 6 wherein: based on the customer portrait, sending a page adjustment message to financial data APP on a mobile terminal of a user corresponding to the customer portrait;
and when the client logs in the financial data APP, adjusting the page display mode of the financial data APP based on the page adjustment information.
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