CN111159479A - Data processing method, device and equipment - Google Patents
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Abstract
The application provides a data processing method, a device and equipment, wherein the method comprises the following steps: acquiring original data of different data sources according to the data processing instruction; respectively analyzing the original data to generate intermediate data corresponding to the different data sources; and performing data mapping on the intermediate data of the different data sources to generate result data. According to the method and the device, data analysis and data mapping are carried out on the original data of different data sources, and the finally obtained processing result is generated into result data. The automatic integration processing of the original data of multiple data sources is realized.
Description
Technical Field
The present application relates to the field of information processing technologies, and in particular, to a data processing method, apparatus and device.
Background
With the development of digital information technology, more and more application scenarios require multiple data source data transmission. For example, a third party platform interfaces data transmissions of data sources of multiple insurance companies. The existing data processing mode is as follows: and the insurance company exports underwriting and security data offline according to the fixed template and then imports the claim settlement system of the third-party platform.
In the existing data processing mode, only data transmission processing of a single insurance company in a single application scene is supported, and data service transmission of multiple data sources and multiple scenes cannot be supported.
Disclosure of Invention
An object of the embodiments of the present application is to provide a data processing method, apparatus and device, so as to implement data analysis and data mapping on original data of different data sources, and then generate result data.
A first aspect of an embodiment of the present application provides a data processing method, including: acquiring original data of different data sources according to the data processing instruction; respectively analyzing the original data to generate intermediate data corresponding to the different data sources; and performing data mapping on the intermediate data of the different data sources to generate result data.
In an embodiment, the analyzing the original data and generating intermediate data corresponding to the different data sources respectively includes: respectively extracting data source identifiers of the original data; acquiring a first data template corresponding to each data source identifier; analyzing the data content of the original data based on the first data template; and importing the data content into the corresponding first data template to generate the intermediate data.
In an embodiment, the performing data mapping on the intermediate data of the different data sources to generate result data includes: respectively identifying field content corresponding to a preset field in each intermediate data; and respectively mapping each field content to a second data template according to the corresponding relation between each field content and the preset field to generate the result data.
In an embodiment, before the mapping each field content to a second data template according to the corresponding relationship between each field content and the preset field, and generating the result data, the method includes: judging whether a plurality of intermediate data belonging to the same account exist or not; when a plurality of intermediate data belonging to the same account exist, marking a plurality of field contents corresponding to the plurality of intermediate data as the same account.
In an embodiment, after performing data mapping on the intermediate data of the different data sources and generating result data, the method further includes: verifying the result data; and deleting the data which do not conform to the preset data format and/or the repeated data in the result data.
A second aspect of the embodiments of the present application provides a data processing apparatus, including: the acquisition module is used for acquiring original data of different data sources according to the data processing instruction; the analysis module is used for respectively analyzing the original data and generating intermediate data corresponding to the different data sources; and the mapping module is used for performing data mapping on the intermediate data of the different data sources to generate result data.
In one embodiment, the parsing module is configured to: respectively extracting data source identifiers of the original data; acquiring a first data template corresponding to each data source identifier; analyzing the data content of the original data based on the first data template; and importing the data content into the corresponding first data template to generate the intermediate data.
In one embodiment, the mapping module is configured to: respectively identifying field content corresponding to a preset field in each intermediate data; judging whether a plurality of intermediate data belonging to the same account exist or not; when a plurality of intermediate data belonging to the same account exist, marking a plurality of field contents corresponding to the plurality of intermediate data as the same account; and respectively mapping each field content to a second data template according to the corresponding relation between each field content and the preset field to generate the result data.
In one embodiment, the method further comprises: the verification module is used for verifying the result data after performing data mapping on the intermediate data of the different data sources to generate the result data; and the deleting module is used for deleting the data which do not accord with the preset data format and/or the repeated data in the result data.
A third aspect of embodiments of the present application provides an electronic device, including: a memory to store a computer program; a processor configured to perform the method of the first aspect of the embodiments of the present application and any of the embodiments of the present application.
According to the data processing method, the data processing device and the data processing equipment, data analysis and data mapping are carried out on original data of different data sources, and finally obtained processing results are generated into result data. The automatic integration processing of the original data of multiple data sources is realized.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments of the present application will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and that those skilled in the art can also obtain other related drawings based on the drawings without inventive efforts.
Fig. 1 is a schematic structural diagram of an electronic device according to an embodiment of the present application;
fig. 2 is a schematic structural diagram of an application scenario between an electronic device and a data source platform according to an embodiment of the present application;
FIG. 3 is a schematic flow chart illustrating a data processing method according to an embodiment of the present application;
FIG. 4 is a schematic flow chart illustrating a data processing method according to an embodiment of the present application;
fig. 5 is a schematic structural diagram of a data processing apparatus according to an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be described below with reference to the drawings in the embodiments of the present application. In the description of the present application, the terms "first," "second," and the like are used solely to distinguish one from another and are not to be construed as indicating or implying relative importance.
As shown in fig. 1, the present embodiment provides an electronic apparatus 1 including: at least one processor 11 and a memory 12, one processor being exemplified in fig. 1. The processor 11 and the memory 12 are connected by a bus 10, and the memory 12 stores instructions executable by the processor 11 and the instructions are executed by the processor 11.
In an embodiment, the electronic device 1 may be a mobile phone, a notebook computer, a desktop computer, or a large server thereof.
In one embodiment, as shown in fig. 2, the electronic device 1 can communicate data with different data source platforms 210. Each data source platform 210 may set a timing point of data transmission, and the electronic device 1 may perform raw data transmission with the corresponding data source platform 210 according to the timing point. The data source platform 210 may be a data platform of an enterprise, such as a data platform of an insurance company, and the raw data may be user insurance data of the insurance company.
Please refer to fig. 3, which is a data processing method according to an embodiment of the present application, and the method can be executed by the electronic device 1 shown in fig. 1 and can be applied to the data processing scenario shown in fig. 2 to implement automatic integration processing on raw data of multiple data sources. The method comprises the following steps:
step 301: and acquiring original data of different data sources according to the data processing instruction.
In this step, the data processing instruction may be a clock instruction issued by a clock timer, and the instruction may be used to instruct to start raw data acquisition. Taking the data source platform 210 of the insurance company as an example, the timing task module starts the data processing task regularly every day, and the starting time of each insurance company can be different. After the data processing task is started, the corresponding underwriting security data is obtained from the data source platform 210 of the corresponding insurance company according to the different data transmission modes and message formats corresponding to the different insurance companies.
The data transmission method includes, but is not limited to: the method is realized by an FTP (File Transfer Protocol) Protocol, an sftp (Secure FTP), a webservice network service mode, a hypertext Transfer Protocol http and the like. Message formats include, but are not limited to: txt text format, excel table format, xml (extensible markup language) format, json (JavaScript Object Notation, JS Object profile, a lightweight data exchange format) format.
Step 302: the original data are respectively analyzed to generate intermediate data corresponding to different data sources.
In this step, the raw data in step 301 may come from different data source platforms 210, and for the raw data of different data sources, data analysis is performed respectively to generate corresponding intermediate data, where the intermediate data corresponds to the data source platform 210.
Step 303: and performing data mapping on the intermediate data of different data sources to generate result data.
In this step, the intermediate data from different data source platforms 210 are respectively subjected to data mapping according to a uniform mapping rule, and the mapped data is the result data.
According to the data processing method, the original data of different data sources are subjected to data analysis and data mapping, and the finally obtained processing result is used for generating result data. The automatic integration processing of the original data of multiple data sources is realized.
Please refer to fig. 4, which is a data processing method according to an embodiment of the present application, and the method can be executed by the electronic device 1 shown in fig. 1 and can be applied to the data processing scenario shown in fig. 2 to implement an automatic integration process on raw data of multiple data sources. The method comprises the following steps:
step 401: and acquiring original data of different data sources according to the data processing instruction. See the description of step 301 in the above embodiments for details.
Step 402: and respectively extracting the data source identification of the original data.
In this step, the original data from different data source platforms 210 respectively carry their respective data source identifiers. Taking the data platforms of different insurance companies as an example, the data source identification may include but is not limited to: and each original data corresponds to one or more of a channel identifier, a service type identifier, an enterprise identifier, a data state identifier and a user identifier. Assuming that the original data is a policy data of an insurance company, the channel identifier, the service identifier, the insurance company identifier, the data state identifier and the policy identifier corresponding to the original data can be extracted from the policy data in a mode of identifying the key field, and the user identifier can be a unique personal identifier of an applicant and can be uniquely determined by the personal policy number, the certificate number and the client number of the applicant in combination with the insurance company identifier.
Step 403: a first data template corresponding to each data source identification is obtained.
In this step, the first data template may be a data parsing manner, and the data source identifiers of the original data for different insurance companies are different, and the data parsing manners adopted are also different, for example, if there is one policy data with a service identifier b and one policy data with a service identifier c in the original data for the insurance company a, the data parsing manners required by the two policy data may also be different, that is, the policy data with the service identifier b corresponds to the first data template x, and the policy data with the service identifier c may correspond to the first template data y.
In an embodiment, different data source platforms 210 may also correspond to different first data templates, for example, policy data from the data platform identified by the insurance company B has the same service identifier as policy data of the insurance company a, but since the policy data and the policy data are from different data platforms, different data parsing manners may be set according to the needs of the respective data platforms.
In an embodiment, the different data source identifiers of the different data source platforms 210 may also correspond to different first data templates, for example, a policy data with a service identifier d in the original data of the insurance company B, which may correspond to the first data template z.
In one embodiment, the first data template may be: one or more of an excel message template, an xml template, a txt template, a json template and a preset general template.
Step 404: and analyzing the data content of the original data based on the first data template.
In this step, according to the first data template obtained in step 403, data analysis is performed on the packet of the original data, so as to obtain the data content in each original data. If the original data is the policy data with the service identifier b, and the corresponding first data template x is an excel message template, performing message analysis on the policy data with the service identifier b based on the excel message template to obtain the data content in the policy data with the service identifier b. Similarly, if the policy data with the original data service identifier d and the corresponding first template data z are xml templates, the policy data with the service identifier d is subjected to message analysis based on the xml templates to obtain the corresponding data content.
Step 405: and importing the data content into the corresponding first data template to generate intermediate data.
In this step, the data content analyzed in step 404 is respectively imported into the respective corresponding first data templates, for example, the data content in the policy data with the service identifier b is imported into the corresponding excel message template, and the generated excel table data is the intermediate data corresponding to the policy data with the service identifier b. Similarly, the data content in the policy data with the service identifier d is imported into the xml template corresponding to the data content, and the generated xml format data is the intermediate data corresponding to the policy data with the service identifier d.
Step 406: and respectively identifying the field content corresponding to the preset field in each intermediate data.
In this step, the preset field may be set according to actual needs, for example, the field identification rule may be: according to the general meaning of fields for identifying, such as certificate numbers, the certificate number field names in the original data of different insurance companies may be inconsistent, and the corresponding field meanings are also inconsistent, such as the certificate number field of insurance company A is called idNO, and the corresponding field contents are: 1-identity card, 2-military personal card. The certificate number field in the original data of the insurance company B is called identityNO, and the content of the corresponding field is as follows: a-identity card, b-military identification card.
Step 407: and judging whether a plurality of intermediate data belonging to the same account exist or not. If so, step 408 is entered, otherwise, step 409 is entered.
In this step, a plurality of original data from different data source platforms 210 may obtain a plurality of corresponding intermediate data through the analysis in the above step, and whether a plurality of intermediate data belonging to the same account exist may be determined based on the account information in the intermediate data. For example, in the original data of the insurance company a, there are one policy data with a service identifier b and one policy data with a service identifier c, and in the intermediate data corresponding to the two policy data, there are account information of the two policy data, if the account information of the two policy data is the same, step 408 is performed, otherwise, step 409 is performed.
Step 408: the contents of the fields corresponding to the intermediate data are marked as the same account.
In this step, when there are a plurality of intermediate data belonging to the same account, for example, the policy data with the service identifier b and the policy data with the service identifier c have the same account information, and in order to facilitate uniform processing of subsequent data, the intermediate data corresponding to both are uniformly marked under the account information.
Step 409: and respectively mapping each field content to a second data template according to the corresponding relation between each field content and the preset field to generate result data.
In this step, the second data template may be a preset general template, and may adopt a uniform data storage format. And mapping the field contents obtained in the step to second data templates respectively. Wherein each field content corresponds to a preset field. Therefore, result data can be obtained, and the result data comprises field contents corresponding to all the preset fields. And corresponding information such as insurance company identification, channel identification, policy identification and the like can be set on the result data. The resulting data may then be written to a database in the data center.
Step 410: and checking the result data.
In this step, data verification may be performed on the result data, and appropriate data may be screened out or data that does not meet the standard may be deleted. For example, a standard data format may be preset for each type of data, and a relationship table between the data type and the data format may be formed, where, for example, the data type is an identification number, which corresponds to the standard identification number data format. Comparing each data format in the result data with the relation table, and if the corresponding relation between the data format and the relation table is different, indicating that the data format is incorrect. And may look for whether duplicate data exists in the resulting data.
Step 411: and deleting the data which do not conform to the preset data format and/or the repeated data in the result data.
In this step, the format and field of the data may be checked first, and the result data with problematic format, the id number format error, the mobile phone number format error, the date format error, etc. may be deleted preliminarily. Or screening out the data with missing key fields, such as the data with missing certificate number and the data with missing client number. And then finding out repeated result data according to a multi-keyword identification algorithm, and deleting the repeated result data from the result data. And further the accuracy of the result data is ensured. The results of the data processing may be fed back to the corresponding data source platform 210 at appropriate times, such as to a corresponding insurance company.
Please refer to fig. 5, which is a data processing apparatus 500 according to an embodiment of the present application, and the apparatus can be applied to the electronic device shown in fig. 1 and can be applied to the data processing scenario shown in fig. 2 to implement an automatic integration process for raw data of multiple data sources. The device includes: the function principle among the modules is as follows:
the obtaining module 501 is configured to obtain original data of different data sources according to the data processing instruction. See the description of step 301 in the above embodiments for details.
The parsing module 502 is configured to parse the original data respectively to generate intermediate data corresponding to different data sources. See the description of step 302 in the above embodiments for details.
The mapping module 503 is configured to perform data mapping on the intermediate data of different data sources to generate result data. See the description of step 303 in the above embodiments for details.
In one embodiment, the parsing module 502 is configured to: and respectively extracting the data source identification of the original data. A first data template corresponding to each data source identification is obtained. And analyzing the data content of the original data based on the first data template. And importing the data content into the corresponding first data template to generate intermediate data. See the description of steps 402-405 in the above embodiments for details.
In one embodiment, the mapping module 503 is configured to: and respectively identifying the field content corresponding to the preset field in each intermediate data. And judging whether a plurality of intermediate data belonging to the same account exist or not. When there are a plurality of intermediate data belonging to the same account, a plurality of field contents corresponding to the plurality of intermediate data are marked as the same account. And respectively mapping each field content to a second data template according to the corresponding relation between each field content and the preset field to generate result data. See the description of step 406 to step 409 in the above embodiments for details.
In one embodiment, the method further comprises: the checking module 504 is configured to check the result data after performing data mapping on the intermediate data of different data sources to generate the result data. A deleting module 505, configured to delete data that does not conform to the preset data format and/or data that appears repeatedly in the result data. Refer to the description of steps 410 to 411 in the above embodiments in detail.
For a detailed description of the data processing apparatus 500, please refer to the description of the related method steps in the above embodiments.
An embodiment of the present invention further provides a non-transitory electronic device readable storage medium, including: a program that, when run on an electronic device, causes the electronic device to perform all or part of the procedures of the methods in the above-described embodiments. The storage medium may be a magnetic Disk, an optical Disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a Flash Memory (Flash Memory), a Hard Disk (Hard Disk Drive, abbreviated as HDD), a Solid State Drive (SSD), or the like. The storage medium may also comprise a combination of memories of the kind described above.
Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations fall within the scope defined by the appended claims.
Claims (10)
1. A data processing method, comprising:
acquiring original data of different data sources according to the data processing instruction;
respectively analyzing the original data to generate intermediate data corresponding to the different data sources;
and performing data mapping on the intermediate data of the different data sources to generate result data.
2. The method of claim 1, wherein said separately parsing said raw data to generate intermediate data corresponding to said different data sources comprises:
respectively extracting data source identifiers of the original data;
acquiring a first data template corresponding to each data source identifier;
analyzing the data content of the original data based on the first data template;
and importing the data content into the corresponding first data template to generate the intermediate data.
3. The method of claim 1, wherein the data mapping the intermediate data of the different data sources to generate result data comprises:
respectively identifying field content corresponding to a preset field in each intermediate data;
and respectively mapping each field content to a second data template according to the corresponding relation between each field content and the preset field to generate the result data.
4. The method according to claim 3, before said mapping each of the field contents to a second data template according to the corresponding relationship between each of the field contents and the preset field, and generating the result data, comprising:
judging whether a plurality of intermediate data belonging to the same account exist or not;
when a plurality of intermediate data belonging to the same account exist, marking a plurality of field contents corresponding to the plurality of intermediate data as the same account.
5. The method of claim 1, further comprising, after data mapping the intermediate data of the different data sources to generate result data:
verifying the result data;
and deleting the data which do not conform to the preset data format and/or the repeated data in the result data.
6. A data processing apparatus, comprising:
the acquisition module is used for acquiring original data of different data sources according to the data processing instruction;
the analysis module is used for respectively analyzing the original data and generating intermediate data corresponding to the different data sources;
and the mapping module is used for performing data mapping on the intermediate data of the different data sources to generate result data.
7. The apparatus of claim 6, wherein the parsing module is configured to:
respectively extracting data source identifiers of the original data;
acquiring a first data template corresponding to each data source identifier;
analyzing the data content of the original data based on the first data template;
and importing the data content into the corresponding first data template to generate the intermediate data.
8. The apparatus of claim 6, wherein the mapping module is configured to:
respectively identifying field content corresponding to a preset field in each intermediate data;
judging whether a plurality of intermediate data belonging to the same account exist or not;
when a plurality of intermediate data belonging to the same account exist, marking a plurality of field contents corresponding to the plurality of intermediate data as the same account;
and respectively mapping each field content to a second data template according to the corresponding relation between each field content and the preset field to generate the result data.
9. The apparatus of claim 6, further comprising:
the verification module is used for verifying the result data after performing data mapping on the intermediate data of the different data sources to generate the result data;
and the deleting module is used for deleting the data which do not accord with the preset data format and/or the repeated data in the result data.
10. An electronic device, comprising:
a memory to store a computer program;
a processor to perform the method of any one of claims 1 to 5.
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Application publication date: 20200515 |