CN117520299A

CN117520299A - Data processing method, device, equipment and medium during migration of financial system

Info

Publication number: CN117520299A
Application number: CN202311494719.0A
Authority: CN
Inventors: 侯森川
Original assignee: Ping An Life Insurance Company of China Ltd
Current assignee: Ping An Life Insurance Company of China Ltd
Priority date: 2023-11-09
Filing date: 2023-11-09
Publication date: 2024-02-06

Abstract

The application is applicable to the technical field of financial system updating, and particularly relates to a data processing method, device, equipment and medium during financial system migration. The method comprises the steps that a front end of a method obtains write-in data sent by a user, an agent interface is called, the write-in data is sent to a local rear end, if the write-in is successful, the agent interface is called, the write-in data is sent to a cloud end, a first data set and a second data set which are written in a preset time period are obtained from a local database and a cloud end database, the first data set and the second data set are compared, if the comparison result is detected to be consistent, a successful check result is generated, the local rear end and the cloud end are guided to be put in storage, the write-in of the data is conducted, the data of an old system is preferentially written in, then the data of the old system is synchronized under the condition that the write-in of the old system is successful, the written-in data of the new system is timely checked, the synchronization of the new and old system can be put in storage under the condition that the write-in of the data of the new system is consistent, and service efficiency is improved.

Description

Data processing method, device, equipment and medium during migration of financial system

Technical Field

The application is applicable to the technical field of financial system updating, and particularly relates to a data processing method, device, equipment and medium during financial system migration.

Background

With development of financial service business, providing financial services in a network form becomes a standardized way, while with increase of users, technology alternation, etc., the original financial system may not be able to afford more advanced and efficient services, so that the service level needs to be improved by updating the system. However, the update of the old and new systems is accompanied by the migration of a large amount of data, the construction of the functions of the new system, and the like, and a certain time is required to completely replace the old system with the new system, but in the update process of the old and new systems, in order to avoid the interruption of the service, the old system still needs to be used for providing the corresponding service. Because the functions of the old system are perfect, and the functions of the new system are still served by the old system for the data provided by the front end in the continuous updating process, the data in the process need to be synchronized to the new system, so that the new system cannot completely synchronize the data of the old system. Therefore, how to process the data provided by the front end under the condition of no stop service so as to ensure the synchronization of the new system and the old system and improve the service efficiency is a problem to be solved urgently.

Disclosure of Invention

In view of this, the embodiments of the present application provide a data processing method, apparatus, device, and medium during migration of a financial system, so as to solve the problem of how to process data provided by a front end under a condition of no stop, so as to ensure synchronization of new and old systems, and improve service efficiency.

In a first aspect, an embodiment of the present application provides a data processing method during migration of a financial system, where an old version of the financial system operates at a local back end, a new version of the financial system operates at a cloud end, where the local back end and the cloud end are both connected to a front end through a proxy interface, the data processing method operates at the front end, and the data processing method includes:

acquiring writing data sent by a user, calling the proxy interface, sending the writing data to the local back end, wherein the local back end is used for calling a local writing component to write the writing data into a corresponding local database after receiving the writing data, and feeding back a writing operation result;

acquiring the writing operation result fed back by the local back end, detecting whether the writing operation result is writing success or not, if the writing operation result is writing success, calling the proxy interface, and sending the writing data to the cloud, wherein the cloud is used for calling a gateway component and a cloud writing component to write the writing data into a corresponding cloud database after receiving the writing data;

When a preset calibration condition is met, a first data set written in a preset time period is obtained from the local database, a second data set written in the preset time period is obtained from the cloud database, and the first data set and the second data set are compared to obtain a comparison result;

and detecting whether the comparison results are consistent, if so, generating a successful checking result, and feeding back to the local back end and the cloud end, wherein the successful checking result is used for guiding the local back end to put the first data set in storage and guiding the cloud end to put the second data set in storage.

In a second aspect, an embodiment of the present application provides a data processing device when a financial system migrates, an old version of the financial system operates at a local back end, a new version of the financial system operates at a cloud end, the local back end and the cloud end are both connected to a front end through proxy interfaces, the data processing device operates at the front end, and the data processing device includes:

the first writing module is used for acquiring writing data sent by a user, calling the proxy interface, sending the writing data to the local back end, and calling a local writing component to write the writing data into a corresponding local database after receiving the writing data by the local back end, and feeding back a writing operation result;

The second writing module is used for acquiring the writing operation result fed back by the local back end, detecting whether the writing operation result is writing success or not, if the writing operation result is writing success, calling the proxy interface, and sending the writing data to the cloud, wherein the cloud is used for calling a gateway component and a cloud writing component to write the writing data into a corresponding cloud database after receiving the writing data;

the data comparison module is used for acquiring a first data set written in a preset time period from the local database when a preset calibration condition is met, acquiring a second data set written in the preset time period from the cloud database, and comparing the first data set with the second data set to obtain a comparison result;

the data checking module is used for detecting whether the comparison results are consistent, if so, generating a successful checking result, feeding the successful checking result back to the local back end and the cloud end, and guiding the local back end to warehouse the first data set and guiding the cloud end to warehouse the second data set.

In a third aspect, embodiments of the present application provide a computer device comprising a processor, a memory and a computer program stored in the memory and executable on the processor, the processor implementing the data processing method according to the first aspect when executing the computer program.

In a fourth aspect, embodiments of the present application provide a computer readable storage medium storing a computer program which, when executed by a processor, implements the data processing method according to the first aspect.

Compared with the prior art, the embodiment of the application has the beneficial effects that: the old version of the financial system runs at the local back end, the new version of the financial system runs at the cloud end, the local back end and the cloud end are connected with a front end through an agent interface, the front end acquires writing data sent by a user, the agent interface is called, the writing data is sent to the local back end, the local back end is used for calling a local writing component to write the writing data into a corresponding local database after receiving the writing data, feeding back a writing operation result, acquiring the writing operation result fed back by the local back end, detecting whether the writing operation result is successful, if the writing operation result is successful, calling the agent interface, sending the writing data to the cloud end, the cloud end is used for calling the gateway component and the cloud writing component to write the writing data into the corresponding cloud database after receiving the writing data, when a preset correction condition is reached, acquiring a first data set written in a preset time period from a local database, acquiring a second data set written in the preset time period from a cloud database, comparing the first data set with the second data set to obtain a comparison result, detecting whether the comparison result is consistent, generating a check-out successful result if the comparison result is consistent, feeding back to a local back end and the cloud, guiding the local back end to put the first data set in storage and guiding the cloud to put the second data set in storage, wherein, aiming at the writing of data, the prior writing of the old system is synchronous with the data of the new system under the condition that the writing of the old system is successful, correcting the written data in time to ensure the synchronous of the new system and the old system under the condition that the writing of the data in the new system is consistent, service efficiency is improved.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required for 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 application, 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 schematic view of an application environment of a data processing method during migration of a financial system according to an embodiment of the present application;

fig. 2 is a flow chart of a data processing method during migration of a financial system according to a second embodiment of the present application;

fig. 3 is a flow chart of a data processing method during migration of a financial system according to a third embodiment of the present application;

FIG. 4 is a schematic diagram of a data processing apparatus during migration of a financial system according to a fourth embodiment of the present application;

fig. 5 is a schematic structural diagram of a computer device according to a fifth embodiment of the present application.

Detailed Description

In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system configurations, techniques, etc. in order to provide a thorough understanding of the embodiments of the present application. It will be apparent, however, to one skilled in the art that the present application may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.

It should be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.

It should also be understood that the term "and/or" as used in this specification and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes such combinations.

As used in this specification and the appended claims, the term "if" may be interpreted as "when..once" or "in response to a determination" or "in response to detection" depending on the context. Similarly, the phrase "if a determination" or "if a [ described condition or event ] is detected" may be interpreted in the context of meaning "upon determination" or "in response to determination" or "upon detection of a [ described condition or event ]" or "in response to detection of a [ described condition or event ]".

In addition, in the description of the present application and the appended claims, the terms "first," "second," "third," and the like are used merely to distinguish between descriptions and are not to be construed as indicating or implying relative importance.

Reference in the specification to "one embodiment" or "some embodiments" or the like means that a particular feature, structure, or characteristic described in connection with the embodiment is included in one or more embodiments of the application. Thus, appearances of the phrases "in one embodiment," "in some embodiments," "in other embodiments," and the like in the specification are not necessarily all referring to the same embodiment, but mean "one or more but not all embodiments" unless expressly specified otherwise. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless expressly specified otherwise.

The embodiment of the application can acquire and process the related data based on the artificial intelligence technology. Among these, artificial intelligence (Artificial Intelligence, AI) is the theory, method, technique and application system that uses a digital computer or a digital computer-controlled machine to simulate, extend and extend human intelligence, sense the environment, acquire knowledge and use knowledge to obtain optimal results.

Artificial intelligence infrastructure technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, operation/interaction systems, mechatronics, and the like. The artificial intelligence software technology mainly comprises a computer vision technology, a robot technology, a biological recognition technology, a voice processing technology, a natural language processing technology, machine learning/deep learning and other directions.

It should be understood that the sequence numbers of the steps in the following embodiments do not mean the order of execution, and the execution order of the processes should be determined by the functions and the internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

In order to illustrate the technical solution of the present application, the following description is made by specific examples.

The data processing method during migration of the financial system provided in the first embodiment of the present application may be applied in an application environment as shown in fig. 1, where the front end communicates with the back end, and the back end includes a local back end and a cloud end. Among them, front end and the like include, but are not limited to, palm top computers, desktop computers, notebook computers, ultra-mobile personal computer (UMPC), netbooks, cloud computing devices, personal digital assistants (personal digital assistant, PDA) and the like. The local backend may be implemented with a stand-alone server or a server cluster made up of multiple servers. The cloud is a server or a server cluster that provides services through a network.

Referring to fig. 2, a flow chart of a data processing method during migration of a financial system according to a second embodiment of the present application is provided, the data processing method during migration of a financial system is applied to the front end in fig. 1, an old version of the financial system is operated at a local back end, a new version of the financial system is operated at a cloud end, the local back end and the cloud end are both connected to a front end through proxy interfaces, the local back end is connected to a local database, the cloud end is connected to a cloud database, the purpose of the database is to store data, wherein the local back end and the cloud end provide services at the same time, the functions of the cloud end may not be complete, but the cloud end can provide basic writing, reading, approval and other services, and these services also exist in the local back end.

As shown in fig. 2, the data processing method during migration of the financial system may include the following steps:

step S201, write-in data sent by a user is obtained, an agent interface is called, and the write-in data is sent to a local back end.

In the application, the computer equipment corresponding to the front end is operated by the user, the user triggers the writing operation from the corresponding interface, and uploads the corresponding writing data, so that the front end can acquire the writing data provided by the user.

The proxy interface is an interface for communication between the front end and the local back end, and may be a separate interface, or may be an interface for communication through a proxy server, for example, ngginx, where ngginx is a high-performance HTTP and reverse proxy server, and is also an IMAP/POP3/SMTP proxy server.

The local back end is used for calling the local writing component to write the writing data into the corresponding local database after receiving the writing data, and feeding back the writing operation result. That is, the local backend is connected to the local database, and the local backend and the local database are provided independently of each other, however, the local database may be provided in the local backend.

The write operation result comprises two types of write success and write failure, if the write failure, the local back end responds to the write operation failure, and if the write success, the local back end responds to the write operation success.

Step S202, a writing operation result fed back by the local back end is obtained, whether the writing operation result is writing success or not is detected, if the writing operation result is writing success is detected, a proxy interface is called, and writing data are sent to the cloud.

In the application, the writing result is fed back to the front end by the local back end, the front end acquires the writing result fed back by the local back end, the writing result is detected, when setting, the parameter corresponding to the writing failure is set to 0, the parameter corresponding to the writing success is set to 1, if the parameter is detected to be 1, the writing success is indicated, and therefore, in order to synchronize the writing data, the proxy interface is still called to send the writing data to the cloud.

The cloud end is used for calling the gateway component and the cloud writing component to write the writing data into the corresponding cloud end database after the writing data are received. The cloud end and the cloud end database are connected through the relevant gateway, so that the gateway component is required to be called when data is written, and the cloud writing component is started to execute writing operation so as to write the written data into the corresponding cloud end database, and the data unification of the local back end and the cloud end is realized.

Step S203, when the preset checking condition is reached, a first data set written in a preset time period is obtained from the local database, a second data set written in the preset time period is obtained from the cloud database, and the first data set and the second data set are compared to obtain a comparison result.

In the application, the preset checking condition can be that when the cloud end and the local back end are used simultaneously, data written in the cloud end and the local back end are required to be checked uniformly, so that the cloud end and the local back end are prevented from being non-uniform. For example, the proofing condition may be a fixed time point, such as 23 points per day, or 10 am on weekday of the week, and the proofing condition may be the first time after each write data process is completed.

The preset time period may be set according to the requirement, specifically corresponds to the calibration condition, for example, if the calibration condition is a fixed time point, the preset time period may be a time between two adjacent fixed time points, where the first data set may include at least one group of data, and of course, there may be a case where the data is 0, for example, if the calibration time is a first time after the data writing process is completed, the preset time period is a processing time of the write-once data, and at this time, the first data set only includes the last write-in data.

Likewise, the preset time period on the cloud end is set to be the same as the preset time period on the local back end, so that the cloud end and the local back end can be ensured to be under the same condition.

And comparing the data in the first data set with the data in the second data set, namely, comparing each data one by one, thereby determining that the data in the first data set is completely consistent with the data in the second data set, of course, arranging the data according to the writing time of the writing data when the first data set and the second data set are acquired, and correspondingly comparing the data according to the writing time when the data are compared, so as to finally obtain a comparison result, wherein the comparison result comprises consistency and inconsistency.

Step S204, whether the comparison results are consistent is detected, if the comparison results are consistent, a successful account checking result is generated, and the result is fed back to the local back end and the cloud end.

In the application, the comparison result is consistent, and can indicate that the cloud end and the local back end are unified, under the condition, the two are confirmed to be successful in checking, the result of the successful checking is fed back to the local back end and the cloud end, and the local back end and the cloud end can store the corresponding data sets, namely, the result of the successful checking is used for guiding the local back end to store the first data set and guiding the cloud end to store the second data set.

Optionally, the data processing method further includes:

acquiring read operation information sent by a user, sending the read operation information to a cloud end, wherein the cloud end is used for matching corresponding first read data according to the read operation information when the read operation information is received, and feeding back the first read data;

acquiring first read data fed back by a cloud, detecting whether an abnormality index of the first read data is larger than an index threshold, if so, sending read operation information to a local rear end, wherein the local rear end is used for matching the read operation information to corresponding second read data when receiving the read operation information, and feeding back the second read data;

And acquiring second read data fed back by the local back end, and feeding the second read data back to the user.

When the new system and the old system are used, the reading operation can be performed, the purpose of the reading operation is to read corresponding data from the cloud or the local back end, and when the data are read, the reading operation information is firstly sent to the cloud to respond, so that a user can use the new system for the first time.

If the acquired first read data is abnormal, acquiring corresponding second read data from the local back end (namely the old system) so as to ensure the accuracy of the read operation of the user.

Optionally, after detecting whether the abnormality index of the first read data is greater than the index threshold, further comprising:

and if the abnormality index of the first read data is detected not to be larger than the index threshold value, feeding back the first read data to the user.

If the abnormality index of the first read data is small, it indicates that the first read data is available, and thus the first read data is directly fed back to the user.

Optionally, the data processing method further includes:

the method comprises the steps that approval operation information sent by a user is obtained, the approval operation information is sent to a cloud end, the approval operation information is used for indicating the cloud end to start an approval process, and the cloud end is used for sending a process node of the approval process to a corresponding approval user to obtain an approval result of the approval user and feeding back the approval result;

The method comprises the steps of obtaining an approval result, sending the approval result to a user, and sending approval operation information and the approval result to a local rear end, wherein the local rear end is used for generating an approval process identical to a cloud end according to the approval operation information and the approval result.

When the new and old systems are used, approval operation can be performed, namely, the new and old systems have approval functions. Because the cloud carries the new system and the approval process and the like are up to date, the approval operation is executed by the cloud, and after the approval is executed by the cloud, the approval result is synchronized to the local back end, so that the synchronization of the new system and the old system is realized.

The old version of the financial system runs on the local back end, the new version of the financial system runs on the cloud end, the local back end and the cloud end are connected with a front end through an agent interface, the front end acquires writing data sent by a user, the agent interface is called, the writing data is sent to the local back end, the local back end is used for calling a local writing component to write the writing data into a corresponding local database after receiving the writing data, and feeding back a writing result, the writing result fed back by the local back end is acquired, whether the writing result is writing success is detected, if the writing result is writing success is detected, the agent interface is called, the writing data is sent to the cloud end, the cloud end is used for calling the gateway component and the cloud writing component to write the writing data into the corresponding cloud database after receiving the writing data, when a preset correction condition is achieved, acquiring a first data set written in a preset time period from a local database, acquiring a second data set written in the preset time period from a cloud database, comparing the first data set with the second data set to obtain a comparison result, detecting whether the comparison result is consistent, generating a check-out successful result if the comparison result is consistent, feeding back to a local back end and the cloud, guiding the local back end to put the first data set in storage and guiding the cloud to put the second data set in storage, wherein, aiming at the writing of data, the prior writing of the old system is synchronous with the data of the new system under the condition that the writing of the old system is successful, correcting the written data in time to ensure the synchronous of the new system and the old system under the condition that the writing of the data in the new system is consistent, service efficiency is improved.

Referring to fig. 3, a flowchart of a data processing method during migration of a financial system according to a third embodiment of the present application is shown in fig. 3, where the data processing method during migration of a financial system may include the following steps:

step S301, write-in data sent by a user is obtained, a proxy interface is called, and the write-in data is sent to a local back end.

Step S302, a writing operation result fed back by the local back end is obtained, whether the writing operation result is writing success or not is detected, if the writing operation result is writing success is detected, a proxy interface is called, and writing data are sent to the cloud.

Step S303, when the preset checking condition is reached, a first data set written in a preset time period is obtained from the local database, and a second data set written in the preset time period is obtained from the cloud database.

The content of step S301 to step S305 is the same as the content of the above-mentioned part of step S201 to step S205, and reference may be made to the descriptions of step S201 to step S205, which are not repeated here.

In step S304, it is detected whether the data amounts of the first data set and the second data set are both greater than a data amount threshold.

The data volume in the data set is detected to determine whether the data volume is too large, if the data volume is smaller, a direct comparison mode can be adopted, and if the data volume is larger, a large data processing mode is needed.

In step S305, if it is detected that the data amounts of the first data set and the second data set are both greater than the data amount threshold, the first data set and the second data set are imported into the Hive library.

The method comprises the steps of storing a first data set and a second data set in a Hive table, and is used for subsequent data comparison.

And step S306, comparing the data quantity of the first data set and the second data set in the Hive library, and comparing the fields of each piece of data corresponding to the first data set and the second data set in the Hive library, wherein if the data quantity is consistent and the fields are consistent, the comparison result is consistent.

The comparison function of the Hive library is used for comparing the fields of each piece of data, so that a comparison result is obtained.

Optionally, after comparing the data amounts of the first data set and the second data set in the Hive library, and comparing the fields of each piece of data corresponding to the first data set and the second data set in the Hive library, the method further includes:

if the data quantity is inconsistent, determining that the comparison result is inconsistent, recording a main key of the missing data, and/or if the fields are inconsistent, determining that the comparison result is inconsistent, and recording the field names of the corresponding fields;

Correspondingly, after detecting whether the comparison results are consistent, the method further comprises:

if the comparison result is detected to be inconsistent, generating an alarm mail according to the main key and/or the field name, and sending the alarm mail to the target address.

And in the comparison process, the main key and the field name of the corresponding data are recorded when the data are inconsistent, so that a corresponding warning mail is generated and used for informing the user corresponding to the target address, and abnormal warning is realized.

Step S307, whether the comparison results are consistent is detected, if the comparison results are consistent, a successful account checking result is generated, and the result is fed back to the local back end and the cloud end.

The content of step S307 is the same as that of the above-mentioned part of step S204, and reference is made to the description of step S204, which is not repeated here.

The old version of the financial system runs on the local back end, the new version of the financial system runs on the cloud end, the local back end and the cloud end are connected with a front end through an agent interface, the front end acquires writing data sent by a user, the agent interface is called, the writing data is sent to the local back end, the local back end is used for calling a local writing component to write the writing data into a corresponding local database after receiving the writing data, and feeding back a writing result, the writing result fed back by the local back end is acquired, whether the writing result is writing success is detected, if the writing result is writing success is detected, the agent interface is called, the writing data is sent to the cloud end, the cloud end is used for calling the gateway component and the cloud writing component to write the writing data into the corresponding cloud database after receiving the writing data, when a preset correction condition is achieved, acquiring a first data set written in a preset time period from a local database, acquiring a second data set written in the preset time period from a cloud database, detecting whether the data amounts of the first data set and the second data set are both larger than a data amount threshold, if so, importing the first data set and the second data set into a Hive database, comparing the data amounts of the first data set and the second data set in the Hive database, comparing the fields of each data corresponding to the first data set and the second data set in the Hive database, if the data amounts are consistent and the fields are consistent, determining whether the comparison results are consistent, if so, generating a successful comparison result, and feeding back the successful comparison result to the local back end and the cloud end, the result of successful account checking is used for guiding the local back end to carry out warehouse entry on the first data set and guiding the cloud end to carry out warehouse entry on the second data set, wherein the old system is written in priority for data writing, then the new system carries out data synchronization under the condition that the old system is written in successfully, and the written data is checked in time, so that the warehouse entry can be carried out under the condition that the written data in the new system is consistent with the written data in the old system, the synchronization of the new system and the old system is ensured, and the service efficiency is improved.

Corresponding to the data processing method during the migration of the financial system in the foregoing embodiment, fig. 4 shows a block diagram of a data processing device during the migration of the financial system in the fourth embodiment of the present application, where the data processing device is applied to the front end in fig. 1, the old version of the financial system is running on the local back end, the new version of the financial system is running on the cloud end, both the local back end and the cloud end are connected to a front end through proxy interfaces, the local back end is connected to a local database, the cloud end is connected to a cloud database, the purpose of the database is to store data, where the local back end and the cloud end provide services at the same time, the cloud end may not have complete functions, but the cloud end can provide basic services such as writing, reading, approval, and the like, and these services also exist in the local back end. For convenience of explanation, only portions relevant to the embodiments of the present application are shown.

Referring to fig. 4, the data processing apparatus includes:

the first writing module 41 is configured to obtain writing data sent by a user, call a proxy interface, send the writing data to a local back end, and the local back end is configured to call a local writing component to write the writing data into a corresponding local database after receiving the writing data, and feed back a writing operation result;

The second writing module 42 is configured to obtain a writing result fed back by the local back end, detect whether the writing result is writing success, and if the writing result is writing success, call the proxy interface, send the writing data to the cloud, and the cloud is configured to call the gateway component and the cloud writing component to write the writing data into the corresponding cloud database after receiving the writing data;

the data comparison module 43 is configured to obtain a first data set written in a preset time period from the local database when a preset calibration condition is reached, obtain a second data set written in the preset time period from the cloud database, and compare the first data set with the second data set to obtain a comparison result;

the data accounting module 44 is configured to detect whether the comparison results are consistent, and if the comparison results are detected to be consistent, generate an accounting success result, and feed back the accounting success result to the local back end and the cloud end, where the accounting success result is used to instruct the local back end to store the first data set and instruct the cloud end to store the second data set.

Optionally, the data processing apparatus further includes:

the first reading module is used for acquiring the reading operation information sent by the user, sending the reading operation information to the cloud end, and the cloud end is used for matching corresponding first reading data according to the reading operation information when the reading operation information is received and feeding back the first reading data;

The read data detection module is used for acquiring first read data fed back by the cloud, detecting whether an abnormality index of the first read data is larger than an index threshold value, if so, sending read operation information to a local rear end, and when the local rear end receives the read operation information, matching the read operation information to corresponding second read data according to the read operation information and feeding back the second read data;

the second reading module is used for acquiring second read data fed back by the local back end and feeding the second read data back to the user.

Optionally, the data processing apparatus further includes:

after detecting whether the abnormality index of the first read data is greater than the index threshold, if the abnormality index of the first read data is not greater than the index threshold, the first read data is fed back to the user.

Optionally, the data comparison module 43 includes:

a data amount detection unit for detecting whether the data amounts of the first data set and the second data set are both greater than a data amount threshold;

the data importing unit is used for importing the first data set and the second data set into the Hive library if the data volume of the first data set and the second data set is detected to be larger than the data volume threshold value;

And the data comparison unit is used for comparing the data quantity of the first data set and the second data set in the Hive library, and comparing the fields of each piece of data corresponding to the first data set and the second data set in the Hive library, and if the data quantity is consistent and the fields are consistent, determining that the comparison result is consistent.

Optionally, the data processing apparatus further includes:

the comparison recording module is used for determining that the comparison result is inconsistent and recording the main key of the missing data after comparing the data amount of the first data set and the second data set in the Hive library and comparing the fields of each piece of data corresponding to the first data set and the second data set in the Hive library, and/or determining that the comparison result is inconsistent and recording the field name of the corresponding field if the data amount is inconsistent;

correspondingly, the data processing device further comprises:

and the mail alarm module is used for generating an alarm mail according to the main key and/or the field name and sending the alarm mail to the target address if the comparison result is detected to be inconsistent after the comparison result is detected to be consistent.

Optionally, the data processing apparatus further includes:

The approval operation module is used for acquiring approval operation information sent by a user, sending the approval operation information to the cloud end, wherein the approval operation information is used for indicating the cloud end to start an approval process, and the cloud end is used for sending a process node of the approval process to a corresponding approval user so as to acquire an approval result of the approval user and feeding back the approval result;

the approval synchronization module is used for acquiring an approval result, sending the approval result to a user, and sending approval operation information and the approval result to the local back end, wherein the local back end is used for generating an approval process identical to the cloud according to the approval operation information and the approval result.

It should be noted that, because the content of information interaction and execution process between the modules is based on the same concept as the method embodiment of the present application, specific functions and technical effects thereof may be referred to in the method embodiment section, and details are not repeated herein.

Fig. 5 is a schematic structural diagram of a computer device according to a fifth embodiment of the present application. As shown in fig. 5, the computer device of this embodiment includes: at least one processor (only one shown in fig. 5), a memory, and a computer program stored in the memory and executable on the at least one processor, the processor executing the computer program to perform the steps of any of the various data processing method embodiments described above when the financial system migrates.

The computer device may include, but is not limited to, a processor, a memory. It will be appreciated by those skilled in the art that fig. 5 is merely an example of a computer device and is not intended to limit the computer device, and that a computer device may include more or fewer components than shown, or may combine certain components, or different components, such as may also include a network interface, a display screen, an input device, and the like.

The processor may be a CPU, but may also be other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), off-the-shelf programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.

The memory includes a readable storage medium, an internal memory, etc., where the internal memory may be the memory of the computer device, the internal memory providing an environment for the execution of an operating system and computer-readable instructions in the readable storage medium. The readable storage medium may be a hard disk of a computer device, and in other embodiments may be an external storage device of the computer device, for example, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), etc. that are provided on the computer device. Further, the memory may also include both internal storage units and external storage devices of the computer device. The memory is used to store an operating system, application programs, boot loader (BootLoader), data, and other programs such as program codes of computer programs, and the like. The memory may also be used to temporarily store data that has been output or is to be output.

It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional units and modules is illustrated, and in practical application, the above-described functional distribution may be performed by different functional units and modules according to needs, i.e. the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-described functions. The functional units and modules in the embodiment may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit, where the integrated units may be implemented in a form of hardware or a form of a software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working process of the units and modules in the above device may refer to the corresponding process in the foregoing method embodiment, which is not described herein again. The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the present application implements all or part of the flow of the method of the above-described embodiments, and may be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, where the computer program, when executed by a processor, may implement the steps of the method embodiments described above. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, executable files or in some intermediate form, etc. The computer readable medium may include at least: any entity or device capable of carrying computer program code, a recording medium, a computer Memory, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), an electrical carrier signal, a telecommunications signal, and a software distribution medium. Such as a U-disk, removable hard disk, magnetic or optical disk, etc. In some jurisdictions, computer readable media may not be electrical carrier signals and telecommunications signals in accordance with legislation and patent practice.

The present application implementing all or part of the flow of the method of the above embodiment may also be implemented by a computer program product, which when run on a computer device causes the computer device to execute the steps of the method embodiment described above.

In the foregoing embodiments, the descriptions of the embodiments are emphasized, and in part, not described or illustrated in any particular embodiment, reference is made to the related descriptions of other embodiments.

Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.

In the embodiments provided in this application, it should be understood that the disclosed apparatus/computer device and method may be implemented in other ways. For example, the apparatus/computer device embodiments described above are merely illustrative, e.g., the division of modules or units is merely a logical functional division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection via interfaces, devices or units, which may be in electrical, mechanical or other forms.

The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

The above embodiments are only for illustrating the technical solution of the present application, and are not limiting thereof; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present application, and are intended to be included in the scope of the present application.

Claims

1. The data processing method is characterized in that an old version of a financial system runs on a local back end, a new version of the financial system runs on a cloud end, the local back end and the cloud end are connected with a front end through a proxy interface, the data processing method runs on the front end, and the data processing method comprises the following steps:

2. The data processing method according to claim 1, characterized in that the data processing method further comprises:

acquiring the read operation information sent by the user, and sending the read operation information to the cloud end, wherein the cloud end is used for matching corresponding first read data according to the read operation information when the read operation information is received, and feeding back the first read data;

acquiring the first read data fed back by the cloud, detecting whether an abnormality index of the first read data is larger than an index threshold, if so, sending the read operation information to the local back end, wherein the local back end is used for matching the read operation information to corresponding second read data according to the read operation information when receiving the read operation information, and feeding back the second read data;

and acquiring second read data fed back by the local back end, and feeding back the second read data to the user.

3. The data processing method according to claim 2, characterized by further comprising, after said detecting whether an abnormality index of the first read data is greater than an index threshold:

4. The data processing method according to claim 1, wherein comparing the first data set and the second data set to obtain a comparison result includes:

detecting whether the data volume of the first data set and the second data set is greater than a data volume threshold;

if the data volume of the first data set and the second data set is detected to be larger than the data volume threshold value, importing the first data set and the second data set into the Hive library;

comparing the data volume of the first data set and the second data set in the Hive library, and comparing the fields of each piece of data corresponding to the first data set and the second data set in the Hive library, wherein if the data volume is consistent and the fields are consistent, the comparison result is consistent.

5. The data processing method according to claim 4, further comprising, after comparing the data amounts of the first data set and the second data set in the Hive library and comparing the fields of each corresponding piece of data of the first data set and the second data set in the Hive library:

correspondingly, after the detecting whether the comparison results are consistent, the method further comprises:

if the comparison result is detected to be inconsistent, generating an alarm mail according to the primary key and/or the field name, and sending the alarm mail to a target address.

6. The data processing method according to any one of claims 1 to 5, characterized in that the data processing method further comprises:

the method comprises the steps that approval operation information sent by a user is obtained, the approval operation information is sent to the cloud end, the approval operation information is used for indicating the cloud end to start an approval process, and the cloud end is used for sending process nodes of the approval process to corresponding approval users to obtain approval results of the approval users and feeding back the approval results;

the approval result is obtained, the approval result is sent to the user, the approval operation information and the approval result are sent to the local back end, and the local back end is used for generating an approval process identical to the cloud according to the approval operation information and the approval result.

7. The utility model provides a data processing device when financial system migrates, its characterized in that, financial system's old version operation is at local backend, financial system's new version operation is at the high in the clouds, local backend with the high in the clouds all pass through proxy interface connection front end, data processing device operation in the front end, data processing device includes:

8. The data processing apparatus of claim 7, wherein the data processing apparatus further comprises:

the first reading module is used for acquiring the reading operation information sent by the user, sending the reading operation information to the cloud end, and when the reading operation information is received, matching the reading operation information to corresponding first reading data according to the reading operation information and feeding back the first reading data;

The read data detection module is used for acquiring the first read data fed back by the cloud, detecting whether an abnormality index of the first read data is larger than an index threshold value, if so, sending the read operation information to the local back end, and when the local back end receives the read operation information, matching the read operation information to corresponding second read data and feeding back the second read data;

and the second reading module is used for acquiring second read data fed back by the local back end and feeding back the second read data to the user.

9. A computer device, characterized in that it comprises a processor, a memory and a computer program stored in the memory and executable on the processor, which processor implements the data processing method according to any of claims 1 to 6 when executing the computer program.

10. A computer-readable storage medium storing a computer program, characterized in that the computer program, when executed by a processor, implements the data processing method according to any one of claims 1 to 6.