CN116842090A - Accounting system, method, equipment and storage medium - Google Patents
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Abstract
The application discloses a reconciliation system, a reconciliation method, a reconciliation device and a storage medium, which relate to the technical field of big data and comprise the following steps: the data acquisition module is used for determining a target reconciliation mode based on the reconciliation mode selection instruction and acquiring data to be compared of the target platform through a preset data receiving interface; the first data storage tool is used for storing the data to be compared into a preset message queue if the target reconciliation mode is a preset first target reconciliation mode; the data converter is used for sequentially carrying out format conversion on the data to be compared which are dequeued in the message queue according to a preset data conversion standard so as to obtain converted data; and the data comparison tool is used for comparing each converted data with corresponding preset standard data to obtain each comparison result, and determining the account comparison result of the data to be compared based on each comparison result. The application abstracts the account checking flow into data comparison, and realizes the processing of mass data through the setting of the message queue.
Description
Technical Field
The present application relates to the field of big data technologies, and in particular, to a reconciliation system, a reconciliation method, a reconciliation device, and a storage medium.
Background
In the prior art, in the checking process, unified management and control cannot be realized in the face of changeable comparison rules, complex service scenes, various interface protocols and various data sources, and the processing load on massive data is heavy. It is therefore desirable to address how to accommodate diverse business reconciliation scenarios and reduce the burden of data processing.
Disclosure of Invention
Accordingly, the present application is directed to a reconciliation system, a method, a device and a storage medium, which can abstract a reconciliation process into data comparison, and realize the processing of mass data through the arrangement of a message queue, so as to adapt to diversified business reconciliation scenes and reduce the burden of data processing. The specific scheme is as follows:
in a first aspect, the present application discloses a reconciliation system comprising:
the data acquisition module is used for determining a target reconciliation mode based on the reconciliation mode selection instruction and acquiring data to be compared of the target platform through a preset data receiving interface;
the first data storage tool is used for storing the data to be compared into a preset message queue if the target reconciliation mode is a preset first target reconciliation mode;
the data converter is used for sequentially carrying out format conversion on the data to be compared which are dequeued in the message queue according to a preset data conversion standard so as to obtain converted data;
and the data comparison tool is used for comparing each converted data with corresponding preset standard data to obtain each comparison result, and determining the account comparison result of the data to be compared based on each comparison result.
Optionally, the data acquisition module includes:
the platform connection unit is used for connecting a preset data receiving interface of a target platform through a preset data acquisition tool so as to acquire data to be compared of the target platform.
Optionally, the first data storage means comprises:
and the data delivery unit is used for delivering the data to be compared to a preset message queue through a Spark engine if the target reconciliation mode is a preset first target reconciliation mode.
Optionally, the data converter includes:
the data sending unit is used for sending the data to be compared which are dequeued in the message queue to a corresponding preset data processing node through the Spark engine so as to obtain data to be converted;
and the data conversion unit is used for carrying out format conversion on the data to be converted through the preset data processing node and the preset data conversion standard so as to obtain all converted data.
Optionally, the data converter includes:
the data loading unit is used for loading the data to be compared of the message queue into a preset event pool in sequence;
and the data reading unit is used for sequentially reading the data to be compared from the preset event pool according to the time sequence and carrying out format conversion according to a preset data conversion standard so as to obtain converted data.
Optionally, the reconciliation system further comprises:
the second data storage tool is used for storing the data to be compared into a preset database if the target reconciliation mode is a preset second target reconciliation mode;
the database segmentation module is used for carrying out segmentation processing on the preset database to obtain segmented databases; and carrying out format conversion on the data to be processed in each segmented database according to a preset data conversion standard in sequence to obtain each converted data.
Optionally, the data comparison tool includes:
the data judging unit is used for judging whether the converted data are consistent with the corresponding preset standard data or not;
and the result acquisition unit is used for recording comparison results and determining the account checking result of the data to be compared based on each comparison result if the converted data are inconsistent with the corresponding preset standard data.
In a second aspect, the application discloses a reconciliation method, comprising:
determining a target reconciliation mode based on the reconciliation mode selection instruction, and acquiring data to be compared of the target platform through a preset data receiving interface;
if the target reconciliation mode is a preset first target reconciliation mode, storing the data to be compared into a preset message queue;
sequentially carrying out format conversion on the to-be-compared data dequeued in the message queue according to a preset data conversion standard to obtain converted data;
and comparing the converted data with corresponding preset standard data to obtain comparison results, and determining the account checking result of the data to be compared based on the comparison results.
In a third aspect, the present application discloses an electronic device, comprising:
a memory for storing a computer program;
and a processor for executing the computer program to implement the aforementioned reconciliation method.
In a fourth aspect, the present application discloses a computer readable storage medium storing a computer program which, when executed by a processor, implements the aforementioned reconciliation method.
In this embodiment, the data acquisition module is configured to determine a target reconciliation mode based on the reconciliation mode selection instruction, and acquire to-be-compared data of the target platform through a preset data receiving interface; the first data storage tool is used for storing the data to be compared into a preset message queue if the target reconciliation mode is a preset first target reconciliation mode; the data converter is used for sequentially carrying out format conversion on the data to be compared which are dequeued in the message queue according to a preset data conversion standard so as to obtain converted data; and the data comparison tool is used for comparing each converted data with corresponding preset standard data to obtain each comparison result, and determining the account comparison result of the data to be compared based on each comparison result. Therefore, on one hand, the application obtains the data to be compared through a preset interface in the process of abstracting the reconciliation data into the data comparison, and converts the data to be compared into converted data according to a preset data conversion standard to realize data comparison so as to obtain a reconciliation result; and the method can adapt to diversified business scenes. On the other hand, the data to be reconciled are scattered and processed through the arrangement of the message queue, and the data blocks are reduced, so that the processing pressure of mass data is reduced.
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In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are required to be used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only embodiments of the present application, and that other drawings can be obtained according to the provided drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic diagram of a reconciliation device according to the disclosure;
FIG. 2 is a flow chart of a specific reconciliation method of the present disclosure;
fig. 3 is a block diagram of an electronic device according to the present disclosure.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
At present, the processing of massive reconciliation data in the face of diversified business scenarios is still relatively laborious. Therefore, a reconciliation system is proposed per se, which can meet the reconciliation requirement under changeable business scenarios.
Referring to fig. 1, an embodiment of the present application discloses a reconciliation system, including:
the data acquisition module 11 is configured to determine a target reconciliation mode based on the reconciliation mode selection instruction, and acquire to-be-compared data of the target platform through a preset data receiving interface.
In this embodiment, the data acquisition module includes: the platform connection unit is used for connecting a preset data receiving interface of a target platform through a preset data acquisition tool so as to acquire data to be compared of the target platform. Namely, the preset data acquisition tool is connected with a preset data receiving interface of the upper target platform, and the data to be compared are acquired through the preset data acquisition tool. The data to be compared is the data to be checked. The preset data capture tool may be optimized according to characteristics of the preset data receiving interface (e.g., paging, rate limiting, etc.). In this way, the access and unified management and control can be realized in the face of changeable comparison rules, complex service scenes, various interface protocols and various data sources, and the inverse check dependence on the interfaces of the access party can be removed without repeated construction. Before the data to be compared are obtained, a user can issue a corresponding checking mode selection instruction according to the data size of the data to be compared, and a target checking mode is determined based on the checking mode selection instruction.
The first data storage means 12 is configured to store the data to be compared in a preset message queue if the target reconciliation mode is a preset first target reconciliation mode.
In this embodiment, when the data amount of the to-be-compared data is greater than a preset data amount threshold, the to-be-compared data may be determined to be massive data, and a preset first target reconciliation mode may be adopted for the massive data. The first data storage means comprises: and the data delivery unit is used for delivering the data to be compared to a preset message queue through a Spark engine if the target reconciliation mode is a preset first target reconciliation mode. And delivering the data to be compared to a preset message queue after the data to be compared is acquired by using a Spark engine. In this way, the data to be compared can be read and processed by another service, so that the processes of acquiring and processing the data can be decoupled, and the throughput of the system can be improved. And Apache Spark (an efficient, general purpose parallel computing framework platform) is used to process large amounts of transfer data in parallel. Spark can distribute tasks in the cluster, and each node only processes a part of data, so that the processing speed can be greatly improved. Wherein the preset message queues include, but are not limited to, apache Kafka or rabitmq (Rabbit Message Queue, message queue middleware). It follows that throughput of the reconciliation system may be improved using distributed computing and message queuing techniques.
And the data converter 13 is configured to sequentially format-convert the to-be-compared data dequeued in the message queue according to a preset data conversion standard to obtain each converted data.
In this embodiment, considering that the data format of the data to be compared may be different from the corresponding format of the preset standard data, in order to reduce the difficulty of checking, the format conversion may be sequentially performed on the data to be compared that is dequeued in the message queue according to the preset data conversion standard. The preset data conversion standard is generated according to the format of the preset standard data. For example, for the data to be checked out of the banking system, the format of the data to be checked out of the banking system may be converted into the data format of the business system corresponding to the banking system; that is, the time formats are unified, the banknote units are unified, and the like.
In this embodiment, the data converter includes: the data sending unit is used for sending the data to be compared which are dequeued in the message queue to a corresponding preset data processing node through the Spark engine so as to obtain data to be converted; and the data conversion unit is used for carrying out format conversion on the data to be converted through the preset data processing node and the preset data conversion standard so as to obtain all converted data. And delivering the data to be compared to a preset message queue through a Spark engine. And delivering the data to be compared to a preset message queue after the data to be compared is acquired by using a Spark engine. Such tens of millions of data become scattered in messages and smaller blocks of data.
The data converter in this embodiment includes: the data loading unit is used for loading the data to be compared of the message queue into a preset event pool in sequence; and the data reading unit is used for sequentially reading the data to be compared from the preset event pool according to the time sequence and carrying out format conversion according to a preset data conversion standard so as to obtain converted data. That is, an event driven architecture is used to improve the real-time performance of the system. For example, when new data to be compared is acquired from the preset data receiving interface, an event may be triggered, and then the corresponding service may immediately start processing the data. Thus, delay can be reduced and data processing speed can be increased. To cope with a large number of concurrent events, event pools (EventPool) may be used to manage events. The preset event pool can be implemented by a thread pool, and each time a new event exists, a thread is acquired from the thread pool to be processed. If all threads are busy, a new event is placed in the blocking queue to wait. This has the advantage that a large number of concurrent events can be prevented from exhausting the system resources, and the processing sequence of the events can be ensured. That is, the data to be compared of the message queue are sequentially loaded into a preset event pool. Before entering the event processing thread pool, the blocking queue needs to be entered, so that a large number of requests are prevented from directly exhausting thread resources, and the asynchronization of event processing is realized. The processing thread periodically fetches tasks from the blocking queue for execution in batches. Meanwhile, by using the delay blocking queue, the characteristic of delay checking can be realized.
The data comparison tool 14 is configured to compare each of the converted data with corresponding preset standard data to obtain each comparison result, and determine a reconciliation result of the data to be compared based on each of the comparison results.
In this embodiment, the converted data are compared with corresponding preset standard data, and the data are required to be ordered one by one without alignment. In addition, the converted data and corresponding preset standard data can be placed into a hash table for comparison. Thus, the accuracy of data comparison can be improved.
In this embodiment, the data comparison tool includes: the data judging unit is used for judging whether the converted data are consistent with the corresponding preset standard data or not; and the result acquisition unit is used for recording comparison results and determining the account checking result of the data to be compared based on each comparison result if the converted data are inconsistent with the corresponding preset standard data. And judging whether the converted data are consistent with the corresponding preset standard data, and if not, recording the inconsistent data to generate a checking result of the data to be compared. And after the account checking result is obtained, a corresponding comparison report can be generated and fed back to related staff, or the account checking result is stored in a preset database for subsequent processing.
In this embodiment, the data acquisition module is configured to determine a target reconciliation mode based on the reconciliation mode selection instruction, and acquire to-be-compared data of the target platform through a preset data receiving interface; the first data storage tool is used for storing the data to be compared into a preset message queue if the target reconciliation mode is a preset first target reconciliation mode; the data converter is used for sequentially carrying out format conversion on the data to be compared which are dequeued in the message queue according to a preset data conversion standard so as to obtain converted data; and the data comparison tool is used for comparing each converted data with corresponding preset standard data to obtain each comparison result, and determining the account comparison result of the data to be compared based on each comparison result. Therefore, on one hand, the application obtains the data to be compared through a preset interface in the process of abstracting the reconciliation data into the data comparison, and converts the data to be compared into converted data according to a preset data conversion standard to realize data comparison so as to obtain a reconciliation result; and the method can adapt to diversified business scenes. On the other hand, the data to be reconciled are scattered and processed through the arrangement of the message queue, and the data blocks are reduced, so that the processing pressure of mass data is reduced. In addition, in order to adapt to diversified business scenes, various differentiated execution components can be placed in the whole reconciliation process. The embedding of the corresponding reconciliation component may be freely selected based on the rules. Second, the data needs to be converted from the original format to the standard format for reconciliation. Through the abstraction, the whole account checking flow is connected in series and is converted into a data comparison process. When executing different reconciliation nodes, different default modules or tools may be selected for execution depending on the configuration. Meanwhile, each flow node can be arranged through a visual interface.
On the basis of any one of the above embodiments, the reconciliation system includes:
and the second data storage tool is used for storing the data to be compared into a preset database if the target reconciliation mode is a preset second target reconciliation mode.
The database segmentation module is used for carrying out segmentation processing on the preset database to obtain segmented databases; and carrying out format conversion on the data to be processed in each segmented database according to a preset data conversion standard in sequence to obtain each converted data.
In this embodiment, when the data amount of the data to be compared is less than or equal to the preset data amount threshold, the data to be compared may be determined as a small amount of data. At this time, a preset second target reconciliation mode may be employed. The method comprises the steps of pushing data to a preset database of a reconciliation system, then enabling a reconciliation system cluster to conduct slicing processing on the data to be compared in the preset database through a slicing strategy, and then sequentially conducting format conversion on the data to be processed in each sliced database according to preset data conversion standards to obtain converted data.
As can be seen, performing slicing processing on the preset database to obtain each sliced database; and carrying out format conversion on the data to be processed in each segmented database according to a preset data conversion standard in sequence to obtain each converted data. The data in the database is subjected to slicing processing, and when the data is compared subsequently, the data is loaded in batches according to paging, and the loaded data is compared. Thus, the data comparison speed can be greatly improved.
For example, for transfer reconciliation data of banking systems, it is assumed that transfer information needs to be acquired from one banking system and then compared with data in a business system to check whether there is an inconsistency. The data comparison method provided by the application can divide the whole account checking flow into four steps: namely data loading, conversion analysis, comparison and result processing. First, a special program is required to connect to a preset data receiving interface of a bank and download data. This procedure may be optimized according to preset data receiving interface characteristics (e.g., paging, rate limiting, etc.). In addition, for a business system, it may be necessary to load data from a database. Then conversion is required since the data format of the banking system and the data format of the business system may be different. The task of this step is to convert the raw data into a unified reconciliation format. For example, it may be necessary to unify time formats, unify monetary units, and so on. Then, the data of the banking system and the data of the business system need to be compared to find out inconsistent places. If inconsistencies are found, we may need to generate reports and send them to the relevant personnel or save the results to a database for later processing. When high throughput and high real-time requirements are met for bank data, distributed computing and message queuing techniques can be utilized to increase the throughput of the system. For example, apache Spark may be used to process a large amount of transfer data in parallel. Spark can distribute tasks in the cluster, and each node only processes a part of data, so that the processing speed can be greatly improved. Meanwhile, data may be asynchronously processed using a message queue (e.g., apache Kafka or rabkitmq). When data is acquired from the bank preset data receiving interface, the data can be sent to a message queue and then read and processed by another service. In this way, the processes of acquiring data and processing data can be decoupled, and the throughput of the system is improved. When new transfer data is acquired from the preset data receiving interface, an event may be triggered, and then the corresponding service may immediately start processing the data. This has the advantage that delay can be reduced and the processing speed of the data can be increased. To handle a large number of concurrent events, an event pool may be used to manage reconciliation events. The event pool may be implemented as a thread pool from which a thread is fetched for processing each time there is a new reconciliation event. If all threads are busy, a new reconciliation event is placed in the blocking queue for waiting. This has the advantage that a large number of concurrent events can be prevented from exhausting the system resources, and the processing sequence of the events can be ensured.
As described with reference to fig. 2, the embodiment of the present application further correspondingly discloses a reconciliation method, including:
step S11: determining a target reconciliation mode based on the reconciliation mode selection instruction, and acquiring data to be compared of the target platform through a preset data receiving interface;
step S12: if the target reconciliation mode is a preset first target reconciliation mode, storing the data to be compared into a preset message queue;
step S13: sequentially carrying out format conversion on the to-be-compared data dequeued in the message queue according to a preset data conversion standard to obtain converted data;
step S14: and comparing the converted data with corresponding preset standard data to obtain comparison results, and determining the account checking result of the data to be compared based on the comparison results.
In the embodiment, a target reconciliation mode is determined based on a reconciliation mode selection instruction, and data to be compared of a target platform is obtained through a preset data receiving interface; if the target reconciliation mode is a preset first target reconciliation mode, storing the data to be compared into a preset message queue; sequentially carrying out format conversion on the to-be-compared data dequeued in the message queue according to a preset data conversion standard to obtain converted data; and comparing the converted data with corresponding preset standard data to obtain comparison results, and determining the account checking result of the data to be compared based on the comparison results. Therefore, on one hand, the application obtains the data to be compared through a preset interface in the process of abstracting the reconciliation data into the data comparison, and converts the data to be compared into converted data according to a preset data conversion standard to realize data comparison so as to obtain a reconciliation result; and the method can adapt to diversified business scenes. On the other hand, the data to be reconciled are scattered and processed through the arrangement of the message queue, and the data blocks are reduced, so that the processing pressure of mass data is reduced.
Further, the embodiment of the present application further discloses an electronic device, and fig. 3 is a block diagram of an electronic device 20 according to an exemplary embodiment, where the content of the figure is not to be considered as any limitation on the scope of use of the present application.
Fig. 3 is a schematic structural diagram of an electronic device 20 according to an embodiment of the present application. The electronic device 20 may specifically include: at least one processor 21, at least one memory 22, a power supply 23, a communication interface 24, an input output interface 25, and a communication bus 26. Wherein the memory 22 is configured to store a computer program that is loaded and executed by the processor 21 to implement the relevant steps in the reconciliation method disclosed in any of the foregoing embodiments. In addition, the electronic device 20 in the present embodiment may be specifically an electronic computer.
In this embodiment, the power supply 23 is configured to provide an operating voltage for each hardware device on the electronic device 20; the communication interface 24 can create a data transmission channel between the electronic device 20 and an external device, and the communication protocol to be followed is any communication protocol applicable to the technical solution of the present application, which is not specifically limited herein; the input/output interface 25 is used for acquiring external input data or outputting external output data, and the specific interface type thereof may be selected according to the specific application requirement, which is not limited herein.
The memory 22 may be a carrier for storing resources, such as a read-only memory, a random access memory, a magnetic disk, or an optical disk, and the resources stored thereon may include an operating system 221, a computer program 222, and the like, and the storage may be temporary storage or permanent storage.
The operating system 221 is used for managing and controlling various hardware devices on the electronic device 20 and computer programs 222, which may be Windows Server, netware, unix, linux, etc. The computer program 222 may further include a computer program that can be used to perform other specific tasks in addition to the computer program that can be used to perform the reconciliation method performed by the electronic device 20 as disclosed in any of the embodiments previously described.
Further, the application also discloses a computer readable storage medium for storing a computer program; wherein the computer program, when executed by a processor, implements the previously disclosed reconciliation method. For specific steps of the method, reference may be made to the corresponding contents disclosed in the foregoing embodiments, and no further description is given here.
In this specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, so that the same or similar parts between the embodiments are referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant points refer to the description of the method section.
Those of skill would further appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative elements and steps are described above generally in terms of functionality in order to clearly illustrate the interchangeability of hardware and software. 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.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. The software modules may be disposed in Random Access Memory (RAM), memory, read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.
Finally, it is further noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The foregoing has outlined rather broadly the more detailed description of the application in order that the detailed description of the application that follows may be better understood, and in order that the present principles and embodiments may be better understood; meanwhile, as those skilled in the art will have variations in the specific embodiments and application scope in accordance with the ideas of the present application, the present description should not be construed as limiting the present application in view of the above.
Claims (10)
1. A reconciliation system comprising:
the data acquisition module is used for determining a target reconciliation mode based on the reconciliation mode selection instruction and acquiring data to be compared of the target platform through a preset data receiving interface;
the first data storage tool is used for storing the data to be compared into a preset message queue if the target reconciliation mode is a preset first target reconciliation mode;
the data converter is used for sequentially carrying out format conversion on the data to be compared which are dequeued in the message queue according to a preset data conversion standard so as to obtain converted data;
and the data comparison tool is used for comparing each converted data with corresponding preset standard data to obtain each comparison result, and determining the account comparison result of the data to be compared based on each comparison result.
2. The reconciliation system of claim 1, wherein the data acquisition module comprises:
the platform connection unit is used for connecting a preset data receiving interface of a target platform through a preset data acquisition tool so as to acquire data to be compared of the target platform.
3. The reconciliation system of claim 1, wherein the first data storage means comprises:
and the data delivery unit is used for delivering the data to be compared to a preset message queue through a Spark engine if the target reconciliation mode is a preset first target reconciliation mode.
4. The reconciliation system of claim 3, wherein the data converter comprises:
the data sending unit is used for sending the data to be compared which are dequeued in the message queue to a corresponding preset data processing node through the Spark engine so as to obtain data to be converted;
and the data conversion unit is used for carrying out format conversion on the data to be converted through the preset data processing node and the preset data conversion standard so as to obtain all converted data.
5. The reconciliation system of claim 1, wherein the data converter comprises:
the data loading unit is used for loading the data to be compared of the message queue into a preset event pool in sequence;
and the data reading unit is used for sequentially reading the data to be compared from the preset event pool according to the time sequence and carrying out format conversion according to a preset data conversion standard so as to obtain converted data.
6. The reconciliation system of claim 1, further comprising:
the second data storage tool is used for storing the data to be compared into a preset database if the target reconciliation mode is a preset second target reconciliation mode;
the database segmentation module is used for carrying out segmentation processing on the preset database to obtain segmented databases; and carrying out format conversion on the data to be processed in each segmented database according to a preset data conversion standard in sequence to obtain each converted data.
7. The reconciliation system of any one of claims 1-6, wherein the data alignment tool comprises:
the data judging unit is used for judging whether the converted data are consistent with the corresponding preset standard data or not;
and the result acquisition unit is used for recording comparison results and determining the account checking result of the data to be compared based on each comparison result if the converted data are inconsistent with the corresponding preset standard data.
8. A method of reconciliation, comprising:
determining a target reconciliation mode based on the reconciliation mode selection instruction, and acquiring data to be compared of the target platform through a preset data receiving interface;
if the target reconciliation mode is a preset first target reconciliation mode, storing the data to be compared into a preset message queue;
sequentially carrying out format conversion on the to-be-compared data dequeued in the message queue according to a preset data conversion standard to obtain converted data;
and comparing the converted data with corresponding preset standard data to obtain comparison results, and determining the account checking result of the data to be compared based on the comparison results.
9. An electronic device, comprising:
a memory for storing a computer program;
a processor for executing the computer program to implement the reconciliation method of claim 8.
10. A computer readable storage medium storing a computer program which when executed by a processor implements the reconciliation method of claim 8.
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| CN117149797A (en) * | 2023-10-27 | 2023-12-01 | 杭银消费金融股份有限公司 | Accounting method and system based on multidimensional data monitoring |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117149797A (en) * | 2023-10-27 | 2023-12-01 | 杭银消费金融股份有限公司 | Accounting method and system based on multidimensional data monitoring |
| CN117149797B (en) * | 2023-10-27 | 2024-01-19 | 杭银消费金融股份有限公司 | Accounting method and system based on multidimensional data monitoring |
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