CN111240714A - Financial data initialization method and system based on template intelligent learning - Google Patents
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Abstract
The invention provides a financial data initialization system based on intelligent template learning, which is characterized in that: the method comprises the following steps: and acquiring an original system data end for acquiring an original system data file. The initialization data generation end is used for generating initialization data and comprises an import module, an identification module, an analysis module, a unified data module, a data reading module and an initialization data generation module. And the management confirmation terminal is used for manually verifying and confirming the user. A financial data initialization method based on template intelligent learning comprises the following specific processes: s1: and acquiring the original system data file, wherein the modes of acquiring the original system data file comprise client-side automatic downloading and manual downloading. S2: importing a file, analyzing and identifying a template or self-defining the template; s3: analyzing the file data according to the template; s4: automatically recording the stencil and the stencil heat; s5: acquiring uniform format data; s6: converting into new system initial data; s7: and finishing initialization after manual confirmation.
Description
Technical Field
The invention relates to the field of financial management software systems, in particular to a financial data initialization method and system based on template intelligent learning.
Background
There are numerous financial management software systems in the market today, and the user changes the upgrading occasionally and takes place, and the data between the system has certain difference: if the file types are inconsistent, the financial settings are inconsistent, the system standards are inconsistent, and the like, data migration cannot be directly performed. The process of data transfer is the process of new system initialization. The process can not be used normally due to uncertainty and diversity of data sources.
Disclosure of Invention
The invention aims to solve the defects in the prior art, and provides a financial data initialization method and system based on intelligent template learning.
In order to achieve the purpose, the invention adopts the following technical scheme: the utility model provides a financial data initialization system based on template intelligence learning which characterized in that: the method comprises the following steps:
and acquiring an original system data end for acquiring an original system data file.
The initialization data generation end is used for generating initialization data and comprises an import module, an identification module, an analysis module, a unified data module, a data reading module and an initialization data generation module.
And the management confirmation terminal is used for manually verifying and confirming the user.
Preferably, the data acquiring end of the original system comprises an automatic downloading module and a manual downloading module, wherein the automatic downloading module is used for automatically downloading the data file of the original system, and the manual downloading module is used for automatically downloading the data file of the original system.
Preferably, the import module is connected to the data of the data end of the original system, and is configured to import the acquired data file of the original system into the new system.
The identification module is used for identifying and judging the type of the data file by template data, and the file type comprises excel, xml, html and pdf.
The analysis module comprises a matching analysis module and a manual analysis module, the matching analysis module is used for analyzing the template data successfully identified by the identification module, and the manual analysis module is used for manually selecting an analysis mode and analyzing the template data failed to be identified by the identification module.
The uniform data module is in data connection with the analysis module, and the analysis module analyzes the data file into uniform JSON format data and records the uniform JSON format data into the same data module.
And the data reading module is connected with the unified data module in a data mode and reads the JSON format data.
The initialization data generation module is used for generating initialization data from the data read by the data reading module and entering a management confirmation terminal of the new system.
Preferably, the data reading module further comprises an intelligent filtering module, the intelligent filtering module is used for intelligently filtering unnecessary data and cleaning useful data according to configuration rules, the JSON format data read by the data reading module is a data file filtered by the intelligent filtering module, and the configuration rules are divided into processing of single content and processing of comparison and analysis of global data.
Preferably, the manual parsing module further comprises a configuration parsing module and a learning generation module, the configuration parsing module is used for manually customizing configuration parsing template data, and the learning generation module is used for autonomously learning and generating new template data.
Preferably, the initialization data generation end further comprises a template library module, and the template library module is in data connection with the analysis module and is used for analyzing template data according to template configuration or receiving and recording new template data autonomously generated by a manual analysis template; the template library module also comprises a statistic module used for counting the times of template matching analysis in the template library module.
A financial data initialization method based on template intelligent learning comprises the following specific processes:
s1: and acquiring the original system data file, wherein the modes of acquiring the original system data file comprise client-side automatic downloading and manual downloading.
S2: importing a file, analyzing and identifying a template or self-defining the template;
the method is characterized in that intelligent matching is adopted for analyzing and identifying the template, and the matching only comprises the following steps:
the first step is that file in various formats is converted into unified structure by using easy excel, the structure comprises four attributes,
1. file name
2. The Sheet data comprises a one-to-many mapping relation between the Sheet serial number and a row set, wherein the row set comprises values of various cols of the row
3. Sheet name and serial number
4. The merge cell information includes a sequence number and single merge information, and the merge information includes startRow (start line), endRow (end line), startCol (start column), and endCol (end column).
And secondly, loading template library template information (a template table/reading configuration table), wherein the template information mainly comprises a template Id, a template name, a template type, a header start line, a header end line, header keywords, form keywords, an identification strategy and a global data processing mode.
The traversal template is set according to the template to carry out matching judgment, and the matching strategies in the template are various and mainly comprise:
a. the name of Sheet is completely consistent, the order and the characters
b. Key Sheet name consistency matching
c. Sheet name keyword matching
d. Specifying row-specific full consistency matches for forms
e. A specific row of a given form contains a keyword match.
Each matching strategy corresponds to a service, the matching strategies are added into the spring object pool in an annotation mode, when a specific strategy needs to be used, the service object is taken out from the spring object pool to perform matching, and if the matching is successful, the service object is read according to the reading configuration of the template.
The configuration of the database in the mode can be effectively decoupled, templates can be visually and efficiently added, and autonomous learning and dynamic sequencing can be realized.
S3: analyzing the file data according to the template;
the template analysis file data adopts a user-defined analysis and automatic learning mode, when all templates of the user file and the template library can not be successfully matched, the user-defined template analysis is carried out, and the specific mode is that
1. The method comprises the steps of firstly grouping excels according to Sheet by utilizing poi, enabling each Sheet to be a JSON object, enabling each row of data in each Sheet to be a JSON array, enabling each JSON array to be composed of single Cell information cells, enabling each Cell to contain five attributes, starting row, ending row, starting Col, ending Col and val Cell content, returning read data to a browser page, and rendering the data by the browser.
2. The browser performs page rendering, all background return values are taken, a table is created, the first row of the table head is 26 English letters, the current table head length is taken, namely, the A-Z cut-off letters are obtained, then each row can display return contents, the first row is the sequence of numerical subscripts, the rest contents need to be combined into the table according to relevant parameters of return objects, and each array is the row of data of each row
Dragging mainly uses a correlation method of native dragging API (application programming interface) drags, dragable labels need to designate dragable as true, in a dragging start dragstart, relevant attributes of current dragging nodes are assigned, such as title, id (information of the current dragging nodes can be taken after dragging is finished, namely, a drop mouse event is dropped), in a dragging process (dragover), a prompt style is displayed in a designated dropable area to prompt the dropable mouse operation, in the dragging process, relevant nodes need to be designated, namely, a first line of a header, after dragging is finished (dragend), relevant class names need to be removed or the labels dragged to the designated area need to be deleted by the events, relevant styles need to be simultaneously cleared, current deleted data is compared, initial position designated data is restored, an end-of-period data form is switched, all dragged nodes need to be cleared, and initial position data is restored
3. And assembling the template according to parameters selected by front-end dragging, wherein the template mainly comprises a template identification strategy, column information corresponding to necessary key values, which can be a header or a column number, and processing strategies aiming at each column of data, such as deleting special symbols, converting a scientific counting method, replacing the special symbols and the like.
4. And reading data according to the assembled template, if the reading is successful, adding the assembled template into a template library, namely inserting corresponding template data information into a template configuration table and a template attribute reading configuration table, and generating new template data for subsequent users and self reuse.
S4: automatically recording the stencil and the stencil heat;
considering that data migration generally refers to the fact that multiple enterprises are migrated from a system A to a system B, original financial data formats of the multiple enterprises are consistent, namely templates are consistent, the size of a template library is larger and larger, and due to default, each sequential traversal can cause a large amount of matching work to be invalid, the concept of template heat is introduced, and a weighted algorithm is calculated according to the using times of users and templates to judge which template is preferentially used for matching analysis. The method is specifically realized in the way that the sum of the times x that a user uses a certain template within one week and the total use times of the current template is recorded to perform descending sorting (x is user weight), data is stored in a redis cache, and the sorting in the cache is taken to perform traversal, so that repeated invalid matching is avoided. With the increase of the number of templates in the template library, the heat system can improve the matching efficiency.
S5: acquiring uniform format data;
s6: converting into new system initial data;
s7: and finishing initialization after manual confirmation.
Further, a user obtains a subject balance list file of an original system in a self mode or a client tool export mode, the original system file is imported into a new system, the new system judges file types including excel, xml, html and pdf according to file streams, and after the judgment is successful, a proper analysis mode is selected to analyze the file into data in a uniform JSON format; and carrying out template identification on JSON data according to a template existing in a template library, wherein template matching of the template library fails, a user carries out custom configuration and analysis of template data by manually selecting an analysis mode, the template is added into the template library after successful analysis for direct use of next configuration and analysis of template data, the processed template data carries out initialization data generation according to a new system financial configuration standard, and finally the generated initialization data is displayed for the user to confirm, and financial data initialization is completed after confirmation.
Further, the template is identified as performing full-scale matching or keyword matching according to the content of the array specified from the beginning line to the end line, the excel-format data file performs full-scale or keyword matching on the form name, and the matching is successful if the matching condition is met.
Compared with the prior art, the invention has the beneficial effects that: the system is suitable for complete migration of data and is suitable for various data types when a financial management software system is updated and upgraded, and has the advantages that a user manually selects an analysis mode, and manually configures analysis template data in a self-defined manner, so that the flexibility of the initialization process of a new system is higher, and the normal use of the new system is ensured.
Drawings
Fig. 1 is a schematic structural diagram of a financial data initialization system based on template intelligent learning according to embodiment 1 of the present invention;
fig. 2 is a schematic diagram illustrating specific steps of a financial data initialization method based on template smart learning according to embodiment 2 of the present invention;
fig. 3 is a schematic flowchart of a financial data initialization method based on template smart learning according to embodiment 2 of the present invention.
Detailed Description
In order to further understand the objects, structures, features and functions of the present invention, the following embodiments are described in detail.
Example 1: as shown in fig. 1, a financial data initialization system based on template smart learning includes:
and acquiring an original system data end for acquiring an original system data file.
The initialization data generation end is used for generating initialization data and comprises an import module, an identification module, an analysis module, a unified data module, a data reading module and an initialization data generation module.
And the management confirmation terminal is used for manually verifying and confirming the user.
As shown in fig. 1, the data acquiring end of the original system includes an automatic downloading module and a manual downloading module, the automatic downloading module is used for automatically downloading the data file of the original system, and the manual downloading module is used for automatically downloading the data file of the original system.
As shown in fig. 1, the import module is connected to the data of the original system data acquisition end, and is configured to import the acquired original system data file into the new system.
The identification module is used for identifying and judging the type of the data file by template data, and the file type comprises excel, xml, html and pdf.
The analysis module comprises a matching analysis module and a manual analysis module, the matching analysis module is used for analyzing the template data successfully identified by the identification module, and the manual analysis module is used for manually selecting an analysis mode and analyzing the template data failed to be identified by the identification module.
The uniform data module is in data connection with the analysis module, and the analysis module analyzes the data file into uniform JSON format data and records the uniform data module.
And the data reading module is connected with the unified data module in a data mode and reads the JSON format data.
The initialization data generation module is used for generating initialization data from the data read by the data reading module and entering a management confirmation terminal of the new system.
As shown in fig. 1, the data reading module further includes an intelligent filtering module, the intelligent filtering module is configured to intelligently filter unnecessary data and clean useful data according to configuration rules, the JSON-format data read by the data reading module is a data file filtered by the intelligent filtering module, and the configuration rules are divided into processing of single content and processing of comparison and analysis of global data.
As shown in fig. 1, the manual parsing module further includes a configuration parsing module and a learning generation module, the configuration parsing module is used for manually customizing configuration parsing template data, and the learning generation module is used for autonomously learning and generating new template data.
As shown in fig. 1, the initialization data generating end further includes a template library module, and the template library module is in data connection with the parsing module and is configured to parse template data according to a template configuration, or to receive and record new template data autonomously generated by a manual parsing template; the template library module also comprises a statistic module used for counting the times of template matching analysis in the template library module.
Example 2: as shown in fig. 2, a financial data initialization method based on template intelligent learning specifically includes the following steps:
s1: and acquiring the original system data file, wherein the modes of acquiring the original system data file comprise client-side automatic downloading and manual downloading.
S2: importing a file, analyzing and identifying a template or self-defining the template;
the method for analyzing and identifying the template adopts an intelligent matching mode, and the matching only comprises the following steps:
the first step is that file in various formats is converted into unified structure by using easy excel, the structure comprises four attributes,
1. file name
2. The Sheet data comprises a one-to-many mapping relation between the Sheet serial number and a row set, wherein the row set comprises values of various cols of the row
3. Sheet name and serial number
4. The merge cell information includes a sequence number and single merge information, and the merge information includes startRow (start line), endRow (end line), startCol (start column), and endCol (end column).
And secondly, loading template library template information (a template table/reading configuration table), wherein the template information mainly comprises a template Id, a template name, a template type, a header start line, a header end line, header keywords, form keywords, an identification strategy and a global data processing mode.
The traversal template is set according to the template to carry out matching judgment, and the matching strategies in the template are various and mainly comprise:
a. the name of Sheet is completely consistent, the order and the characters
b. Key Sheet name consistency matching
c. Sheet name keyword matching
d. Specifying row-specific full consistency matches for forms
e. A specific row of a given form contains a keyword match.
Each matching strategy corresponds to a service, the matching strategies are added into the spring object pool in an annotation mode, when a specific strategy needs to be used, the service object is taken out from the spring object pool to perform matching, and if the matching is successful, the service object is read according to the reading configuration of the template.
The configuration of the database in the mode can be effectively decoupled, templates can be visually and efficiently added, and autonomous learning and dynamic sequencing can be realized.
S3: analyzing the file data according to the template;
the template analysis file data adopts a user-defined analysis and automatic learning mode, when all templates of the user file and the template library can not be successfully matched, the user-defined template analysis is carried out, and the specific mode is that
1. The method comprises the steps of firstly grouping excels according to Sheet by utilizing poi, enabling each Sheet to be a JSON object, enabling each row of data in each Sheet to be a JSON array, enabling each JSON array to be composed of single Cell information cells, enabling each Cell to contain five attributes, starting row, ending row, starting Col, ending Col and val Cell content, returning read data to a browser page, and rendering the data by the browser.
2. The browser performs page rendering, all background return values are taken, a table is created, the first row of the table head is 26 English letters, the current table head length is taken, namely, the A-Z cut-off letters are obtained, then each row can display return contents, the first row is the sequence of numerical subscripts, the rest contents need to be combined into the table according to relevant parameters of return objects, and each array is the row of data of each row
Dragging mainly uses a correlation method of native dragging API (application programming interface) drags, dragable labels need to designate dragable as true, in a dragging start dragstart, relevant attributes of current dragging nodes are assigned, such as title, id (information of the current dragging nodes can be taken after dragging is finished, namely, a drop mouse event is dropped), in a dragging process (dragover), a prompt style is displayed in a designated dropable area to prompt the dropable mouse operation, in the dragging process, relevant nodes need to be designated, namely, a first line of a header, after dragging is finished (dragend), relevant class names need to be removed or the labels dragged to the designated area need to be deleted by the events, relevant styles need to be simultaneously cleared, current deleted data is compared, initial position designated data is restored, an end-of-period data form is switched, all dragged nodes need to be cleared, and initial position data is restored
3. And assembling the template according to parameters selected by front-end dragging, wherein the template mainly comprises a template identification strategy, column information corresponding to necessary key values, which can be a header or a column number, and processing strategies aiming at each column of data, such as deleting special symbols, converting a scientific counting method, replacing the special symbols and the like.
4. And reading data according to the assembled template, if the reading is successful, adding the assembled template into a template library, namely inserting corresponding template data information into a template configuration table and a template attribute reading configuration table, and generating new template data for subsequent users and self reuse.
S4: automatically recording the stencil and the stencil heat;
considering that data migration generally refers to the fact that multiple enterprises are migrated from a system A to a system B, original financial data formats of the multiple enterprises are consistent, namely templates are consistent, the size of a template library is larger and larger, and due to default, each sequential traversal can cause a large amount of matching work to be invalid, the concept of template heat is introduced, and a weighted algorithm is calculated according to the using times of users and templates to judge which template is preferentially used for matching analysis. The method is specifically realized in the way that the sum of the times x that a user uses a certain template within one week and the total use times of the current template is recorded to perform descending sorting (x is user weight), data is stored in a redis cache, and the sorting in the cache is taken to perform traversal, so that repeated invalid matching is avoided. With the increase of the number of templates in the template library, the heat system can improve the matching efficiency.
S5: acquiring uniform format data;
s6: converting into new system initial data;
s7: and finishing initialization after manual confirmation.
As shown in fig. 3, a user obtains a subject balance table file of an original system by a self-way or a client tool export way, and imports the original system file into a new system, wherein the new system judges file types including excel, xml, html and pdf according to a file stream, and after the judgment is successful, selects a proper analytic way to analyze the file into data in a uniform JSON format; and carrying out template identification on JSON data according to a template existing in a template library, wherein template matching of the template library fails, a user carries out custom configuration and analysis of template data by manually selecting an analysis mode, the template is added into the template library after successful analysis for direct use of next configuration and analysis of template data, the processed template data carries out initialization data generation according to a new system financial configuration standard, and finally the generated initialization data is displayed for the user to confirm, and financial data initialization is completed after confirmation.
The template is identified as full matching or keyword matching according to the contents of the array specified starting line to ending line, full matching or keyword matching is carried out on the form name by the excel-format data file, and matching is successful if the matching condition is met.
The present invention has been described in relation to the above embodiments, which are only exemplary of the implementation of the present invention. It should be noted that the disclosed embodiments do not limit the scope of the invention. Rather, it is intended that all such modifications and variations be included within the spirit and scope of this invention.
Claims (8)
1. The utility model provides a financial data initialization system based on template intelligence learning which characterized in that: the method comprises the following steps:
acquiring an original system data end for acquiring an original system data file;
the initialization data generation end is used for generating initialization data and comprises an import module, an identification module, an analysis module, a unified data module, a data reading module and an initialization data generation module;
and the management confirmation terminal is used for manually verifying and confirming the user.
2. The financial data initialization system based on template smart learning of claim 1, wherein: the data acquisition terminal of the original system comprises an automatic downloading module and a manual downloading module, wherein the automatic downloading module is used for automatically downloading the data file of the original system, and the manual downloading module
The financial data initialization system based on template smart learning of claim 1, wherein: the import module is connected with the data of the original system data acquisition end and is used for importing the acquired original system data file into a new system;
the identification module is used for identifying and judging the type of the data file by template data, wherein the file type comprises excel, xml, html and pdf;
the analysis module comprises a matching analysis module and a manual analysis module, the matching analysis module is used for analyzing the template data successfully identified by the identification module, and the manual analysis module is used for manually selecting an analysis mode and analyzing the template data failed to be identified by the identification module;
the uniform data module is in data connection with the analysis module, and the analysis module analyzes the data file into uniform JSON format data and records the uniform JSON format data into the same data module;
the data reading module is in data connection with the unified data module and reads the JSON format data;
the initialization data generation module is used for generating initialization data from the data read by the data reading module and entering a management confirmation terminal of the new system.
3. The financial data initialization system based on template smart learning of claim 3, wherein: the data reading module is also provided with an intelligent filtering module, the intelligent filtering module is used for intelligently filtering unnecessary data and cleaning useful data according to configuration rules, the JSON format data read by the data reading module is a data file filtered by the intelligent filtering module, and the configuration rules are divided into processing of single content and processing of comparison and analysis of global data.
4. The financial data initialization system based on template smart learning of claim 3, wherein: the manual analysis module further comprises a configuration analysis module and a learning generation module, the configuration analysis module is used for manually and custom configuring and analyzing template data, and the learning generation module is used for autonomously learning and generating new template data.
5. The financial data initialization system based on template smart learning of claim 5, wherein: the initialization data generation end also comprises a template library module which is in data connection with the analysis module and is used for configuring analysis template data according to the template or recording new template data autonomously generated by the manual analysis template; the template library module also comprises a statistic module used for counting the times of template matching analysis in the template library module.
6. A financial data initialization method based on template intelligent learning is characterized in that: the specific process is as follows:
s1: acquiring an original system data file, wherein the mode for acquiring the original system data file comprises client automatic downloading and manual downloading;
s2: importing a file, analyzing and identifying a template or self-defining the template;
s3: analyzing the file data according to the template;
s4: automatically recording the stencil and the stencil heat;
s5: acquiring uniform format data;
s6: converting into new system initial data;
s7: and finishing initialization after manual confirmation.
7. The financial data initialization method based on template smart learning of claim 7, wherein: a user acquires a subject balance list file of an original system in a self mode or a client tool export mode, and imports the original system file into a new system, wherein the new system judges file types including excel, xml, html and pdf according to file streams, and selects a proper analysis mode to analyze the file into data in a uniform JSON format after successful judgment; and carrying out template identification on JSON data according to a template existing in a template library, wherein template matching of the template library fails, a user carries out custom configuration and analysis of template data by manually selecting an analysis mode, the template is added into the template library after successful analysis for direct use of next configuration and analysis of template data, the processed template data carries out initialization data generation according to a new system financial configuration standard, and finally the generated initialization data is displayed for the user to confirm, and financial data initialization is completed after confirmation.
8. The method of claim 1, wherein: the template is identified as full matching or keyword matching according to the contents of the array specified starting line to ending line, full matching or keyword matching is carried out on the form name by the excel-format data file, and matching is successful if the matching condition is met.
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| CN109062872A (en) * | 2018-07-13 | 2018-12-21 | 摩诃科技无锡有限公司 | The algorithm that a kind of pair of different-format entry is uniformly processed |
| CN109949877A (en) * | 2019-03-15 | 2019-06-28 | 北京科技大学 | A kind of data fusion method and system based on Experiment of Material Science |
| CN110457302A (en) * | 2019-07-31 | 2019-11-15 | 河南开合软件技术有限公司 | A kind of structural data intelligence cleaning method |
| CN110489398A (en) * | 2019-08-14 | 2019-11-22 | 南京云帐房网络科技有限公司 | A kind of method and system of difference financial system batch financial data migration |
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| CN112183047A (en) * | 2020-09-29 | 2021-01-05 | 成都商通数治科技有限公司 | Big data cleaning configuration type field analysis method |
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