CN115713261A

CN115713261A - Index data processing method and device, computer equipment and storage medium

Info

Publication number: CN115713261A
Application number: CN202211445942.1A
Authority: CN
Inventors: 钱建
Original assignee: Ping An Property and Casualty Insurance Company of China Ltd
Current assignee: Ping An Property and Casualty Insurance Company of China Ltd
Priority date: 2022-11-18
Filing date: 2022-11-18
Publication date: 2023-02-24

Abstract

The embodiment of the application belongs to the field of artificial intelligence, and relates to a method for processing index data, which comprises the following steps: acquiring historical claim settlement case data; performing statistical processing on the historical claim settlement case data, and screening out historical index data of each service index in a preset time period from the claim settlement case data; establishing an index feature library based on the historical index data; analyzing and processing historical index data in the index feature library based on a preset association analysis algorithm to obtain an association relation among all the service indexes; and constructing derivation formulas respectively corresponding to the service indexes based on the incidence relation. The application also provides a processing device, computer equipment and storage medium of the index data. In addition, the present application also relates to a blockchain technique, and the derivation formula can be stored in the blockchain. By the method and the device, the construction efficiency of the derivation formula of the service index is improved, and the accuracy of the generated derivation formula of the service index is ensured.

Description

Index data processing method and device, computer equipment and storage medium

Technical Field

The present application relates to the field of artificial intelligence technologies, and in particular, to a method and an apparatus for processing index data, a computer device, and a storage medium.

Background

In the current insurance claim business scenario, calculation formulas of some important business indexes are usually required to be constructed based on historical case data of the insurance claim business, so that business personnel can use the calculation formulas to perform a process of making index targets of related businesses. The existing method for constructing the calculation formula of the service index mainly depends on manual experience to conduct discussion and analysis, the method for constructing the calculation formula through a manual processing mode has the problem of low efficiency, and due to the fact that data auxiliary verification is not available, the generated calculation formula can possibly exist in an unreasonable place due to insufficient experience of personnel, and therefore the accuracy of the obtained calculation formula cannot be guaranteed.

Disclosure of Invention

The embodiment of the application aims to provide an index data processing method, an index data processing device, a computer device and a storage medium, and aims to solve the technical problems that an existing method for constructing a calculation formula of a business index mainly depends on manual experience for discussion and analysis, the method for constructing the calculation formula by a manual processing method is low in efficiency, and due to the fact that data auxiliary verification is not available, the generated calculation formula is possibly in an unreasonable place due to insufficient experience of personnel, and the accuracy of the obtained calculation formula cannot be guaranteed.

In order to solve the above technical problem, an embodiment of the present application provides a method for processing index data, which adopts the following technical solutions:

acquiring historical claim settlement case data;

performing statistical processing on the historical claim settlement case data, and screening out historical index data of each service index in a preset time period from the claim settlement case data;

establishing an index feature library based on the historical index data;

analyzing and processing historical index data in the index feature library based on a preset association analysis algorithm to obtain an association relation among all the service indexes;

and constructing derivation formulas respectively corresponding to the service indexes based on the incidence relation.

Further, the step of analyzing and processing the historical index data in the index feature library based on a preset association analysis algorithm to obtain an association relationship between the service indexes specifically includes:

carrying out standardization processing on numerical values of all the service indexes contained in the index feature library to obtain sample data;

acquiring data of a first preset proportion from the sample data as training data, and acquiring data of a second preset proportion as test data;

fitting the training data based on a support vector regression algorithm to obtain a corresponding fitting result;

testing the fitting result based on the test data to obtain a corresponding test error;

and if the test error is smaller than a preset error threshold value, obtaining the association relation between the service indexes based on the fitting result.

Further, after the step of constructing derivation formulas corresponding to the service indicators based on the association relationship, the method further includes:

acquiring data occupation spaces of all the derivation formulas;

acquiring a local available storage space;

calculating a difference value between the available storage space and the data occupation space, and judging whether the difference value is larger than a preset value or not;

if the difference value is larger than the preset value, storing all the derivation formulas into a local preset database;

and if the difference value is not larger than the preset value, storing all the derivation formulas into a block chain.

Further, the step of storing all the derivation formulas in a local preset database specifically includes:

configuring first formula identifications corresponding to each derivation formula one by one, and using the first formula identifications as index information of the corresponding derivation formula;

storing the first formula identification and the derivation formula into the database based on a correspondence between the first formula identification and the derivation formula;

and creating a search engine corresponding to the database.

Further, the step of storing all the derivation formulas in the blockchain specifically includes:

configuring a one-to-one corresponding second formula identifier for each derivation formula;

determining a target sub-block chain from the block chains; wherein the block chain at least comprises two sub-block chains;

and storing the second formula identification and the derivation formula into the target sub-block chain based on the corresponding relation between the second formula identification and the derivation formula.

Further, the step of determining a target sub-blockchain from the blockchains specifically includes:

determining a data type corresponding to the derivation formula;

judging whether a first sub-block chain corresponding to the data type exists in all sub-block chains contained in the block chain or not based on the data type;

if a first sub-block chain corresponding to the data type exists, taking the first sub-block chain as the target sub-block chain;

if the first sub-block chain corresponding to the data type does not exist, screening out a second sub-block chain meeting a preset condition from all the sub-block chains based on the storage frequency and the storage space of each sub-block chain;

and taking the second sub-block chain as the target sub-block chain.

judging whether an index target formulation request triggered by a user is received or not; wherein the index target formulation request carries a formula identifier;

extracting the formula identification from the service target formulation request;

acquiring a target derivation formula corresponding to the formula identification from all the derivation formulas which are prestored;

displaying an index value configuration page corresponding to the target derivation formula;

receiving index value data input by the user in the index value configuration page;

calculating the target derivation formula based on the index numerical data to generate an index target value corresponding to the index numerical data;

and displaying the index target value.

In order to solve the foregoing technical problem, an embodiment of the present application further provides a device for processing index data, where the following technical solutions are adopted:

the first acquisition module is used for acquiring historical claim settlement case data;

the screening module is used for carrying out statistical processing on the historical claim settlement case data and screening out historical index data of each service index in a preset time period from the claim settlement case data;

the establishing module is used for establishing an index feature library based on the historical index data;

the analysis module is used for analyzing and processing historical index data in the index feature library based on a preset association analysis algorithm to obtain an association relation among all the service indexes;

and the construction module is used for constructing derivation formulas respectively corresponding to the service indexes based on the incidence relation.

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

acquiring historical claim settlement case data;

establishing an index feature library based on the historical index data;

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

acquiring historical claim settlement case data;

establishing an index feature library based on the historical index data;

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

the method comprises the steps of firstly, obtaining historical claim settlement case data; then, statistical processing is carried out on the historical claim settlement case data, and historical index data of each service index in a preset time period are screened out from the claim settlement case data; then, establishing an index feature library based on historical index data; analyzing and processing historical index data in the index feature library based on a preset association analysis algorithm to obtain association relations among all the service indexes; and finally, constructing derivation formulas respectively corresponding to the service indexes based on the incidence relation. According to the embodiment of the application, the incidence relation among the business indexes can be accurately deduced through the use of the incidence analysis algorithm, and then the derivation formulas respectively corresponding to the business indexes can be accurately constructed based on the incidence relation, so that the construction efficiency of the derivation formulas of the business indexes is effectively improved, and the accuracy of the derivation formulas of the generated business indexes is ensured.

Drawings

In order to more clearly illustrate the solution of the present application, the drawings needed for describing the embodiments of the present application will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present application, and that other drawings can be obtained by those skilled in the art without inventive effort.

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

FIG. 2 is a flow diagram of one embodiment of a method of processing metric data according to the present application;

FIG. 3 is a schematic block diagram of an embodiment of a device for processing index data according to the present application;

FIG. 4 is a schematic block diagram of one embodiment of a computer device according to 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 application herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application; the terms "including" and "having," and any variations thereof, in the description and claims of this application and the description of the above figures are intended to cover non-exclusive inclusions. The terms "first," "second," and the like in the description and claims of this application or in the above-described drawings are used for distinguishing between different objects and not for describing a particular order.

Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase 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. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.

In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings.

As shown in fig. 1, the system architecture 100 may include

terminal devices

101, 102, 103, a network 104, and a server 105. Network 104 is the medium used to provide communication links between

terminal devices

101, 102, 103 and server 105. Network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.

A user may use

terminal devices

101, 102, 103 to interact with a server 105 over a network 104 to receive or send messages or the like. The

terminal devices

101, 102, 103 may have various communication client applications installed thereon, such as a web browser application, a shopping application, a search application, an instant messaging tool, a mailbox client, social platform software, and the like.

The

terminal devices

101, 102, 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, e-book readers, MP3 players (Mov I picture ExpertsGroup Aud I o Layer I, mpeg compression standard audio Layer 3), MP4 players (Mov I ng P I ctu re experts G roup Aud I o Layer I V, mpeg compression standard audio Layer 4), laptop portable computers, 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 method for processing the index data provided in the embodiment of the present application is generally executed by a server/terminal device, and accordingly, the processing device for the index data is generally disposed in the server/terminal device.

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 diagram of one embodiment of a method of processing metric data in accordance with the present application is shown. The index data processing method comprises the following steps:

in step S201, historical claim settlement case data is acquired.

In this embodiment, the electronic device (for example, the server/terminal device shown in fig. 1) on which the processing method of the index data operates may acquire the historical claim settlement case data by a wired connection manner or a wireless connection manner. It should be noted that the wireless connection means may include, but is not limited to, a 3G/4G/5G connection, a wifi connection, a bluetooth connection, a wimax connection, a Z i gbee connection, a UWB (u l t ra W i deband) connection, and other wireless connection means now known or developed in the future. The historical claim settlement case data can be historical case data in a vehicle insurance claim settlement scene.

Step S202, the historical claim case data is subjected to statistical processing, and historical index data of each service index in a preset time period is screened out from the claim case data.

In this embodiment, the preset time period is not particularly limited, and may be, for example, the previous year from the current time. The service indexes can correspond to service indexes of a vehicle insurance claim settlement scene, and specifically can include vehicle insurance plan total insurance payment periods, thousands of plans total insurance payment periods, thousands of vehicle property plans, thousands of human injury plans, accessories, working hours, maintenance duty, repair and replacement, whole plan claim rate, vehicle property claim rate and the like.

Step S203, establishing an index feature library based on the historical index data.

In this embodiment, the index feature library can be obtained by storing the historical index data in an initial database. The initial database is a database which does not store other data except the historical index data.

And step S204, analyzing and processing historical index data in the index feature library based on a preset association analysis algorithm to obtain an association relation among the service indexes.

In this embodiment, the specific implementation process of analyzing and processing the historical index data in the index feature library based on the preset association analysis algorithm to obtain the association relationship between the service indexes is described in further detail in the following specific embodiment, and will not be described in detail herein.

Step S205, constructing derivation formulas corresponding to the service indexes based on the association relationship.

In this embodiment, the association relationship is a balanced relationship including each service index, so that a derivation formula corresponding to each service index can be sequentially constructed based on the association relationship. The derivation formula corresponding to the specified service index is a formula containing the relation logic of all the specified service indexes and other service indexes, and the specified service index is any one of all the service indexes. For example, if the service indexes include the vehicle insurance plan average insurance payment period, the number of the previous vehicle insurance plans, the number of the next vehicle insurance plans, the number of the accessories, the working hours, the maintenance ratio, the repair and replacement, the entire plan odds ratio and the vehicle insurance odds ratio. The derivation formulas respectively corresponding to the service indicators, which are correspondingly constructed based on the association relationship between the service indicators, may include: the vehicle insurance plan total insurance payment cycle = (the thousands of upper records per insurance payment cycle + the thousands of lower records per insurance payment cycle)/(the thousands of upper records + the thousands of lower records) N; the number of the ten-thousand-plan-based insurance payment cycles is not less than the number of the ten-thousand-plan-based insurance payment cycles (the number of the ten-thousand-plan-based insurance plans plus the number of the ten-thousand-plan-based insurance plans); the ten-thousand-case-average-risk-paying period = (the ten-thousand-case-event-result-paying period + the ten-thousand-person-injury-event-paying period × ten-thousand-person-injury-event-result number)/(the ten-thousand-case-event number + the ten-person-injury-event number) × N; the payment period of each of the ten thousand vehicle projects is = checking and determining the effectiveness of each of the ten thousand vehicles, determining the damage effectiveness of each of the ten thousand vehicles, checking and damaging the articles, re-checking the articles and paying the articles; the ten-thousand-person injury case pay-out period = ten-thousand quick-loss pay period + ten-thousand outpatient-service pay period + ten-thousand inpatient-service pay period + ten-thousand disability pay period + ten-thousand death pay period; the all-vehicle-leaving scheme insurance payment period is = all-vehicle-leaving vehicle investigation effectiveness + all-vehicle-leaving vehicle loss effectiveness + all-vehicle-leaving vehicle nuclear loss effectiveness + all-vehicle-leaving vehicle re-investigation effectiveness + all-vehicle-leaving vehicle payment effectiveness; the every ten thousands of people's injury cases take out the insurance payment cycle = ten thousands of fast indemnity payment cycles + ten thousands of outpatient service payment cycles + ten thousands of inpatient payment cycles + ten thousands of disability payment cycles + ten thousands of death payment cycles; average case claim = reported odds rate (bottom line objective) car has earned premium/risk frequency N; vehicle property reported average claim = vehicle property reported odds ratio-vehicle has earned premium/risk-out frequency N; vehicle feature settled plan average claim = (vehicle has earned premium/frequency of occurrence of insurance as vehicle feature reported rate of payment (bottom line target) · number of claim plan (pending claim of vehicle feature on the day-pending claim of vehicle feature on the last year))/number of result plan; the national vehicle distribution number = (the sigma vehicle distribution number ×/loss-assessment task flow)/(the sigma loss-assessment task flow) × N; vehicle and property reported odds = vehicle and property reported average claim amount, insurance frequency/vehicle has earned premium N; overall reported odds = (vehicle and object reported odds + injury reported odds) × N. Wherein, N is a preset weight coefficient and can be dynamically adjusted according to historical data.

The method comprises the steps of firstly, obtaining historical claim settlement case data; then, statistical processing is carried out on the historical claim settlement case data, and historical index data of each service index in a preset time period are screened out from the claim settlement case data; then, establishing an index feature library based on historical index data; analyzing and processing historical index data in the index feature library based on a preset association analysis algorithm to obtain association relations among all the service indexes; and finally, constructing derivation formulas respectively corresponding to the service indexes based on the incidence relation. According to the method and the device, the incidence relation among all the service indexes can be accurately deduced through the use of the incidence analysis algorithm, and then the derivation formulas respectively corresponding to all the service indexes can be accurately constructed based on the incidence relation, so that the construction efficiency of the derivation formulas of the service indexes is effectively improved, and the accuracy of the derivation formulas of the generated service indexes is ensured.

In some optional implementations, step S204 includes the following steps:

and carrying out standardization processing on the numerical values of the service indexes contained in the index feature library to obtain sample data.

In this embodiment, the numerical values of the service indexes included in the index feature library may be normalized based on a normalization processing formula.

And acquiring data with a first preset proportion from the sample data as training data, and acquiring data with a second preset proportion as test data.

In this embodiment, the values of the first preset proportion and the second preset proportion are not specifically limited, and may be set according to actual use requirements, for example, the first preset proportion may be set to 80%, and the second preset proportion may be set to 20%.

And fitting the training data based on a support vector regression algorithm to obtain a corresponding fitting result.

In this embodiment, support Vector Regression (SVR) is a regression algorithm that applies similar techniques of Support Vector Machines (SVMs) for regression analysis. The regression data contains continuous real numbers. To fit this type of data, the SVR model approaches the optimal value with a given margin called the ε -tube (epsilon denotes the width of the tube) taking into account the complexity and error rate of the model.

And testing the fitting result based on the test data to obtain a corresponding test error.

In this embodiment, the value of the error threshold is not specifically limited, and may be set according to actual use requirements, for example, may be set to 0.2%. And when the test error is smaller than the error threshold value, the training result is considered to be satisfactory, the fitting result is stored, and the fitting relation of each service index contained in the fitting result is used as the association relation among each service index.

The method comprises the steps of carrying out standardization processing on numerical values of all service indexes contained in an index feature library to obtain sample data; then, acquiring data of a first preset proportion from the sample data as training data, and acquiring data of a second preset proportion as test data; fitting the training data based on a support vector regression algorithm to obtain a corresponding fitting result; subsequently, testing the fitting result based on the test data to obtain a corresponding test error; when the test error is detected to be smaller than the preset error threshold value, the incidence relation among the service indexes is obtained based on the fitting result, so that the incidence relation among the service indexes is accurately deduced based on the use of the support vector regression algorithm, the derivation formulas respectively corresponding to the service indexes can be accurately built subsequently based on the incidence relation, the building efficiency of the derivation formulas of the service indexes is improved, and the accuracy of the derivation formulas of the generated service indexes is ensured.

In some optional implementation manners of this embodiment, after step S205, the electronic device may further perform the following steps:

and acquiring the data occupation space of all the derivation formulas.

In the embodiment, the data occupation space is determined by calculating the sum of the data sizes of all the derived formulas.

And acquiring local available storage space.

And calculating a difference value between the available storage space and the data occupation space, and judging whether the difference value is larger than a preset value or not.

In this embodiment, the value of the error threshold is not specifically limited, and may be set according to actual use requirements.

And if the difference value is larger than the preset value, storing all the derivation formulas into a local preset database.

In this embodiment, the above-mentioned specific implementation process of storing all the derivation formulas in the local preset database will be described in further detail in the following specific embodiments, and will not be described in detail herein.

In the present embodiment, the above-mentioned implementation procedure for storing all the derived formulas in the blockchain will be described in further detail in the following embodiments, which are not set forth herein too much.

After the derivation formulas respectively corresponding to the business indexes are constructed and generated based on the incidence relation, the data occupation space of all the derivation formulas is obtained, the local available storage space is obtained, the difference between the available storage space and the data occupation space is calculated, and the derivation formulas can be stored in a database storage mode or a block chain storage mode intelligently based on the comparison result of the difference and the numerical value of the preset numerical value, so that the storage intelligence of the derivation formulas is improved, and the data safety of the derivation formulas is guaranteed.

In some alternative implementations, the step of storing all the derivation formulas in a local preset database includes the following steps:

and configuring a first formula identifier in one-to-one correspondence to each derivation formula, and using the first formula identifier as index information of the corresponding derivation formula.

In this embodiment, the formula identifier may be formed by numbers, english, or the like, and the formula identifier of each derived formula has uniqueness.

Storing the first formula identification and the derivation formula into the database based on a correspondence between the first formula identification and the derivation formula.

In the present embodiment, it is preferred that,

and creating a search engine corresponding to the database.

In this embodiment, the use of the search engine is not limited, and any engine middleware such as Sp l un, E l ast i csearch, so l r, etc. may be specifically used as the search engine. By implanting the search engine into the electronic equipment, the keyword query based on formula identification or related to the derivation formula can be conveniently carried out in the database in which the derivation formula is stored in the electronic equipment, so that the purpose of finding the required derivation formula is realized.

The method includes the steps that corresponding first formula identifications are configured for all derivation formulas, and the first formula identifications are used as index information of the corresponding derivation formulas; and then based on the corresponding relation between the first formula identification and the derivation formula, storing the first formula identification and the derivation formula into the database, and creating a search engine corresponding to the database so as to realize the purpose of finding the required derivation formula from the database based on the search engine. When the difference value between the available storage space and the data occupation space is larger than a preset value, the derivation formula is stored in a local preset database, so that the storage of the derivation formula cannot affect the normal operation of the electronic equipment, the storage intelligence of the derivation formula is improved, and the data safety of the derivation formula is ensured.

In some optional implementations, the step of storing all the derivation formulas in the blockchain includes the steps of:

and configuring a one-to-one corresponding second formula identifier for each derivation formula.

Determining a target sub-block chain from the block chains; wherein the block chain at least comprises two sub-block chains.

In this embodiment, the specific implementation process of determining the target sub-blockchain from the blockchain is described in further detail in the following specific embodiments, and will not be described in detail herein.

The method includes the steps that corresponding second formula identifications are configured for all derivation formulas, then a target sub-area block chain is determined from the block chain, and then the second formula identifications and the derivation formulas are stored in the target sub-area block chain based on the corresponding relation between the second formula identifications and the derivation formulas. So that the required derivation formula can be found from the blockchain in the following. When the difference value between the available storage space and the data occupation space is not larger than a preset value, the derivation formula is stored in the target sub-block chain of the block chain, so that the storage of the derivation formula cannot affect the normal operation of the electronic equipment, the storage intelligence of the derivation formula is improved, and the data safety of the derivation formula is ensured.

In some optional implementations of this embodiment, the step of determining a target sub-blockchain from the blockchains includes the following steps:

determining a data type corresponding to the derivation formula.

In the present embodiment, the data type of the derivation formula is a formula type.

And judging whether a first sub-block chain corresponding to the data type exists in all the sub-block chains contained in the block chain or not based on the data type.

In this embodiment, the block chain includes at least two sub-block chains, and each sub-block chain corresponds to one data type, that is, different sub-block chains are configured to store data of the corresponding data type.

And if the first sub-block chain corresponding to the data type exists, taking the first sub-block chain as the target sub-block chain.

And if the first sub-block chain corresponding to the data type does not exist, screening out a second sub-block chain meeting a preset condition from all the sub-block chains based on the storage frequency and the storage space of each sub-block chain.

In this embodiment, the preset condition is not limited, and the preset condition may include that a product between a storage frequency of the sub-block chain and the storage space is greater than a preset product threshold, for example.

And taking the second sub-block chain as the target sub-block chain.

According to the method and the device, the data type corresponding to the derivation formula is determined, if it is detected that the first sub-block chain corresponding to the data type exists in all the sub-block chains contained in the block chain, the first sub-block chain is used as a target sub-block chain, and the first sub-block chain matched with the data type is determined from all the sub-block chains contained in the block chain based on the data type to store the derivation formula, so that the normalization of the derivation formula storage can be ensured. And if the first sub-block chain corresponding to the data type does not exist, screening out a second sub-block chain meeting the preset condition from all the sub-block chains based on the storage frequency and the storage space of each sub-block chain, and storing the derivation formula by taking the second sub-block chain as a target sub-block chain, so that the reasonability of storing the derivation formula by using the screened second sub-block chain can be ensured, and the storage intelligence of the derivation formula can be improved.

judging whether an index target formulation request triggered by a user is received or not; and the index target formulation request carries a formula identifier.

In the present embodiment, it is preferred that,

and extracting the formula identification from the service target formulation request.

In this embodiment, the formula identifier may be extracted from the business target formulation request by analyzing the business target formulation request.

And acquiring a target derivation formula corresponding to the formula identifier from all the derivation formulas which are pre-stored.

In this embodiment, based on the storage location of the derivation formula, the target derivation formula corresponding to the formula identifier may be obtained by accessing all the derivation formulas pre-stored in the storage medium (database or block chain) corresponding to the storage location.

And displaying an index value configuration page corresponding to the target derivation formula.

In this embodiment, the index value configuration page is a page including an index value corresponding to the target derivation formula to be filled.

And receiving index value data input by the user in the index value configuration page.

And calculating the target derivation formula based on the index numerical data to generate an index target value corresponding to the index numerical data.

In this embodiment, the target value of the index corresponding to the index value data can be generated by performing calculation processing by substituting the index value data into the target derivation formula.

When an index target formulation request triggered by a user is received; extracting a formula identifier from a business target formulation request; then obtaining a target derivation formula corresponding to the formula identification from all the derivation formulas which are pre-stored; then displaying an index value configuration page corresponding to the target derivation formula; subsequently, receiving index value data input by a user in an index value configuration page; and finally, calculating the target derivation formula based on the index numerical data to generate an index target value corresponding to the index numerical data, and displaying the index target value, so that the target derivation formula is automatically, quickly and accurately calculated based on the formula identifier and the index numerical data input by the user to generate the required index target value, the generation efficiency and the accuracy of the index target value are improved, and the use experience of the user is improved.

It is emphasized that the derivation formula can also be stored in a node of a block chain in order to further ensure the privacy and security of the derivation formula.

The block chain referred by the application is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, a consensus mechanism, an encryption algorithm and the like. The block chain (B l ockcha i n), which is essentially a decentralized database, is a string of data blocks associated by using cryptography, and each data block contains information of a batch of network transactions, which is used for verifying the validity (anti-counterfeiting) of the information and generating the next block. The blockchain may include a blockchain underlying platform, a platform product service layer, an application service layer, and the like.

The embodiment of the application can acquire and process related data based on an artificial intelligence technology. The artificial intelligence (Art I f I c I a l I nte l I gene, A I) is a theory, method, technology and application system for simulating, extending and expanding human intelligence, sensing environment, acquiring knowledge and using knowledge to obtain optimal results by using a digital computer or a machine controlled by the digital computer.

The artificial intelligence infrastructure generally includes 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 the like.

It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware associated with computer readable instructions, which can be stored in a computer readable storage medium, and when executed, the processes of the embodiments of the methods described above can be included. The storage medium may be a non-volatile storage medium such as a magnetic disk, an optical disk, a Read-only Memory (ROM), or a 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, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and may be performed in other orders unless explicitly stated herein. Moreover, at least a portion of the steps in the flow chart of the figure may include multiple sub-steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, which are not necessarily performed in sequence, but may be performed alternately or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

With further reference to fig. 3, as an implementation of the method shown in fig. 2, the present application provides an embodiment of an apparatus for processing index data, 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. 3, the index data processing device 300 according to the present embodiment includes: a first acquisition module 301, a screening module 302, a building module 303, an analysis module 304, and a construction module 305. Wherein:

a first obtaining module 301, configured to obtain historical claim settlement case data;

a screening module 302, configured to perform statistical processing on the historical claim settlement case data, and screen historical index data of each service index in a preset time period from the claim settlement case data;

an establishing module 303, configured to establish an index feature library based on the historical index data;

an analysis module 304, configured to analyze and process historical index data in the index feature library based on a preset association analysis algorithm to obtain an association relationship between the service indexes;

a building module 305, configured to build derivation formulas corresponding to the service indicators based on the association relationship.

In this embodiment, the operations executed by the modules or units respectively correspond to the steps of the index data processing method of the foregoing embodiment one by one, and are not described herein again.

In some optional implementations of this embodiment, the analysis module 304 includes:

the first processing submodule is used for carrying out standardization processing on the numerical value of each service index contained in the index feature library to obtain sample data;

the first determining submodule is used for acquiring data of a first preset proportion from the sample data as training data and acquiring data of a second preset proportion as test data;

the second processing submodule is used for fitting the training data based on a support vector regression algorithm to obtain a corresponding fitting result;

the test sub-module is used for carrying out test processing on the fitting result based on the test data to obtain a corresponding test error;

and the second determining submodule is used for obtaining the association relation between the service indexes based on the fitting result if the test error is smaller than a preset error threshold value.

In this embodiment, the operations respectively executed by the modules or units correspond to the steps of the index data processing method of the foregoing embodiment one by one, and are not described herein again.

In some optional implementations of this embodiment, the apparatus for processing index data further includes:

the second acquisition module is used for acquiring the data occupation space of all the derivation formulas;

the third acquisition module is used for acquiring a local available storage space;

the first judgment module is used for calculating a difference value between the available storage space and the data occupation space and judging whether the difference value is larger than a preset value or not;

the first storage module is used for storing all the derivation formulas into a local preset database if the difference value is larger than the preset value;

and the second storage module is used for storing all the derivation formulas into the block chain if the difference value is not larger than the preset value.

In this embodiment, the operations executed by the modules or units respectively correspond to the steps of the index data processing method in the foregoing embodiment one by one, and are not described herein again.

In some optional implementations of this embodiment, the first storage module includes:

the first configuration submodule is used for configuring a one-to-one corresponding first formula identifier for each derivation formula and taking the first formula identifier as index information of the corresponding derivation formula;

a first storage sub-module, configured to store the first formula identifier and the derivation formula into the database based on a correspondence between the first formula identifier and the derivation formula;

and the creating sub-module is used for creating a search engine corresponding to the database.

In some optional implementations of this embodiment, the second storage module includes:

the second configuration submodule is used for configuring a one-to-one corresponding second formula identifier for each derivation formula;

a third determining submodule, configured to determine a target sub-block chain from the block chains; wherein the block chain at least comprises two sub-block chains;

and the second storage submodule is used for storing the second formula identification and the derivation formula into the target subblock chain based on the corresponding relation between the second formula identification and the derivation formula.

In some optional implementations of this embodiment, the third determining sub-module includes:

a first determining unit, configured to determine a data type corresponding to the derivation formula;

a determining unit, configured to determine, based on the data type, whether a first sub-block chain corresponding to the data type exists in all sub-block chains included in the block chain;

a second determining unit, configured to, if a first sub-chunk chain corresponding to the data type exists, use the first sub-chunk chain as the target sub-chunk chain;

the screening unit is used for screening out a second sub-block chain which meets a preset condition from all the sub-block chains based on the storage frequency and the storage space of each sub-block chain if the first sub-block chain corresponding to the data type does not exist;

a second determining unit, configured to use the second sub-block chain as the target sub-block chain.

the second judgment module is used for judging whether an index target formulation request triggered by a user is received or not; wherein the index target formulation request carries a formula identifier;

the extraction module is used for extracting the formula identification from the business target formulation request;

the fourth obtaining module is used for obtaining a target derivation formula corresponding to the formula identifier from all the derivation formulas which are pre-stored;

the first display module is used for displaying an index value configuration page corresponding to the target derivation formula;

the receiving module is used for receiving the index value data input by the user in the index value configuration page;

the calculation module is used for calculating the target derivation formula based on the index numerical data and generating an index target value corresponding to the index numerical data;

and the second display module is used for displaying the index target value.

In order to solve the technical problem, an embodiment of the present application further provides a computer device. Referring to fig. 4, fig. 4 is a block diagram of a basic structure of a computer device according to the present embodiment.

The computer device 4 comprises a memory 41, a processor 42, and a network interface 43, which are communicatively connected to each other via a system bus. It is noted that only computer device 4 having components 41-43 is shown, but it is understood that not all of the shown components are required to be implemented, and that more or fewer components may be implemented instead. AS will be understood by those skilled in the art, the computer device herein is a device capable of automatically performing numerical calculation and/or information processing according to a preset or stored instruction, and the hardware includes, but is not limited to, a microprocessor, an application specific integrated circuit (App I cat I on Spec I C I integrated C I rcu I, AS ic), a programmable Gate array (F I l D-programmable ab l Gate Ar ray, FPGA), a digital Processor (D I ta l S I gna l Processor, DSP), an embedded device, and the like.

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

The memory 41 includes at least one type of readable storage medium including a flash memory, a hard disk, a multimedia card, a card type memory (e.g., SD or DX memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a Read Only Memory (ROM), an Electrically Erasable Programmable Read Only Memory (EEPROM), a Programmable Read Only Memory (PROM), a magnetic memory, a magnetic disk, an optical disk, etc. In some embodiments, the memory 41 may be an internal storage unit of the computer device 4, such as a hard disk or a memory of the computer device 4. In other embodiments, the memory 41 may also be an external storage device of the computer device 4, such as a plug-in hard disk, a Smart Memory Card (SMC), a Secure digital (Secure D i g i ta l, SD) Card, a flash memory Card (F l ash Card), and the like, which are provided on the computer device 4. Of course, the memory 41 may also include both internal and external storage devices of the computer device 4. In this embodiment, the memory 41 is generally used for storing an operating system installed in the computer device 4 and various types of application software, such as computer readable instructions of a processing method of index data. Further, the memory 41 may also be used to temporarily store various types of data that have been output or are to be output.

The processor 42 may be a Central Processing Unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chip in some embodiments. The processor 42 is typically used to control the overall operation of the computer device 4. In this embodiment, the processor 42 is configured to execute computer readable instructions stored in the memory 41 or computer readable instructions for processing data, such as executing a processing method of the index data.

The network interface 43 may comprise a wireless network interface or a wired network interface, and the network interface 43 is generally used for establishing communication connection between the computer device 4 and other electronic devices.

in the embodiment of the application, historical claim settlement case data is obtained firstly; then, statistical processing is carried out on the historical claim settlement case data, and historical index data of each service index in a preset time period are screened out from the claim settlement case data; then, establishing an index feature library based on historical index data; analyzing and processing historical index data in the index feature library based on a preset association analysis algorithm to obtain association relations among all the service indexes; and finally, constructing derivation formulas respectively corresponding to the service indexes based on the incidence relation. According to the embodiment of the application, the incidence relation among all the service indexes can be accurately deduced through the use of the incidence analysis algorithm, and then the derivation formulas respectively corresponding to all the service indexes can be accurately constructed on the basis of the incidence relation, so that the construction efficiency of the derivation formulas of the service indexes is effectively improved, and the accuracy of the derivation formulas of the generated service indexes is ensured.

The present application further provides another embodiment, which is to provide a computer-readable storage medium storing computer-readable instructions executable by at least one processor to cause the at least one processor to perform the steps of the method for processing index data as described above.

in the embodiment of the application, historical claim settlement case data is obtained firstly; then, statistical processing is carried out on the historical claim settlement case data, and historical index data of each service index in a preset time period are screened out from the claim settlement case data; then, establishing an index feature library based on historical index data; analyzing and processing historical index data in the index feature library based on a preset association analysis algorithm to obtain association relations among all the service indexes; and finally, constructing derivation formulas respectively corresponding to the service indexes based on the incidence relation. According to the embodiment of the application, the incidence relation among the business indexes can be accurately deduced through the use of the incidence analysis algorithm, and then the derivation formulas respectively corresponding to the business indexes can be accurately constructed based on the incidence relation, so that the construction efficiency of the derivation formulas of the business indexes is effectively improved, and the accuracy of the derivation formulas of the generated business indexes is ensured.

Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solutions of the present application may be embodied in the form of a software product, which is stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal device (such as a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the method according to the embodiments of the present application.

It is to be understood that the above-described embodiments are merely illustrative of some, but not restrictive, of the broad invention, and that the appended drawings illustrate preferred embodiments of the invention and do not limit the scope of the invention. This application is capable of embodiments in many different forms and the embodiments are provided so that this disclosure will be thorough and complete. Although the present application has been described in detail with reference to the foregoing embodiments, it will be apparent to one skilled in the art that the present application may be practiced without modification or with equivalents of some of the features described in the foregoing embodiments. All equivalent structures made by using the contents of the specification and the drawings of the present application are directly or indirectly applied to other related technical fields and are within the protection scope of the present application.

Claims

1. A method for processing index data is characterized by comprising the following steps:

acquiring historical claim settlement case data;

establishing an index feature library based on the historical index data;

2. The index data processing method according to claim 1, wherein the step of analyzing and processing the historical index data in the index feature library based on a preset association analysis algorithm to obtain the association relationship between the service indexes specifically comprises:

carrying out standardization processing on the numerical values of the service indexes contained in the index feature library to obtain sample data;

3. The method for processing index data according to claim 1, further comprising, after the step of constructing derivation formulas corresponding to the respective service indexes based on the association relationship, the steps of:

acquiring data occupation spaces of all the derivation formulas;

acquiring a local available storage space;

4. The index data processing method according to claim 3, wherein the step of storing all the derivation formulas in a local preset database specifically includes:

and creating a search engine corresponding to the database.

5. The method of claim 3, wherein the step of storing all the derivation formulas in the blockchain comprises:

6. The index data processing method according to claim 5, wherein the step of determining the target sub-block chain from the block chains specifically includes:

determining a data type corresponding to the derivation formula;

taking the second sub-block chain as the target sub-block chain.

7. The method for processing index data according to claim 1, further comprising, after the step of constructing derivation formulas corresponding to the respective service indexes based on the association relationship, the steps of:

and displaying the index target value.

8. An apparatus for processing index data, comprising:

9. A computer device comprising a memory having computer readable instructions stored therein and a processor which when executed implements the steps of a method of processing metric data as claimed in any one of claims 1 to 7.

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