CN117909358A

CN117909358A - Data analysis method and device, storage medium and electronic equipment

Info

Publication number: CN117909358A
Application number: CN202410018505.4A
Authority: CN
Inventors: 张菡
Original assignee: Advanced Nova Technology Singapore Holdings Ltd
Current assignee: Advanced Nova Technology Singapore Holdings Ltd
Priority date: 2024-01-04
Filing date: 2024-01-04
Publication date: 2024-04-19

Abstract

The specification discloses a data analysis method, a device, a storage medium and an electronic device, wherein the method comprises the following steps: the method comprises the steps of obtaining first transaction data, analyzing the first transaction data to obtain first upstream data corresponding to the first transaction data, wherein the first upstream data comprises at least one first upstream sub-data used for forming the first transaction data, obtaining second transaction data in a transaction database based on the first upstream sub-data, analyzing the first transaction data and the second transaction data to obtain a data relationship aiming at the first transaction data and the second transaction data, obtaining the second transaction data based on the first upstream sub-data of the first transaction data by adopting the embodiment of the specification, and further obtaining the data relationship between the first transaction data and the second transaction data based on the determined data relationship, so that a transaction processor can conveniently conduct data processing on the transaction data, and the high efficiency and the accuracy of transaction data processing are improved.

Description

Data analysis method and device, storage medium and electronic equipment

Technical Field

The present disclosure relates to the field of computer technologies, and in particular, to a data analysis method, a data analysis device, a storage medium, and an electronic device.

Background

At present, with the continuous increase of the operation duration of transaction parties, transaction data corresponding to each transaction is inevitably generated while each transaction is executed, the transaction data stored by the transaction parties are increased, and analysis of numerous and complicated transaction data is one of the problems faced by the transaction parties.

Disclosure of Invention

The specification provides a data analysis method, a device, a storage medium and electronic equipment, which can acquire second transaction data based on first upstream sub-data of the first transaction data to obtain a data relationship between the first transaction data and the second transaction data, and further facilitate data processing of transaction data by a transaction processor based on the determined data relationship between the transaction data, thereby improving the efficiency and accuracy of transaction data processing.

In a first aspect, embodiments of the present disclosure provide a data analysis method, the method including:

acquiring first transaction data, and analyzing the first transaction data to obtain first upstream data corresponding to the first transaction data, wherein the first upstream data comprises at least one first upstream sub-data for forming the first transaction data;

Acquiring second transaction data in a transaction database based on the first upstream sub-data;

and analyzing the first transaction data and the second transaction data to obtain a data relationship aiming at the first transaction data and the second transaction data.

In a second aspect, embodiments of the present disclosure provide a data generating method, including:

Deleting the second transaction data if the data relationship is that the first transaction data contains the second transaction data;

deleting the first transaction data if the data relationship is that the second transaction data contains the first transaction data;

And if the data relationship is that the first transaction data and the second transaction data have an intersection part, merging the first transaction data and the second transaction data.

In a third aspect, embodiments of the present specification provide a data analysis apparatus, including:

An upstream data obtaining unit, configured to obtain first transaction data, parse the first transaction data, and obtain first upstream data corresponding to the first transaction data, where the first upstream data includes at least one first upstream sub-data that is used to compose the first transaction data;

A second transaction data acquisition unit, configured to acquire second transaction data in a transaction database based on the first upstream sub-data;

and the data analysis unit is used for analyzing the first transaction data and the second transaction data to obtain a data relationship aiming at the first transaction data and the second transaction data.

In a fourth aspect, embodiments of the present disclosure provide a data generating apparatus, including:

an upstream data determining unit configured to acquire demand data for generating third transaction data, and determine third upstream data based on the demand data;

a fourth transaction data acquisition unit that acquires fourth transaction data including the third upstream data in a transaction database;

And the data generating unit is used for generating the third transaction data based on the third upstream data if the fourth transaction data does not accord with the requirement data.

In a fifth aspect, the present description embodiments provide a computer program product storing at least one instruction adapted to be loaded by a processor and to perform the above-described method steps.

In a sixth aspect, the present description provides a computer storage medium storing a plurality of instructions adapted to be loaded by a processor and to perform the steps of the method described above.

In a seventh aspect, embodiments of the present disclosure provide an electronic device, including: a processor and a memory; wherein the memory stores a computer program adapted to be loaded by the processor and to perform the steps of the method described above.

In the embodiment of the present disclosure, the first upstream data corresponding to the first transaction data is obtained by analyzing the obtained first transaction data, the second transaction data is obtained in the transaction database based on the first upstream sub-data in the first upstream data, and the data relationship between the first transaction data and the second transaction data is obtained by analyzing the first transaction data and the second transaction data, so that the data relationship between the first transaction data and the second transaction data is obtained based on the first upstream sub-data of the first transaction data, and the data relationship between the first transaction data and the second transaction data is obtained, and further based on the determined data relationship between the transaction data, so that a transaction processor is facilitated to perform data processing on the transaction data, and the efficiency and the accuracy of transaction data processing are improved.

Drawings

In order to more clearly illustrate the embodiments of the present description or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present description, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

Fig. 1 is a system architecture diagram of a data analysis method according to an embodiment of the present disclosure;

fig. 2 is a flow chart of a data analysis method according to an embodiment of the present disclosure;

FIG. 3 is an exemplary schematic diagram of a data blood lineage according to an embodiment of the present disclosure;

FIG. 4 is a schematic diagram of an example of a data comparison result provided in the embodiment of the present disclosure;

FIG. 5 is an exemplary diagram of a data processing advice output provided by an embodiment of the present disclosure;

FIG. 6 is a schematic diagram illustrating an example of a data processing hint output according to an embodiment of the present disclosure

Fig. 7 is a flow chart of a data analysis method according to an embodiment of the present disclosure;

Fig. 8 is a flow chart of a data generating method according to an embodiment of the present disclosure;

Fig. 9 is a schematic structural diagram of a data analysis device according to an embodiment of the present disclosure;

FIG. 10 is a schematic diagram of an upstream data acquisition unit according to an embodiment of the present disclosure;

fig. 11 is a schematic structural diagram of a second transaction data acquiring unit according to an embodiment of the present disclosure;

Fig. 12 is a schematic structural diagram of a data analysis unit according to an embodiment of the present disclosure;

fig. 13 is a schematic structural diagram of a data analysis device according to an embodiment of the present disclosure;

fig. 14 is a schematic structural diagram of a data generating device according to an embodiment of the present disclosure;

Fig. 15 is a schematic structural diagram of a fourth transaction data acquiring unit according to an embodiment of the present disclosure;

fig. 16 is a schematic structural diagram of a data generating device according to an embodiment of the present disclosure;

Fig. 17 is a schematic structural diagram of an electronic device according to an embodiment of the present disclosure;

fig. 18 is a schematic structural diagram of an electronic device according to an embodiment of the present disclosure.

Detailed Description

In order to make the features and advantages of the present specification more comprehensible, the following description refers to the accompanying drawings in which embodiments of the present specification are described in detail, and it is apparent that the described embodiments are only some, but not all embodiments of the present specification. All other embodiments, which can be made by those skilled in the art based on the embodiments herein without making any inventive effort, are intended to be within the scope of the present disclosure.

In the prior art, when transaction data analysis is performed, the transaction data is manually analyzed, so that the problems that the data to be processed is complicated, the transaction data need to be fully known and the like exist, the data analysis efficiency is low, and the high efficiency and accuracy of processing the data cannot be met.

Based on the above, the embodiment of the present disclosure provides a data analysis method, by adopting the embodiment of the present disclosure, the first upstream data corresponding to the first transaction data is obtained by analyzing the obtained first transaction data, the second transaction data is obtained in the transaction database based on the first upstream sub-data in the first upstream data, the data relationship between the first transaction data and the second transaction data is obtained by analyzing the first transaction data and the second transaction data, thereby implementing the first upstream sub-data based on the first transaction data, the data relationship between the first transaction data and the second transaction data is obtained, and further based on the determined data relationship between the transaction data, the transaction data is facilitated to be processed by a transaction processor, and the efficiency and the accuracy of the transaction data processing are improved.

Referring to fig. 1, a system architecture diagram of data analysis is provided for an embodiment of the present disclosure. As shown in fig. 1, the data analysis method provided in the embodiment of the present disclosure may be applied to a terminal device to implement a process of obtaining a data result by data analysis, and the system structure provided in the embodiment of the present disclosure mainly includes a terminal device 10 and a transaction database 20. The terminal device 10 may be a device with a data analysis function, for comparing different data by using data blood edges, specifically may be an electronic device with data display and data processing functions, such as a desktop computer, a notebook computer, a tablet computer, etc., or may be implemented by an independent server or a server cluster formed by multiple servers used by an enterprise, including but not limited to a hardware server, a virtual server, a cloud server, or may be a micro computer, such as a personal computer, etc.; the transaction database 20 may be a data storage device storing transaction data, and may be implemented by a server used by an enterprise or a server cluster formed by a plurality of servers, including but not limited to a hardware server, a virtual server, a cloud server, and may also be a micro-computer, such as a personal computer, etc.

In this embodiment of the present disclosure, after the terminal device 10 obtains the first transaction data, the first transaction data is parsed to obtain first upstream data, the first upstream data is searched in the transaction database 20 to obtain second transaction data, and the first transaction data and the second transaction data are analyzed to obtain a data relationship for the first transaction data and the second transaction data.

Based on the system architecture shown in fig. 1, the data analysis method provided in the embodiment of the present disclosure will be described in detail below with reference to fig. 2 to 6.

Referring to fig. 2, a flow chart of a data analysis method is provided in the embodiment of the present disclosure. As shown in fig. 2, the method may include the following steps S102-S106.

S102, acquiring first transaction data, and analyzing the first transaction data to obtain first upstream data corresponding to the first transaction data;

In one embodiment, first transaction data to be consolidated is acquired, the first transaction data is parsed to obtain first data attributes corresponding to the first transaction data, and first upstream data corresponding to the first transaction data is determined based on the first data attributes.

The first data attribute may be a data type, a data name, etc. of the first transaction data. For example, the first transaction data may be "operational benefits of item a", a table for operational analysis of transaction "item a", and the first data attribute of the table may be a name including "item a", benefit information, and the like.

The first upstream data may be various data for generating the first transaction data, wherein the first upstream data includes at least one first upstream sub-data for composing the first transaction data. For example, the first transaction data may be a table for performing operation analysis with respect to the transaction "item a", and the first upstream data of the first transaction data may be tables for generating the table, and each table is each first upstream sub-data in the first upstream data.

Illustratively, the first transaction data is data generated based on four tables "table a", "table B", "table C", and "table D", and "table a", "table B", "table C", and "table D" are first upstream data, and each table is each first upstream sub-data included in the first upstream data. As shown in fig. 3, the data of "table 1" in fig. 3 is the first transaction data, and the "table a", "table B", "table C" and "table D" are the upstream data of "table 1".

For example, one possible method for determining the first upstream data corresponding to the first transaction data based on the first data attribute may be to trace back the generation manner of the first transaction data according to the data blood edge of the first transaction data based on the first data attribute to obtain a data blood edge map of the first transaction data, so as to determine the first upstream data corresponding to the first transaction data. The data blood-lineage diagram can be as shown in fig. 3.

S104, acquiring second transaction data from a transaction database based on the first upstream sub-data;

in one embodiment, after first upstream sub-data of first upstream data of the first transaction data is obtained, a second data attribute of the first upstream sub-data is obtained, and a lookup is performed in the transaction database based on the second data attribute, so as to obtain second transaction data containing at least one first upstream sub-data.

For example, the method for searching in the transaction database based on the second data attribute may be to determine the upstream data corresponding to each transaction data in the transaction database, compare the second data attribute of the first upstream sub-data with the data attribute of each upstream transaction data, and if the second data attribute matches the data attribute, consider that the transaction data corresponding to the data attribute and the first upstream sub-data are the same data.

For example, if the second data attribute of the first upstream sub-data is "advertising benefit of item a", and transaction data with another data attribute being "advertising benefit of item a" is found in the transaction database, the transaction data corresponding to the data attribute and the first upstream sub-data are the same data; the other second data attribute of the first upstream sub-data is "intra-project benefit of project a", and transaction data having a data attribute that is also "intra-project benefit of project a" is not found in the transaction database, it can be considered that transaction data identical to the first upstream sub-data does not exist in the transaction data. The term "project internal benefit of the project a" may be amount information paid by a user when using the project a during the running process of the project a, and the like.

It should be noted that, the first upstream data includes at least one first upstream sub-data, and if the upstream data of any one transaction data in the database matches any one of the first upstream sub-data, the transaction data may be used as the second transaction data.

It should be noted that the second transaction data may be at least one transaction data including the first upstream sub-data, and the specific data amount of the second transaction data is based on the search result of the first upstream sub-data in the transaction database.

S106, analyzing the first transaction data and the second transaction data to obtain a data relation aiming at the first transaction data and the second transaction data;

In one embodiment, after the first transaction data and the second transaction data are acquired, the first upstream data of the first transaction data and the second upstream data of the second transaction data are compared to obtain a comparison result, and a data relationship between the first transaction data and the second transaction data is obtained based on the comparison result.

The comparison result may be a data relationship indicating an inclusion relationship between the first upstream data and the second upstream data, from which the data relationship between the first transaction data and the second transaction data can be determined. The method specifically comprises the following steps: if the comparison result indicates that the first upstream data comprises the second upstream data, the obtained data relationship is that the first transaction data comprises the second transaction data; if the comparison result indicates that the second upstream data comprises the first upstream data, the obtained data relationship is that the second transaction data comprises the first transaction data; if the comparison result indicates that the first upstream data and the second upstream data have no inclusion relationship, the obtained data relationship is that an intersection part exists between the first transaction data and the second transaction data.

Illustratively, as shown in fig. 4, the first upstream data of the first transaction data "table 1" in fig. 4A is "table a", "table B", "table C", and "table D", and the second upstream data of the second transaction data "table 2" in fig. 4B is "table a" and "table B". As can be seen from the content shown in fig. 4, the comparison result of the first upstream data and the second upstream data indicates that the first upstream data includes the second upstream data, and the obtained data relationship is that the first transaction data includes the second transaction data.

It will be appreciated that if the first upstream data is "table a" and "table B", and the second upstream data is "table a" and "table C", it can be seen that the first upstream data and the second upstream data both have upstream sub-data of "table a", but the first upstream data contains "table B" and does not contain "table C", and the second upstream data contains "table C" and does not contain "table B", so that there is no containing relationship, and therefore the data relationship between the first transaction data and the second transaction data is regarded as having an intersection portion, which is "table a".

Further, based on the obtained relation between the first transaction data and the second transaction data, a corresponding data processing suggestion is generated, and the generated data processing suggestion is output in a preset mode.

The data processing proposal can be a data processing mode such as data deletion, data combination and the like.

One possible method for outputting the data processing advice may be to convert the generated data processing advice into any one or more of text information, voice information, image information and video information, and transmit the generated and converted data processing advice to a display interface of a terminal device of a transaction processor responsible for processing the first transaction data and/or the second transaction data. The specific proposal output mode can be set according to actual needs.

For example, as shown in fig. 5, the method of displaying text information in fig. 5 outputs a data processing suggestion, and since the first transaction data includes the second transaction data, in order to save the storage space in the transaction database, it is suggested to delete the second transaction data, so that the text information is output: the data processing proposal comprises second transaction data, and the proposal of deleting the second transaction data is provided with two keys of delete and cancel, and if an operation instruction aiming at delete is received, the second transaction data is deleted; if an operation instruction for cancel is received, the second transaction data is not operated.

It should be noted that, in order to avoid damaging important data due to misoperation of data, one feasible method may be to output advice only to the transaction processor, and execute the data processing instruction to process the transaction data after receiving the data processing instruction input by the transaction processor; if the data processing instruction is not received, the transaction data is not processed.

Further, another possible method may be to perform data on the first transaction data and the second transaction data based on the data relationship. Specifically, if the data relationship is that the first transaction data includes the second transaction data, deleting the second transaction data; deleting the first transaction data if the data relationship is that the second transaction data contains the first transaction data; and if the data relationship is that the intersection part exists between the first transaction data and the second transaction data, merging the first transaction data and the second transaction data.

It will be appreciated that there is part of the transaction database as temporary transaction data which is accessed only once after generation, such transaction data can be considered as disposable data which can be deleted directly in order to save storage space in the transaction database.

For example, the first transaction data is "operation benefit of item a", the second transaction data is "advertising benefit of item a in 2021", it can be seen that the second transaction data is data for reporting the advertising benefit of item a in 2022, after reporting, the second transaction data is not accessed any more, and the second transaction data is included in the first transaction data, so that the second transaction data can be deleted.

It should be noted that, in order to avoid deleting the transaction data by mistake, one possible method may be to set a corresponding storage duration for each transaction data, and if the transaction data is not accessed and is contained by another transaction data within the storage duration corresponding to the transaction data, the transaction data may be deleted and recorded in a log in the transaction database.

Further, in order to avoid deleting the transaction data by mistake, a feasible method may be to judge that the first transaction data and the second transaction data are not intersected if the data relationship between the first transaction data and the second transaction data is that an intersection part exists, and to merge the first transaction data and the second transaction data if the importance of the non-intersection part is lower.

For example, the first transaction data is "operation benefit of the item a", and the second transaction data is "advertisement benefit and advertiser information of the item a", so that the importance of "advertiser information" not included in the first transaction data in the second transaction data is considered to be low, and the first transaction data and the second transaction data can be combined, so that the benefit information of the item a in the first transaction data is perfected, and the storage space in the transaction database is saved.

The method for determining the importance of the non-intersecting portion and the degree of importance may be set according to actual needs.

Further, a possible method may be to output a prompt message to the transaction operator after deleting the transaction data, where the prompt message is used to prompt that the transaction data is deleted, and if the transaction operator considers that the transaction data should not be deleted, the transaction data may be restored by one key. For example, as shown in fig. 6, a prompt message is output by using a text message output mode, where the prompt message includes "data processing prompt: because the first transaction data comprises the second transaction data, deleting the second transaction data, providing two keys of 'determining' and 'recovering', and if an operation instruction aiming at 'determining' is received, maintaining the deleting state of the second transaction data; if an operation instruction for 'recovery' is received, recovering the deleted second transaction data in the transaction database, and inheriting the information of the second transaction data before being deleted.

Referring to fig. 7, a flow chart of a data analysis method is provided in the embodiment of the present disclosure. As shown in fig. 7, the method may include the following steps S202 to S216.

S202, acquiring first transaction data, and analyzing the first transaction data to obtain a first data attribute corresponding to the first transaction data;

In one embodiment, after the first transaction data is acquired, each item of information in the first transaction data is parsed to obtain a first data attribute corresponding to the first transaction data.

S204, determining first upstream data corresponding to the first transaction data based on the first data attribute;

In one embodiment, after the first data attribute corresponding to the first transaction data is obtained, the first transaction data is searched by adopting the data blood margin based on the first data attribute so as to determine first upstream data corresponding to the first transaction data.

The first upstream data may be various data for generating the first transaction data, wherein the first upstream data includes at least one first upstream sub-data for composing the first transaction data. For example, the first transaction data may be a table for performing operation analysis on the transaction "item a", and the first upstream data of the first transaction data may be tables for generating the table, where each table is each first upstream sub-data in the first upstream data.

S206, acquiring a second data attribute of the first upstream sub-data;

in an embodiment, the method for acquiring the second sub-data attribute may refer to the method for acquiring the first data attribute in step S202, which is not described herein.

S208, searching in a transaction database based on the second data attribute to acquire second transaction data containing at least one first upstream sub-data;

In one embodiment, the method for searching in the transaction database based on the second data attribute may be that determining upstream data corresponding to each transaction data in the transaction database, comparing the second data attribute of the first upstream sub-data with the data attribute of the upstream sub-data included in each upstream data, and if the second data attribute matches with the data attribute, considering that the upstream sub-data corresponding to the data attribute and the first upstream sub-data are the same data.

S210, acquiring second upstream data of the second transaction data;

In an embodiment, the method for acquiring the second upstream data may refer to the method for acquiring the first upstream data in step S204, which is not described herein.

S212, comparing the first upstream data with the second upstream data to obtain a comparison result;

In one embodiment, each first upstream sub-data in the first upstream data and each second upstream sub-data in the second upstream data are compared to obtain a comparison result between the first upstream sub-data and the second upstream sub-data.

The comparison result may be an indication of an inclusion relationship between the first upstream data and the second upstream data. For example, the first upstream data may include the second upstream data, or the second upstream data may include the first upstream data.

Illustratively, as shown in fig. 4, the first upstream data of the first transaction data "table 1" in fig. 4A is "table a", "table B", "table C", and "table D", and the second upstream data of the second transaction data "table 2" in fig. 4B is "table a" and "table B". As can be seen from what is shown in fig. 4, the comparison of the first upstream data and the second upstream data indicates that the first upstream data contains the second upstream data.

S214, based on the comparison result, obtaining a data relationship between the first transaction data and the second data;

In one embodiment, the obtaining the data relationship between the first transaction data and the second transaction data based on the comparison result may be that, if the comparison result indicates that the first upstream data includes the second upstream data, the obtained data relationship is that the first transaction data includes the second transaction data; if the comparison result indicates that the second upstream data comprises the first upstream data, the obtained data relationship is that the second transaction data comprises the first transaction data; if the comparison result indicates that the first upstream data and the second upstream data have no inclusion relationship, the obtained data relationship is that an intersection part exists between the first transaction data and the second transaction data.

S216, generating and outputting data processing suggestions based on the data relationship;

in one embodiment, after the data relationship between the first transaction data and the second transaction data is obtained, to facilitate the transaction operator's ability to process the data, a data processing recommendation may be generated and output based on the data relationship and the first transaction data and the second transaction data may be processed in response to the transaction operator's manipulation of the data processing recommendation.

The data processing proposal can be a data processing mode including data deletion, data combination and the like.

It should be noted that, in order to avoid deleting the transaction data by mistake, one possible method may be to set a corresponding storage duration for each transaction data in the transaction database, and if the transaction data is not accessed and is contained by another transaction data within the storage duration corresponding to the transaction data, then the transaction data may be deleted and recorded in a log in the transaction database.

For example, if a certain transaction data is "2021 advertising benefit of item a", the storage duration of the transaction data is set to 400 days, the transaction data is not accessed within 400 days after being generated and accessed, and after being found on the 401 st day, deletion or merging processing can be performed according to the situation.

In the embodiment of the present disclosure, the first upstream data corresponding to the first transaction data is obtained by analyzing the obtained first transaction data, the second transaction data is obtained in the transaction database based on the first upstream sub-data in the first upstream data, the data relationship between the first transaction data and the second transaction data is obtained by analyzing the first transaction data and the second transaction data, and the data processing suggestion is generated and output based on the data relationship, so that the first upstream sub-data based on the first transaction data is realized, the second transaction data is obtained, the data relationship between the first transaction data and the second transaction data is obtained, and the data processing suggestion is generated based on the determined data relationship, so that a transaction operator can conveniently perform data processing on the transaction data according to the data processing suggestion, and the efficiency and accuracy of transaction data processing are improved.

Referring to fig. 8, a flowchart of a data generating method is provided in an embodiment of the present disclosure. As shown in fig. 8, the method may include the following steps S302-S306.

S302, acquiring demand data for generating third transaction data, and determining third upstream data based on the demand data;

in one embodiment, after the demand data of the third transaction data is acquired, searching is performed in the transaction data based on the demand data, and if the repeated data meeting the demand data is not found, the corresponding third upstream data is determined based on the demand data.

The demand data may be a screening condition for data required in generating the third transaction data, for example, when the third transaction data is a profit case of one transaction, the demand data may be a name of the one transaction, profit information of the one transaction, or the like.

For example, the third transaction data is a table for analyzing the operation condition of a transaction, and the corresponding requirement data may be a related transaction name, a transaction type, and the like of the transaction.

The third upstream data may be data for generating third transaction data, for example, tables required for generating the third transaction data.

Illustratively, the generated third transaction data is a table for analyzing an operation condition of one transaction, and the third upstream data may be a transaction name of the one transaction, a running time of the transaction, benefit information, expense information, and the like.

Further, if duplicate data meeting the demand data exists in the transaction database when searching is performed in the transaction database based on the demand data, the duplicate data is determined to be third transaction data.

The duplicate data may be the same data as the third transaction data determined in the transaction database based on the demand data. For example, the demand data indicates that the third transaction data is "operational benefit of item a", and when the demand data is searched in the transaction database, the duplicate data "operational benefit of item a" is found, and the duplicate data is determined as the third transaction data.

S304, fourth transaction data containing the third upstream data is acquired in a transaction database;

In one embodiment, after determining third upstream data corresponding to the third transaction data, acquiring a third data attribute in the third upstream data, and searching in the transaction database based on the third data attribute to acquire fourth transaction data including the third upstream data.

The third data attribute may be a data type, a data name, etc. of the third transaction data. For example, the third transaction data may be "operational benefit of item a", a table for operational analysis of the transaction "item a", and the third data attribute of the table may be a name including "item a", benefit information, and the like.

For example, one possible method for acquiring the fourth transaction data may be to acquire the data attribute of the upstream data of each transaction data in the transaction database, compare the third data attribute with each data attribute in the transaction database, and if there is transaction data whose data attribute includes the third data attribute, determine the transaction data as the fourth transaction data.

It will be appreciated that, since the required data is the third transaction data, in order to ensure that the acquired data meets the required data content, the data attribute of the upstream data of the fourth transaction data should include all the content of the third data attribute.

For example, the demand indicated by the demand data is "operational benefit of item a", the acquired transaction data is "all content of item a from standing" or "benefit and expense of item a", and it can be seen that both transaction data includes relevant data of "operational benefit of item a", so that both transaction data can be determined as fourth transaction data. If the transaction data of the advertisement benefit and the advertiser information of the item a exists in the transaction database, the transaction data can be regarded as including part of the benefit information of the item a, but only including the related data of the advertisement benefit, and not including all the contents of the third transaction data, so that the transaction data is insufficient as the fourth transaction data.

The fourth transaction data includes data included in the third transaction data and other data than the third transaction data, and in order to determine whether the fourth transaction data can be used as the third transaction data, whether the fourth transaction data can meet the requirement data may be determined.

S306, if the fourth transaction data does not meet the requirement data, generating third transaction data based on the third upstream data;

In one embodiment, after the fourth transaction data is obtained, the fourth transaction data is determined, and if the fourth transaction data does not meet the demand data, third transaction data is generated based on third upstream data.

For example, one possible method for determining the fourth transaction data may be to identify the fourth transaction data according to the requirement data, and if the fourth transaction data does not meet the requirement indicated by the requirement data, consider that the fourth transaction data does not meet the requirement data.

For example, the requirement indicated by the requirement data is to obtain "operation benefit of the project a", the obtained fourth transaction data is "all contents of the project a from the stand," and it can be seen that the benefit condition of the project a can be found according to the fourth transaction data, but the analysis on the operation benefit of the project a is inconvenient because the fourth transaction data includes excessive contents, so that the fourth transaction data is considered to be inconsistent with the requirement data.

Further, if the fourth transaction data meets the demand data, the fourth transaction data is determined to be the third transaction data.

For example, the third transaction data is "operation benefit of the project a", the fourth transaction data is "benefit and expense of the project a", it can be seen that the third transaction data and the fourth transaction data are both related data of profit for the project a, the fourth transaction data includes content of the third transaction data, and the content included in the fourth transaction data is not excessively complicated, and the understanding of "operation benefit of the project a" is not affected, so that the fourth transaction data can be used as the third transaction data.

Further, if third upstream data corresponding to the demand data does not exist in the transaction database, outputting a data supplementing prompt, receiving the third upstream data returned in response to the data supplementing prompt, and generating third transaction data based on the third upstream data.

The data replenishment hint may be hint information for prompting the transaction processor to supplement the data, which may be data needed to include replenishment of the third upstream data. For example, the third upstream data includes three upstream sub-data of "table a", "table B" and "table C", and the transaction database includes "table a" and "table B", so that the generated data supplement hint may be used for supplementing "table C" for the hint.

Illustratively, the third upstream data includes "project name of project a, project date", "project internal profit of project a", and "advertisement profit of project a", but "project internal profit of project a" is not stored in the transaction database, so the input data supplement hint may be text information "please input data related to project internal profit of project a".

The data supplement prompt can be in any one or more information forms of text information, voice information, image information and video information, and can be specifically set according to actual needs.

In the embodiment of the specification, by acquiring the demand data for generating the third transaction data, determining the third upstream data based on the demand data, acquiring the fourth transaction data containing the third upstream data in the transaction database, and generating the third transaction data based on the third upstream data under the condition that the fourth transaction data does not meet the demand data, the fourth transaction data searched in the transaction database based on the demand data is used as the third transaction data, so that the generation time of the third transaction data is reduced, and when the transaction data meeting the demand data is not found, the third transaction data is generated based on the third upstream data, thereby reducing the time required for generating the transaction data and improving the efficiency and the accuracy of the generation of the transaction data.

Based on the system architecture shown in fig. 1, the data analysis device provided in the embodiment of the present disclosure will be described in detail below with reference to fig. 9 to 13. It should be noted that, the data analysis device in fig. 9-13 is used to execute the method of the embodiment shown in fig. 2-7 of the present specification, and for convenience of explanation, only the portion relevant to the embodiment of the present specification is shown, and specific technical details are not disclosed, please refer to the embodiment shown in fig. 2-7 of the present specification.

Referring to fig. 9, a schematic structural diagram of a data analysis device is provided in an embodiment of the present disclosure. As shown in fig. 9, the data analysis device 1 of the embodiment of the present specification may include: an upstream data acquisition unit 11, a second transaction data acquisition unit 12, and a data analysis unit 13.

An upstream data obtaining unit 11, configured to obtain first transaction data, parse the first transaction data, and obtain first upstream data corresponding to the first transaction data, where the first upstream data includes at least one first upstream sub-data for forming the first transaction data;

A second transaction data acquisition unit 12, configured to acquire second transaction data in a transaction database based on the first upstream sub-data;

And the data analysis unit 13 is used for analyzing the first transaction data and the second transaction data to obtain a data relationship of the first transaction data and the second transaction data.

Alternatively, as shown in fig. 10, the upstream data acquisition unit 11 includes:

A first attribute obtaining subunit 111, configured to obtain first transaction data, and parse the first transaction data to obtain a first data attribute corresponding to the first transaction data;

a first upstream data subunit 112, configured to determine, based on the first data attribute, first upstream data corresponding to the first transaction data.

Alternatively, as shown in fig. 11, the second transaction data acquisition unit 12 includes:

A second attribute acquiring subunit 121, configured to acquire a second data attribute of the first upstream sub data;

a second transaction data obtaining subunit 122, configured to perform a lookup in a transaction database based on the second data attribute, to obtain second transaction data that includes at least one of the first upstream sub-data.

Alternatively, as shown in fig. 12, the data analysis unit 13 includes:

A second upstream data acquisition subunit 131, configured to acquire second upstream data of the second transaction data;

A data comparing subunit 132, configured to compare the first upstream data and the second upstream data to obtain a comparison result, where the comparison result is used to indicate a inclusion relationship between the first upstream data and the second upstream data;

a relationship obtaining subunit 133, configured to obtain a data relationship between the first transaction data and the second data based on the comparison result.

Optionally, the relationship acquisition subunit 133 is further configured to:

If the comparison result indicates that the first upstream data comprises the second upstream data, the obtained data relationship is that the first transaction data comprises the second transaction data;

If the comparison result indicates that the second upstream data comprises the first upstream data, the obtained data relationship is that the second transaction data comprises the first transaction data;

and if the comparison result indicates that the first upstream data and the second upstream data have no inclusion relationship, the obtained data relationship is that an intersection part exists between the first transaction data and the second transaction data.

Optionally, as shown in fig. 13, the data analysis device 1 further includes:

And a advice output unit 14 for generating and outputting data processing advice including data deletion, data merging, based on the data relationship.

Optionally, as shown in fig. 13, the data analysis device 1 further includes:

and a data processing unit 15, configured to perform data processing on the first transaction data and the second transaction data based on the data relationship.

Optionally, the data processing unit 15 is further configured to:

Based on the system architecture shown in fig. 1, the data generating apparatus provided in the embodiment of the present specification will be described in detail with reference to fig. 14 to 16. It should be noted that, the data generating apparatus in fig. 14 to fig. 16 is used to execute the method of the embodiment shown in fig. 8 of the present specification, and for convenience of explanation, only the portion relevant to the embodiment of the present specification is shown, and specific technical details are not disclosed, please refer to the embodiment shown in fig. 8 of the present specification.

Referring to fig. 14, a schematic structural diagram of a data generating device is provided in an embodiment of the present disclosure. As shown in fig. 14, the data generating apparatus 2 of the embodiment of the present specification may include: an upstream data determination unit 21, a fourth transaction data acquisition unit 22, and a data generation unit 23.

An upstream data determination unit 21 for acquiring demand data for generating third transaction data, determining third upstream data based on the demand data;

a fourth transaction data acquisition unit 22 that acquires fourth transaction data including the third upstream data in a transaction database;

A data generating unit 23, configured to generate the third transaction data based on the third upstream data if the fourth transaction data does not meet the requirement data.

Optionally, as shown in fig. 15, the fourth transaction data acquiring unit 22 includes:

a third attribute obtaining subunit 221, configured to obtain a third data attribute in the third upstream data;

A fourth transaction data obtaining subunit 222, configured to perform a lookup in a transaction database based on the third data attribute, to obtain fourth transaction data including the third upstream data.

Optionally, as shown in fig. 16, the data generating apparatus 2 further includes:

And a third transaction data determining unit 24, configured to determine, if duplicate data that meets the requirement data exists in the transaction database, the duplicate data as the third transaction data.

Optionally, the data generating unit 23 is further configured to:

outputting a data supplementing prompt if third upstream data corresponding to the demand data does not exist in the transaction database;

and receiving third upstream data returned in response to the data supplementing prompt, and generating third transaction data based on the third upstream data.

Optionally, the third transaction data determining unit 24 is further configured to:

and if the fourth transaction data accords with the requirement data, determining the fourth transaction data as third transaction data.

The embodiments of the present disclosure further provide a computer storage medium, where a plurality of program instructions may be stored, where the program instructions are adapted to be loaded by a processor and execute the steps of the method of the embodiments shown in fig. 1 to 8, and the specific execution process may refer to the specific description of the embodiments shown in fig. 1 to 8, which is not repeated herein.

The present disclosure further provides a computer program product, where at least one instruction is stored, where the at least one instruction is loaded by the processor and executed by the processor to perform the data analysis method according to the embodiment shown in fig. 1 to 8, and the specific execution process may refer to the specific description of the embodiment shown in fig. 1 to 8, which is not repeated herein.

Referring to fig. 17, a schematic structural diagram of an electronic device is provided in an embodiment of the present disclosure. As shown in fig. 17, the electronic device 1000 may include: at least one processor 1001, such as a CPU, at least one network interface 1004, an input output interface 1003, a memory 1005, at least one communication bus 1002. Wherein the communication bus 1002 is used to enable connected communication between these components. The network interface 1004 may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface), among others. The memory 1005 may be a high-speed RAM memory or a non-volatile memory (non-volatile memory), such as at least one disk memory. The memory 1005 may also optionally be at least one storage device located remotely from the processor 1001. As shown in fig. 17, an operating system, a network communication module, an input-output interface module, and a data analysis application program may be included in the memory 1005, which is one type of computer storage medium.

In the electronic device 1000 shown in fig. 17, the input-output interface 1003 is mainly used as an interface for providing input for a user, and acquires data input by the user.

In one embodiment, the processor 1001 may be configured to invoke a data analysis application stored in the memory 1005 and specifically perform the following operations:

Optionally, when executing to acquire first transaction data and parse the first transaction data to obtain first upstream data corresponding to the first transaction data, the processor 1001 specifically executes the following operations:

Acquiring first transaction data, and analyzing the first transaction data to obtain a first data attribute corresponding to the first transaction data;

And determining first upstream data corresponding to the first transaction data based on the first data attribute.

Optionally, the processor 1001, when executing the acquiring the second transaction data in the transaction database based on the first upstream sub-data, specifically performs the following operations:

Acquiring a second data attribute of the first upstream sub-data;

And searching in a transaction database based on the second data attribute to acquire second transaction data containing at least one first upstream sub-data.

Optionally, when the processor 1001 performs analysis on the first transaction data and the second transaction data to obtain a data relationship for the first transaction data and the second transaction data, the following operations are specifically performed:

acquiring second upstream data of the second transaction data;

Comparing the first upstream data with the second upstream data to obtain a comparison result, wherein the comparison result is used for indicating the inclusion relationship between the first upstream data and the second upstream data;

and obtaining the data relationship between the first transaction data and the second data based on the comparison result.

Optionally, when the processor 1001 obtains the data relationship between the first transaction data and the second data based on the comparison result, the following operations are specifically performed:

Optionally, the processor 1001 further performs the following operations:

Based on the data relationship, generating and outputting data processing suggestions, wherein the data processing suggestions comprise data deletion and data combination.

Optionally, the processor 1001 further performs the following operations:

and carrying out data processing on the first transaction data and the second transaction data based on the data relationship.

Optionally, when performing data processing on the first transaction data and the second transaction data based on the data relationship, the processor 1001 specifically performs the following operations:

Referring to fig. 18, a schematic structural diagram of an electronic device is provided in an embodiment of the present disclosure. As shown in fig. 18, the electronic device 2000 may include: at least one processor 2001, such as a CPU, at least one network interface 2004, an input output interface 2003, a memory 2005, at least one communication bus 2002. Wherein a communication bus 2002 is used to enable connected communications between these components. The network interface 2004 may optionally include standard wired interfaces, wireless interfaces (e.g., WI-FI interfaces), among others. The memory 2005 may be a high-speed RAM memory or a non-volatile memory (non-volatile memory), such as at least one disk memory. The memory 2005 may also optionally be at least one storage device located remotely from the aforementioned processor 2001. As shown in fig. 18, an operating system, a network communication module, an input-output interface module, and a data generation application program may be included in the memory 2005 as one type of computer storage medium.

In the electronic device 2000 shown in fig. 18, the input/output interface 2003 is mainly used as an interface for providing input to a user, and data input by the user is acquired.

In one embodiment, processor 2001 may be used to invoke a data generating application stored in memory 2005 and specifically perform the following operations:

acquiring demand data for generating third transaction data, and determining third upstream data based on the demand data;

acquiring fourth transaction data containing the third upstream data in a transaction database;

and if the fourth transaction data does not meet the requirement data, generating the third transaction data based on the third upstream data.

Optionally, the processor 2001, when executing the obtaining fourth transaction data including the third upstream data in the transaction database, specifically performs the following operations:

Acquiring a third data attribute in the third upstream data;

And searching in a transaction database based on the third data attribute to acquire fourth transaction data containing the third upstream data.

Optionally, after executing the acquisition of the requirement data for generating the third transaction data, the processor 2001 further executes the following operations:

And if repeated data conforming to the required data exist in the transaction database, determining the repeated data as the third transaction data.

Optionally, the processor 2001 further performs the following operations:

Those skilled in the art will appreciate that implementing all or part of the above-described methods in accordance with the embodiments may be accomplished by way of a computer program stored on a computer readable storage medium, which when executed may comprise the steps of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a read-only memory (ROM), a random-access memory (Random Access Memory, RAM), or the like.

The foregoing disclosure is only illustrative of the preferred embodiments of the present invention and is not to be construed as limiting the scope of the claims, which follow the meaning of the claims of the present invention.

Claims

1. A method of data analysis, the method comprising:

2. The method of claim 1, wherein the obtaining the first transaction data, analyzing the first transaction data, and obtaining first upstream data corresponding to the first transaction data, includes:

3. The method of claim 1, the obtaining second transaction data in a transaction database based on the first upstream sub-data, comprising:

Acquiring a second data attribute of the first upstream sub-data;

4. The method of claim 1, the analyzing the first transaction data and the second transaction data to obtain a data relationship for the first transaction data and the second transaction data, comprising:

acquiring second upstream data of the second transaction data;

5. The method of claim 4, the deriving a data relationship of the first transaction data and the second data based on the comparison result, comprising:

6. The method of claim 1, the method further comprising:

7. The method of claim 1, the method further comprising:

8. The method of claim 7, the data processing the first transaction data and the second transaction data based on the data relationship, comprising:

9. A method of data generation, the method comprising:

10. The method of claim 9, the retrieving fourth transaction data including the third upstream data in a transaction database, comprising:

Acquiring a third data attribute in the third upstream data;

11. The method of claim 9, after the obtaining the requirement data for generating the third transaction data, further comprising:

12. The method of claim 9, after the obtaining the requirement data for generating the third transaction data, further comprising:

13. The method of claim 9, the method further comprising:

14. A data analysis device, the device comprising:

15. A data generation apparatus, the apparatus comprising:

16. A computer storage medium storing a plurality of instructions adapted to be loaded by a processor and to perform the steps of the method according to any one of claims 1 to 8 and 9 to 13.

17. An electronic device, comprising: a processor and a memory; wherein the memory stores a computer program adapted to be loaded by the processor and to perform the steps of the method according to any of claims 1-8 and 9-13.

18. A computer program product having stored thereon at least one instruction which when executed by a processor implements the steps of the method of any of claims 1 to 8 and 9 to 13.