CN114817250A - Report data processing method and device, electronic equipment and storage medium - Google Patents

Abstract

The disclosure provides a report data processing method and device, electronic equipment and a storage medium, which can be applied to the financial field or other fields. The method comprises the following steps: performing first processing on source data acquired from at least one source database to generate first data, wherein the first data at least meets a report data preset rule and a target database preset type; performing second processing on the first data to generate second data containing preset attributes of the target database, wherein the preset attributes of the target database of the second data meet preset rules of the target database; determining a target characteristic attribute and a target category of the original report data set according to the acquired original report data set; and classifying the second data according to the target characteristic attribute and the target category to generate a basic report data set, wherein the basic report data set is used for generating the target report.

Description

Report data processing method and device, electronic equipment and storage medium

Technical Field

The present disclosure relates to the field of data processing technologies, and in particular, to a method and an apparatus for processing report data, an electronic device, and a readable storage medium.

Background

With the increasing number and types of services related to financial institutions, risk monitoring management becomes more and more important, and in the process of performing risk monitoring management on different services, different source data need to be processed to generate risk monitoring reports meeting different requirements. In the related art, the risk monitoring system adopts a single product processing mode for processing the basic data of the risk report, for example: the products such as public loan, bond, bill, release, personal loan, credit card and the like are respectively processed after the source data is acquired, and different risk monitoring statements corresponding to the business but with different data contents and the like are generated. With the increase of the number and the types of related services, if a single product respective processing mode is adopted for processing when the risk monitoring report is manufactured, different risk monitoring report data are different and cannot be used universally, so that the labor consumption is huge, the procedure becomes redundant and complicated, the later maintenance difficulty is high, and the system operation efficiency is low. In the process of reporting and supervising the risk report, if data problems occur, the data cannot be reported to the corresponding mechanism on schedule, and the risk monitoring of each service is greatly influenced.

Disclosure of Invention

In view of the above problems, the present disclosure provides a report data processing method, an apparatus, an electronic device, a readable storage medium, and a computer program product, which can process different source data to generate a basic report data set for generating a target report, effectively implement unified management on basic report data, improve report display efficiency, and save data storage resources at the same time.

According to a first aspect of the present disclosure, there is provided a report data processing method, including but not limited to: performing first processing on source data acquired from at least one source database to generate first data, wherein the first data at least meets a report data preset rule and a target database preset type; performing second processing on the first data to generate second data containing preset attributes of a target database, wherein the preset attributes of the target database of the second data meet preset rules of the target database; determining a target characteristic attribute and a target category of an original report data set according to the acquired original report data set; and classifying the second data according to the target characteristic attribute and the target category to generate a basic report data set, wherein the basic report data set is used for generating a target report.

In some exemplary embodiments of the present disclosure, the determining a target characteristic attribute and a target category of an original report dataset according to an obtained original report dataset includes: acquiring an original report data set, and extracting elements and attributes in the original report data set; and determining the target characteristic attribute and the target category of each original report data according to the elements and the attributes.

In some exemplary embodiments of the disclosure, the classifying the second data according to the target characteristic attribute and the target category to generate a basic report dataset includes: determining a first probability of each target characteristic attribute corresponding to each target category according to the target characteristic attribute and the target category of the original report data set; acquiring a second data characteristic attribute of the second data, and determining at least one second probability of the second data corresponding to a target class according to the second data characteristic attribute and the first probability; determining a target category corresponding to the second data according to the value of the at least one second probability; and generating a basic report data set according to the target category corresponding to each piece of second data.

In some exemplary embodiments of the present disclosure, the performing a first processing on source data obtained from at least one source database to generate first data includes: determining the same source data in the source data according to the acquired data characteristics of each source data; screening the source data based on the same source data to generate first intermediate data; removing the first intermediate data according to the report data preset rule; and transcoding the first intermediate data subjected to the elimination according to the preset type of the target database to generate first data.

In some exemplary embodiments of the disclosure, the performing, for the first data, the second processing to generate second data including a preset attribute of a target database includes: acquiring a target database preset rule, and determining a target database preset attribute according to the target database preset rule; and performing second processing on the first data according to the target database preset rule and the target database preset attribute to generate second data so that the target database preset attribute of the second data meets the target database preset rule.

In some exemplary embodiments of the present disclosure, the target database preset rule includes at least one of a data format rule, a data precision rule, a data category rule, a data code rule, a data type rule, and a data threshold rule.

A second aspect of the present disclosure provides a report data processing apparatus, including but not limited to: the first generation module is configured to perform first processing on source data acquired from at least one source database to generate first data, and the first data at least meets a report data preset rule and a target database preset type; the second generation module is configured to perform second processing on the first data to generate second data containing a target database preset attribute, wherein the target database preset attribute of the second data meets a target database preset rule; the determining module is configured to determine a target characteristic attribute and a target category of the original report data set according to the acquired original report data set; and the third generation module is configured to classify the second data according to the target characteristic attribute and the target category to generate a basic report data set, wherein the basic report data set is used for generating a target report.

In some exemplary embodiments of the present disclosure, the determining module comprises a determining submodule configured to: acquiring an original report data set, and extracting elements and attributes in the original report data set; and determining the target characteristic attribute and the target category of each original report data according to the elements and the attributes.

In some exemplary embodiments of the present disclosure, the third generation module comprises a third generation submodule configured to: determining a first probability of each target characteristic attribute corresponding to each target category according to the target characteristic attribute and the target category of the original report data set; acquiring a second data characteristic attribute of the second data, and determining at least one second probability of the second data corresponding to a target class according to the second data characteristic attribute and the first probability; determining a target category corresponding to the second data according to the value of the at least one second probability; and generating a basic report data set according to the target category corresponding to each piece of second data.

In some exemplary embodiments of the present disclosure, the first generation module comprises a first generation submodule configured to: determining the same source data in the source data according to the acquired data characteristics of each source data; screening the source data based on the same source data to generate first intermediate data; removing the first intermediate data according to the report data preset rule; and transcoding the first intermediate data after the elimination processing according to the preset type of the target database to generate first data.

In some exemplary embodiments of the present disclosure, the second generation module comprises a second generation submodule configured to: acquiring a target database preset rule, and determining a target database preset attribute according to the target database preset rule; and performing second processing on the first data according to the target database preset rule and the target database preset attribute to generate second data so that the target database preset attribute of the second data meets the target database preset rule.

A third aspect of the present disclosure provides an electronic device, comprising: one or more processors; a storage device for storing executable instructions that, when executed by the processor, implement the method according to the above.

A fourth aspect of the disclosure provides a computer-readable storage medium having stored thereon executable instructions that, when executed by a processor, implement a method according to the above.

A fifth aspect of the disclosure provides a computer program product comprising a computer program which, when executed by a processor, implements a method according to the above.

According to the embodiment of the disclosure, the source data in the source database is processed for the first time and processed for the second time, the second data is generated to filter the unnecessary fields, the data storage resources are saved, meanwhile, the second data is classified according to the target characteristic attribute and the target category of the original report data set to generate the basic report data, and the error rate of generating the report is reduced through the accurate classification of the data.

Drawings

The foregoing and other objects, features and advantages of the disclosure will be apparent from the following description of embodiments of the disclosure, which proceeds with reference to the accompanying drawings, in which:

FIG. 1 is a schematic diagram schematically illustrating a system architecture to which the report data processing method according to the embodiment of the present disclosure may be applied;

FIG. 2 schematically shows a flow chart of a report data processing method according to an embodiment of the present disclosure;

FIG. 3 schematically shows a flowchart at operation S210 of a report data processing method according to an embodiment of the present disclosure;

FIG. 4 schematically shows a flowchart at operation S220 of a report data processing method according to an embodiment of the present disclosure;

FIG. 5 schematically shows a flowchart at operation S230 of a report data processing method according to an embodiment of the present disclosure;

FIG. 6 schematically shows a flowchart at operation S240 of a report data processing method according to an embodiment of the present disclosure;

FIG. 7 is a block diagram schematically illustrating the structure of a report data processing apparatus according to an embodiment of the present disclosure; and

FIG. 8 schematically illustrates a block diagram of an electronic device adapted to implement the report data processing method according to an embodiment of the present disclosure.

Detailed Description

Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. It should be understood that the description is illustrative only and is not intended to limit the scope of the present disclosure. In the following detailed description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the disclosure. It may be evident, however, that one or more embodiments may be practiced without these specific details. Moreover, in the following description, descriptions of well-known structures and techniques are omitted so as to not unnecessarily obscure the concepts of the present disclosure.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. The terms "comprises," "comprising," and the like, as used herein, specify the presence of stated features, steps, operations, and/or components, but do not preclude the presence or addition of one or more other features, steps, operations, or components.

All terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art unless otherwise defined. It is noted that the terms used herein should be interpreted as having a meaning that is consistent with the context of this specification and should not be interpreted in an idealized or overly formal sense.

Where a convention analogous to "at least one of A, B and C, etc." is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., "a system having at least one of A, B and C" would include but not be limited to systems that have a alone, B alone, C alone, a and B together, a and C together, B and C together, and/or A, B, C together, etc.).

In the technical scheme of the disclosure, the acquisition, storage, application and the like of the personal information of the related user all accord with the regulations of related laws and regulations, necessary confidentiality measures are taken, and the customs of the public order is not violated.

In the technical scheme of the disclosure, the operations of obtaining, storing, applying and the like of the related user personal information all obtain the authorization of the user.

In order to solve the problem that a unified data set for generating a target report cannot be generated effectively aiming at different source data in the related art, the disclosure provides a report data processing method, a report data processing device, an electronic device, a readable storage medium and a computer program product. The method and the device can effectively realize processing aiming at different source data, generate a basic report data set for generating the target report, effectively realize unified management on the basic report data, improve the report display efficiency and save data storage resources at the same time. The report data processing method comprises but is not limited to the following steps: performing first processing on source data acquired from at least one source database to generate first data, wherein the first data at least meets a report data preset rule and a target database preset type; performing second processing on the first data to generate second data containing preset attributes of the target database, wherein the preset attributes of the target database of the second data meet preset rules of the target database; determining a target characteristic attribute and a target category of the original report data set according to the acquired original report data set; and classifying the second data according to the target characteristic attribute and the target category to generate a basic report data set, wherein the basic report data set is used for generating the target report.

According to the embodiment of the disclosure, the source data in the source database is processed for the first time and processed for the second time, the second data is generated to filter the unnecessary fields, the data storage resources are saved, meanwhile, the second data is classified according to the target characteristic attribute and the target category of the original report data set, the basic report data used for manufacturing the target report is generated, the accurate classification of the data is realized, and the error rate of generating the report is reduced.

Fig. 1 schematically shows a schematic diagram of a system architecture to which the report data processing method according to the embodiment of the present disclosure can be applied. It should be noted that fig. 1 is only an example of a system architecture to which the embodiments of the present disclosure may be applied to help those skilled in the art understand the technical content of the present disclosure, and does not mean that the embodiments of the present disclosure may not be applied to other devices, systems, environments or scenarios. It should be noted that the method and the apparatus for processing report data provided by the embodiment of the present disclosure may be used in the related fields of the data processing technology field and the financial field, and may also be used in any field other than the financial field.

As shown in FIG. 1, an exemplary system architecture 100 to which the report data processing method may be applied may include terminal devices 101, 102, a network 103, and a server 104. The network 103 serves as a medium for providing communication links between the terminal devices 101, 102 and the server 104. Network 103 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.

A user may use the terminal devices 101, 102 to interact with the server 104 over the network 103 to receive or send messages, instructions or the like. Various payment client applications or various transaction client applications may be installed on the terminal devices 101 and 102, such as an online banking client processing application, a shopping application, a mobile payment application, a public transportation payment application, an investment financing application, and so on (for example only), and various source data are stored on the terminal devices 101 and 102, and the source data may be transmitted, read, stored, and so on with the server 104 through the network 103.

The terminal devices 101, 102 may be various electronic devices having a display screen and supporting functions of mobile payment, electronic transaction, and the like, including but not limited to tablet computers, laptop portable computers, desktop computers, and the like.

The server 104 may be a server providing various services, such as a background management server (for example only) providing support for information or data acquired by the user using the terminal device 101, 102. The background management server can analyze and process the received information or data such as the user request and feed back the processing result to the terminal equipment. The data transmitted by the user may be analyzed or processed and fed back to the terminal device based on the processing result, or the source data in the terminals 101 and 102 may be processed.

It should be noted that the report data processing method provided by the embodiment of the present disclosure may be generally executed by the server 104. Accordingly, the report data processing apparatus provided by the embodiment of the present disclosure may be generally disposed in the server 104. The report data processing method provided by the embodiment of the present disclosure may also be executed by a server or a server cluster that is different from the server 104 and is capable of communicating with the terminal devices 101 and 102 and/or the server 104. Correspondingly, the report data processing apparatus provided by the embodiment of the present disclosure may also be disposed in a server or a server cluster different from the server 104 and capable of communicating with the terminal devices 101 and 102 and/or the server 104.

It should be understood that the number of terminal devices, networks, and servers in fig. 1 is merely illustrative in embodiments of the present disclosure. There may be any number of terminal devices, networks, and servers according to implementation needs, and the embodiments of the present disclosure are not limited thereto.

The report data processing method of the disclosed embodiment will be described in detail below with reference to fig. 2 to 6.

FIG. 2 schematically shows a flowchart of a report data processing method according to an embodiment of the present disclosure.

As shown in fig. 2, the flow 200 of the report data processing method of the present disclosure includes operations S210 to S240.

In operation S210, the source data obtained from the at least one source database is processed for the first time to generate first data, where the first data at least meets the report data preset rule and the target database preset type.

In operation S220, the first data is processed for the second time to generate second data including a preset attribute of the target database, where the preset attribute of the target database of the second data satisfies a preset rule of the target database.

In operation S230, a target characteristic attribute and a target category of the original report dataset are determined according to the acquired original report dataset.

In operation S240, the second data is classified according to the target characteristic attribute and the target category, so as to generate a basic report dataset, where the basic report dataset is used for generating a target report.

The following describes in detail a specific flow of the report data processing method of the present disclosure.

In embodiments of the present disclosure, the source data may be, for example, a variety of data from different regions, different platforms, and different types. The method includes the steps of collecting or acquiring source data through a data collection tool, storing and processing the collected different source data, for example, first processing and/or second processing and/or data classification and other operations described below, and finally generating a basic report data set, where the basic report data set is used for generating a target report, that is, when the target report is generated, data is directly acquired from the basic report data set.

After the source data is acquired, a first processing is performed on at least one source data to generate a first data. For example, the first processing may be deleting, transcoding, and the like, of data in the source data.

The first processing in the report data processing method according to the embodiment of the present disclosure is described below with reference to fig. 3.

Fig. 3 schematically shows a flowchart at operation S210 of a report data processing method according to an embodiment of the present disclosure.

As shown in fig. 3, the first processing is performed on at least one source data, and the flow of operation S210 of generating the first data may specifically include operations S211 to S214.

In operation S211, the same source data in the source data is determined according to the acquired data characteristics of each source data.

After each source data is obtained, the data characteristics of the source data are obtained according to each source data, and the data in the source data are compared and identified according to the data characteristics to determine the same source data in the source data.

For example, a ROW _ NUMBER window function is established for the source data in each data source according to the corresponding data characteristics through a data structure query statement SQL, the source data with the same data characteristics are determined, and the source data with the same data characteristics are sorted.

In operation S212, the source data is filtered based on the same source data, generating first intermediate data.

Illustratively, after determining the same source data, the source data is filtered according to the same source data. For example, a sorting table of source data having the same data characteristic is acquired, and the sequence number of each source data having the same data characteristic is sequentially recorded in an increasing order. The first intermediate data is generated by extracting the source data with the sequence number of 1, deleting other data in the same source data, and only retaining one data in one same source data.

In operation S213, the first intermediate data is removed according to the report data preset rule.

For example, after the first intermediate data is generated, there may be a case where the value of the intermediate data and the like do not satisfy the condition that is finally used for generating the target report, that is, do not satisfy the report data preset rule. And removing the first intermediate data according to the report data preset rule. The report data preset rule may be a requirement for generating data of the target report, such as a value range of the data, a length range of the data, and the like.

For example, the removing process of the first intermediate data may be to set a WHERE condition in the database through the data structure query statement SQL, and remove the first intermediate data that does not meet the preset rule of the report data, for example, to remove null values, negative numbers, and/or data that exceeds the data range in the first intermediate data. And enabling the first intermediate data after the elimination processing to meet the preset rule of the report data.

In operation S214, the first intermediate data after being removed is transcoded according to the preset type of the target database, so as to generate first data.

In an embodiment of the present disclosure, the target database preset type is a database for storing or reading first data, and has a corresponding data encoding requirement, and the target database preset type is determined by acquiring information of the target database, and determining the encoding requirement of the data according to the target database preset type.

Illustratively, the first data is corresponding to the preset type of the target database, so as to meet the requirement of the preset type of the target database. And transcoding the first intermediate data subjected to the elimination processing according to the preset type of the target database to generate first data.

For example, a target database preset type to be imported with first data is obtained, the target database preset type may be a relational database, the first intermediate data after being subjected to the elimination processing is converted into a variable length character coding (UTF-8) code system through an iconv command in a Linux operating system to generate the first data, and then the first data is imported into the target database.

Fig. 4 schematically shows a flowchart at operation S220 of a report data processing method according to an embodiment of the present disclosure.

In the embodiment of the present disclosure, as shown in fig. 4, the process of operation S220 of performing the second processing on the first data and generating the second data including the preset attribute of the target database may include operations S221 to S222.

In operation S221, a target database preset rule is obtained, and a target database preset attribute is determined according to the target database preset rule.

In an embodiment of the present disclosure, the target database preset rule includes at least one of a data format rule, a data precision rule, a data category rule, a data code rule, a data type rule, and a data threshold rule.

In the embodiment of the present disclosure, the target database preset attribute may include contents included in the target database, such as a date, a numerical precision, a foreign currency type, a dictionary value, a data type, and the like.

Illustratively, by obtaining a preset rule of the target database, specific content of a preset attribute of the target database defined by the preset rule is determined. For example, if a data format rule is obtained, it may be determined that the preset attribute of the target database includes content in a daily period according to the rule. Alternatively, the target database preset attribute may be determined to include the content of the foreign currency by acquiring the data category rule (e.g., the balance conversion of the foreign currency). Therefore, all target database preset attributes can be determined according to the target database preset rules.

In operation S222, second processing is performed on the first data according to the target database preset rule and the target database preset attribute, so as to generate second data, so that the target database preset attribute of the second data meets the target database preset rule.

Illustratively, the second processing is performed on the first DATA TO generate second DATA, for example, the target database in the first DATA is preset with date attribute, and the date format is specified as YYYYMMDD (year, month and day) or yyyyyy-MM-DD (year, month and day) by the TO _ DATA and TO _ CHAR functions in the relational database according TO the target database preset rule.

For another example, for the content of the target database preset attribute in the first data being a numeric value, the target database preset rule may include a data precision rule, where the type numeric field precision such as balance, amount, etc. is set TO be length 22, the format of decimal place 2, and the numeric field precision such as ratio, parameter, exchange rate, etc. is set TO be length 9, the format of decimal place 6, etc. through the TO _ NUMBER function in the relational database.

For another example, the target database in the first data is preset with the content of currency, the target database preset rule may include a data type rule (foreign currency), the latest exchange rate data is obtained in real time through the exchange rate table in the relational database, and the balance of the coin a is converted into the amount of the coin B by using the exchange rate calculation formula.

For another example, the target database in the first data is preset with the attribute as the content of the code, the target database preset rule may include a data code rule, and the DICTIONARY value conversion between different DICTIONARY codes is realized by acquiring the DICTIONARY table in the relational database, such as organization codes, country codes, industry codes, product codes, business variety codes, and the like, and by using the database structure query statement SQL program.

For another example, the preset attribute of the target database in the first data is content of a data type, and the preset rule of the target database may include a data type rule, where there is a case where a unit is added to a numerical value of a data type to be displayed in the generated different reports, for example: 75.3%, 10 ten thousand yuan, 1345 thousand dollars, etc. For which special fields need to be converted to string type storage in a relational database. The value is checked by a regular expression [ ^0-9+. + - ] in the relational database, and then is checked by another regular expression ^ (\\ - | \ +)? Determining that the data is positive and the decimal place is reserved with 2 digits, converting the numerical value into a character by utilizing a T0_ CHAR function in the database, and connecting the converted numerical value and the unit into a complete character string form by utilizing a CONCAT function, so that the preset attribute of the target database of the generated second data meets the preset rule of the target database.

For another example, the target database in the first data is preset with content having an attribute of a data threshold, and the target database preset rule may include a data threshold rule. Data whose data threshold does not satisfy the data threshold rule is processed, for example, abnormal and redundant null data can be filtered out. For some important data with small part of null value, the data is supplemented by a regression replacement method, firstly, a plurality of independent variables for predicting the missing value are selected, then a regression equation is established for estimating the missing value, namely, the missing value is replaced by the condition expected value of the missing data. And establishing a custom regression equation function in a relational database through a data structure query statement SQL, and inputting custom expected condition parameter values to calculate a result. And therefore the finally generated target database preset attribute of the second data meets the target database preset rule.

In the embodiment of the present disclosure, the foregoing exemplarily shows the process of the second processing, and the specific process of each example may be combined with the processes of other examples, or may be performed separately, which is not limited in the present disclosure.

Fig. 5 schematically shows a flowchart at operation S230 of a report data processing method according to an embodiment of the present disclosure.

As shown in fig. 5, the flow of operation S230 may include operations S231 through S232.

In operation S231, the original report dataset is obtained, and elements and attributes in the original report dataset are extracted.

In the embodiment of the disclosure, an original report dataset is obtained first, and the original report dataset has an element and an attribute corresponding to each report, where the element may be, for example, content such as an item in the report, and the attribute may be, for example, content such as a specific numerical value corresponding to different elements.

In operation S232, a target feature attribute and a target category of each original report data are determined according to the elements and the attributes.

According to the contents of elements, attributes and the like in the original report data set, determining the characteristic attribute of each original report data, taking the characteristic attribute as a target characteristic attribute, and simultaneously determining the target type of each original report data.

For example, the determined target characteristic attributes comprise data date, client number, country of the client, organization of the client, name of the client, industry of the client, credit rating of the client, rating of country of counterparty, rating model, rating result of the client, area number, website number, product, balance, deduction preparation, five-level classification, currency, credit total amount, risk exposure, risk weighted asset, business contract effective date, business contract due date and the like.

The determination of the target category according to the target data characteristic attribute of the original report can be determined manually or by a machine or equipment according to a preset standard. For example comprising m classes (C) ₁ ，C ₂ ，...，C _m )。

Next, after the execution of operation S230 is completed, operation S240 is performed.

Fig. 6 schematically shows a flowchart at operation S240 of a report data processing method according to an embodiment of the present disclosure.

As shown in fig. 6, the process of operation S240 of classifying the second data according to the target characteristic attribute and the target category and generating the base report dataset may include operations S241 to S244.

In operation S241, a first probability that each target feature attribute corresponds to each target category is determined according to the target feature attributes and the target categories of the original report dataset.

In the embodiment of the disclosure, the original report data set includes the number of original reports, and the first probability that each target characteristic attribute corresponds to each target category can be determined by counting the number of reports in the original report data set, the target characteristic attribute and the target category of each original report data.

In operation S242, a second data characteristic attribute of the second data is obtained, and at least one second probability of the second data corresponding to the target category is determined according to the second data characteristic attribute and the first probability.

In the embodiment of the present disclosure, the second data is analyzed to obtain all second data characteristic attributes of the second data, where the second data characteristic attributes correspond to the target characteristic attributes, that is, the second data characteristic attributes are included in all the target characteristic attributes. The second data characteristic attribute of each second data includes one or more. By obtaining all of the second data characteristic attributes, a first probability may be determined that each second data characteristic attribute corresponds to each target class.

Illustratively, the at least one second probability of the second data corresponding to the target class is determined according to the second data feature attributes and the first probability, for example, the at least one second probability of each second data corresponding to the target class when having the corresponding second data feature attributes is obtained through a bayesian classification algorithm.

For example, the second data X includes second data characteristic attributes X ₁ ，x ₂ ，x ₃ ，...，x _L . Object classes include, for example, C ₁ ，C ₂ ，...，C _m For each object class, calculating a second feature attribute X having a correspondence in the second data X ₁ ，x ₂ ，x ₃ ，...，x _L A second probability of time. For example, the second probability includes P (C) ₁ |X)、P(C ₂ |X)、P(C ₃ |X)、......P(C _m |X)。

In operation S243, a target class corresponding to the second data is determined according to the value of the at least one second probability.

In an embodiment of the present disclosure, P (C) is included by determining the second probability ₁ |X)、P(C ₂ |X)、P(C ₃ |X)、......P(C _m | X), the magnitude of the second probability that the second data corresponds to the target class is determined, e.g., when P (C) _i |X)＞P(C _j If | X) (j is not less than 1 and not more than m, j is not equal to i), determining C _i Is a target class of the second data.

In operation S244, a base report dataset is generated according to the target category corresponding to each second data.

In the embodiment of the disclosure, after different source data are processed, a plurality of second data are generated, and a base report data set is generated by classifying each second data, where the base report data set includes all the second data and categories corresponding to the second data. When the target report is generated in advance, the data required for generating the target report can be acquired from the corresponding target category according to the characteristic attribute of the target report.

According to the embodiment of the disclosure, the second data is generated by performing the first processing and the second processing on the source data in the source database. The source data from different sources can be effectively filtered and processed, data unification is realized, and the workload of business personnel for checking the source data in the later period is reduced.

In addition, the second data are classified according to the target characteristic attributes and the target categories of the original report data set to generate basic report data for making the target report, so that the accurate classification of the data can be realized when more characteristic attributes exist in different source data, and the error rate of generating the report can be reduced. On the other hand, by processing the source data, redundant data which are not needed for generating the target report are filtered and deleted, the data capacity is reduced, and the data storage resource can be saved.

Fig. 7 schematically shows a block diagram of the report data processing apparatus according to the embodiment of the present disclosure.

As shown in fig. 7, the report data processing apparatus 300 according to the embodiment of the present disclosure includes a first generation module 301, a second generation module 302, a determination module 303, and a third generation module 304.

The first generating module 301 is configured to perform first processing on source data acquired from at least one source database to generate first data, where the first data at least meets a preset rule of report data and a preset type of a target database. In an embodiment, the first generating module 301 may be configured to perform the operation S210 described above, which is not described herein again.

The second generating module 302 is configured to perform a second processing on the first data to generate second data including a preset attribute of the target database, where the preset attribute of the target database of the second data satisfies a preset rule of the target database. In an embodiment, the second generating module 302 may be configured to perform the operation S220 described above, which is not described herein again.

The determining module 303 is configured to determine the target characteristic attribute and the target category of the original report data set according to the obtained original report data set. In an embodiment, the determining module 303 may be configured to perform the operation S230 described above, which is not described herein again.

And the third generating module 304 is configured to classify the second data according to the target characteristic attribute and the target category, and generate a basic report data set, where the basic report data set is used for generating a target report. In an embodiment, the third generating module 304 may be configured to perform the operation S240 described above, which is not described herein again.

In some exemplary embodiments of the present disclosure, the determining module includes a determining submodule configured to: acquiring an original report data set, and extracting elements and attributes in the original report data set; and determining the target characteristic attribute and the target category of each original report data according to the elements and the attributes.

In some exemplary embodiments of the present disclosure, the third generation module includes a third generation submodule configured to: determining a first probability of each target characteristic attribute corresponding to each target category according to the target characteristic attribute and the target category of the original report data set; acquiring a second data characteristic attribute of the second data, and determining at least one second probability of the second data corresponding to the target class according to the second data characteristic attribute and the first probability; determining a target category corresponding to the second data according to at least one second probability value; and generating a basic report data set according to the target category corresponding to each second data.

In some exemplary embodiments of the present disclosure, the first generation module includes a first generation submodule configured to: determining the same source data in the source data according to the acquired data characteristics of each source data; screening source data based on the same source data to generate first intermediate data; removing the first intermediate data according to a report data preset rule; and transcoding the first intermediate data subjected to the elimination processing according to the preset type of the target database to generate first data.

In some exemplary embodiments of the present disclosure, the second generation module includes a second generation submodule configured to: acquiring a target database preset rule, and determining a target database preset attribute according to the target database preset rule; and performing second processing on the first data according to the target database preset rule and the target database preset attribute to generate second data so that the target database preset attribute of the second data meets the target database preset rule.

According to the embodiment of the present disclosure, any plurality of the first generation module 301, the second generation module 302, the determination module 303, the third generation module 304, the first generation sub-module, the second generation sub-module, the determination sub-module, and the third generation sub-module may be combined and implemented in one module, or any one of them may be split into a plurality of modules. Alternatively, at least part of the functionality of one or more of these modules may be combined with at least part of the functionality of the other modules and implemented in one module. According to an embodiment of the present disclosure, at least one of the first generation module 301, the second generation module 302, the determination module 303, the third generation module 304, the first generation sub-module, the second generation sub-module, the determination sub-module, and the third generation sub-module may be at least partially implemented as a hardware circuit, such as a Field Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), a system on a chip, a system on a substrate, a system on a package, an Application Specific Integrated Circuit (ASIC), or may be implemented by hardware or firmware in any other reasonable manner of integrating or packaging a circuit, or implemented by any one of three implementations of software, hardware, and firmware, or by a suitable combination of any of them. Alternatively, at least one of the first generation module 301, the second generation module 302, the determination module 303, the third generation module 304, the first generation sub-module, the second generation sub-module, the determination sub-module, and the third generation sub-module may be at least partially implemented as a computer program module which, when executed, may perform a corresponding function.

FIG. 8 schematically illustrates a block diagram of an electronic device adapted to implement the report data processing method according to an embodiment of the present disclosure. The electronic device shown in fig. 8 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present disclosure.

As shown in fig. 8, an electronic device 400 according to an embodiment of the present disclosure includes a processor 401 that can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM)402 or a program loaded from a storage section 408 into a Random Access Memory (RAM) 403. Processor 401 may include, for example, a general purpose microprocessor (e.g., a CPU), an instruction set processor and/or associated chipset, and/or a special purpose microprocessor (e.g., an Application Specific Integrated Circuit (ASIC)), among others. The processor 401 may also include onboard memory for caching purposes. Processor 401 may include a single processing unit or multiple processing units for performing the different actions of the method flows in accordance with embodiments of the present disclosure.

In the RAM 403, various programs and data necessary for the operation of the electronic apparatus 400 are stored. The processor 401, ROM 402 and RAM 403 are connected to each other by a bus 404. The processor 401 performs various operations of the method flows according to the embodiments of the present disclosure by executing programs in the ROM 402 and/or the RAM 403. Note that the programs may also be stored in one or more memories other than the ROM 402 and RAM 403. The processor 401 may also perform various operations of the method flows according to embodiments of the present disclosure by executing programs stored in the one or more memories.

According to an embodiment of the present disclosure, electronic device 400 may also include an input/output (I/O) interface 405, input/output (I/O) interface 405 also being connected to bus 404. Electronic device 400 may also include one or more of the following components connected to I/O interface 405: an input section 406 including a keyboard, a mouse, and the like; an output section 407 including a display device such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), an organic electroluminescent display (OLED), and other display devices or devices, and a speaker; a storage section 408 including a hard disk and the like; and a communication section 409 including a network interface card such as a LAN card, a modem, or the like. The communication section 409 performs communication processing via a network such as the internet. A driver 410 is also connected to the I/O interface 405 as needed. A removable medium 411 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 410 as necessary, so that a computer program read out therefrom is mounted into the storage section 408 as necessary.

The present disclosure also provides a computer-readable storage medium, which may be contained in the apparatus/device/system described in the above embodiments; or may exist separately and not be assembled into the device/apparatus/system. The computer-readable storage medium carries one or more programs which, when executed, implement a report data processing method according to an embodiment of the present disclosure.

According to embodiments of the present disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium, which may include, for example but is not limited to: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. For example, according to embodiments of the present disclosure, a computer-readable storage medium may include ROM 402 and/or RAM 403 and/or one or more memories other than ROM 402 and RAM 403 described above.

Embodiments of the present disclosure also include a computer program product comprising a computer program containing program code for performing the method illustrated in the flow chart. When the computer program product runs in a computer system, the program code is used for causing the computer system to realize the report data processing method provided by the embodiment of the disclosure.

The computer program performs the above-described functions defined in the system/apparatus of the embodiments of the present disclosure when executed by the processor 401. The systems, apparatuses, modules, units, etc. described above may be implemented by computer program modules according to embodiments of the present disclosure.

In one embodiment, the computer program may be hosted on a tangible storage medium such as an optical storage device, a magnetic storage device, or the like. In another embodiment, the computer program may also be transmitted, distributed in the form of a signal on a network medium, downloaded and installed through the communication section 409, and/or installed from the removable medium 411. The computer program containing program code may be transmitted using any suitable network medium, including but not limited to: wireless, wired, etc., or any suitable combination of the foregoing.

In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 409, and/or installed from the removable medium 411. The computer program, when executed by the processor 401, performs the above-described functions defined in the system of the embodiments of the present disclosure. The systems, devices, apparatuses, modules, units, etc. described above may be implemented by computer program modules according to embodiments of the present disclosure.

In accordance with embodiments of the present disclosure, program code for executing computer programs provided by embodiments of the present disclosure may be written in any combination of one or more programming languages, and in particular, these computer programs may be implemented using high level procedural and/or object oriented programming languages, and/or assembly/machine languages. The programming language includes, but is not limited to, programming languages such as Java, C + +, python, the "C" language, or the like. The program code may execute entirely on the user computing device, partly on the user device, partly on a remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., through the internet using an internet service provider).

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

Those skilled in the art will appreciate that various combinations and/or combinations of features recited in the various embodiments and/or claims of the present disclosure can be made, even if such combinations or combinations are not expressly recited in the present disclosure. In particular, various combinations and/or combinations of the features recited in the various embodiments and/or claims of the present disclosure may be made without departing from the spirit or teaching of the present disclosure. All such combinations and/or associations are within the scope of the present disclosure.

The embodiments of the present disclosure have been described above. However, these examples are for illustrative purposes only and are not intended to limit the scope of the present disclosure. Although the embodiments are described separately above, this does not mean that the measures in the embodiments cannot be used in advantageous combination. The scope of the disclosure is defined by the appended claims and equivalents thereof. Various alternatives and modifications can be devised by those skilled in the art without departing from the scope of the present disclosure, and such alternatives and modifications are intended to be within the scope of the present disclosure.

Claims (10)

1. A report data processing method comprises the following steps:

performing first processing on source data acquired from at least one source database to generate first data, wherein the first data at least meets a report data preset rule and a target database preset type;

performing second processing on the first data to generate second data containing preset attributes of a target database, wherein the preset attributes of the target database of the second data meet preset rules of the target database;

determining a target characteristic attribute and a target category of an original report data set according to the acquired original report data set;

and classifying the second data according to the target characteristic attribute and the target category to generate a basic report data set, wherein the basic report data set is used for generating a target report.

2. The method of claim 1, wherein,

the determining the target characteristic attribute and the target category of the original report data set according to the acquired original report data set comprises the following steps:

acquiring an original report data set, and extracting elements and attributes in the original report data set;

and determining the target characteristic attribute and the target category of each original report data according to the elements and the attributes.

3. The method of claim 2, wherein,

classifying the second data according to the target characteristic attribute and the target category to generate a basic report data set, including:

determining a first probability of each target characteristic attribute corresponding to each target category according to the target characteristic attribute and the target category of the original report data set;

acquiring a second data characteristic attribute of the second data, and determining at least one second probability of the second data corresponding to a target class according to the second data characteristic attribute and the first probability;

determining a target category corresponding to the second data according to the value of the at least one second probability;

and generating a basic report data set according to the target category corresponding to each piece of second data.

4. The method of claim 1, wherein,

the first processing is performed on the source data acquired from the at least one source database to generate first data, and the method includes:

determining the same source data in the source data according to the acquired data characteristics of each source data;

screening the source data based on the same source data to generate first intermediate data;

removing the first intermediate data according to the preset rule of the report data;

and transcoding the first intermediate data after the elimination processing according to the preset type of the target database to generate first data.

5. The method of claim 1, wherein,

the second processing is performed on the first data to generate second data containing preset attributes of a target database, and the second processing includes:

acquiring a target database preset rule, and determining a target database preset attribute according to the target database preset rule;

and performing second processing on the first data according to the target database preset rule and the target database preset attribute to generate second data so that the target database preset attribute of the second data meets the target database preset rule.

6. The method of claim 5, wherein,

the target database preset rule comprises at least one of a data format rule, a data precision rule, a data category rule, a data code rule, a data type rule and a data threshold value rule.

7. A report data processing apparatus comprising:

the first generation module is configured to perform first processing on source data acquired from at least one source database to generate first data, and the first data at least meets a report data preset rule and a target database preset type;

the second generation module is configured to perform second processing on the first data to generate second data containing a target database preset attribute, wherein the target database preset attribute of the second data meets a target database preset rule;

the determining module is configured to determine a target characteristic attribute and a target category of the original report data set according to the acquired original report data set;

and the third generation module is configured to classify the second data according to the target characteristic attribute and the target category to generate a basic report data set, wherein the basic report data set is used for generating a target report.

8. An electronic device, comprising:

one or more processors;

storage means for storing executable instructions that, when executed by the processor, implement the method of any one of claims 1 to 6.

9. A computer readable storage medium having stored thereon executable instructions which, when executed by a processor, implement the method of any one of claims 1 to 6.

10. A computer program product comprising a computer program which, when executed by a processor, implements the method according to any one of claims 1 to 6.

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