CN111191429A - System and method for automatic filling of data table - Google Patents
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Abstract
The invention discloses a system and a method for automatically filling a data form, which comprises the steps of obtaining source data, identifying and extracting the data, bidirectionally matching and filling the extracted data and the data form to be filled, and storing and exporting a filled data form object; the user uploads or inputs source data through the source data acquisition module; the source data analysis module analyzes the source data and presents an analysis result; the target data table analysis module identifies the meaning of the cells in the target data table provided by the user; the bidirectional matching module is used for bidirectionally matching the analysis result of the source data with the target data table and automatically filling the data, and then the data table export module is used for exporting the filled data or the analysis result data with the assistance of user confirmation.
Description
Technical Field
The invention belongs to the technical field of automatic filling of data forms, and particularly relates to a system and a method for automatically filling a data form.
Background
In many industries, a large amount of complex data needs to be extracted from source data with wide source and complex structure and filled into an existing data table.
In the past, required data is extracted from a large amount of data with wide sources and complex structures and is filled into a data table to be filled, and the required data is often extracted manually by a practitioner, so that the efficiency is very low.
For this problem, the current tool for Extracting data in a targeted manner mainly has a method in patent US 9,292,485Extracting data cell convertible to model object, which enables a user to extract relevant data from a relational database to an existing form, but the tool requires the user to be skilled in mastering relevant operations of the database and to formulate a set of complex query rules. This requirement raises the threshold for using the tool, making it time and effort for the relevant practitioner to learn the use of the tool and to master more complex relational database query rules and operations before using the tool. The method provided by the patent US 10,235,028Text extraction graphs allows the user to extract data from the images and generate reports. However, the data extracted by the tool is only extracted from the image by characters, and cannot be directly used for filling the data table to generate the data table ultimately required by the user.
While the tool software for filling data into the data table is mainly through spreadsheet editing software, such as Microsoft Excel, Google Sheets, etc., these general spreadsheet editing software allow users to edit the data table in spreadsheet form in what you see is what you get, and can directly write various types of contents including text, numerical values, formulas, etc. into the data table. The flexibility of common spreadsheet software makes it possible to edit almost all types of data forms to be filled, but the flexibility of this manner of operation makes it easy for data content filling errors to occur when large amounts of data are manually filled using this manner, and the manner of operation of this data filling is time consuming and laborious. To improve the efficiency of data population requires users to master many advanced spreadsheet editing skills, supplemented with a large number of exercises. Whether data is filled or advanced skill learning requires the relevant practitioner to spend a great deal of time and effort on repetitive work, greatly limiting the productivity of the relevant practitioner.
Tools for automatically filling data into data forms are mainly the method in patent US 7,426,496Assisted filing, which enables a user to automatically fill data into a data form using unlabelled text data or media data, and provides a graphical user interface to present the filling result to the user, allowing the user to manually modify and confirm the automatically filled data through the graphical user interface. However, the method only structures the data without labels, and does not provide the realization of matching and filling in the existing data table model. Since the fields, data names, formats and organization forms of the target data table and the data table as the data source may be different, the method cannot well solve the problem of automatic data filling in the situation.
Disclosure of Invention
Aiming at the defects of the prior art, the invention designs a system and a method for automatically analyzing data from source data and automatically matching and filling the data into the existing data form, which have high automation and stronger flexibility. The method can be used for filling the data table with the highest utilization rate, and is quicker and more accurate when being used for filling the data table, and is simple and convenient to use. The data form can be filled by relevant practitioners by using the tool, so that the time and energy of the practitioners in data screening, manual extraction and manual filling can be reduced, the time and energy of the practitioners are relieved from a large amount of complicated and repetitive work, the energy is put into creative work, and the productivity is improved.
The invention provides the following technical scheme
A system for automatically filling a data table comprises a memory, a source data acquisition module and a source data uploading module; the system is characterized by further comprising a bidirectional data matching module and an analysis data automatic filling module, wherein the bidirectional data matching module is connected with the source data acquisition module, the data uploading module and the storage;
acquiring source data through a source data acquisition module or a source data uploading module, storing the source data in a memory, analyzing the source data through a source data analysis module, and presenting an analysis result;
analyzing the target data table through a target data table analysis module to obtain an analysis result;
and matching the source data analysis result and the target table data analysis result through the bidirectional data matching module, and filling data in the table through the analysis data automatic filling module to obtain a target data table for table export.
Preferably, the data acquired by the source data acquisition module is one or more of text, image, pdf file, spreadsheet file, database query result and network search result.
Preferably, the source data analysis module analyzes and presents the data obtained by the source data uploading module, the user can manually confirm or modify the analysis and identification results, and the source data analysis module records the operation results of the user and modifies the analyzed data in real time.
Preferably, the source data analysis module comprises a background learning analysis data selection module, and can formulate an analysis mode according to an analysis rule customized by a user and a rule for analyzing certain industry or certain type of data customized by the user, so as to strengthen the analysis rule.
Preferably, the target data table parsing module parses a target data table provided by a user, and identifies the meaning of each cell therein.
Preferably, the bidirectional data matching module identifies items or area blocks to be matched in an existing data form to be filled in advance, and when the source data analysis module completes analysis, the data identified by the source data analysis module is bidirectionally matched with the data form to be filled, and the data form to be filled is automatically filled with data; the user can manually confirm or modify the matching and filling results, and when the user clicks or selects a certain item or a certain area in the data table to be filled, the matching module can intelligently prompt the item or the area corresponding to the item or the area in the analysis data and other items or areas which can be matched; when a user clicks or selects a certain item or a certain area in the analytical data, the bidirectional data matching module can intelligently prompt the item or the area to correspond to the item or the area in the data form to be filled and other items or areas which can be matched, and the bidirectional data matching module records the operation result of the user and modifies the data in the data form to be filled in real time.
Preferably, the system for automatically filling the data table further comprises a data table export module, and the data table export module is connected with the analysis data automatic filling module; exporting the data table, the user can choose to export the filled target data table or the parsing result data into a plurality of formats or directly store the data on the cloud server.
The invention also discloses a method for automatically filling the data table, which comprises the following steps:
s1, data acquisition: acquiring a source data analysis result and a target data table analysis result;
s2, bidirectional matching: selecting and matching the source data analysis result and the target data table analysis result;
s3, automatic filling of analysis data: and carrying out data filling on the table through an analysis data automatic filling module to obtain a target data table, and carrying out table export.
Preferably, in step S1, the source data of the source data parsing result is acquired by a source data acquiring module or a source data uploading module.
Preferably, in step S1, the target table data analysis result is obtained by analyzing the target data table by the target data table analysis module.
Preferably, the memory: and the source data acquisition module or the source data uploading module acquires the source data and stores the source data in the memory.
Preferably, the analysis data result is automatically filled in and exported through a data table export module.
Preferably, the populated target data is derived by a data table derivation module.
Compared with the prior art, the system and the method for automatically filling the data table have the following beneficial effects:
(1) according to the system and the method for automatically filling the data form, the efficiency of filling data into the data form is improved through the analysis data automatic filling module, and the problem of low filling efficiency is solved.
(2) The system and the method for automatically filling the data table automatically analyze the source data and the target data table through the data analysis module when filling the data.
(3) The system and the method for automatically filling the data form have different fields, data names, formats and organization forms of the data form of the target data form and the data form of the data source, and can automatically fill the data by utilizing the data form automatic filling module.
(4) The system and the method for automatically filling the data table can extract characters from the image and directly fill the characters into the data table to generate the data table finally required by a user.
(5) The system and the method for automatically filling the data table fill the analyzed data to the target data table through the analysis data automatic filling module.
(6) The system and the method for automatically filling the data forms have high automation degree and strong flexibility, solve the problem of manual filling, are used for filling the data forms with the highest utilization rate, and have wide application range and high working efficiency.
(7) The system and the method for automatically filling the data form can ensure that the time and the energy of a practitioner on data discrimination, manual extraction and manual filling can be reduced by using the tool to fill the data form, so that the time and the energy of the practitioner are released from a large amount of complicated and repetitive work, the energy is put into creative work, and the productivity of related practitioners is improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are required to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
FIG. 1 is a flow chart of the operation of the present invention.
FIG. 2 is a flow diagram of a data acquisition module of the present invention.
FIG. 3 is a flow diagram of the operation of the data parsing module of the present invention.
FIG. 4 is a flow diagram of a target data table parsing module of the present invention.
FIG. 5 is a flow chart of the bi-directional data matching module operation of the present invention.
FIG. 6 is a flow chart of the identify target data workflow of the present invention.
Fig. 7 is a schematic diagram of the first step in the operation of the matching module of the present invention.
Fig. 8 is a schematic diagram of a second step in the operation of the matching module of the present invention.
Fig. 9 is a third schematic diagram of the operation of the matching module of the present invention.
Fig. 10 is a diagram illustrating a fourth step in the operation of the matching module of the present invention.
Fig. 11 is a diagram illustrating a fifth step in the operation of the matching module of the present invention.
FIG. 12 is a data table export module workflow diagram of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be described in detail and completely with reference to the accompanying drawings. It is to be understood that the described embodiments are only a few embodiments of the present invention, and not all embodiments. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.
Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, fall within the scope of the present invention.
The invention provides a system and a method for automatically extracting data from source data and filling the data into a target data table, wherein the system comprises the following modules:
1. a data acquisition module;
2. a source data parsing module;
3. a target data table parsing module;
4. a bidirectional data matching module;
5. and a data table export module.
The work flow is shown in figure 1: the data acquisition module (101) receives a target data table (102) and source data (103) provided by a user or generated by a system, and the source data analysis module (105) analyzes the source data into structured analysis result data (107); the target data table analyzing module (104) analyzes the target data table and identifies the meaning of the cells in the target data table; the bidirectional data matching module (108) is used for bidirectionally matching the analysis result data with the target data table (106) and automatically filling (109) the analysis data into the target data table; a data table derivation module (110) derives (111) the populated target data table.
Data acquisition module
The data acquisition module receives at least one of source data and target data tables provided by a user or generated by a system for the parsing module to extract data. The structure of the module is shown in fig. 2, and the module comprises at least one of an arbitrary input module (201) and a file uploading module (202).
At any input module, the user can paste a link to text data or a network file, and drag a data file in any recognizable format to an input area, including but not limited to an image file, a pdf file, an excel form file, and the like.
And in the file uploading module, a user can upload a local data file in any recognizable format.
In addition to user manual input or upload, the module may also interface with data interfaces such as commercial financial databases and the like. The data obtained from these data interfaces is structured in itself, and the source data parsing module can directly read the data for matching.
Source data parsing module
And the source data analysis module receives the source data from the data acquisition module and analyzes the data into structured data. The process mainly comprises the following steps:
1. identifying a data type;
2. calling the type of analysis engine to analyze the source data into structured result data;
3. presenting the result data on a result display page;
4. correcting the analysis result by the user;
5. learning corrective actions of the user;
the workflow of the parsing module is as shown in fig. 3, after receiving source data (301), the parsing module identifies a data type (302), parses the source data into structured data (305), and then presents the parsing result on a result presentation page (306). The user may select the result of the corrected data analysis (307) and the analysis module learns the user's corrected behavior (308).
1. Identifying data types
The analysis module receives the data from the data acquisition module, and identifies the data type of the data by methods of identifying a file suffix name, a data coding mode and the like so as to conveniently use a corresponding analysis interface to further identify the content of the data. For example, if the suffix of the file is jpg, the file is data in a picture format, and if the suffix is xls, the file is an Excel form file.
2. Analytical process
After the data is finished, if the data is a text, the data is analyzed into structured data by using a natural language recognition algorithm. If the type of the data is other types, determining the data type of the data according to the encoding rule of the data, and then calling an analysis interface corresponding to the data type to analyze the data of the type into structured data. For example, if the data type is an image, the analysis engine calls an image text processing interface, extracts character information in the image, and generates analysis result structured data according to information such as the position and the size of characters in the image. The module can also convert files in other formats such as PDF and the like into a picture form and uniformly analyze the files through an image recognition algorithm.
3. Analysis result
The results are stored in memory in a structured data structure, each data item in the data structure storing the value of the data item and the semantics of the data item. For source data in tabular form, one implementation of the data structure is that each data item contains two matching attributes row, column and one value attribute value of the node. For text source data, one implementation of the data structure is that each data item contains a list of strings representing semantics and a value attribute value. And the analysis result is displayed on an analysis result display page. Any data change of the analysis result data displayed on the analysis result display page can be in bidirectional linkage with the target data table in a response mode under the action of the bidirectional data matching module.
4. Manual correction
The result analyzed by the analysis engine is not always completely correct, and a user can manually change and correct the analysis result on a result display page. For example, when the user considers that the result of a certain item in the parsing result is different from the source data, the user may click the item with a mouse and then input a new value using a keyboard. The value of the data item in the parsing result data is replaced with the value input by the user. In other cases, such as when the source data is a table file in a picture format, the semantic information of some items in the parsing result is not accurately identified, for example, the row tag or column tag of some item is not accurately identified, and the user may click to select the row tag or column tag for secondary identification, or may directly modify the data manually.
5. Corrective action
The analysis module can learn the selection standard and the modification result of the correction behavior of the user, strengthen the analysis rule of the analysis engine and improve the accuracy of analysis and identification. For example, when a user modifies a certain item in the parsing result, the parsing module records the related semantic information of the item, compares the parsing result with the result of manual modification of the user, trains the parsing engine with the recorded data, and when similar semantic items are parsed next time, the parsed result is more inclined to the result of manual modification of the user.
Target data table analysis module
The target data table parsing module parses a target data table provided by a user into a structured data table. As shown in fig. 4, the module may be comprised of the following steps:
1. identifying a format (402) of a user-provided target data table (401);
2. identifying each cell in the target data table (403);
3. identifying the content of each cell and semantics associated therewith (404);
4. user confirmation or modification (405);
5. a structured target data table is generated (406).
The target data table provided by the user is not necessarily a data table which can be directly filled, and can be in various formats, such as an Excel table in an xls format, a picture table in a jpg format, and the like. The target data table analysis module firstly identifies the format of a target data table according to the relevant information of the target data table provided by a user; then, identifying each cell in the table according to the format of the cell, for example, for the table with the picture format, identifying all possible cells by an analysis engine according to information such as line positions in the picture; for each cell, identifying the content of the cell and semantic information related to the cell, such as a row label and a column label of each cell; the result of automatic identification may not be completely accurate, and the user may modify the identified result manually; and finally, generating a structured target data table meeting the matching requirement.
In some implementations of the invention, the target data table provided by the user is already a structured data table that can be directly used for matching and populating, and the target data table parsing module can directly read the data for matching.
Bidirectional data matching module
The bidirectional data matching module receives the analysis result data from the analysis module and the structured target data table from the target table source data analysis module, bidirectionally matches the analysis result data with the target data table, and automatically fills the data in the analysis result into the target data table. The module work flow is shown in fig. 5, and the flow mainly includes the following steps:
1. identifying items needing to be filled in the target data table model in advance (501);
2. bidirectionally comparing node matching information of items to be filled in the target data table with items in the analysis result (504);
3. establishing a connection relation (506) between the items in the data table with high matching degree and matching score and the analysis result items;
4. automatically populating the matching result to the location of the item in the data table (508);
5. prompting the data table item to be filled with the connection relation and the single or a plurality of analysis result items to wait for confirmation or selection of a user (510);
6. the user selects and confirms the results of the auto-fill (511).
1. Identifying items to be filled in a data table to be filled
Upon startup of the overall system, the two-way matching module begins to identify the items in the target data table that need to be populated. The flow of identification is shown in fig. 6. Generally, a blank cell in the target data table is a data item to be filled. In particular, if the target data table is an electronic table file such as xls, xlsx, etc., then a formula may be contained therein. All formulas in the spreadsheet constitute the computational dependencies between different cells in the spreadsheet. In determining whether an item or cell needs to be filled with data, it is usually dependent on the value attribute of the cell and the calculation dependent attribute. The value attribute refers to a data value filled in the cell, and the calculation dependency attribute refers to a position of the cell in the relationship graph since the calculation of the spreadsheet, that is, whether the cell depends on or is depended on by another cell. If the value attribute of a certain cell is not empty (601) or the cell is filled with a formula (602), filling is not needed; in a spreadsheet, there often exist calculation dependency relationships between cells, and if a cell depends on other cell or cells, the cell or item does not need to be filled with data (603); if a cell is relied upon by other cells and the value attribute of the cell is null (604), then padding data is needed, otherwise the cell does not need padding data (605).
2. Bidirectional data match comparison
After the source data is analyzed into analysis result data, the bidirectional data matching module traverses items needing to be filled in the target data table, and traverses and calculates the matching degree and the matching score between each item in the analysis result and the items needing to be filled in the target data table in the analysis result data. When the matching score reaches a certain threshold value, the matching module considers that a strong matching relation exists between the two items, and a connection relation is established between the two items.
(1) Degree of match and match score
In the process of bidirectional comparison, the matching score between the items in the target data table and the items in the analysis result needs to be calculated, and the matching degree between the two items is judged by taking the matching score as a judgment basis. One implementation method of the matching score calculation is that according to the row labels and the column labels of the table items of the data to be filled and the analysis result data items, the matching module carries out semantic analysis on the row labels and the column labels, and gives the matching score between the two items according to the semantic similarity degree of the two attributes between the two items. One implementation of semantic analysis is to compute for each word its word vector, then compute the distance between the word vectors of the two words, and give a matching score between the two words based on the distance.
3. Connection relation
The connection relationship is a data structure for storing a matching relationship between an item in the data table to be populated and an item in the parsing result data. One implementation of this data structure is that each connection structure has three attributes, A, B, score, where the A, B attribute points to an item in the target data table and an item in the parse result data, respectively, and the score attribute represents the matching score between the two items of the connection.
4. Automatic filling
And for the items to be filled in each data table to be filled after the matching scores are obtained, filling the items with the highest matching scores in the items with the connection relationship in the analysis result data to the corresponding items of the data table to be filled, and taking a plurality of items with the highest matching scores as standby matching items for the user to select and confirm.
5. Prompt emphasis
After the connection relationship is established and the partial data is automatically filled, as shown in fig. 7, the matching module highlights the table item (701) of the data to be filled and one or more spare matching items (702) in the analysis result data mentioned in the above 2 in a certain order. When a selection cursor or a mouse is moved to an item of a data table to be filled (fig. 8), an alternative window (801) appears beside the item, the content in the window is related information of the values of the alternative items in the analysis result data corresponding to the item of the data table and the alternative values, and each alternative value in the alternative window is in one-to-one correspondence with the alternative items in the analysis result data and is bound in two directions (802). When the cursor or mouse moves to the alternative value (801), the alternative value and the related information thereof are highlighted for the second time (figure 9), the other alternative value is highlighted, the alternative item (902) corresponding to the alternative value in the corresponding analysis result data is highlighted for the second time, and the other alternative item (903) is highlighted; when the user selects one of the alternative values, the alternative value fills in the data form item, the alternative window disappears, the data form item is highlighted and weakened, the selected item in the corresponding analysis result data is highlighted and weakened, and the unselected alternative item is highlighted and disappears (fig. 10); in addition to the item currently undergoing the selection and confirmation process, the previous and next processed items in the table of data to be filled are highlighted differently from the currently processed item (fig. 11).
6. Selection confirmation mechanism
After highlighting the prompt, the user may select or confirm the fill result using a mouse, keyboard, touch screen, or the like. When the mouse is used for selection, a user clicks a certain alternative value in an alternative window, or clicks a certain alternative item or other items in an analysis data display page, the matching module determines that the user finishes the value attribute filling selection of the data form item to be filled at the moment, automatically highlights a next item to be filled in the data form to be filled and an alternative matching item in a data analysis result corresponding to the next item to be filled, and repeatedly executes the operations 5 and 6 until all data filling is finished. When a user makes a selection using a touch screen device, the operating logic is the same as the mouse selection. When the keyboard equipment is used for selection, a user can press up and down direction keys to move a selection cursor, press an enter key to confirm the selection, press an n key to skip the current data table item to enter the next data table item to be filled, and press a b key to return to the last processing item. When the user presses the enter key for confirmation, the matching module automatically moves the cursor to the next processing item.
The matching system records the selection of each processing item by the user, and if the user directly presses an enter key and adopts the matching value automatically recommended by the matching system, the matching system records the user selection of the processing item as a selection a; if the user does not adopt the automatically recommended matching value to select one alternative value in other values in the alternative window, the matching system records the user selection of the processing item as selection b; if the user does not select the alternative value in the alternative window but selects a non-alternative in the analysis result data in the selection and confirmation process of a certain processing item, the matching system records the user selection of the processing item as selection c. For the processing item of selection c, at 5, the hint emphasis mechanism highlights the processing item in a characteristic way that the user can check the correctness of the data selected by the selection at the time the data is filled or at any other time.
Data table export module
The data table export module is used for exporting the data table. The user can choose to export the populated target data table or parsing result data into multiple formats or directly store the data on the cloud server. As shown in fig. 12, the derivation process consists of the following steps:
1. the user selects a data table to be exported (1202);
2. user selection or default designation of export format and destination location (1203);
3. the data export module exports data (1204).
In the above steps, the user selects the data table to be exported, and the data table may be the whole or part of the filled target data table, or may be the analysis result data; then, selecting an export format, wherein the export format can be various, such as an xls format electronic table, a csv format file and the like; secondly, selecting an exported destination position, wherein the destination position can be a local path, a network mail, a database and the like; and after the selection is finished, the data export module exports the data.
Besides data export, the data can be directly stored in the cloud server, and a user can check and download the stored data by logging in the cloud server at any time and any place.
Claims (18)
1. A system for automatically filling a data table comprises a memory, a source data acquisition module and a source data uploading module; the system is characterized by further comprising a bidirectional data matching module and an analysis data automatic filling module, wherein the bidirectional data matching module is connected with the source data acquisition module, the data uploading module and the storage;
acquiring source data through a source data acquisition module or a source data uploading module, storing the source data in a memory, analyzing the source data through a source data analysis module, and presenting an analysis result;
analyzing the target data table through a target data table analysis module to obtain an analysis result;
and matching the source data analysis result with the target table data analysis result through the bidirectional data matching module, and filling data in the table through the analysis data automatic filling module to obtain the target data table.
2. The system of claim 1, wherein the data obtained by the source data obtaining module is one or more of text, image, pdf file, spreadsheet file, database query result, and web search result.
3. The system for automatically filling the data table as claimed in claim 1 or 2, wherein the source data analysis module analyzes and presents the data obtained by the source data uploading module, a user can manually confirm or modify the analysis and identification results, and the source data analysis module records the operation results of the user.
4. The system of claim 3, further comprising a source data parsing module for recording the operation result of the user and modifying the parsed data in real time.
5. The system of claim 1, wherein the source data parsing module comprises a parsing data selection learning module capable of learning user operations and enforcing parsing rules.
6. The system of claim 1, wherein the target data table parsing module parses a user-provided target data table to identify the meaning of each cell therein.
7. A system for automatically populating a data form according to claim 6, wherein said identifying the meaning of each cell therein includes identifying the row label and the column label of said each cell.
8. The system according to claim 1, wherein the bidirectional data matching module identifies items or area blocks to be matched in the data table to be filled, and when the source data parsing module completes parsing, bidirectionally matches the data identified by the source data parsing module with the data table to be filled, and automatically fills data in the data table to be filled.
9. The system of claim 8, wherein the automatic population of data into the data form to be populated further comprises a user being able to manually confirm or modify the match and fill results.
10. The system of claim 9, wherein the user manually confirms or modifies the matching and filling result, further comprising the bidirectional data matching module intelligently prompting a corresponding item or area of the item or area in the parsed data and other possible matching items or areas when the user clicks or selects the item or area.
11. The system of claim 10, wherein the user manually confirms or modifies the matching and filling result, and further comprising the bidirectional data matching module capable of intelligently prompting a corresponding item or area of the item or area in the data form to be filled and other possible matching items or areas when the user clicks or selects the item or area.
12. The system of claim 11, wherein the user manually confirms or modifies the match and fill results, further comprising the bi-directional data matching module recording the match results selected by the user and modifying the data in the data form to be filled in real time.
13. The system for automatically populating a data form according to claim 1, further comprising a data form export module, said data form export module coupled to the parsed data auto-population module; exporting the data table, the user can choose to export the filled target data table or the parsing result data into a plurality of formats or directly store the data on the cloud server.
14. The system of claim 1, wherein the bidirectional data matching module further calculates a distance between a word vector corresponding to a row label or a column label of the data table to be filled and a word vector corresponding to a row label or a column label of the source data.
15. A method of data form autofill for use in the system of data form autofill of any of claims 1-14, the method comprising the steps of:
s1, data acquisition: acquiring a source data analysis result and a target data table analysis result;
s2, bidirectional matching: selecting and matching the source data analysis result and the target data table analysis result;
s3, automatic filling of analysis data: and carrying out data filling on the table through an analysis data automatic filling module to obtain a target data table, and carrying out table export.
16. The method for automatically populating the data table according to claim 15, wherein in step S1, the source data of the source data parsing result is obtained through a source data obtaining module or a source data uploading module.
17. The method for automatically populating data form according to claim 15 or 16, wherein in step S1, the target form data parsing result is obtained by parsing the target data form through a target data form parsing module.
18. The method of claim 15, further comprising deriving the populated data form.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910924724.8A CN111191429A (en) | 2019-09-27 | 2019-09-27 | System and method for automatic filling of data table |
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| CN111797601A (en) * | 2020-06-28 | 2020-10-20 | 北京字节跳动网络技术有限公司 | Form processing method, device, equipment and storage medium |
| CN112052653A (en) * | 2020-09-04 | 2020-12-08 | 国网福建省电力有限公司 | Filling method of statement remark information and financial robot |
| CN112528613A (en) * | 2020-12-21 | 2021-03-19 | 金蝶软件（中国）有限公司 | Data table generation method and related equipment thereof |
| CN113627892A (en) * | 2021-08-16 | 2021-11-09 | 深圳市云采网络科技有限公司 | BOM data identification method and electronic equipment thereof |
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| CN111797601A (en) * | 2020-06-28 | 2020-10-20 | 北京字节跳动网络技术有限公司 | Form processing method, device, equipment and storage medium |
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| CN112052653A (en) * | 2020-09-04 | 2020-12-08 | 国网福建省电力有限公司 | Filling method of statement remark information and financial robot |
| CN112528613A (en) * | 2020-12-21 | 2021-03-19 | 金蝶软件（中国）有限公司 | Data table generation method and related equipment thereof |
| CN113627892A (en) * | 2021-08-16 | 2021-11-09 | 深圳市云采网络科技有限公司 | BOM data identification method and electronic equipment thereof |
| CN113627892B (en) * | 2021-08-16 | 2023-09-01 | 深圳市云采网络科技有限公司 | BOM data identification method and electronic equipment thereof |
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