CN112258191A - Data reconciliation method, device, equipment and storage medium - Google Patents
Data reconciliation method, device, equipment and storage medium Download PDFInfo
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Abstract
The invention relates to the technical field of information processing, and discloses a data reconciliation method, a device, equipment and a storage medium, wherein the method comprises the following steps: the method comprises the steps of obtaining local order data and channel order data, storing the local order data and the channel order data into a redis database to serve as first reconciliation data and second reconciliation data, comparing the first reconciliation data with the second reconciliation data to obtain difference reconciliation data, and obtaining a judgment result according to the difference reconciliation data. Compared with the prior art that the total amount of the account checking data of the two parties is directly compared, the account checking data of the two parties is compared after being processed by a difference comparison method, so that the complexity of an account checking program can be effectively reduced, the occupation of database resources is reduced, and the overall efficiency of account checking is improved.
Description
Technical Field
The present invention relates to the field of information processing technologies, and in particular, to a data reconciliation method, apparatus, device, and storage medium.
Background
With the rapid development of internet technology, enterprises can complete order transaction by connecting a plurality of third-party payment channel systems through self-built payment systems. However, data reconciliation is particularly important because local order data may not be consistent with channel order data due to network delay, network error or system vulnerability during the payment process.
At present, most systems directly compare the total amount of system data of both parties, for example, all local order data and channel order data are stored in a temporary database, continuous large-batch traversal comparison query is performed based on the temporary database, and difference data is gradually found out from the temporary database and recorded.
However, the account checking procedure is complicated and time-consuming due to huge data volume, and resources of a database are always occupied in the account checking process, so that the account checking efficiency is low.
The above is only for the purpose of assisting understanding of the technical aspects of the present invention, and does not represent an admission that the above is prior art.
Disclosure of Invention
The invention mainly aims to provide a data reconciliation method, a device, equipment and a storage medium, and aims to solve the technical problems of complicated and time-consuming reconciliation procedure, more database resources occupied by the reconciliation process and low reconciliation efficiency.
In order to achieve the above object, the present invention provides a data reconciliation method, which comprises the following steps:
acquiring local order data and channel order data;
storing the local order data serving as first reconciliation data and the channel order data serving as second reconciliation data into a redis database;
comparing the first reconciliation data with the second reconciliation data to obtain difference reconciliation data;
and performing data updating judgment on account checking results in the local order data and/or the channel order data according to the difference account checking data, and obtaining a judgment result.
Preferably, the acquiring local order data and channel order data specifically includes:
sending a channel order data request to a channel operator, and receiving channel order data fed back by the channel operator according to the channel order data request;
storing the channel order data into a local server;
local order data stored in a non-volatile memory of a local server is obtained.
Preferably, the storing the local order data as the first reconciliation data and the channel order data as the second reconciliation data in a redis database specifically includes:
extracting order numbers and order amount information of the local order data as first account checking data and storing the first account checking data in a redis database;
and extracting order numbers and order amount information of the channel order data as second account checking data and storing the second account checking data in a redis database.
Preferably, the comparing the first reconciliation data with the second reconciliation data to obtain difference reconciliation data specifically includes:
acquiring daily money data according to the first reconciliation data and the second reconciliation data;
reading historical in-doubt order data, and obtaining in-doubt leveling data according to the historical in-doubt order data and the money-on-day data;
and determining actual short money data and actual long money data according to the historical doubt order data, the doubt leveling data and the daily long money data, and taking the actual short money data and the actual long money data as difference reconciliation data.
Preferably, the acquiring of the money data on the day according to the first reconciliation data and the second reconciliation data specifically includes:
acquiring intersection data between the first reconciliation data and the second reconciliation data;
comparing the second reconciliation data with the intersection data, and determining the money data on the day according to the comparison result;
the step of reading the historical in-doubt order data and obtaining in-doubt leveling data according to the historical in-doubt order data and the money on the day data comprises the following steps:
reading historical in-doubt order data and storing the historical in-doubt order data into the redis database;
and performing intersection operation on the historical doubt order data and the money data on the day to obtain doubt leveling data.
Preferably, the determining actual short money data and actual long money data according to the historical in-doubt order data, the in-doubt leveling data and the daily long money data specifically includes:
performing difference set operation on the historical in-doubt order data and the in-doubt leveling data, acquiring reconciliation data exceeding a preset in-doubt duration according to a difference set operation result, and taking the acquired reconciliation data as actual short-fund data;
and performing difference set operation on the daily long data and the doubt leveling data to obtain actual long data.
Preferably, the performing data update judgment on the reconciliation result in the local order data and/or the channel order data according to the difference reconciliation data and obtaining a judgment result specifically includes:
when the difference reconciliation data only contains the actual long-money data, judging that the local order data is to-be-updated data;
when the difference reconciliation data only contains the actual short payment data, judging that the channel order data is to-be-updated data;
and when the difference reconciliation data comprises the actual long payment data and the actual short payment data, judging that the local order data and the channel order data are the data to be updated.
In addition, in order to achieve the above object, the present invention further provides a data reconciliation apparatus, which includes the following modules:
the data acquisition module is used for acquiring local order data and channel order data;
the data storage module is used for storing the local order data serving as first reconciliation data and the channel order data serving as second reconciliation data into a redis database;
the data reconciliation module is used for comparing the first reconciliation data with the second reconciliation data to obtain difference reconciliation data;
and the result judging module is used for carrying out data updating judgment on the reconciliation result in the local order data and/or the channel order data according to the difference reconciliation data and obtaining a judgment result.
In addition, in order to achieve the above object, the present invention further provides a data reconciliation device, including: the system comprises a memory, a processor and a data reconciliation program stored on the memory and capable of running on the processor, wherein the data reconciliation program realizes the steps of the data reconciliation method in any item when being executed by the processor.
In addition, in order to achieve the above object, the present invention further provides a computer-readable storage medium, on which a data reconciliation program is stored, which when executed implements the steps of the data reconciliation method described in any one of the above.
The method comprises the steps of obtaining local order data and channel order data, storing the local order data and the channel order data into a redis database to serve as first reconciliation data and second reconciliation data, comparing the first reconciliation data with the second reconciliation data to obtain difference reconciliation data, and obtaining a judgment result according to the difference reconciliation data. Compared with the prior art that the total amount of the account checking data of the two parties is directly compared, the account checking data of the two parties is compared after being processed by a difference comparison method, and an account checking result is obtained, so that the complexity of an account checking program can be effectively reduced, the occupation of database resources is reduced, and the overall efficiency of account checking is improved.
Drawings
Fig. 1 is a schematic structural diagram of a data reconciliation device of a hardware operating environment according to an embodiment of the present invention;
FIG. 2 is a flowchart illustrating a first embodiment of a data reconciliation method according to the present invention;
FIG. 3 is a flowchart illustrating a data reconciliation method according to a second embodiment of the present invention;
fig. 4 is a block diagram of a first embodiment of the data reconciliation apparatus according to the present invention.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Referring to fig. 1, fig. 1 is a schematic structural diagram of a data reconciliation device in a hardware operating environment according to an embodiment of the present invention.
As shown in fig. 1, the data reconciliation device may include: a processor 1001, such as a Central Processing Unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. Wherein a communication bus 1002 is used to enable connective communication between these components. The user interface 1003 may include a Display screen (Display), an input unit such as a Keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface, a wireless interface. The network interface 1004 may optionally include a standard wired interface, a WIreless interface (e.g., a WIreless-FIdelity (WI-FI) interface). The Memory 1005 may be a Random Access Memory (RAM) Memory, or may be a Non-Volatile Memory (NVM), such as a disk Memory. The memory 1005 may alternatively be a storage device separate from the processor 1001.
Those skilled in the art will appreciate that the configuration shown in fig. 1 does not constitute a limitation of the data reconciliation device, and may include more or fewer components than those shown, or some components in combination, or a different arrangement of components.
As shown in fig. 1, the memory 1005, which is a storage medium, may include therein an operating system, a data storage module, a network communication module, a user interface module, and a data reconciliation program.
In the data reconciliation apparatus shown in fig. 1, the network interface 1004 is mainly used for data communication with the network server; the user interface 1003 is mainly used for data interaction with a user; the processor 1001 and the memory 1005 in the data reconciliation device of the present invention can be provided in the data reconciliation device, and the data reconciliation device calls the data reconciliation program stored in the memory 1005 through the processor 1001 and executes the data reconciliation method provided by the embodiment of the present invention.
An embodiment of the present invention provides a data reconciliation method, and referring to fig. 2, fig. 2 is a schematic flow diagram of a first embodiment of the data reconciliation method of the present invention.
In this embodiment, the data reconciliation method includes the following steps:
step S10: acquiring local order data and channel order data;
it should be noted that the execution main body of the embodiment may be a computing service device with data processing, network communication and program running functions, such as a smart phone, a tablet computer, a personal computer, and the like, or may be other devices capable of implementing the above functions, which is not limited in the embodiment. The present embodiment and the following embodiments will be specifically described by taking a data reconciliation device as an example.
In this embodiment, the order data may be information that is generated in the payment process and can indicate details of the order, for example: order number, order amount, commission charge amount, order creation time and the like, wherein the local order data is order data locally generated by the data reconciliation equipment, and the channel order data is order data generated by a channel operator.
In a specific implementation, the data reconciliation equipment sends a channel order data request to a channel operator, receives channel order data sent by the channel operator, and then acquires local order data stored in a nonvolatile memory of a local server in advance.
Further, in order to improve the data reconciliation efficiency, the step S10 may be further refined as follows:
step S101: sending a channel order data request to a channel operator, and receiving channel order data fed back by the channel operator according to the channel order data request;
it should be understood that the data reconciliation facility sending the channel order data request may be sent over a connection network, such as: a 2G network, a 3G network, a 4G network, a 5G network, a broadband network, an optical fiber network, etc., which are not limited in this embodiment.
In the specific implementation, the data reconciliation equipment is connected with the network to send the channel order data to the channel operator, and the channel operator receives the channel order data request, analyzes the request and sends the channel order data back to the data reconciliation equipment through the network.
Step S102: storing the channel order data into a local server;
in a specific implementation, after the data reconciliation equipment acquires the channel order data, the channel order data is stored in a nonvolatile memory of a local server for storage.
Step S103: local order data stored in a non-volatile memory of a local server is obtained.
It should be understood that the local order data is pre-stored in the non-volatile memory of the local server.
In a specific implementation, the data reconciliation device reads local order data pre-stored in a non-volatile memory of the local server.
Step S20: storing the local order data serving as first reconciliation data and the channel order data serving as second reconciliation data into a redis database;
it should be understood that the first reconciliation data is information that is part of the intercepted local order data and may indicate characteristics of the local order data, such as: the amount of the commission charge of the local order data, the order creation time, etc.; the second reconciliation data is information that is part of the intercepted channel order data and may indicate characteristics of the channel order data, such as: the amount of commission charges for the channel order data, the order creation time, etc.
In a specific implementation, the data reconciliation device may use the local order data as first reconciliation data, use the received channel order data as second reconciliation data, and then store the first reconciliation data and the second reconciliation data in a redis database.
Further, in order to solve the defects of the independent step or achieve a further object, the step S20 may be further refined as follows:
step S201: extracting order numbers and order amount information of the local order data as first account checking data and storing the first account checking data in a redis database;
in a specific implementation, the data reconciliation device extracts an order number and an order amount of the local order data, and stores the extracted order number and the extracted order amount as first reconciliation data in a redis database as the first reconciliation data.
Step S202: and extracting order numbers and order amount information of the channel order data as second account checking data and storing the second account checking data in a redis database.
In a specific implementation, the data reconciliation device extracts an order number and an order amount of the channel order data, and stores the extracted order number and the extracted order amount as second reconciliation data into a redis database as the second reconciliation data.
Step S30: comparing the first reconciliation data with the second reconciliation data to obtain difference reconciliation data;
it should be noted that the differential reconciliation data refers to reconciliation data in which local order data and channel order data are inconsistent due to network delay, network error, or system bug. For example: the difference order data caused by network delay is that when the creation time of the local trade order is 23:59:59, due to network delay, the order data can be 00:00:01 when the order reaches a third-party channel, and the phenomenon that the order data exists locally and the order data does not exist in the channel can occur when the order data is summarized in the previous day.
It should be understood that the data reconciliation device compares the first reconciliation data with the second reconciliation data in order to obtain differential reconciliation data that eliminates the effects of network delays, for example: including differential reconciliation data caused by network errors or system vulnerabilities, which is not limited by the embodiment.
In a specific implementation, the data reconciliation equipment performs intersection difference set and other comparison operations on the first reconciliation data and the second reconciliation data to obtain difference reconciliation data for eliminating network delay influence.
Step S40: and performing data updating judgment on account checking results in the local order data and/or the channel order data according to the difference account checking data, and obtaining a judgment result.
It should be understood that, in order to obtain the final reconciliation result, the data reconciliation equipment generally needs to analyze and compare the difference reconciliation data and output the judgment result.
In a specific implementation, the data reconciliation equipment judges whether the local order data or the channel order data is to-be-updated data or not according to a reconciliation result of the difference reconciliation data.
Further, in order to improve the reconciliation efficiency, step S40 may be further refined as follows:
s401: when the difference reconciliation data only contains the actual long-money data, judging that the local order data is to-be-updated data;
it should be noted that the actual growth data refers to growth data from which the network delay influence is eliminated, for example: the local order data is not available, and the channel order data has data.
In a specific implementation, if the data reconciliation device detects that the differential reconciliation data only contains the actual long-money data, the local order data is determined to be the data to be updated, and the actual long-money data needs to be analyzed and determined to determine the place where the local order data needs to be updated.
S402: when the difference reconciliation data only contains the actual short payment data, judging that the channel order data is to-be-updated data;
it should be noted that the actual short data refers to short data with the effect of network delay eliminated, for example: the local order data is data with the channel order data is data without the channel order data.
In a specific implementation, if the data reconciliation device detects that the differential reconciliation data only contains the actual short fund data, the channel order data is judged to be the data to be updated, and the actual short fund data needs to be analyzed and judged to later determine the place where the channel order data needs to be updated.
S403: and when the difference reconciliation data comprises the actual long payment data and the actual short payment data, judging that the local order data and the channel order data are the data to be updated.
In a specific implementation, if the data reconciliation device detects that the difference reconciliation data contains both the actual long-fund data and the actual short-fund data. Judging that the local order data and the channel order data are both data to be updated, and analyzing and judging the actual long money data and the actual short money data to determine the places where the local order data and the channel order data need to be updated.
In the embodiment, the local order data and the channel order data are acquired and stored in the redis database as the first reconciliation data and the second reconciliation data, the first reconciliation data and the second reconciliation data are compared to obtain the difference reconciliation data, and the judgment result is obtained according to the difference reconciliation data. Compared with the prior art that the total amount of the account checking data of the two parties is directly compared, the account checking data of the two parties is compared after being processed by a difference comparison method, and an account checking result is obtained, so that the complexity of an account checking program can be effectively reduced, the occupation of database resources is reduced, and the overall efficiency of account checking is improved.
Referring to fig. 3, fig. 3 is a flowchart illustrating a data reconciliation method according to a second embodiment of the present invention.
Based on the first embodiment described above, in the present embodiment, the step S30 includes:
step S301: acquiring daily money data according to the first reconciliation data and the second reconciliation data;
it should be noted that the daily surplus data refers to order data that may be caused by network delay or other factors, but not local order data, but channel order data.
It should be understood that the difference comparison operation refers to an algorithm for comparing the first reconciliation data with the second reconciliation data, and the purpose of the difference comparison operation is to obtain a difference result of the first reconciliation data and the second reconciliation data.
In a specific implementation, the data reconciliation device may perform intersection operation on the first reconciliation data and the second reconciliation data, and then further determine the daily fund data according to a result of the intersection operation.
Further, in order to improve the reconciliation efficiency, the step S301 may be further refined as follows:
step S3011: acquiring intersection data between the first reconciliation data and the second reconciliation data;
step S3012: and comparing the second reconciliation data with the intersection data, and determining the money data on the day according to the comparison result.
It should be noted that the intersection operation refers to an operation of performing intersection on 2 sets by using a redis intersection operation instruction, for example: and adopting a sinter method to take intersection of the first reconciliation data and the second reconciliation data.
It should be understood that there is an overlapping portion between the first reconciliation data and the second reconciliation data, and the overlapping portion is a portion where the local order data and the channel order data are aligned and do not have a difference, i.e. the intersection data.
In specific implementation, the data reconciliation equipment calculates the first reconciliation data and the second reconciliation data by using an intersection operation instruction of redis, and after the intersection data is obtained, the second reconciliation data and the intersection data can be compared, and then the money data on the day is determined according to a comparison result.
It should be understood that the difference between the second reconciliation data and the intersection data is a set of long-amount data, that is, order data that the local order data does not have and the channel order data has, but the set of long-amount data may be caused by network delay, so that the set of long-amount data is taken as the day-long-amount data so that the subsequent operation can obtain the difference reconciliation data without the influence of the network delay.
In a specific implementation, the data reconciliation equipment calculates the second reconciliation data and the intersection data by using a difference set operation instruction of redis, and determines the daily long data according to an operation result.
Step S302: reading historical in-doubt order data, and obtaining in-doubt leveling data according to the historical in-doubt order data and the money-on-day data;
it should be noted that, the historical in-doubt order data, i.e. the inventory in-doubt order, normally, the number of days the order remains in doubt is fixed (generally, one day). The in-doubt leveling data is data with uneven reconciliation and accounting converted into data with even reconciliation and accounting. For example: the daily growth data and the historical in-doubt order data have overlapped partial data due to network delay, and the partial data is extracted to be a group of in-doubt leveling data.
It should be understood that there is an intersection between the historical in-doubt order data and the current day money data, i.e., there is historical in-doubt order data the day before and the current day money data the day after the network delay. Therefore, the intersection operation is performed on the two data, and the data which can be leveled, namely the data converted into the leveling data, can be found out.
In a specific implementation, the data reconciliation device performs intersection operation on the first long-money data and the first short-money data, and takes the result of the intersection operation as the doubt leveling data. Specifically, the data reconciliation equipment can read the historical doubt order data, store the historical doubt order data into a redis database, and then perform intersection operation on the historical doubt order data and the daily long money data to obtain the doubt leveling data.
Step S303: and determining actual short money data and actual long money data according to the historical doubt order data, the doubt leveling data and the daily long money data, and taking the actual short money data and the actual long money data as difference reconciliation data.
In a specific implementation, the data reconciliation equipment can carry out difference set operation on historical in-doubt order data and in-doubt leveling data, then obtain reconciliation data exceeding a preset in-doubt duration according to a difference set operation result, and take the obtained reconciliation data as actual short-fund data; and performing difference set operation on the current-day long-money data and the doubt leveling data to obtain actual long-money data, and finally taking the actual short-money data and the actual long-money data as difference reconciliation data. Wherein the preset in-doubt duration is also typically set to one day.
In addition, an embodiment of the present invention further provides a storage medium, where the storage medium stores a data reconciliation program, and the data reconciliation program, when executed by a processor, implements the steps of the data reconciliation method described above.
Referring to fig. 4, fig. 4 is a block diagram of a first embodiment of the data reconciliation apparatus according to the present invention.
As shown in fig. 4, a data reconciliation apparatus according to an embodiment of the present invention includes:
a data obtaining module 401, configured to obtain local order data and channel order data;
a data storage module 402, configured to store the local order data as first reconciliation data and the channel order data as second reconciliation data in a redis database;
a data reconciliation module 403, configured to compare the first reconciliation data with the second reconciliation data to obtain difference reconciliation data;
and the result judgment module 404 is configured to perform data update judgment on the reconciliation result in the local order data and/or the channel order data according to the difference reconciliation data, and obtain a judgment result.
In the embodiment, the local order data and the channel order data are acquired and stored in the redis database as the first reconciliation data and the second reconciliation data, the first reconciliation data and the second reconciliation data are compared to obtain the difference reconciliation data, and the judgment result is obtained according to the difference reconciliation data. Compared with the prior art that the total amount of the account checking data of the two parties is directly compared, the account checking data of the two parties is compared after being processed by a difference comparison method, and an account checking result is obtained, so that the complexity of an account checking program can be effectively reduced, the occupation of database resources is reduced, and the overall efficiency of account checking is improved.
Based on the first embodiment of the data reconciliation device, a second embodiment of the data reconciliation device is provided.
In this embodiment, the data obtaining module 401 is configured to send a channel order data request to a channel operator, and receive channel order data fed back by the channel operator according to the channel order data request; storing the channel order data into a local server; local order data stored in a non-volatile memory of a local server is obtained.
Further, the data storage module 402 is configured to extract the order number and the order amount information of the local order data, and store the order number and the order amount information as first reconciliation data in a redis database; and extracting order numbers and order amount information of the channel order data as second account checking data and storing the second account checking data in a redis database.
Further, the data reconciliation module 403 is configured to obtain money-on-day data according to the first reconciliation data and the second reconciliation data; reading historical in-doubt order data, and obtaining in-doubt leveling data according to the historical in-doubt order data and the money-on-day data; and determining actual short money data and actual long money data according to the historical doubt order data, the doubt leveling data and the daily long money data, and taking the actual short money data and the actual long money data as difference reconciliation data.
Further, the data reconciliation module 403 is further configured to obtain intersection data between the first reconciliation data and the second reconciliation data; comparing the second reconciliation data with the intersection data, and determining the money data on the day according to the comparison result; reading historical in-doubt order data and storing the historical in-doubt order data into the redis database; and performing intersection operation on the historical doubt order data and the money data on the day to obtain doubt leveling data.
Further, the data reconciliation module 403 is further configured to perform difference set operation on the historical in-doubt order data and the in-doubt leveling data, obtain reconciliation data exceeding a preset in-doubt duration according to a difference set operation result, and use the obtained reconciliation data as actual short-fund data; and performing difference set operation on the daily long data and the doubt leveling data to obtain actual long data.
Further, the result determining module 404 is configured to determine that the local order data is to-be-updated data when the difference reconciliation data only includes the actual long-money data; when the difference reconciliation data only contains the actual short payment data, judging that the channel order data is to-be-updated data; and when the difference reconciliation data comprises the actual long payment data and the actual short payment data, judging that the local order data and the channel order data are the data to be updated.
Other embodiments or specific implementation manners of the data reconciliation device of the invention can refer to the above method embodiments, and are not described again here.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system 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 system. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or system that comprises the element.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium (e.g., ROM/RAM, magnetic disk, optical disk) as described above and includes instructions for enabling a terminal device (e.g., a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the method according to the embodiments of the present invention.
The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (10)
1. A data reconciliation method, characterized in that the method comprises the steps of:
acquiring local order data and channel order data;
storing the local order data serving as first reconciliation data and the channel order data serving as second reconciliation data into a redis database;
comparing the first reconciliation data with the second reconciliation data to obtain difference reconciliation data;
and performing data updating judgment on account checking results in the local order data and/or the channel order data according to the difference account checking data, and obtaining a judgment result.
2. The method of claim 1, wherein the obtaining local order data and channel order data comprises:
sending a channel order data request to a channel operator, and receiving channel order data fed back by the channel operator according to the channel order data request;
storing the channel order data into a local server;
local order data stored in a non-volatile memory of a local server is obtained.
3. The method of claim 2, wherein the storing the local order data as first reconciliation data and the channel order data as second reconciliation data in a redis database specifically comprises:
extracting order numbers and order amount information of the local order data as first account checking data and storing the first account checking data in a redis database;
and extracting order numbers and order amount information of the channel order data as second account checking data and storing the second account checking data in a redis database.
4. The method of claim 3, wherein the comparing the first reconciliation data with the second reconciliation data to obtain differential reconciliation data specifically comprises:
acquiring daily money data according to the first reconciliation data and the second reconciliation data;
reading historical in-doubt order data, and obtaining in-doubt leveling data according to the historical in-doubt order data and the money-on-day data;
and determining actual short money data and actual long money data according to the historical doubt order data, the doubt leveling data and the daily long money data, and taking the actual short money data and the actual long money data as difference reconciliation data.
5. The method according to claim 4, wherein the obtaining of the daily fund data from the first reconciliation data and the second reconciliation data specifically comprises:
acquiring intersection data between the first reconciliation data and the second reconciliation data;
comparing the second reconciliation data with the intersection data, and determining the money data on the day according to the comparison result;
the step of reading the historical in-doubt order data and obtaining in-doubt leveling data according to the historical in-doubt order data and the money on the day data comprises the following steps:
reading historical in-doubt order data and storing the historical in-doubt order data into the redis database;
and performing intersection operation on the historical doubt order data and the money data on the day to obtain doubt leveling data.
6. The method of claim 5, wherein determining actual short-money data and actual long-money data from the historical in-doubt order data, the in-doubt leveling data and the on-day long-money data comprises:
performing difference set operation on the historical in-doubt order data and the in-doubt leveling data, acquiring reconciliation data exceeding a preset in-doubt duration according to a difference set operation result, and taking the acquired reconciliation data as actual short-fund data;
and performing difference set operation on the daily long data and the doubt leveling data to obtain actual long data.
7. The method according to claim 6, wherein the performing a data update determination on the reconciliation result in the local order data and/or the channel order data according to the difference reconciliation data and obtaining a determination result specifically comprises:
when the difference reconciliation data only contains the actual long-money data, judging that the local order data is to-be-updated data;
when the difference reconciliation data only contains the actual short payment data, judging that the channel order data is to-be-updated data;
and when the difference reconciliation data comprises the actual long payment data and the actual short payment data, judging that the local order data and the channel order data are the data to be updated.
8. A data reconciliation apparatus, the apparatus comprising:
the data acquisition module is used for acquiring local order data and channel order data;
the data storage module is used for storing the local order data serving as first reconciliation data and the channel order data serving as second reconciliation data into a redis database;
the data reconciliation module is used for comparing the first reconciliation data with the second reconciliation data to obtain difference reconciliation data;
and the result judging module is used for carrying out data updating judgment on the reconciliation result in the local order data and/or the channel order data according to the difference reconciliation data and obtaining a judgment result.
9. A data reconciliation device, characterized in that the data reconciliation device comprises: a memory, a processor, and a data reconciliation program stored on the memory and executable on the processor, the data reconciliation program when executed by the processor implementing the steps of the data reconciliation method of any of claims 1-7.
10. A computer-readable storage medium, having stored thereon a data reconciliation program, which when executed performs the steps of the data reconciliation method of any of claims 1-7.
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