CN116089490A

CN116089490A - Data analysis method, device, terminal and storage medium

Info

Publication number: CN116089490A
Application number: CN202211600477.4A
Authority: CN
Inventors: 李乌英嘎; 杨虹洪; 项子林; 华桊兴
Original assignee: Ping An Bank Co Ltd
Current assignee: Ping An Bank Co Ltd
Priority date: 2022-12-13
Filing date: 2022-12-13
Publication date: 2023-05-09

Abstract

The embodiment of the invention provides a data analysis method, a data analysis device, a terminal and a storage medium; the embodiment of the invention can acquire the data analysis requirement; acquiring metadata according to the data analysis requirement; constructing index metadata according to the attribute information of the metadata; storing the index metadata in an index metadata base; and responding to the triggering operation of the user, and calling index metadata corresponding to the triggering operation from the index metadata base to perform data analysis. In the embodiment of the invention, a technician can quickly call the index metadata through the data analysis terminal under a specific data analysis scene, so that the data analysis operation is performed by utilizing the information contained in the index metadata, and the data analysis efficiency is improved.

Description

Data analysis method, device, terminal and storage medium

Technical Field

The present invention relates to the field of computer technologies, and in particular, to a data analysis method, apparatus, terminal, and storage medium.

Background

The data analysis means that a large amount of collected data is analyzed by a proper statistical analysis method, and the collected data are summarized and understood so as to maximally develop the function of the data and play the role of the data. Data analysis is the process of detailed research and summarization of data in order to extract useful information and form conclusions. Data analysis is now an important application in the computer field.

In the prior art, when a technician performs data analysis, an operation rule related to the data analysis often needs to be built on site, and the built standard is not uniform, so that the efficiency of the data analysis is affected.

Disclosure of Invention

The embodiment of the application provides a data analysis method, a data analysis device, a terminal and a storage medium, which are used for solving the problem of low data analysis efficiency in the prior art.

The embodiment of the application provides a data analysis method, which comprises the following steps:

acquiring data analysis requirements;

acquiring metadata according to the data analysis requirement, wherein the metadata comprises attribute information of data;

constructing index metadata according to the attribute information of the metadata;

storing the index metadata in an index metadata base;

and responding to the triggering operation of the user, acquiring index metadata corresponding to the triggering operation in the index metadata base, and carrying out data analysis.

The embodiment of the application also provides a data analysis device, which comprises:

the demand module is used for acquiring data analysis demands;

the acquisition module is used for acquiring metadata according to the data analysis requirement, wherein the metadata comprises attribute information of the data;

the construction module is used for constructing index metadata according to the attribute information of the metadata;

The storage module is used for storing the index metadata in an index metadata base;

and the calling module is used for responding to the triggering operation of the user and calling index metadata corresponding to the triggering operation from the index metadata base to perform data analysis.

In some embodiments, the build module further comprises:

the first response sub-module is used for responding to a first operation of a user and determining an index name, wherein the index name is the name of target index data;

a statistical range sub-module for determining a statistical range of the metadata;

an analysis dimension sub-module for determining an analysis dimension of the metadata;

the second response sub-module is used for responding to the second operation of the user and determining the operation rule of the target index data;

and the construction sub-module is used for constructing index metadata based on the index name, the operation rule, the statistical range and the analysis dimension.

In some embodiments, the statistical range sub-module further comprises:

the first analysis submodule is used for intersecting and combining any processing of the statistical ranges of the metadata based on the operation rule to obtain a range analysis result;

and the first determining submodule is used for determining the range analysis result as the statistical range of the metadata.

In some embodiments, the analysis dimension submodule further includes:

the second analysis submodule is used for intersecting and combining any processing of analysis dimensions of the metadata based on the operation rule to obtain a dimension analysis result;

and the second determining submodule is used for determining the dimension analysis result as the analysis dimension of the metadata.

In some embodiments, the building sub-module further comprises:

the first acquisition sub-module is used for acquiring a preset assembly sequence;

and the assembly sub-module is used for assembling the index name, the operation rule, the statistical range and the analysis dimension according to the preset assembly sequence to generate index metadata.

In some embodiments, the data analysis method further comprises:

the building module is used for building a dimension database;

and the dimension storage module is used for storing the analysis dimension of the index metadata in the dimension database.

In some embodiments, the data analysis method further comprises:

the blood margin analysis module is used for carrying out blood margin analysis on the index metadata and determining all upstream metadata forming the index metadata;

the image generation module is used for generating a blood margin relation graph based on the upstream metadata and the index metadata;

And the image display module is used for responding to the display operation of a user, generating a display interface and displaying the blood relationship graph.

In the data analysis method provided by the embodiment of the application, first, data analysis requirements are acquired; then obtaining metadata according to the data analysis requirement, wherein the metadata comprises attribute information of the data; further, index metadata is constructed according to the attribute information of the metadata; the index metadata is then saved in an index metadata base; and finally, responding to the triggering operation of the user, and calling index metadata corresponding to the triggering operation from the index metadata base to perform data analysis. In the embodiment of the application, since the index metadata comprises the operation rule, the operation rule included in the index metadata can be called to realize the data analysis without the need of on-site construction of operation rules by technicians, so that the data analysis efficiency is improved.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following description will briefly explain the drawings needed in the description of the embodiments, and it is obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.

Fig. 1a is a schematic view of a scenario of a data analysis method provided in an embodiment of the present application;

FIG. 1b is a schematic flow chart of a data analysis method according to an embodiment of the present disclosure;

FIG. 2 is a schematic flow chart of constructing index metadata in the data analysis method provided in the embodiment of the present application;

FIG. 3 is a schematic illustration of a blood relationship graph provided by an embodiment of the present application;

FIG. 4 is an interface schematic diagram of a first data analysis interface provided in an embodiment of the present application;

FIG. 5 is an interface schematic diagram of a second data analysis interface provided in an embodiment of the present application;

FIG. 6a is a schematic flow chart of a specific embodiment of the present invention;

FIG. 6b is a schematic diagram of an audit process provided by an embodiment of the present invention;

FIG. 7 is a schematic diagram of a data analysis device according to an embodiment of the present invention;

fig. 8 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.

Detailed Description

It is noted that the terminology used in the examples section of the embodiments of the present application is used for the purpose of explaining specific embodiments of the present application only and is not intended to limit the present application. In addition, in the description of the embodiments of the present application, unless otherwise indicated, "a plurality" means two or more, and "at least one" means one, two or more. The term "first" is used for descriptive purposes only and is not to be interpreted as indicating or implying relative importance or implicitly indicating the number of technical features indicated. The term "upper level" is used for descriptive purposes only and is not to be construed as implying that the described object is relatively more important. Reference in the specification to "one embodiment" or "some embodiments" or the like means that a particular feature, structure, or characteristic described in connection with the embodiment is included in one or more embodiments of the application. Thus, appearances of the phrases "in one embodiment," "in some embodiments," and the like in various places throughout this specification are not necessarily all referring to the same embodiment, but mean "one or more, but not all embodiments" unless expressly specified otherwise. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless expressly specified otherwise.

The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are only some, but not all, of the embodiments of the present application. All other embodiments, which can be made by those skilled in the art based on the embodiments herein without making any inventive effort, are intended to be within the scope of the present application.

First, basic concepts of understanding the related nouns of the present invention are described:

metadata: metadata is data describing other data, such as base data, index data, etc., or metadata is structural data that is used to provide information about a resource, which is not necessarily in digital form, but may be from different resources. The purpose of the metadata is to: identifying a resource; evaluating the resource; tracking the change of the resource in the using process; the realization is simple and the management of a large amount of networking data is high-efficient; the method and the device realize effective discovery, searching, integrated organization and effective management of the used resources of the information resources.

Index data: i.e. an index, which is used in statistics to represent the overall composite quantitative characteristics. For example, in an industrial census, all industrial enterprises make up an ensemble, and the industrial enterprise count, the industrial employee count, the payroll count, the average payroll, the fixed asset count, the profit count, etc., are indicators that reflect the quantitative characteristics of the ensemble from different aspects.

Index metadata: index metadata refers to metadata describing index data, and in the embodiment of the invention, the index metadata is text data, and besides the name of the index data, the index metadata can also include information such as operation rules, statistical ranges, analysis dimensions and the like of the index data.

Basic data: the basic data is the basis of the operation of the data system, and is used for supporting other various data and parameters of the operation of the data system, and can be generally understood as data directly acquired through a statistical tool, wherein the acquired basic data does not contain other operation processing procedures except coverage updating, such as single transaction amount, single transaction time, customer age information and the like. The basic data has corresponding data names, and is stored in a database in the form of specific numerical values under a certain list structure.

The embodiment of the application provides a data analysis method, a data analysis device, a terminal and a storage medium.

The data analysis method can be integrated in electronic equipment, and the electronic equipment can be a terminal, a server and other equipment. The terminal can be a mobile phone, a tablet computer, an intelligent Bluetooth device, a notebook computer or a personal computer (Personal Computer, PC) and other devices; the server may be a single server or a server cluster composed of a plurality of servers.

In some embodiments, the data analysis method may also be integrated in a plurality of electronic devices, for example, the data analysis method may be integrated in a plurality of servers, and the data analysis method of the present application is implemented by the plurality of servers.

In some embodiments, the server may also be implemented in the form of a terminal.

For example, referring to fig. 1a, an application scenario schematic diagram of a data analysis method provided in an embodiment of the present application is shown. As shown in fig. 1a, the data analysis terminal 101, the index metadata base 102, the dimension database 103, the base database 104 and the data auditing platform 105 are located in a wireless or wired network, and the data analysis terminal 101 can perform data interaction with the index metadata base 102, the dimension database 103, the base database 104 and the data auditing platform 105, and the index metadata base 102, the dimension database 103 and the base database 104 can perform data interaction.

And (3) a data analysis terminal: the data analysis terminal 101 may acquire data analysis requirements including the purpose of data analysis, specific metadata required to achieve the purpose, operation rules, and the like; after the data analysis requirement is acquired, acquiring metadata from the index metadata base 102 according to the data analysis requirement, wherein the metadata comprises attribute information of the data; index metadata meeting the data analysis requirement is further constructed according to the metadata and is stored in the index metadata base 102; finally, the base data may be obtained from the base database 104, and the index metadata of the index metadata database 102 may be obtained for data analysis.

Index metadata base: the index metadata base 102 stores metadata, wherein the metadata comprises metadata corresponding to data in the base database 104 and index metadata constructed by technicians, and the metadata has an association relationship with the base data in the base database 104 and dimension data in the dimension database 103; when the data analysis terminal 101 has a data analysis demand, the metadata is transmitted to the data analysis terminal 101.

Dimension database: the dimension database 103 stores dimension data, wherein the dimension data has an association relationship with the basic data in the basic database 104 and the metadata in the index metadata database 102; when the index metadata in the data analysis terminal 101 is constructed and stored in the index metadata database 102, the dimension database 103 is updated accordingly, and the analysis dimension of the index metadata is stored.

Basic database: the base database 104 holds base data having an association relationship with the dimension data in the dimension database 103 and the metadata in the index metadata database 102. The base data may be transmitted to the data analysis terminal for data analysis in response to a request of the data analysis terminal 101.

And a data auditing platform: the data auditing platform 105 can audit the data analysis requirements and index metadata according to the requirements of the user, and when the auditing result indicates that the data analysis requirements and index metadata cannot pass, the auditing intention can be sent to see the data analysis terminal 101 so that the user can modify the corresponding content, and then the modified data analysis requirements and index metadata are received to audit until the auditing result indicates that the data analysis requirements and index metadata pass.

Specifically, after the data analysis terminal 101 acquires the data analysis requirement, the data analysis requirement is sent to the data auditing platform 105; acquiring metadata from the index metadata base 102 according to the data analysis requirement after passing the auditing; the data analysis terminal 101 constructs index metadata based on the metadata, and transmits the index metadata to the data auditing platform 105; the data analysis terminal 101 stores the index metadata after passing the auditing into an index metadata database 102, and the dimension database is updated correspondingly at the same time; finally, the data analysis terminal 101 obtains the basic data from the basic database 104 according to the data analysis requirement, and invokes the index metadata stored in the index metadata database 102 to perform data analysis.

The following will describe in detail. The numbers of the following examples are not intended to limit the preferred order of the examples.

In this embodiment, description will be made from the point of view of a data analysis device which may be integrated in a data analysis terminal for receiving a data analysis demand of a user, constructing and saving index metadata in an index metadata base, and calling the index metadata for data analysis. The data analysis terminal may be an electronic device, for example, a server, a terminal, or the like. For example, the server may be a server, such as a single server, a cluster of servers, and so forth. For another example, the terminal may be a mobile phone, a notebook computer, a personal computer, etc.

As shown in fig. 1b, the specific flow of the data analysis method may be as follows steps S110 to S150:

s110, acquiring data analysis requirements.

The data analysis requirement refers to text information in a specific data analysis scene, wherein the text information comprises a data analysis purpose, specific metadata required for realizing the data analysis purpose, possible operation rules and the like, and the data analysis purpose and the specific metadata required for realizing the data analysis purpose are necessary items. The data analysis purpose is to achieve the objective through data analysis, for example, in order to know the capability of obtaining the benefits of the operating capital of the enterprise, know the capability of resisting risks of the enterprise, judge the asset condition of the individual user, etc., the specific metadata required for achieving the data analysis purpose can be determined by technicians according to the existing data, or can be selected according to personal experience, the metadata is processed through specific operation rules and is expected to achieve the corresponding data analysis purpose, for example, the result obtained by dividing the two metadata of the net profit of the enterprise and the average capital can be used for representing the capability of obtaining the benefits of the operating capital of the enterprise. In some embodiments, the data analysis requirements may not include operational rules, the purpose of the data analysis must be explicit, and the metadata needs to be determined through multiple adjustments.

Various ways of obtaining the data analysis requirements exist, for example, a technician may input corresponding text information in a text input window of the data analysis terminal to form a corresponding text file, where the text file is the data analysis requirement, the text information of the data analysis requirement sent by other terminal devices may also be received through a wireless network, the data analysis requirement may also be received through a communication medium, for example, the data analysis requirement may be received through a network cable, a usb disk, a floppy disk, an optical disk, etc.

In some embodiments, the process of obtaining the data analysis requirements may include examining the data analysis requirements to adjust the data analysis requirements according to actual requirements, where the process of examining includes the following steps A1-A3:

A1. responding to uploading operation of a user, and uploading data analysis requirements to a data auditing platform so that the data auditing platform can generate auditing comments on the analysis requirements;

A2. when the audit opinion indicates that the audit is not passed, the data analysis requirement can be adjusted according to the audit opinion, and the step is skipped: uploading data analysis requirements to a data auditing platform;

A3. And when the audit opinion indicates that the audit is passed, acquiring the data analysis requirement.

In some embodiments, the data analysis terminal sends an audit request to the data audit platform, where the audit request may include a file to be audited, i.e., a data analysis requirement, and source information, and the data audit platform audits the data analysis requirement according to the audit request.

The source information may include device information of the data analysis terminal that makes the audit request and personal information of the user who performs the corresponding operation. The device information may include a network IP address of the data analysis terminal, geographical location information, physical device information, etc., where the physical device information may include device identification information, which may be user-defined and uploaded to the data auditing platform to characterize the device's uniqueness within a certain space-time range. The personal information may include user account information, user identity information, etc. for the user initiating the audit request to log into the data analysis terminal. The device information and the personal information can be character strings formed by combining elements such as English letters, numbers, separators and the like, or can be in a format converted by a specific standard, such as binary conversion, hexadecimal conversion and the like. The format of the specific device information and the user information may be determined according to the will of the skilled person.

In some embodiments, the step of the data auditing platform generating auditing opinions is specifically as follows: receiving an audit request; analyzing the auditing request to obtain files to be audited and source information; determining auditing standards for the files to be audited according to the source information; pre-auditing the file to be audited by a computer; and (5) carrying out expert manual auditing on the file to be audited to obtain auditing opinions.

In a specific embodiment, a data auditing platform receives an auditing request that a file to be audited is a data analysis requirement, and an auditing process is established; analyzing the audit request to obtain data analysis requirements and source information containing personal information and equipment information; judging that the data analysis requirement comes from an insurance department according to personal information, and determining a data asset auditing standard corresponding to the department; performing computer pre-examination on the data analysis requirement, judging whether index metadata for achieving the same purpose according to the specific metadata exist, whether the required specific metadata exist or not and the like, and generating examination comments which indicate that the pre-examination is unqualified if the pre-examination is not passed; arranging 1-3 experts in the corresponding field for manual auditing through the pre-auditing data analysis requirement, and collecting auditing opinions of the experts after the auditing of the experts is finished; when the audit opinion indicates that the audit opinion does not pass, the audit opinion is sent to a corresponding data analysis terminal according to the equipment information so as to carry out subsequent processing; and when the audit opinion indicates that the audit opinion passes, the audit opinion is sent to the corresponding data analysis terminal, and the audit process is ended.

In the embodiment, the data analysis requirements are audited so that the user can adjust the data analysis requirements, so that the user can clearly determine the purpose of data analysis and find the most suitable specific metadata, and more reasonable index metadata can be constructed.

S120, acquiring metadata according to the data analysis requirement, wherein the metadata comprises attribute information of the data.

The data can be basic data or index data; attribute information refers to information about data that may be used to represent characteristics of the data for a user to perform data analysis based on the characteristics. In some embodiments, the attribute information may include a statistical range for the bank data, where the statistical range is a statistical source of the data, and the statistical range may be divided into seven upper-level ranges including a customer range, an organization range, a product or service range, a channel range, a subject range, an account range, and other ranges, where each upper-level range may be specifically described, for example, the customer range may be subdivided into a lower-level range for public customers, individual customers, peer customers, and the like, and may be further defined, for example, for public credit customers, small business owner customers, and the like, so as to define the source of the data.

In some embodiments, a dimension database is also created prior to obtaining metadata from the data analysis requirements.

The dimension database is used for storing analysis dimensions, and the analysis dimensions can be text structure data. The analysis dimension stored in the dimension database has an association relationship with the basic data in the basic database and the metadata in the index metadata database, for example, each analysis dimension of the basic data and the metadata can find a corresponding analysis dimension in the dimension database. The analysis dimension refers to a view angle of analysis data, and corresponds to an analysis angle of a certain index data. For example, for bank index data "deposit balance", analysis may be performed from the product dimension to analyze the balance of demand deposit, or to analyze the balance of regular deposit, using the index data; analysis may also be performed from the dimensions of the institution, such as analyzing the deposit balance of Beijing branches, or analyzing the deposit balance of Tianjin branches, etc. The analysis dimensions may include granularity attributes, where granularity is the thickness degree of data statistics under the same analysis dimension, the higher the refinement degree is, the smaller the granularity value is, the lower the refinement degree is, the larger the granularity value is, and hierarchical management is performed between the analysis dimensions according to granularity, for example, under the mechanism dimension, the analysis dimensions may be represented as: beijing headquarter-Shanghai branch-Shanghai iridescent bridge branch-Shanghai ancient North ruby road community branch. Each time index metadata is newly added in the index metadata database, whether an analysis dimension corresponding to the index metadata exists or not and whether granularity of the analysis dimension is consistent or not are checked in the dimension database. If no corresponding analysis dimension exists, the new analysis dimension needs to be stored in a dimension database, the new analysis dimension can be distinguished from the existing analysis dimension through naming, the naming mode can be any combination of Chinese characters, letters, arabic numerals, special symbols and the like, the names of the analysis dimensions are easy to distinguish and query as standards, and the names of the analysis dimensions are not to be understood as limiting the invention.

In the embodiment, the method for unified management of analysis dimensions by establishing the dimension database is convenient for the analysis dimensions of the index data to be clearly represented when the index metadata are called in the data analysis process, so that the understanding of technicians on the corresponding index data is enhanced, and the efficiency of data analysis is improved.

S130, constructing index metadata according to the attribute information of the metadata.

In some embodiments, the attribute information includes a statistical range and an analysis dimension, and the constructing index metadata according to the attribute information of the metadata, as shown in fig. 2, includes the following steps S210 to S250:

s210, determining an operation rule of target index data in response to a first operation of a user;

s220, determining a statistical range of the metadata;

s230, determining analysis dimensions of the metadata;

s240, determining an index name in response to a second operation of the user, wherein the index name is the name of target index data;

s250, constructing index metadata of target index data based on the index name, the operation rule, the statistical range and the analysis dimension.

The target index data is index data which is generated based on the metadata and can achieve the purpose of data analysis of the data analysis requirement. The operation rule is used for accurately expressing how the index data is calculated, and one operation rule generally comprises three elements, namely one operation symbol or function, and the function can be a common SQL function; 2. a constant, which may be a fixed value; 3. the variables may be index data, base data, or other intermediate variables that may be variables that have no actual analytical value and are not stored in the index metadata database in the form of index metadata. For example, a complete operational rule may be: (net profit-net asset average balance x capital cost)/net asset average balance x 100%, wherein net profit, net asset average balance and capital cost are all index data, connected by operation symbols, and finally the index data is changed into a percentage form by multiplying a constant.

The first operation may be that when the index metadata is constructed, the terminal device generates a text input window, and the user directly inputs text information of the operation rule through the text input window; the terminal device may also generate an operation rule determining interface according to the specific metadata, where the operation rule determining interface may include a plurality of controls except for a confirmation control, each control corresponds to an operation symbol, an SQL function, or a data name corresponding to the specific metadata, and the user may trigger the control to implement generation of text content through clicking, dragging, long pressing, etc. until the user clicks the confirmation control to complete construction of the operation rule. The first operation is determined at the discretion of the skilled person and should not be construed as limiting the invention.

Since the attribute information of the specific metadata includes the statistical range and the analysis dimension, the corresponding statistical range and analysis dimension can be directly determined.

In some embodiments, after determining the statistical range of the metadata, any processing needs to be intersected and combined on the basis of the operation rule to obtain a range analysis result; and determining the range analysis result as the statistical range of the metadata.

Specifically, when an operation rule of target index data only relates to one metadata, the statistical range of the metadata is a range analysis result; when an operation rule of target index data relates to a plurality of metadata, if the statistical ranges of the metadata are the same, the same statistical range is a range analysis result; if the statistical ranges of the metadata are different, when the statistical ranges are all lower ranges in the same upper range, determining the lowest lower range as a range analysis result, wherein the range analysis result is the intersection processing of the statistical ranges; when the statistical ranges of the plurality of metadata do not belong to the same upper-level range, for example, when the statistical ranges of the two metadata are the client range and the organization range, respectively, the range analysis result may be defined as "client range and organization range", which is the merging process of the statistical ranges. In some embodiments, the user may determine that the element of the statistical range is not significant for the target index data, and may directly set the range analysis result to be null, and the statistical range of the corresponding index metadata is also null. The obtained range analysis result is the statistical range of the index metadata. In some embodiments, the intersection and merging of the statistical ranges can be automatically completed by the data analysis terminal according to a preset rule.

In the embodiment, the statistical range of the index metadata is processed to obtain the statistical range of the index metadata, so that the statistical range of the index data can be clearly represented when the index metadata is called in the data analysis process, the understanding of technicians to the corresponding index data is enhanced, and the efficiency of data analysis is improved.

In some embodiments, after determining the analysis dimension of the metadata, any processing of intersecting, merging and updating the analysis dimension of the metadata based on the operation rule is required to obtain a dimension analysis result; and determining the dimension analysis result as the analysis dimension of the metadata.

Specifically, when an operation rule of one target index data only relates to one metadata, the analysis dimension of the metadata is a dimension analysis result; when an operation rule of target index data relates to a plurality of metadata, if the analysis dimensions of the metadata are the same, the same analysis dimension is a dimension analysis result; if the analysis dimensions of the metadata are different, when the analysis dimensions are all analysis dimensions with different granularities under the same analysis dimension, determining the analysis dimension with the smallest granularity as an analysis dimension result, namely intersecting the analysis dimensions; when the statistical ranges of the plurality of metadata do not belong to the same analysis dimension and are not analysis dimensions of different granularities in the same analysis dimension, for example, when the analysis dimensions of the two metadata are respectively a mechanism dimension and a client dimension, the dimension analysis result can be determined as a mechanism dimension and a client dimension, which is the merging process of the analysis dimensions; in addition, for a brand new index metadata, in order to describe the characteristics of the more fitting index data, a brand new analysis dimension which does not belong to the existing analysis dimension can be defined, wherein the brand new analysis dimension is a dimension analysis result, and is update processing of the analysis dimension. The obtained dimension analysis result is the analysis dimension of the index metadata. In some embodiments, the intersecting and merging processes of the analysis dimensions can be automatically completed by the data analysis terminal according to preset rules, and for the result of the automatic completion, the user can further consider whether to perform the updating operation.

In the embodiment, the analysis dimension of the metadata is processed to obtain the analysis dimension of the index metadata, so that the analysis dimension can accurately represent the characteristics of the corresponding index data when the index metadata is called in the data analysis process, the understanding of technicians on the corresponding index data is enhanced, and the data analysis efficiency is improved.

The second operation is similar to the first operation, and the purpose of generating text information of the index name is not described herein.

In some embodiments, the index name may be normalized in the following manner: index name = statistical range + modifier + metric name, wherein modifier depends on the naming habit of different business departments, usually refers to the business term base of the whole industry to determine, and directly taking the statistical range of index metadata as a part of index names may result in overlong index names, influence the use of index metadata, and users can perform moderate optimization processing according to personal understanding.

In some embodiments, after the index name, the operation rule, the statistical range and the analysis dimension are obtained, a preset assembly order is further required to be obtained, and the index name, the operation rule, the statistical range and the analysis dimension are further assembled according to the preset assembly order to generate index metadata.

The preset assembly sequence is a manner of arranging the acquired text data of each element, for example, in some embodiments, index metadata is set as an index name, a statistical range, an operation rule, and an analysis dimension are sequentially displayed in a horizontal row from top to bottom. The specific preset assembly sequence is determined according to actual use requirements. In some embodiments, after the data analysis terminal obtains the preset assembly order, the text content of the index metadata is generated according to the preset assembly order in response to an execution operation of the user. In other embodiments, after the dimension database is established and the index metadata is constructed according to the preset assembly sequence, if the analysis dimension corresponding to the index metadata does not exist in the dimension database, the analysis dimension of the index metadata may be stored in the dimension database.

In the embodiment, by assembling the index names, the statistical ranges, the operation rules and the analysis dimensions according to the specific sequence, when the index metadata are called in the actual data analysis scene, technicians can quickly and comprehensively understand the attribute of the index data, and the improvement of the data analysis efficiency is facilitated.

In some embodiments, other elements that aid the understanding of the technician may also be added to the index metadata, e.g., technical fields, analysis purposes, etc. For analysis purposes, the text content of the part can be consistent with the data analysis purposes in the data analysis requirements, so that the meaning of the index data corresponding to the index metadata can be defined when the index metadata is called by a user.

In some embodiments, after constructing index metadata from attribute information of the metadata, the data analysis method further includes the steps of B1-B3:

B1. performing blood-margin analysis on the index metadata to determine all upstream metadata constituting the index metadata;

B2. generating a blood relationship graph based on the upstream metadata and the index metadata;

B3. and responding to the display operation of the user, generating a display interface to display the blood relationship graph.

For one index metadata, since the operation rule included in the index metadata relates to other index data or basic data, the upper metadata can be metadata corresponding to the other index data or basic data, and the upper metadata of the upper metadata can be obtained by analogy until the upper metadata are metadata of the basic data, all the upper metadata obtained through the tracing process can be collectively referred to as upstream metadata of the index metadata, and the tracing process is a blood edge analysis process. And (3) using the corresponding data name as a mark for metadata related to the whole blood edge analysis process, connecting by using connecting lines, and arranging according to a certain rule, for example, arranging in a step-by-step and line-by-line manner, wherein the obtained image is the blood edge relation graph.

In some embodiments, as shown in fig. 3, the display interface is an interactive interface, and the interactive interface includes a metadata control and an expansion control, where the metadata control uses a data name corresponding to the metadata as an identifier, the expansion control may have two display states, which respectively represent expansion/folding of the upper metadata, and the control may control the display interface to display all or part of the blood-edge relationship graph. The index name of index metadata to be subjected to blood edge analysis is target data, the operation rule of the index metadata comprises index data 1 and index data 2, a user can trigger the expansion control 301 to expand upper metadata representing the target data through operations such as clicking, double clicking, long pressing and the like, if the upper metadata also has the upper metadata, the user can trigger the expansion control corresponding to the upper metadata through the same operations until all the upper metadata are metadata of basic data, and the expansion control cannot be further expanded. In addition, the user may trigger again for the triggered control to switch its display state so that the superior metadata is no longer displayed. Different colors can be used for distinguishing between the metadata control corresponding to the basic data and the metadata control corresponding to the index data, for example, blue marks can be used for the basic data metadata control 303 corresponding to the basic data 1, and orange marks can be used for the index data metadata control 302 corresponding to the target data. Each metadata control may be repeatedly triggered in response to a single click, double click, long press, etc. operation by the user to reveal/close specific text information that reveals metadata.

And S140, storing the index metadata in an index metadata base.

In some embodiments, before saving the obtained index metadata in the index metadata base, it is further necessary to perform a step of examining the index metadata:

uploading index metadata to a data auditing platform so that the data auditing platform generates auditing comments on the index metadata to facilitate adjustment of the constructed index metadata, and the index metadata more accords with actual data analysis requirements, and the auditing process comprises the following steps of;

when the audit opinion indicates that the audit is not passed, the index metadata can be adjusted according to the audit opinion, and the step is skipped: uploading index metadata to a data auditing platform;

and when the audit opinion indicates that the audit is passed, storing the index metadata in an index metadata database.

The specific steps of the inspection process are substantially identical to the inspection steps A1-A3 of the data analysis requirements, and are not described herein.

In the embodiment, the index metadata are audited so that the user can adjust the index metadata, so that the user can construct more reasonable index metadata, the requirement of actual data analysis is met, and the data analysis efficiency is improved.

And S150, responding to the triggering operation of the user, and acquiring index metadata corresponding to the triggering operation in the index metadata base to perform data analysis.

In some embodiments, the index metadata stored in the index metadata database includes an analysis purpose, and the user performs a data analysis operation based on the data analysis purpose, where the data analysis purpose is different from the data analysis purpose in the data analysis requirement, and there is no series of operations performed by the user for auditing, constructing the index metadata, storing the index metadata, and the like. At this time, the trigger operation is an index metadata retrieval operation. As shown in fig. 4, the data analysis terminal provides an index data retrieval interface including a keyword reception window 401, a filtering area 402, a selection area 403, and a presentation area 404. The keyword receiving window 401 is configured to facilitate a user to input keywords related to data analysis purposes through the keyword window, where the keywords may be separated by using specific symbols, for example, a semicolon, a comma, and the data analysis terminal retrieves corresponding index metadata in the index metadata base according to the keywords input by the user, and the retrieval policy may be to determine whether the index metadata includes the input keywords; the filtering area 402 is used for further filtering index metadata obtained by a user through keyword searching; further, the user may select one of the index metadata selected in the selection area 403, and in response to the user's operation on the index metadata, the presentation area 404 presents the specific content of the index metadata of the selected index data; in addition, a blood-margin display control may be included in the display area 404, and a user may generate a blood-margin relationship graph in which the index metadata is displayed by the display interface by triggering the control. And the user actively obtains basic data required by operation according to the index metadata obtained by retrieval and the operation rule to obtain a numerical result of index data corresponding to the index metadata so as to realize the data analysis purpose of the user.

In other embodiments, the user performs a data analysis operation based on existing base data, as shown in fig. 5, and the data analysis terminal provides a data analysis interface, where the data analysis interface includes a base data area 501 and an index data area 502, where the index data area initially has only a first row and a column of display cells containing drop-down controls 503. The user first determines the basic data to be analyzed in the basic data area, the basic data can be imported into the data analysis terminal from the basic database, and because the metadata of the basic data comprise elements such as analysis dimension, statistical range and the like, at this time, the user clicks the drop-down control 503 to display a list of index data which can be formed based on the attribute information of the basic data, and further the user selects one index data, and the data analysis terminal can add related index data in the index data area according to the index metadata operation rule of the index data. Taking the target data of fig. 3 as an example, a user first imports base data 1, base data 2 and base data 3 in a base data area, each row representing a different value of these base data; the user clicks the drop-down control 503 and selects target data in the drop-down list, and the data analysis terminal adds index data 1 and index data 2 in the index data area according to the operation rule of the target data; further, each index data displays the index value obtained according to the operation rule in the display grid of the corresponding row of the column according to the operation rule and the specific value of all the basic data of a certain row; the user can then perform subsequent data analysis operations based on the obtained index values.

In the embodiment of the invention, the index metadata is easy for technicians to understand, and the normalized index metadata can be repeatedly used in various scenes, so that the technicians can quickly call the normalized index metadata according to the structure of the index metadata in a specific data analysis scene, further process the basic data by utilizing the information contained in the index metadata, and the efficiency of the technicians to cooperatively execute the data analysis task can be greatly improved.

In the embodiment of the present application, a specific embodiment of a data analysis method is also provided, as shown in fig. 6a, a specific flow of the data analysis method may include steps S601 to S610:

s601, a data analysis terminal acquires a data analysis requirement;

s602, the data analysis terminal sends the data analysis requirement to a data auditing platform;

s603, the data auditing platform audits the data analysis requirements and sends auditing intention to the data analysis terminal;

s604, the data analysis terminal acquires metadata from an index metadata base according to the data analysis requirement after the data analysis terminal passes the auditing;

s605, the data analysis terminal responds to user operation and constructs index metadata according to the metadata;

S606, the data analysis terminal sends the index metadata to a data auditing platform;

s607, the data auditing platform audits the index metadata and sends audit intention seeing data analysis terminals;

s608, the data analysis terminal stores the index metadata which pass the auditing in an index metadata database;

s609, updating analysis dimensions according to the index metadata by a dimension database;

and S610, the data analysis terminal responds to the user operation to acquire basic data from the basic database, and invokes the index metadata stored in the index metadata database to perform data analysis.

In the specific embodiment, after a data analysis terminal acquires a data analysis requirement, the data analysis requirement is sent to a data auditing platform; acquiring metadata from an index metadata base according to the data analysis requirement after the verification; constructing index metadata based on metadata, and sending the index metadata to a data auditing platform; storing the index metadata which pass the auditing into an index metadata database, and correspondingly updating the dimension database at the same time; and finally, acquiring basic data from a basic database according to the data analysis requirement, and calling the index metadata stored in the index metadata database to perform data analysis.

The auditing related process in this embodiment can be shown in fig. 6b, and it is obvious that multiple audits are required to form appropriate index metadata, so that the index metadata is online in the index metadata base, and the auditing process is beneficial to standardization of the index metadata, and is beneficial to improving the collaboration efficiency among data analysts.

As can be seen from the above, in this embodiment, by using the normalized index metadata to perform data analysis, a technician can quickly determine the most suitable index data according to the data analysis scene, so as to improve the efficiency of data analysis; in addition, the repeated auditing and standardized index metadata construction are beneficial to unifying standards, so that the index metadata have index multiplexing capability, the cooperation among data analysis personnel is facilitated, and the data analysis efficiency is further improved.

In order to better implement the above method, the embodiment of the present invention provides a data analysis device, which may be specifically integrated in an electronic device, where the electronic device may be a terminal, a server, or other devices.

For example, in the present embodiment, a description will be given in terms of a data analysis device, and a method of an embodiment of the present invention will be described in detail taking the data analysis device as an example of being specifically integrated in a data analysis terminal.

For example, as shown in fig. 7, the data analysis device 700 may include a requirement module 710, an acquisition module 720, a construction module 730, a storage module 740, and a calling module 750.

A requirement module 710, configured to obtain a data analysis requirement;

an obtaining module 720, configured to obtain metadata according to the data analysis requirement, where the metadata includes attribute information of data;

a construction module 730, configured to construct index metadata according to attribute information of the metadata;

a saving module 740, configured to save the index metadata in an index metadata database;

and the invoking module 750 is used for responding to the triggering operation of the user, acquiring index metadata corresponding to the triggering operation in the index metadata base and carrying out data analysis.

In some embodiments, the build module further comprises:

In some embodiments, the statistical range sub-module further comprises:

In some embodiments, the analysis dimension submodule further includes:

In some embodiments, the building sub-module further comprises:

In some embodiments, the data analysis method further comprises:

the building module is used for building a dimension database;

In some embodiments, the data analysis method further comprises:

In the implementation, each unit may be implemented as an independent entity, or may be implemented as the same entity or several entities in any combination, and the implementation of each unit may be referred to the foregoing method embodiment, which is not described herein again.

It can be known from the above that, in this embodiment, by analyzing the data analysis requirement, the appropriate index metadata is constructed, and the normalized index metadata is further used to perform the data analysis, so that a technician can quickly determine the most appropriate index metadata according to the data analysis scene where the technician is located, and automatically obtain the index data according to the operation rule and the basic data of the index metadata, thereby improving the efficiency of data analysis.

The embodiment of the invention also provides electronic equipment which can be a terminal, a server and other equipment.

For example, the terminal can be a mobile phone, a tablet computer, an intelligent Bluetooth device, a notebook computer, a personal computer and other devices; the server may be a single server or a server cluster composed of a plurality of servers.

In this embodiment, a detailed description will be given taking an example in which the electronic device of this embodiment is a server, for example, as shown in fig. 8, which shows a schematic structural diagram of the server according to an embodiment of the present invention, specifically:

the server may include components such as a processor 801 of one or more processing cores, a memory 802 of one or more computer-readable storage media, a power supply 803, an input module 804, and a communication module 805. Those skilled in the art will appreciate that the server architecture shown in fig. 8 is not limiting of the server and may include more or fewer components than shown, or may combine certain components, or a different arrangement of components. Wherein:

the processor 801 is a control center of the server, connects respective portions of the entire server using various interfaces and lines, and performs various functions of the server and processes data by running or executing software programs and/or modules stored in the memory 802 and calling data stored in the memory 802, thereby performing overall monitoring of the server. In some embodiments, processor 801 may include one or more processing cores; in some embodiments, the processor 801 may integrate an application processor that primarily processes operating systems, user pages, applications, and the like, with a modem processor that primarily processes wireless communications. It will be appreciated that the modem processor described above may not be integrated into the processor 801.

The memory 802 may be used to store software programs and modules, and the processor 801 executes various functional applications and data processing by executing the software programs and modules stored in the memory 802. The memory 802 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program (such as a sound playing function, an image playing function, etc.) required for at least one function, and the like; the storage data area may store data created according to the use of the server, etc. In addition, memory 802 may include high-speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid-state storage device. Accordingly, the memory 802 may also include a memory controller to provide the processor 801 with access to the memory 802.

The server also includes a power supply 803 for powering the various components, and in some embodiments, the power supply 803 may be logically coupled to the processor 801 via a power management system such that charge, discharge, and power consumption management functions are performed by the power management system. The power supply 803 may also include one or more of any components, such as a direct current or alternating current power supply, a recharging system, a power failure detection circuit, a power converter or inverter, a power status indicator, and the like.

The server may also include an input module 804, which input module 804 may be used to receive entered numeric or character information and to generate keyboard, mouse, joystick, optical or trackball signal inputs related to user settings and function control.

The server may also include a communication module 805, and in some embodiments the communication module 805 may include a wireless module, through which the server may wirelessly transmit over short distances, thereby providing wireless broadband internet access to the user. For example, the communication module 805 may be used to assist a user in e-mail, browsing web pages, accessing streaming media, and the like.

Although not shown, the server may further include a display unit or the like, which is not described herein. In this embodiment, the processor 801 in the server loads executable files corresponding to the processes of one or more application programs into the memory 802 according to the following instructions, and the processor 801 executes the application programs stored in the memory 802, so as to implement various functions as follows:

acquiring data analysis requirements;

storing the index metadata in an index metadata base;

The specific implementation of each operation above may be referred to the previous embodiments, and will not be described herein.

Those of ordinary skill in the art will appreciate that all or a portion of the steps of the various methods of the above embodiments may be performed by instructions, or by instructions controlling associated hardware, which may be stored in a computer-readable storage medium and loaded and executed by a processor.

To this end, embodiments of the present invention provide a computer readable storage medium having stored therein a plurality of instructions capable of being loaded by a processor to perform the steps of any of the data analysis methods provided by the embodiments of the present invention. For example, the instructions may perform the steps of:

Acquiring data analysis requirements;

storing the index metadata in an index metadata base;

The instructions stored in the storage medium may perform steps in any data analysis method provided by the embodiments of the present invention, so that the beneficial effects that any data analysis method provided by the embodiments of the present invention can be achieved are detailed in the previous embodiments, and are not repeated herein.

The foregoing has described in detail a method and apparatus for data analysis provided by embodiments of the present invention, and specific examples have been set forth herein to illustrate the principles and embodiments of the present invention, the above description of embodiments being only for the purpose of aiding in the understanding of the method and core ideas of the present invention; meanwhile, as those skilled in the art will have variations in specific embodiments and application scope in light of the ideas of the present invention, the present disclosure should not be construed as limiting the present application.

Claims

1. A data analysis method applied to a terminal device, comprising:

acquiring data analysis requirements;

storing the index metadata in an index metadata base;

2. The data analysis method as claimed in claim 1, wherein the attribute information includes a statistical range and an analysis dimension, and the constructing index metadata from the attribute information of the metadata includes:

determining an operation rule of target index data in response to a first operation of a user;

determining a statistical range of the metadata;

determining an analysis dimension of the metadata;

determining an index name in response to a second operation of the user, wherein the index name is the name of target index data;

and constructing index metadata of target index data based on the index name, the operation rule, the statistical range and the analysis dimension.

3. A method of data analysis according to claim 2, wherein said determining the statistical range of the metadata comprises:

intersecting and merging any one of the statistical ranges of the metadata based on the operation rule to obtain a range analysis result;

and determining the range analysis result as the statistical range of the metadata.

4. A method of data analysis according to claim 2, wherein said determining the analysis dimension of the metadata comprises:

any processing of intersecting, merging and updating the analysis dimensionality of the metadata is carried out on the basis of the operation rule, so that a dimensionality analysis result is obtained;

and determining the dimension analysis result as the analysis dimension of the metadata.

5. The data analysis method according to claim 2, wherein the constructing the index metadata of the target index data based on the index name, the operation rule, the statistical range, and the analysis dimension includes:

acquiring a preset assembly sequence;

and assembling the index names, the operation rules, the statistical ranges and the analysis dimensions according to the preset assembly sequence to generate index metadata.

6. A data analysis method according to claim 2, wherein before said obtaining metadata according to said data analysis requirements, comprising:

establishing a dimension database;

after the index metadata is constructed according to the attribute information of the metadata, the method comprises the following steps:

and storing the analysis dimension of the index metadata in the dimension database.

7. The data analysis method of claim 1, wherein after constructing index metadata from attribute information of the metadata, comprising:

performing blood-margin analysis on the index metadata to determine all upstream metadata constituting the index metadata;

generating a blood relationship graph based on the upstream metadata and the index metadata;

and responding to the display operation of the user, generating a display interface to display the blood relationship graph.

8. A data analysis device, comprising:

the demand module is used for acquiring data analysis demands;

9. A terminal comprising a processor and a memory, the memory storing a plurality of instructions; the processor loads instructions from the memory to perform the steps in the data analysis method as claimed in any one of claims 1 to 7.

10. A storage medium storing a plurality of instructions adapted to be loaded by a processor to perform the steps of the data analysis method of any one of claims 1 to 7.