CN112183047A - A big data cleaning configuration field parsing method - Google Patents
A big data cleaning configuration field parsing method Download PDFInfo
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- CN112183047A CN112183047A CN202011050140.1A CN202011050140A CN112183047A CN 112183047 A CN112183047 A CN 112183047A CN 202011050140 A CN202011050140 A CN 202011050140A CN 112183047 A CN112183047 A CN 112183047A
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Abstract
The invention discloses a big data cleaning configuration type field analysis method, which comprises the following specific steps: s1, setting the type ID of the file, and setting the type ID of the file by the user through the data of the excel template, wherein the data type corresponding to each field can be character strings, shaping and floating point numbers; s2, establishing a program configuration file directory, and uploading the type ID of the setting file to the program configuration file directory; s3, cleaning the template data, and uploading an excel template and selecting the ID of the template type when a user cleans the template data; and S4, field analysis, wherein the cleaning program automatically calls the corresponding field of the analysis configuration of the response to analyze according to the data source. The type ID of the file is set through the data of the excel template, the ID is uploaded to a program configuration file directory, and the template ID can automatically call the corresponding field of the analysis configuration of the response for analysis according to the data source by the cleaning program, so that the workload of workers is reduced.
Description
Technical Field
The invention relates to the technical field of field analysis of large data cleaning, in particular to a field analysis method for a large data cleaning configuration type.
Background
With the advent of the big data era, mass data is increasing rapidly, and various industries can realize integration and readjustment of the existing resources through the support of big data technology, improve the operation efficiency of the industries, develop the huge potential of the industries, and need to acquire the data continuously.
At present, the biggest difference is that when a worker needs to collect new data, a developer needs to write a corresponding analysis program according to a new template every time, and then the corresponding analysis program is tested and updated to a production server to perform new data warehousing work, so that huge workload is caused, a business process is more, and mistakes are easy to make.
Disclosure of Invention
The invention aims to provide a big data cleaning configuration type field analysis method, which solves the problem that when a developer needs to collect new data, the developer needs to write a corresponding analysis program according to a new template every time, and then the developer tests and updates the new data to a production server to perform new data warehousing work, so that the workload is large.
In order to achieve the purpose, the invention provides the following technical scheme: a big data cleaning configuration type field analysis method comprises the following specific steps:
s1, setting the type ID of the file, and setting the type ID of the file by the user through the data of the excel template, wherein the data type corresponding to each field can be character strings, shaping and floating point numbers;
s2, establishing a program configuration file directory, and uploading the type ID of the setting file to the program configuration file directory;
s3, cleaning the template data, and uploading an excel template and selecting the ID of the template type when a user cleans the template data;
and S4, field analysis, wherein the cleaning program automatically calls the corresponding field of the analysis configuration of the response to analyze according to the data source.
As a still further scheme of the invention: the program configuration file directory establishing module comprises a plurality of program configuration file directory modules, a program configuration file directory detection template and a program configuration file directory storage module;
the program configuration file directory modules run independently and correspond to the data types corresponding to the fields;
the program configuration file directory detection template is used for detecting whether data loss occurs or not when the type ID of the uploaded file exists, and an intermediate data service module is arranged in the program configuration file directory detection template and can differentiate data into a plurality of intermediate points;
and the program configuration file directory storage module is used for storing the IDs uploaded to the program configuration file directory modules in a database.
As a still further scheme of the invention: two points of an intermediate point section of the intermediate data service module are used as an output point X and an input point Y, and collected information is detected by detecting the two points of the output point X and the input point Y through a program configuration file directory detection template;
when the uploaded data is not lost, the data information a1 and a2 of the input point X and the data information B1 and B2 of the output point Y are generated into a one-dimensional quadratic equation according to a first formula, as shown in formula 1:
Y＝KX
(1)
when the uploaded data is not lost, the method can be transformed into the method shown in formula 2 through formula 1:
Y＝KX+B
(2)
wherein B is the data intercept, generating formula (3):
Y＝X+B
(3)
the coordinates X of the input point are (A1, A2), and the output point Y is (B1, B2).
As a still further scheme of the invention: the template data cleaning comprises a single uploading module, a plurality of uploading modules and a batch uploading module;
the single uploading module selects the IDs of the excel templates individually, uploads the IDs one by one, and the IDs of the templates are numbered as 1, 2, 3 and 4.. N during selection;
the multiple uploading modules are used for performing multiple combined uploading, and when the templates are selected, the ID number of each template is one of 12, 13.. 1N or 123,134.. 13N or 2678.. 2345N;
and the batch uploading module is used for performing batch combined uploading, and the ID number of the template is 123456.. N during selection.
As a still further scheme of the invention: when the uploading template data is cleaned, the single uploading module, the multiple uploading modules and the batch uploading module are independently uploaded respectively, and the single uploading module, the multiple uploading modules and the batch uploading module can be synchronously uploaded independently or in combination of the single uploading module, the multiple uploading module and the batch uploading module.
As a still further scheme of the invention: the field analysis comprises an automatic import analysis module, an automatic matching analysis module and a storage analysis module;
the automatic import analysis module is used for importing the cleaning program data template ID automatically according to an uploading format;
the automatic matching and analyzing module is used for matching and analyzing the program data template ID of the format uploaded by the automatic leading-in and analyzing module;
and the storage analysis module is used for storing the program data template ID analyzed by the automatic matching analysis module.
As a still further scheme of the invention: the automatic matching and analyzing module comprises a data recovery module and a data clearing module;
the data recovery module is used for recovering the DI of the data through the data recovery module when the automatic matching analysis module analyzes that the data has errors or gaps;
and the data clearing module is used for clearing the DI of the data through the data clearing module when the automatic matching analysis module analyzes that the data is repeated or wrong.
Compared with the prior art, the invention has the beneficial effects that:
the type ID of the file is set through the data of the excel template, the ID is uploaded to a program configuration file directory, and the template ID can automatically call the corresponding field of the analysis configuration of the response for analysis according to the data source by the cleaning program, so that the workload of workers is reduced, and errors are avoided.
Drawings
FIG. 1 is a step diagram of a big data cleansing configuration field parsing method according to the present invention;
FIG. 2 is a flow chart of the present invention for creating a program configuration file directory;
FIG. 3 is a flow chart of stencil data cleaning in accordance with the present invention;
FIG. 4 is a flow chart of field resolution of the present invention;
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 case of the example 1, the following examples are given,
referring to fig. 1-4, in an embodiment of the present invention, a big data cleansing configured field parsing method includes the following steps:
s1, setting the type ID of the file, and setting the type ID of the file by the user through the data of the excel template, wherein the data type corresponding to each field can be character strings, shaping and floating point numbers;
s2, establishing a program configuration file directory, and uploading the type ID of the setting file to the program configuration file directory;
s3, cleaning the template data, and uploading an excel template and selecting the ID of the template type when a user cleans the template data;
and S4, field analysis, wherein the cleaning program automatically calls the corresponding field of the analysis configuration of the response to analyze according to the data source.
In this embodiment, the big database based server is used by a big database server, which has a data file combination, the data file combination creates objects, views, texts, functions, pictures, and various combination files, and sets a file type ID on the data file, wherein the ID may correspond to a character string, a shape, a floating point number, and the like.
Examples 2,
With reference to embodiment 1, the difference is that, as shown in fig. 2, the creating of the program configuration file directory includes a plurality of program configuration file directory modules, a program configuration file directory detection template, and a program configuration file directory storage module; the program configuration file directory modules run independently and correspond to the data type corresponding to each field;
the ID can be uploaded to different program configuration file directory modules corresponding to character strings, shaping and floating point numbers, and each corresponding ID can be uploaded to template data cleaning independently or in a combined mode.
The system comprises a program configuration file directory detection template, an intermediate data service module and a data processing module, wherein the program configuration file directory detection template is used for detecting whether data are lost or not when the type ID of an uploaded file is detected, and the intermediate data service module is arranged in the program configuration file directory detection template and can divide the data into a plurality of intermediate points; and the program configuration file directory storage module is used for storing the IDs uploaded to the program configuration file directory modules in a database.
Two points of an intermediate point section of the intermediate data service module are used as an output point X and an input point Y, and information is detected and collected by detecting the two points of the output point X and the input point Y through a program configuration file directory detection template;
when the uploaded data is not lost, the data information a1 and a2 of the input point X and the data information B1 and B2 of the output point Y are generated into a one-dimensional quadratic equation according to a first formula, as shown in formula 1:
Y＝KX
(1)
when the uploaded data is not lost, the method can be transformed into the method shown in formula 2 through formula 1:
Y＝KX+B
(2)
wherein B is the data intercept, generating formula (3):
Y＝X+B
(3)
the coordinates X of the input point are (A1, A2), and the output point Y is (B1, B2).
When the detection and analysis of the program configuration file directory detection template can be changed into a formula 2 and a formula 3 through the formula 1, the interceptable distance appears, the intercept represents that the data is lack, and the lack part is the intercept display part.
In the case of the example 3, the following examples are given,
with reference to the embodiment 1 and the embodiment 2, the difference is that, as shown in fig. 3, the stencil data cleaning includes a single upload module, a plurality of upload modules, and a batch upload module;
the single uploading module is used for selecting the IDs of the excel templates individually, uploading the IDs one by one, and numbering the IDs of the templates as 1, 2, 3 and 4.. N during selection;
a plurality of uploading modules, which are used for carrying out a plurality of combined uploading, and when in selection, the ID number of the template is one of 12, 13.. 1N or 123,134.. 13N or 2678.. 2345N;
and the batch uploading module is used for performing batch combined uploading, and the ID number of the template is 123456.. N during selection.
When the uploading template data is cleaned, the single uploading module, the multiple uploading modules and the batch uploading module are independently uploaded respectively, and the single uploading module, the multiple uploading modules and the batch uploading module can be synchronously uploaded independently or in combination of the single uploading module, the multiple uploading module and the batch uploading module.
In the case of the example 4, the following examples are given,
with reference to the embodiments 1, 2 and 3, the difference is that, as shown in fig. 4, the field parsing includes an automatic import parsing module, an automatic matching parsing module, and a save parsing module;
the automatic import analysis module is used for automatically importing the cleaning program data template ID according to an uploading format;
the automatic matching and analyzing module is used for matching and analyzing the program data template ID of the format uploaded by the automatic leading-in analyzing module;
and the storage analysis module is used for storing the program data template ID analyzed by the automatic matching analysis module.
The automatic matching analysis module comprises a data recovery module and a data clearing module;
the data recovery module is used for recovering the DI of the data through the data recovery module when the automatic matching analysis module analyzes that the data has errors or gaps;
and the data clearing module is used for clearing the DI of the data through the data clearing module when the automatic matching analysis module analyzes that the data is repeated or wrong.
In summary, the type ID of the file is set through the data of the excel template, the ID is uploaded to a program configuration file directory, and the template ID can automatically call the corresponding field of the analysis configuration of the response for analysis according to the data source by the cleaning program, so that the workload of workers is reduced.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Furthermore, it should be understood that although the present description refers to embodiments, not every embodiment may contain only a single embodiment, and such description is for clarity only, and those skilled in the art should integrate the description, and the embodiments may be combined as appropriate to form other embodiments understood by those skilled in the art.
Claims (7)
1. A big data cleaning configuration type field analysis method is characterized by comprising the following specific steps:
s1, setting the type ID of the file, and setting the type ID of the file by the user through the data of the excel template, wherein the data type corresponding to each field can be character strings, shaping and floating point numbers;
s2, establishing a program configuration file directory, and uploading the type ID of the setting file to the program configuration file directory;
s3, cleaning the template data, and uploading an excel template and selecting the ID of the template type when a user cleans the template data;
and S4, field analysis, wherein the cleaning program automatically calls the corresponding field of the analysis configuration of the response to analyze according to the data source.
2. The big data washing configured field parsing method according to claim 1, wherein the creating of the program configuration file directory comprises a plurality of program configuration file directory modules, a program configuration file directory detection template, and a program configuration file directory saving module;
the program configuration file directory modules run independently and correspond to the data types corresponding to the fields;
the program configuration file directory detection template is used for detecting whether data loss occurs or not when the type ID of the uploaded file exists, and an intermediate data service module is arranged in the program configuration file directory detection template and can differentiate data into a plurality of intermediate points;
and the program configuration file directory storage module is used for storing the IDs uploaded to the program configuration file directory modules in a database.
3. The big data cleansing configuration type field parsing method of claim 2, wherein two points of the middle point segment of the middle data service module are used as an output point X and an input point Y, and information collection is detected by detecting two points of the output point X and the input point Y through a program configuration file directory detection template;
when the uploaded data is not lost, the data information a1 and a2 of the input point X and the data information B1 and B2 of the output point Y are generated into a one-dimensional quadratic equation according to a first formula, as shown in formula 1:
Y＝KX
(1)
when the uploaded data is not lost, the method can be transformed into the method shown in formula 2 through formula 1:
Y＝KX+B
(2)
wherein B is the data intercept, generating formula (3):
Y＝X+B
(3)
the coordinates X of the input point are (A1, A2), and the output point Y is (B1, B2).
4. The big data cleaning configured field parsing method according to claim 1, wherein the template data cleaning comprises a single-item uploading module, a plurality of uploading modules and a batch uploading module;
the single uploading module selects the IDs of the excel templates individually, uploads the IDs one by one, and the IDs of the templates are numbered as 1, 2, 3 and 4.. N during selection;
the multiple uploading modules are used for performing multiple combined uploading, and when the templates are selected, the ID number of each template is one of 12, 13.. 1N or 123,134.. 13N or 2678.. 2345N;
and the batch uploading module is used for performing batch combined uploading, and the ID number of the template is 123456.. N during selection.
5. The big data cleaning configured field analysis method according to claim 4, wherein when the upload template data is cleaned, the single upload module, the multiple upload modules and the batch upload module are independently uploaded, and the single upload module, the multiple upload modules and the batch upload module can be synchronously uploaded independently by combining the single upload module, the multiple upload modules and the batch upload module.
6. The big data cleaning configured field analysis method according to claim 1, wherein the field analysis comprises an automatic import analysis module, an automatic matching analysis module and a saving analysis module;
the automatic import analysis module is used for importing the cleaning program data template ID automatically according to an uploading format;
the automatic matching and analyzing module is used for matching and analyzing the program data template ID of the format uploaded by the automatic leading-in and analyzing module;
and the storage analysis module is used for storing the program data template ID analyzed by the automatic matching analysis module.
7. The big data washing configured field parsing method according to claim 6, wherein the automatic matching parsing module comprises a data recovery module, a clear data module;
the data recovery module is used for recovering the DI of the data through the data recovery module when the automatic matching analysis module analyzes that the data has errors or gaps;
and the data clearing module is used for clearing the DI of the data through the data clearing module when the automatic matching analysis module analyzes that the data is repeated or wrong.
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Citations (3)
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| US20020078406A1 (en) * | 2000-10-24 | 2002-06-20 | Goh Kondoh | Structure recovery system, parsing system, conversion system, computer system, parsing method, storage medium, and program transmission apparatus |
| CN108920638A (en) * | 2018-07-02 | 2018-11-30 | 山东浪潮商用系统有限公司 | Web terminal data collector file method and device based on data dictionary configuration |
| CN111240714A (en) * | 2019-12-29 | 2020-06-05 | 南京云帐房网络科技有限公司 | Financial data initialization method and system based on template intelligent learning |
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Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20020078406A1 (en) * | 2000-10-24 | 2002-06-20 | Goh Kondoh | Structure recovery system, parsing system, conversion system, computer system, parsing method, storage medium, and program transmission apparatus |
| CN108920638A (en) * | 2018-07-02 | 2018-11-30 | 山东浪潮商用系统有限公司 | Web terminal data collector file method and device based on data dictionary configuration |
| CN111240714A (en) * | 2019-12-29 | 2020-06-05 | 南京云帐房网络科技有限公司 | Financial data initialization method and system based on template intelligent learning |
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