CN115295166A

CN115295166A - Index data processing method and device

Info

Publication number: CN115295166A
Application number: CN202210867039.8A
Authority: CN
Inventors: 杜其芳
Original assignee: Taikang Insurance Group Co Ltd; Taikang Pension Insurance Co Ltd
Current assignee: Taikang Insurance Group Co Ltd; Taikang Pension Insurance Co Ltd
Priority date: 2022-07-21
Filing date: 2022-07-21
Publication date: 2022-11-04

Abstract

The invention discloses an index data processing method and device, wherein one specific embodiment comprises the steps of responding to a processing request received by an external application function layer, identifying the scene type of the processing request, and calling an index recommendation program or an index simulation program in a knowledge base application service layer based on the scene type; and executing an index recommendation program or an index simulation program, acquiring index information of the processing request, inquiring the association level in a preset index knowledge base, acquiring all index information related to the index or acquiring a subgraph related to the index, and outputting the subgraph through an external application function layer. Therefore, the invention realizes rapid and accurate data query and provides related indexes with service value for intelligent prompt; and can provide the index data calculation route, for the analog calculation of the index and for making, improving the service scheme; therefore, a new service form is formed, the user requirements are better met, and the user stickiness is increased.

Description

Index data processing method and device

Technical Field

The invention relates to the technical field of computers, in particular to an index data processing method and device.

Background

In the process of implementing the invention, the inventor finds that at least the following problems exist in the prior art:

in the prior art, a medical insurance big data platform provides medical insurance evaluation business services aiming at a huge number of medical insurance index libraries, and is very difficult to break through. Moreover, the existing medical insurance index database only can maintain the hierarchical relationship between indexes with lower depth, and if the depth is increased, the query pressure of the relational data is greatly increased.

Disclosure of Invention

In view of this, embodiments of the present invention provide an index data processing method and apparatus, which can implement fast and accurate data query and provide an intelligent prompt for relevant indexes with business values; and can provide the index data calculation route, for the analog calculation of the index and for making, improving the service scheme; therefore, a new service form is formed, the user requirements are better met, and the user stickiness is increased.

To achieve the above object, according to an aspect of an embodiment of the present invention, there is provided an index data processing method, including identifying a scene category of a processing request received by an external application functional layer, to invoke an index recommender or an index simulator in a knowledge base application service layer based on the scene category;

and executing an index recommendation program or an index simulation program, acquiring index information of the processing request, inquiring the association level in a preset index knowledge base, acquiring all index information related to the index or acquiring a subgraph related to the index, and outputting the subgraph through an external application function layer.

Optionally, before responding to the processing request received by the external application function layer, the method includes:

reading an index library of an external data source to obtain index basic information and logic information, constructing an index node list according to the index basic information, taking the indexes as entity nodes, and further constructing a calculation type relation and an association type relation through the logic information among the indexes;

and storing the entity nodes in the index node list and the calculation type relation and the association type relation thereof to generate an index knowledge base.

Optionally, comprising: the index node list is stored in a key-value pair form;

wherein, KEY is index code as graph node; VALUE is index node information as an attribute of a graph node.

Optionally, the association relationship includes a data association relationship and a service association relationship;

constructing an association relation through logic information among indexes, wherein the association relation comprises the following steps:

acquiring basic index information, determining a plurality of indexes which have the same index field for calculation, and constructing a data association relation according to logic information among the plurality of indexes;

acquiring basic index information, determining a plurality of indexes with the same preset index library dimension, and constructing a business association relation according to logic information among the plurality of indexes.

Optionally, before responding to the processing request received by the external application function layer, the method further includes:

reading an index query record of an external data source to obtain index basic information and query logic information, updating an index node list according to the index basic information, and further constructing an industry experience relation through the query logic information among indexes;

and storing entity nodes in the index node list and the industry experience type relation thereof, and updating the index knowledge base.

Optionally, reading a metric query record of an external data source, including:

and reading the index query record of the external data source through the BI tool and the index data query interface.

Optionally, obtaining a subgraph having a computational relationship with the index, and outputting through an external application functional layer, includes:

acquiring a subgraph having a calculation relationship with the index and a corresponding index calculation formula;

bringing specific index values of entity nodes in the subgraph into the subgraph for calculation through an external application function layer based on the index calculation formula;

and adjusting the specific index values of the entity nodes in the subgraph according to the calculation result until a preset target result is met, and outputting the index value of each current entity node.

In addition, the invention also provides an index data processing device, which comprises an acquisition module, a processing module and a display module, wherein the acquisition module is used for responding to a processing request received by an external application functional layer, identifying the scene category of the processing request and calling an index recommendation program or an index simulation program in a knowledge base application service layer based on the scene category; and the processing module is used for executing an index recommendation program or an index simulation program, acquiring the index information of the processing request, inquiring the association level in a preset index knowledge base, acquiring all index information related to the index or acquiring a subgraph related to the index, and outputting the subgraph through an external application functional layer.

One embodiment of the above invention has the following advantages or benefits: the invention provides intelligent support for medical insurance evaluation business services, and the needle is no longer fished from the sea in the index sea by manpower, so that support is provided for the establishment of an improved scheme for an index analysis object, and the expansion of the business can be accelerated; moreover, the construction of an index database is completed through a knowledge graph to make up for the design capability of a relation network of a common relation database and gradually form the standard of the index knowledge database; establishing a knowledge base with various application relations, managing and maintaining uniformly, and realizing the automatic learning of the implicit knowledge by collecting daily work records of users; meanwhile, the method promotes the addition of the special service to a great extent, simplifies the business process, improves the user experience, improves the timeliness and saves the manpower.

Further effects of the above-mentioned non-conventional alternatives will be described below in connection with the embodiments.

Drawings

The drawings are included to provide a better understanding of the invention and are not to be construed as unduly limiting the invention. Wherein:

fig. 1 is a schematic view of a main flow of an index data processing method according to a first embodiment of the present invention;

FIG. 2 is a diagram of an index data processing architecture according to an embodiment of the present invention;

FIG. 3 is a schematic diagram of a structure of an index knowledge base of an index recommendation scenario according to an embodiment of the invention;

FIG. 4 is a diagram illustrating a structure of an index knowledge base of an index simulation scenario, according to an embodiment of the present invention;

FIG. 5 is a schematic view of a main flow of an index data processing method according to a second embodiment of the present invention;

FIG. 6 is a diagram illustrating an exemplary structure of index information according to an embodiment of the present invention;

FIG. 7 is a schematic diagram of the main blocks of an index data processing apparatus according to an embodiment of the present invention;

FIG. 8 is an exemplary device architecture diagram in which embodiments of the present invention may be employed;

fig. 9 is a schematic structural diagram of a computer apparatus of a terminal device or a server adapted to implement an embodiment of the present invention.

Detailed Description

Exemplary embodiments of the present invention are described below with reference to the accompanying drawings, in which various details of embodiments of the invention are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the invention. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.

Fig. 1 is a schematic diagram of a main flow of an index data processing method according to a first embodiment of the present invention, the index data processing method including:

step S101, responding to a processing request received by an external application function layer, identifying a scene category of the processing request, and calling an index recommendation program or an index simulation program in a knowledge base application service layer based on the scene category.

Step S102, an index recommendation program or an index simulation program is executed, index information of the processing request is obtained, a preset index knowledge base is inquired about an associated level, all index information related to the index or a subgraph related to the index is obtained, and the subgraph is output through an external application function layer.

As an embodiment, as shown in fig. 2, the index knowledge base may be supported by using a graph database, and after accessing the graph base source data from an external data source, the graph base source data is classified and analyzed to generate different relationships, so as to support multiple application scenarios. The knowledge base application service can read the index knowledge base and provide index association recommendation and index simulation services, namely, the index association recommendation service queries the associated indexes or the index simulation implements an analysis improvement method. Meanwhile, an application end of the index knowledge base can provide various use scenes, such as: the evaluation function is to define a set of related index sets through the index knowledge base, and to perform evaluation analysis on the basis of the set of related index sets, namely, after a certain service index is selected, the related index is recommended through the index knowledge base for selection, and an evaluation index set is constructed. And the analysis function is to perform the next analysis for evidence collection through the recommended associated indexes when the user analyzes the index data to find abnormality. The simulation function is generally used for simulating and adjusting the influence on the related indexes when one index is adjusted, so as to perform simulation measurement and calculation.

In addition, the data source constructed by the index knowledge base may be an operation log of the medical insurance index base (i.e. providing an operation relationship between the business indexes), the metadata of the medical insurance data (i.e. providing an association relationship between fields in the data structure), the index analysis and query functions (for example, a BI tool, an index analysis data interface, etc. may prompt an implicit business relationship, i.e. convert the business analysis experience into the index knowledge base).

As another embodiment, before responding to the processing request received by the external application function layer, step S101 includes: reading an index library of an external data source to obtain index basic information and logic information, constructing an index node list according to the index basic information, taking the indexes as entity nodes, and further constructing a calculation type relation and an association type relation through the logic information among the indexes; and storing the entity nodes in the index node list and the calculation type relation and the association type relation thereof to generate an index knowledge base. The calculation type relation is obtained based on analysis of a medical insurance index library, the medical insurance indexes are divided into atomic indexes and composite indexes, the atomic indexes correspond to specific tables and fields of a medical insurance data structure, and the composite indexes are obtained by using atomic index composite operation. In addition, the association relationship comprises a data association relationship and a service association relationship. The data association relation is obtained based on structural analysis of medical insurance index data. The business association relation is calculated based on the medical insurance data, and the business indexes can be mapped to a table and a field of the specific medical insurance data, so that a plurality of business indexes can be calculated by using the same index field. For example, the indexes such as total medical cost, hospitalization cost, clinic cost and the like are counted by using medical cost fields, and only the analysis dimensions are different, and the business indexes are specifically related at the data level. For example, indexes such as drug cost, drug proportion, drug quantity and the like are all analyzed from drug dimension and have direct relevance in business.

In a further embodiment, the association relationship is constructed through the logic information between the indexes, and the method comprises the following steps: acquiring basic index information, determining a plurality of indexes which have the same index field for calculation, and constructing a data association relation according to logic information among the plurality of indexes; acquiring basic index information, determining a plurality of indexes with the same preset index library dimension, and constructing a business association relation according to logic information among the plurality of indexes.

In the preferred embodiment, the index node list is stored in a KEY-value pair form, and KEY is used as index code and is used as a graph node; VALUE is index node information as an attribute of a graph node. For example:

KPI_0001:base{code:’KPI_0001’,name:’KPI_A’}

KPI_0003:complex{code:’KPI_0003’,name:’KPI_C’,formula:’(KPI_0001-KPI_0002)/KPI_0002’}

as still other embodiments, before responding to the processing request received by the external application functional layer, step S101 may further read an index query record of an external data source to obtain index basic information and query logic information, so as to update an index node list according to the index basic information, and further construct an industry experience type relationship through the query logic information between indexes; and storing entity nodes in the index node list and the industry experience type relation thereof, and updating the index knowledge base. The industry experience type relation is obtained based on medical insurance industry experience collection and analysis, namely, is obtained from the records of daily work and operation of medical insurance users. For example, when the user checks the medical expense in the BI tool, and simultaneously queries the index of the number of patients in the same dimension, the number of patients and the medical expense are specifically associated and analyzed in the index analysis. That is, the index query record of the external data source can be read through the BI tool (business intelligence analysis tool) and the index data query interface.

It is worth to be noted that the class relationship, the data association class relationship, the service association class relationship and the industry experience class relationship are directly calculated, wherein the first 3 are explicit relationships which can be constructed by data extraction of configuration and operation modeling, and the 4 th is a implicit relationship which is the digitization of industry experience knowledge. The 4 relations can coexist and can be used in different application scenes.

In a further embodiment, step S102 obtains all index information related to the index, and further outputs the index information through an external application functional layer, so that an index recommendation function is realized. For example, the index code is input, and meanwhile, the input recommendation relation level (namely the query level or the depth of the map) can be supported, the index application analysis scene is specified, and the output is all related index codes and names having the relation with the index. It should be noted that the indexes and relationships queried by the index recommendation function during query may be sorted according to the relationship types. Illustratively, the industry experience type relation, the business association type relation, the data association type relation and the calculation type relation are reduced in sequence.

As an embodiment, as shown in fig. 3, an evaluation index set for a insured person, that is, a subset of the index knowledge base, is obtained from the index knowledge base, and then the hospital admission fee is input to query relevant recommendation indexes related to the hospital admission fee, so that 6 relevant indexes for drug proportion, hospital admission times, hospital admission days, outpatient service fees, and drug purchase fees can be obtained, and the 6 relevant indexes are used for assisting in perfecting the evaluation index set from the aspects of the fees, diagnosis and treatment behaviors, and the like of the insured person, thereby realizing the index recommendation function of the knowledge base application service. The specific implementation process of obtaining the evaluation index set aiming at the ginseng insurance person from the index knowledge base comprises the following steps: reading an index library, wherein the index library comprises relevant information of hospitalization cost, clinic cost, medicine purchasing cost, hospitalization times, hospitalization days and medicine proportion. Identifying node information of 7 indexes to construct an index node:

CREATE (KPI _0001

CREATE (KPI _0002, base, ready code: 'KPI _0001', name: 'clinic cost' })

CREATE (KPI _0003, base ready code: 'KPI _0001', name: 'cost of purchasing drugs')

CREATE (KPI _0004

CREATE (KPI _0005

CREATE (KPI _0006

Then, identifying that the hospitalization cost, the medicine purchase cost and the clinic cost all use the same index field (medical cost), defining that the 3 indexes respectively have data association relations, and adding relations to construct the data association relations:

MERGE(KPI_0001)-[:dataRel]->(KPI_0002)

MERGE(KPI_0001)-[:dataRel]->(KPI_0003)

MERGE(KPI_0002)-[:dataRel]->(KPI_0003)

identifying the hospital frequency, the hospital days and the hospital cost by using the same analysis dimension (a participant, an organization and a department), performing business association with each other, and adding a relationship to construct a business association relationship:

MERGE (KPI _ 0001) - [: businesserel { scene: 'ginseng, insurance, institution, department' } ] - > (KPI _ 0004)

MERGE (KPI _ 0001) - [: businessesRel { scene: 'ginseng, institution, department' } ] - > (KPI _ 0005)

MERGE (KPI _ 0004) - [: businessesRel { scene: 'ginseng, institution, department' } ] - > (KPI _ 0005)

Reading a BI and an index query interface, querying a medicine ratio index, using a dimension as a participant, adding a relationship to construct an industry experience relationship:

MERGE (KPI _ 0001) - [: experience Rel { scene: 'Ginseng' } ] - > (KPI _ 0007)

In a further embodiment, step S102 obtains a subgraph having a calculation relationship with the index, and then outputs the subgraph through an external application functional layer, including obtaining the subgraph having a calculation relationship with the index and a corresponding index calculation formula; bringing specific index values of entity nodes in the subgraph into the subgraph for calculation through an external application function layer based on the index calculation formula; and adjusting the specific index values of the entity nodes in the subgraph according to the calculation result until the preset target result is met, and outputting the index values of the current entity nodes.

The method is applied to an index simulation scene, namely, a knowledge base application service realizes an index simulation function, the input of the index simulation function is index coding and query relation hierarchy, and the output of the index simulation function is a subgraph which has a direct calculation relation with the index, wherein a composite index calculation formula can be included. And after the calculation is obtained by an external application system, the direction is calculated according to a calculation formula and the relation, and a specific index value is brought in for calculation. For example, as shown in fig. 4, the user analyzes his own index, finds that the drug ratio ring ratio increase rate increases month by month in approximately 3 months, and then wants to achieve the object by simulating the change of the relevant index. Therefore, all factors (indexes) related to the index calculation of the ring ratio increase rate of the drug ratio need to be found, and then the simulation adjustment is carried out according to a formula. Specifically, in response to a simulation-type function processing request of the external application function, an index simulation program of the knowledge base application service is executed, an external data source (for example, a business index library) is read, and the following 4 indexes are obtained and added:

CREATE (KPI _0001

CREATE (KPI _0002

CREATE (KPI _0003

CREATE (KPI _0004: base ready code: 'KPI _0004', name: 'medicine cost' }) adds the calculation relationship of 4 indexes:

MERGE(KPI_0002)-[:calculateRel]->(KPI_0001)

MERGE(KPI_0003)-[:calculateRel]->(KPI_0002)

MERGE(KPI_0004)-[:calculateRel]->(KPI_0002)

therefore, an index knowledge base is built, a user can transmit the drug ratio ring ratio increase rate index, a calculation link related to the period is obtained, the data shown in the figure 4 is obtained, meanwhile, a calculation formula of the index is obtained, and after the drug cost or the medical cost is tried to be modified, the change condition of the drug ratio ring ratio increase rate is observed, so that a target value is designed to serve as an improvement target.

Fig. 5 is a schematic view of a main flow of an index data processing method according to a second embodiment of the present invention. The index data processing method comprises the following steps:

step S501, reading an index library of an external data source to obtain index basic information and logic information, constructing an index node list according to the index basic information, and taking the index as an entity node.

In an embodiment, as shown in fig. 6, the index information includes basic information and logical information. The basic information is used for constructing knowledge base index nodes and comprises the following fields: index code, index name. The logic information is used for constructing the relation between the calculation class and the service association class, and comprises calculation logic and statistical dimensions. The statistical dimension is a dimension combination (such as institutions, departments, treatment modes and the like) supported by the statistical index. The calculation logic is divided into an atomic index calculation logic and a composite index calculation logic according to different calculation modes of the atomic index and the composite index. The atomic index calculation logic comprises a database table name, a field name and a fixed query dimension value (for example, the out-patient cost is an index of a combination of the fixed dimension value and the index field, the dimension of the out-patient is a treatment mode, and the value-out-patient is taken as a combination of the fixed dimension value and the medical cost). The computational logic of the composite index is complex, and information is configured for structured indexes, such as: the index A-index B)/the index B, wherein the correlation calculation index is an index A and an index B, and the calculation formula is the structure of (A-B)/B. The composite index comprises a plurality of calculation formulas, but the content of the corresponding associated index and the formula can be split.

Preferably, the step S501 of reading the index library of the external data source may output an index basic information list, an index dimension combination, an atomic index calculation logic list, and a composite index calculation logic list.

And step S502, constructing a calculation type relation through the logic information between the indexes. The specific implementation process comprises the following steps:

and defining the calculation logic between the service indexes, namely the relation between the composite index and the atomic index. The relationship type is defined as calculteRel, the relationship direction is an atom index- > composite index, and the relationship is a one-way relationship. For example: (KPI _ 0001) - [: calculateRel ] - > (KPI _ 0003).

Step S503, acquiring the basic information of the indexes, determining a plurality of indexes which have the same index field for calculation, and constructing a data association relation according to the logic information among the plurality of indexes.

In the embodiment, data association conditions among the atomic indexes are defined, and the atomic indexes of the same calculation field can establish a data association type relation and are mainly applied to the atomic indexes. The relationship type is defined as dataRel, and the relationship direction is undirected, for example: (KPI _ 0001) - [: dataRel ] - (KPI _ 0003).

Step S504, acquiring the basic index information, determining a plurality of indexes with the same preset index library dimension, and constructing a business association relation according to the logic information among the plurality of indexes.

In an embodiment, business association conditions between business indexes are defined, and the indexes with the same dimension can establish the relationship without distinguishing the atomic indexes from the composite indexes. The relationship type is defined as businesserl, the relationship direction is undirected, a relationship attribute is added as an application service scene, and the dimension name is used for assignment, for example: (KPI _ 0001) - [: businessRel { scene: 'insu' } ] - (KPI _ 0003).

Step S505, index query records of an external data source are read to obtain index basic information and query logic information, so that an index node list is updated according to the index basic information, and further an industry experience relation is established through the query logic information among indexes.

In the embodiment, the index query record is read, the data has two most valuable data sources, namely a BI tool and an index data query interface, and certainly, tools such as a large screen and a statistical report of each medical insurance business product can also be used as data sources. For example, through the fact tables and fields mainly analyzed by the BI tool user, a user-defined query SQL can be collected in the tool, and the tables and fields involved therein can be extracted. For example: select mdrtt _ way, count (mdrtt _ id), sum (amt) from tk _ setl _ info group by mdrtt _ way. Extracting mdrtid and amt fields from the SQL, querying the fields together, and querying an index library according to the 2 fields, wherein the atomic index using the 2 fields is considered to have an industry experience type relationship. In an example, an index code list which is put together for query by a user can be directly obtained in an index query interface, and an industry experience relation can be directly established among the indexes. In addition, the relation type is defined as experience Rel, the relation scheme is undirected, a relation attribute is added to serve as an application service scene, and dimension name assignment is used. For example: (KPI _ 0001) - [: experienceRel { scene: 'insu' } ] - (KPI _ 0003).

Step S506, storing the entity nodes in the index node list and the calculation type relation, the data association type relation, the service association type relation and the industry experience type relation thereof to generate an index knowledge base.

In an embodiment, the generated nodes and relationships are stored one by one in a library, taking neo4j as an example:

and (4) index node warehousing:

CREATE(KPI_0001:base{code:‘KPI_0001’，name:‘KPI_A’})

CREATE KPI_0003:complex{code:‘KPI_0003’，name:‘KPI_C’，formula:‘(KPI_0001-KPI_0002)/KPI_0002’}

and (4) storing the node relationship:

MERGE(KPI_0001)-[:calculateRel]->(KPI_0003)

MERGE(KPI_0001)-[:dataRel]->(KPI_0003)

MERGE(KPI_0001)-[:businessRel{scene:’insu’}]->(KPI_0003)

MERGE(KPI_0001)-[:experienceRel{scene:’insu’}]->(KPI_0003)

when the node relation is put in storage, except that calcuteRel is unidirectional, the other types are unidirectional. Because neo4j does not support undirected storage, unidirectional storage is still used as long as undirected queries are used at query time.

Step S507, in response to the processing request received by the external application functional layer, identifying a scene category of the processing request, so as to invoke an index recommendation program or an index simulation program in the knowledge base application service layer based on the scene category.

Step S508, an index recommendation program or an index simulation program is executed to obtain the index information of the processing request, and query the association levels in a preset index knowledge base to obtain all the index information related to the index or obtain a subgraph related to the index, and then output the subgraph through an external application function layer.

Fig. 7 is a schematic diagram of main blocks of an index data processing apparatus according to an embodiment of the present invention, and as shown in fig. 7, the index data processing apparatus 700 includes an acquisition module 701 and a processing module 702. The obtaining module 701 responds to a processing request received by an external application function layer, identifies a scene category of the processing request, and calls an index recommendation program or an index simulation program in a knowledge base application service layer based on the scene category; the processing module 702 executes an index recommendation program or an index simulation program, obtains index information of the processing request, queries an association level in a preset index knowledge base, obtains all index information related to the index or obtains a subgraph related to the index, and then outputs the subgraph through an external application function layer.

In some embodiments, the obtaining module 701, before responding to the processing request received by the external application function layer, includes:

reading an index library of an external data source to obtain index basic information and logic information, constructing an index node list according to the index basic information, taking the indexes as entity nodes, and further constructing a calculation type relation and an association type relation through the logic information among the indexes; and storing the entity nodes in the index node list and the calculation type relation and the association type relation thereof to generate an index knowledge base.

In some embodiments, the method comprises: the index node list is stored in a key-value pair form; wherein, KEY is index code as graph node; VALUE is index node information as an attribute of a graph node.

In some embodiments, the association class relationship comprises a data association class relationship and a business association class relationship;

the obtaining module 701 constructs an association relationship through the logic information between the indexes, including:

acquiring basic index information, determining a plurality of indexes which have the same index field for calculation, and constructing a data association relation according to logic information among the plurality of indexes; acquiring basic index information, determining a plurality of indexes with the same preset index library dimension, and constructing a business association relation according to logic information among the plurality of indexes.

In some embodiments, before the obtaining module 701 responds to the processing request received by the external application function layer, the obtaining module further includes:

reading an index query record of an external data source to obtain index basic information and query logic information, updating an index node list according to the index basic information, and further constructing an industry experience relation through the query logic information among indexes; and storing entity nodes in the index node list and the industry experience type relation thereof, and updating the index knowledge base.

In some embodiments, the obtaining module 701 reads a metric query record of an external data source, including: and reading the index query record of the external data source through the BI tool and the index data query interface.

In some embodiments, the processing module 702 obtains a subgraph having a calculation relationship with the index, and then outputs the subgraph through an external application function layer, including:

acquiring subgraphs with calculation relation with the indexes and corresponding index calculation formulas; carrying out calculation on specific index values brought into entity nodes in the subgraph based on the index calculation formula through an external application functional layer; and adjusting the specific index values of the entity nodes in the subgraph according to the calculation result until a preset target result is met, and outputting the index value of each current entity node.

It should be noted that, the index data processing method and the index data processing apparatus according to the present invention have corresponding relationships in the specific implementation contents, and therefore, the repeated contents are not described again.

Fig. 8 shows an exemplary device architecture 800 to which the index data processing method or the index data processing device of the embodiment of the present invention can be applied.

As shown in fig. 8, the apparatus architecture 800 may include

terminal devices

801, 802, 803, a network 804, and a server 805. The network 804 is used to provide a medium for communication links between

terminal devices

801, 802, 803 and a server 805. Network 804 may include a variety of connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.

A user may use the

terminal devices

801, 802, 803 to interact with a server 805 over a network 804 to receive or send messages or the like. The

end devices

801, 802, 803 may have installed thereon various communication client applications, such as, for example, shopping applications, web browser applications, search applications, instant messaging tools, mailbox clients, social platform software, and the like (by way of example only).

The

terminal devices

801, 802, 803 may be various electronic devices having index data processing screens and supporting web browsing, including but not limited to smart phones, tablet computers, laptop portable computers, desktop computers, and the like.

The server 805 may be a server that provides various services, an example background management server (for example only) that supports shopping-like websites browsed by users using the

terminal devices

801, 802, 803. The background management server may analyze and perform other processing on the received data such as the product information query request, and feed back a processing result (the example target push information and the product information — only the example) to the terminal device.

It should be noted that the index data processing method provided by the embodiment of the present invention is generally executed by the server 805, and accordingly, the computing device is generally disposed in the server 805.

It should be understood that the number of terminal devices, networks, and servers in fig. 8 are merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.

Reference is now made to fig. 9, which shows a schematic block diagram of a computer arrangement 900 suitable for implementing a terminal device according to an embodiment of the invention. The terminal device shown in fig. 9 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present invention.

As shown in fig. 9, the computer apparatus 900 includes a Central Processing Unit (CPU) 901 that can perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM) 902 or a program loaded from a storage section 908 into a Random Access Memory (RAM) 903. In the RAM903, various programs and data necessary for the operation of the computer apparatus 900 are also stored. The CPU901, ROM902, and RAM903 are connected to each other via a bus 904. An input/output (I/O) interface 905 is also connected to bus 904.

The following components are connected to the I/O interface 905: an input portion 906 including a keyboard, a mouse, and the like; an output section 907 including components such as a Cathode Ray Tube (CRT), a liquid crystal index data processor (LCD), and the like, and a speaker and the like; a storage portion 908 including a hard disk and the like; and a communication section 909 including a network interface card such as a LAN card, a modem, or the like. The communication section 909 performs communication processing via a network such as the internet. A drive 910 is also connected to the I/O interface 905 as needed. A removable medium 911 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 910 as necessary, so that a computer program read out therefrom is mounted into the storage section 908 as necessary.

In particular, according to the embodiments of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. The disclosed embodiments of the invention include, by way of example, a computer program product comprising a computer program embodied on a computer readable medium, the computer program containing program code for performing the method illustrated in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 909, and/or installed from the removable medium 911. The above-described functions defined in the apparatus of the present invention are executed when the computer program is executed by a Central Processing Unit (CPU) 901.

It should be noted that the computer readable medium shown in the present invention can be a computer readable signal medium or a computer readable storage medium or any combination of the two. Examples of a computer readable storage medium may be, but are not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor device, apparatus, or a combination of any of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present invention, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution apparatus, device, or apparatus. In contrast, in the present invention, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution apparatus, device, or apparatus. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.

The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. As an example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based apparatus that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

The modules described in the embodiments of the present invention may be implemented by software or hardware. The described modules may also be provided in a processor, which may be described, for example, as: a processor includes an acquisition module and a processing module. Wherein the names of the modules do not in some cases constitute a limitation of the module itself.

As another aspect, the present invention also provides a computer-readable medium, which may be contained in the apparatus described in the above embodiments; or may be separate and not assembled into the device. The computer readable medium carries one or more programs which, when executed by a device, cause the device to include identifying a scenario category of a processing request received in response to the external application function layer to invoke an index recommendation program or an index simulation program in a knowledge base application service layer based on the scenario category; and executing an index recommendation program or an index simulation program, acquiring index information of the processing request, inquiring the association level in a preset index knowledge base, acquiring all index information related to the index or acquiring a subgraph related to the index, and outputting the subgraph through an external application function layer.

According to the technical scheme of the embodiment of the invention, the embodiment of the invention can realize rapid and accurate data query and provide intelligent prompt related indexes with service value; and can provide the index data calculation route, for the analog calculation of the index and for making, improving the service scheme; therefore, a new service form is formed, the user requirements are better met, and the user stickiness is increased.

The above-described embodiments should not be construed as limiting the scope of the invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may occur depending on design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. An index data processing method, characterized by comprising:

in response to a processing request received by an external application functional layer, identifying a scene category of the processing request to call an index recommendation program or an index simulation program in a knowledge base application service layer based on the scene category;

2. The method of claim 1, wherein prior to responding to the processing request received by the external application function layer, comprising:

3. The method of claim 2, comprising:

the index node list is stored in a key-value pair form;

4. The method of claim 2, wherein the association class relationship comprises a data association class relationship and a business association class relationship;

5. The method of claim 2, wherein prior to responding to the processing request received by the external application function layer, further comprising:

6. The method of claim 5, wherein reading the metric query record of the external data source comprises:

7. The method according to any one of claims 1 to 6, wherein obtaining a subgraph having a computational relationship with the index for output through an external application functional layer comprises:

acquiring subgraphs with calculation relation with the indexes and corresponding index calculation formulas;

carrying out calculation on specific index values brought into entity nodes in the subgraph based on the index calculation formula through an external application functional layer;

and adjusting the specific index values of the entity nodes in the subgraph according to the calculation result until the preset target result is met, and outputting the index values of the current entity nodes.

8. An index data processing apparatus characterized by comprising:

the acquisition module is used for responding to a processing request received by an external application function layer, identifying the scene category of the processing request and calling an index recommendation program or an index simulation program in a knowledge base application service layer based on the scene category;

and the processing module is used for executing an index recommendation program or an index simulation program, acquiring the index information of the processing request, inquiring the association level in a preset index knowledge base, acquiring all index information related to the index or acquiring a subgraph related to the index, and outputting the subgraph through an external application function layer.

9. An electronic device, comprising:

one or more processors;

a storage device for storing one or more programs,

when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-7.

10. A computer-readable medium, on which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1-7.