CN114677206A - Data checking system for performing autonomous financial checking through data checking - Google Patents
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Abstract
The invention relates to the technical field of data verification, in particular to a data verification system for performing autonomous financial verification through data verification. It includes that data check system includes at least: acquiring a data unit; a data classification unit; the data checking unit is used for analyzing and checking the acquired data and generating correct checking information and error information; the extraction checking unit is used for receiving the error information generated during the checking of the data checking unit, extracting the error data range covered by the error information, judging the analysis condition, correcting the error data and generating integrated data; and a verification completion unit. By analyzing, contrasting and checking the data report, when the data report is checked wrongly, the currently extracted error data is judged, so that the error data correction speed can be increased, and the integrated data is sent to the data checking unit for secondary checking.
Description
Technical Field
The invention relates to the technical field of data verification, in particular to a data verification system for performing autonomous financial verification through data verification.
Background
In an enterprise, every time the month, the quarter or the year ends, workers in the enterprise can check data of financial statements made at that time to analyze initial measurement and consumption measurement of goods resources so as to prepare for subsequent development planning of the enterprise;
when the current enterprise checks the data, the enterprise checks and analyzes the data manually, so that great human resource loss is formed, the reason for forming the wrong data cannot be obtained when the wrong data is checked by the data, the follow-up personnel cannot make data report numbers to propose opinions, and the current data needs to be checked by the personnel in a whole and unified manner.
Disclosure of Invention
The present invention is directed to a data collation system for autonomously collating a financial affair by data collation, to solve the problems of the background art described above.
In order to achieve the above object, the present invention provides a data collation system for autonomously collating a finance by data collation, including at least:
the data acquisition unit is used for acquiring a data report and a data comparison table;
a data classification unit for classifying the data acquired by the data acquisition unit;
the data checking unit is used for analyzing and checking the acquired data and generating correct checking information and error information;
the extraction and checking unit is used for receiving the error information generated during the checking of the data checking unit, extracting the error data range covered by the error information, judging the analysis condition, checking the error data, generating integrated data, and sending the integrated data into the data checking unit for analysis and checking again;
and a verification completion unit for receiving the information verified by the data verification unit and generating a verification completion report.
As a further improvement of the technical solution, the data acquiring unit includes a data acquiring comparison table module and an initial data acquiring module, and the data acquiring comparison table module is used for acquiring a data comparison table collated with the data report; the initial data acquisition module is used for acquiring a large number of data reports.
As a further improvement of the technical scheme, the data obtaining comparison table module and the initial data obtaining module both adopt a web crawler algorithm, and the algorithm steps are as follows:
selecting a seed URL of a webpage to be crawled;
putting the URLs into a URL queue to be crawled;
acquiring corresponding web pages according to URLs in a queue to be crawled, analyzing a DNS, downloading the web pages corresponding to the URLs, storing the web pages into a downloaded web page library, and putting the URLs into a crawled URL queue;
and analyzing the URL in the crawled URL queue, analyzing other URLs in the crawled URL queue, and putting the URL into a to-be-crawled URL queue.
As a further improvement of the technical solution, the data classification unit includes a monthly data classification module, a quarterly data classification module and an annual data classification module; the monthly data classification module is used for classifying the data report according to months; the quarterly data classification module is used for classifying the data report according to the quarterly; the quarterly data classification module is used for classifying the data report according to the year.
As a further improvement of the technical solution, the data checking unit includes a data checking module, a normal checking module and an abnormal checking module; the data checking module is used for checking the acquired data report and generating correct checking information and error information; the normal checking module is used for receiving correct information checked by the data checking module; the abnormal checking module is used for receiving error information checked by the data checking module.
As a further improvement of the technical solution, the data checking module adopts a structured comparison algorithm, and the algorithm steps are as follows:
the method comprises the following steps: taking all functions in an executable file as an initial function set
And
will be
Put into a data report pending queue
Performing the following steps;
step two: slave queue
Set of mid-elimination functions
Will be
Pairing the unique and same function data of the report table data, and collecting all the subfunctions of the paired functions
Put into queue
Performing the following steps;
step three: repeating the step two until the queue is reached
When the data report is empty, the comparison algorithm is finished, and the data report is checked and finished.
As a further improvement of the technical scheme, the extraction and check unit comprises an error list data and corresponding comparison table data extraction module, a data secondary comparison module and a judgment processing module; the error list data and corresponding comparison table data extraction module is used for extracting error report data covered by error information; the data secondary comparison module is used for secondarily comparing the extracted error report data with the data report; and the judgment processing module is used for judging and analyzing the error condition of the data report after the data secondary comparison module finishes comparison.
As a further improvement of the technical solution, the extracting and checking unit further includes a data sequence error module, a data input error module, and a data calculation error module, where the data sequence error module is configured to receive a data sequence exception report obtained by the judging and processing module after the judgment; the data input error module is used for receiving an arithmetic data input error report obtained by the judgment processing module after judgment; and the data calculation error module is used for receiving a data calculation error report obtained after judgment by the judgment processing module during conveying.
As a further improvement of the technical solution, the extracting and checking unit further includes a calibration processing module, the calibration processing module is configured to calibrate and repair a data report problem formed by the data sequence error module, the data input error module, and the data calculation error module, and the calibration processing module sends the data to the data checking module for secondary checking after calibration and repair.
As a further improvement of the technical scheme, the data checking module comprises a basic checking module and a secondary checking module, wherein the basic checking module is used for checking the overall calculation data report; the secondary checking module is used for receiving and checking the data report sent by the calibration processing module, and outputting the data report through the checking completion unit when the data report is checked to be correct, so as to form a checking completion report.
Compared with the prior art, the invention has the beneficial effects that:
in the data checking system for automatically checking the finance through data checking, through checking the data report, when the data report is checked wrongly, only the range of error data covered by error information is extracted, so that the problem that the data report needs to be checked repeatedly in the whole at present is solved, the currently extracted error data is analyzed, the reason for forming the error data is identified, specific operation checking and repairing is carried out according to specific reasons, the working efficiency is improved, secondary checking is carried out after the error data is repaired, the error-free data report after checking and repairing is ensured, and the checking and repairing speed of the current data report is accelerated.
Drawings
FIG. 1 is an overall block diagram of the present invention;
FIG. 2 is a block diagram of a data unit module of the present invention;
FIG. 3 is a block diagram of a data sorting unit module according to the present invention;
FIG. 4 is a flow chart of the data verification unit, the extraction verification unit and the verification completion unit according to the present invention.
The various reference numbers in the figures mean:
100. acquiring a data unit;
110. acquiring a data comparison table module; 120. acquiring an initial data module;
200. a data classification unit;
210. a monthly data classification module; 220. a quarterly data classification module; 230. an annual data classification module;
300. a data collation unit;
310. a data collation module; 311. a base verification module; 312. a secondary checking module; 320. a normal checking module; 330. an abnormality checking module;
400. an extraction and check unit;
410. an error list data and corresponding comparison table data extraction module; 420. a data secondary comparison module; 430. a judgment processing module; 431. a data sequence error module; 432. a data input error module; 433. a data calculation error module; 440. a calibration processing module;
500. and a verification completion unit.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious 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.
In the description of the present invention, it is to be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", and the like, indicate orientations and positional relationships based on those shown in the drawings, and are used only for convenience of description and simplicity of description, and do not indicate or imply that the equipment or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus, should not be considered as limiting the present invention.
Example 1
Referring to fig. 1-4, the present embodiment provides a data collation system for performing an autonomous collation of finance through data collation, including the data collation system at least comprising:
the data acquiring unit 100, wherein the data acquiring unit 100 is used for acquiring a data report and a data comparison table;
a data classification unit 200, the data classification unit 200 being configured to classify the data acquired by the data acquisition unit 100;
a data checking unit 300, wherein the data checking unit 300 is used for analyzing and checking the acquired data and generating correct checking information and error information;
the extraction and checking unit 400 is used for receiving error information generated during checking of the data checking unit 300, extracting an error data range covered by the error information, judging an analysis condition, checking error data, generating integrated data, and sending the integrated data to the data checking unit 300 for analysis and checking again;
and a collation completion unit 500, wherein the collation completion unit 500 receives information collated by the data collation unit 300 and generates a collation completion report.
The data report needing to be checked and the data comparison table are obtained by the data obtaining unit 100, the obtained data report is subjected to multiple classification by the data classifying unit 200, so that the obtained large number of data reports are classified, the classified data report corresponds to the data comparison table, the situation that a large number of data reports needing not to be checked are also in the data report is avoided, then the data report needing to be checked in the data classifying unit 200 is analyzed, compared and checked by the data checking unit 300, when the current data report is checked, correct checking information is generated and sent to the checking finishing unit 500, at the moment, the checking work of the data report is finished, when the current data report is checked, wrong checking information is generated, the range of error data covered by the error information is extracted, and the currently extracted error data is judged, the reason for identifying the error data is formed, the speed of checking the error data is accelerated, the checked information data is formed into integrated data, the integrated data is sent to the data checking unit 300 for secondary checking, correct information is generated if checking is not correct and sent to the checking completion unit 500, and the checking and processing work of the data report is completed at this time (wherein the data report is a page table made by the entrance, the exit and the sale inside an enterprise, such as an excle table, the data comparison table is an initial original file table, and the original file table is a correct statistical table).
The data acquiring unit 100 includes an data acquiring comparison table module 110 and an initial data acquiring module 120, where the data acquiring comparison table module 110 is configured to acquire a data comparison table collated with the data report; the initial data obtaining module 120 is configured to obtain a large number of data reports, where the data reports and the data report comparison table are both obtained for reason of normal operation of subsequent checking of information data.
The data obtaining comparison table module 110 and the initial data obtaining module 120 both use a web crawler algorithm, and the algorithm steps are as follows:
selecting a seed URL of a webpage to be crawled;
putting the URLs into a URL queue to be crawled;
acquiring corresponding web pages according to URLs in a queue to be crawled, analyzing a DNS, downloading the web pages corresponding to the URLs, storing the web pages into a downloaded web page library, and putting the URLs into a crawled URL queue;
and analyzing the URL in the crawled URL queue, analyzing other URLs in the crawled URL queue, and putting the URL into a to-be-crawled URL queue.
In order to deal with the situation that the data report is checked to further analyze and process the data report checking, the data checking unit 300 comprises a data checking module 310, a normal checking module 320 and an abnormal checking module 330; the data checking module 310 is used for checking the acquired data report and generating correct checking information and error information; the normal check module 320 is used for receiving correct information checked by the data check module 310; the abnormal checking module 330 is used for receiving error information checked by the data checking module 310, checking a data report by the data checking module 310, sending correct information formed by the data report checked by the data checking module 310 to the normal checking module 320, sending the correct information to the checking completion unit 500 by the normal checking module 320 to be received, and completing the checking, if the error information formed by the data report checked by the data checking module 310 is sent to the abnormal checking module 330, after the abnormal checking module 330 receives the error information, the error information is sent to the extraction checking unit 400, the error data report covered by the error information is extracted and checked, and the data checking unit 300 is used for checking again, and if the error does not exist, the checking completion unit 500 is used for sending a checking completion prompt.
The data checking module 310 adopts a structured comparison algorithm, and the algorithm steps are as follows:
the method comprises the following steps: taking all functions in an executable file as an initial function set
And
will be
Put into a data report pending queue
Performing the following steps;
step two: slave queue
Set of mid-elimination functions
Will be
Pairing the unique and same function data of the report table data, and collecting all the subfunctions of the paired functions
Put into queue
Performing the following steps;
step three: repeating the step two until the queue is reached
When the data report is empty, the comparison algorithm is finished, and the data report is checked and finished.
In order to perform further detailed analysis on the operation job of the above-mentioned extraction and check unit 400, the extraction and check unit 400 includes an error list data and corresponding comparison table data extraction module 410, a data secondary comparison module 420 and a judgment processing module 430; the error list data and corresponding look-up table data extraction module 410 is used for extracting error report data covered by the error information; the data secondary comparison module 420 is used for performing secondary comparison on the extracted error report data and the data report; the judgment processing module 430 is configured to judge and analyze an error condition of the data report according to the comparison of the data secondary comparison module 420, extract error information of the data report for error report calculation data covered by multiple columns or multiple rows close to each other in the data report through the error list data and corresponding comparison table data extraction module 410, extract a comparison table data range corresponding to the error data report, perform secondary comparison and correction through the data secondary comparison module 420 after extraction, and perform judgment and analysis on the compared error report data through the judgment processing module 430 to obtain a specific reason for forming the error report data, thereby facilitating an operator to accelerate correction processing on the data report.
The extracting and checking unit 400 further includes a data sequence error module 431, a data input error module 432, and a data calculation error module 433, where the data sequence error module 431 is configured to receive a data sequence exception report obtained by the judging and processing module 430 after judgment; the data input error module 432 is configured to receive an arithmetic data input error report obtained by the judgment processing module 430 after the judgment; the data calculation error module 433 is configured to receive a data calculation error report obtained by the judgment processing module 430 after the judgment during the transportation, classify the specific analysis condition obtained by the judgment processing module 430 after the judgment, and locate a specific problem of an error of the data report through the data sequence error module 431, the data input error module 432, and the data calculation error module 433, so that a person can further correct the data report.
The extracting and checking unit 400 further includes a calibration processing module 440, the calibration processing module 440 is configured to calibrate and repair the data report problem formed by the data sequence error module 431, the data input error module 432, and the data calculation error module 433, and the calibration processing module 440 sends the data report problem to the data checking module 310 for secondary checking after calibration and repair. In order to process the situations of the data sequence error module 431, the data input error module 432 and the data calculation error module 433 which are formed in the data report, so that the data report with errors is subjected to secondary proofreading after calibration and repair (wherein the problem formed by the data sequence error module 431 is that two adjacent data columns are reversed, only the reversed data columns need to be reset through the calibration processing module 440, and multiple times of complicated repair are not needed, the problem formed by the data input error module 432 is a stroke error formed when a person initially inputs the data report, so that accurate positioning repair can be achieved, and the problem formed by the data calculation error module 433 is an abnormality generated when the person operates through a calculation formula in the data report, so that specific analysis is performed on the possible situations, and the repair efficiency of the calibration processing module 440 is accelerated).
In order to ensure that the calibrated and repaired data report is circularly and specifically checked, the data checking module 310 comprises a basic checking module 311 and a secondary checking module 312, wherein the basic checking module 311 is used for checking the overall calculation data report; the secondary checking module 312 is configured to receive and check the data report sent by the calibration processing module 440, output the data report through the checking completion unit 500 and form a checking completion report when there is no error in the checking, receive and check the data report after calibration and repair sent by the calibration processing module 440 by the secondary checking module 312 of the data checking module 310, output the data report through the checking completion unit 500 when there is no error in the checking, form an error abnormal report to send to the normal checking module 320 when there is still an error in the secondary checking performed by the secondary checking module 312, and perform cyclic extraction, analysis and checking through the extraction checking unit 400 by the normal checking module 320.
Example 2
In order to classify a large amount of data in the data classification unit 200, avoid checking a data report that does not need to be checked, and accelerate the checking efficiency, the embodiment further improves on the basis of embodiment 1, as shown in fig. 3 specifically:
for further analysis of the data classification unit 200, the data classification unit 200 includes a monthly data classification module 210, a quarterly data classification module 220, and an annual data classification module 230; the monthly data classification module 210 is used for classifying the data report according to months; the quarterly data classification module 220 is used for classifying the data report according to the quarterly; the quarterly data classification module 220 is used for classifying the data reports according to the year, a large number of data reports obtained in the above are classified according to the parts to be checked under the monthly data classification module 210, the quarterly data classification module 220 and the annual data classification module 230, the monthly data classification module 210 is used when the monthly data reports need to be checked, the quarterly data classification module 220 is used when the quarterly data reports are checked, namely the annual data classification module 230 is used when the annual data reports are checked, the secondary checking of the previously checked data reports when the data reports are checked is avoided, and the checking efficiency of the data reports is improved.
The foregoing shows and describes the general principles, essential features, and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, and the preferred embodiments of the present invention are described in the above embodiments and the description, and are not intended to limit the present invention. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (10)
1. A data checking system for performing autonomous financial checking through data checking comprises a data checking system and is characterized in that: the data collation system includes at least:
the data acquisition unit (100) is used for acquiring a data report and a data comparison table;
a data classification unit (200), the data classification unit (200) being configured to classify data acquired by the acquisition data unit (100);
a data checking unit (300), wherein the data checking unit (300) is used for analyzing and checking the acquired data and generating correct checking information and error information;
the extraction and checking unit (400) is used for receiving error information generated when the data checking unit (300) is checked, extracting an error data range covered by the error information, judging the analysis condition, checking the error data, generating integrated data, and sending the integrated data to the data checking unit (300) for analysis and checking again;
a verification completion unit (500), wherein the verification completion unit (500) is used for receiving the information verified by the data verification unit (300) and generating a verification completion report.
2. A data reconciliation system for autonomous reconciliation of financial transactions by data reconciliation according to claim 1 wherein: the data acquiring unit (100) comprises an acquiring data comparison table module (110) and an acquiring initial data module (120), wherein the acquiring data comparison table module (110) is used for acquiring a data comparison table which is collated with a data report; the get initial data module (120) is used for getting a large number of data reports.
3. A data reconciliation system for autonomous reconciliation of financial transactions by data reconciliation according to claim 2 wherein: the data acquisition comparison table module (110) and the initial data acquisition module (120) both adopt a web crawler algorithm, and the algorithm steps are as follows:
selecting a seed URL of a webpage to be crawled;
putting the URLs into a URL queue to be crawled;
acquiring corresponding web pages according to URLs in a queue to be crawled, analyzing a DNS, downloading the web pages corresponding to the URLs, storing the web pages into a downloaded web page library, and putting the URLs into a crawled URL queue;
and analyzing the URL in the crawled URL queue, analyzing other URLs in the crawled URL queue, and putting the URL into a to-be-crawled URL queue.
4. A data reconciliation system for autonomous reconciliation of financial transactions by data reconciliation according to claim 1 wherein: the data classification unit (200) comprises a monthly data classification module (210), a quarterly data classification module (220) and an annual data classification module (230); the monthly data classification module (210) is used for classifying the data report according to months; the quarterly data classification module (220) is used for classifying the data report according to the quarterly; the quarterly data classification module (220) is used for classifying the data report according to the year.
5. A data reconciliation system for autonomous reconciliation of financial transactions by data reconciliation according to claim 1 wherein: the data checking unit (300) comprises a data checking module (310), a normal checking module (320) and an abnormal checking module (330); the data checking module (310) is used for checking the acquired data report and generating correct checking information and error checking information; the normal checking module (320) is used for receiving correct information checked by the data checking module (310); the abnormal checking module (330) is used for receiving error information checked by the data checking module (310).
6. The data reconciliation system for an autonomous reconciliation of a financial transaction with data from claim 5 wherein: the data checking module (310) adopts a structured comparison algorithm, and the algorithm steps are as follows:
the method comprises the following steps: taking all functions in an executable file as an initial function set
And
will be
Put into a data report pending queue
Performing the following steps;
step two: slave queue
Set of mid-elimination functions
Will be
Pairing the unique and same function data of the report table data, and collecting all the subfunctions of the paired functions
Put into queue
Performing the following steps;
step three: repeating the step two until the queue is reached
When the data report is empty, the comparison algorithm is finished, and the data report is checked and finished.
7. The data reconciliation system for the autonomous reconciliation of financials by data reconciliation of claim 5 wherein: the extraction checking unit (400) comprises an error list data and corresponding comparison table data extraction module (410), a data secondary comparison module (420) and a judgment processing module (430); the error list data and corresponding comparison table data extraction module (410) is used for extracting error report data covered by error information; the data secondary comparison module (420) is used for comparing the extracted error report data with the data report for the second time; and the judgment processing module (430) is used for judging the error condition of the analysis data report after the comparison of the data secondary comparison module (420) is finished.
8. A data reconciliation system for autonomous reconciliation of financial transactions by data reconciliation of claim 7 wherein: the extraction checking unit (400) further comprises a data sequence error module (431), a data input error module (432) and a data calculation error module (433), wherein the data sequence error module (431) is used for receiving a data sequence abnormal report obtained by the judgment processing module (430) after judgment; the data input error module (432) is used for receiving an arithmetic data input error report obtained by the judgment processing module (430) after judgment; and the data calculation error module (433) is used for receiving a data calculation error report obtained by the judgment processing module (430) after judgment.
9. A data reconciliation system for an autonomous reconciliation of financials by data reconciliation according to claim 8 wherein: the extracting and checking unit (400) further comprises a calibration processing module (440), the calibration processing module (440) is used for calibrating and repairing data report problems formed by the data sequence error module (431), the data input error module (432) and the data calculation error module (433), and the calibration processing module (440) sends the data report problems to the data checking module (310) for secondary checking after calibration and repair.
10. A data reconciliation system for an autonomous reconciliation of financials by data reconciliation according to claim 9 wherein: the data checking module (310) comprises a basic checking module (311) and a secondary checking module (312), wherein the basic checking module (311) is used for checking the overall calculation data report; the secondary checking module (312) is used for receiving the data report sent by the calibration processing module (440), checking the data report, and outputting and forming a checking completion report through the checking completion unit (500) when the data report is checked.
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| CN113806400A (en) * | 2021-01-21 | 2021-12-17 | 京东安联财产保险有限公司 | Financial data processing method and device, storage medium and electronic equipment |
| CN114218110A (en) * | 2021-12-17 | 2022-03-22 | 平安养老保险股份有限公司 | Account checking test method and device for financial data, computer equipment and storage medium |
| CN114358903A (en) * | 2022-01-04 | 2022-04-15 | 中国工商银行股份有限公司 | RPA-based multi-system accounting checking method and device and computer equipment |
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