CN117827841A

CN117827841A - Index calculation method and device based on multiple data tables

Info

Publication number: CN117827841A
Application number: CN202410009637.0A
Authority: CN
Inventors: 马云霞; 刘晨阳; 周新宇; 张鹏; 王振兴; 郭涛
Original assignee: China International Financial Ltd By Share Ltd
Current assignee: China International Financial Ltd By Share Ltd
Priority date: 2024-01-02
Filing date: 2024-01-02
Publication date: 2024-04-05

Abstract

The present application provides an index calculation method based on a plurality of data tables stored in at least one data source, comprising: receiving at least one target parameter corresponding to a target index; determining an intermediate result set according to the at least one target parameter and a plurality of preset calculation rules, wherein the intermediate result set comprises an output result of each calculation rule in the plurality of calculation rules; calculating a target index according to the intermediate result set; wherein the output result of each calculation rule is determined according to at least one of: at least one dimension corresponding to at least one target parameter in a plurality of dimensions, wherein the plurality of dimensions are in one-to-one correspondence with a plurality of fields required by calculating target indexes in the plurality of data tables; and an output result of another calculation rule different from the calculation rule among the plurality of calculation rules. The application also provides a scene analysis method based on the multiple data tables and a report generation method based on the multiple data tables.

Description

Index calculation method and device based on multiple data tables

Technical Field

The present application relates to the field of computer technology, and in particular, to a method, an apparatus, a computing device, a computer readable storage medium, and a computer program product for calculating an index based on a plurality of data tables. In addition, the application also relates to a scene analysis method based on the multiple data tables and a report generation method based on the multiple data tables.

Background

With the continuous development of information technology, data required to be processed, processed and analyzed in various industries is explosively increased, such as traditional databases, distributed repositories, cloud repositories and the like, and become main data sources of enterprises, platforms and even individuals. Under different service scenes, data are often required to be acquired from a plurality of data sources, and real-time query results are output through steps of key value association, data processing calculation and the like. The acquired data often includes data stored in different databases.

In the related art, to obtain and display the corresponding query result, the interface output is generally customized by combining database programming, application programming and report forms. However, since report data may be from multiple data tables, where the data tables are from different data sources (e.g., different databases), the filtering and processing logic of the data from the different data sources may be different, and when a user makes a query, a user needs to wait for a back-end service to perform a large amount of complex computation, and the computation consumes a long time, which affects the user experience. In addition, the historical data processing results are often not multiplexed, which causes waste of calculation resources, and when processing of data in a data source and calculation logic change, reports on a user side using related data need to be modified one by one, which easily causes inconsistency of the data.

Disclosure of Invention

In view of the foregoing, the present application provides a method, apparatus, computing device, computer-readable storage medium, and computer program product for index calculation based on multiple data tables to alleviate, mitigate, or even eliminate the above-mentioned problems.

According to one aspect of the present application, there is provided a method of index calculation based on a plurality of data tables stored in at least one data source, the method comprising: receiving at least one target parameter corresponding to a target index; determining an intermediate result set according to the at least one target parameter and a plurality of preset calculation rules, wherein the intermediate result set comprises an output result of each calculation rule in the plurality of calculation rules; calculating the target index according to the intermediate result set; wherein an output result of each of the plurality of calculation rules is determined according to at least one of: at least one dimension corresponding to the at least one target parameter in a plurality of dimensions, wherein the plurality of dimensions are in one-to-one correspondence with a plurality of fields required by calculating the target index in the plurality of data tables; and an output result of another calculation rule different from the calculation rule among the plurality of calculation rules.

According to another aspect of the present application, there is provided a scene analysis method based on a plurality of data tables stored in at least one data source, the method comprising: determining a plurality of target indexes corresponding to the target scene; acquiring a plurality of target parameter sets corresponding to the target indexes one by one; determining an intermediate result set corresponding to the target parameter set according to each target parameter set in the plurality of target parameter sets and a plurality of preset calculation rules corresponding to the target parameter set, wherein the intermediate result set corresponding to the target parameter set comprises an output result of each calculation rule in the plurality of preset calculation rules corresponding to the target parameter set; calculating the plurality of target indexes according to an intermediate result set corresponding to each of the plurality of target parameter sets; determining an analysis result of the target scene according to the target indexes; wherein an output result of each of the pre-configured plurality of calculation rules corresponding to each of the target parameter sets is determined according to at least one of: at least one dimension corresponding to the target parameter set in a plurality of dimensions, wherein the dimensions correspond to a plurality of fields needed by calculating target indexes corresponding to the target parameter set in the data tables one by one; and outputting results of other calculation rules different from the calculation rule among the preconfigured plurality of calculation rules corresponding to the target parameter set.

According to yet another aspect of the present application, there is provided a report generating method based on a plurality of data tables stored in at least one data source, the method comprising: determining a target scene and a plurality of target indexes corresponding to the target scene; acquiring a plurality of target parameter sets corresponding to the target indexes one by one; determining an intermediate result set corresponding to the target parameter set according to each target parameter set in the plurality of target parameter sets and a plurality of preset calculation rules corresponding to the target parameter set, wherein the intermediate result set corresponding to the target parameter set comprises an output result of each calculation rule in the plurality of preset calculation rules corresponding to the target parameter set; determining a target index result report corresponding to each target parameter set according to an intermediate result set corresponding to the target parameter set in the plurality of target parameter sets, wherein the target index result report corresponding to the target parameter set stores the calculation result of the target index corresponding to the target parameter set; and determining a target report based on a target index result report corresponding to each of the plurality of target parameter sets according to a mapping relation between the target report and the plurality of target indexes; wherein an output result of each of the pre-configured plurality of calculation rules corresponding to each of the target parameter sets is determined according to at least one of: at least one dimension corresponding to the target parameter set in a plurality of dimensions, wherein the dimensions correspond to a plurality of fields needed by calculating target indexes corresponding to the target parameter set in the data tables one by one; and outputting results of other calculation rules different from the calculation rule among the preconfigured plurality of calculation rules corresponding to the target parameter set.

According to still another aspect of the present application, there is provided an index calculation device based on a plurality of data tables, including: the target parameter receiving module is configured to receive at least one target parameter corresponding to the target index; an intermediate result set determination module configured to determine an intermediate result set from the at least one target parameter and a plurality of calculation rules configured in advance, the intermediate result set including an output result of each of the plurality of calculation rules; and a target index calculation module configured to calculate the target index from the intermediate result set; wherein an output result of each of the plurality of calculation rules is determined according to at least one of: at least one dimension corresponding to the at least one target parameter in a plurality of dimensions, wherein the plurality of dimensions are in one-to-one correspondence with a plurality of fields required by calculating the target index in the plurality of data tables; and an output result of another calculation rule different from the calculation rule among the plurality of calculation rules.

According to yet another aspect of the present application, there is provided a scene analysis device based on a plurality of data tables stored in at least one data source, the device comprising: the target index determining module is configured to determine a plurality of target indexes corresponding to the target scene; a target parameter set acquisition module configured to acquire a plurality of target parameter sets corresponding to the plurality of target indexes one by one; an intermediate result set determining module configured to determine an intermediate result set corresponding to the target parameter set according to each of the plurality of target parameter sets and a plurality of preconfigured calculation rules corresponding to the target parameter set, the intermediate result set corresponding to the target parameter set including an output result of each of the plurality of preconfigured calculation rules corresponding to the target parameter set; a target index calculation module configured to calculate the plurality of target indexes from an intermediate result set corresponding to each of the plurality of target parameter sets; and a scene analysis module configured to determine an analysis result of the target scene according to the plurality of target indexes; wherein an output result of each of the pre-configured plurality of calculation rules corresponding to each of the target parameter sets is determined according to at least one of: at least one dimension corresponding to the target parameter set in a plurality of dimensions, wherein the dimensions correspond to a plurality of fields needed by calculating target indexes corresponding to the target parameter set in the data tables one by one; and outputting results of other calculation rules different from the calculation rule among the preconfigured plurality of calculation rules corresponding to the target parameter set.

According to yet another aspect of the present application, there is provided a report generating apparatus, the plurality of data tables being stored in at least one data source, the apparatus comprising: a target index determining module configured to determine a target scene and a plurality of target indexes corresponding to the target scene; a target parameter set acquisition module configured to acquire a plurality of target parameter sets corresponding to the plurality of target indexes one by one; an intermediate result set determining module configured to determine an intermediate result set corresponding to the target parameter set according to each of the plurality of target parameter sets and a plurality of preconfigured calculation rules corresponding to the target parameter set, the intermediate result set corresponding to the target parameter set including an output result of each of the plurality of preconfigured calculation rules corresponding to the target parameter set; an index result report determining module configured to determine a target index result report corresponding to each of the plurality of target parameter sets according to an intermediate result set corresponding to the target parameter set, the target index result report corresponding to the target parameter set storing a calculation result of a target index corresponding to the target parameter set; and a target report determining module configured to determine a target report based on a target index result report corresponding to each of the plurality of target parameter sets according to a mapping relationship between the target report and the plurality of target indexes; wherein an output result of each of the pre-configured plurality of calculation rules corresponding to each of the target parameter sets is determined according to at least one of: at least one dimension corresponding to the target parameter set in a plurality of dimensions, wherein the dimensions correspond to a plurality of fields needed by calculating target indexes corresponding to the target parameter set in the data tables one by one; and outputting results of other calculation rules different from the calculation rule among the preconfigured plurality of calculation rules corresponding to the target parameter set.

According to yet another aspect of the present application, there is provided a computing device comprising: a memory configured to store computer-executable instructions; a processor configured to perform any of the methods provided according to the foregoing aspects of the present application when the computer-executable instructions are executed by the processor.

According to yet another aspect of the present application, there is provided a computer readable storage medium storing computer executable instructions that, when executed, perform any of the methods provided according to the foregoing aspects of the present application.

According to yet another aspect of the present application, there is provided a computer program product comprising computer executable instructions which when executed by a processor perform any of the methods provided according to the preceding aspects of the present application.

According to the index calculation method based on the plurality of data tables stored in the at least one data source, an intermediate result set can be determined according to the received at least one target parameter corresponding to the target index and a plurality of preset calculation rules, wherein the intermediate result set comprises an output result of each calculation rule in the plurality of calculation rules; and further, calculating the target index according to the intermediate result set. Compared with the index calculation method in the related art, the method provided by the application can be used for carrying out standardized processing on the data from different data tables, and multiplexing of different calculation rules and related output results can be realized in the calculation process of the target index, so that corresponding calculation resources can be saved, the calculation flow is simplified, the calculation time is shortened, and the consistency of the data is ensured.

These and other aspects of the present application will be apparent from, and elucidated with reference to, the embodiments described hereinafter.

Drawings

Further details, features and advantages of the technical solutions of the present application are disclosed in the following description of exemplary embodiments with reference to the attached drawings, in which:

FIG. 1 schematically illustrates an example scenario in which a technical solution provided according to some embodiments of the present application may be applied;

FIG. 2 schematically illustrates an example schematic diagram of a method of index calculation based on multiple data tables according to some embodiments of the present application;

FIG. 3 schematically illustrates an example architecture diagram of an metrics calculation and query service, according to some embodiments of the present application;

FIGS. 4A-4C schematically illustrate example hierarchical graphs of relationships between different metrics according to some embodiments of the present application;

FIG. 5 schematically illustrates an example service request processing link diagram of a business topic query service in accordance with some embodiments of the present application;

FIG. 6 schematically illustrates an example schematic diagram of entity relationships according to some embodiments of the present application;

FIG. 7 schematically illustrates an example block diagram of an index computing device according to some embodiments of the present application;

FIG. 8 schematically illustrates an example block diagram of a scene analysis device according to some embodiments of the application;

FIG. 9 schematically illustrates an example block diagram of a report generating device according to some embodiments of this application; and

FIG. 10 illustrates an example system including an example computing device that represents one or more systems and/or devices that can implement the various techniques described herein.

Detailed Description

Several embodiments of the present application will be described in more detail below with reference to the accompanying drawings in order to enable those skilled in the art to practice the technical solutions of the present application. The technical solutions of the present application may be embodied in many different forms and objects and should not be limited to the embodiments set forth herein. These examples are provided so that this disclosure will be thorough and complete, and should not be construed as limiting the scope of the disclosure.

Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and/or the present specification and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.

Fig. 1 schematically illustrates an example scenario 100 in which a technical solution provided according to some embodiments of the present application may be applied. As shown in fig. 1, scenario 100 may include a user 110, a terminal device 120 (e.g., a computer), a terminal device 130 (e.g., a tablet), a network 140, and a remote facility 150. By way of example, the remote facility 150 includes a server 151 and optionally a database device 152 for storing related data tables, which servers or devices may communicate via the network 140.

Illustratively, at the terminal device 120, at least one target parameter corresponding to the target index provided by the user 110 may be obtained. The target metrics may be various metrics related to the service that user 110 needs to acquire, depending on the service requirements. For example, in the industry, when a user 110 needs to obtain a historical sales volume of a product in a region, the historical sales volume of the product in the region may be regarded as a target index. In the financial field, when a user 110 needs to acquire the holding data of a certain type of financial product on a certain date, the holding data of the certain type of financial product on the date can be regarded as a target index. It will be appreciated by those skilled in the art that the corresponding target metrics may be various business-related objects to be analyzed, calculated, depending on the particular application area and the identity of the user.

At the remote facility 150 side, at least one target parameter corresponding to the target index may be obtained from the terminal device 120 via the network 140, and then an intermediate result set is determined by using the server 151 in the remote facility 150 according to the at least one target parameter and a plurality of calculation rules configured in advance, where the intermediate result set includes an output result of each calculation rule in the plurality of calculation rules. These preconfigured calculation rules may be deployed in the form of a program on a server 151 in the remote facility 150, or on other computing devices and invoked by the server 151. Depending on the definition of the target index, these calculation rules may have a corresponding structure/definition. The intermediate result set may be stored in a database device 152 in the remote facility 150, or in other storage devices, such as in a distributed storage device or cloud storage device.

The target index may then be calculated from the intermediate result set. Wherein an output result of each of the plurality of calculation rules is determined according to at least one of: at least one dimension corresponding to the at least one target parameter in a plurality of dimensions, wherein the plurality of dimensions are in one-to-one correspondence with a plurality of fields required by calculating the target index in the plurality of data tables; and an output result of another calculation rule different from the calculation rule among the plurality of calculation rules. The plurality of data tables may all be stored in a database device 152 in the remote facility 150, alternatively one or more of the plurality of data tables may be stored in other database devices than the database device 152. In one example, each of the plurality of data tables is stored in a different database device, i.e., they are from different databases.

It should be noted that although the above steps may be performed at the remote facility 150 (e.g., in the form of a program or system deployed on the server 151). Those skilled in the art will appreciate that these steps may be performed on the terminal device side (e.g., on terminal device 120). Alternatively, these steps may be performed on the side of the terminal device 120 and the side of the remote facility 150, respectively, for example, the step of acquiring at least one target parameter corresponding to the target index may be performed on the side of the terminal device 120, and the terminal device 120 may send the at least one target parameter corresponding to the acquired target index to the remote facility 150 via the network 140, and further, the remote facility 150 performs the subsequent other steps.

In this application, the server 151 in the remote facility 150 may be a single server or a cluster of servers, and the database device 152 in the remote facility 150 may store various data (at least a portion of the plurality of data tables, intermediate data in the index calculation process, intermediate result sets, etc.) required in the index calculation process. Illustratively, the user 110 may access the remote facility 150 via the terminal device 120 or the terminal device 130 in a web page. Alternatively, the user may communicate with the remote facility 150 through a client installed on the terminal device 120 or the terminal device 130 to participate in the index calculation process. Alternatively, the server 151 may also run other applications and store other data. For example, the server 151 may include multiple virtual hosts to run different applications and provide different services.

In the present application, the terminal devices 120 and 130 may be various types of devices, such as mobile phones, tablet computers, notebook computers, in-vehicle devices, and the like. The terminal devices 120 and 130 may have disposed thereon a client that may be used to perform index queries or index calculation related operations (e.g., select an index of interest) and optionally provide other services, and may take any of the following forms: locally installed applications, applets accessed via other applications, web programs accessed via a browser, etc. (it should be understood that "applications" in this application may include any of these various forms of programs, and may even include other types of programs not listed herein). User 110 may view information presented by clients and perform corresponding interactions through the input/output interfaces of terminal devices 120 and 130. Alternatively, the terminal devices 120 and 130 may be integrated with the server 151.

In this application, the database device 152 may be regarded as an electronic file cabinet, i.e. a place where electronic files are stored, and a user may perform operations such as adding, querying, updating, deleting, etc. on data in the files. A "database" is a collection of data stored together in a manner that can be shared with multiple objects, with as little redundancy as possible, independent of the application.

Further, in the present application, the network 140 may be a wired network connected via a cable, an optical fiber, or the like, or may be a wireless network such as 2G, 3G, 4G, 5G, wi-Fi, bluetooth, zigBee, li-Fi, or the like.

It should be noted that the term "user" as used herein refers to any party that may interact data with a system (e.g., a client system deployed on a terminal device 120), including but not limited to, a person, program software, a network platform, or even a machine.

FIG. 2 schematically illustrates an example schematic diagram of a method of index calculation based on multiple data tables according to some embodiments of the present application. The metric calculation method may be implemented by the remote facility 150 shown in fig. 1, for example, although this is not limiting.

As shown in fig. 2, the data table 211 is stored in the data source 210, the data table 221 is stored in the data source 220, and the intermediate result set 230 is determined according to at least one target parameter corresponding to the received target index and a plurality of calculation rules configured in advance, and the intermediate result set 230 includes an output result of each calculation rule of the plurality of calculation rules. In the example of fig. 2, these output results include 231 and 232, where output result 231 is an output result obtained by one of the preconfigured calculation rules according to at least one of the plurality of dimensions corresponding to the at least one target parameter, and output result 232 is an output result obtained by another of the preconfigured calculation rules according to at least one of the plurality of dimensions corresponding to the at least one target parameter, and the above-described calculation rule corresponding to output result 231. In other words, the corresponding fields in the data table 211 are utilized in determining the output result 231, and the corresponding fields in the output result 231 and the data table 221 are utilized in determining the output result 232. Finally, the target index 240 is calculated from the intermediate result set 230, i.e. the target index 240 is calculated from the output result 231 and the output result 232.

It should be noted that although two data tables (data table 211 and data table 221) are provided in the example of fig. 2, those skilled in the art will appreciate that there may be more data tables depending on the actual service requirements. In addition, depending on the definition of the target index and the corresponding configuration means, there may be a greater number of calculation rules. To further illustrate the principle of the index calculation method based on a plurality of data tables shown in fig. 2, a more specific example is given below.

As shown below, the data table T1 is used for storing fund accounts of different departments, and the data table T2 is used for storing market values of different fund accounts on different dates.

Department (dept_code)	Fund account number (account_number)
		A	A1
A	A2
		A	A3
B	B1
		B	B2
B	B3
		C	C1
C	C2
		C	C3

One example requirement faced by a user may be: and calculating the total market value of each fund account of the A department of 2023, 9 months and 16 days based on the data table T1 and the data table T2. Accordingly, the total market value of each fund account of the department a can be regarded as an index (i.e., the target index). Data table T1 and data table T2 contain a total of four dimensions: department, fund account number, date of data, and market value.

Illustratively, the following calculation rules may be configured separately:

Rule R1: for example, the target parameters corresponding to the target indexes provided by the user may include a date "20230916" and a department "a", and in the rule, the screening conditions may be: the corresponding program executing the rule may be a database function in the database storing the data table T1, and the output result of the rule may be:

dept_code
	A

wherein the data "dept_code" may be passed to other rules, as described in more detail below.

Rule R2: which is used to receive the output of rule R1 and filter out the fund account number under dept_code= "a", the corresponding procedure to execute the rule may be a database function in the database storing data table T1, and the output of the rule may be:

cptl_acct
	A1
A2
	A3

in the output result of this rule, "account_number" is renamed to "cptl_acct", i.e. the cptl_acct column data is passed to other rules, as described in detail below.

Rule R3: the method is used for receiving the output result of the rule R2 and screening out the value_date= "20230916" of the holding market value of the corresponding fund account, the corresponding program for executing the rule may include a database function in a database of the stored data table T2, and a Java function for executing the market value summation processing, and accordingly, the output result of the rule may be:

Department(s)	Date of data	Market value
			A	20230916	21000

Rule R4: the rule R3 is used for filtering output results (namely the screened departments and the total market value) according to the dept_code configured by the rule R1. Since in this example rule R2 has screened the fund account number by dept_code, the output of rule R3 is already a market value for department a. Generally, the filtering of the dept_code is not required to be configured in the rule R1, and the filtering is only required to be configured in the rule R4, but the filtering in the rule R1 can improve the efficiency of the whole index calculation under the condition that the data size of the data table T2 is relatively large. Accordingly, the output of the rule may be:

department(s)	Date of data	Market value
			A	20230916	21000

Accordingly, one example of the structure of query entries and output results is given herein:

inquiring and entering parameters:

{"businesssubjectid":"Asset_analysis","ignoreCache":"true","reportDataType":"Sql,JqGrid","valuedate":"2023-09-06","dept_code":"A"}

outputting a result:

it should be noted that, for standardization and generality, information such as related indexes, entities, metadata, etc. may be defined by a series of configuration tables, and for the above examples, the corresponding information may be summarized as follows:

index name: designating department clients to hold market values;

the calculation formula is as follows: SUM (market in hand), the "market" is derived from the market in hand field of data table T2;

And (3) inputting parameters: department, date;

metadata related to: data table T1 and fields in data table T1; data table T2 and fields in data table T2. Each data table may be collectively referred to simply as an "entity".

For the above example, specific configurations and descriptions are as follows:

1. information defining the data tables T1, T2 in the "entity configuration table":

the configuration in the entity configuration table illustrates the actual STORAGE database of the data table, and the system implementing the method of the present application will submit a query against the table to the corresponding query service based on its STORAGE field attribute. Each entity only needs to be configured once in the table, and all subsequent configurations of the index, dimension columns from the table are applicable.

2. Parameters for adaptation of data tables T1 and T2 are defined in the "parameter configuration table":

the parameters defined in the parameter configuration table may be used to adapt a variety of entries as follows:

in actual business, the index may be a lower-layer and basic calculation object, and various scenes exist on the basis of the index, and various parameters shown in the table above can reflect the distinction between the scenes. Still in combination with the above example, the application scenario embodied by the entries v_dept_code and v_value_date in the above table is an asset analysis of a specified department (radio) on a specified date. And if the participation is changed into hs_dept_code and v_value_date, the embodied application scene is the asset analysis of a certain group of designated departments (multiple choices) on a certain designated date.

3. Defining an index application scene and a calculation rule:

(1) Defining application scenarios in a "topic definition table

(2) Defining data classifications in a "data Classification Table

The data classification rule may be defined as the same rule if the data sources are consistent, the calculation rule is consistent, and the output binding dimensions are consistent, for example, when the stock keeping market values of all departments are counted, the data are all derived from a product keeping table (for example, the data table T2), the calculation rule is a summary of the keeping market values, and the output columns include the departments and the keeping market values.

(3) Configuring index-related definitions in an index definition table

/>

A system (e.g., a computer system, or an index computing system developed using the methods disclosed herein, or other system implementing the methods disclosed herein) may have the following three processes in parsing the field "indicator" by priority: the bs_indicator field is not empty, and the configuration content of the indicator is ignored, and the bs_indicator is taken as the reference; configuring a mode definition of "@ field content", wherein the displayed content is the field content; configuring "field content", the code layer will spell the two columns together in the manner of "indicator_entity" by changing the field configuration, which is to prevent the duplication of field names in the same standard rule calculation logic.

In the method, a series of calculations are realized through db functions and a plurality of packaged Java functions and by configuring index calculation rules, so that detail data meeting the conditions (which form an intermediate result set) can be screened, and corresponding indexes are calculated according to the detail data.

In addition, regarding the relationship between metadata and an index, the metadata is basic data and rules of the index. Regarding the relationship between (application) subject and index, a subject may include a plurality of indexes under one subject, for example, a subject of "A, B two-department total market value" may be defined, and two indexes exist under the subject: the department A holds the total market value of the storehouse and the department B holds the total market value of the storehouse. By permutation and combination of different dimensions, different "market value" results can be output, for example: to calculate the total market value under a certain fund account number of the department A, the data needs to be screened and summed according to two dimensions of the department and the fund account number; to calculate the total market value of the department a on a certain day under a certain fund account, a dimension of "date of data" needs to be added to calculate the corresponding index.

In the application, the execution of the preset calculation rules is similar to the execution flow of a pipeline, the database is accessed to execute SQL (comprising db functions) or java functions packaged in a memory according to the configured sequence rules, so that corresponding output results are obtained, designated data in the output results are transmitted to the next calculation rule, and the calculation results of the indexes are obtained by analogy.

In connection with the above description, examples of the calculation rules described above with reference to the data tables T1 and T2 (i.e., rules R1, R2, R3, and R4) may have configurations shown in the following tables:

sequence number	R3
		Rule name	Index calculation rule-division gate
Index ID	100023
		Index name	Market value of holding bin
Table ID	T2
		fieldid	scrt_mval
Function grammar	sum(scrt_mval)
		Function type	Database function
Binding column	cptl_acct
		Output rule	###.000000
Screening rules
		Whether or not to act as an intermediate result	Is that

Sequence number	R4
		Rule name	Index calculation rule-summary
Index ID	100023
		Index name	Department(s)
Table ID
		fieldid
Function grammar	$sum$({scrt_mval})
		Function type	Java function
Binding column	dept_code
		Output rule	###.000000
Screening rules
		Whether or not to act as an intermediate result

For example, the rule R3 includes calculating a rule sum (scrt_mval), and the corresponding output result is scrt_mval (total result of the holding amounts).

According to the method, a calculation system based on detail data can be constructed, metadata such as application subject, dimension, calculation rule, calculation configuration, calculation parameters and the like are defined by analyzing the detail data, index calculation, release and query services are provided, and calculated data can be stored in an index result table. For some scenes needing report generation, the report does not need to be calculated in real time, and only the mapping relation between the report and the index is configured, so that correct index result data can be obtained, thereby greatly improving the calculation performance and improving the user experience. In the process, index data only needs to be calculated once, the subsequent report needs can be summarized upwards in different dimensions, the data is not summarized from detail data, but the index result table is sourced, and when the index calculation logic changes, only index calculation rules are required to be modified, so that the data consistency among different reports is ensured. In addition, the calculation rules defined by the application can support the page visual data, namely, the complete link for displaying index calculation, so that a user can know the whole data-system from different levels.

Accordingly, in some embodiments, a method of scene analysis based on a plurality of data tables stored in at least one data source is provided, the method comprising: determining a plurality of target indexes corresponding to the target scene; acquiring a plurality of target parameter sets corresponding to the target indexes one by one; determining an intermediate result set corresponding to the target parameter set according to each target parameter set in the plurality of target parameter sets and a plurality of preset calculation rules corresponding to the target parameter set, wherein the intermediate result set corresponding to the target parameter set comprises an output result of each calculation rule in the plurality of preset calculation rules corresponding to the target parameter set; calculating the plurality of target indexes according to an intermediate result set corresponding to each of the plurality of target parameter sets; determining an analysis result of the target scene according to the target indexes; wherein an output result of each of the pre-configured plurality of calculation rules corresponding to each of the target parameter sets is determined according to at least one of: at least one dimension corresponding to the target parameter set in a plurality of dimensions, wherein the dimensions correspond to a plurality of fields needed by calculating target indexes corresponding to the target parameter set in the data tables one by one; and outputting results of other calculation rules different from the calculation rule among the preconfigured plurality of calculation rules corresponding to the target parameter set.

In the above-mentioned scene analysis method, the analysis result of the target scene is obtained based on the calculation results of the plurality of target indexes corresponding to the target scene. Accordingly, the calculation steps of these target indexes may refer to the index calculation method described above, and will not be described herein.

In addition, in some embodiments, a report generating method based on a plurality of data tables stored in at least one data source is provided, the method comprising: determining a target scene and a plurality of target indexes corresponding to the target scene; acquiring a plurality of target parameter sets corresponding to the target indexes one by one; determining an intermediate result set corresponding to the target parameter set according to each target parameter set in the plurality of target parameter sets and a plurality of preset calculation rules corresponding to the target parameter set, wherein the intermediate result set corresponding to the target parameter set comprises an output result of each calculation rule in the plurality of preset calculation rules corresponding to the target parameter set; determining a target index result report corresponding to each target parameter set according to an intermediate result set corresponding to the target parameter set in the plurality of target parameter sets, wherein the target index result report corresponding to the target parameter set stores the calculation result of the target index corresponding to the target parameter set; and determining a target report based on a target index result report corresponding to each of the plurality of target parameter sets according to a mapping relation between the target report and the plurality of target indexes; wherein an output result of each of the pre-configured plurality of calculation rules corresponding to each of the target parameter sets is determined according to at least one of: at least one dimension corresponding to the target parameter set in a plurality of dimensions, wherein the dimensions correspond to a plurality of fields needed by calculating target indexes corresponding to the target parameter set in the data tables one by one; and outputting results of other calculation rules different from the calculation rule among the preconfigured plurality of calculation rules corresponding to the target parameter set.

It should be noted that, in the report generating method, the target report is obtained according to the calculation results of the target indexes, where the target indexes may be regarded as the indexes corresponding to the corresponding scenes (topics), and accordingly, the calculation steps of the target indexes may refer to the index calculating method described above, which is not repeated herein.

FIG. 3 schematically illustrates an example architecture diagram of an metrics calculation and query service, according to some embodiments of the present application. As shown in fig. 3, the architecture of index calculation and query service adapted to the business complexity can be set according to the needs, and the index calculation and query service is provided for the user from the data source, the basic definition, the basic service and the analysis service of the bottom layer to the output service of the top layer, wherein the different layers are mutually matched. For example, at the base service level, the computing object has extensibility, e.g., when the definition of a composite index of two indices (which may correspond to one of the above-described (application) topics) changes, only the definition of the two indices that make up the index needs to be changed.

Fig. 4A-4C schematically illustrate example hierarchical graphs of relationships between different metrics, which provide examples of the resolution of the metrics in a real scenario, i.e., the metrics are summarized from underlying data to different levels, in accordance with some embodiments of the present application. For example, according to the data table A, B, C, D, I, M, H shown in fig. 4B, the indexes of different dimensions such as the securities dimension index and the currency dimension index shown in fig. 4C are obtained through the processing such as the result set association screening, the exchange rate conversion and the summarization calculation shown in fig. 4A. From these examples it can be seen that starting from the currency dimension index, all indices are from the recalculation of the existing index.

Fig. 5 schematically illustrates an example service request processing link diagram of a (business) topic query service (which may have the architecture shown in fig. 3) according to some embodiments of the present application, including the following services: user requests (services), business topic query services, business topic services, index calculation services, query result construction services, query construction services, and data query services. For example, the data query service may be a service provided by a corresponding database device, where the query result construction service is configured to provide a query result conforming to a preset format, and the index calculation service calculates a corresponding index according to a corresponding input parameter, and different indexes are summarized to obtain a corresponding topic query result (service topic query service). Additionally, FIG. 6 schematically illustrates an example schematic diagram of entity relationships, namely a DB-ER diagram, showing the link relationships between different tables of the foregoing example metric calculation process provided by the present application, according to some embodiments of the present application.

Fig. 7 schematically illustrates an example block diagram of an index computing device 700 based on a plurality of data tables stored in at least one data source, according to some embodiments of this application. As shown in fig. 7, the index calculation device 700 includes a target parameter receiving module 710, an intermediate result set determining module 720, and a target index calculation module 730.

Specifically, the target parameter receiving module 710 may be configured to receive at least one target parameter corresponding to the target index; the intermediate result set determination module 720 may be configured to determine an intermediate result set comprising an output result of each of the plurality of calculation rules based on the at least one target parameter and a plurality of calculation rules that are pre-configured; and a target index calculation module 730 may be configured to calculate the target index from the intermediate result set; wherein an output result of each of the plurality of calculation rules is determined according to at least one of: at least one dimension corresponding to the at least one target parameter in a plurality of dimensions, wherein the plurality of dimensions are in one-to-one correspondence with a plurality of fields required by calculating the target index in the plurality of data tables; and output results of other calculation rules of the plurality of calculation rules different from the calculation rule

Fig. 8 schematically illustrates an example block diagram of a scene analysis device 800 based on a plurality of data tables stored in at least one data source, according to some embodiments of the application. As shown in fig. 8, the scene analysis device 800 includes a target index determination module 810, a target parameter set acquisition module 820, an intermediate result set determination module 830, a target index calculation module 840, and a scene analysis module 850.

Specifically, the target index determination module 810 may be configured to determine a plurality of target indexes corresponding to the target scene; the target parameter set acquisition module 820 may be configured to acquire a plurality of target parameter sets corresponding to the plurality of target indexes one to one; the intermediate result set determining module 830 may be configured to determine an intermediate result set corresponding to the target parameter set according to each of the plurality of target parameter sets and a plurality of preconfigured calculation rules corresponding to the target parameter set, the intermediate result set corresponding to the target parameter set including an output result of each of the plurality of preconfigured calculation rules corresponding to the target parameter set; the target index calculation module 840 may be configured to calculate the plurality of target indexes from the intermediate result set corresponding to each of the plurality of target parameter sets; and scene analysis module 850 may be configured to determine an analysis result of the target scene from the plurality of target metrics; wherein an output result of each of the pre-configured plurality of calculation rules corresponding to each of the target parameter sets is determined according to at least one of: at least one dimension corresponding to the target parameter set in a plurality of dimensions, wherein the dimensions correspond to a plurality of fields needed by calculating target indexes corresponding to the target parameter set in the data tables one by one; and outputting results of other calculation rules different from the calculation rule among the preconfigured plurality of calculation rules corresponding to the target parameter set.

FIG. 9 schematically illustrates an example block diagram of a report generating device 900 based on a plurality of data tables stored in at least one data source, according to some embodiments of this application. As shown in fig. 9, the report generating apparatus 900 includes a target index determining module 910, a target parameter set acquiring module 920, an intermediate result set determining module 930, an index result report determining module 940, and a target report determining module 950.

Specifically, the target indicator determination module 910 may be configured to determine a target scene and a plurality of target indicators corresponding to the target scene; the target parameter set obtaining module 920 may be configured to obtain a plurality of target parameter sets corresponding to the plurality of target indexes one to one; the intermediate result set determining module 930 may be configured to determine an intermediate result set corresponding to the target parameter set according to each of the plurality of target parameter sets and a plurality of preconfigured calculation rules corresponding to the target parameter set, the intermediate result set corresponding to the target parameter set including an output result of each of the plurality of preconfigured calculation rules corresponding to the target parameter set; the index result report determining module 940 may be configured to determine a target index result report corresponding to each of the plurality of target parameter sets according to an intermediate result set corresponding to the target parameter set, the target index result report corresponding to the target parameter set storing a calculation result of a target index corresponding to the target parameter set; and the target report determining module 950 may be configured to determine the target report based on a target index result report corresponding to each of the plurality of target parameter sets according to a mapping relationship between the target report and the plurality of target indexes; wherein an output result of each of the pre-configured plurality of calculation rules corresponding to each of the target parameter sets is determined according to at least one of: at least one dimension corresponding to the target parameter set in a plurality of dimensions, wherein the dimensions correspond to a plurality of fields needed by calculating target indexes corresponding to the target parameter set in the data tables one by one; and outputting results of other calculation rules different from the calculation rule among the preconfigured plurality of calculation rules corresponding to the target parameter set.

It should be understood that any of the index calculation device 700, the scene analysis device 800, and the report generation device 900 may be implemented in software, hardware, or a combination of software and hardware, and that a plurality of different modules in these devices may be implemented in the same software or hardware structure, or one module may be implemented by a plurality of different software or hardware structures.

In addition, the index calculating device 700, the scene analysis device 800, and the report generating device 900 may be used to implement the index calculating method, the scene analysis method, and the report generating method described above, respectively, and their related details are described in detail in the foregoing, and for brevity, they will not be repeated here. In addition, these devices may have the same features and advantages as described for the corresponding methods.

FIG. 10 illustrates an example system including an example computing device 1000 that represents one or more systems and/or devices that can implement the various techniques described herein. Computing device 1000 may be, for example, a server used by nodes in a blockchain, a device associated with a server, a system-on-chip, and/or any other suitable computing device or computing system. The index calculating means 700, the scene analysis means 800, and the report generating means 900 described above with reference to fig. 7, 8, and 9, respectively, may each take the form of a computing device 1000. Alternatively, the index calculation means 700, the scene analysis means 800, and the report generation means 900 may all be implemented as computer programs in the form of an application 1016.

The example computing device 1000 as illustrated in fig. 10 includes a processing system 1011, one or more computer-readable media 1012, and one or more I/O interfaces 1013 communicatively coupled to each other. Although not shown, the computing device 1000 may also include a system bus or other data and command transfer system that couples the various components to one another. A system bus may include any one or combination of different bus structures, such as a memory bus or memory controller, a peripheral bus, a universal serial bus, and/or a processor or local bus that utilizes any of a variety of bus architectures. Various other examples are also contemplated, such as control and data lines.

The processing system 1011 represents functionality that performs one or more operations using hardware. Thus, the processing system 1011 is illustrated as including hardware elements 1014 that may be configured as processors, functional blocks, and the like. This may include implementation in hardware as application specific integrated circuits or other logic devices formed using one or more semiconductors. The hardware elements 1014 are not limited by the materials from which they are formed or the processing mechanisms employed therein. For example, the processor may be comprised of semiconductor(s) and/or transistors (e.g., electronic Integrated Circuits (ICs)). In such a context, the processor-executable instructions may be electronically-executable instructions.

Computer-readable medium 1012 is illustrated as including memory/storage 1015. Memory/storage 1015 represents memory/storage capacity associated with one or more computer-readable media. Memory/storage 1015 may include volatile media such as Random Access Memory (RAM) and/or nonvolatile media such as Read Only Memory (ROM), flash memory, optical disks, magnetic disks, and so forth. The memory/storage 1015 may include fixed media (e.g., RAM, ROM, a fixed hard drive, etc.) and removable media (e.g., flash memory, a removable hard drive, an optical disk, and so forth). The computer-readable medium 1012 may be configured in a variety of other ways as described further below.

The one or more I/O interfaces 1013 represent functions that allow a user to input commands and information to the computing device 1000 using various input devices, and optionally also allow information to be presented to the user and/or other components or devices using various output devices. Examples of input devices include keyboards, cursor control devices (e.g., mice), microphones (e.g., for voice input), scanners, touch functions (e.g., capacitive or other sensors configured to detect physical touches), cameras (e.g., motion that does not involve touches may be detected as gestures using visible or invisible wavelengths such as infrared frequencies), and so forth. Examples of output devices include a display device (e.g., projector), speakers, printer, network card, haptic response device, and so forth. Accordingly, the computing device 1000 may be configured in a variety of ways to support user interaction as described further below.

Computing device 1000 also includes applications 1016. The application 1016 may be, for example, a software instance of any one of the index computing device 700, the scene analysis device 800, and the report generation device 900, or of two or all of these devices, and implements the techniques described herein in combination with other elements in the computing device 1000.

Various techniques may be described herein in the general context of software hardware elements or program modules. Generally, these modules include routines, programs, elements, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The terms "module," "functionality," and "component" as used herein generally represent software, firmware, hardware, or a combination thereof. The features of the techniques described herein are platform-independent, meaning that the techniques may be implemented on a variety of computing platforms having a variety of processors.

An implementation of the described modules and techniques may be stored on or transmitted across some form of computer readable media. Computer readable media can include a variety of media that are accessible by computing device 1000. By way of example, and not limitation, computer readable media may comprise "computer readable storage media" and "computer readable signal media".

"computer-readable storage medium" refers to a medium and/or device that can permanently store information and/or a tangible storage device, as opposed to a mere signal transmission, carrier wave, or signal itself. Thus, computer-readable storage media refers to non-signal bearing media. Computer-readable storage media include hardware such as volatile and nonvolatile, removable and non-removable media and/or storage devices implemented in methods or techniques suitable for storage of information such as computer-readable instructions, data structures, program modules, logic elements/circuits or other data. Examples of a computer-readable storage medium may include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital Versatile Disks (DVD) or other optical storage, hard disk, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or other storage devices, tangible media, or articles of manufacture adapted to store the desired information and which may be accessed by a computer.

"computer-readable signal medium" refers to a signal bearing medium configured to transmit instructions to hardware of the computing device 1000, such as via a network. Signal media may typically be embodied in a modulated data signal, such as a carrier wave, data signal, or other transport mechanism, with computer readable instructions, data structures, program modules, or other data. Signal media also include any information delivery media. The term "modulated data signal" means a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal. By way of example, and not limitation, communication media includes wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, RF, infrared and other wireless media.

As previously described, the hardware elements 1014 and computer-readable media 1012 represent instructions, modules, programmable device logic, and/or fixed device logic implemented in hardware that may be used in some embodiments to implement at least some aspects of the techniques described herein. The hardware elements may include integrated circuits or components of a system on a chip, application Specific Integrated Circuits (ASICs), field Programmable Gate Arrays (FPGAs), complex Programmable Logic Devices (CPLDs), and other implementations in silicon or other hardware devices. In this context, the hardware elements may be implemented as processing devices that perform program tasks defined by instructions, modules, and/or logic embodied by the hardware elements, as well as hardware devices that store instructions for execution, such as the previously described computer-readable storage media.

Combinations of the foregoing may also be used to implement the various techniques and modules described herein. Thus, software, hardware, or program modules and other program modules may be implemented as one or more instructions and/or logic embodied on some form of computer readable storage medium and/or by one or more hardware elements 1014. Computing device 1000 may be configured to implement particular instructions and/or functions corresponding to software and/or hardware modules. Thus, for example, modules may be implemented at least in part in hardware as modules executable by computing device 1000 as software using computer-readable storage media of a processing system and/or hardware elements 1014. The instructions and/or functions may be executable/operable by one or more articles of manufacture (e.g., one or more computing devices 1000 and/or processing systems 1011) to implement the techniques, modules, and examples described herein.

In various implementations, the computing device 1000 may take on a variety of different configurations. For example, computing device 1000 may be implemented as a computer-like device including a personal computer, desktop computer, multi-screen computer, laptop computer, netbook, and the like. Computing device 1000 may also be implemented as a mobile appliance-like device including mobile devices such as mobile telephones, portable music players, portable gaming devices, tablet computers, multi-screen computers, and the like. The computing device 1000 may also be implemented as a television-like device that includes devices having or connected to generally larger screens in casual viewing environments. Such devices include televisions, set-top boxes, gaming machines, and the like.

The techniques described herein may be supported by these various configurations of computing device 1000 and are not limited to the specific examples of techniques described herein. The functionality may also be implemented in whole or in part on the "cloud" 1020 through the use of a distributed system, such as through the platform 1022 described below.

Cloud 1020 includes and/or is representative of a platform 1022 for resources 1024. The platform 1022 abstracts underlying functionality of hardware (e.g., servers) and software resources of the cloud 1020. The resources 1024 may include applications and/or data that can be used when executing computer processing on servers remote from the computing device 1000. The resources 1024 may also include services provided over the internet and/or over subscriber networks such as cellular or Wi-Fi networks.

Platform 1022 may abstract resources and functions to connect computing device 1000 with other computing devices. The platform 1022 may also be used to abstract a hierarchy of resources to provide a corresponding level of hierarchy of encountered demand for resources 1024 implemented via the platform 1022. Thus, in an interconnect device embodiment, implementation of the functionality described herein may be distributed throughout system 1000. For example, the functionality may be implemented in part on the computing device 1000 and by the platform 1022 that abstracts the functionality of the cloud 1020.

It should be understood that for clarity, embodiments of the present application have been described with reference to different functional units. However, it will be apparent that the functionality of each functional unit may be implemented in a single unit, in a plurality of units or as part of other functional units without departing from the present application. For example, functionality illustrated to be performed by a single unit may be performed by multiple different units. Thus, references to specific functional units are only to be seen as references to suitable units for providing the described functionality rather than indicative of a strict logical or physical structure or organization. Thus, the present application may be implemented in a single unit or may be physically and functionally distributed between different units and circuits.

It will be understood that, although the terms first, second, third, etc. may be used herein to describe various devices, elements, components or sections, these devices, elements, components or sections should not be limited by these terms. These terms are only used to distinguish one device, element, component, or section from another device, element, component, or section.

Although the present application has been described in connection with some embodiments, it is not intended to be limited to the specific form set forth herein. Rather, the scope of the present application is limited only by the appended claims. Additionally, although individual features may be included in different claims, these may possibly be advantageously combined, and the inclusion in different claims does not imply that a combination of features is not feasible and/or advantageous. The order of features in the claims does not imply any specific order in which the features must be worked. Furthermore, in the claims, the word "comprising" does not exclude other elements, and the term "a" or "an" does not exclude a plurality. Reference signs in the claims are provided merely as a clarifying example and shall not be construed as limiting the scope of the claims in any way.

The present application provides a computer readable storage medium having stored thereon computer readable instructions that when executed implement the training method for applying classification models described above.

The present application provides a computer program product or computer program comprising computer instructions stored in a computer readable storage medium. The computer instructions are read from the computer-readable storage medium by a processor of a computing device, and executed by the processor, cause the computing device to perform the training method of the application classification model provided in the various alternative implementations described above.

Variations to the disclosed embodiments can be understood and effected by those skilled in the art in practicing the claimed subject matter, from a study of the drawings, the disclosure, and the appended claims. In the claims, the word "comprising" does not exclude other elements or steps, and the "a" or "an" does not exclude a plurality. The mere fact that certain measures are recited in mutually different dependent claims does not indicate that a combination of these measures cannot be used to advantage.

Claims

1. A method of index calculation based on a plurality of data tables stored in at least one data source, the method comprising:

receiving at least one target parameter corresponding to a target index;

determining an intermediate result set according to the at least one target parameter and a plurality of preset calculation rules, wherein the intermediate result set comprises an output result of each calculation rule in the plurality of calculation rules;

calculating the target index according to the intermediate result set;

wherein an output result of each of the plurality of calculation rules is determined according to at least one of:

at least one dimension corresponding to the at least one target parameter in a plurality of dimensions, wherein the plurality of dimensions are in one-to-one correspondence with a plurality of fields required by calculating the target index in the plurality of data tables; and

The output results of other calculation rules of the plurality of calculation rules different from the calculation rule.

2. The method of claim 1, wherein the determining an intermediate result set from the at least one target parameter and a plurality of pre-configured calculation rules comprises:

and according to a pre-configured execution sequence, sequentially calling corresponding programs in the at least one data source or in a memory of a computer system where the at least one data source is located to execute the plurality of calculation rules so as to determine the intermediate result set.

3. The method of claim 2, wherein the at least one data source comprises at least one database, and wherein the corresponding program in the at least one data source comprises at least one database function of the at least one database.

4. The method of claim 1, wherein the plurality of calculation rules comprises a first screening rule comprising the steps of:

passing a target parameter of the at least one target parameter that satisfies a first filtering condition to a second one of the plurality of calculation rules that is different from the first filtering rule, wherein

The first screening condition is determined from a corresponding field in at least one of the plurality of data tables.

5. The method of claim 4, further comprising:

acquiring corresponding target data from the plurality of data tables according to the target parameters transferred to the second calculation rule;

and determining an output result of the second calculation rule based on the target data.

6. The method of claim 1, further comprising:

and in response to receiving the at least one target parameter from the client, transmitting the at least one target parameter and the calculation result of the target index together as output to the client.

7. A scene analysis method based on a plurality of data tables stored in at least one data source, the method comprising:

determining a plurality of target indexes corresponding to the target scene;

acquiring a plurality of target parameter sets corresponding to the target indexes one by one;

determining an intermediate result set corresponding to the target parameter set according to each target parameter set in the plurality of target parameter sets and a plurality of preset calculation rules corresponding to the target parameter set, wherein the intermediate result set corresponding to the target parameter set comprises an output result of each calculation rule in the plurality of preset calculation rules corresponding to the target parameter set;

Calculating the plurality of target indexes according to an intermediate result set corresponding to each of the plurality of target parameter sets; and

determining an analysis result of the target scene according to the target indexes;

wherein an output result of each of the pre-configured plurality of calculation rules corresponding to each of the target parameter sets is determined according to at least one of:

at least one dimension corresponding to the target parameter set in a plurality of dimensions, wherein the dimensions correspond to a plurality of fields needed by calculating target indexes corresponding to the target parameter set in the data tables one by one; and

output results of other calculation rules different from the calculation rule among the pre-configured plurality of calculation rules corresponding to the target parameter set.

8. A report generation method based on a plurality of data tables stored in at least one data source, the method comprising:

determining a target scene and a plurality of target indexes corresponding to the target scene;

Determining a target index result report corresponding to each target parameter set according to an intermediate result set corresponding to the target parameter set in the plurality of target parameter sets, wherein the target index result report corresponding to the target parameter set stores the calculation result of the target index corresponding to the target parameter set; and

determining a target report based on a target index result report corresponding to each of the plurality of target parameter sets according to the mapping relation between the target report and the plurality of target indexes;

9. An index computing device based on a plurality of data tables stored in at least one data source, the device comprising:

The target parameter receiving module is configured to receive at least one target parameter corresponding to the target index;

an intermediate result set determination module configured to determine an intermediate result set from the at least one target parameter and a plurality of calculation rules configured in advance, the intermediate result set including an output result of each of the plurality of calculation rules; and

a target index calculation module configured to calculate the target index from the intermediate result set;

10. A computing device, comprising:

a memory configured to store computer-executable instructions;

a processor configured to perform the method according to any one of claims 1 to 8 when the computer executable instructions are executed by the processor.

11. A computer readable storage medium storing computer executable instructions which, when executed, perform the method of any one of claims 1 to 8.

12. A computer program product comprising computer executable instructions which when executed by a processor perform the method according to any one of claims 1 to 8.