CN111553556A

CN111553556A - Business data analysis method and device, computer equipment and storage medium

Info

Publication number: CN111553556A
Application number: CN202010235048.6A
Authority: CN
Inventors: 郭凌峰; 黄北辰; 杨镭
Original assignee: OneConnect Financial Technology Co Ltd Shanghai
Current assignee: OneConnect Smart Technology Co Ltd; OneConnect Financial Technology Co Ltd Shanghai
Priority date: 2020-03-27
Filing date: 2020-03-27
Publication date: 2020-08-18

Abstract

The embodiment of the application belongs to the field of big data and discloses a business data analysis method and device, computer equipment and a storage medium. The method comprises the steps of obtaining and analyzing data to be analyzed to obtain at least one field for analyzing business data; converting the field into field information in an SQL statement, taking an index field in the field information as a parent index, and matching an index hierarchical relation table and an index algorithm relation table corresponding to the index field; acquiring sub-level indexes from the lower layer based on the index hierarchical relation table until a terminal index of the bottommost layer is obtained; and when data analysis is carried out, acquiring the data value of each end point index from the database, calculating the data value of the index of the upper level layer by layer according to the index algorithm relation table until the data value of the index field is acquired, and completing the service data analysis. The method and the device can automatically configure the indexes and perform flow calculation, and efficiency of analyzing the service data aiming at the indexes is improved.

Description

Business data analysis method and device, computer equipment and storage medium

Technical Field

The present application relates to the field of big data technologies, and in particular, to a method and an apparatus for analyzing business data, a computer device, and a storage medium.

Background

The statistical indexes are concepts and numerical values reflecting the overall performance characteristics of the business data, for example, hundreds of indexes such as the living asset proportion, each stock of net assets and the like form a financial index system of an enterprise, and the conditions of aspects such as the enterprise assets, the liability ratio, the profitability and the like are reflected, so that how to view and analyze each index becomes one of the key points of the business data analysis of the enterprise.

At present, in the business of each large enterprise, each index usually exists in an offline manual form, enterprise departments spend a large amount of manpower for collection every month, management confusion and non-uniform access apertures are easily caused, and further analysis is difficult to perform on an Excel table.

However, in the existing online BI tool (Business Intelligence analysis tool), for example, BDP, Tableau and the like are mostly used as fields, and a user needs to manually configure a drill-down path, otherwise, operations such as drill-down or scroll-up cannot be performed, and due to the huge number of indexes of the financial system of an enterprise, the operation amount is extremely large and the configuration time is long due to independent configuration, so that the Business data analysis efficiency is low.

Disclosure of Invention

An embodiment of the present application provides a method and an apparatus for analyzing business data, a computer device, and a storage medium, which improve the efficiency of analyzing business data.

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

a business data analysis method comprises the following steps:

acquiring data to be analyzed input by a user, and analyzing the data to be analyzed to obtain at least one field for analyzing business data;

converting the field into field information in an SQL statement, wherein the field information at least comprises an index field, taking the index field as a parent index, and matching a pre-created index hierarchical relation table and an index algorithm relation table according to the parent index;

acquiring all sub-level indexes corresponding to the parent-level indexes based on the index hierarchy relation table;

taking the obtained sub-level indexes as new parent-level indexes, repeating the process of obtaining the sub-level indexes based on the index hierarchy relation table until the new sub-level indexes cannot be obtained, and taking all the indexes which cannot obtain the sub-level indexes as end point indexes;

and accessing a database based on the SQL statement, acquiring the data value of each end point index from the database, calculating the data value of the index of the upper level layer by layer according to the data value of each end point index and the index algorithm relation table until acquiring the data value of the index field, and completing business data analysis.

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

a business data analysis apparatus comprising:

the field obtaining module is used for obtaining data to be analyzed input by a user and analyzing the data to be analyzed to obtain at least one field for analyzing the service data;

a parent index obtaining module, configured to convert the field into field information in an SQL statement, where the field information at least includes one index field, and the index field is used as a parent index and matches a pre-created index hierarchical relationship table and an index algorithm relationship table according to the parent index;

a sub-level index obtaining module, configured to obtain all sub-level indexes corresponding to the parent-level indexes based on the index hierarchy relation table;

an end point index obtaining module, configured to use the obtained sub-level index as a new parent-level index, repeat the process of obtaining the sub-level index based on the index hierarchy relation table until the new sub-level index cannot be obtained, and use all indexes that cannot obtain the sub-level index as end point indexes;

and the data analysis module is used for accessing a database based on the SQL statement, acquiring the data value of each end point index from the database, calculating the data value of the index of the upper level layer by layer according to the data value of each end point index and the index algorithm relation table until acquiring the data value of the index field, and completing business data analysis.

In order to solve the foregoing technical problem, an embodiment of the present application further provides a computer device, which includes a memory and a processor, where the memory stores a computer program, and the processor implements the steps of the service data analysis method when executing the computer program.

In order to solve the above technical problem, an embodiment of the present application further provides a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the computer program implements the steps of the business data analysis method described above.

According to the business data analysis method, the device, the computer equipment and the storage medium, the field for business data analysis is obtained by obtaining and analyzing the data to be analyzed input by the user, and the index field is extracted to obtain the parent index, so that the parent index is automatically extracted from the data to be analyzed; matching a pre-created index level relation table and an index algorithm relation table according to the parent indexes to obtain all child indexes corresponding to the parent indexes; the acquired sub-level indexes are used as new parent-level indexes to acquire sub-level indexes until the new sub-level indexes cannot be acquired, and a plurality of end point indexes are acquired, so that automatic configuration of the indexes is realized, and configuration efficiency is improved; when data analysis is carried out, the data value of each end point index is obtained from the database, the data value of the index of the upper level is calculated layer by combining the index algorithm relation table, the service data analysis can be completed when the data value of the index field is obtained, and the efficiency of the service data analysis is improved through the process calculation.

Drawings

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

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

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

FIG. 3 is a flowchart of one embodiment of step S202 of FIG. 2;

FIG. 4 is a diagram of an index hierarchy table in one embodiment;

FIG. 5 is a flowchart of one embodiment of step S202 in FIG. 2;

FIG. 6 is a flowchart of one embodiment after step S205 in FIG. 2;

FIG. 7 is a schematic illustration of data analysis according to the present application;

FIG. 8 is a schematic block diagram of an embodiment of a business data analysis apparatus according to the present application;

FIG. 9 is a schematic block diagram of one embodiment of a computer device according to the present application.

Reference numerals: 401 field get module; 402 a parent index obtaining module; 403, a sublevel index obtaining module; 404 an endpoint indicator obtaining module; 405 a data analysis module.

Detailed Description

Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs; the terminology used in the description of the application herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application; the terms "including" and "having," and any variations thereof, in the description and claims of this application and the description of the above figures are intended to cover non-exclusive inclusions. The terms "first," "second," and the like in the description and claims of this application or in the above-described drawings are used for distinguishing between different objects and not for describing a particular order.

Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.

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

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

terminal devices

101, 102, 103, a network 104, and a server 105. The network 104 serves as a medium for providing communication links between the

terminal devices

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

The user may use the

terminal devices

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

terminal devices

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

The

terminal devices

101, 102, 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, e-book readers, MP3 players (Moving Picture experts Group Audio Layer III, mpeg compression standard Audio Layer 3), MP4 players (Moving Picture experts Group Audio Layer IV, mpeg compression standard Audio Layer 4), laptop portable computers, desktop computers, and the like.

The server 105 may be a server providing various services, such as a background server providing support for pages displayed on the

terminal devices

101, 102, 103.

It should be noted that, the service data analysis method provided in the embodiment of the present application is generally executed by a server, and accordingly, the service data analysis apparatus is generally disposed in the server.

It should be understood that the number of terminal devices, networks, and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.

With continuing reference to FIG. 2, a flow diagram of one embodiment of a business data analysis method in accordance with the present application is shown. The business data analysis method comprises the following steps:

step S201, acquiring data to be analyzed input by a user, and analyzing the data to be analyzed to obtain at least one field for analyzing the service data.

The data to be analyzed may be text data input by a user through the terminal device.

Specifically, the user inputs data to be analyzed at the terminal device. The server receives the data to be analyzed sent by the terminal device through the network 104. The server can analyze the data to be analyzed in the modes of word segmentation, part of speech tagging, named entity identification and the like to obtain at least one field for analyzing the business data.

In one embodiment, the server may process the text data based on StanfordNLP, split nouns, adjectives, verbs, and the like in the text data. StanfordNLP is a Natural Language Processing (NLP) toolkit released by the StanfordNLP team that contains multi-Language pre-training models, which supports a complete text analysis pipeline for multiple languages, including word segmentation, part-of-speech tagging, word shape merging, and dependency resolution. The server carries out part-of-speech tagging on the adjectives and verbs so as to identify the structure of the sentences in the text data; wherein the sentence structure may be at least one of a host predicate structure, a bingo structure, and a host predicate structure. The server performs named entity recognition on the noun to determine whether the noun is a specific identification word such as some time, place name or name.

In one embodiment, the server may split and label a statement entity in the data to be analyzed through the BERT model, thereby identifying a field of the data to be analyzed. The BERT model (collectively called Bidirectional encoding retrieval from transforms) is a natural language processing model issued by Google, and has high accuracy in NLP application.

In one embodiment, the step of analyzing the data to be analyzed to obtain at least one field for analyzing the service data specifically includes: performing word segmentation on data to be analyzed to obtain a plurality of fields; performing semantic annotation on the fields according to a semantic knowledge base to generate a semantic annotation result; determining the dependency relationship of a plurality of fields based on the semantic annotation result; and determining at least one field used for business data analysis in the plurality of fields according to the dependency relationship.

Specifically, the server performs word segmentation on data to be analyzed to obtain a plurality of fields. And the server inquires the field obtained by word segmentation in the semantic knowledge base, and performs semantic annotation to obtain a semantic annotation result. The semantic annotation result can comprise the meaning and the part of speech of the field, and the dependency relationship between the fields can be determined, so that at least one field used for business data analysis is determined from the fields.

The semantic knowledge base can be a dictionary set generated based on habitual expression in natural language; the semantic knowledge base can help a computer to quickly locate the content corresponding to the information input by the user.

For example, the data to be analyzed is "i want to inquire yesterday's data", which the server can disassemble into "i", "want", "inquire", "yesterday's" and "data" through the StanfordNLP tool; according to the semantic knowledge base, "me" and "data" belong to nouns, "want" and "query" belong to verbs, and "yesterday" belongs to adjectives; the query is followed by the query's purpose, which is depended on by the query's data, followed by fields including, but not limited to, data, pages, scores, prices, etc., which represent specific content, and in turn, results that the user wants to obtain. The server determines three fields for business data analysis, namely 'query', 'yesterday' and 'data', through the semantics of the fields.

In the embodiment, the purpose of business data analysis can be determined by performing word segmentation and semantic annotation on the data to be analyzed, so that the normal operation of business data analysis is ensured.

In one embodiment, the plurality of fields are divided into index fields and range fields by semantic annotation results; the index field is a target of business data analysis, and the range field is used for indicating the server to acquire a data value from the database for business data analysis.

The step of obtaining the data value of each endpoint indicator from the database comprises: and reading the data value meeting each end point index of the range field from the database according to the range field.

Specifically, the semantic annotation result includes the semantics of the field, and the server can determine the purpose of inputting the data to be analyzed by the user through the semantics and classify the field into an index field and a range field according to the semantics. The index field is a field which is determined through semantic analysis and needs to be subjected to business data analysis, the range field limits the range of time, region or organization, when specific business data analysis is carried out, the index field serves as a parent index and can be drilled down to at least one end point index, and the server reads data values of all end point indexes meeting the range field from the database according to the range field to carry out analysis and calculation.

For example, the data to be analyzed is "deposit business flow information of the last half bank of 2019", 4 fields can be generated: the service data analysis method comprises the steps of '2019', 'first half year', 'saving service' and 'streamline information', wherein the '2019' and the 'first half year' are range fields, and when a server analyzes service data, the server can obtain data values of the first half year of 2019 according to the range fields without obtaining data values of other times; the 'deposit business' and 'running water information' are index fields and are targets of business data analysis. In this embodiment, the index field is divided into the index field and the range field, so that the service data analysis is performed on the index field according to the range field, and the accuracy of the service data analysis is improved.

Step S202, converting the field into field information in the SQL statement, wherein the field information at least comprises one index field, taking the index field as a parent index, and matching a pre-created index hierarchical relation table and an index algorithm relation table according to the parent index.

Among them, SQL (Structured Query Language) is a database Query and programming Language for accessing data and querying, updating, and managing a relational database system. The index hierarchical relation table can be obtained based on the index algorithm relation table, and records the relation among different indexes; the index algorithm relation table can be a table for recording operation relation among indexes.

Specifically, the server converts the field information obtained by parsing into a code of the SQL statement according to the syntax of the SQL statement, that is, the field information in the SQL statement, where the field information includes at least one index field. The server takes the index field as the parent index.

The database stores established index hierarchical relation tables and index algorithm relation tables, and the server inquires the index hierarchical relation tables and the index algorithm relation tables matched with the parent-level indexes.

For example, the data to be analyzed is "the savings business running information of the bank in the first half of 2019", and the server analyzes the data to obtain the fields "2019", "the first half of the year", "the savings business" and "the running information". The server can determine the intention of the data to be analyzed through semantic analysis to inquire the running water in the deposit business of the bank in the time period from 1/2019 to 6/30/2019, wherein the running water (B) is a deposit-withdrawal condition.

The server converts the data to be processed into sql statements, and obtains field information by taking a time period as a query condition and taking a running water B as a result value of the query:

select B from tb_a where time between 20190101 and 20190630.

where B denotes pipelining, tb _ a denotes savings business record data, time denotes time, 20190101 and 20190630 denote two time nodes.

Step S203, all the sub-level indexes corresponding to the parent-level indexes are obtained based on the index hierarchy relation table.

Specifically, the relationship between different indexes is described in the index hierarchy relationship table. And the server inquires the parent-level indexes in the index hierarchical relation table and extracts all the child-level indexes corresponding to the inquired parent-level indexes. The parent-level index is obtained by computing the child-level index.

For example, the data to be analyzed is "the saving business running information of the bank in the first half of 2019", the server takes the saving business "and the running information" as a first parent-level index, stores and takes out the running information (B) in the bank saving business according to the index hierarchical relation table, takes the running information B as a parent-level index, and takes the child-level indexes of the running information B as the storage amount and the taking amount.

And step S204, taking the obtained sub-level indexes as new parent-level indexes, repeating the process of obtaining the sub-level indexes based on the index hierarchical relation table until the new sub-level indexes cannot be obtained, and taking all the indexes which cannot be obtained as the sub-level indexes as end point indexes.

Specifically, for each acquired sub-level index, the server takes the sub-level index as a new parent-level index, acquires the sub-level index of the new parent-level index based on the index hierarchical relation table, and repeats the above operations until the new sub-level index can not be acquired. And the server takes the index which can not obtain the sublevel index any more as the end point index.

For example, if the remaining amount of a certain commodity is the deposit amount-the shipment amount + the original amount, the remaining amount is the parent level indicator, and the deposit amount, the shipment amount, and the original amount are the child level indicators. The following steps are provided: and taking the sub-level index storage amount as a parent level index, and further acquiring a new sub-level index X index storage amount and a new y index storage amount. The new sub-level indexes (the X index storage amount and the y index storage amount) can not be used as parent indexes to carry out next-level index acquisition, and the acquired sub-level indexes (the X index storage amount and the y index storage amount) are end point indexes.

Step S205, accessing the database based on SQL statements, obtaining the data value of each end point index from the database, and calculating the data value of the index of the upper level layer by layer according to the data value of each end point index and the index algorithm relation table until obtaining the data value of the index field, and completing the service data analysis.

Specifically, the server acquires the data value of each end point index from the database, takes the end point index as a starting point, takes the parent-level index in the index field as an end point, and calculates the upper-level index step by step based on the index algorithm relation until the data value of the parent-level index in the index field is obtained through calculation, thereby completing the service data analysis. When the server needs to access the database in the calculation process, the server can access the database based on the SQL statement.

In one embodiment, the server sends the data value of the parent index to the specified terminal for presentation.

In the embodiment, a field for analyzing business data is obtained by obtaining and analyzing data to be analyzed input by a user, and a parent index is obtained by extracting an index field, so that the parent index is automatically extracted from the data to be analyzed; matching a pre-created index level relation table and an index algorithm relation table according to the parent indexes to obtain all child indexes corresponding to the parent indexes; the acquired sub-level indexes are used as new parent-level indexes to acquire sub-level indexes until the new sub-level indexes cannot be acquired, and a plurality of end point indexes are acquired, so that automatic configuration of the indexes is realized, and configuration efficiency is improved; when data analysis is carried out, the data value of each end point index is obtained from the database, the data value of the index of the upper level is calculated layer by combining the index algorithm relation table, the service data analysis can be completed when the data value of the index field is obtained, and the efficiency of the service data analysis is improved through the process calculation.

Referring to fig. 3, in some alternative implementations of the present embodiment, after step 201 and before step 202, the server may further perform the following steps:

step S301, index identification is obtained.

The index identifier may be an identifier of the index, such as a name of the index.

Specifically, the server may obtain the index identifier from a database or a terminal device, and the index identifier may include identifiers of various indexes.

Step S302, receiving the algorithm relation aiming at the index identification.

The algorithm relationship may be a calculation manner between the index identifiers, and the algorithm relationship may be presented in the form of a formula. The user can customize the algorithm relationship between the index identifications in the visual configuration page and send the algorithm relationship to the server. The server receives an algorithmic relationship identified for the metric.

Step S303, an index algorithm relation table is created according to the algorithm relation.

Specifically, the server creates an index algorithm relationship table for storing all received algorithm relationships.

And step S304, creating an index hierarchical relation table according to the index algorithm relation table.

Specifically, the server may determine a hierarchical relationship between indexes corresponding to the index identifier according to the index algorithm relationship table, and create an index hierarchical relationship table for storing the determined hierarchical relationship between the indexes.

In one embodiment, creating the index hierarchical relationship table from the index algorithm relationship table comprises: acquiring algorithm relation information in an index algorithm relation table; identifying a first index and a second index in the obtained algorithm relation information; the first index is obtained by the second index through operation; determining parent-child relationship information of the first index and the second index; in the parent-child relationship information, the first index is a parent index of the second index, and the second index is a child index of the first index; and obtaining an index hierarchical relation table according to the determined parent-child relation information.

Specifically, the index algorithm relationship table may be composed of a piece of algorithm relationship information, and the algorithm relationship information records the operation relationship between the indexes. The server identifies a first index and a second index in the algorithm relation information, wherein the first index is obtained by calculating other indexes, and the second index is an index participating in calculation, namely the first index is obtained by calculating the second index. In one piece of algorithm relation information, the number of the first index and the second index may be more than one.

The server determines the parent-child relationship information of the first index and the second index, wherein in the parent-child relationship information, the first index is the parent index of the second index, and the second index is the child index of the first index. And the server can obtain an index hierarchical relation table according to all the parent-child relation information.

In the embodiment, the parent-child relationship information among the indexes is determined according to the algorithm relationship table, and the index hierarchical relationship table is obtained according to the parent-child relationship information, so that the accuracy of the generated index hierarchical relationship table is ensured.

In one embodiment, when a new index algorithm relationship is added, deleted, or updated, the index hierarchy relationship table needs to be updated accordingly. When a specific algorithm relation is deleted, the hierarchical relation under the algorithm relation does not exist any more, and a plurality of indexes related to the algorithm relation in the hierarchical relation table are deleted; when a specific algorithm relation is newly added, the algorithm relation generates a new hierarchical relation, and a plurality of indexes related to the algorithm relation are written into a hierarchical relation table according to the hierarchical relation.

For example, there are three indicators: shipment volume, deposit volume and remaining volume. The user defines equation (1):

the residual amount is the amount of stock, the amount of shipment and the original amount

The formula (1) is an index algorithm relation, and the server stores the formula (1) in an index algorithm relation table. It can be seen that the index algorithm relationship table represents a series of index algorithm relationship sets. Table 1 may be an index algorithm relationship table in one embodiment:

an index hierarchical relationship table created according to the index algorithm relationship table 1 is shown in fig. 4. An example of creating or updating an index hierarchical relationship based on an index algorithm relationship table is as follows: as can be seen from the formula (1), the remaining amount is calculated from the amount of stock, the amount of shipment and the original amount. Therefore, the residual amount is used as an upper-layer index in the index hierarchical relation table, and the stored amount, the shipped amount and the original amount are used as a lower-layer index of the residual amount in the index hierarchical relation table.

If the algorithm relation (1) is deleted, the formula (1) corresponding to the index algorithm relation is meaningless, and the relation between the upper-layer index and the lower-layer index does not exist among the residual quantity, the stored quantity and the original quantity of the shipment quantity; if the hierarchical relationship already exists in the hierarchical relationship table, the indexes contained in the formula (1) are deleted.

If the indexes in the algorithm relation (1) are updated as follows: and if the water flow is the deposit amount and the shipment amount, adding a new index into the upper-layer index of the deposit amount and the shipment amount, writing the index of the water flow into an index hierarchical relation table, and placing the index at the upper layer of the deposit amount and the shipment amount.

Simultaneously, an algorithm relation is obtained: the remaining amount is the flow rate + the original amount. Therefore, a new hierarchical relationship should be created, with the remaining amount being level one, the flow amount and the original amount being level two, and the deposit amount and the shipment amount being level three.

The hierarchical relationship table indicates the hierarchical relationship between the indexes, but the hierarchical levels of the indexes are not fixed, and the hierarchical levels are continuously created or updated based on the updating of the index algorithm relationship.

In the embodiment, the index algorithm relation table is created according to the algorithm relation among the indexes, and the index hierarchical relation table is created according to the index algorithm relation table, so that the speed and the accuracy of creating the index hierarchical relation table are guaranteed.

Referring to fig. 5, in some alternative implementations of the present embodiment, step S202: the specific steps of matching the pre-created index hierarchical relationship table and the index algorithm relationship table according to the parent-level indexes comprise:

step S2021: and matching a first algorithm corresponding to the parent-level index from the index algorithm relation table.

Specifically, after determining the parent index, the server reads the index algorithm relation table, queries the parent index from the index algorithm relation table, and extracts a first algorithm related to the parent index.

In one embodiment, the server matches a first algorithm corresponding to the parent index from the index algorithm relationship table according to the text matching.

Step S2022: according to a first algorithm, an index hierarchical relation table corresponding to a parent index is matched.

Specifically, the server obtains the child indexes of the parent indexes according to a first algorithm, so as to obtain an index hierarchy relation table corresponding to the parent indexes.

In this embodiment, the first algorithm corresponding to the parent index is matched from the index algorithm relationship table, and then the index hierarchy relationship table corresponding to the parent index is matched, so that the matching speed is ensured.

Referring to fig. 6, in some alternative implementations of the present embodiment, the step of calculating the data value of the index of the upper stage layer by layer according to the data value of each endpoint index and the index algorithm relation table in step 205 further includes:

and step S206, after each calculation is carried out according to the index algorithm relation table, judging whether the data value corresponding to the current index obtained by calculation is within a preset index value interval.

Specifically, each index is preset with an index value interval, and the value range of the data value corresponding to the index is recorded. And after finishing the calculation of one index according to the index algorithm relation table, the server compares the data value corresponding to the current index obtained by calculation with the index value interval and judges whether the data value obtained by calculation falls in the index value interval.

In one embodiment, the index value interval is set by the user according to a desired value for the index.

Step S207, if not, the current index is marked as an abnormal index, and the current index is displayed after the data analysis is finished.

Specifically, when the calculated data value corresponding to the current index is not within the index value interval, the server marks the current index as an abnormal index. After the service data analysis is completed, the server counts all indexes marked as abnormal indexes, generates an abnormal index table according to the index values and the index value intervals of the abnormal indexes, and sends the abnormal index table to the specified terminal equipment for displaying so that a user can perform data analysis, drill down analysis and the like. When the calculated data value corresponding to the current index is within the index value interval, the current index is within a preset expected range, and the current index is normal without special marking and feedback.

On the basis of the parent-level indexes, the child-level indexes of each index are continuously acquired, and all the indexes can exist in the form of an index tree. And when the server calculates the parent indexes in the index field from the terminal indexes, counting all the indexes participating in calculation, displaying the index tree in a visual mode through the terminal equipment, and highlighting the indexes participating in calculation to display the path of data analysis.

Fig. 7 is a schematic diagram showing data analysis by a terminal device in an embodiment, specifically, referring to fig. 7, an ROE (Return on demand, ROE for short, net asset profitability) in month of july is a finally calculated index, where the index ROE is a parent index of an index ROA (Return 0n Assets, ROA for short, asset profitability) and an index asset/rights; the ROA index is a parent index that targets net interest income/assets, non-interest income ratio and cost income ratio. The server calculates the ROA of July according to the non-interest income ratio and the cost-income ratio of July, and then obtains the ROE of July according to the ROA of July, and the indexes participating in the calculation are shown in dark colors.

In this embodiment, the server determines whether the calculated data value of the current index meets expectations, and if not, the current index is marked as an abnormal index and displayed, so as to perform further analysis, thereby improving the accuracy of data analysis.

It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and can include the processes of the embodiments of the methods described above when the computer program is executed. The storage medium may be a non-volatile storage medium such as a magnetic disk, an optical disk, a Read-Only Memory (ROM), or a Random Access Memory (RAM).

It should be understood that, although the steps in the flowcharts of the figures are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and may be performed in other orders unless explicitly stated herein. Moreover, at least a portion of the steps in the flow chart of the figure may include multiple sub-steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, which are not necessarily performed in sequence, but may be performed alternately or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

With further reference to fig. 8, as an implementation of the method shown in fig. 2, the present application provides an embodiment of a service data analysis apparatus, where the embodiment of the apparatus corresponds to the embodiment of the method shown in fig. 2, and the apparatus may be specifically applied to various electronic devices.

As shown in fig. 7, the service data analysis apparatus 400 according to this embodiment includes: a field obtaining module 401, a parent index obtaining module 402, a child index obtaining module 403, an end point index obtaining module 404, and a data analysis module 405. Wherein:

the field obtaining module 401 is configured to obtain data to be analyzed input by a user, analyze the data to be analyzed, and obtain at least one field used for analyzing service data.

A parent index obtaining module 402, configured to convert the field into field information in the SQL statement, where the field information at least includes one index field, and the index field is used as a parent index, and matches a pre-created index hierarchical relationship table and an index algorithm relationship table according to the parent index.

A sub-level index obtaining module 403, configured to obtain all sub-level indexes corresponding to parent-level indexes based on the index hierarchical relationship table.

An end point index obtaining module 404, configured to use the obtained sub-level index as a new parent-level index, repeat the process of obtaining the sub-level index based on the index hierarchy relation table until the new sub-level index cannot be obtained, and use all indexes that cannot be obtained as the end point indexes.

And a data analysis module 405, configured to access the database based on the SQL statement, obtain the data value of each endpoint indicator from the database, and calculate the data value of the upper-level indicator layer by layer according to the data value of each endpoint indicator and the indicator algorithm relation table until obtaining the data value of the indicator field, thereby completing service data analysis.

In some optional implementation manners of this embodiment, the field obtaining module 401 is further configured to perform word segmentation on the data to be analyzed to obtain a plurality of fields; performing semantic annotation on the fields according to a semantic knowledge base to generate a semantic annotation result; determining the dependency relationship of a plurality of fields based on the semantic annotation result; and determining at least one field used for business data analysis in the plurality of fields according to the dependency relationship.

In some optional implementation manners of this embodiment, the field obtaining module 401 is further configured to divide the plurality of fields into an index field and a range field according to a semantic annotation result; the index field is a target of business data analysis, and the range field is used for indicating the server to acquire a data value from the database for business data analysis. The step of obtaining the data value of each endpoint indicator from the database comprises: and reading the data value meeting each end point index of the range field from the database according to the range field.

In this embodiment, the index field is divided into the index field and the range field, so that the service data analysis is performed on the index field according to the range field, and the accuracy of the service data analysis is improved.

In some optional implementations of this embodiment, the service data analysis apparatus 400 further includes: the system comprises an identification acquisition module, an algorithm receiving module, an algorithm relation creating module and a hierarchical relation creating module, wherein:

and the identifier acquisition module is used for acquiring the index identifier.

And the algorithm receiving module is used for receiving the algorithm relation aiming at the index identification.

And the algorithm relation creating module is used for creating an index algorithm relation table according to the algorithm relation.

And the hierarchical relation creating module is used for creating an index hierarchical relation table according to the index algorithm relation table.

In some optional implementation manners of this embodiment, the hierarchical relationship creating module is further configured to obtain algorithm relationship information in the index algorithm relationship table; identifying a first index and a second index in the obtained algorithm relation information; the first index is obtained by the second index through operation; determining parent-child relationship information of the first index and the second index; in the parent-child relationship information, the first index is a parent index of the second index, and the second index is a child index of the first index; and obtaining an index hierarchical relation table according to the determined parent-child relation information.

In some optional implementation manners of this embodiment, the parent index obtaining module 402 is further configured to match a first algorithm corresponding to the parent index from the index algorithm relation table; according to a first algorithm, an index hierarchical relation table corresponding to a parent index is matched.

In some optional implementations of this embodiment, the service data analysis apparatus 400 further includes: data judge module and index display module, wherein:

and the data judgment module is used for judging whether the data value corresponding to the current index obtained by calculation is within a preset index value interval or not after each calculation is carried out according to the index algorithm relation table.

And the index display module is used for marking the current index as an abnormal index when the data value corresponding to the current index is not in the preset index value interval, and displaying the current index after the data analysis is finished.

In order to solve the technical problem, an embodiment of the present application further provides a computer device. Referring to fig. 8, fig. 8 is a block diagram of a basic structure of a computer device according to the present embodiment.

The computer device 9 comprises a memory 51, a processor 52, a network interface 53 communicatively connected to each other via a system bus. It is noted that only a computer device 5 having components 51-53 is shown, but it is understood that not all of the shown components are required to be implemented, and that more or fewer components may be implemented instead. As will be understood by those skilled in the art, the computer device is a device capable of automatically performing numerical calculation and/or information processing according to a preset or stored instruction, and the hardware includes, but is not limited to, a microprocessor, an Application Specific Integrated Circuit (ASIC), a Programmable Gate Array (FPGA), a Digital Signal Processor (DSP), an embedded device, and the like.

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

The memory 51 includes at least one type of readable storage medium including a flash memory, a hard disk, a multimedia card, a card type memory (e.g., SD or DX memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a Read Only Memory (ROM), an Electrically Erasable Programmable Read Only Memory (EEPROM), a Programmable Read Only Memory (PROM), a magnetic memory, a magnetic disk, an optical disk, etc. In some embodiments, the memory 51 may be an internal storage unit of the computer device 5, such as a hard disk or a memory of the computer device 5. In other embodiments, the memory 51 may also be an external storage device of the computer device 5, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a flash Card (FlashCard), or the like, provided on the computer device 5. Of course, the memory 51 may also comprise both an internal storage unit of the computer device 5 and an external storage device thereof. In this embodiment, the memory 51 is generally used for storing an operating system installed in the computer device 5 and various types of application software, such as program codes of a business data analysis method. Further, the memory 51 may also be used to temporarily store various types of data that have been output or are to be output.

The processor 52 may be a Central Processing Unit (CPU), controller, microcontroller, microprocessor, or other data Processing chip in some embodiments. The processor 52 is typically used to control the overall operation of the computer device 5. In this embodiment, the processor 52 is configured to execute the program code stored in the memory 51 or process data, for example, execute the program code of the X method.

The network interface 53 may comprise a wireless network interface or a wired network interface, and the network interface 53 is generally used for establishing communication connections between the computer device 5 and other electronic devices.

The computer device provided by this embodiment can execute the steps of the above-mentioned service data analysis method, and obtain a field for service data analysis by obtaining and analyzing data to be analyzed input by a user, and extract an index field to obtain a parent index, thereby implementing automatic extraction of the parent index from the data to be analyzed; matching a pre-created index level relation table and an index algorithm relation table according to the parent indexes to obtain all child indexes corresponding to the parent indexes; the acquired sub-level indexes are used as new parent-level indexes to acquire sub-level indexes until the new sub-level indexes cannot be acquired, and a plurality of end point indexes are acquired, so that automatic configuration of the indexes is realized, and configuration efficiency is improved; when data analysis is carried out, the data value of each end point index is obtained from the database, the data value of the index of the upper level is calculated layer by combining the index algorithm relation table, the service data analysis can be completed when the data value of the index field is obtained, and the efficiency of the service data analysis is improved through the process calculation.

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

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

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

Claims

1. A business data analysis method is characterized by comprising the following steps:

2. The method according to claim 1, wherein the step of analyzing the data to be analyzed to obtain at least one field for analyzing the service data specifically comprises:

performing word segmentation on the data to be analyzed to obtain a plurality of fields;

performing semantic annotation on the fields according to a semantic knowledge base to generate a semantic annotation result;

determining the dependency relationship of the plurality of fields based on the semantic annotation result;

and determining at least one field used for business data analysis in the plurality of fields according to the dependency relationship.

3. The method for analyzing business data according to claim 2, wherein after said step of semantically labeling said plurality of fields according to a semantic knowledge base and generating a semantic labeling result, further comprising:

dividing the fields into index fields and range fields according to the semantic annotation result; the index field is a target of business data analysis, and the range field is used for indicating the server to acquire a data value from a database for business data analysis;

the step of obtaining the data value for each endpoint indicator from the database comprises:

and reading the data value of each end point index meeting the range field from the database according to the range field.

4. The business data analysis method according to claim 1, wherein said step of converting said field into field information in SQL statements, said field information at least comprising an index field, said index field being used as a parent index, and said step of matching a pre-created index hierarchical relationship table and an index algorithm relationship table according to said parent index further comprises:

acquiring an index identifier;

receiving an algorithm relationship for the indicator identification;

creating an index algorithm relation table according to the algorithm relation;

and creating an index hierarchical relation table according to the index algorithm relation table.

5. The business data analysis method according to claim 4, wherein the step of creating an index hierarchical relationship table according to the index algorithm relationship table specifically comprises:

acquiring algorithm relation information in the index algorithm relation table;

identifying a first index and a second index in the obtained algorithm relation information; the first index is obtained by the second index through operation;

determining parent-child relationship information of the first index and the second index; in the parent-child relationship information, the first index is a parent index of the second index, and the second index is a child index of the first index;

and obtaining an index hierarchical relation table according to the determined parent-child relation information.

6. The business data analysis method according to claim 1, wherein the concrete step of matching a pre-created index hierarchical relationship table and an index algorithm relationship table according to the parent index comprises:

matching a first algorithm corresponding to the parent-level index from the index algorithm relation table;

and matching an index hierarchical relation table corresponding to the parent index according to the first algorithm.

7. The business data analysis method according to claim 1, wherein the step of calculating the data value of the index of the upper stage layer by layer based on the data value of each of the end point indexes and the index algorithm relation table further comprises:

after each calculation is carried out according to the index algorithm relation table, whether the data value corresponding to the current index obtained by calculation is within a preset index value interval or not is judged;

if not, marking the current index as an abnormal index, and displaying the current index after the business data analysis is completed.

8. A service data analysis apparatus, comprising:

9. A computer device comprising a memory in which a computer program is stored and a processor which, when executing the computer program, implements the steps of the business data analysis method of any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that a computer program is stored thereon, which computer program, when being executed by a processor, carries out the steps of the business data analyzing method according to any one of claims 1 to 7.