CN117333272A - Intelligent management system for banking data - Google Patents
Intelligent management system for banking data Download PDFInfo
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- CN117333272A CN117333272A CN202311070947.5A CN202311070947A CN117333272A CN 117333272 A CN117333272 A CN 117333272A CN 202311070947 A CN202311070947 A CN 202311070947A CN 117333272 A CN117333272 A CN 117333272A
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- G—PHYSICS
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
- G06Q40/02—Banking, e.g. interest calculation or account maintenance
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- H03M—CODING; DECODING; CODE CONVERSION IN GENERAL
- H03M7/00—Conversion of a code where information is represented by a given sequence or number of digits to a code where the same, similar or subset of information is represented by a different sequence or number of digits
- H03M7/02—Conversion to or from weighted codes, i.e. the weight given to a digit depending on the position of the digit within the block or code word
- H03M7/04—Conversion to or from weighted codes, i.e. the weight given to a digit depending on the position of the digit within the block or code word the radix thereof being two
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- H—ELECTRICITY
- H03—ELECTRONIC CIRCUITRY
- H03M—CODING; DECODING; CODE CONVERSION IN GENERAL
- H03M7/00—Conversion of a code where information is represented by a given sequence or number of digits to a code where the same, similar or subset of information is represented by a different sequence or number of digits
- H03M7/02—Conversion to or from weighted codes, i.e. the weight given to a digit depending on the position of the digit within the block or code word
- H03M7/06—Conversion to or from weighted codes, i.e. the weight given to a digit depending on the position of the digit within the block or code word the radix thereof being a positive integer different from two
- H03M7/08—Conversion to or from weighted codes, i.e. the weight given to a digit depending on the position of the digit within the block or code word the radix thereof being a positive integer different from two the radix being ten, i.e. pure decimal code
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Abstract
The invention provides a banking data intelligent management system, which comprises a banking data acquisition module, a data similarity acquisition module, a priority dividing index acquisition module, a target banking data acquisition module and a grid dividing module, wherein the data similarity of any two types of banking data is acquired firstly, then the priority dividing index of the banking data is determined according to the data similarity and the data condition of the banking data, the optimal priority dividing index and the corresponding banking data are obtained, the target banking data is obtained, finally the banking data is subjected to grid division according to the target banking data, the accurate grid division of various types of banking data can be realized, the obtained grid data is closely related to the banking data, and the accuracy and the reliability are higher.
Description
Technical Field
The invention relates to an intelligent management system for banking data.
Background
People transact various services through banks, such as: a large amount of banking data is correspondingly generated by the deposit and withdrawal service, the financial transaction, the related financial consultation service, the enterprise financial service, etc. And banking data includes various types of data. In order to facilitate the gridding storage of the banking data, multiple kinds of banking data need to be gridded, and each gridded data is stored respectively. The existing grid division mode is rough, different grid division is only carried out according to different times, and reliability is poor.
Disclosure of Invention
In order to solve the technical problems, the invention provides an intelligent management system for banking data.
An intelligent banking data management system, comprising:
the banking data acquisition module is used for acquiring various types of banking data;
the data similarity acquisition module is used for acquiring the data similarity of any two types of banking data;
the priority index dividing acquisition module is used for determining priority indexes of the banking data of any type according to the data similarity between the banking data of the type and other banking data of other types and the data condition of the banking data of the type;
the target banking data acquisition module is used for taking the maximum value in the priority dividing indexes of various types of banking data as an optimal priority dividing index, acquiring banking data corresponding to the optimal priority dividing index and obtaining target banking data;
and the grid division module is used for carrying out grid division on the banking data of the multiple types according to the target banking data.
Optionally, the acquiring multiple types of banking data includes:
acquiring various types of original banking data;
binary coding is carried out on various types of original banking data to obtain binary coding sequences corresponding to various types of original banking data;
and converting binary code sequences corresponding to various types of original banking data into decimal numbers to obtain various types of banking data.
Optionally, the acquiring the data similarity of any two types of banking data includes:
for any type of banking data, acquiring characteristic values corresponding to all data points in the banking data of the type, wherein the characteristic values corresponding to all data points in the banking data of the type form characteristic vectors;
and obtaining the similarity of the feature vectors corresponding to any two types of banking data to obtain the data similarity.
Optionally, the characteristic value is a variance of the data point from within a predetermined window centered on the data point.
Optionally, the determining the priority index of the type of banking data according to the data similarity between the type of banking data and other types of banking data and the data condition of the type of banking data includes:
calculating the average value of the data similarity between the banking data of the type and the banking data of other types, and carrying out normalization processing to obtain a data similarity index;
obtaining the number of extreme value data points in the banking data of the type, and carrying out normalization processing to obtain an extreme value data point number index, wherein the extreme value data points comprise maximum value data points and minimum value data points;
and dividing the extreme value data point quantity index with the data similarity index to obtain a quotient value which is a priority index for dividing the banking data of the type.
Optionally, the meshing of the multiple types of banking data according to the target banking data includes:
and dividing the banking data of various types according to the positions of the extreme value data points in the banking data of the target as dividing points, wherein each data group obtained by dividing is grid data.
The invention has the following beneficial effects: firstly, acquiring data similarity of any two types of banking data, then for any one type of banking data, determining a priority dividing index of the type of banking data according to the data similarity between the type of banking data and other types of banking data and the data condition of the type of banking data, wherein the priority dividing index is related to the similarity of other types of banking data and also related to the self data condition of the type of banking data, so that the obtained priority dividing index is closely related to the self data, the optimal priority dividing index is obtained, the various types of banking data are subjected to grid division according to target banking data corresponding to the optimal priority dividing index, the accurate grid division of the various types of banking data can be realized, the obtained grid data is closely related to the banking data, and the accuracy and the reliability are relatively high.
Drawings
Fig. 1 is a block diagram of an intelligent management system for banking data.
Detailed Description
As shown in fig. 1, the present embodiment provides an intelligent management system for banking data, which includes a banking data acquisition module, a data similarity acquisition module, a priority index dividing acquisition module, a target banking data acquisition module, and a grid dividing module.
And the banking data acquisition module is used for acquiring various types of banking data. The type of banking data is determined by banking actual business, such as: banking data corresponding to deposit and withdrawal businesses, financial businesses, related financial consultation businesses, enterprise financial businesses and the like.
In this embodiment, in order to facilitate subsequent calculation, multiple types of original banking data are acquired first, that is, original banking data which is acquired from a banking server and is not subjected to data processing is acquired; then binary encoding is carried out on various types of original banking data to obtain binary encoding sequences corresponding to various types of original banking data, and further, bit filling operation can be carried out on the binary encoding sequences, specifically: acquiring the longest binary code sequence in binary code sequences corresponding to various types of original banking data, and supplementing binary digits 0 to the tail ends of other binary code sequences so that the lengths of all binary code sequences are the same as the length of the longest binary code sequence; then, binary code sequences corresponding to various types of original banking data are converted into decimal numbers to obtain various types of banking data, so that the various types of banking data are composed of decimal numbers, and the various types of banking data are decimal number sequences.
And the data similarity acquisition module is used for acquiring the data similarity of any two types of banking data.
In this embodiment, for any type of banking data, feature values corresponding to data points in the type of banking data are obtained, and feature vectors corresponding to the type of banking data are formed by feature values corresponding to all data points in the type of banking data. The feature value is used to reflect the relevant feature of the data point, in this embodiment, the feature value is the variance of the data point within a preset window centered on the data point, specifically: the size of the preset window is set according to actual needs, for example, 15, namely, a certain data point is taken as a central data point, 7 data points are left and right, 15 data points formed by the central data point are taken as data points in the preset window of the central data point, the variance of the data points in the preset window is calculated, and the obtained variance is taken as a characteristic value of the central data point.
And obtaining the similarity of the feature vectors corresponding to any two types of banking data, wherein the obtained similarity is data similarity. The higher the similarity, the more similar the two are, and the similarity is calculated by the prior art, for example: the pearson correlation coefficient, euclidean distance, etc., are not described in detail.
The priority index dividing acquisition module is used for determining priority indexes of the banking data of any type according to the data similarity between the banking data of the type and other banking data of other types and the data condition of the banking data of the type.
In this embodiment, for any one type of banking data, an average value of data similarity between the banking data of that type and other banking data of each type is calculated, and normalization processing is performed to obtain a data similarity index. The normalization algorithm is an existing algorithm and will not be described in detail.
And obtaining extreme value data points in the type of banking data, wherein the extreme value data points comprise maximum value data points and minimum value data points, then obtaining the maximum value and the minimum value of the data points in the type of banking data, wherein the total number of the maximum value data points and the minimum value data points is the number of the extreme value data points, and carrying out normalization processing to obtain an extreme value data point number index.
Dividing the obtained extreme value data point quantity index with the data similarity index to obtain a quotient value which is a priority index for dividing the banking data of the type.
The target banking data acquisition module is used for taking the maximum value in the priority dividing indexes of the banking data of various types as the optimal priority dividing index to acquire the banking data corresponding to the optimal priority dividing index, so as to obtain the target banking data.
And the grid division module is used for carrying out grid division on the banking data of the multiple types according to the target banking data.
And dividing various types of banking data including the target banking data according to the positions of the extreme value data points in the target banking data by taking the extreme value data points in the target banking data as dividing points, wherein each data group obtained by dividing is each grid data of the banking data. Such as: if the target banking data includes 7 extremum data points, the positions are respectively: the 8 th, 12 th, 16 th, 19 th, 22 nd, 27 th and 31 th data points in the target banking data, then, for any one banking data, the 8 th, 12 th, 16 th, 19 th, 22 nd, 27 th and 31 th data points in the banking data of that type are taken as division points, and the banking data of that type are divided into 8 data groups to constitute 8 grid data. The data points corresponding to the dividing points can be divided into the previous data set or the next data set according to the actual situation. And processing other banking data of all types according to the segmentation process to obtain all grid data corresponding to all banking data.
Claims (6)
1. An intelligent banking data management system, comprising:
the banking data acquisition module is used for acquiring various types of banking data;
the data similarity acquisition module is used for acquiring the data similarity of any two types of banking data;
the priority index dividing acquisition module is used for determining priority indexes of the banking data of any type according to the data similarity between the banking data of the type and other banking data of other types and the data condition of the banking data of the type;
the target banking data acquisition module is used for taking the maximum value in the priority dividing indexes of various types of banking data as an optimal priority dividing index, acquiring banking data corresponding to the optimal priority dividing index and obtaining target banking data;
and the grid division module is used for carrying out grid division on the banking data of the multiple types according to the target banking data.
2. The intelligent banking data management system according to claim 1, wherein the acquiring a plurality of types of banking data includes:
acquiring various types of original banking data;
binary coding is carried out on various types of original banking data to obtain binary coding sequences corresponding to various types of original banking data;
and converting binary code sequences corresponding to various types of original banking data into decimal numbers to obtain various types of banking data.
3. The intelligent banking data management system according to claim 1, wherein the acquiring data similarity of any two types of banking data includes:
for any type of banking data, acquiring characteristic values corresponding to all data points in the banking data of the type, wherein the characteristic values corresponding to all data points in the banking data of the type form characteristic vectors;
and obtaining the similarity of the feature vectors corresponding to any two types of banking data to obtain the data similarity.
4. A banking data intelligence management system according to claim 3 wherein the characteristic value is a variance of data points within a predetermined window centered on the data points.
5. The intelligent banking data management system according to claim 1, wherein determining the priority index of the type of banking data based on the data similarity between the type of banking data and other types of banking data and the data condition of the type of banking data includes:
calculating the average value of the data similarity between the banking data of the type and the banking data of other types, and carrying out normalization processing to obtain a data similarity index;
obtaining the number of extreme value data points in the banking data of the type, and carrying out normalization processing to obtain an extreme value data point number index, wherein the extreme value data points comprise maximum value data points and minimum value data points;
and dividing the extreme value data point quantity index with the data similarity index to obtain a quotient value which is a priority index for dividing the banking data of the type.
6. The intelligent banking data management system of claim 5, wherein the meshing of the plurality of types of banking data according to the target banking data includes:
and dividing the banking data of various types according to the positions of the extreme value data points in the banking data of the target as dividing points, wherein each data group obtained by dividing is grid data.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311070947.5A CN117333272A (en) | 2023-08-24 | 2023-08-24 | Intelligent management system for banking data |
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| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311070947.5A CN117333272A (en) | 2023-08-24 | 2023-08-24 | Intelligent management system for banking data |
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| Publication Number | Publication Date |
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| CN117333272A true CN117333272A (en) | 2024-01-02 |
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| CN202311070947.5A Pending CN117333272A (en) | 2023-08-24 | 2023-08-24 | Intelligent management system for banking data |
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| CN (1) | CN117333272A (en) |
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- 2023-08-24 CN CN202311070947.5A patent/CN117333272A/en active Pending
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