CN111240714B - 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 template intelligent learning, which is characterized in that: comprising the following steps: and acquiring the original system data end, which is used for acquiring the original system data file. The initialization data generation end is used for generating initialization data and comprises an importing 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 the manual verification and confirmation of the user. A financial data initialization method based on template intelligent learning comprises the following specific processes: s1: the method for acquiring the original system data file comprises automatic downloading and manual downloading of the client. S2: importing a file, analyzing and identifying a template or a custom template; s3: analyzing file data according to the template; s4: automatically recording the heat of the template; s5: acquiring unified 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 template intelligent learning-based financial data initialization method and system.
Background
There are numerous financial management software systems in the market today, and when users perform system replacement and upgrade, data between systems has a certain difference: if the file types are inconsistent, the financial settings are inconsistent, the system standards are inconsistent, and the data migration cannot be directly performed. The data transfer process is the process of initializing a new system. This process will result in the new system not being used properly because of uncertainty and diversity in the data sources.
Disclosure of Invention
The invention aims to solve the defects in the prior art, and provides a financial data initialization method and a system based on template intelligent learning, wherein the system is suitable for complete migration of data and various data types when a financial management software system is updated and upgraded, has a manual analysis mode selected by a user, and manually self-defines configuration analysis template data, so that the initialization process of a new system is higher in flexibility, and the normal use of the new system is ensured.
In order to achieve the above purpose, the invention adopts the following technical scheme: a financial data initialization system based on template intelligent learning is characterized in that: comprising the following steps:
and acquiring the original system data end, which is used for acquiring the original system data file.
The initialization data generation end is used for generating initialization data and comprises an importing 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 the manual verification and confirmation of the user.
Preferably, the acquiring the original system data end includes an automatic downloading module and a manual downloading module, wherein the automatic downloading module is used for automatically downloading the original system data file, and the manual downloading module is used for automatically downloading the original system data file.
Preferably, the importing module is connected with the data of the original system data acquisition end and is used for importing the obtained 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, wherein 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 to analyze the template data failed to be identified by the identification module.
The unified data module is in data connection with the analysis module, and the analysis module analyzes the data file into unified JSON format data and records the data file into the same data module.
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 is further 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 analysis post-processing of global data.
Preferably, the manual analysis module further comprises a configuration analysis module and a learning generation module, wherein the configuration analysis module is used for manually customizing configuration analysis template data, and the learning generation module is used for autonomously learning to generate new template data.
Preferably, the initialization data generating end further comprises a template library module, wherein the template library module is in data connection with the analysis module and is used for analyzing template data according to template configuration or recording new template data which is automatically generated by manually analyzing the template; the template library module also comprises a statistics module 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: the method for acquiring the original system data file comprises automatic downloading and manual downloading of the client.
S2: importing a file, analyzing and identifying a template or a custom template;
the intelligent matching is adopted for analyzing and identifying the templates, and the matching only comprises the following steps:
the first step is to convert files in multiple formats into a unified structure, which contains four attributes,
1. file name
2. Sheet data comprising a one-to-many mapping relationship of Sheet sequence numbers and a row set of values for each col of the row
3. Sheet name and number
4. Merging cell information including a sequence number and single merging information, and merging information including startRow, endRow, startCol, endCol.
Secondly, loading template information (template list/reading configuration list) of a template library, wherein the template information mainly comprises a template Id, a template name, a template type, a header start line, a header end line, a header keyword, a form keyword, an identification strategy and a global data processing mode.
Traversing templates to carry out matching judgment according to template setting, wherein the matching strategies in the templates are various and mainly comprise:
a. the Sheet names are completely consistent, sequential and literal
b. Key Sheet name consistency matching
c. Sheet name keyword matching
d. Specific row complete consistency matching of specified forms
e. The specific row of the designated form contains a keyword match.
Each matching strategy corresponds to a service, the service is 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 be matched, and if the matching is successful, the service object is read according to the reading configuration of the template.
The database configuration can be effectively decoupled in the mode, templates can be intuitively and efficiently added, and the database configuration can be independently learned and dynamically ordered.
S3: analyzing file data according to the template;
the template analysis file data adopts a self-defined analysis and automatic learning mode, when all templates of the user file and the template library cannot be successfully matched, the self-defined template analysis is carried out, and the specific mode is that
1. Firstly, grouping excel by virtue of poi, wherein each Sheet is a JSON object, each line of data in a single Sheet is a JSON array, each Cell consists of single Cell information Cell, each Cell contains five attributes, startRow (beginning line), endRow (ending line), startCol (beginning column), endCol (ending column), value (Cell content), and the read data is returned to a browser page to be rendered by the browser.
2. The browser performs page rendering, obtains all background return values, creates a table, the first row of the table takes the current table length as the 26 English letters of the table head, namely the cut-off letters of A-Z, then each row can display the return content, the first row is the digital subscript sequence, the rest of the contents need to merge the table according to the related parameters of the return object, and each array is the data of each row
The method mainly uses related methods of original drag API drags in drag, a dragable label needs to be assigned with a dragable as true, in a drag start dragstart, related attributes of a current drag node, such as title, id (information of the current drag node can be taken after the drag is finished, namely drop mouse event), in the drag process (dragover), a prompt style is displayed in an assigned dripable area to prompt the drop mouse operation, in the drag process, related nodes, namely a first row of a table head, related class names need to be removed or labels dragged to the assigned area need to be deleted by events need to be removed after the drag is finished, related styles need to be cleared simultaneously, current delete data need to be compared simultaneously, initial position assigned data need to be restored, end-of-period data form needs to be cleared, and initial position data need to be restored simultaneously by all dragged nodes need to be cleared
3. According to the parameters selected by front end dragging, template assembly is carried out, which mainly comprises a template recognition strategy, column information corresponding to the necessary key value can be a header or a column number, and the processing strategy is carried out for each column of data, such as deleting special symbols, converting by a scientific counting method, replacing the special symbols and the like.
4. And reading data according to the assembled templates, if the data is successfully read, adding the assembled templates 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 heat of the template;
considering that data migration is generally that a plurality of enterprises are migrated from an A system to a B system, original financial data formats of the enterprises are consistent, namely templates are consistent, the template library is larger and larger in scale, and a large number of matching works are invalid due to sequential traversal by default, so that the concept of template heat is introduced, and the template is used for judging which template is preferentially used for matching analysis according to the number of times of using the user and the template by means of weighting algorithm calculation. The method is characterized by comprising the steps of recording the sum of the times of using a certain template by a user in one week and the total times of using the current template, performing descending order sorting (x is user weight), storing data in a redis cache, and traversing by taking the sorting in the cache, so that repeated invalid matching is avoided. As the number of templates in the template library increases, the heat system can improve the matching efficiency.
S5: acquiring unified 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 the file type according to the file stream, wherein the file type comprises excel, xml, html and pdf forms, and after judging success, a proper analysis mode is selected to analyze the file into unified JSON format data; and then performing template recognition on the JSON data according to templates in a template library, wherein template matching of the template library fails, a user performs custom configuration analysis on the template data by manually selecting an analysis mode, the template is added into the template library for direct use of next configuration analysis on the template data after successful analysis, the processed template data is generated according to new system financial configuration standards, and finally the generated initialization data is displayed to the user for confirmation, so that financial data initialization is completed after confirmation.
Further, the template is identified to perform full-quantity matching or keyword matching according to the content from the beginning to the end of array designation, the data file in excel format performs full-quantity or keyword matching on the form name, and 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 various data types when updating and upgrading the financial management software system, has a manual analysis mode selected by a user, and manually self-defines configuration analysis template data, so that the initialization process of the new system is higher in flexibility, and normal use of the new system is ensured.
Drawings
FIG. 1 is a schematic diagram of a financial data initializing system based on template intelligent learning according to embodiment 1 of the present invention;
FIG. 2 is a schematic diagram showing specific steps of a method for initializing financial data based on intelligent learning of a template according to embodiment 2 of the present invention;
fig. 3 is a flowchart of a financial data initializing method based on template intelligent learning according to embodiment 2 of the present invention.
Detailed Description
For a further understanding of the objects, construction, features, and functions of the invention, reference should be made to the following detailed description of the preferred embodiments.
Example 1: as shown in fig. 1, a financial data initializing system based on template intelligent learning includes:
and acquiring the original system data end, which is used for acquiring the original system data file.
The initialization data generation end is used for generating initialization data and comprises an importing 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 the manual verification and confirmation of the user.
As shown in fig. 1, the acquiring the original system data end includes an automatic downloading module and a manual downloading module, where the automatic downloading module is used to automatically download the original system data file, and the manual downloading module is used to manually download the original system data file.
As shown in fig. 1, the import module is connected with the data end of the original system and is used for importing the obtained 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, wherein 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 to analyze the template data failed to be identified by the identification module.
The unified data module is in data connection with the analysis module, and the analysis module analyzes the data file into unified JSON format data and records the unified data module.
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 is further 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, 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 analysis post-processing of global data.
As shown in fig. 1, the manual analysis module further includes a configuration analysis module and a learning generation module, wherein the configuration analysis module is used for manually customizing configuration analysis template data, and the learning generation module is used for generating new template data through autonomous learning.
As shown in fig. 1, the initialization data generating end further includes a template library module, which is in data connection with the analysis module and is used for analyzing template data according to template configuration or recording new template data generated automatically by manually analyzing the template; the template library module also comprises a statistics module for counting the times of template matching analysis in the template library module.
Example 2: as shown in FIG. 2, the method for initializing financial data based on template intelligent learning comprises the following specific processes:
s1: the method for acquiring the original system data file comprises automatic downloading and manual downloading of the client.
S2: importing a file, analyzing and identifying a template or a custom template;
the analyzing and identifying the template adopts an intelligent matching mode, and the matching only comprises the following steps:
the first step is to convert files in multiple formats into a unified structure, which contains four attributes,
1. file name
2. Sheet data comprising a one-to-many mapping relationship of Sheet sequence numbers and a row set of values for each col of the row
3. Sheet name and number
4. Merging cell information including a sequence number and single merging information, and merging information including startRow, endRow, startCol, endCol.
Secondly, loading template information (template list/reading configuration list) of a template library, wherein the template information mainly comprises a template Id, a template name, a template type, a header start line, a header end line, a header keyword, a form keyword, an identification strategy and a global data processing mode.
Traversing templates to carry out matching judgment according to template setting, wherein the matching strategies in the templates are various and mainly comprise:
a. the Sheet names are completely consistent, sequential and literal
b. Key Sheet name consistency matching
c. Sheet name keyword matching
d. Specific row complete consistency matching of specified forms
e. The specific row of the designated form contains a keyword match.
Each matching strategy corresponds to a service, the service is 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 be matched, and if the matching is successful, the service object is read according to the reading configuration of the template.
The database configuration can be effectively decoupled in the mode, templates can be intuitively and efficiently added, and the database configuration can be independently learned and dynamically ordered.
S3: analyzing file data according to the template;
the template analysis file data adopts a self-defined analysis and automatic learning mode, when all templates of the user file and the template library cannot be successfully matched, the self-defined template analysis is carried out, and the specific mode is that
1. Firstly, grouping excel by virtue of poi, wherein each Sheet is a JSON object, each line of data in a single Sheet is a JSON array, each Cell consists of single Cell information Cell, each Cell contains five attributes, startRow (beginning line), endRow (ending line), startCol (beginning column), endCol (ending column), value (Cell content), and the read data is returned to a browser page to be rendered by the browser.
2. The browser performs page rendering, obtains all background return values, creates a table, the first row of the table takes the current table length as the 26 English letters of the table head, namely the cut-off letters of A-Z, then each row can display the return content, the first row is the digital subscript sequence, the rest of the contents need to merge the table according to the related parameters of the return object, and each array is the data of each row
The method mainly uses related methods of original drag API drags in drag, a dragable label needs to be assigned with a dragable as true, in a drag start dragstart, related attributes of a current drag node, such as title, id (information of the current drag node can be taken after the drag is finished, namely drop mouse event), in the drag process (dragover), a prompt style is displayed in an assigned dripable area to prompt the drop mouse operation, in the drag process, related nodes, namely a first row of a table head, related class names need to be removed or labels dragged to the assigned area need to be deleted by events need to be removed after the drag is finished, related styles need to be cleared simultaneously, current delete data need to be compared simultaneously, initial position assigned data need to be restored, end-of-period data form needs to be cleared, and initial position data need to be restored simultaneously by all dragged nodes need to be cleared
3. According to the parameters selected by front end dragging, template assembly is carried out, which mainly comprises a template recognition strategy, column information corresponding to the necessary key value can be a header or a column number, and the processing strategy is carried out for each column of data, such as deleting special symbols, converting by a scientific counting method, replacing the special symbols and the like.
4. And reading data according to the assembled templates, if the data is successfully read, adding the assembled templates 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 heat of the template;
considering that data migration is generally that a plurality of enterprises are migrated from an A system to a B system, original financial data formats of the enterprises are consistent, namely templates are consistent, the template library is larger and larger in scale, and a large number of matching works are invalid due to sequential traversal by default, so that the concept of template heat is introduced, and the template is used for judging which template is preferentially used for matching analysis according to the number of times of using the user and the template by means of weighting algorithm calculation. The method is characterized by comprising the steps of recording the sum of the times of using a certain template by a user in one week and the total times of using the current template, performing descending order sorting (x is user weight), storing data in a redis cache, and traversing by taking the sorting in the cache, so that repeated invalid matching is avoided. As the number of templates in the template library increases, the heat system can improve the matching efficiency.
S5: acquiring unified 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 list file of an original system by self mode or using a client tool export mode, and imports the original system file into a new system, the new system judges the file type according to the file stream, including excel, xml, html and pdf forms, and after judging success, selects a proper analysis mode to analyze the file into unified JSON format data; and then performing template recognition on the JSON data according to templates in a template library, wherein template matching of the template library fails, a user performs custom configuration analysis on the template data by manually selecting an analysis mode, the template is added into the template library for direct use of next configuration analysis on the template data after successful analysis, the processed template data is generated according to new system financial configuration standards, and finally the generated initialization data is displayed to the user for confirmation, so that financial data initialization is completed after confirmation.
And the template is identified as carrying out full-quantity matching or keyword matching on the contents of the rows from the beginning to the end according to array designation, the data files in excel format are subjected to full-quantity or keyword matching on the names of the forms, and matching conditions are met, so that the matching is successful.
The invention has been described with respect to the above-described embodiments, however, the above-described embodiments are merely examples of practicing the invention. It should be noted that the disclosed embodiments do not limit the scope of the invention. On the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the invention.
Claims (8)
1. A financial data initialization method based on template intelligent learning is characterized in that: the specific process is as follows:
s1: the method comprises the steps of obtaining an original system data file, wherein the method for obtaining the original system data file comprises automatic downloading and manual downloading of a client;
s2: importing a file, analyzing and identifying a template or a custom template;
the recognition template specifically comprises:
firstly, converting files in various formats into a unified structure by utilizing easy excel;
the unified structure comprises four attributes, namely a file name, sheet data, sheet names, serial numbers and merging cell information;
secondly, loading template information of a template library, traversing templates, and carrying out matching judgment according to template setting;
the specific analysis mode of the custom template is as follows:
1) Firstly, grouping excel by virtue of poi, wherein each Sheet is a JSON object, each row of data in a single Sheet is a JSON array, and the JSON array consists of single Cell information Cell;
2) Returning the read data to the browser page, and rendering by the browser;
3) Assembling templates according to the parameters selected by front-end dragging, wherein the templates comprise a template identification strategy;
4) Reading data according to the assembled templates, if the data is successfully read, adding the assembled templates 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;
s3: analyzing file data according to the template;
s4: automatically recording the heat of the template;
s5: acquiring unified format data;
s6: converting into new system initial data;
s7: and finishing initialization after manual confirmation.
2. The template intelligent learning-based financial data initialization method as claimed in claim 1, wherein: the user obtains the subject balance list file of the 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 the file type according to the file flow, wherein the file type comprises excel, xml, html and pdf forms, and after judging success, the user selects an analysis mode to analyze the file into unified JSON format data; and then performing template recognition on the JSON data according to templates in a template library, wherein template matching of the template library fails, a user performs custom configuration analysis on the template data by manually selecting an analysis mode, the template is added into the template library for direct use of next configuration analysis on the template data after successful analysis, the processed template data is generated according to new system financial configuration standards, and finally the generated initialization data is displayed to the user for confirmation, so that financial data initialization is completed after confirmation.
3. The template intelligent learning-based financial data initialization method as claimed in claim 1, wherein: and the template is identified as carrying out full-quantity matching or keyword matching on the contents of the rows from the beginning to the end according to array designation, the data files in excel format are subjected to full-quantity or keyword matching on the names of the forms, and matching conditions are met, so that the matching is successful.
4. A system for performing a template intelligent learning-based financial data initialization method according to claim 1, characterized in that: comprising the following steps:
the method comprises the steps of acquiring an original system data end, wherein the original system data end is used for acquiring an original system data file;
the initialization data generation end is used for generating initialization data and comprises an importing 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 the manual verification and confirmation of the user.
5. A system for a template intelligent learning based financial data initialization method of claim 4, wherein: the method comprises the steps that an original system data acquisition end comprises an automatic downloading module and a manual downloading module, wherein the automatic downloading module is used for automatically downloading an original system data file, and the manual downloading module is used for automatically downloading the original system data file;
the importing module is connected with the data of the original system data acquisition end and is used for importing the obtained 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 type of the data file comprises excel, xml, html and pdf;
the analysis module comprises a matching analysis module and a manual analysis module, wherein 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 to analyze the template data failed to be identified by the identification module;
the unified data module is in data connection with the analysis module, and the analysis module analyzes the data file into unified JSON format data and records the unified data file into the unified 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.
6. A system for a template intelligent learning based financial data initialization method of claim 5, wherein: the data reading module is also provided with an intelligent filtering module which 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 analysis post-processing of global data.
7. A system for a template intelligent learning based financial data initialization method of claim 5, wherein: the manual analysis module further comprises a configuration analysis module and a learning generation module, wherein the configuration analysis module is used for manually customizing configuration analysis template data, and the learning generation module is used for autonomously learning to generate new template data.
8. A system for a template intelligent learning based financial data initialization method of claim 7, wherein: the initialization data generating end also comprises a template library module which is in data connection with the analysis module and is used for analyzing template data according to template configuration or recording new template data which is automatically generated by manually analyzing the template; the template library module also comprises a statistics module for counting the times of template matching analysis in the template library module.
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