CN112598289A - Index configuration method, system, computer device and computer readable storage medium - Google Patents

Index configuration method, system, computer device and computer readable storage medium Download PDF

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CN112598289A
CN112598289A CN202011558433.0A CN202011558433A CN112598289A CN 112598289 A CN112598289 A CN 112598289A CN 202011558433 A CN202011558433 A CN 202011558433A CN 112598289 A CN112598289 A CN 112598289A
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index
data
target
service
configuration
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宣建露
严方
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Ping An Bank Co Ltd
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Ping An Bank Co Ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
    • G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/22—Indexing; Data structures therefor; Storage structures
    • G06F16/2282—Tablespace storage structures; Management thereof
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/24—Querying
    • G06F16/242—Query formulation
    • G06F16/2433—Query languages
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/27—Replication, distribution or synchronisation of data between databases or within a distributed database system; Distributed database system architectures therefor
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/28—Databases characterised by their database models, e.g. relational or object models
    • G06F16/283—Multi-dimensional databases or data warehouses, e.g. MOLAP or ROLAP
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/067—Enterprise or organisation modelling
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
    • G06Q40/02—Banking, e.g. interest calculation or account maintenance

Abstract

The embodiment of the invention provides an index configuration method, which comprises the steps of obtaining a service request, wherein the service request comprises at least one configuration data; acquiring an index model corresponding to the service request according to the service request; generating a target index according to the at least one configuration data and the index model; generating a query statement according to the target index; acquiring at least one index datum according to the query statement; and performing characteristic calculation on the at least one index data to obtain an index result. According to the embodiment of the invention, the target index is generated by configuring the data and the index model, so that the efficiency of index expansion is effectively improved.

Description

Index configuration method, system, computer device and computer readable storage medium
Technical Field
The embodiment of the invention relates to the technical field of big data, in particular to an index configuration method, an index configuration system, computer equipment and a computer readable storage medium.
Background
The business intelligence becomes an important tool for supporting the strategic direction of banks, the business operation is quickly and efficiently observed, and the business strategy is adjusted, so that the business intelligence is an important appeal for leadership and business. Wherein, the processing efficiency of the index determines the quality of service enabling.
At present, the following problems exist in the index processing method: when the dimension of the index is expanded, a developer needs to modify the script manually, the expansion is very inflexible, and the index expansion efficiency is low.
Disclosure of Invention
In view of this, embodiments of the present invention provide an index configuration method, an index configuration system, a computer device, and a computer-readable storage medium, which are used to solve the problem of low efficiency of index expansion by using the existing index processing method.
The embodiment of the invention solves the technical problems through the following technical scheme:
an index configuration method, comprising:
acquiring a service request, wherein the service request comprises at least one configuration data;
acquiring an index model corresponding to the service request according to the service request;
generating a target index according to the at least one configuration data and the index model;
generating a query statement according to the target index;
acquiring at least one index datum according to the query statement; and
and performing characteristic calculation on the at least one index data to obtain an index result.
Optionally, the step of obtaining, according to the service request, an index model corresponding to the service request includes:
acquiring service scene data from the service request;
and acquiring an index model corresponding to the service scene data in a preset database according to the service scene data.
Optionally, the method comprises:
acquiring at least one service scene data, wherein the service scene data comprises statistical logic and corresponding atomic indexes;
the statistical logic decomposes the at least one service scene data according to the statistical logic of the at least one service scene data;
defining the decomposed at least one service scene data to obtain at least one template index element, wherein the at least one template index element comprises statistical dimensions, time granularity and service limitation;
acquiring at least one preset index metric and at least one preset dimension according to the at least one service scene data;
and combining the atomic index, the at least one template index element, the preset index metric and the preset dimension of each service scene data to generate an index model corresponding to the service scene data.
Optionally, the method comprises:
pre-building a first incidence relation between the atomic index in the index model and at least one data table in a preset data warehouse;
pre-building a second incidence relation between the at least one template index element in the index model and at least one data table in a preset data warehouse;
and pre-building a third association relation between the preset index measurement in the index model and at least one data table in a preset data warehouse.
Optionally, the step of generating a target index according to the at least one configuration data and the index model includes:
obtaining the atomic index, the index metric and at least one target element from the index model according to the at least one configuration data, wherein the at least one target element is: in the index model, a template index element having an association relationship with the at least one configuration data;
generating a target index from the at least one configuration data, the atomic index, the index metric, and the at least one target element.
Optionally, the step of generating a query statement according to the target index includes:
extracting at least one target field from at least one data table in a preset data warehouse according to the atomic index in the target index, the index metric and the incidence relation between the at least one target element and the at least one data table;
generating a query statement according to the at least one target field and the at least one configuration data in the target metric.
Optionally, the step of performing feature calculation on the at least one index data to obtain an index result includes:
and according to a preset calculation time period, performing characteristic calculation on the at least one index data to obtain an index result.
In order to achieve the above object, an embodiment of the present invention further provides an index configuration system, including:
a first obtaining module, configured to obtain a service request, where the service request includes at least one configuration data;
the second acquisition module is used for acquiring an index model corresponding to the service request according to the service request;
a first generation module, configured to generate a target index according to the at least one configuration data and the index model;
the second generation module is used for generating a query statement according to the target index;
the third acquisition module is used for acquiring at least one index datum according to the query statement; and
and the characteristic calculation module is used for executing characteristic calculation on the at least one index data to obtain an index result.
In order to achieve the above object, an embodiment of the present invention further provides a computer device, where the computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the steps of the index configuration method as described above when executing the computer program.
In order to achieve the above object, an embodiment of the present invention further provides a computer-readable storage medium, in which a computer program is stored, where the computer program is executable by at least one processor, so as to cause the at least one processor to execute the steps of the index configuration method as described above.
According to the index configuration method, the index configuration system, the computer equipment and the computer readable storage medium provided by the embodiment of the invention, the target index is generated through at least one configuration data in the service request and the index model corresponding to the service request; generating a query statement according to the target index; acquiring at least one index datum according to the query statement; performing characteristic calculation on the at least one index data to obtain an index result; the efficiency of index expansion is effectively improved.
The invention is described in detail below with reference to the drawings and specific examples, but the invention is not limited thereto.
Drawings
FIG. 1 is a flowchart illustrating a pointer allocation method according to an embodiment of the present invention;
FIG. 2 is a flowchart illustrating a step of obtaining an index model according to a service request in an index configuration method according to an embodiment of the present invention;
FIG. 3 is a flowchart illustrating steps of pre-constructing a pointer model in a pointer allocation method according to a first embodiment of the present invention;
FIG. 4 is a flowchart illustrating a step of generating a target pointer in a pointer allocation method according to a first embodiment of the present invention;
FIG. 5 is a flowchart illustrating steps of generating a query statement in a pointer allocation method according to a first embodiment of the present invention;
FIG. 6 is a block diagram of a pointer allocation system according to a second embodiment of the present invention;
fig. 7 is a schematic hardware structure diagram of a computer device according to a third embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Technical solutions between various embodiments may be combined with each other, but must be realized by those skilled in the art, and when the technical solutions are contradictory or cannot be realized, such a combination should not be considered to exist, and is not within the protection scope of the present invention.
Example one
Referring to fig. 1, a flowchart illustrating steps of a pointer allocation method according to an embodiment of the invention is shown. It is to be understood that the flow charts in the embodiments of the present method are not intended to limit the order in which the steps are performed. The following description is given by taking a computer device as an execution subject, specifically as follows:
as shown in fig. 1, the index configuration method may include steps S100 to S600, in which:
step S100, a service request is obtained, where the service request includes at least one configuration data.
In an exemplary embodiment, a service request sent by a service end is received through an index configuration platform, wherein the service request includes service scene data and at least one configuration data input by the service end.
The service request is used for requesting to acquire a target index related to the service request and acquiring an index result of the target index. The traffic scenario data includes, but is not limited to: staged order handling business, consumption flow inquiry business and turnover inquiry business.
The index configuration platform can be connected with a plurality of banking business interfaces through a unified communication protocol. When the banking business interface needs to request to obtain the index, the index configuration platform receives the business request sent by the banking business interface, and the index configuration platform responds to the business request to generate index data and an index result.
The plurality of banking interfaces comprise a banking loan service interface, a banking staging service interface, a banking retail service interface and the like.
Illustratively, the bank loan service interface is connected to the index configuration platform, and the bank loan service interface sends a service request to the index configuration platform through a unified communication protocol, so as to obtain the target index according to the service request for obtaining the turnover of the north china street, merchant a, merchant B, merchant C and merchant D in shenzhen futian, china north china, 2020.
The system comprises a service request, an index configuration platform, a service limit and an index measurement platform, wherein the index configuration platform comprises a plurality of index concepts, the index concept comprises a plurality of index concepts, the index configuration platform comprises a plurality of index configuration platforms, the index configuration platforms are used for associating the index concepts to corresponding index models, the generation time granularity is '2020 top half year', the dimension is 'merchant', the service limit is 'merchant A', 'merchant B', 'merchant C', 'merchant D', 'Shenzhen', 'Futian region' and 'North China street', and the index measurement is 'RMB': target index of Yuan ". In the embodiment of the invention, the computer equipment converts the index concept into the target index, and the target index is a computer language for the computer equipment to execute index data query.
And step S200, acquiring an index model corresponding to the service request according to the service request.
Illustratively, the metric model is pre-created in a metric modeling component of the metric-deployment platform. Different service types have corresponding index models. The service type can be understood as service scene data.
In an exemplary embodiment, as shown in fig. 2, the step S200 includes steps S201 to S202, in which: step S201, obtaining service scene data from the service request; step S202, according to the service scene data, obtaining an index model corresponding to the service scene data in a preset database.
Constructing a corresponding index model on an index configuration platform in advance according to the service scene data; other systems connected to the index configuration platform can share the index model stored on the index configuration platform, different systems do not need to write additional database scripts to develop indexes, the indexes can be shared among service scene data of different systems, and the processing efficiency of the indexes is improved.
In an exemplary embodiment, each index may be constructed into a different index model according to the type of service. The method comprises the pre-construction of an index model.
Exemplarily, as shown in fig. 3, the method includes a process of constructing at least one index model, and specifically includes steps S701 to S705, where: step S701, acquiring at least one service scene data, wherein the service scene data comprises statistical logic and corresponding atomic indexes; step S702, the statistical logic decomposes the at least one service scene data according to the statistical logic of the at least one service scene data; step S703, defining the decomposed at least one service scene data to obtain at least one template index element, where the at least one template index element includes a statistical dimension, a time granularity, and a service definition; step S704, acquiring at least one preset index metric and at least one preset dimension according to the at least one service scene data; step S705, respectively combining the atomic index, the at least one template index element, the preset index metric, and the preset dimension of each service scene data to generate an index model corresponding to the service scene data.
And the atomic index, at least one template index element, the index measurement and the data source corresponding to the dimension of each index model are all stored in at least one data table in a preset data warehouse.
Wherein, the atom index is an index without any modifier, and is a noun with definite business meaning. The atomic index describes a type of index such as an order payment amount, a payment amount of orders, an order placing amount of orders, and the like. However, only one atomic index in the index model is not directly retrievable from at least one data table of the data warehouse. Therefore, an index metric needs to be introduced.
Besides the atomic index and the index measurement, the index model also needs template index elements capable of forming derivative indexes, and at least one template index element is obtained by decomposing the statistical logic of the business scene data and defining the statistical logic of the decomposed business scene data. At least one template index element is defined to make the generated index model more fit to the actual business scene data.
Wherein, at least one template index element comprises time granularity, statistical dimension and service limitation. The time granularity includes, but is not limited to, hours, days, weeks, months, quarters, first half year, second half year, years; the statistical dimension comprises a qualitative statistical dimension and a quantitative statistical dimension, wherein the qualitative statistical dimension comprises but is not limited to cities, sexes and the like; the quantitative statistics dimension includes order quantity statistics among sales sections, and the like. Business definition may be understood as a dimension filter condition.
Illustratively, the index model is composed of an atomic index, an index measure, a dimension, and modifiers (i.e., at least one template index element) of derivative indexes corresponding to the atomic index.
And acquiring corresponding preset index measurement and preset dimensionality according to the service scene data, wherein the measurement is a standard measurement unit. For example, the service scene data corresponds to a consumption stream query service of a certain user, and the metric may be "rmb: yuan ". The dimension refers to an angle describing service scenario data, and if the service scenario data corresponds to an order query service, the dimension includes, but is not limited to: buyer, seller, merchandise, time of purchase, amount of order, etc.
Illustratively, the pre-construction of the metric model is performed in a data specification definition component of the metric deployment platform.
The operation of constructing the index model according to the service scene data is exemplarily described below by taking the service scene data as the turnover query service of the Shenzhen Futian Huaqiang North street merchant 2020 in the first half of the year.
Acquiring service scene data of' turnover query service of the North street merchant 2020 in Shenzhen Futian Huaqiang in 2020; the atomic index of the service scene data corresponds to 'turnover', the service scene data is decomposed according to the statistical logic of the service scene data to obtain at least one field corresponding to 'Shenzhen', 'Futian region', 'Huaqiang north street', 'merchant', 'last half year of 2020', 'turnover' and 'query service', the at least one field is defined to obtain at least one template index element corresponding to the at least one field, for example, the 'merchant' is defined as 'statistical dimension', 'last half year of 2020' is defined as 'time granularity', 'Shenzhen', 'Futian region', 'Huaqiang north street' is defined as 'service definition'; the index metric obtained from the service scene data is "renminbi: meta ", the dimension obtained from the business scenario data is" merchant "; and combining the time granularity, the statistical dimension, the service limitation, the atomic index, the index measurement and the dimension to generate an index model corresponding to the turnover query service.
The above definition of the decomposed service scene data and the generation of the template index element are realized on the index configuration platform according to the data specification definition rule. The definition of the index elements of the template is realized according to the data specification definition rules, and the unified management of the index model is facilitated.
The corresponding index model is constructed according to different service scene data, the requirement of service scenes of different systems for the generation of the index model as required is met, and the service scene of index application is expanded, so that the index model can be applied to the service scenes corresponding to more systems.
In an exemplary embodiment, the method further comprises: pre-building a first incidence relation between the atomic index in the index model and at least one data table in a preset data warehouse; pre-building a second incidence relation between the at least one template index element in the index model and at least one data table in a preset data warehouse; pre-building a third association relation between the preset index measurement in the index model and at least one data table in a preset data warehouse; and pre-building a fourth incidence relation between the preset dimension in the index model and at least one data table in a preset data warehouse.
At least one data table in the preset data warehouse comprises a summary fact table, a detail fact table, a dimension table and the like, wherein the summary fact table is a fact table aggregating the detail facts; the detail fact table is detail data with the most original granularity; the dimension table is a wide table for physicochemically managing logical dimensions. The dimension table is associated with the detail fact table and the summary fact table through external keys. The data stored in at least one of the data tables has a corresponding primary key.
Illustratively, the atomic indexes in the index model have corresponding first association relations with the summary fact table.
At least one template index element in the index model and the summary fact table have corresponding second incidence relation. And the index measurement in the index model and the detailed fact table have a corresponding third correlation.
And the dimension in the index model and the dimension table have a corresponding fourth incidence relation.
Through the association relation and the configuration data, the required data source can be acquired from the corresponding data table. And the incidence relation between the data table and the index model is set up in advance, so that the follow-up data query can be effectively carried out.
Step S300, generating a target index according to the at least one configuration data and the index model.
In an exemplary embodiment, the configuration data may be configuration data input by the service side. And configuring target indexes at the product end according to the configuration data and the index model.
In an exemplary embodiment, referring to fig. 4, the step S300 includes steps S301 to S302, wherein: step S301, according to the at least one configuration data, obtaining the atomic index, the index metric, and at least one target element from the index model, where the at least one target element is: in the index model, a template index element having an association relationship with the at least one configuration data; step S302, generating a target index according to the at least one configuration data, the atomic index, the index metric, and the at least one target element.
According to the embodiment, the target index corresponding to the service scene data is generated according to at least one piece of configuration data of the service request, and the use experience of the service end is improved.
And S400, generating a query statement according to the target index.
In an exemplary embodiment, a standard Query statement is generated based on at least one data table and a target index in a preset data warehouse, wherein the Query statement is an sql (Structured Query Language) script.
In an exemplary embodiment, as shown in fig. 5, the step S400 includes steps S401 to S402, in which: step S401, extracting at least one target field from at least one data table in a preset data warehouse according to the atomic index in the target index, the index metric and the incidence relation between the at least one target element and the at least one data table; step S402, generating a query statement according to the at least one target field and the at least one configuration data in the target index.
Illustratively, according to the atomic index, the index metric and the association relationship between the target element of the at least one index element and the at least one data table, which field of the at least one data table is defined to correspond to the target element is identified. And acquiring required fields from the data table to generate a standard sql statement.
Illustratively, at least one sub-query statement is generated according to at least one target field, the generated at least one sub-query statement is assembled to obtain a query statement, and feature calculation can be performed on the query statement subsequently.
Step S500, at least one index datum is obtained according to the query statement.
Illustratively, at least one data source is obtained according to the index model in a corresponding data table through a query statement; the acquired data source is at least one index data.
Step S600, performing feature calculation on the at least one index data to obtain an index result.
In an exemplary embodiment, the method further comprises converting the query statement into a corresponding query task, facilitating subsequent feature computation.
In an exemplary embodiment, a calculation engine performs feature calculation on at least one acquired index data to obtain an index result, returns the index result to a service end, and stores the index result in a preset database.
In an exemplary embodiment, the step S600 may further include: and according to a preset calculation time period, performing characteristic calculation on the at least one index data to obtain an index result.
For example, in the calculation process, the feature calculation is performed on the at least one index data through a preset calculation time period, for example, setting the frequency of the calculation, and then the obtained index result is stored.
Performing feature calculation on at least one index data according to a preset calculation time period, and avoiding performing feature calculation on at least one index data in the same time period; load pressure calculated by the computer device on the at least one metric data is dispersed.
The index configuration method further includes: and storing the preset database storing the index result in a block chain.
The block chain (Blockchain), which is essentially a decentralized database, is a string of data blocks associated by using a cryptographic method, 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 a next block. The blockchain may include a blockchain underlying platform, a platform product service layer, an application service layer, and the like.
Example two
Continuing to refer to FIG. 6, a program module diagram of the index configuration system of the present invention is shown. In this embodiment, the index configuration system 20 may include or be divided into one or at least one program module, and the one or at least one program module is stored in a storage medium and executed by one or at least one processor, so as to implement the present invention and implement the index configuration method. The program module referred to in the embodiments of the present invention refers to a series of computer program instruction segments capable of performing specific functions, and is more suitable for describing the execution process of the index configuration system 20 in the storage medium than the program itself. The following description will specifically describe the functions of the program modules of the present embodiment:
a first obtaining module 800, configured to obtain a service request, where the service request includes at least one configuration data;
a second obtaining module 810, configured to obtain, according to the service request, an index model corresponding to the service request;
a first generating module 820, configured to generate a target index according to the at least one configuration data and the index model;
a second generating module 830, configured to generate a query statement according to the target indicator;
a third obtaining module 840, configured to obtain at least one index data according to the query statement; and
and the feature calculation module 850 is configured to perform feature calculation on the at least one index data to obtain an index result.
In an exemplary embodiment, the second obtaining module 810 is further configured to: acquiring an atomic index associated with the service request; and acquiring an index model corresponding to the atomic index in a preset database according to the atomic index.
In an exemplary embodiment, the system includes: an index model building module 860; the metric model building module 860 is configured to: acquiring at least one service scene data, wherein the service scene data comprises statistical logic and corresponding atomic indexes; the statistical logic decomposes the at least one service scene data according to the statistical logic of the at least one service scene data; defining the decomposed at least one service scene data to obtain at least one template index element, wherein the at least one template index element comprises statistical dimensions, time granularity and service limitation; acquiring at least one preset index metric and at least one preset dimension according to the at least one service scene data; and combining the atomic index, the at least one template index element, the preset index metric and the preset dimension of each service scene data to generate an index model corresponding to the service scene data.
In an exemplary embodiment, the system includes: an association relation pre-construction module 870; the association relationship pre-construction module 870 is configured to: and pre-building an incidence relation among the atomic index, the at least one template index element and the preset index metric in the index model and at least one data table in a preset data warehouse.
In an exemplary embodiment, the first generating module 820 is further configured to: obtaining the atomic index, the index metric and at least one target element from the index model according to the at least one configuration data, wherein the at least one target element is: in the index model, a template index element having an association relationship with the at least one configuration data; generating a target index from the at least one configuration data, the atomic index, the index metric, and the at least one target element.
In an exemplary embodiment, the second generating module 830 is further configured to: extracting at least one target field from at least one data table in a preset data warehouse according to the atomic index in the target index, the index metric and the incidence relation between the at least one target element and the at least one data table; generating a query statement according to the at least one target field and the at least one configuration data in the target metric.
In an exemplary embodiment, the feature calculation module 850 is further configured to: and according to a preset calculation time period, performing characteristic calculation on the at least one index data to obtain an index result.
EXAMPLE III
Fig. 7 is a schematic diagram of a hardware architecture of a computer device according to a third embodiment of the present invention. In the present embodiment, the computer device 2 is a device capable of automatically performing numerical calculation and/or information processing in accordance with a preset or stored instruction. The computer device 2 may be a rack server, a blade server, a tower server or a rack server (including an independent server or a server cluster composed of at least one server), and the like. As shown in FIG. 7, the computer device 2 includes, but is not limited to, at least a memory 21, a processor 22, a network interface 23, and an index configuration system 20, which may be communicatively coupled to each other via a system bus. Wherein:
in this embodiment, the memory 21 includes at least one type of computer-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, and the like. In some embodiments, the storage 21 may be an internal storage unit of the computer device 2, such as a hard disk or a memory of the computer device 2. In other embodiments, the memory 21 may also be an external storage device of the computer device 2, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), or the like provided on the computer device 2. Of course, the memory 21 may also comprise both internal and external memory units of the computer device 2. In this embodiment, the memory 21 is generally used for storing an operating system installed in the computer device 2 and various types of application software, such as the program codes of the index configuration system 20 of the above-described embodiment. Further, the memory 21 may also be used to temporarily store various types of data that have been output or are to be output.
Processor 22 may be a Central Processing Unit (CPU), controller, microcontroller, microprocessor, or other data Processing chip in some embodiments. The processor 22 is typically used to control the overall operation of the computer device 2. In this embodiment, the processor 22 is configured to run the program code stored in the memory 21 or process data, for example, run the index configuration system 20, so as to implement the index configuration method of the above embodiment.
The network interface 23 may comprise a wireless network interface or a wired network interface, and the network interface 23 is generally used for establishing communication connection between the computer device 2 and other electronic apparatuses. For example, the network interface 23 is used to connect the computer device 2 to an external terminal through a network, establish a data transmission channel and a communication connection between the computer device 2 and the external terminal, and the like. The network may be a wireless or wired network such as an Intranet (Intranet), the Internet (Internet), a Global System of Mobile communication (GSM), Wideband Code Division Multiple Access (WCDMA), a 4G network, a 5G network, Bluetooth (Bluetooth), Wi-Fi, and the like.
It is noted that fig. 7 only shows the computer device 2 with components 20-23, but it is to be understood that not all shown components are required to be implemented, and that more or less components may be implemented instead.
In this embodiment, the index configuration system 20 stored in the memory 21 may be further divided into one or at least one program module, and the one or at least one program module is stored in the memory 21 and executed by one or at least one processor (in this embodiment, the processor 22) to complete the present invention.
For example, fig. 6 shows a schematic diagram of program modules of a second embodiment of the index configuration system 20, in this embodiment, the index configuration system 20 may be divided into a first obtaining module 800, a second obtaining module 810, a first generating module 820, a second generating module 830, a third obtaining module 840 and a feature calculating module 850. The program module referred to in the present invention refers to a series of computer program instruction segments capable of performing specific functions, and is more suitable than a program for describing the execution process of the index configuration system 20 in the computer device 2. The specific functions of the program modules 800 and 850 have been described in detail in the second embodiment, and are not described herein again.
Example four
The present embodiment also provides a computer-readable storage medium, such as 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, a server, an App application mall, etc., on which a computer program is stored, which when executed by a processor implements corresponding functions. The computer-readable storage medium of the present embodiment is used for storing the index configuration system 20, and when being executed by a processor, the index configuration method of the above embodiment is implemented.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
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.
The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.

Claims (10)

1. An index configuration method, comprising:
acquiring a service request, wherein the service request comprises at least one configuration data;
acquiring an index model corresponding to the service request according to the service request;
generating a target index according to the at least one configuration data and the index model;
generating a query statement according to the target index;
acquiring at least one index datum according to the query statement; and
and performing characteristic calculation on the at least one index data to obtain an index result.
2. The index configuration method according to claim 1, wherein the step of obtaining the index model corresponding to the service request according to the service request comprises:
acquiring service scene data from the service request;
and acquiring an index model corresponding to the service scene data in a preset database according to the service scene data.
3. The index configuration method according to claim 2, characterized by comprising:
acquiring at least one service scene data, wherein the service scene data comprises statistical logic and corresponding atomic indexes;
the statistical logic decomposes the at least one service scene data according to the statistical logic of the at least one service scene data;
defining the decomposed at least one service scene data to obtain at least one template index element, wherein the at least one template index element comprises statistical dimensions, time granularity and service limitation;
acquiring at least one preset index metric and at least one preset dimension according to the at least one service scene data;
and combining the atomic index, the at least one template index element, the preset index metric and the preset dimension of each service scene data to generate an index model corresponding to the service scene data.
4. An index configuration method according to claim 3, characterized in that the method comprises:
pre-building a first incidence relation between the atomic index in the index model and at least one data table in a preset data warehouse;
pre-building a second incidence relation between the at least one template index element in the index model and at least one data table in a preset data warehouse;
and pre-building a third association relation between the preset index measurement in the index model and at least one data table in a preset data warehouse.
5. The method of claim 4, wherein the step of generating a target metric from the at least one configuration datum and the metric model comprises:
obtaining the atomic index, the index metric and at least one target element from the index model according to the at least one configuration data, wherein the at least one target element is: in the index model, a template index element having an association relationship with the at least one configuration data;
generating a target index from the at least one configuration data, the atomic index, the index metric, and the at least one target element.
6. The index configuration method of claim 5, wherein the step of generating a query statement according to the target index comprises:
extracting at least one target field from at least one data table in a preset data warehouse according to the atomic index in the target index, the index metric and the incidence relation between the at least one target element and the at least one data table;
generating a query statement according to the at least one target field and the at least one configuration data in the target metric.
7. The index configuration method according to claim 1, wherein the step of performing a feature calculation on the at least one index data to obtain an index result includes:
and according to a preset calculation time period, performing characteristic calculation on the at least one index data to obtain an index result.
8. An index configuration system, comprising:
a first obtaining module, configured to obtain a service request, where the service request includes at least one configuration data;
the second acquisition module is used for acquiring an index model corresponding to the service request according to the service request;
a first generation module, configured to generate a target index according to the at least one configuration data and the index model;
the second generation module is used for generating a query statement according to the target index;
the third acquisition module is used for acquiring at least one index datum according to the query statement; and
and the characteristic calculation module is used for executing characteristic calculation on the at least one index data to obtain an index result.
9. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the steps of the index configuration method according to any of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, in which a computer program is stored which is executable by at least one processor to cause the at least one processor to perform the steps of the index configuration method as claimed in any one of claims 1 to 7.
CN202011558433.0A 2020-12-25 2020-12-25 Index configuration method, system, computer device and computer readable storage medium Pending CN112598289A (en)

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