CN112288585B - Insurance business refined data processing method and device and electronic equipment - Google Patents
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Abstract
One or more embodiments of the present specification provide an insurance business refined data processing method, the processing method including reading insurance business data from a table of a distributed column-oriented database in a predetermined format and converting it into business entity objects; processing the business entity object by using a parallel computing engine to obtain an accurate calculation index; according to a preset checking rule, performing compliance checking on attribute values of the business entity objects, and repairing the non-compliance insurance business data found in the compliance checking; saving insurance business data passing compliance checking or repaired to a data warehouse based on a distributed system architecture; and carrying out insurance business fine calculation and data summarization according to the fine calculation index and the insurance business data stored in the data warehouse. According to the embodiment of the invention, the processed data is detected and repaired through distributed extraction and parallel computing processing, so that the time consumed for extracting, detecting and repairing the data is less, and the data summarizing efficiency is improved.
Description
Technical Field
One or more embodiments of the present disclosure relate to the field of data processing technologies, and in particular, to a method and an apparatus for processing insurance business refined data, and an electronic device.
Background
Insurance calculation refers to the application of knowledge and principles of mathematics, statistics, finance, insurance and demographics to solve the problems of business insurance and various social security business, such as mortality measurement, life form construction, rate box, preparation of gold, and business surplus distribution, so as to ensure the stability and safety of insurance management.
At present, the short insurance calculation data in insurance is mainly processed by single thread processing and manual processing for data summarization, and the processing time is too long, so that the data summarization efficiency is low.
Disclosure of Invention
In view of the foregoing, one or more embodiments of the present disclosure are directed to a method, an apparatus and an electronic device for processing insurance business refined data, so as to solve the problem of long processing time for big data in short risk refining.
In view of the above object, one or more embodiments of the present disclosure provide an insurance business accurate data processing method, including:
Reading insurance business data from a table of a distributed column-oriented database in a predetermined format and converting the insurance business data into business entity objects;
processing the business entity object by using a parallel computing engine to obtain an accurate calculation index;
According to a preset checking rule, performing compliance checking on the attribute value of the business entity object, and repairing the non-compliance insurance business data found in the compliance checking;
saving insurance business data passing the compliance check or the repair to a data warehouse based on a distributed system architecture;
and carrying out insurance business fine calculation and data summarization according to the fine calculation index and the insurance business data stored in the data warehouse.
As an alternative embodiment, the distributed column-oriented database is an HBase database.
As an alternative embodiment, the parallel computing engine is a MapReduce or Spark computing engine.
As an alternative implementation, the data warehouse is a Hadoop-based Hive library.
As an alternative embodiment, the refinement index includes at least one of the following: group/personal logo, death guard, severe disease guard, claims and pending claims.
As an alternative embodiment, repairing the non-compliant insurance business data includes: and repairing the non-compliant insurance business data by using a pre-trained random forest model.
As an alternative embodiment, the non-compliant insurance service data includes insurance service data in which a blank necessary-filled information field exists or the format of filling content is inconsistent with the requirements of the information field.
As an alternative embodiment, the non-compliant insurance service data includes insurance service data with unsuitable validation date and expiration date.
Corresponding to the obtaining method, the embodiment of the invention also provides an insurance business refined data processing device, which comprises:
the reading module is used for reading insurance business data from a table of the distributed column-oriented database in a preset format and converting the insurance business data into business entity objects;
The processing module is used for processing the business entity object by using a parallel computing engine to obtain an accurate calculation index;
The checking and repairing module is used for performing compliance checking on the attribute values of the business entity objects according to a preset checking rule and repairing the non-compliance insurance business data found in the compliance checking;
a storage module for storing the insurance business data passing the compliance check or the repair to a data warehouse based on a distributed system architecture;
And the fine calculation and summarization module is used for carrying out fine calculation and summarization of insurance business according to the fine calculation index and the insurance business data stored in the data warehouse.
Corresponding to the above-mentioned obtaining method, the embodiment of the invention also provides an electronic device, which comprises a memory, a processor and a computer program stored on the memory and executable by the processor, wherein the processor implements the method as described above when executing the computer program.
As can be seen from the foregoing, according to the method, the device and the electronic device for processing insurance business refined data provided in one or more embodiments of the present disclosure, data is read from a distributed database and converted into a business entity object, and a parallel computing engine is used to process the business entity object, so that compared with single-thread processing efficiency, detection and repair of a machine learning algorithm are performed on the processed data, and time consumed for extracting, detecting and repairing the data is less, thereby improving efficiency of data summarization.
Drawings
For a clearer description of one or more embodiments of the present description or of the solutions of the prior art, the drawings that are necessary for the description of the embodiments or of the prior art will be briefly described, it being apparent that the drawings in the description below are only one or more embodiments of the present description, from which other drawings can be obtained, without inventive effort, for a person skilled in the art.
FIG. 1 is a flow diagram of a method of insurance business refinement data processing in accordance with one or more embodiments of the present disclosure;
FIG. 2 is a schematic diagram of an insurance business resolution data processing device according to one or more embodiments of the present disclosure;
fig. 3 is a schematic structural diagram of an electronic device according to one or more embodiments of the present disclosure.
Detailed Description
For the purposes of promoting an understanding of the principles and advantages of the disclosure, reference will now be made in detail to the following specific examples.
In order to achieve the above object, an embodiment of the present invention provides a method for processing insurance business calculation data, including:
Reading insurance business data from a table of a distributed column-oriented database in a predetermined format and converting the insurance business data into business entity objects;
processing the business entity object by using a parallel computing engine to obtain an accurate calculation index;
According to a preset checking rule, performing compliance checking on the attribute value of the business entity object, and repairing the non-compliance insurance business data found in the compliance checking;
saving insurance business data passing the compliance check or the repair to a data warehouse based on a distributed system architecture;
and carrying out insurance business fine calculation and data summarization according to the fine calculation index and the insurance business data stored in the data warehouse.
In the embodiment of the invention, aiming at insurance business data, the data are extracted in a distributed mode and converted into entity objects, the entity objects are processed by parallel operation to obtain accurate calculation indexes, the entity objects are detected according to preset rules, the non-compliance data are repaired, compliance data or the repaired data are stored in a data warehouse, and the insurance business accurate calculation and the data summarization are carried out on the accurate calculation indexes and the data stored in the data warehouse to obtain a data summary table. In the embodiment of the invention, compared with single-thread processing efficiency, the distributed extraction and parallel computing processing improves, the machine learning algorithm detection and repair are carried out on the processed data, the time consumed for the extraction, detection and repair of the data is less, and the data summarizing efficiency is improved.
Referring to fig. 1, an embodiment of the present invention provides a method for processing insurance business refined data, including:
S100, reading insurance business data from a table of a distributed column-oriented database in a preset format, and converting the insurance business data into business entity objects.
As an alternative embodiment, the distributed column-oriented database is an HBase database.
Alternatively, yaml is used to extract the data from the HBase database and parse the data.
And S200, processing the business entity object by using a parallel computing engine to obtain an accurate calculation index.
As an alternative embodiment, the parallel computing engine is a MapReduce or Spark computing engine.
Alternatively, mapReduce is used for processing, data is classified in the Map stage, each type of data is processed separately to generate a specific index, and the results processed in the Map stage are summarized in the Reduce stage.
As an alternative embodiment, the refinement index includes at least one of the following: group/personal logo, death guard, severe disease guard, claims and pending claims.
And S300, performing compliance checking on the attribute values of the business entity objects according to a preset checking rule, and repairing the non-compliance insurance business data found in the compliance checking.
As an alternative embodiment, the non-compliant insurance service data includes insurance service data in which a blank necessary-filled information field exists or the format of filling content is inconsistent with the requirements of the information field.
Optionally, the type of policy is filled in with errors, and the amount under different types is confused.
As an alternative embodiment, repairing the non-compliant insurance business data includes: and repairing the non-compliant insurance business data by using a pre-trained random forest model.
As an alternative embodiment, the non-compliant insurance service data includes insurance service data with unsuitable validation date and expiration date.
Optionally, the training method of the effective date model of the random forest model comprises the following steps:
acquiring a training set, wherein the training set comprises data which does not contain effective date and only contains relevant fields of effective date and data which contains effective date and relevant fields;
inputting training data in the training set into the random forest model, and performing iterative computation until the difference between the predicted effective date and the actual effective date is smaller than a threshold value;
and obtaining a trained random forest model with effective date.
Optionally, the training method of the expiration date model of the random forest model comprises the following steps:
Acquiring a training set, wherein the training set comprises data which does not contain an expiration date and only contains related fields of the expiration date and data which contains the expiration date and related fields;
Inputting training data in the training set into the random forest model, and performing iterative computation until the difference value between the predicted termination date and the actual termination date is smaller than a threshold value;
Obtaining a trained expiration date random forest model.
The principle of the random forest regression algorithm is as follows:
And (1) randomly extracting m sample points from the training sample set S to obtain a new S1, S2 and S3.
And 2, training the CART regression tree by using a sub-training set, wherein in the training process, the segmentation rule of each node is to randomly select k features from all the features, then select the optimal segmentation point from the k features, and then divide the left subtree and the right subtree.
And step3, generating a plurality of CART regression tree models according to the second step.
And 4, the final prediction result of each CART regression tree is the average value of leaf nodes to which the sample points reach.
And 5, the final prediction result of the random forest is the average value of all CART regression tree prediction results.
And S400, saving the insurance business data passing the compliance check or the repaired insurance business data to a data warehouse based on a distributed system architecture.
As an alternative implementation, the data warehouse is a Hadoop-based Hive library.
Structured data files can be mapped to a database table by a hive data warehouse tool.
S500, according to the calculation index and the insurance business data stored in the data warehouse, carrying out insurance business calculation and data summarization.
It should be noted that the methods of one or more embodiments of the present description may be performed by a single device, such as a computer or server. The method of the embodiment can also be applied to a distributed scene, and is completed by mutually matching a plurality of devices. In the case of such a distributed scenario, one of the devices may perform only one or more steps of the methods of one or more embodiments of the present description, the devices interacting with each other to accomplish the methods.
Based on any one of the embodiments of the insurance business accurate data processing method described above, the present invention further provides an insurance business accurate data processing device, as shown in fig. 2, including:
A reading module 10 for reading insurance business data from a table of the distributed column-oriented database in a predetermined format and converting it into business entity objects;
The processing module 20 is configured to process the business entity object by using a parallel computing engine to obtain a refined calculation index;
the checking and repairing module 30 is configured to perform compliance checking on the attribute value of the service entity object according to a predetermined checking rule, and repair the non-compliance insurance service data found in the compliance checking;
A storage module 40 for saving insurance business data passing the compliance check or the repair to a data warehouse based on a distributed system architecture;
And the calculation and summarization module 50 is used for carrying out calculation and summarization of insurance business according to the calculation index and the insurance business data stored in the data warehouse.
In the embodiment of the invention, aiming at insurance business data, the data are extracted in a distributed mode and converted into entity objects, the entity objects are processed by parallel operation to obtain accurate calculation indexes, the entity objects are detected according to preset rules, the non-compliance data are repaired, compliance data or the repaired data are stored in a data warehouse, and the insurance business accurate calculation and the data summarization are carried out on the accurate calculation indexes and the data stored in the data warehouse to obtain a data summary table. In the embodiment of the invention, compared with single-thread processing efficiency, the distributed extraction and parallel computing processing improves, the machine learning algorithm detection and repair are carried out on the processed data, the time consumed for the extraction, detection and repair of the data is less, and the data summarizing efficiency is improved.
It is noted that unless otherwise defined, technical or scientific terms used in one or more embodiments of the present disclosure should be taken in a general sense as understood by one of ordinary skill in the art to which the present disclosure pertains. The word "comprising" or "comprises", and the like, means that elements or items preceding the word are included in the element or item listed after the word and equivalents thereof, but does not exclude other elements or items.
Based on any one of the embodiments of the insurance business accurate data processing method described above, the present invention further provides an insurance business accurate data processing electronic device, as shown in fig. 3, including: a processor 1010, a memory 1020, an input/output interface 1030, a communication interface 1040, and a bus 1050. Wherein processor 1010, memory 1020, input/output interface 1030, and communication interface 1040 implement communication connections therebetween within the device via a bus 1050.
The processor 1010 may be implemented by a general-purpose CPU (Central Processing Unit ), a microprocessor, an Application SPECIFIC INTEGRATED Circuit (ASIC), or one or more integrated circuits, etc. for executing related programs to implement the technical solutions provided in the embodiments of the present disclosure.
The Memory 1020 may be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory ), static storage, dynamic storage, etc. Memory 1020 may store an operating system and other application programs, and when the embodiments of the present specification are implemented in software or firmware, the associated program code is stored in memory 1020 and executed by processor 1010.
The input/output interface 1030 is used to connect with an input/output module for inputting and outputting information. The input/output module may be configured as a component in a device (not shown) or may be external to the device to provide corresponding functionality. Wherein the input devices may include a keyboard, mouse, touch screen, microphone, various types of sensors, etc., and the output devices may include a display, speaker, vibrator, indicator lights, etc.
Communication interface 1040 is used to connect communication modules (not shown) to enable communication interactions of the present device with other devices. The communication module may implement communication through a wired manner (such as USB, network cable, etc.), or may implement communication through a wireless manner (such as mobile network, WIFI, bluetooth, etc.).
Bus 1050 includes a path for transferring information between components of the device (e.g., processor 1010, memory 1020, input/output interface 1030, and communication interface 1040).
It should be noted that although the above-described device only shows processor 1010, memory 1020, input/output interface 1030, communication interface 1040, and bus 1050, in an implementation, the device may include other components necessary to achieve proper operation. Furthermore, it will be understood by those skilled in the art that the above-described apparatus may include only the components necessary to implement the embodiments of the present description, and not all the components shown in the drawings.
Those of ordinary skill in the art will appreciate that: the discussion of any of the embodiments above is merely exemplary and is not intended to suggest that the scope of the disclosure, including the claims, is limited to these examples; combinations of features of the above embodiments or in different embodiments are also possible within the spirit of the present disclosure, steps may be implemented in any order, and there are many other variations of the different aspects of one or more embodiments described above which are not provided in detail for the sake of brevity.
The present disclosure is intended to embrace all such alternatives, modifications and variances which fall within the broad scope of the appended claims. Any omissions, modifications, equivalents, improvements, and the like, which are within the spirit and principles of the one or more embodiments of the disclosure, are therefore intended to be included within the scope of the disclosure.
Claims (7)
1. A method for processing insurance business refinement data, comprising:
Reading insurance business data from a table of a distributed column-oriented database in a predetermined format and converting the insurance business data into business entity objects;
Processing the business entity object by using a parallel computing engine to obtain a refined calculation index, wherein the method comprises the following steps: using a MapReduce parallel computing engine to classify the business entity object in a Map stage; the business entity objects of each type are independently processed to generate specific indexes, and the results in the Map stage are summarized in the Reduce stage to obtain the refined indexes;
And according to a preset checking rule, performing compliance checking on the attribute value of the business entity object, and repairing the non-compliance insurance business data found in the compliance checking, wherein repairing the non-compliance insurance business data comprises the following steps: repairing the non-compliance insurance business data by using a pre-trained random forest model; the non-compliant insurance business data comprises an information bar which is filled with blank or the format of filling content is inconsistent with the requirements of the information bar;
saving insurance business data passing the compliance check or the repair to a data warehouse based on a distributed system architecture;
and carrying out insurance business fine calculation and data summarization according to the fine calculation index and the insurance business data stored in the data warehouse.
2. The insurance business accuracy data processing method according to claim 1, wherein said distributed column-oriented database is an HBase database.
3. The insurance business refined data processing method according to claim 1 or 2, wherein said data warehouse is a Hadoop-based Hive library.
4. The insurance business accuracy data processing method according to claim 1 or 2, wherein said accuracy index includes at least one of: group/personal logo, death guard, severe disease guard, claims and pending claims.
5. The insurance business accuracy data processing method according to claim 1, wherein said non-compliant insurance business data includes insurance business data whose effective date and expiration date are not appropriate.
6. An insurance business refinement data processing device, comprising:
the reading module is used for reading insurance business data from a table of the distributed column-oriented database in a preset format and converting the insurance business data into business entity objects;
The processing module is used for processing the business entity object by using a parallel computing engine to obtain an accurate calculation index, and comprises the following steps: classifying the business entity objects by using a MapReduce parallel computing engine, processing each type of business entity object independently to generate a specific index in a Map stage, and summarizing the results in the Map stage in a Reduce stage to obtain the accurate index;
The checking and repairing module is used for performing compliance checking on the attribute values of the business entity objects according to a preset checking rule and repairing the non-compliance insurance business data found in the compliance checking; wherein repairing the non-compliant insurance business data comprises: repairing the non-compliance insurance business data by using a pre-trained random forest model; the non-compliant insurance business data comprises an information bar which is filled with blank or the format of filling content is inconsistent with the requirements of the information bar;
a storage module for storing the insurance business data passing the compliance check or the repair to a data warehouse based on a distributed system architecture;
And the fine calculation and summarization module is used for carrying out fine calculation and summarization of insurance business according to the fine calculation index and the insurance business data stored in the data warehouse.
7. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable by the processor, wherein the processor implements the method of any one of claims 1 to 5 when executing the computer program.
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Citations (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2004038590A (en) * | 2002-07-03 | 2004-02-05 | Nissay Dowa General Insurance Co Ltd | Data management system, method and program, and computer-readable storage medium with the program stored therein |
| JP2009259233A (en) * | 2008-03-19 | 2009-11-05 | Dai-Ichi Mutual Life Insurance Co | Group insurance management device and program |
| CN105938573A (en) * | 2016-03-10 | 2016-09-14 | 深圳市前海安测信息技术有限公司 | Actuarial early-warning system and method for medical benefits fund |
| CN106097104A (en) * | 2016-06-07 | 2016-11-09 | 众安在线财产保险股份有限公司 | A kind of judge the method and system of internet data integrity under distributed system |
| CN107688591A (en) * | 2017-04-06 | 2018-02-13 | 平安科技（深圳）有限公司 | A kind of actuarial treating method and apparatus |
| CN109409430A (en) * | 2018-10-26 | 2019-03-01 | 江苏智通交通科技有限公司 | Traffic accident intelligent data analysis and comprehensive application system |
| CN109597967A (en) * | 2018-11-20 | 2019-04-09 | 江苏云上电力科技有限公司 | A kind of electric system distribution transforming power station load data abnormality detection and restorative procedure |
| CN110033382A (en) * | 2019-02-12 | 2019-07-19 | 阿里巴巴集团控股有限公司 | A kind of processing method of insurance business, device and equipment |
| CN110555064A (en) * | 2018-03-27 | 2019-12-10 | 中国人民财产保险股份有限公司 | data service system and method for insurance business |
| CN110659273A (en) * | 2019-09-20 | 2020-01-07 | 南方电网科学研究院有限责任公司 | Data abnormity monitoring and repairing method of distributed big data acquisition platform |
| CN110675243A (en) * | 2019-08-30 | 2020-01-10 | 北京银联金卡科技有限公司 | Machine learning-fused credit prediction overdue method and system |
| CN111275524A (en) * | 2020-01-19 | 2020-06-12 | 北京众信易保科技有限公司 | Insurance product recommendation method and system |
| CN111444159A (en) * | 2020-03-03 | 2020-07-24 | 中国平安人寿保险股份有限公司 | Actuarial data processing method, actuarial data processing device, electronic equipment and storage medium |
| CN111506908A (en) * | 2020-04-10 | 2020-08-07 | 深圳新致软件有限公司 | Big data recommendation method, system and equipment for insurance industry |
| CN111524017A (en) * | 2019-02-01 | 2020-08-11 | 天津幸福生命科技有限公司 | Claim data auditing method, device and medium |
Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8571897B2 (en) * | 2009-11-23 | 2013-10-29 | The Prudential Insurance Company Of America | System and method for administering insurance policies issued before comprehensive underwriting |
| US20130013344A1 (en) * | 2011-07-08 | 2013-01-10 | Ernstberger Kelly A | Systems and methods for determining optional insurance coverages |
| US20140278573A1 (en) * | 2013-03-15 | 2014-09-18 | State Farm Mutual Automobile Insurance Company | Systems and methods for initiating insurance processing using ingested data |
| US20190130226A1 (en) * | 2017-10-27 | 2019-05-02 | International Business Machines Corporation | Facilitating automatic handling of incomplete data in a random forest model |
-
2020
- 2020-11-20 CN CN202011312384.2A patent/CN112288585B/en active Active
Patent Citations (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2004038590A (en) * | 2002-07-03 | 2004-02-05 | Nissay Dowa General Insurance Co Ltd | Data management system, method and program, and computer-readable storage medium with the program stored therein |
| JP2009259233A (en) * | 2008-03-19 | 2009-11-05 | Dai-Ichi Mutual Life Insurance Co | Group insurance management device and program |
| CN105938573A (en) * | 2016-03-10 | 2016-09-14 | 深圳市前海安测信息技术有限公司 | Actuarial early-warning system and method for medical benefits fund |
| CN106097104A (en) * | 2016-06-07 | 2016-11-09 | 众安在线财产保险股份有限公司 | A kind of judge the method and system of internet data integrity under distributed system |
| CN107688591A (en) * | 2017-04-06 | 2018-02-13 | 平安科技（深圳）有限公司 | A kind of actuarial treating method and apparatus |
| CN110555064A (en) * | 2018-03-27 | 2019-12-10 | 中国人民财产保险股份有限公司 | data service system and method for insurance business |
| CN109409430A (en) * | 2018-10-26 | 2019-03-01 | 江苏智通交通科技有限公司 | Traffic accident intelligent data analysis and comprehensive application system |
| WO2020083400A1 (en) * | 2018-10-26 | 2020-04-30 | 江苏智通交通科技有限公司 | Traffic accident data intelligent analysis and comprehensive application system |
| CN109597967A (en) * | 2018-11-20 | 2019-04-09 | 江苏云上电力科技有限公司 | A kind of electric system distribution transforming power station load data abnormality detection and restorative procedure |
| CN111524017A (en) * | 2019-02-01 | 2020-08-11 | 天津幸福生命科技有限公司 | Claim data auditing method, device and medium |
| CN110033382A (en) * | 2019-02-12 | 2019-07-19 | 阿里巴巴集团控股有限公司 | A kind of processing method of insurance business, device and equipment |
| CN110675243A (en) * | 2019-08-30 | 2020-01-10 | 北京银联金卡科技有限公司 | Machine learning-fused credit prediction overdue method and system |
| CN110659273A (en) * | 2019-09-20 | 2020-01-07 | 南方电网科学研究院有限责任公司 | Data abnormity monitoring and repairing method of distributed big data acquisition platform |
| CN111275524A (en) * | 2020-01-19 | 2020-06-12 | 北京众信易保科技有限公司 | Insurance product recommendation method and system |
| CN111444159A (en) * | 2020-03-03 | 2020-07-24 | 中国平安人寿保险股份有限公司 | Actuarial data processing method, actuarial data processing device, electronic equipment and storage medium |
| CN111506908A (en) * | 2020-04-10 | 2020-08-07 | 深圳新致软件有限公司 | Big data recommendation method, system and equipment for insurance industry |
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