CN114155076A - Method, device and equipment for checking business data and financial data - Google Patents
Method, device and equipment for checking business data and financial data Download PDFInfo
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Abstract
The invention discloses a method, a device and equipment for checking business data and financial data, which are applied to a Hadoop distributed file system, wherein the Hadoop distributed file system comprises a data warehouse, and the method comprises the following steps: importing the acquired business data and financial data in a preset time period into a data warehouse; comparing the business data with the financial data according to a preset rule in a data warehouse to obtain difference data, wherein the difference data is part of data of the business data and the financial data which are different aiming at the same type of business activities in a comparison result, and the preset rule is used for limiting a key field for comparing the business data with the financial data; generating a verification result based on the difference data. The technical scheme provided by the invention greatly improves the efficiency of checking the business data and the financial data.
Description
Technical Field
The invention relates to the field of business and financial data processing, in particular to a method, a device and equipment for checking business data and financial data.
Background
The business scope of an enterprise often relates to various fields. Enterprise operations include various types of business activities, such as: house rental management, building cleaning services, merchandise sales, and the like. In order to ensure that the operation flow of an enterprise is transparent and reliable, financial statistics needs to be carried out on business data generated corresponding to business activities, and the business data and the financial data are checked regularly. The existing business and financial checking method is that financial staff pull business data from a background database through a business system technical department, pull financial data from the background database through the financial system technical department, and respectively export the financial data to EXCEL for manual checking, if the pull data is abnormal or the data-taking logic is changed, multiple times of pull verification are needed, and the problems of multiple butt joint times, multiple manpower consumption, data safety and the like are caused. In addition, when the enterprise is large in operation scale, the business and property data often break through millions or millions, manual checking is almost impossible to complete, even if checking is carried out through one computer, checking time is long, so that frequent business and property checking work cannot be carried out, and the business and property data checking efficiency is low. Therefore, how to improve the checking efficiency of the industry and property data is a problem to be solved urgently.
Disclosure of Invention
In view of this, the embodiment of the present invention provides a method, an apparatus, and a device for checking business data and financial data, so as to improve the checking efficiency of the business data and the financial data.
According to a first aspect, the present invention provides a method for checking business data and financial data, which is applied to a Hadoop distributed file system, wherein the Hadoop distributed file system comprises a data warehouse, and the method comprises: importing the acquired business data and financial data in a preset time period into a data warehouse; comparing the business data with the financial data according to a preset rule in a data warehouse to obtain difference data, wherein the difference data is part of data of the business data and the financial data which are different aiming at the same type of business activities in a comparison result, and the preset rule is used for limiting a key field for comparing the business data with the financial data; generating a verification result based on the difference data.
Optionally, the comparing the business data and the financial data according to a preset rule in a data warehouse to obtain difference data includes: converting the comparison operation of the business data and the financial data into a task of a mapping-specification calculation model by using a data warehouse tool; comparing the business data and the financial data belonging to the same type of business activity according to the preset rule through a mapping-stipulation calculation model; generating difference data based on the comparison result.
Optionally, the method further comprises: timing is started after each comparison is finished; and when the timing time reaches the set comparison interval time, returning to the step of importing the acquired business data and financial data in the preset time period into a data warehouse.
Optionally, the method further comprises: acquiring the blending information generated by blending the difference data based on an external user, wherein the blending information is used for eliminating the next service activity type which does not need to be compared; and sending the blending information to the data warehouse, and adjusting key fields in the preset rules based on the blending information by using a data warehouse tool.
Optionally, the business activity type of the business data and the financial data is a distributed outbound contract, and the comparing the business data and the financial data according to a preset rule in a data warehouse includes: and checking data of a first key field in the business data and the financial data, wherein the first key field comprises a company code, a house-leaving contract number, a contract initial lease date, a contract deadline, a contract house lease total amount, a house-receiving contract number and a contract version number.
Optionally, the business activity type of the business data and the business activity type of the financial data are money collection, and the comparing the business data and the financial data according to a preset rule in a data warehouse includes: and checking data of a second key field in the business data and the financial data, wherein the second key field comprises a company code, a delivery contract number, an order number, a collection expense item, a collection method, a collection date, an accounting date and a collection amount.
Optionally, the generating a verification result based on the difference data includes: classifying the difference data according to the service activity type of each data entry in the difference data; and generating a plurality of check tables based on the classification result, and taking each check table as the check result.
According to a second aspect, the present invention provides a device for collating business data and financial data, which is applied to a Hadoop distributed file system, wherein the Hadoop distributed file system comprises a data warehouse, and the device comprises: the data acquisition module is used for importing the acquired business data and financial data in a preset time period into a data warehouse; the data comparison module is used for comparing the business data with the financial data according to a preset rule in a data warehouse to obtain difference data, wherein the difference data is the difference result of the business data and the financial data in the comparison result aiming at the same type of business activities, and the preset rule is used for limiting key fields for comparing the business data with the financial data; and the result generation module is used for generating a checking result based on the difference data.
According to a third aspect, the present invention provides a device for collating business data and financial data, comprising: a memory and a processor, the memory and the processor being communicatively coupled to each other, the memory having stored therein computer instructions, and the processor performing the method of the first aspect, or any one of the optional embodiments of the first aspect, by executing the computer instructions.
According to a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, which stores computer instructions for causing a computer to thereby perform the method of the first aspect, or any one of the optional implementation manners of the first aspect.
The technical scheme provided by the application has the following advantages:
according to the technical scheme, aiming at the problem that the comparison efficiency of business data and financial data is low, the business data and the financial data are imported into a data warehouse of a Hadoop distributed file system, relevant comparison rules are configured in the data warehouse, and by means of the advantages of Hadoop distributed calculation, the comparison process of the business data and the financial data is achieved on a plurality of servers, and the characteristics of supporting super-large files, high expansibility, high efficiency, low cost and high fault tolerance of the Hadoop distributed file system enable the comparison of million-level and millions-level business data and financial data to be obviously improved under the condition that excessive hardware and software cost is not increased, and the comparison time of the business data and the financial data is greatly reduced.
In addition, by means of a mapping-stipulation calculation model in the Hadoop distributed file system, the complex and parallel comparison process on the cluster is highly abstracted into two functions: "mapping" and "reduction" can be accomplished by simply implementing a number of interfaces, and the distributed program can be distributed to a large number of inexpensive machines for operation. Thereby greatly saving the cost of comparing the business data with the financial data. After each comparison is finished, timing is performed again, business data and financial data are compared in a periodic mode, comparison interval time can be set to be in a day level according to user requirements, the computing power of the Hadoop distributed file system can be completely supported, and the problem that the comparison frequency is too low in the prior art is solved. In addition, after the difference data are obtained through comparison every time, the comparison information obtained through manually comparing the difference data can be sent to the data warehouse, so that comparison key fields limited in the preset rules are adjusted, a large amount of compared repeated data are prevented from being doped in the high-frequency comparison process, and the comparison efficiency is further improved.
Drawings
The features and advantages of the present invention will be more clearly understood by reference to the accompanying drawings, which are illustrative and not to be construed as limiting the invention in any way, and in which:
FIG. 1 is a schematic diagram illustrating the steps of a method for reconciling business data with financial data in accordance with an embodiment of the present invention;
FIG. 2 is a flow chart illustrating a method for reconciling business data with financial data in accordance with an embodiment of the present invention;
FIG. 3 is a schematic diagram showing a checking apparatus for business data and financial data according to an embodiment of the present invention;
FIG. 4 is a schematic structural diagram of a device for collating business data and financial data according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be described clearly and completely with reference to the accompanying drawings of the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without inventive step based on the embodiments of the present invention, are within the scope of the present invention.
Referring to fig. 1 and fig. 2, in an embodiment, a method for checking business data and financial data is applied to a Hadoop distributed file system, where the Hadoop distributed file system includes a data warehouse, and specifically includes the following steps:
step S101: and importing the acquired business data and financial data in the preset time period into a data warehouse.
Step S102: and comparing the business data with the financial data according to a preset rule in the data warehouse to obtain difference data, wherein the difference data is part of data of the business data and the financial data which are different aiming at the same type of business activities in the comparison result, and the preset rule is used for limiting key fields for comparing the business data with the financial data.
Step S103: generating a verification result based on the difference data.
Specifically, the Hadoop Distributed File System (HDFS) is a Distributed System infrastructure, and a user can develop a Distributed program without knowing details of a Distributed bottom layer. The power of the cluster is fully utilized to carry out high-speed operation and storage. The files stored by the HDFS can support TB-level data and PB-level data, the data of the files automatically store a plurality of copies, and the copies are automatically recovered after being lost. HDFS distributes data among available computer clusters and performs computational tasks, the clusters can be easily expanded into thousands of nodes, HDFS can dynamically move data among nodes and ensure dynamic balance of each node, and thus the processing speed is very fast. The HDFS is open-source, the software cost is low, the requirement of the HDFS on hardware is low, and the HDFS only needs to be operated on a low-cost commercial machine cluster instead of using expensive high-availability machines. In the embodiment of the invention, business data and financial data are respectively stored in respective relational databases (including but not limited to a Mysql database and an Oracle database), the business data and the financial data of a business system and a financial system are imported into a data warehouse of the HDFS by adopting a sqoop technology (the data warehouse is a theme-oriented, integrated, non-volatile, time-varying data set for supporting the decision of a manager) and then the same type of data in a preset time period (for example, 1 day) is compared according to the data types (including the type of contract, collection, payment, configuration cost, balance of a customer wallet and the like) of the business data and the financial data, wherein the compared part is a key field specified in a preset rule, and the key fields which are not specified do not need to be compared. The data comparison process is completed by dispersing the data to a large number of nodes, and finally, the data are summarized in one node, the difference data are generated according to the comparison result, and the difference data are stored in the independent relational database, so that the data comparison efficiency is greatly improved. For example: for the settlement data of the house renters, the renting period is 2020-01-01 to 2022, 12 months and 31 days; the company needs to pay the house rents for the owners in a fixed period, the owners propose the settlement in 10 months and 1 days in 2021, the settlement date is agreed in 10 months and 10 days in 2021, theoretically, the cost of the house rents paid for the owners should be settled to 10 months and 10 days in 2021, but because the business system is not notified in time, 11 months and 5 in 2021 notify the financial system that the contract is settled in 10 months and 10 months in 2021, so that the settlement cost of the financial system is always settled in 10 months and 11 months in 2021, and the financial system is inconsistent with the financial reality. Through the steps, data differences in the scene can be compared, and the scene that some extreme multi-core computer owners rent tens of millions of times is avoided. And finally, generating a check result according to the difference data, sending the check result to an external user (by means of mails, internal OA messages and the like, but the invention is not limited thereto), giving an alarm to the external user to inform the external user that the business data and the financial data have difference, and providing downloading and displaying functions in a relation database of the difference data for analysis and use by the user.
Specifically, in an embodiment, the step S102 specifically includes the following steps:
the method comprises the following steps: and converting the comparison operation of the business data and the financial data into a task of a mapping-reduction calculation model by using a data warehouse tool.
Step two: and comparing the business data and the financial data belonging to the same type of business activity according to a preset rule through a mapping-stipulation calculation model.
Step three: generating difference data based on the comparison result.
Specifically, the business data and the financial data are usually extracted from a relational database storing the data, the business data and the financial data are compared based on a mapping-reduction calculation model in the HDFS, and a mapping (Map) -reduction (Reduce) calculation model, namely MapReduce, is operated in a complex parallel calculation process on a large-scale cluster, is highly abstracted into two functions, namely 'mapping' and 'reduction' MapReduce, is easy to understand, can complete a distributed program by simply realizing some interfaces, and can be distributed to a large number of cheap PC machines for operation. The model has the advantages of high expansibility and low cost, and based on the characteristics of the model, the cost of comparing business data with financial data is effectively reduced in a distributed file system needing a large number of nodes. In this embodiment, the data warehouse tool (HVIE tool) in the HDFS data warehouse is used to extract, convert, and load data, and the SQL statement that is the comparison operation between the business data and the financial data is converted into a MapReduce task to be executed, so as to perform comparison, and finally generate difference data.
Specifically, in an embodiment, a method for checking business data and financial data further includes the following steps:
step four: timing was started after each alignment was completed.
Step five: and when the timing time reaches the set comparison interval time, returning to the step of importing the acquired business data and financial data in the preset time period into the data warehouse.
Specifically, based on the high performance of the HDFS, the efficiency of comparing the business data with the financial data is greatly improved, so that the timing is started from 0 moment when each comparison is finished, and when the timing time reaches the preset interval time (in the embodiment of the present invention, 1 day is used as the interval time), the next round of comparison is performed. The comparison frequency of the prior art is once a year, and the time length of each time exceeds two months, so that compared with the prior art, the problems of low comparison frequency and long comparison time of the prior art are solved.
Specifically, in an embodiment, a method for checking business data and financial data further includes the following steps:
step six: and acquiring the blending information generated by blending the difference data based on the external user, wherein the blending information is used for rejecting the next business activity type which does not need to be compared.
Step seven: the redemption information is sent to a data warehouse and key fields in the preset rules are adjusted based on the redemption information using a data warehouse tool.
Specifically, in the above embodiment, the HDFS-based business and financial data comparison frequency is once every 1 day, the length set in the general preset time period is greater than 1 day, that is, the time length of the general preset time period is greater than the comparison interval time, and the high-frequency data comparison causes the data compared each time to include a large amount of repeated data, which reduces the comparison efficiency. Therefore, in the embodiment, the external user performs the blending according to the difference data, removes the data types which are not required to be compared in the next comparison to generate the blending information, then sends the blending information to the data warehouse, and modifies the key fields of the preset rules in the data warehouse according to the blending information, so that the key fields of the data which are not required to be compared are set as not to be compared in the preset rules, and the comparison efficiency of the industrial and financial data in each comparison is further improved. For example: the client pays 3000 yuan for the house renting at 23 o' clock 10/31/2021, the theory should be paid in the same day, and the accounting period is 31/10/2021. But the document is not synchronized to the financial system at that time due to the reason of a business system, but is synchronized to the financial system at the 11 th, 1 st, 0 th point 10 th point in 2021, the accounting period is 11 th, 1 st in 2021, for external auditing, the data belongs to the data in a period of time, and after manual blending of the data in the period of time in the last preset time, repeated comparison is not needed to be carried out in the next preset time.
Specifically, in one implementation, the type of business activity of the business data and the financial data is a decentralised outbound contract, and step S102 includes:
and checking business data and financial data of the distributed house-leaving contracts, wherein data of a first key field in a preset rule needs to be checked, and the first key field comprises a company code, a house-leaving contract number, a contract start lease date, a contract deadline date, a contract house lease total amount, a house-receiving contract number and a contract version number. If the situation that one piece of business data and the corresponding financial data are different occurs in the content of the key fields, the two pieces of data are extracted to be used as difference data, and the content of the different key fields is highlighted.
Specifically, in another embodiment, the transaction activity type of the transaction data and the financial data is collection, and step S102 includes:
and checking the collected business data and financial data, wherein the data of a second key field in the preset rule needs to be checked, and the second key field comprises a company code, a delivery contract number, a collection contract number, an order number, a collection expense item, a collection method, a collection date, an accounting date and a collection amount. If the situation that one piece of business data and the corresponding financial data are different occurs in the content of the key fields, the two pieces of data are extracted to be used as difference data, and the content of the different key fields is highlighted.
Specifically, in an embodiment, the step S103 specifically includes the following steps:
step eight: classifying the difference data according to the service activity type of each data entry in the difference data;
step nine: and generating a plurality of check tables based on the classification result, and taking each check table as the check result.
Specifically, in this embodiment, the business activity types to which the data entries in the differential data belong are analyzed, and the differential data entries belonging to the same business activity type are divided into the same check table, for example: all collected difference data is in one check list, all distributed outbound contract difference data is in another check list, and all distributed inbound contract difference data is in a third check list. Then, the external user can analyze and process the difference data of various business activity types in a targeted manner according to the classified checking result, and correspondingly adjust the business activity, so that the use efficiency of the difference data is improved.
Through the steps, according to the technical scheme provided by the application, aiming at the problem that the comparison efficiency of the business data and the financial data is low, the business data and the financial data are imported into a data warehouse of a Hadoop distributed file system, relevant comparison rules are configured in the data warehouse, and by means of the advantages of Hadoop distributed calculation, the comparison process of the business data and the financial data is realized on a plurality of servers, and the characteristics of supporting super-large files, high expansibility, high efficiency, low cost and high fault tolerance of the Hadoop distributed file system are utilized, so that the comparison efficiency of million-level and million-level business data and financial data is obviously improved under the condition that the cost of excessive hardware and software is not increased, and the comparison time of the business data and the financial data is greatly reduced.
In addition, by means of a mapping-protocol calculation model in the Hadoop distributed file system, a complex parallel comparison process on a cluster is highly abstracted into two functions, mapping and reduction are achieved, interfaces are simply achieved, a distributed program can be completed, and the distributed program can be distributed to a large number of cheap machines to operate. Thereby greatly saving the cost of comparing the business data with the financial data. After each comparison is finished, timing is performed again, business data and financial data are compared in a periodic mode, comparison interval time can be set to be in a day level according to user requirements, the computing power of the Hadoop distributed file system can be completely supported, and the problem that the comparison frequency is too low in the prior art is solved. In addition, after the difference data are obtained through comparison every time, the comparison information obtained through manually comparing the difference data can be sent to the data warehouse, so that comparison key fields limited in the preset rules are adjusted, a large amount of compared repeated data are prevented from being doped in the high-frequency comparison process, and the comparison efficiency is further improved.
As shown in fig. 3, this embodiment further provides a device for checking business data and financial data, which is applied to a Hadoop distributed file system, where the Hadoop distributed file system includes a data warehouse, and the device includes:
the data acquisition module 101 is configured to import the acquired business data and financial data in a preset time period into a data warehouse. For details, refer to the related description of step S101 in the above method embodiment, and no further description is provided here.
The data comparison module 102 is configured to compare the business data and the financial data according to a preset rule in the data warehouse to obtain difference data, where the difference data is a difference result of the business data and the financial data in the comparison result for the same type of business activity, and the preset rule is used to limit a key field for comparing the business data and the financial data. For details, refer to the related description of step S102 in the above method embodiment, and no further description is provided here.
A result generating module 103 for generating a collation result based on the difference data. For details, refer to the related description of step S103 in the above method embodiment, and no further description is provided here.
The device for checking business data and financial data provided by the embodiment of the present invention is configured to execute the method for checking business data and financial data provided by the above embodiment, and the implementation manner and the principle thereof are the same, and the details refer to the related description of the above method embodiment and are not repeated.
Through the cooperative cooperation of the components, the technical scheme provided by the application aims at the problem of low comparison efficiency of business data and financial data, the business data and the financial data are imported into a data warehouse of a Hadoop distributed file system, relevant comparison rules are configured in the data warehouse, and by means of the advantages of Hadoop distributed calculation, the comparison process of the business data and the financial data is realized on a plurality of servers, and the characteristics of supporting oversized files, high expansibility, high efficiency, low cost and high fault tolerance of the Hadoop distributed file system, so that the comparison efficiency of million-level and millions-level business data and financial data is remarkably improved under the condition of not increasing too much hardware and software cost, and the comparison time of the business data and the financial data is greatly reduced.
In addition, by means of a mapping-protocol calculation model in the Hadoop distributed file system, a complex parallel comparison process on a cluster is highly abstracted into two functions, mapping and reduction are achieved, interfaces are simply achieved, a distributed program can be completed, and the distributed program can be distributed to a large number of cheap machines to operate. Thereby greatly saving the cost of comparing the business data with the financial data. After each comparison is finished, timing is performed again, business data and financial data are compared in a periodic mode, comparison interval time can be set to be in a day level according to user requirements, the computing power of the Hadoop distributed file system can be completely supported, and the problem that the comparison frequency is too low in the prior art is solved. In addition, after the difference data are obtained through comparison every time, the comparison information obtained through manually comparing the difference data can be sent to the data warehouse, so that comparison key fields limited in the preset rules are adjusted, a large amount of compared repeated data are prevented from being doped in the high-frequency comparison process, and the comparison efficiency is further improved.
Fig. 4 shows a device for collating business data and financial data according to an embodiment of the present invention, which includes a processor 901 and a memory 902, and may be connected by a bus or by other means, and fig. 4 illustrates the connection by the bus as an example.
The memory 902, which is a non-transitory computer-readable storage medium, may be used to store non-transitory software programs, non-transitory computer-executable programs, and modules, such as program instructions/modules corresponding to the methods in the above-described method embodiments. The processor 901 executes various functional applications and data processing of the processor by executing non-transitory software programs, instructions and modules stored in the memory 902, that is, implements the methods in the above-described method embodiments.
The memory 902 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application program required for at least one function; the storage data area may store data created by the processor 901, and the like. Further, the memory 902 may include high speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory 902 may optionally include memory located remotely from the processor 901, which may be connected to the processor 901 via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
One or more modules are stored in the memory 902, which when executed by the processor 901 performs the methods in the above-described method embodiments.
The specific details of the device for checking the business data and the financial data may be understood by referring to the corresponding related description and effects in the above method embodiments, and are not described herein again.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, and the implemented program can be stored in a computer-readable storage medium, and when executed, can include the processes 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 (RAM), a Flash Memory (Flash Memory), a Hard Disk (Hard Disk Drive, abbreviated as HDD) or a Solid State Drive (SSD), etc.; the storage medium may also comprise a combination of memories of the kind described above.
Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations fall within the scope defined by the appended claims.
Claims (10)
1. A method for checking business data and financial data is applied to a Hadoop distributed file system, the Hadoop distributed file system comprises a data warehouse, and the method comprises the following steps:
importing the acquired business data and financial data in a preset time period into a data warehouse;
comparing the business data with the financial data according to a preset rule in a data warehouse to obtain difference data, wherein the difference data is part of data of the business data and the financial data which are different aiming at the same type of business activities in a comparison result, and the preset rule is used for limiting a key field for comparing the business data with the financial data;
generating a verification result based on the difference data.
2. The method according to claim 1, wherein the comparing the business data and the financial data according to a preset rule in a data warehouse to obtain difference data comprises:
converting the comparison operation of the business data and the financial data into a task of a mapping-specification calculation model by using a data warehouse tool;
comparing the business data and the financial data belonging to the same type of business activity according to the preset rule through a mapping-stipulation calculation model;
generating difference data based on the comparison result.
3. The method of claim 2, further comprising:
timing is started after each comparison is finished;
and when the timing time reaches the set comparison interval time, returning to the step of importing the acquired business data and financial data in the preset time period into a data warehouse.
4. The method of claim 3, further comprising:
acquiring the blending information generated by blending the difference data based on an external user, wherein the blending information is used for eliminating the next service activity type which does not need to be compared;
and sending the blending information to the data warehouse, and adjusting key fields in the preset rules based on the blending information by using a data warehouse tool.
5. The method of claim 1, wherein the type of business activity of the business data and the financial data is a distributed outbound contract, and the comparing the business data and the financial data according to a preset rule in a data warehouse comprises:
and checking data of a first key field in the business data and the financial data, wherein the first key field comprises a company code, a house-leaving contract number, a contract initial lease date, a contract deadline, a contract house lease total amount, a house-receiving contract number and a contract version number.
6. The method of claim 1, wherein the type of business activity of the business data and the financial data is cash collection, and wherein comparing the business data and the financial data according to a predetermined rule in a data repository comprises:
and checking data of a second key field in the business data and the financial data, wherein the second key field comprises a company code, a delivery contract number, an order number, a collection expense item, a collection method, a collection date, an accounting date and a collection amount.
7. The method of claim 1, wherein generating a reconciliation result based on the difference data comprises:
classifying the difference data according to the service activity type of each data entry in the difference data;
and generating a plurality of check tables based on the classification result, and taking each check table as the check result.
8. A device for checking business data and financial data is applied to a Hadoop distributed file system, the Hadoop distributed file system comprises a data warehouse, and the device comprises:
the data acquisition module is used for importing the acquired business data and financial data in a preset time period into a data warehouse;
the data comparison module is used for comparing the business data with the financial data according to a preset rule in a data warehouse to obtain difference data, wherein the difference data is the difference result of the business data and the financial data in the comparison result aiming at the same type of business activities, and the preset rule is used for limiting key fields for comparing the business data with the financial data;
and the result generation module is used for generating a checking result based on the difference data.
9. A device for collating business data with financial data, comprising:
a memory and a processor communicatively coupled to each other, the memory having stored therein computer instructions, the processor executing the computer instructions to perform the method of any of claims 1-7.
10. A computer-readable storage medium having stored thereon computer instructions for causing a computer to thereby perform the method of any one of claims 1-7.
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| CN114708080A (en) * | 2022-06-06 | 2022-07-05 | 湖南涉外经济学院 | Distributed financial data online processing method |
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