CN110297833A - A kind of bordereau error correction method - Google Patents
A kind of bordereau error correction method Download PDFInfo
- Publication number
- CN110297833A CN110297833A CN201910601980.3A CN201910601980A CN110297833A CN 110297833 A CN110297833 A CN 110297833A CN 201910601980 A CN201910601980 A CN 201910601980A CN 110297833 A CN110297833 A CN 110297833A
- Authority
- CN
- China
- Prior art keywords
- field
- bordereau
- feature library
- subject
- error correction
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/22—Indexing; Data structures therefor; Storage structures
- G06F16/2282—Tablespace storage structures; Management thereof
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/22—Matching criteria, e.g. proximity measures
Abstract
Bordereau data error-correcting method disclosed by the invention, it is related to bordereau processing technology field, utilize machine learning model, the bordereau that user uploads is analyzed and handled, the feature of each subject corresponding field of bordereau is obtained, fisrt feature library is generated, field each in fisrt feature library and each field in preset second feature library is compared one by one, judge that fisrt feature library and second feature library with the presence or absence of different field, improve the accuracy and efficiency of error correction.
Description
Technical field
The present invention relates to bordereau processing technology fields, and in particular to a kind of bordereau error correction method.
Background technique
Currently, due to various businesses report (such as: it is balance sheet, profit flow table, value-added tax table, income-tax schedules, customized
Table) version it is different, cause software program analysis difficulty increase.By taking financial statement as an example, the annual tax bureau also can be to original report
Table is modified, and the difficulty of the bordereau correcting data error to user's input is increased.
Summary of the invention
To solve the deficiencies in the prior art, the embodiment of the invention provides a kind of bordereau error correction method, this method packets
Include following steps:
Using machine learning model, the bordereau that user uploads is analyzed and handled, it is each to obtain bordereau
The feature of subject corresponding field generates fisrt feature library；
Field each in fisrt feature library and each field in preset second feature library are compared one by one, described in judgement
Fisrt feature library and the second feature library whether there is different field, if so, user is reminded to change the field simultaneously
The corresponding criteria field of the field is provided to select for user.
Preferably, the generating process in the second feature library includes:
Magnanimity bordereau is obtained from Internet resources and utilizes machine learning model, is extracted each in magnanimity bordereau
The feature of subject corresponding field generates second feature library.
Preferably, the bordereau uploaded to user, which is analyzed and handled, includes:
Delete the spcial character in each subject of bordereau, wherein the spcial character includes punctuation mark.
Preferably, judge that the fisrt feature library includes: with the presence or absence of different field with the second feature library
Obtain the column position at corresponding first field of each subject in the fisrt feature library and place；
According to the column position, each subject the second field corresponding in the second feature library is determined；
Judge whether first field and second field are identical, if not, it is determined that the fisrt feature library and institute
Second feature inventory is stated in different field.
Bordereau data error-correcting method provided in an embodiment of the present invention has the advantages that
By collecting the feature of each subject corresponding field of magnanimity bordereau, while the business report uploaded according to user
Table data are analysed and compared, if it find that the bordereau data of mismatch or mistake, carry out friendly prompt to user, together
When to prevent data maloperation, need user to confirm manually, improve the accuracy and efficiency of error correction.
Detailed description of the invention
Fig. 1 is bordereau error correction method flow diagram provided in an embodiment of the present invention；
Fig. 2 is the system prompt interface schematic diagram generated using bordereau error correction method provided in an embodiment of the present invention.
Specific embodiment
Specific introduce is made to the present invention below in conjunction with the drawings and specific embodiments.
Referring to Fig. 1, bordereau data error-correcting method provided in an embodiment of the present invention the following steps are included:
S101 analyzes and handles to the bordereau that user uploads, obtain bordereau using machine learning model
The feature of each subject corresponding field generates fisrt feature library.
Field each in fisrt feature library and each field in preset second feature library are compared one by one, are sentenced by S102
Disconnected fisrt feature library and second feature library whether there is different field, if so, user is reminded to change the field and provide
The corresponding criteria field of field is selected for user.
Optionally, the generating process in second feature library includes:
Magnanimity bordereau is obtained from Internet resources and utilizes machine learning model, is extracted each in magnanimity bordereau
The feature of subject corresponding field generates second feature library.
As, there are field A, 2017 version profit flow tables exist in a specific embodiment, such as 2014 version profit flow tables
Field A-a, then second feature library includes field A and A-a.
Optionally, the bordereau uploaded to user, which is analyzed and handled, includes:
Delete the spcial character in each subject of bordereau, wherein the spcial character includes punctuation mark.
It include: " one, operating income " as the corresponding field of subject in a specific embodiment, such as profit flow table,
The corresponding field of the subject is " operating income " after then deleting spcial character, by " operating income " and predefined spy
Field in sign library compares.
Optionally, judge that fisrt feature library includes: with the presence or absence of different field with second feature library
Obtain the column position at corresponding first field of each subject and place in fisrt feature library；
According to column position, each subject the second field corresponding in second feature library is determined；
Judge whether the first field and second field are identical, if not, it is determined that fisrt feature library and second feature library
There are different fields.
As shown in Fig. 2, will pop up drop-down when the feature database and preset feature database of the bordereau that user uploads are inconsistent
Frame prompts user to carry out selection criteria field.
Bordereau data error-correcting method provided in an embodiment of the present invention uploads user using machine learning model
Bordereau is analyzed and is handled, and obtains the feature of each subject corresponding field of bordereau, generates fisrt feature library, by the
Each field compares one by one with each field in preset second feature library in one feature database, judges fisrt feature library and second
Feature database whether there is different field, improve the accuracy and efficiency of error correction.
In the above-described embodiments, it all emphasizes particularly on different fields to the description of each embodiment, there is no the portion being described in detail in some embodiment
Point, reference can be made to the related descriptions of other embodiments.
It is understood that the correlated characteristic in the above method and device can be referred to mutually.In addition, in above-described embodiment
" first ", " second " etc. be and not represent the superiority and inferiority of each embodiment for distinguishing each embodiment.
It is apparent to those skilled in the art that for convenience and simplicity of description, the system of foregoing description,
The specific work process of device and unit, can refer to corresponding processes in the foregoing method embodiment, and details are not described herein.
Algorithm and display are not inherently related to any particular computer, virtual system, or other device provided herein.
Various general-purpose systems can also be used together with teachings based herein.As described above, it constructs required by this kind of system
Structure be obvious.In addition, the present invention is also not directed to any particular programming language.It should be understood that can use various
Programming language realizes summary of the invention described herein, and the description done above to language-specific is to disclose this hair
Bright preferred forms.
In addition, memory may include the non-volatile memory in computer-readable medium, random access memory
(RAM) and/or the forms such as Nonvolatile memory, such as read-only memory (ROM) or flash memory (flash RAM), memory includes extremely
A few storage chip.
It should be understood by those skilled in the art that, embodiments herein can provide as method, system or computer program
Product.Therefore, complete hardware embodiment, complete software embodiment or reality combining software and hardware aspects can be used in the application
Apply the form of example.Moreover, it wherein includes the computer of computer usable program code that the application, which can be used in one or more,
The computer program implemented in usable storage medium (including but not limited to magnetic disk storage, CD-ROM, optical memory etc.) produces
The form of product.
The application is referring to method, the process of equipment (system) and computer program product according to the embodiment of the present application
Figure and/or block diagram describe.It should be understood that every one stream in flowchart and/or the block diagram can be realized by computer program instructions
The combination of process and/or box in journey and/or box and flowchart and/or the block diagram.It can provide these computer programs
Instruct the processor of general purpose computer, special purpose computer, Embedded Processor or other programmable data processing devices to produce
A raw machine, so that being generated by the instruction that computer or the processor of other programmable data processing devices execute for real
The device for the function of being specified in present one or more flows of the flowchart and/or one or more blocks of the block diagram.
These computer program instructions, which may also be stored in, is able to guide computer or other programmable data processing devices with spy
Determine in the computer-readable memory that mode works, so that it includes referring to that instruction stored in the computer readable memory, which generates,
Enable the manufacture of device, the command device realize in one box of one or more flows of the flowchart and/or block diagram or
The function of being specified in multiple boxes.
These computer program instructions also can be loaded onto a computer or other programmable data processing device, so that counting
Series of operation steps are executed on calculation machine or other programmable devices to generate computer implemented processing, thus in computer or
The instruction executed on other programmable devices is provided for realizing in one or more flows of the flowchart and/or block diagram one
The step of function of being specified in a box or multiple boxes.
In a typical configuration, calculating equipment includes one or more processors (CPU), input/output interface, net
Network interface and memory.
Memory may include the non-volatile memory in computer-readable medium, random access memory (RAM) and/
Or the forms such as Nonvolatile memory, such as read-only memory (ROM) or flash memory (flash RAM).Memory is computer-readable Jie
The example of matter.
Computer-readable medium includes permanent and non-permanent, removable and non-removable media can be by any method
Or technology come realize information store.Information can be computer readable instructions, data structure, the module of program or other data.
The example of the storage medium of computer includes, but are not limited to phase change memory (PRAM), static random access memory (SRAM), moves
State random access memory (DRAM), other kinds of random access memory (RAM), read-only memory (ROM), electric erasable
Programmable read only memory (EEPROM), flash memory or other memory techniques, read-only disc read only memory (CD-ROM) (CD-ROM),
Digital versatile disc (DVD) or other optical storage, magnetic cassettes, tape magnetic disk storage or other magnetic storage devices
Or any other non-transmission medium, can be used for storage can be accessed by a computing device information.As defined in this article, it calculates
Machine readable medium does not include temporary computer readable media (transitory media), such as the data-signal and carrier wave of modulation.
It should also be noted that, the terms "include", "comprise" or its any other variant are intended to nonexcludability
It include so that the process, method, commodity or the equipment that include a series of elements not only include those elements, but also to wrap
Include other elements that are not explicitly listed, or further include for this process, method, commodity or equipment intrinsic want
Element.In the absence of more restrictions, the element limited by sentence "including a ...", it is not excluded that including element
There is also other identical elements in process, method, commodity or equipment.
It will be understood by those skilled in the art that embodiments herein can provide as method, system or computer program product.
Therefore, complete hardware embodiment, complete software embodiment or embodiment combining software and hardware aspects can be used in the application
Form.It is deposited moreover, the application can be used to can be used in the computer that one or more wherein includes computer usable program code
The shape for the computer program product implemented on storage media (including but not limited to magnetic disk storage, CD-ROM, optical memory etc.)
Formula.
The above is only embodiments herein, are not intended to limit this application.To those skilled in the art,
Various changes and changes are possible in this application.It is all within the spirit and principles of the present application made by any modification, equivalent replacement,
Improve etc., it should be included within the scope of the claims of this application.
Claims (4)
1. a kind of bordereau error correction method characterized by comprising
Using machine learning model, the bordereau that user uploads is analyzed and handled, each subject of bordereau is obtained
The feature of corresponding field generates fisrt feature library；
Field each in fisrt feature library and each field in preset second feature library are compared one by one, judge described first
Feature database and the second feature library whether there is different field, if so, reminding user to change the field and providing
The corresponding criteria field of the field is selected for user.
2. bordereau error correction method according to claim 1, which is characterized in that the generating process in the second feature library
Include:
Magnanimity bordereau is obtained from Internet resources and utilizes machine learning model, extracts each subject in magnanimity bordereau
The feature of corresponding field generates second feature library.
3. bordereau error correction method according to claim 1, which is characterized in that carried out to the bordereau that user uploads
It analyzes and handles and include:
Delete the spcial character in each subject of bordereau, wherein the spcial character includes punctuation mark.
4. bordereau error correction method according to claim 1, which is characterized in that judge the fisrt feature library with it is described
Second feature library includes: with the presence or absence of different field
Obtain the column position at corresponding first field of each subject in the fisrt feature library and place；
According to the column position, each subject the second field corresponding in the second feature library is searched；
Judge whether first field and second field are identical, if not, it is determined that the fisrt feature library and described the
There are different fields for two feature databases.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910601980.3A CN110297833A (en) | 2019-07-05 | 2019-07-05 | A kind of bordereau error correction method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910601980.3A CN110297833A (en) | 2019-07-05 | 2019-07-05 | A kind of bordereau error correction method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN110297833A true CN110297833A (en) | 2019-10-01 |
Family
ID=68030364
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910601980.3A Pending CN110297833A (en) | 2019-07-05 | 2019-07-05 | A kind of bordereau error correction method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110297833A (en) |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102855229A (en) * | 2011-06-30 | 2013-01-02 | 镇江雅迅软件有限责任公司 | Self-defined statistical report form generating system based on EXCEL form |
| CN104699748A (en) * | 2014-12-19 | 2015-06-10 | 深圳市燃气集团股份有限公司 | Method and system for showing report form with non-fixed column numbers |
| US20160337228A1 (en) * | 2014-01-23 | 2016-11-17 | Huawei Technologies Co., Ltd. | Flow table modifying method, flow table modifying apparatus, and openflow network system |
| CN108090516A (en) * | 2017-12-27 | 2018-05-29 | 第四范式（北京）技术有限公司 | Automatically generate the method and system of the feature of machine learning sample |
| CN108197207A (en) * | 2017-12-28 | 2018-06-22 | 南京涵韬信息科技有限公司 | Batch data matches introduction method |
| CN109271411A (en) * | 2018-09-28 | 2019-01-25 | 中国平安财产保险股份有限公司 | Report form generation method, device, computer equipment and storage medium |
| CN109344831A (en) * | 2018-08-22 | 2019-02-15 | 中国平安人寿保险股份有限公司 | A kind of tables of data recognition methods, device and terminal device |
| CN109766534A (en) * | 2018-12-19 | 2019-05-17 | 益萃网络科技（中国）有限公司 | Report form generation method, device, computer equipment and readable storage medium storing program for executing |
-
2019
- 2019-07-05 CN CN201910601980.3A patent/CN110297833A/en active Pending
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102855229A (en) * | 2011-06-30 | 2013-01-02 | 镇江雅迅软件有限责任公司 | Self-defined statistical report form generating system based on EXCEL form |
| US20160337228A1 (en) * | 2014-01-23 | 2016-11-17 | Huawei Technologies Co., Ltd. | Flow table modifying method, flow table modifying apparatus, and openflow network system |
| CN104699748A (en) * | 2014-12-19 | 2015-06-10 | 深圳市燃气集团股份有限公司 | Method and system for showing report form with non-fixed column numbers |
| CN108090516A (en) * | 2017-12-27 | 2018-05-29 | 第四范式（北京）技术有限公司 | Automatically generate the method and system of the feature of machine learning sample |
| CN108197207A (en) * | 2017-12-28 | 2018-06-22 | 南京涵韬信息科技有限公司 | Batch data matches introduction method |
| CN109344831A (en) * | 2018-08-22 | 2019-02-15 | 中国平安人寿保险股份有限公司 | A kind of tables of data recognition methods, device and terminal device |
| CN109271411A (en) * | 2018-09-28 | 2019-01-25 | 中国平安财产保险股份有限公司 | Report form generation method, device, computer equipment and storage medium |
| CN109766534A (en) * | 2018-12-19 | 2019-05-17 | 益萃网络科技（中国）有限公司 | Report form generation method, device, computer equipment and readable storage medium storing program for executing |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US10817271B2 (en) | Automated dependency analyzer for heterogeneously programmed data processing system | |
| US11893500B2 (en) | Data classification for data lake catalog | |
| TWI718643B (en) | Method and device for identifying abnormal groups | |
| KR102432104B1 (en) | Systems and methods for determining relationships between data elements | |
| US9336184B2 (en) | Representation of an interactive document as a graph of entities | |
| US9576072B2 (en) | Database calculation using parallel-computation in a directed acyclic graph | |
| CN106557486A (en) | A kind of storage method and device of data | |
| US20170161323A1 (en) | Automatic generation of sub-queries | |
| US20120159333A1 (en) | Representation of an interactive document as a graph of entities | |
| van Zelst et al. | ILP-Based Process Discovery Using Hybrid Regions. | |
| CN107943465B (en) | Method and device for generating HTML (Hypertext markup language) form | |
| CN105824855B (en) | Method and device for screening and classifying data objects and electronic equipment | |
| CN111046052B (en) | Method, device and equipment for storing operation records in database | |
| US10963963B2 (en) | Rule based hierarchical configuration | |
| US20170300461A1 (en) | Representation of an Interactive Document as a Graph of Entities | |
| US10437857B2 (en) | Mapping device, mapping method, and recording medium | |
| CN109583473A (en) | A kind of generation method and device of characteristic | |
| CN109656946A (en) | A kind of multilist relation query method, device and equipment | |
| CN108665142A (en) | A kind of the recommendation method, apparatus and equipment of rule | |
| CN110019195A (en) | A kind of storage method and device of data | |
| US20200409670A1 (en) | Automatic software generation for computer systems | |
| CN110297833A (en) | A kind of bordereau error correction method | |
| US20150170068A1 (en) | Determining analysis recommendations based on data analysis context | |
| CN109376285A (en) | Data sorting verification method, electronic equipment and medium based on json format | |
| US10509659B1 (en) | Input processing logic to produce outputs for downstream systems using configurations |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20191001 |
|
| RJ01 | Rejection of invention patent application after publication |