CN113935787A - Financial information management system based on association rule mining algorithm - Google Patents
Financial information management system based on association rule mining algorithm Download PDFInfo
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Abstract
The invention discloses a financial information management system based on an association rule mining algorithm, which relates to the technical field of financial management and comprises a management center, wherein the management center is in communication connection with a data acquisition module, a data processing module, a data analysis module, a behavior prediction module and a financial planning module, whether association exists between each consumption is analyzed through an association rule algorithm, so that the association relation between each consumption is obtained, the association degree of each consumption type is obtained through the occurrence times of the association relation, and the next predicted consumption amount of a user is obtained through the association degree and the consumption amount of the consumption type; through the association rule algorithm, the potential association among different consumption types can be analyzed, so that the interconnection among different consumptions can be clearly obtained, and the association can be considered in the predicted consumption amount of the user in the next month, so that the prediction result is more accurate and reasonable.
Description
Technical Field
The invention belongs to the technical field of financial management, and particularly relates to a financial information management system based on an association rule mining algorithm.
Background
Most of financial management software is only used for recording consumption contents and money, in the actual use process, different consumptions often have great relevance, and by means of the relevance, the relationship behind the consumption contents can be analyzed, so that consumption data of the next month can be predicted more accurately, and therefore a financial information management system based on a relevance rule mining algorithm is provided.
Disclosure of Invention
The invention aims to provide a financial information management system based on an association rule mining algorithm.
The purpose of the invention can be realized by the following technical scheme: the financial information management system based on the association rule mining algorithm comprises a management center, wherein the management center is in communication connection with a data acquisition module, the data acquisition module is used for acquiring historical consumption data of a user so as to acquire online consumption data and offline consumption data of the user, the online consumption data and the offline consumption data are sent to a data processing module, the historical consumption data of the user are processed through the data processing module so as to classify consumption of the user and acquire different consumption types, then association relations among the consumption types are acquired, the association relations are sent to a data analysis module, the data analysis module analyzes consumption habits of the user according to the historical consumption data processed by the data processing module so as to acquire association degrees among the consumption types, and then analysis results are sent to a behavior prediction module, and predicting the consumption data of the user in the next month through the behavior prediction module, generating a prediction result, and sending the prediction result to the financial planning module.
Further, the obtaining process of the historical consumption data comprises: the method comprises the steps of obtaining a month in which a current date is located by taking the month degree as a unit, and obtaining all offline consumption data and online consumption data in the month; and summarizing the online consumption data and the offline consumption data.
Further, the on-line consumption data is acquired in the following manner: and acquiring the bill acquisition authority of the payment software for online consumption, directly acquiring the consumption bill of the payment software, and acquiring online consumption data from the consumption bill.
Further, the off-line consumption data is acquired in the following manner: and setting an offline expense account book, and recording offline expense data in the online expense account book by a user.
Further, the processing procedure of the data processing module on the user historical consumption data comprises the following steps:
classifying historical consumption data of a user according to consumption types, marking each consumption type, acquiring consumption times of each consumption type, and acquiring consumption time of each consumption; integrating consumption data through an association rule algorithm, acquiring the total consumption times in each consumption type, acquiring all association relations among the consumption types, and acquiring the occurrence times of each association relation; and sorting the incidence relations according to the sequence of the occurrence times from high to low, and removing the incidence relation with the minimum occurrence time.
Further, the analysis process of the data analysis module comprises:
acquiring the times of incidence relation between the two consumption types, and then acquiring the incidence degree of the two consumption types through the sum of the times of incidence relation between the two consumption types and the consumption times of the two consumption types.
Further, the process of predicting the consumption data of the user in the next month by the behavior prediction module comprises the following steps:
combining a certain consumption type and other consumption types pairwise to obtain the association degree and the sum of the consumption amount of the two consumption types; and multiplying the sum of the consumption amount by the correlation degree to obtain the correlation amount of the consumption type, and summing all the obtained correlation amounts of the consumption types to obtain the predicted consumption amount of the next month of the consumption type.
Further, the financial planning module is used for planning the financial affairs of the next month according to the predicted consumption amount obtained by the data analysis module, and the specific process comprises the following steps:
generating a predicted consumption plan bill according to the predicted consumption amount of each consumption type;
acquiring consumption data of each month in the last half year, acquiring the consumption amount of each consumption type in the last half year, and then acquiring the average consumption amount of each consumption type;
the amount of consumption of each type of consumption in the predicted consumption plan bill is compared to the average consumption of each type of consumption per month over the past half year, and types of consumption exceeding the average amount of consumption are flagged.
Compared with the prior art, the invention has the beneficial effects that: analyzing whether correlation exists between each consumption through a correlation rule algorithm so as to obtain the correlation relation between each consumption, obtaining the correlation degree of each consumption type through the occurrence frequency of the correlation relation, and obtaining the next predicted consumption amount of the user through the correlation degree and the consumption amount of the consumption type; through the association rule algorithm, the potential association among different consumption types can be analyzed, so that the interconnection among different consumptions can be clearly obtained, and the association can be considered in the predicted consumption amount of the user in the next month, so that the prediction result is more accurate and reasonable.
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FIG. 1 is a schematic diagram of the system of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the following embodiments, and it should be understood that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, the financial information management system based on association rule mining algorithm includes a management center, wherein the management center is in communication connection with a data acquisition module, a data processing module, a data analysis module, a behavior prediction module and a financial planning module;
the data acquisition module is used for acquiring historical consumption data of a user, and the acquisition process of the historical consumption data specifically comprises the following steps:
the method comprises the steps of obtaining a month in which a current date is located by taking the month degree as a unit, and obtaining all consumption data in the month, wherein the consumption data comprise offline consumption data and online consumption data;
acquiring bill acquisition permission of payment software for online consumption, directly acquiring a consumption bill of the payment software, and acquiring online consumption data from the consumption bill;
setting an offline expense account book, and recording offline expense data in the online expense account book by a user;
and summarizing the online consumption data and the offline consumption data, and uploading the summarized historical consumption data to a data processing module.
The data processing module is used for processing historical consumption data of a user, and the specific processing process comprises the following steps:
classifying historical consumption data of a user according to consumption types, wherein the consumption types comprise catering A, cosmetics B, communication C, traffic D, medical E, daily necessities F and leisure and entertainment G;
marking each consumption type, acquiring the consumption times of each consumption type, and acquiring the time of each consumption;
integrating consumption data through an association rule algorithm so as to obtain the association degree between each consumption;
for example:
the first embodiment is as follows:
at a certain day, a user goes out to a certain restaurant for consumption, then leisure and entertainment activities are carried out for a period of time, and partial daily necessities and cosmetics are purchased in the return process;
then traffic-catering-daily necessities-leisure entertainment-cosmetics is in an incidence relation, and the occurrence frequency of the incidence relation is added by 1;
example two:
on a certain day, a user goes out to a certain restaurant for consumption, then leisure and entertainment activities are carried out for a period of time, and partial daily necessities are purchased in the return process;
the traffic-food and beverage-daily necessities-leisure entertainment is in an incidence relation, and the occurrence frequency of the incidence relation is added with 1;
example three:
on a certain day, the user goes out to a certain restaurant for consumption and then returns immediately;
the traffic-food and beverage are in an incidence relation, and the occurrence frequency of the incidence relation is added by 1;
in the first, second and third embodiments, the frequency of occurrence of the three correlations is 1, 2 and 3 times, respectively; namely, the association relationship in the first embodiment includes the association relationship in the first embodiment and the association relationship in the second embodiment, and the association relationship in the second embodiment includes the association relationship in the first embodiment;
acquiring the total consumption times in each consumption type, acquiring all incidence relations among the consumption types, and acquiring the occurrence times of each incidence relation;
sorting the incidence relations according to the sequence of the occurrence times from high to low, removing the incidence relation with the least occurrence times, and sending the processed historical consumption data to a data analysis module;
the data analysis module is used for analyzing the consumption habits of the users according to the historical consumption data processed by the data processing module, and the specific analysis process comprises the following steps:
acquiring the times of incidence relation between two consumption types, and then acquiring the incidence degree of the two consumption types through the sum of the times of incidence relation between the two consumption types and the consumption times of the two consumption types;
taking catering and transportation as examples, the times of consumption of catering is a, the times of consumption of transportation is b, and the times of incidence relation between the catering and the transportation is c, wherein c is less than or equal to a, and c is less than or equal to b;
the association degree between the catering and the traffic is as follows: c/(a + b);
and summarizing the total consumption amount of all consumption types and the association degree between each consumption type, and then sending the data to the behavior prediction module.
The behavior prediction module is used for predicting consumption data of the user in the next month according to the analysis result of the data analysis module, and the specific process comprises the following steps:
combining a certain consumption type and other consumption types pairwise to obtain the association degree and the sum of the consumption amount of the two consumption types; multiplying the sum of the consumption amount by the correlation degree to obtain a correlation amount of the consumption type, and summing all the obtained correlation amounts of the consumption type to obtain a predicted consumption amount of the next month of the consumption type;
and sending the obtained predicted consumption amount to a financial planning module.
The financial planning module is used for planning the financial affairs of the next month according to the predicted consumption amount obtained by the data analysis module, and the specific process comprises the following steps:
generating a predicted consumption plan bill according to the predicted consumption amount of each consumption type;
acquiring consumption data of each month in the last half year, acquiring the consumption amount of each consumption type in the last half year, and then acquiring the average consumption amount of each consumption type;
comparing the consumption amount of each consumption type in the predicted consumption plan bill with the average consumption of each consumption type in each month in the last half year, and marking the consumption types exceeding the average consumption amount so as to set a reminding message.
It should be further noted that, in the specific implementation process, after a certain consumption type is set with the reminding message, when the subsequent user consumes the consumption type, the reminding message is sent to the user, so as to help the user reduce unnecessary consumption.
Although the present invention has been described in detail with reference to the preferred embodiments, it will be understood by those skilled in the art that various changes may be made and equivalents may be substituted for elements thereof without departing from the spirit and scope of the present invention.
Claims (8)
1. The financial information management system based on the association rule mining algorithm comprises a management center and is characterized in that the management center is in communication connection with a data acquisition module, the data acquisition module is used for acquiring historical consumption data of a user so as to acquire online consumption data and offline consumption data of the user, the online consumption data and the offline consumption data are sent to a data processing module, the historical consumption data of the user are processed through the data processing module so as to classify consumption of the user and obtain different consumption types, then association relations among the consumption types are acquired, the association relations are sent to a data analysis module, the data analysis module analyzes consumption habits of the user according to the historical consumption data processed by the data processing module so as to obtain association degrees among the consumption types, and then an analysis result is sent to a behavior prediction module, and predicting the consumption data of the user in the next month through the behavior prediction module, generating a prediction result, and sending the prediction result to the financial planning module.
2. A financial information management system based on association rule mining algorithm according to claim 1 wherein the acquisition process of historical consumption data comprises: the method comprises the steps of obtaining a month in which a current date is located by taking the month degree as a unit, and obtaining all offline consumption data and online consumption data in the month; and summarizing the online consumption data and the offline consumption data.
3. A financial information management system based on association rule mining algorithm as claimed in claim 2 wherein the on-line consumption data is obtained by: and acquiring the bill acquisition authority of the payment software for online consumption, directly acquiring the consumption bill of the payment software, and acquiring online consumption data from the consumption bill.
4. A financial information management system based on association rule mining algorithm as claimed in claim 2 wherein the off-line expense data is obtained by: and setting an offline expense account book, and recording offline expense data in the online expense account book by a user.
5. A financial information management system based on association rule mining algorithm according to claim 4 wherein the processing of the user's historical consumption data by the data processing module comprises:
classifying historical consumption data of a user according to consumption types, marking each consumption type, acquiring consumption times of each consumption type, and acquiring consumption time of each consumption; integrating consumption data through an association rule algorithm, acquiring the total consumption times in each consumption type, acquiring all association relations among the consumption types, and acquiring the occurrence times of each association relation; and sorting the incidence relations according to the sequence of the occurrence times from high to low, and removing the incidence relation with the minimum occurrence time.
6. A financial information management system based on association rule mining algorithm according to claim 5 wherein the analysis process of the data analysis module includes:
acquiring the times of incidence relation between the two consumption types, and then acquiring the incidence degree of the two consumption types through the sum of the times of incidence relation between the two consumption types and the consumption times of the two consumption types.
7. A financial information management system based on association rule mining algorithm according to claim 6 wherein the process of predicting the consumption data of the user in the next month by the behavior prediction module comprises:
combining a certain consumption type and other consumption types pairwise to obtain the association degree and the sum of the consumption amount of the two consumption types; and multiplying the sum of the consumption amount by the correlation degree to obtain the correlation amount of the consumption type, and summing all the obtained correlation amounts of the consumption types to obtain the predicted consumption amount of the next month of the consumption type.
8. A financial information management system based on association rule mining algorithm according to claim 7 wherein the financial planning module is configured to plan the financial affairs of the next month according to the predicted consumption amount obtained by the data analysis module, and the specific process includes:
generating a predicted consumption plan bill according to the predicted consumption amount of each consumption type;
acquiring consumption data of each month in the last half year, acquiring the consumption amount of each consumption type in the last half year, and then acquiring the average consumption amount of each consumption type;
the amount of consumption of each type of consumption in the predicted consumption plan bill is compared to the average consumption of each type of consumption per month over the past half year, and types of consumption exceeding the average amount of consumption are flagged.
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Application publication date: 20220114 |