Transaction data checking method, device and equipment, readable storage medium and product

Abstract

The invention relates to a transaction data checking method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the steps that a container about a transaction data checking task is acquired, the container is used for importing original transaction data and submitted transaction data generated in a target area, and the submitted transaction data is transaction data submitted to a transaction data management mechanism; under the condition that the container is checked to be in the check state, performing data conversion on original transaction data related to the transaction data check task in the container to obtain transaction data to be submitted in accordance with a data format of a transaction data management mechanism; and according to a preset comparison rule, based on the transaction data to be submitted and the submitted transaction data related to the transaction data inspection task in the container, performing data inspection to obtain a target inspection result, thereby improving the transaction data inspection efficiency.

Description

Technical Field

[0001] This application relates to the field of computer technology, and in particular to a method, apparatus, computer equipment, computer-readable storage medium, and computer program product for inspecting transaction data. Background Technology

[0002] With the development of computer technology, the scale of e-commerce transactions has grown exponentially, and cross-platform and cross-border transactions have become increasingly complex. The integrity and compliance of transaction data is not only related to the protection of consumer rights, but also the cornerstone of building a trustworthy digital business ecosystem. Therefore, the inspection of transaction data is very important.

[0003] In related technologies, on-site inspectors or internal bank compliance personnel typically retrieve all transaction data for a specific period from the bank's back-end database, such as overseas transaction data. They then obtain the reported data from the regulatory agency responsible for the transaction data. The inspection of both the transaction and reported data is then conducted manually, using spreadsheet software for sorting and filtering. However, this method of manually verifying transaction data is inefficient. Summary of the Invention

[0004] Therefore, it is necessary to provide a transaction data inspection method, apparatus, computer equipment, computer-readable storage medium, and computer program product that can improve the efficiency of transaction data inspection in response to the above-mentioned technical problems.

[0005] Firstly, this application provides a method for inspecting transaction data, including:

[0006] Obtain a container for the transaction data inspection task, the container being used to import the original transaction data generated in the target area and the reported transaction data, which is the transaction data reported to the transaction data management agency;

[0007] If the container is found to be in an inspection state, the original transaction data related to the transaction data inspection task in the container is converted to obtain the transaction data to be reported that conforms to the data format of the transaction data management agency.

[0008] According to the preset comparison rules, based on the transaction data to be reported and the reported transaction data in the container related to the transaction data inspection task, data inspection is performed to obtain the target inspection result.

[0009] Secondly, this application also provides a transaction data inspection device, comprising:

[0010] The container acquisition module is used to acquire a container for the transaction data inspection task. The container is used to import the original transaction data generated in the target area and the reported transaction data, which is the transaction data reported to the transaction data management agency.

[0011] The format conversion module is used to convert the original transaction data related to the transaction data inspection task in the container when the container is found to be in the inspection state, so as to obtain the transaction data to be reported that conforms to the data format of the transaction data management agency.

[0012] The data inspection module is used to perform data inspection based on the transaction data to be reported and the reported transaction data related to the transaction data inspection task in the container, according to preset comparison rules, and to obtain the target inspection result.

[0013] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0014] Obtain a container for the transaction data inspection task, the container being used to import the original transaction data generated in the target area and the reported transaction data, which is the transaction data reported to the transaction data management agency;

[0015] If the container is found to be in an inspection state, the original transaction data related to the transaction data inspection task in the container is converted to obtain the transaction data to be reported that conforms to the data format of the transaction data management agency.

[0016] According to the preset comparison rules, based on the transaction data to be reported and the reported transaction data in the container related to the transaction data inspection task, data inspection is performed to obtain the target inspection result.

[0017] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the following steps:

[0018] Obtain a container for the transaction data inspection task, the container being used to import the original transaction data generated in the target area and the reported transaction data, which is the transaction data reported to the transaction data management agency;

[0019] If the container is found to be in an inspection state, the original transaction data related to the transaction data inspection task in the container is converted to obtain the transaction data to be reported that conforms to the data format of the transaction data management agency.

[0020] According to the preset comparison rules, based on the transaction data to be reported and the reported transaction data in the container related to the transaction data inspection task, data inspection is performed to obtain the target inspection result.

[0021] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, performs the following steps:

[0022] Obtain a container for the transaction data inspection task, the container being used to import the original transaction data generated in the target area and the reported transaction data, which is the transaction data reported to the transaction data management agency;

[0023] If the container is found to be in an inspection state, the original transaction data related to the transaction data inspection task in the container is converted to obtain the transaction data to be reported that conforms to the data format of the transaction data management agency.

[0024] According to the preset comparison rules, based on the transaction data to be reported and the reported transaction data in the container related to the transaction data inspection task, data inspection is performed to obtain the target inspection result.

[0025] The aforementioned transaction data inspection method, apparatus, computer equipment, computer-readable storage medium, and computer program product acquire a container for the transaction data inspection task. This container imports raw transaction data generated in the target area and reported transaction data (data submitted to the transaction data management agency). The status of the container determines whether to initiate inspection of the imported transaction data, ensuring the effectiveness of the inspection. When the container is detected as being in inspection mode, the system automatically converts the raw transaction data related to the inspection task within the container to obtain reportable transaction data conforming to the data format specified by the transaction data management agency. This ensures that the data inspection can be performed in the format prescribed by the agency, eliminating the need for manual conversion and improving efficiency. Then, according to preset comparison rules, based on the reportable transaction data and the reported transaction data related to the inspection task within the container, the system automatically initiates the data inspection to obtain the target inspection result, eliminating the need for manual verification and comparison. This significantly improves the efficiency of transaction data inspection. Attached Figure Description

[0026] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0027] Figure 1 This is a diagram illustrating the application environment of a transaction data inspection method in one embodiment.

[0028] Figure 2 This is a flowchart illustrating a transaction data inspection method in one embodiment;

[0029] Figure 3 This is a schematic diagram of the target inspection results in one embodiment;

[0030] Figure 4 This is a schematic diagram illustrating the checking of query results in one embodiment;

[0031] Figure 5 This is a schematic diagram of the transaction data inspection process in one embodiment;

[0032] Figure 6 This is a structural block diagram of a transaction data detection device in one embodiment;

[0033] Figure 7 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0034] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0035] It should be noted that the terms "first," "second," etc., used in this application can be used to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish the first element from the second element. The terms "comprising" and "having," and any variations thereof, used in this application, are intended to cover non-exclusive inclusion. The term "multiple" used in this application refers to two or more. The term "and / or" used in this application refers to one of the embodiments, or any combination of multiple embodiments.

[0036] The transaction data inspection method provided in this application embodiment can be applied to, for example... Figure 1In the application environment shown, the target server 102 communicates with the transaction data management agency 104 via a network. In some embodiments, the target server 102 obtains a container for a transaction data inspection task. This container is used to import raw transaction data generated in the target area and reported transaction data, where the reported transaction data is the transaction data reported to the transaction data management agency. When the container is verified to be in an inspection state, the target server 102 performs data conversion on the raw transaction data related to the transaction data inspection task in the container to obtain reportable transaction data conforming to the data format of the transaction data management agency 104. Based on the reportable transaction data and the reported transaction data related to the transaction data inspection task in the container, a data inspection is performed according to preset comparison rules to obtain the target inspection result. The container is generated based on the reported transaction data sent by the transaction data management agency 104 and the raw transaction data sent by the original transaction provider. The original transaction provider can be the target server 102, or other servers or other terminals. Other servers send raw transaction data to the target server 102 to generate the container.

[0037] Other terminals are terminals that provide raw transaction data. These terminals can be, but are not limited to, various personal computers, laptops, smartphones, tablets, etc. The target server is the server that performs data inspection. This server can be a standalone physical server, a server cluster or distributed system consisting of multiple physical servers, or a cloud server providing cloud computing services.

[0038] In one exemplary embodiment, such as Figure 2 As shown, a method for inspecting transaction data is provided, which can be applied to... Figure 1 Taking target server 102 as an example, the explanation includes the following steps 202 to 206. Wherein:

[0039] Step 202: Obtain a container for the transaction data inspection task. The container is used to import the original transaction data generated in the target area and the reported transaction data. The reported transaction data is the transaction data reported to the transaction data management agency.

[0040] The transaction data inspection task involves examining transaction data. Optionally, this task can inspect both raw transaction data originating from the target bank and the reported transaction data submitted by the target bank to the transaction data management agency. The inspection aims to check for omissions, over-reporting, or errors in the transaction data originating from the target bank. Raw transaction data refers to transaction data generated using the target bank's bank card. The transaction data management agency manages transaction data from different banks and can be considered a regulatory body for transaction data. Reported transaction data refers to the transaction data submitted by the target bank to the transaction data management agency. Raw transaction data can originate in the target region; that is, it can be data generated after transactions are performed using the target bank's bank card within the target region. The target region can be other countries or regions; for example, the raw transaction data could be overseas transaction data. A container is used to import the relevant transaction data for the transaction data inspection task. Each transaction data inspection task has a corresponding container. This container can be understood as a data import component to isolate different transaction data inspection tasks.

[0041] Optionally, a transaction data inspection task is initiated when the transaction data inspection conditions are met at the current moment. The target server determines the container corresponding to the transaction data inspection task. For example, after initiating the transaction data inspection task, the target server can query containers that are currently idle (i.e., empty) and inject the original transaction data and reported data related to the transaction data inspection task into those containers. The transaction data inspection condition can be that the duration between the end time of the previous transaction data inspection task and the current time meets a preset duration.

[0042] In some embodiments, obtaining a container for a transaction data inspection task includes: after obtaining the transaction data inspection task, parsing the transaction data inspection task and determining the scope of data inspection; based on the scope of data inspection, obtaining the original transaction data and the reported transaction data corresponding to the scope of data inspection from the original transaction data provider and the transaction data management agency, respectively; obtaining a container template, and importing the original transaction data and the reported transaction data into the container template to obtain a container for the transaction data inspection task.

[0043] The data inspection scope specifies the conditions for the transaction data inspection task, such as which bank, year, and time period's transaction data will be inspected. The container template is a template without any injected transaction data. Optionally, after obtaining the transaction data inspection task, the target server parses the task to obtain the data inspection scope. It retrieves the original transaction data conforming to the data inspection scope from the original transaction provider and the reported transaction data conforming to the data inspection scope from the transaction data management structure. For example, the target server identifies keywords in the transaction data inspection task to determine time keywords and bank identification keywords. The data inspection scope is determined based on the time keywords and bank identification keywords. For instance, the transaction data inspection task is: inspect the transaction data of bank identifier A in the third quarter of year B. The data inspection scope includes bank identifier A, year B, and the third quarter; this transaction data is generated in the target region. Then, retrieve the original transaction data data1 for the third quarter of year b from the database of bank identifier a, and retrieve the reported transaction data data2 for the third quarter of year b from the transaction data management agency, which was submitted by the server of bank identifier a.

[0044] Optionally, the target server selects a container template corresponding to the data inspection scope from multiple candidate container templates, and imports the original transaction data and the reported transaction data into the container template respectively to obtain a container for the transaction data inspection task. For example, after selecting a container template corresponding to the data inspection scope, the sum of the data volume of the original transaction data and the data volume of the reported transaction data is calculated. The container template with the largest upper limit for imported data among the selected container templates is selected as the container template corresponding to the transaction data inspection task. The original transaction data and the reported transaction data are then imported into the container template corresponding to the transaction data inspection task to obtain a container for the transaction data inspection task.

[0045] In this embodiment, by creating corresponding containers for transaction data inspection tasks, it is ensured that different transaction data inspection tasks can be implemented in their respective containers, thereby achieving transaction data inspection isolation and ensuring the effectiveness and efficiency of transaction data inspection.

[0046] In some embodiments, the method further includes: obtaining a time inspection range from the data inspection range; determining the amount of data to be reported per unit time based on the amount of reported transaction data and the transaction duration involved in the time inspection range; and, if the amount of reported data is greater than or equal to a quantity threshold, returning to the step of importing the original transaction data and the reported transaction data into the container template respectively to obtain a container for the transaction data inspection task and continuing to execute.

[0047] The data inspection scope includes a time inspection scope, which indicates the duration of the transaction. For example, the time inspection scope is the third quarter of year b, that is, from July 1st to September 30th of year b. The unit of time can be days, months, weeks, etc.

[0048] For example, the target server obtains the time inspection range from the data inspection range, determines the transaction duration involved in the time inspection range, divides the transaction duration into units of time, obtains the number of divisions, and calculates the ratio of the reported transaction data to the number of divisions to obtain the amount of reported data per unit time. The target server compares the amount of reported data per unit time with the data threshold corresponding to the unit time. If the reported amount is greater than or equal to the data threshold, the target server returns to the step of importing the original transaction data and the reported transaction data into the container template to obtain the container for the transaction data inspection task and continues execution. If the reported amount is less than the data threshold, a reporting exception notification is sent to the transaction data management agency. For example, if the transaction duration of the time inspection range is from July 1st to September 30th, and the unit time is days, then the number of divisions is the total number of days in the third quarter. The ratio of the reported transaction data to the total number of days reflects the daily reporting amount, and the corresponding data threshold is the minimum daily reporting amount, i.e., the minimum daily data amount lower limit configuration.

[0049] In other embodiments, the method further includes: obtaining a time inspection range from the data inspection range; dividing the time inspection range based on unit duration to obtain a preset number of sub-time inspection ranges; obtaining sub-transaction data corresponding to each sub-time inspection range from the reported transaction data, and calculating the data volume of the sub-transaction data corresponding to each sub-time inspection range; selecting the smallest data volume among multiple data volumes, comparing the smallest data volume with a data threshold, and if the smallest data volume is greater than or equal to the data threshold, then returning to the step of importing the original transaction data and the reported transaction data into the container template to obtain a container for the transaction data inspection task and continuing execution.

[0050] In some embodiments, the data inspection scope includes a time inspection scope, which indicates the trading period. For example, the time inspection scope is the third quarter of year b, i.e., from July 1st to September 30th of year b. The unit duration is the length of a unit of time, such as a day, a week, or a month. The duration of the trading period corresponding to each sub-time inspection scope is the unit duration. For example, if the unit duration is one day, then the sub-time inspection scope is each day of the third quarter.

[0051] For example, the target server obtains the time inspection range from the data inspection range, divides the time inspection range sequentially using unit duration as the unit, and obtains a preset number of sub-time inspection ranges. The preset number is determined based on the ratio of the duration of the transaction period corresponding to the time inspection range to the unit duration.

[0052] For example, based on the transaction timestamps indicated by each piece of data in the submitted data, the target server counts the amount of data whose transaction timestamps fall within each sub-time check range. It then selects the minimum data amount from the data amounts within each sub-time check range and verifies whether this minimum data amount is greater than or equal to a quantity threshold. If it is greater than or equal to the quantity threshold, the server returns to the step of importing the original transaction data and the submitted transaction data into the container template to obtain the container for the transaction data check task, and continues execution.

[0053] In the above embodiments, the time check range is obtained from the data check range, thereby enabling the determination of the amount of data to be reported per unit time based on the amount of reported transaction data and the transaction duration of the time check range. This allows for automatic verification of whether the amount of data to be reported per unit time meets the lower limit configuration, ensuring the accuracy and effectiveness of the transaction data check task.

[0054] Step 204: If the container is found to be in the inspection state, perform data conversion on the original transaction data in the container related to the transaction data inspection task to obtain the transaction data to be reported that conforms to the data format of the transaction data management agency.

[0055] Among them, "inspection status" means that the transaction data in the container can be inspected, and "data conversion" means that the data format is converted.

[0056] Optionally, the target server obtains the container tag of the container and verifies whether the container is in an inspection state based on the container tag. If in an inspection state, the target server performs data transformation on the original transaction data related to the transaction data inspection task in the container based on preset transformation rules, obtaining the transaction data to be reported that conforms to the data format of the transaction data management agency. The transformation rules illustrate field mapping rules, whether decimal places in the amount are converted, etc. The container tag is used to instantiate, manage the lifecycle and state of each independent transaction data inspection task. A corresponding container and a corresponding container tag are set for each transaction data inspection task. The metadata associated with this container tag includes: the tag name of the container tag: identifying the transaction data inspection task; start date / end date: specifying the time range of this inspection.

[0057] In some embodiments, the method further includes: obtaining an ordered plurality of status flags corresponding to the container based on the container label; if the data import flag indicates that transaction data has been imported, reading the data to be reported generation flag located after the data import flag; if the data to be reported generation flag indicates that it has not been generated, determining that the container is in an inspection state, and returning to the step of performing data conversion on the original transaction data related to the transaction data inspection task in the container to obtain data to be reported transaction data conforming to the data format of the transaction data management agency, and continuing to execute the step.

[0058] The container's label is associated with multiple status flags, which indicate that the container has completed a corresponding subtask. These subtasks are derived from the transaction data inspection task. For example, there are a first data import flag indicating whether the original transaction data has been imported into the container, a second data import flag indicating whether the reported transaction data has been imported into the container, a flag indicating whether the application-reported data has been generated, and an inspection completion flag indicating whether the data inspection is complete. The first and second data import flags are data import flags. Optionally, the multiple status flags are ordered. For example, the order of the multiple status flags might be: first data import flag, second data import flag, reported data generation flag, and inspection completion flag, each corresponding to an ordered subtask: the subtask of importing the original transaction data, the subtask of importing the reported transaction data, the subtask of generating the reported data, and the subtask of data inspection, collectively forming the transaction data inspection task. These status flags provide control nodes for the automated workflow (the workflow of the transaction data inspection task), and these status flags can also be displayed to the user, providing the user with a clear task progress dashboard.

[0059] For example, the target server obtains a sequence of multiple status flags corresponding to the container based on the container tag. After determining that the first data import flag indicates that the original transaction data has been imported and the second data import flag indicates that the reported transaction data has been imported, the target server obtains the data to be reported generation flag located after the second data import flag. If the target server verifies that the data to be reported generation flag is not generated and the check completion flag is not completed, it determines that the container is in the check state and returns to the step of performing data conversion on the original transaction data related to the transaction data check task in the container to obtain the data to be reported transaction data that conforms to the data format of the transaction data management agency, and continues to execute.

[0060] In the above embodiments, multiple ordered status flags reflecting the task execution progress can be automatically obtained through container tags, thereby enabling timely knowledge of the data inspection progress of the original transaction data and reported transaction data for the container, ensuring the accuracy of data inspection.

[0061] In some embodiments, data conversion is performed on the original transaction data related to the transaction data inspection task in the container to obtain the transaction data to be reported that conforms to the data format of the transaction data management agency. This includes: obtaining different field mapping relationships, where the field mapping relationship is the mapping relationship between the data format of the first field corresponding to the target area and the data format of the second field indicated by the transaction data management agency; for each field mapping relationship, the data format of the corresponding first field in the original transaction data related to the transaction data inspection task in the container is converted to the data format of the corresponding second field according to the field mapping relationship to obtain the corresponding converted data; and based on the converted data corresponding to each field mapping relationship, the transaction data to be reported that conforms to the data format of the transaction data management agency is determined.

[0062] The different field mapping relationships are used to define the field mapping relationships between the original transaction data and the reported transaction data. The different field mapping relationships include institution code mapping relationship, region code mapping relationship (such as country code mapping relationship), document type mapping relationship, currency type mapping relationship, and transaction type mapping relationship.

[0063] For example, the target server obtains pre-stored different field mapping relationships, which are the mapping relationships between the data format of the first field corresponding to the target area and the data format of the second field indicated by the transaction data management agency.

[0064] For each field mapping relationship, the target server retrieves the corresponding first field from the original transaction data related to the transaction data inspection task in the container. Based on this field mapping relationship, it converts the data format of the first field into the data format of the corresponding second field, obtaining the corresponding converted data. The target server combines the converted data for each field mapping relationship to obtain the transaction data to be reported, which conforms to the data format of the transaction data management agency.

[0065] For example, the method further includes: the target server performing data verification on both the original transaction data and the reported transaction data, such as verifying whether key fields in the original transaction data and the reported transaction data contain null values, whether the time ranges match, etc., to ensure the quality of the input data and avoid data check failures due to data problems. If both the original transaction data and the reported transaction data pass their respective data verifications, the target server performs data cleaning on the original verified data, and then performs data transformation on the original transaction data to obtain the reportable transaction data that conforms to the data format of the transaction data management agency.

[0066] In this embodiment, the original transaction data generated in the target area can be processed for data compliance through different field mapping relationships, and a set of "theoretically fully compliant" transaction data to be reported can be automatically generated, further ensuring the accuracy and effectiveness of data inspection.

[0067] Step 206: According to the preset comparison rules, perform data inspection based on the transaction data to be reported and the reported transaction data in the container that is related to the transaction data inspection task, and obtain the target inspection result.

[0068] The comparison rules define data inspection principles to detect over-reporting, under-reporting, or misreporting in the original transaction data. Since each piece of data in the transaction data includes multiple fields, each field can be an amount field or a non-amount field recording the transaction amount. For example, non-amount fields can include transaction type, region code, etc. Therefore, the comparison rules include comparison rules for amount fields and comparison rules for non-amount fields. For instance, the comparison rule for amount fields indicates that the numerical difference between the amount field in the original transaction data and the corresponding amount field in the reported transaction data is small; the comparison rule for non-amount fields indicates that the non-amount fields in the original transaction data are consistent with the corresponding non-amount fields in the reported transaction data.

[0069] In some embodiments, the transaction data to be reported includes multiple first transaction data, and the reported transaction data in the container related to the transaction data inspection task includes multiple second transaction data. According to a preset comparison rule, based on the transaction data to be reported and the reported transaction data in the container related to the transaction data inspection task, a data inspection is performed to obtain a target inspection result, including: comparing data identifiers based on the data identifiers of each first transaction data and each second transaction data to determine multiple data pairs with the same data identifier, each data pair including first transaction data and second transaction data with the same data identifier; and based on the first transaction data in each data pair... According to the data, the system checks whether there is any missing first transaction data in the transaction data that should be reported, and obtains the missing data check result; based on the second transaction data in each data pair, it checks whether there is any over-reported second transaction data in the reported transaction data, and obtains the over-reporting check result; for each data pair, it compares the difference between the amount values ​​of the amount fields of the first and second transaction data in the data pair and the fault tolerance difference, as well as whether the information of the non-amount fields of the first and second transaction data in the data pair is consistent, and obtains the data pair check result, which is used to indicate whether there is a misreport; based on the missing data check result, the over-reporting check result, and the data pair check result, the target check result is generated.

[0070] Optionally, the reported transaction data includes multiple first transaction data sets, each containing multiple first fields, which can be amount fields or non-amount fields. The reported transaction data includes multiple second transaction data sets, where the second fields can be amount fields or non-amount fields. Further, each first transaction data set has a corresponding data identifier to identify it; similarly, each second transaction data set has a corresponding data identifier to identify it. The data identifier can be a transaction serial number.

[0071] Optionally, the target server may generate a first set of identifiers based on the data identifiers of each first transaction data, and a second set of identifiers based on the data identifiers of each second transaction data, and determine the intersection of the first set of identifiers and the second set of identifiers. For each data identifier in the intersection, the first transaction data for that data identifier is retrieved from the transaction data to be reported, and the second transaction data for that data identifier is retrieved from the transaction data already reported. Based on the retrieved first and second transaction data, a data pair corresponding to that data identifier is determined, wherein the data identifiers of the first and second transaction data in the data pair are the same.

[0072] Optionally, based on the first transaction data in each data pair, check whether there is any first transaction data in the reportable transaction data that is not in any data pair, to obtain a false negative check result. For example, if it exists, the first transaction data that is not in any data pair is taken as false negative transaction data, and a false negative check result indicating false negative transaction data is obtained. If it does not exist, a false negative check result indicating that there is no false negative is obtained.

[0073] Optionally, for the second transaction data in each data pair, check whether there is any second transaction data that is not in any data pair among the reported transaction data to obtain a multi-reporting check result. For example, if it exists, the second transaction data that is not in any data pair is regarded as multi-reported transaction data, and a multi-reporting check result indicating multi-reported transaction data is obtained. If it does not exist, a multi-reporting check result indicating that there is no multi-reporting is obtained.

[0074] Optionally, for each data pair, the target server retrieves the first transaction data and the second transaction data from that data pair. The target server compares the field names and the number of fields in the first and second transaction data to ensure they are consistent. If there are discrepancies in field names or the number of fields, a data pair check result indicating a misreport is generated for that data pair. If the field names and the number of fields are consistent, for each field, if it is an amount field, the difference between the amount value of the amount field in the first transaction data and the amount value of the amount field in the second transaction data is compared, and this difference is compared with a tolerance difference to obtain the field comparison result for the amount field. If the field is a non-amount field, the information of the non-amount field in the first transaction data is compared to the information of the non-amount field in the second transaction data to obtain the field comparison result for the non-amount field. Based on the field comparison results for the non-amount fields and the field comparison results for the amount fields, the data pair check result for that data pair is determined. For example, if the comparison result of the amount field indicates a difference greater than the tolerance difference, or if the comparison result of the non-amount field indicates a discrepancy, the data indicates a false alarm in the inspection result. If the comparison result of the amount field indicates a difference less than or equal to the tolerance difference, and the comparison result of the non-amount field indicates a consistency, the data indicates no false alarm in the inspection result.

[0075] Optionally, the target server generates a target inspection result for the transaction data inspection task based on the results of the missed detection, over-reporting detection, and data pair detection. If the missed detection result indicates a missed report, the over-reporting detection result indicates an over-reporting, or the data pair detection result indicates an incorrect report, the target inspection result is determined to be a failed inspection. If the missed detection result indicates no missed reports, the over-reporting detection result indicates no over-reporting, or the data pair detection result indicates no incorrect reports, the target inspection result is determined to be a successful inspection.

[0076] In other embodiments, the method further includes: the server can also display target inspection results indicating failed checks, which can be filtered and sorted by dimensions such as result type and severity, and supports exporting the target inspection results as structured reports (such as Excel and PDF). The reports clearly list problem records, discrepancy fields, and possible causes, directly serving inspection or internal rectification. Result types can be missed reports, false alarms, multiple reports, or a combination of both. A combination type includes both false alarms and missed reports, multiple reports and missed reports, multiple reports and false alarms, or false alarms, multiple reports, and missed reports. Figure 3 The image shown is a schematic diagram of the target inspection results in one embodiment. Figure 3 The diagram illustrates the target inspection results of the transaction data inspection task, showing instances of false positives, omissions, and overreporting. For example... Figure 4 The diagram shown is a schematic representation of checking query results in one embodiment. Figure 4 A query page is provided for completed transaction data inspection tasks. The query page offers search keywords; by entering the corresponding search results in the input boxes for container label, result type, and transaction type, the page displays the relevant search results. See details below. Figure 4 .

[0077] In the above embodiments, transaction data with the same data identifier is queried based on the data identifiers of the first transaction data and the second transaction data to accurately check for misreporting. Then, based on the data pair, it can automatically check for omissions or overreporting, ensuring the accuracy and efficiency of data checking.

[0078] In the aforementioned transaction data inspection method, a container for the transaction data inspection task is obtained. This container is used to import raw transaction data generated in the target area and reported transaction data, which is the transaction data reported to the transaction data management agency. The status of the container determines whether to initiate the inspection of the imported transaction data, ensuring the effectiveness of the inspection. When the container is detected as being in inspection mode, the raw transaction data related to the inspection task within the container is automatically converted to obtain the reportable transaction data conforming to the data format specified by the transaction data management agency. This ensures that the data inspection can be performed in the data format prescribed by the transaction data management agency, eliminating the need for manual conversion and improving the efficiency of the inspection. Then, according to preset comparison rules, based on the reportable transaction data and the reported transaction data related to the inspection task in the container, the data inspection is automatically initiated to obtain the target inspection result, eliminating the need for manual verification and comparison. This significantly improves the efficiency of the transaction data inspection.

[0079] In a specific embodiment, such as Figure 5The diagram shown illustrates a transaction data inspection process in one embodiment. The specific steps are as follows:

[0080] Phase 1: Initialization, i.e., parameter configuration. Specifically, the target server performs the following configuration set to solidify business knowledge into the tool, namely, basic tool parameter configuration: setting system-level parameters, such as allowing cross-year checks—configuring whether the start and end time span of newly created container tags can exceed 1 year; import data file character set settings—configuring the character set of imported files (currently only GBK format is supported); maximum number of container tags—configuring the maximum number of newly created container tags; raw data volume warning start point (within 10 million)—configuring the basic data alarm data volume of the tool to ensure the tool's calculation efficiency; number of transaction data import threads—the system's most recommended thread count is 100; head office financial institution code—the foreign exchange bureau code (6 digits) of the card issuing branch's location; bank financial institution code—the card issuing bank financial institution code (4 digits); whether to convert the decimal places of the transaction currency amount—configuring whether the transaction currency amount needs decimal conversion. If you select "No", the transaction amount does not need decimal conversion; if you select "Yes", decimal conversion is required based on the decimal place configuration in the currency parameters. Whether to convert the decimal place of the transaction currency to the target currency amount – whether the transaction currency to the target currency amount needs decimal conversion configuration. If you select "No", the transaction currency to the target currency amount does not need decimal conversion; if you select "Yes", decimal conversion is required based on the decimal place configuration in the currency parameters, providing basic support for tool checks; Field mapping relationship configuration; Comparison rule configuration: Defines the specific business logic for identifying "false alarms". Amount difference tolerance threshold (unit: yuan) – the tolerance difference between the amount to be reported and the amount already reported. If |Amount to be reported - Amount already reported| < "Tolerance difference", it is screened as normal data; otherwise, it is a false alarm; Minimum daily reported data volume – configuration of the minimum daily data volume.

[0081] The second phase involves container creation, which involves creating new containers and container labels, and configuring the time check range and container name. Specifically, after receiving the transaction data check task, the target server parses the task, determines the data check range (including the time check range), and then determines the container label and container name.

[0082] The third stage: Data import, which involves importing both raw and reported transaction data. Specifically, the target server, based on the data inspection scope, obtains the raw and reported transaction data corresponding to the data inspection scope from both the raw transaction data provider and the transaction data management agency; it obtains a container template and the time inspection scope from the data inspection scope; based on the amount of reported transaction data and the transaction duration involved in the time inspection scope, it determines the amount of data to be reported per unit time; if the amount of reported data is greater than or equal to the quantity threshold, the target server imports the raw and reported transaction data into the container template respectively, and the steps for obtaining the container for the transaction data inspection task continue. The unit time is a day, and the quantity threshold is the minimum daily reported data amount mentioned above.

[0083] The fourth stage is data inspection. After performing a pre-inspection, the transaction data to be reported is generated. Then, according to the comparison rules, the reported transaction data and the transaction data to be reported are compared to obtain the target inspection result. Specifically, the target server obtains multiple ordered status flags corresponding to the container based on the container tag. If the data import flag indicates that the transaction data has been imported, the target server reads the data to be reported generation flag located after the data import flag. If the data to be reported generation flag indicates that the data has not been generated, the target server determines that the container is in the inspection state. The target server obtains different field mapping relationships. The field mapping relationship is the mapping relationship between the data format of the first field corresponding to the target area and the data format of the second field indicated by the transaction data management agency. For each field mapping relationship, the target server converts the data format of the corresponding first field in the original transaction data related to the transaction data inspection task in the container to the corresponding data format of the second field according to the field mapping relationship to obtain the corresponding converted data. Based on the converted data corresponding to each field mapping relationship, the target server determines the transaction data to be reported that conforms to the data format of the transaction data management agency. Since the transaction data to be reported includes multiple first transaction data, and the reported transaction data related to the transaction data inspection task in the container includes multiple second transaction data, the target server compares the data identifiers of each first transaction data and each second transaction data based on the data identifiers of each first transaction data and the data identifiers of each second transaction data to identify multiple data pairs with the same data identifier. Each data pair includes first transaction data and second transaction data with the same data identifier. Based on the first transaction data in each data pair, the server checks whether there is any missing first transaction data in the transaction data to be reported, and obtains a missing data check result. Based on the second transaction data in each data pair, the server checks whether there is any over-reported second transaction data in the reported transaction data, and obtains an over-report check result. For each data pair, the server compares the difference between the amount values ​​of the amount fields of the first and second transaction data in the data pair and the fault tolerance difference, as well as whether the information of the non-amount fields of the first and second transaction data in the data pair is consistent, and obtains a data pair check result. The data pair check result is used to indicate whether there is a misreport. Based on the missing data check result, the over-report check result, and the data pair check result, the target inspection result is generated.

[0084] Phase 5: Results output, i.e., the target server displays and reports the results of the target inspection.

[0085] It should be noted that the above implements a container tagging mechanism, which involves instantiating, managing the lifecycle and state of each transaction data inspection task.

[0086] In this embodiment, a container for transaction data inspection tasks is acquired. This container imports raw transaction data and reported transaction data generated in the target area. The reported transaction data is the data submitted to the transaction data management agency. The container's state determines whether to initiate inspection of the imported transaction data, ensuring the effectiveness of the inspection. When the container is detected as being in inspection mode, the raw transaction data related to the inspection task within the container is automatically converted to obtain the reported transaction data conforming to the data format specified by the transaction data management agency. This ensures that the data inspection can be performed in the format prescribed by the agency, eliminating the need for manual conversion and improving efficiency. Then, based on preset comparison rules and the reported transaction data related to the inspection task within the container, data inspection is automatically initiated according to the reported transaction data and the reported transaction data, yielding the target inspection result without manual verification. This significantly improves the efficiency of transaction data inspection. Furthermore, the challenge of identifying complex false alarms is solved by using configurable comparison rules for deep logical comparison, surpassing the shallow data matching capabilities of traditional tools. In addition, it has a high degree of flexibility and maintainability. When the rules change, there is no need to modify the program code. It can adapt simply by updating the configuration, which significantly reduces maintenance costs and time.

[0087] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages in other steps. It is understood that the steps in different embodiments can be freely combined as needed, and all non-contradictory solutions formed by such combinations are within the scope of protection of this application.

[0088] Based on the same inventive concept, this application also provides a transaction data inspection apparatus for implementing the transaction data inspection method described above. The solution provided by this apparatus is similar to the implementation described in the above method; therefore, the specific limitations in one or more transaction data inspection apparatus embodiments provided below can be found in the limitations of the transaction data inspection method described above, and will not be repeated here.

[0089] In one exemplary embodiment, such as Figure 6 As shown, a transaction data inspection device 600 is provided, including: a container acquisition module 602, a format conversion module 604, and a data inspection module 606, wherein:

[0090] The container acquisition module 602 is used to acquire a container for the transaction data inspection task. The container is used to import the original transaction data generated in the target area and the reported transaction data. The reported transaction data is the transaction data reported to the transaction data management agency.

[0091] The format conversion module 604 is used to convert the original transaction data related to the transaction data inspection task in the container when the container is found to be in the inspection state, so as to obtain the transaction data to be reported that conforms to the data format of the transaction data management agency.

[0092] The data inspection module 606 is used to perform data inspection according to preset comparison rules, based on the transaction data to be reported and the reported transaction data in the container related to the transaction data inspection task, and to obtain the target inspection result.

[0093] In some embodiments, the container acquisition module 602 is used to, after acquiring the transaction data inspection task, parse the transaction data inspection task and determine the data inspection scope; based on the data inspection scope, acquire the original transaction data and the reported transaction data corresponding to the data inspection scope from the original transaction data provider and the transaction data management agency, respectively; acquire the container template, and import the original transaction data and the reported transaction data into the container template to obtain the container for the transaction data inspection task.

[0094] In some embodiments, the container acquisition module 602 is used to acquire the time inspection range from the data inspection range; determine the amount of data to be reported per unit time based on the amount of reported transaction data and the transaction duration involved in the time inspection range; and if the amount of reported data is greater than or equal to the quantity threshold, return to the step of importing the original transaction data and the reported transaction data into the container template respectively to obtain the container for the transaction data inspection task and continue execution.

[0095] In some embodiments, the apparatus further includes a status check module, configured to obtain an ordered plurality of status flags corresponding to the container based on the container tag; if the data import flag indicates that transaction data has been imported, read the data to be reported generation flag located after the data import flag; if the data to be reported generation flag indicates that it has not been generated, determine that the container is in a check state, and return to the step of performing data conversion on the original transaction data related to the transaction data check task in the container to obtain data to be reported transaction data conforming to the data format of the transaction data management agency and continue execution.

[0096] In some embodiments, the format conversion module 604 is used to obtain different field mapping relationships, wherein the field mapping relationship is the mapping relationship between the data format of the first field corresponding to the target area and the data format of the second field indicated by the transaction data management agency; for each field mapping relationship, according to the field mapping relationship, the data format of the corresponding first field in the original transaction data related to the transaction data inspection task in the container is converted into the data format of the corresponding second field to obtain the corresponding converted data; based on the converted data corresponding to each field mapping relationship, the transaction data to be reported that conforms to the data format of the transaction data management agency is determined.

[0097] In some embodiments, the transaction data to be reported includes multiple first transaction data, and the reported transaction data related to the transaction data inspection task in the container includes multiple second transaction data; the data inspection module 606 is used to compare data identifiers based on the data identifiers of each first transaction data and the data identifiers of each second transaction data to determine multiple data pairs with the same data identifier, each data pair including first transaction data and second transaction data with the same data identifier; based on the first transaction data in each data pair, check whether there is any missing first transaction data in the transaction data to be reported, and obtain a missing detection result; based on the second transaction data in each data pair, check whether there is any over-reported second transaction data in the reported transaction data, and obtain an over-reporting detection result; for each data pair, compare the difference between the amount values ​​of the amount fields of the first transaction data and the second transaction data in the data pair and the fault tolerance difference, and whether the information of the non-amount fields of the first transaction data and the second transaction data in the data pair is consistent, and obtain a data pair inspection result, which is used to indicate whether there is a misreport; based on the missing detection result, the over-reporting result, and the data pair inspection result, generate a target inspection result.

[0098] Each module in the aforementioned transaction data inspection device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the operations corresponding to each module.

[0099] In one exemplary embodiment, a computer device is provided, which may be a server or a terminal, and its internal structure diagram may be as follows. Figure 7As shown, this computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operating system and computer programs stored in the non-volatile storage media to run. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communicating with external terminals via a network connection. When the computer program is executed by the processor, it implements a transaction data verification method.

[0100] Those skilled in the art will understand that Figure 7 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0101] In one embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above method embodiments.

[0102] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the steps in the above method embodiments.

[0103] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0104] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0105] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0106] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0107] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for inspecting transaction data, characterized in that, The method includes: Obtain a container for the transaction data inspection task, the container being used to import the original transaction data generated in the target area and the reported transaction data, which is the transaction data reported to the transaction data management agency; If the container is found to be in an inspection state, the original transaction data related to the transaction data inspection task in the container is converted to obtain the transaction data to be reported that conforms to the data format of the transaction data management agency. According to the preset comparison rules, based on the transaction data to be reported and the reported transaction data in the container related to the transaction data inspection task, data inspection is performed to obtain the target inspection result.

2. The method according to claim 1, characterized in that, The container for acquiring transaction data inspection tasks includes: After obtaining the transaction data inspection task, the transaction data inspection task is parsed to determine the scope of data inspection; Based on the data inspection scope, original transaction data and reported transaction data corresponding to the data inspection scope are obtained from the original transaction data provider and the transaction data management agency, respectively. Obtain the container template, and import the original transaction data and the reported transaction data into the container template respectively to obtain a container for the transaction data inspection task.

3. The method according to claim 2, characterized in that, The method further includes: Obtain the time inspection range from the data inspection range; Based on the amount of reported transaction data and the transaction duration covered by the time inspection scope, determine the amount of reported data per unit time. If the amount of reported data is greater than or equal to the quantity threshold, the process returns to importing the original transaction data and the reported transaction data into the container template respectively to obtain a container for the transaction data inspection task and continues execution.

4. The method according to claim 1, characterized in that, The method further includes: Based on the container label of the container, obtain an ordered set of multiple status flag bits corresponding to the container; If the data import flag indicates that transaction data has been imported among the plurality of status flags, then the data to be reported generation flag located after the data import flag is read. If the flag indicating that the data to be reported has not been generated, the container is determined to be in an inspection state, and the process of converting the original transaction data related to the transaction data inspection task in the container to obtain the transaction data to be reported that conforms to the data format of the transaction data management agency continues.

5. The method according to claim 1, characterized in that, The process of converting the raw transaction data related to the transaction data inspection task in the container to obtain the transaction data to be reported that conforms to the data format of the transaction data management agency includes: Obtain different field mapping relationships, wherein the field mapping relationship is the mapping relationship between the data format of the first field corresponding to the target area and the data format of the second field indicated by the transaction data management agency; For each field mapping relationship, according to the field mapping relationship, the data format of the corresponding first field in the original transaction data related to the transaction data inspection task in the container is converted into the data format of the corresponding second field to obtain the corresponding converted data; Based on the conversion data corresponding to each field mapping relationship, the transaction data to be reported that conforms to the data format of the transaction data management agency is determined.

6. The method according to claim 1, characterized in that, The transaction data to be reported includes multiple first transaction data sets, and the reported transaction data related to the transaction data inspection task in the container includes multiple second transaction data sets; the step of performing data inspection based on the transaction data to be reported and the reported transaction data related to the transaction data inspection task in the container according to preset comparison rules, and obtaining the target inspection result, includes: Based on the data identifiers of each first transaction data and each second transaction data, data identifier comparison is performed to determine multiple data pairs with the same data identifier. Each data pair includes first transaction data and second transaction data with the same data identifier. Based on the first transaction data in each data pair, check whether there is any missing first transaction data in the transaction data that should be reported, and obtain the missing data check result. Based on the second transaction data in each data pair, check whether there is any over-reported second transaction data in the reported transaction data, and obtain the over-reporting check result; For each data pair, compare the difference between the amount values ​​of the amount fields of the first and second transaction data in the data pair and the fault tolerance difference, as well as whether the information of the non-amount fields of the first and second transaction data in the data pair is consistent, to obtain the data pair check result. The data pair check result is used to indicate whether there is a false alarm. Based on the results of missed inspections, over-reported inspections, and data comparisons of inspection results, target inspection results are generated.

7. A transaction data inspection device, characterized in that, The device includes: The container acquisition module is used to acquire a container for the transaction data inspection task. The container is used to import the original transaction data generated in the target area and the reported transaction data, which is the transaction data reported to the transaction data management agency. The format conversion module is used to convert the original transaction data related to the transaction data inspection task in the container when the container is found to be in the inspection state, so as to obtain the transaction data to be reported that conforms to the data format of the transaction data management agency. The data inspection module is used to perform data inspection based on the transaction data to be reported and the reported transaction data related to the transaction data inspection task in the container, according to preset comparison rules, and to obtain the target inspection result.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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