CN117172226A

CN117172226A - Form reconstruction method, device, equipment and storage medium thereof

Info

Publication number: CN117172226A
Application number: CN202311112157.9A
Authority: CN
Inventors: 唐明建
Original assignee: Ping An Health Insurance Company of China Ltd
Current assignee: Ping An Health Insurance Company of China Ltd
Priority date: 2023-08-30
Filing date: 2023-08-30
Publication date: 2023-12-05

Abstract

The embodiment of the application belongs to the technical fields of artificial intelligence and digital medical treatment, and relates to a form reconstruction method, a device, equipment and a storage medium thereof, which are applied to the medical premium calculation process in a comprehensive medical insurance system, and comprise the steps of respectively setting inquiry identification codes for different rate factors and generating a first structure sub-table; calculating a corresponding rate factor according to each medical risk price to generate a second structure sub-table; generating a third structure sub-table corresponding to each rate factor according to the factor values and the calculation coefficients corresponding to all the rate factors; and writing all rate factors affecting the pricing calculation of the medical risk in the target medical insurance data broad table, corresponding factor values and calculation coefficients of all rate factors into the corresponding first structure sub-table, second structure sub-table and third structure sub-table respectively, so as to complete the form reconstruction of the medical insurance data in the target medical insurance data broad table and improve the medical risk premium calculation efficiency and the maintainability of the system.

Description

Form reconstruction method, device, equipment and storage medium thereof

Technical Field

The application relates to the technical field of artificial intelligence and digital medical treatment, which is applied to the medical premium calculation process in a comprehensive medical insurance system, in particular to a form reconstruction method, a form reconstruction device, form reconstruction equipment and a storage medium thereof.

Background

With the development of the computer industry, the traditional medical industry is gradually transformed into digital medical treatment, and particularly in a medical application system crossing multiple platforms, with the wider access to medical institutions, the wider the medical service range and coverage range, the more and more corresponding medical insurance services are processed.

The traditional medical premium calculation data storage is a chimney type development thinking, each newly added rate factor and related data thereof are gradually added into a data wide table in an item accumulation mode, but when one factor is newly added as the service development product demand is iterated, the more and more rate data are changed, a new column is required to be added in the table, a trial calculation interface is also required to be modified to enter a reference, trial calculation logic codes are adjusted, the online release is carried out in a scheduling mode, the maintenance workload is larger, and the higher and higher maintenance cost is very easy to cause.

Disclosure of Invention

The embodiment of the application aims to provide a form reconstruction method, a device, equipment and a storage medium thereof, which are used for solving the problems that the prior art uses a data wide table to store rate data, so that the maintenance workload is larger and the maintenance cost is higher.

In order to solve the above technical problems, the embodiment of the present application provides a form reconstruction method, which adopts the following technical scheme:

A form reconstruction method comprising the steps of:

acquiring all rate factors affecting the current medical risk pricing calculation in a target medical insurance data wide table, and factor values and calculation coefficients corresponding to all rate factors;

sorting all rate factors according to a preset sorting rule to obtain a sorting result;

based on the sorting result, respectively setting inquiry identification codes for different rate factors, and generating a first structure sub-table, wherein column field information of the first structure sub-table comprises a rate factor name, a calculation coefficient address code corresponding to the rate factor and the inquiry identification code of the rate factor;

calculating the corresponding rate factors according to the current medical risk, and generating a second structure sub-table, wherein the column field information of the second structure sub-table comprises the current medical risk, all rate factor names affecting the pricing of the current medical risk and query identification codes corresponding to each rate factor;

generating a third structural sub-table corresponding to each rate factor according to the factor value and the calculation coefficient corresponding to each rate factor, wherein column field information of each third structural sub-table comprises the calculation coefficient and the calculation coefficient distinguishing code of each rate factor, and factor value representation data corresponding to the calculation coefficients respectively;

And writing all rate factors affecting the pricing calculation of each medical risk in the target medical insurance data broad table, factor values and calculation coefficients corresponding to all rate factors into a corresponding first structure sub-table, a corresponding second structure sub-table and a corresponding third structure sub-table respectively, and completing the form reconstruction of medical insurance data in the target medical insurance data broad table.

Further, before executing the step of obtaining all rate factors affecting the pricing calculation of the current medical risk in the target medical insurance data broad table, the factor values corresponding to all rate factors, and the calculation coefficients, the method further includes:

based on preset identification conditions and a monitoring calculation component, identifying whether the total data items in the medical insurance data wide table meet the reconstruction requirements or not;

if the total data items in the medical insurance data wide table reach the reconstruction requirement, acquiring all rate factors affecting the pricing calculation of the current medical insurance risk, and factor values and calculation coefficients corresponding to all rate factors in the medical insurance data wide table;

and if the total data items in the medical insurance data wide table do not meet the reconstruction requirement, continuing to adopt the monitoring and calculating component to carry out data entry monitoring on the medical insurance data wide table.

Further, the step of obtaining all rate factors affecting the pricing calculation of the current medical risk, the factor values corresponding to all rate factors and the calculation coefficients in the target medical insurance data broad table specifically includes:

acquiring each piece of data information in the medical insurance data wide table row by row according to the row number information in the medical insurance data wide table;

based on a preset structured analysis template, carrying out structured analysis on each piece of data information in the medical insurance data broad table acquired line by line to acquire a structured analysis result;

classifying and sorting the structural analysis results by taking different medical risk types as classification items, and obtaining all rate factors corresponding to each medical risk type, factor values corresponding to all rate factors and calculation coefficients as classification and sorting results;

selecting one medical risk from the different medical risk types as a current medical risk type, and screening all rate factors corresponding to the current medical risk type, factor values corresponding to all rate factors and calculation coefficients from the classification and arrangement results.

Further, the step of sorting all rate factors according to a preset sorting rule to obtain a sorting result specifically includes:

According to the structural analysis result, table entry time stamp information of all rate factors corresponding to the current medical risk is obtained;

and based on the table entry timestamp information of all the rate factors corresponding to the current medical risk, sorting all the rate factors corresponding to the current medical risk, and obtaining the sorting result.

Further, the step of setting query identification codes for different rate factors based on the sorting result specifically includes:

according to the sorting result, sequentially setting positive integer numbers for all rate factors corresponding to the current medical risk according to a descending order, wherein the initial value of the positive integer numbers is the minimum positive integer;

the English identifiers preset for the current medical risk are obtained, and query identification codes of all rate factors corresponding to the current medical risk are generated by splicing the corresponding English identifiers and positive integer numbers.

Further, the step of generating the first structure sub-table specifically includes:

acquiring name information of all rate factors corresponding to the current medical risk;

setting name information of all rate factors corresponding to the current medical risk to a first column item preset in the first structure sub-table;

Acquiring inquiry identification codes of all rate factors corresponding to the current medical dangerous seed;

according to the name information of all the rate factors corresponding to the current medical risk, setting the query identification codes of all the rate factors corresponding to the current medical risk into a second column item preset in the first structure sub-table;

and redundancy of a blank column in the first structure sub-table is performed in advance to serve as a third column item of the first structure sub-table.

Further, before executing the step of generating the third structural sub-table corresponding to each rate factor according to the factor values and the calculation coefficients corresponding to all rate factors, the method further includes:

according to the structural analysis result, different calculation coefficients corresponding to each rate factor and factor value representation data corresponding to the different calculation coefficients are obtained;

judging the data type of the factor value representation data respectively corresponding to different calculation coefficients of the same rate factor;

if the data type of the factor value representation data corresponding to the different calculation coefficients of the same rate factor is a Boolean type, counting Boolean values corresponding to the different calculation coefficients of the same rate factor according to the factor value representation data, and constructing a one-to-one association relationship between the calculation coefficients and the corresponding Boolean values;

If the data type of the factor value representation data corresponding to the different calculation coefficients of the same rate factor is an interval numerical value type, counting the value interval corresponding to the different calculation coefficients of the same rate factor according to the factor value representation data, and constructing a one-to-one association relation between the calculation coefficients and the corresponding value interval;

the step of generating a third structural sub-table corresponding to each rate factor according to the factor values and the calculation coefficients corresponding to all rate factors specifically comprises the following steps:

different calculation coefficients corresponding to each rate factor are obtained, different codes are set according to the difference of the calculation coefficients, table name information of the preset third structure sub-table is obtained, the table name information and the different codes are spliced, calculation coefficient difference codes corresponding to the different calculation coefficients are generated, and the calculation coefficient difference codes corresponding to the different calculation coefficients are set in a preset first column item in the third structure sub-table;

setting different calculation coefficients into a preset second column item in the third structure sub-table according to calculation coefficient distinguishing codes respectively corresponding to the different calculation coefficients;

According to the data types of the factor value representation data respectively corresponding to different calculation coefficients, acquiring a Boolean value or a value interval corresponding to the different calculation coefficients, setting the Boolean value or the value interval into a preset third column item in the third structure sub-table according to the corresponding relation with the corresponding calculation coefficients, and generating a third structure sub-table corresponding to each rate factor;

after executing the step of generating the third structural sub-table corresponding to each rate factor according to the factor values and the calculation coefficients corresponding to all rate factors, the method further includes:

acquiring table name information of a third structure sub-table corresponding to each rate factor;

and setting the table name information of the third structural sub-table corresponding to each rate factor into the third column item of the first structural sub-table according to the name information of each rate factor, and completing filling of the third column item in the first structural sub-table.

In order to solve the above technical problems, the embodiment of the present application further provides a form reconstruction device, which adopts the following technical scheme:

a form reconstruction apparatus comprising:

the rate data acquisition module is used for acquiring all rate factors affecting the pricing calculation of the current medical risk in the target medical insurance data wide table, and factor values and calculation coefficients corresponding to all rate factors;

The rate factor ordering module is used for ordering all rate factors according to a preset ordering rule to obtain an ordering result;

the first structure sub-table generation module is used for setting inquiry identification codes for different rate factors respectively based on the sorting result and generating a first structure sub-table, wherein the column field information of the first structure sub-table comprises a rate factor name, a calculation coefficient address code corresponding to the rate factor respectively and the inquiry identification code of the rate factor;

the second structure sub-table generation module is used for calculating the corresponding rate factors according to the current medical risk, and generating a second structure sub-table, wherein the column field information of the second structure sub-table comprises the current medical risk, all rate factor names affecting the pricing of the current medical risk and query identification codes corresponding to each rate factor;

the third structure sub-table generation module is used for generating a third structure sub-table corresponding to each rate factor according to the factor value and the calculation coefficient corresponding to each rate factor, wherein the column field information of each third structure sub-table comprises the calculation coefficient and the calculation coefficient distinguishing code of each rate factor, and the factor value representation data corresponding to the calculation coefficient respectively;

And the rate data writing module is used for writing all rate factors affecting the pricing calculation of each medical insurance risk in the target medical insurance data broad table, factor values and calculation coefficients corresponding to all rate factors into the corresponding first structure sub-table, second structure sub-table and third structure sub-table respectively to finish the form reconstruction of the medical insurance data in the target medical insurance data broad table.

In order to solve the above technical problems, the embodiment of the present application further provides a computer device, which adopts the following technical schemes:

a computer device comprising a memory having stored therein computer readable instructions which when executed by a processor implement the steps of the form reconstruction method described above.

In order to solve the above technical problems, an embodiment of the present application further provides a computer readable storage medium, which adopts the following technical schemes:

a computer readable storage medium having stored thereon computer readable instructions which when executed by a processor implement the steps of a form reconstruction method as described above.

Compared with the prior art, the embodiment of the application has the following main beneficial effects:

according to the form reconstruction method, all rate factors influencing the pricing calculation of medical risk seeds in the target medical insurance data wide table, and corresponding factor values and calculation coefficients of all rate factors are obtained; respectively setting inquiry identification codes for different rate factors, and generating a first structure sub-table; calculating a corresponding rate factor according to each medical risk price to generate a second structure sub-table; generating a third structure sub-table corresponding to each rate factor according to the factor values and the calculation coefficients corresponding to all the rate factors; and writing all rate factors affecting the pricing calculation of the medical risk in the target medical insurance data broad table, corresponding factor values and calculation coefficients of all rate factors into the corresponding first structure sub-table, second structure sub-table and third structure sub-table respectively, so as to complete the form reconstruction of the medical insurance data in the target medical insurance data broad table and improve the medical risk premium calculation efficiency and the maintainability of the system.

Drawings

In order to more clearly illustrate the solution of the present application, a brief description will be given below of the drawings required for the description of the embodiments of the present application, it being apparent that the drawings in the following description are some embodiments of the present application, and that other drawings may be obtained from these drawings without the exercise of inventive effort for a person of ordinary skill in the art.

FIG. 1 is an exemplary system architecture diagram in which the present application may be applied;

FIG. 2 is a flow chart of one embodiment of a form reconstruction method according to the present application;

FIG. 3 is a flow chart of one embodiment of step 201 of FIG. 2;

FIG. 4 is a flow chart of one embodiment of step 202 of FIG. 2;

FIG. 5 is a flow chart of one embodiment of setting the query identification code in a form reconstruction method according to the present application;

FIG. 6 is a flow chart of one embodiment of generating the first structural sub-table in a form reconstruction method according to the present application;

FIG. 7 is a flow chart of one embodiment of step 205 of FIG. 2;

FIG. 8 is a schematic diagram of one embodiment of a form reconstruction device according to the present application;

FIG. 9 is a schematic diagram of an embodiment of a computer device in accordance with the present application.

Detailed Description

Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs; the terminology used in the description of the applications herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application; the terms "comprising" and "having" and any variations thereof in the description of the application and the claims and the description of the drawings above are intended to cover a non-exclusive inclusion. The terms first, second and the like in the description and in the claims or in the above-described figures, are used for distinguishing between different objects and not necessarily for describing a sequential or chronological order.

Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the application. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments.

In order to make the person skilled in the art better understand the solution of the present application, the technical solution of the embodiment of the present application will be clearly and completely described below with reference to the accompanying drawings.

As shown in fig. 1, a system architecture 100 may include terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 is used as a medium to provide communication links between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, among others.

The user may interact with the server 105 via the network 104 using the terminal devices 101, 102, 103 to receive or send messages or the like. Various communication client applications, such as a web browser application, a shopping class application, a search class application, an instant messaging tool, a mailbox client, social platform software, etc., may be installed on the terminal devices 101, 102, 103.

The terminal devices 101, 102, 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smartphones, tablet computers, electronic book readers, MP3 players (Moving Picture ExpertsGroup Audio Layer III, dynamic video expert compression standard audio plane 3), MP4 (Moving PictureExperts Group Audio Layer IV, dynamic video expert compression standard audio plane 4) players, laptop and desktop computers, and the like.

The server 105 may be a server providing various services, such as a background server providing support for pages displayed on the terminal devices 101, 102, 103.

It should be noted that, the form reconstruction method provided by the embodiment of the present application is generally executed by a server, and accordingly, the form reconstruction device is generally disposed in the server.

It should be understood that the number of terminal devices, networks and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.

With continued reference to FIG. 2, a flow chart of one embodiment of a form reconstruction method according to the present application is shown. The form reconstruction method comprises the following steps:

Step 201, obtaining all rate factors affecting the pricing calculation of the current medical risk in the target medical insurance data broad table, and factor values and calculation coefficients corresponding to all rate factors.

In this embodiment, before executing the step of obtaining all rate factors affecting the current medical risk pricing calculation, the factor values corresponding to all rate factors, and the calculation coefficients in the target medical insurance data broad table, the method further includes: based on preset identification conditions and a monitoring calculation component, identifying whether the total data items in the medical insurance data wide table meet the reconstruction requirements or not; if the total data items in the medical insurance data wide table reach the reconstruction requirement, acquiring all rate factors affecting the pricing calculation of the current medical insurance risk, and factor values and calculation coefficients corresponding to all rate factors in the medical insurance data wide table; and if the total data items in the medical insurance data wide table do not meet the reconstruction requirement, continuing to adopt the monitoring and calculating component to carry out data entry monitoring on the medical insurance data wide table.

Through the monitoring and calculating component, excessive data input in the medical insurance data wide table can be avoided. Meanwhile, the form reconstruction method can be conveniently set as an automatic arrangement method, and when the total data items in the medical insurance data wide table reach a certain set value, the form reconstruction is automatically carried out, so that the expansibility of a program is increased.

Wherein the rate factor affecting the medical insurance rate comprises: the medical insurance comprises: health risks, serious diseases risks, basic medical insurance for urban and rural residents, and the like.

With continued reference to fig. 3, fig. 3 is a flow chart of one embodiment of step 201 of fig. 2, comprising:

step 301, acquiring each piece of data information in the medical insurance data wide table row by row according to the row number information in the medical insurance data wide table;

step 302, based on a preset structural analysis template, carrying out structural analysis on each piece of data information in the medical insurance data wide table acquired line by line to acquire a structural analysis result;

the structural analysis templates comprise JSON analysis templates, the JSON analysis templates comprise JSON array analysis templates and JSON set analysis templates, and the JSON set analysis templates comprise List analysis templates or/and Map analysis templates.

Step 303, classifying and sorting the structured analysis results by taking different medical risk types as classification items, and obtaining all rate factors corresponding to each medical risk type, factor values corresponding to all rate factors and calculation coefficients as classification and sorting results;

Step 304, selecting one medical risk from the different medical risks as the current medical risk, and screening all rate factors corresponding to the current medical risk, factor values corresponding to all rate factors and calculation coefficients from the classification and arrangement results.

Through the structural analysis template and by taking different medical risk types as classification items, all the rate factors of each medical risk type obtained through analysis, the factor values and the calculation coefficients corresponding to all the rate factors are classified, so that the method is more scientific, the fuzzy arrangement of the rate factors of all the medical risk types is avoided, the rate factors of each medical risk type are directly and accurately obtained, and the complexity of a data structure is reduced.

Step 202, sorting all rate factors according to a preset sorting rule, and obtaining a sorting result.

With continued reference to FIG. 4, FIG. 4 is a flow chart of one embodiment of step 202 of FIG. 2, including:

step 401, obtaining the entry time stamp information of all rate factors corresponding to the current medical risk according to the structural analysis result;

specifically, through traversing the structural analysis result, obtaining the schedule entry time stamp information of all the rate factors corresponding to each medical risk respectively, and if a plurality of schedule entry time stamp information of a certain target rate factor of the current medical risk is obtained in the traversing process, only caching the earliest schedule entry time stamp information as the schedule entry time stamp information of the target rate factor.

Step 402, based on the entry time stamp information of all the rate factors corresponding to the current medical risk, sorting all the rate factors corresponding to the current medical risk, and obtaining the sorting result.

Specifically, all the rate factors corresponding to each medical risk are ordered according to the time stamp information of the entry table of all the rate factors corresponding to each medical risk.

In addition, the ranking may also be performed according to the influence weights of all the rate factors corresponding to each medical risk, and the ranking may be performed according to the magnitude relation of the influence weights, so that the ranking is not repeated herein.

Through the sorting process, when other processes are carried out on all rate factors corresponding to each medical risk respectively, a specific processing sequence is corresponding, and the processing efficiency of the subsequent form reconstruction is further improved.

Step 203, based on the sorting result, respectively setting query identification codes for different rate factors, and generating a first structure sub-table.

In this embodiment, the column field information of the first structural sub-table includes a rate factor name, a calculated coefficient address code corresponding to the rate factor, and a query identifier of the rate factor, where the rate factor name, the calculated coefficient address code, and the query identifier are in a one-to-one association relationship.

In this embodiment, the address code of each calculation coefficient is the table name information of the storage table corresponding to each calculation coefficient after the current form is reconstructed.

With continued reference to FIG. 5, FIG. 5 illustrates a flow chart of one embodiment of setting the query identification code in a form reconstruction method according to the present application, comprising:

step 501, sequentially setting positive integer numbers for all rate factors corresponding to the current medical risk according to the sorting result from small to large, wherein the initial value of the positive integer number is the minimum positive integer;

step 502, acquiring an English identifier preset for the current medical risk, and generating query identification codes of all rate factors corresponding to the current medical risk by splicing the corresponding English identifier and positive integer numbers.

By generating the query identification codes of all the rate factors corresponding to each medical risk respectively, the corresponding rate factors can be screened out directly through a configuration file and a machine query mode when the risk premium accounting is carried out later, and the method is more intelligent, wherein the configuration file contains a one-to-one correspondence between all the rate factors corresponding to each medical risk respectively and the query identification codes of all the rate factors corresponding to each medical risk respectively.

With continued reference to FIG. 6, FIG. 6 illustrates a flow chart of one embodiment of generating the first structural sub-table in a form reconstruction method according to the present application, comprising:

step 601, acquiring name information of all rate factors corresponding to the current medical risk;

step 602, setting name information of all rate factors corresponding to the current medical risk to a first column item preset in the first structure sub-table;

step 603, acquiring inquiry identification codes of all rate factors corresponding to the current medical risk;

step 604, according to the name information of all the rate factors corresponding to the current medical risk, setting the query identification codes of all the rate factors corresponding to the current medical risk into a second column item preset in the first structure sub-table;

step 605, redundancy a blank column in the first structure sub-table is used as a third column item of the first structure sub-table.

By taking different medical risk types as categories, a first structure sub-table corresponding to each medical risk type is constructed, and one-to-one association relationship among the rate factor name, the calculation coefficient address code and the inquiry identification code is constructed, so that the follow-up inquiry is convenient, the premium calculation is not needed to be carried out through wide-table inquiry, the system maintenance is convenient, and the premium calculation efficiency is improved. Specifically, the name of the current medical dangerous seed is a distinguishing identification field of a first structure table, a first column item of the first structure table is filled with the name information of the rate factor, a second column item is filled with the query identification code of the rate factor, and a third column item is filled with the calculation coefficient address code corresponding to the rate factor, wherein the calculation coefficient address code is the table name address or the table name information stored by the calculation coefficient.

And 204, calculating the corresponding rate factors according to the current medical risk, and generating a second structure sub-table, wherein the column field information of the second structure sub-table comprises the current medical risk, all rate factor names affecting the pricing of the current medical risk and the query identification codes corresponding to the rate factors.

In this embodiment, the current medical risk and all rate factor names affecting the pricing of the current medical risk are named as one-to-many associations. Specifically, the first column of the second structural sub-table is filled with the current medical risk, the second column is filled with all rate factor names of the current medical risk pricing, and the third column is filled with the query identification code corresponding to each rate factor.

And after that, when the rate factors corresponding to the pricing calculation of different medical dangerous seeds are obtained, the second structure sub-table is gradually perfected directly in a table row-by-row splicing mode.

And 205, generating a third structural sub-table corresponding to each rate factor according to the factor values and the calculation coefficients corresponding to all the rate factors.

In this embodiment, the column field information of each third structural sub-table includes a calculation coefficient of each rate factor, a calculation coefficient distinguishing code, and factor value representation data corresponding to the calculation coefficient, where the three values of the different calculation coefficients, the calculation coefficient address code, and the factor value representation data corresponding to the different calculation coefficients are in a one-to-one association relationship.

In this embodiment, before the step of generating the third structural sub-table corresponding to each rate factor according to the factor values and the calculation coefficients corresponding to all rate factors, the method further includes: according to the structural analysis result, different calculation coefficients corresponding to each rate factor and factor value representation data corresponding to the different calculation coefficients are obtained; judging the data type of the factor value representation data respectively corresponding to different calculation coefficients of the same rate factor; if the data type of the factor value representation data corresponding to the different calculation coefficients of the same rate factor is a Boolean type, counting Boolean values corresponding to the different calculation coefficients of the same rate factor according to the factor value representation data, and constructing a one-to-one association relationship between the calculation coefficients and the corresponding Boolean values; if the data type of the factor value representation data corresponding to the different calculation coefficients of the same rate factor is an interval numerical value type, counting the value interval corresponding to the different calculation coefficients of the same rate factor according to the factor value representation data, and constructing a one-to-one association relation between the calculation coefficients and the corresponding value interval.

In this embodiment, it is assumed that the rate factor of a certain medical risk includes gender, at this time, the factor value representation data corresponding to the gender of the rate factor can only be male or female, or 1 or 0 is preset, where 1 represents male and 0 represents female, that is, the data type of the factor value representation data is a Boolean type, and the male/female correspond to different calculation coefficients; similarly, the rate factor for a medical risk includes age, for example: the incidence of a disease is different in different age ranges, such as heart disease and hypertension, and the incidence rate of 20 years to 30 years is generally far lower than the incidence rate of 50 years to 60 years, at this time, the age of 20 years to 30 years corresponds to one calculation coefficient, the age of 50 years to 60 years corresponds to another calculation coefficient, namely, the data type of the factor value representation data is a range numerical value type, and different age ranges correspond to different calculation coefficients.

With continued reference to fig. 7, fig. 7 is a flow chart of one embodiment of step 205 shown in fig. 2, comprising:

step 701, obtaining different calculation coefficients corresponding to each rate factor, setting a distinguishing code according to different calculation coefficients, obtaining preset table name information of the third structural sub-table, splicing the table name information and the distinguishing code, generating calculation coefficient distinguishing codes corresponding to the different calculation coefficients, and setting the calculation coefficient distinguishing codes corresponding to the different calculation coefficients into preset first column items in the third structural sub-table;

Essentially, the computational coefficient differential code is formed by jointly concatenating a computational coefficient address code and a corresponding differential code.

Step 702, setting different calculation coefficients into a preset second column item in the third structural sub-table according to calculation coefficient distinguishing codes respectively corresponding to the different calculation coefficients;

step 703, obtaining the Boolean value or the value interval corresponding to the different calculation coefficients according to the data types of the factor value characterization data corresponding to the different calculation coefficients, and setting the Boolean value or the value interval into a preset third column item in the third structure sub-table according to the corresponding relation with the corresponding calculation coefficients, so as to generate a third structure sub-table corresponding to each rate factor.

In this embodiment, the first column entry of the third structural sub-table is filled with the computation coefficient distinguishing codes corresponding to the different computation coefficients respectively, the second column entry is filled with the different computation coefficients, and the third column entry is filled with the factor value characterization data corresponding to the different computation coefficients respectively.

In this embodiment, after the step of generating the third structural sub-table corresponding to each rate factor according to the factor values and the calculation coefficients corresponding to all rate factors is performed, the method further includes: acquiring table name information of a third structure sub-table corresponding to each rate factor; and setting the table name information of the third structural sub-table corresponding to each rate factor into the third column item of the first structural sub-table according to the name information of each rate factor, and completing filling of the third column item in the first structural sub-table.

And 206, writing all rate factors affecting the pricing calculation of each medical risk in the target medical insurance data broad table, factor values and calculation coefficients corresponding to all rate factors into the corresponding first structure sub-table, second structure sub-table and third structure sub-table respectively, and completing the form reconstruction of medical insurance data in the target medical insurance data broad table.

In this embodiment, the first structural sub-table is a rate factor table, the second structural sub-table is a dangerous rate table, and the third structural sub-table is a factor coefficient table, and the form reconstruction of the medical insurance data in the target medical insurance data wide table is substantially to reconstruct rate factor related data of the medical insurance data in the target medical insurance data wide table.

In this embodiment, after the step 206 is performed, the method further includes: and cleaning the data in the target medical insurance data wide table so that the target medical insurance data wide table is in a zero data state. The excessive data quantity cached in the database is avoided, the resource consumption is reduced, and the premium calculation efficiency is improved.

The target medical insurance data wide table is arranged into the first structure sub-table, the second structure sub-table and the third structure sub-table, so that all rate data are prevented from being extruded into one table, and the sub-table is adopted, thereby being more beneficial to premium calculation, improving calculation efficiency and being easy to maintain.

The application obtains all rate factors influencing the pricing calculation of the medical risk in the target medical insurance data wide table, and factor values and calculation coefficients corresponding to all rate factors; respectively setting inquiry identification codes for different rate factors, and generating a first structure sub-table; calculating a corresponding rate factor according to each medical risk price to generate a second structure sub-table; generating a third structure sub-table corresponding to each rate factor according to the factor values and the calculation coefficients corresponding to all the rate factors; and writing all rate factors affecting the pricing calculation of the medical risk in the target medical insurance data broad table, corresponding factor values and calculation coefficients of all rate factors into the corresponding first structure sub-table, second structure sub-table and third structure sub-table respectively, so as to complete the form reconstruction of the medical insurance data in the target medical insurance data broad table and improve the medical risk premium calculation efficiency and the maintainability of the system.

The embodiment of the application can acquire and process the related data based on the artificial intelligence technology. Among these, artificial intelligence (Artificial Intelligence, AI) is the theory, method, technique and application system that uses a digital computer or a digital computer-controlled machine to simulate, extend and extend human intelligence, sense the environment, acquire knowledge and use knowledge to obtain optimal results.

Artificial intelligence infrastructure technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, operation/interaction systems, mechatronics, and the like. The artificial intelligence software technology mainly comprises a computer vision technology, a robot technology, a biological recognition technology, a voice processing technology, a natural language processing technology, machine learning/deep learning and other directions.

In the embodiment of the application, query identification codes are respectively set for different rate factors, and a first structure sub-table is generated; calculating a corresponding rate factor according to each medical risk price to generate a second structure sub-table; generating a third structure sub-table corresponding to each rate factor according to the factor values and the calculation coefficients corresponding to all the rate factors; and writing all rate factors affecting the pricing calculation of the medical risk in the target medical insurance data broad table, corresponding factor values and calculation coefficients of all rate factors into the corresponding first structure sub-table, second structure sub-table and third structure sub-table respectively, so as to complete the form reconstruction of the medical insurance data in the target medical insurance data broad table and improve the medical risk premium calculation efficiency and the maintainability of the system.

With further reference to fig. 8, as an implementation of the method shown in fig. 2, the present application provides an embodiment of a form reconstruction apparatus, where the embodiment of the apparatus corresponds to the embodiment of the method shown in fig. 2, and the apparatus may be specifically applied to various electronic devices.

As shown in fig. 8, the form reconstruction device 800 according to the present embodiment includes: rate data acquisition module 801, rate factor ordering module 802, first structure sub-table generation module 803, second structure sub-table generation module 804, third structure sub-table generation module 805, and rate data writing module 806.

Wherein:

the rate data obtaining module 801 is configured to obtain all rate factors affecting the pricing calculation of the current medical risk, factor values corresponding to all rate factors, and calculation coefficients in the target medical insurance data broad table;

the rate factor sorting module 802 is configured to sort all rate factors according to a preset sorting rule, and obtain a sorting result;

a first structure sub-table generating module 803, configured to set query identifier codes for different rate factors respectively based on the sorting result, and generate a first structure sub-table, where column field information of the first structure sub-table includes a rate factor name, a calculated coefficient address code corresponding to the rate factor respectively, and a query identifier code of the rate factor;

A second structural sub-table generating module 804, configured to calculate a corresponding rate factor according to the current medical risk, and generate a second structural sub-table, where column field information of the second structural sub-table includes the current medical risk, all rate factor names affecting the pricing of the current medical risk, and a query identifier corresponding to each rate factor;

a third structural sub-table generating module 805, configured to generate a third structural sub-table corresponding to each rate factor according to the factor value and the calculation coefficient corresponding to each rate factor, where column field information of each third structural sub-table includes the calculation coefficient and the calculation coefficient distinguishing code of each rate factor, and factor value characterization data corresponding to the calculation coefficient respectively;

and a rate data writing module 806, configured to write all rate factors affecting the pricing calculation of each medical risk in the target medical insurance data broad table, factor values and calculation coefficients corresponding to all rate factors into the corresponding first structural sub-table, second structural sub-table and third structural sub-table respectively, so as to complete the form reconstruction of the medical insurance data in the target medical insurance data broad table.

Those skilled in the art will appreciate that implementing all or part of the above described embodiment methods may be accomplished by computer readable instructions, stored on a computer readable storage medium, that the program when executed may comprise the steps of embodiments of the methods described above. The storage medium may be a nonvolatile storage medium such as a magnetic disk, an optical disk, a Read-Only Memory (ROM), or a random access Memory (Random Access Memory, RAM).

It should be understood that, although the steps in the flowcharts of the figures are shown in order as indicated by the arrows, these steps are not necessarily performed in order as indicated by the arrows. The steps are not strictly limited in order and may be performed in other orders, unless explicitly stated herein. Moreover, at least some of the steps in the flowcharts of the figures may include a plurality of sub-steps or stages that are not necessarily performed at the same time, but may be performed at different times, the order of their execution not necessarily being sequential, but may be performed in turn or alternately with other steps or at least a portion of the other steps or stages.

In order to solve the technical problems, the embodiment of the application also provides computer equipment. Referring specifically to fig. 9, fig. 9 is a basic structural block diagram of a computer device according to the present embodiment.

The computer device 9 comprises a memory 9a, a processor 9b, a network interface 9c communicatively connected to each other via a system bus. It should be noted that only a computer device 9 having components 9a-9c is shown in the figures, but it should be understood that not all of the illustrated components need be implemented, and that more or fewer components may alternatively be implemented. It will be appreciated by those skilled in the art that the computer device herein is a device capable of automatically performing numerical calculations and/or information processing in accordance with predetermined or stored instructions, the hardware of which includes, but is not limited to, microprocessors, application specific integrated circuits (Application Specific Integrated Circuit, ASICs), programmable gate arrays (fields-Programmable Gate Array, FPGAs), digital processors (Digital Signal Processor, DSPs), embedded devices, etc.

The computer equipment can be a desktop computer, a notebook computer, a palm computer, a cloud server and other computing equipment. The computer equipment can perform man-machine interaction with a user through a keyboard, a mouse, a remote controller, a touch pad or voice control equipment and the like.

The memory 9a includes at least one type of readable storage medium including flash memory, hard disk, multimedia card, card memory (e.g., SD or DX memory, etc.), random Access Memory (RAM), static Random Access Memory (SRAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), programmable Read Only Memory (PROM), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the storage 9a may be an internal storage unit of the computer device 9, such as a hard disk or a memory of the computer device 9. In other embodiments, the memory 9a may also be an external storage device of the computer device 9, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash Card (Flash Card) or the like, which are provided on the computer device 9. Of course, the memory 9a may also comprise both an internal memory unit of the computer device 9 and an external memory device. In this embodiment, the memory 9a is typically used to store an operating system and various application software installed on the computer device 9, such as computer readable instructions of a form reconstruction method. Further, the memory 9a may be used to temporarily store various types of data that have been output or are to be output.

The processor 9b may be a central processing unit (Central Processing Unit, CPU), controller, microcontroller, microprocessor, or other data processing chip in some embodiments. The processor 9b is typically used to control the overall operation of the computer device 9. In this embodiment, the processor 9b is configured to execute computer readable instructions stored in the memory 9a or process data, such as computer readable instructions for executing the form reconstruction method.

The network interface 9c may comprise a wireless network interface or a wired network interface, which network interface 9c is typically used for establishing a communication connection between the computer device 9 and other electronic devices.

The computer equipment provided by the embodiment belongs to the technical field of artificial intelligence and digital medical treatment, and is applied to the medical premium calculation process in the comprehensive medical insurance system. The application obtains all rate factors influencing the pricing calculation of the medical risk in the target medical insurance data wide table, and factor values and calculation coefficients corresponding to all rate factors; respectively setting inquiry identification codes for different rate factors, and generating a first structure sub-table; calculating a corresponding rate factor according to each medical risk price to generate a second structure sub-table; generating a third structure sub-table corresponding to each rate factor according to the factor values and the calculation coefficients corresponding to all the rate factors; and writing all rate factors affecting the pricing calculation of the medical risk in the target medical insurance data broad table, corresponding factor values and calculation coefficients of all rate factors into the corresponding first structure sub-table, second structure sub-table and third structure sub-table respectively, so as to complete the form reconstruction of the medical insurance data in the target medical insurance data broad table and improve the medical risk premium calculation efficiency and the maintainability of the system.

The present application also provides another embodiment, namely, a computer readable storage medium storing computer readable instructions executable by a processor to cause the processor to perform the steps of a form reconstruction method as described above.

The computer readable storage medium provided by the embodiment belongs to the technical field of artificial intelligence and digital medical treatment, and is applied to the medical premium calculation process in the comprehensive medical insurance system. The application obtains all rate factors influencing the pricing calculation of the medical risk in the target medical insurance data wide table, and factor values and calculation coefficients corresponding to all rate factors; respectively setting inquiry identification codes for different rate factors, and generating a first structure sub-table; the corresponding rate factor is calculated based on each medical risk pricing,

generating a second structure sub-table; generating a third structure sub-table corresponding to each rate factor according to the factor values and the calculation coefficients corresponding to all the rate factors; and writing all rate factors affecting the pricing calculation of the medical risk in the target medical insurance data broad table, corresponding factor values and calculation coefficients of all rate factors into the corresponding first structure sub-table, second structure sub-table and third structure sub-table respectively, so as to complete the form reconstruction of the medical insurance data in the target medical insurance data broad table and improve the medical risk premium calculation efficiency and the maintainability of the system.

From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (e.g. ROM/RAM, magnetic disk, optical disk) comprising instructions for causing a terminal device (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to perform the method according to the embodiments of the present application.

It is apparent that the above-described embodiments are only some embodiments of the present application, but not all embodiments, and the preferred embodiments of the present application are shown in the drawings, which do not limit the scope of the patent claims. This application may be embodied in many different forms, but rather, embodiments are provided in order to provide a thorough and complete understanding of the present disclosure. Although the application has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that modifications may be made to the embodiments described in the foregoing description, or equivalents may be substituted for elements thereof. All equivalent structures made by the content of the specification and the drawings of the application are directly or indirectly applied to other related technical fields, and are also within the scope of the application.

Claims

1. A form reconstruction method, comprising the steps of:

2. The form reconstruction method according to claim 1, wherein before performing the step of affecting all rate factors, factor values corresponding to all rate factors, and calculation coefficients of the current medical risk pricing calculation in the acquisition target medical insurance data wide table, the method further comprises:

3. The method for reconstructing a form according to claim 1 or 2, wherein the step of obtaining all rate factors affecting the current medical risk pricing calculation, factor values corresponding to all rate factors, and calculation coefficients in the target medical insurance data wide table specifically comprises:

4. The method for reconstructing a form according to claim 3, wherein the step of sorting all rate factors according to a preset sorting rule to obtain a sorting result specifically comprises:

5. The method for reconstructing forms according to claim 4, wherein said step of setting query identification codes for different rate factors based on said sorting result, respectively, comprises:

6. The method for reconstructing a form according to claim 5, wherein said generating a first structural sub-table comprises:

7. The form reconstruction method according to claim 6, wherein before the step of generating the third structural sub-table corresponding to each rate factor according to the factor values and the calculation coefficients corresponding to all rate factors is performed, the method further comprises:

8. A form reconstruction apparatus, comprising:

9. A computer device comprising a memory having stored therein computer readable instructions which when executed by a processor implement the steps of the form reconstruction method of any one of claims 1 to 7.

10. A computer readable storage medium having stored thereon computer readable instructions which when executed by a processor implement the steps of the form reconstruction method of any one of claims 1 to 7.