CN113918593A - Method and device for identifying financial data abnormity, storage medium and computing equipment - Google Patents
Method and device for identifying financial data abnormity, storage medium and computing equipment Download PDFInfo
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Abstract
A method and device for identifying financial data abnormity, a storage medium and a computing device, wherein the method comprises the following steps: acquiring an identification request and a rule description file, wherein the identification request comprises address information of a target database, and the rule description information comprises: the method comprises a target data table, an exception type and a method description statement, wherein the target data table is a data table to which data to be identified belongs in the target database, the method description statement is used for describing a method for identifying an exception, and the method description statement is determined according to the exception type; reading data in the target data table from the target database; and performing exception identification on the data in the target data table according to the method description statement to obtain an identification result, wherein the identification result comprises the position of the exception data in the target data table. By the method, the abnormal data in the target database can be accurately and efficiently identified.
Description
Technical Field
The invention relates to the technical field of big data processing, in particular to a method and a device for identifying financial data abnormity, a storage medium and computing equipment.
Background
At present, more and more financial institutions adopt a big data processing technology to analyze and process a large amount of financial data so as to realize the supervision of the financial data, and thus inappropriate financial behaviors can be found in time. For example, the financial institution enters financial data into the anti-money laundering model to identify money laundering behavior during the financial transaction. However, since the financial data is usually massive, problems such as data errors or data loss usually occur in the processes of acquisition, storage and transmission, and if these problems are not identified and processed, errors may occur in the results of subsequent data analysis and processing.
Therefore, a method for accurately and efficiently identifying financial data anomalies is needed.
Disclosure of Invention
The invention aims to provide a method for accurately and efficiently identifying financial data abnormity.
To solve the above technical problem, an embodiment of the present invention provides a method for identifying financial data anomalies, where the method includes: acquiring an identification request and a rule description file, wherein the identification request comprises address information of a target database, and the rule description information comprises: identification information, an exception type and a method description statement of a target data table, wherein the target data table is a data table to which data to be identified belongs in the target database, the method description statement is used for describing a method for identifying an exception, and the method description statement is determined according to the exception type; reading data in the target data table from the target database; and performing exception identification on the data in the target data table according to the method description statement to obtain an identification result, wherein the identification result comprises the position of the exception data in the target data table.
Optionally, before performing exception identification on the data in the target data table according to the method description statement, the method further includes: reading preset storage structure information of the target data table, wherein the preset storage structure information comprises a standard field attribute of the target data table; judging whether the field attribute of the data in the target data table is consistent with the standard field attribute according to the preset storage structure information to obtain a judgment result; and sending the judgment result to a user terminal, wherein the judgment result comprises: a location of data in the target data table where the field attribute is inconsistent with the standard field attribute.
Optionally, the preset storage structure information further includes a standard index of the target data table, and before the determination result is sent to the user terminal, the method further includes: and judging whether the actual index of the target data table comprises the standard index, and if not, taking the standard index which is not included as a part of the judgment result.
Optionally, the preset storage structure information further includes a preset padding value, and before the judging result is sent to the user terminal after judging whether the field attribute of the data in the target data table is consistent with the standard field attribute, the method further includes: and judging whether data with the field attribute of the data in the target data table consistent with the standard field attribute has data missing, if so, judging whether an actual filling value at the missing part is consistent with the preset filling value, and if not, taking the position of the actual filling value in the target data table as a part of the judgment result.
Optionally, performing exception identification on the data in the target data table according to the method description statement includes: and searching the preset filling value in the target data table, and taking the position of the preset filling value as the position of the abnormal data in the target data table.
Optionally, before performing exception identification on the data in the target data table according to the method description statement, the method further includes: acquiring updating data from the user terminal, wherein the updating data is determined according to the judgment result; updating the data of the target data table according to the updating data; and judging whether the data in the target data table meets the requirement of the preset storage structure information, if so, performing exception identification on the data in the target data table according to the method description statement.
Optionally, the method description statement includes a first key field, and performing exception identification on data in the target data table according to the method description statement includes: determining an associated data table of the target data table, wherein the associated data table is a data table where data having an association relation with the data to be identified are located; determining a plurality of data belonging to the first key field in the target data table, and recording the data as a plurality of first key data; and judging whether the plurality of first key data exist in the associated data table, if not, taking the first key data which do not exist in the associated data table as the abnormal data.
Optionally, the target data table is a transaction data table, the transaction data table includes a plurality of transaction records, the associated data table is an account information table, and performing exception identification on data in the target data table according to the method description statement includes: determining the transaction time of each transaction record and the account information of a transaction party; and looking up the account creating time of the transaction party in the account information table, judging whether the account creating time is after the transaction time, and if so, taking the transaction record as the abnormal data.
The embodiment of the invention also provides a device for identifying financial data abnormity, which comprises: an obtaining module, configured to obtain an identification request and a rule description file, where the identification request includes address information of a target database, and the rule description information includes: identification information, an exception type and a method description statement of a target data table, wherein the target data table is a data table to which data to be identified belongs in the target database, the method description statement is used for describing a method for identifying an exception, and the method description statement is determined according to the exception type; the reading module is used for reading the data in the target data table from the target database; and the identification module is used for carrying out exception identification on the data in the target data table according to the method description statement to obtain an identification result, wherein the identification result comprises the position of the exception data in the target data table.
Embodiments of the present invention further provide a storage medium, on which a computer program is stored, where the computer program, when executed by a processor, performs the steps of the above method for identifying financial data anomalies.
The embodiment of the present invention further provides a computing device, which includes a memory and a processor, where the memory stores a computer program operable on the processor, and the processor executes the above-mentioned steps of the method for identifying financial data anomalies when executing the computer program.
Compared with the prior art, the technical scheme of the embodiment of the invention has the following beneficial effects:
in the scheme of the embodiment of the invention, the identification request and the rule description file are obtained, wherein the rule description file comprises the identification information, the abnormal type and the method description statement of the target data table. Since the target data table is a data table to which the data to be identified belongs in the target database, and the identification request includes address information of the target database, the data in the target data table can be read from the target database. Further, since the method description statement is used for describing the method for identifying the exception and the method description statement is determined according to the exception type, the data in the target data table is subjected to exception identification according to the method description statement, an identification result corresponding to the exception type can be obtained, and the identification result comprises the position of the exception data in the target data table. Therefore, abnormal data in the target data table can be accurately and efficiently identified.
Further, in the solution of the embodiment of the present invention, before the data in the target data table is identified according to the method description statement, it is determined whether the field attribute, the index, and the missing value of the data in the target data table are consistent with the preset storage structure information according to the preset storage structure information of the target data table, so that the abnormality of the data in the target data table in the storage structure aspect can be identified, and the user can correct the abnormality, thereby improving the accuracy when the abnormality is identified according to the method description statement.
Further, in the solution of the embodiment of the present invention, it is first determined whether the field attribute of the data in the target data table is consistent with the standard field attribute, and then determined whether the data with the field attribute consistent with the standard field attribute has data missing, and if so, it is further determined whether the actual padding value at the missing position is consistent with the preset padding value. By adopting the scheme, the data missing judgment of the data with inconsistent field attributes and standard field attributes can be avoided, and the abnormal identification efficiency can be improved.
Drawings
FIG. 1 is a diagram illustrating an application scenario of a method for identifying financial data anomalies according to an embodiment of the present invention;
FIG. 2 is a flow chart illustrating a method for identifying financial data anomalies in accordance with an embodiment of the present invention;
FIG. 3 is a partial flow diagram of another method for identifying financial data anomalies in accordance with an embodiment of the present invention;
FIG. 4 is a schematic structural diagram of an apparatus for identifying financial data anomalies according to an embodiment of the present invention.
Detailed Description
As discussed in the background, there is a need for an accurate and efficient method for identifying financial data anomalies.
The inventor of the present invention has found that, in the prior art, a large amount of financial data is usually directly analyzed and processed to determine whether there is an inappropriate financial behavior. However, since the financial data is usually massive, problems such as data errors or data loss usually occur in the processes of acquisition, storage and transmission, and if these problems are not identified and processed, errors may occur in the results of subsequent data analysis and processing.
In order to solve the above technical problem, an embodiment of the present invention provides a method for identifying financial data anomalies. In the scheme of the embodiment of the invention, the identification request and the rule description file are obtained, wherein the rule description file comprises a target data table, an exception type and a method description statement. Since the target data table is a data table to which the data to be identified belongs in the target database, and the identification request includes address information of the target database, the data in the target data table can be read from the target database. Further, since the method description statement is used for describing the method for identifying the exception and the method description statement is determined according to the exception type, the data in the target data table is subjected to exception identification according to the method description statement, an identification result corresponding to the exception type can be obtained, and the identification result comprises the position of the exception data in the target data table. Therefore, abnormal data in the target data table can be accurately and efficiently identified.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in detail below.
Referring to fig. 1, fig. 1 is a schematic view of an application scenario of a method for identifying financial data anomalies according to an embodiment of the present invention. The financial data may be various data for financial statistics, financial analysis, for example, transaction data, financial data of a company entity, and the like, but is not limited thereto. It should be noted that, in the solution of the embodiment of the present invention, the abnormality that needs to be identified is not an abnormality caused by an inappropriate financial transaction behavior, for example, whether an abnormality caused by a money laundering behavior exists in the financial data is not identified, but whether the financial data is standardized data is identified before identifying whether an abnormality caused by an inappropriate financial transaction behavior exists, so that the user can correct the financial data according to the identification result, which is beneficial to accurately identifying whether an abnormality caused by an inappropriate financial transaction behavior exists in the financial data subsequently. In other words, the anomaly to be identified in the embodiment of the present invention is a case where the financial data in the database is inconsistent with the standardized financial data.
In particular, user terminal 11 may be coupled to computing platform 12 for data interaction with computing platform 12. The user terminal 11 may also be coupled to a plurality of databases 13 for data interaction with the plurality of databases 13. Wherein a plurality of databases 13 may be used to store financial data. It should be noted that, the computing platform 12 may be configured to execute the method for identifying the financial data abnormality in the embodiment of the present invention, before executing the method for identifying the financial data abnormality, the computing platform 12 is not connected to the database 13, and during executing the method for identifying the financial data abnormality, the computing platform 12 may obtain address information of a target database from the user terminal 11, establish a connection with the target database according to the address information of the target database, and obtain the financial data from the target database and identify the abnormality. The target database may be any one of the databases 13, and the target database may be determined by the user terminal 11.
Further, the user terminal 11 may also be connected with a third party terminal 14 to perform data interaction with the third party terminal 14. In a specific example, the user terminal 11 may obtain the rule description file from the third party terminal 14 and send the rule description file to the computing platform 12, so that the computing platform 12 performs the anomaly identification on the financial data according to the rule description file, but is not limited thereto.
Referring to fig. 2, fig. 2 is a flowchart illustrating a method for identifying financial data anomalies according to an embodiment of the present invention. The method may be performed by a computing device, which may be any of various existing devices with data receiving and processing functions, such as, but not limited to, a server, and the like, and may also be, for example, the computing platform 12 shown in fig. 1, and the like. By the method for identifying financial data abnormity shown in fig. 2, abnormal data in the target database can be accurately and efficiently identified. The method of identifying financial data anomalies illustrated in FIG. 2 may include the steps of:
step S201: acquiring an identification request and a rule description file;
step S202: reading data in the target data table from the target database;
step S203: and performing exception identification on the data in the target data table according to the method description statement to obtain an identification result.
It is understood that in a specific implementation, the method may be implemented by a software program running in a processor integrated within a chip or a chip module; alternatively, the method can be implemented in hardware or a combination of hardware and software.
In the specific implementation of step S201, the identification request and the rule description file may be obtained from the outside, for example, the identification request and the rule description file may be obtained from the user terminal 11 shown in fig. 1, where the rule description file may be obtained by the user terminal 11 from the third party terminal 14. It should be noted that, the order of acquiring the identification request and the rule description file is not limited in the embodiment of the present invention.
Specifically, the identification request may include address information of the target database, and a connection may be established with the target database according to the address information of the target database.
Further, the rule description file may include identification information of a target data table, where the target data table is a data table to which data to be identified belongs in the target database, and the identification information is used to uniquely determine the target data table. For example, if the data to be identified is transaction data, the target data table may be a transaction data table in the target database, and the identification information may be a name of the data table, but is not limited thereto.
Further, the rule description file may further include exception types and method description statements, where the method description statements and the exception types are in one-to-one correspondence, and the method description statements may be used to describe a method for identifying a corresponding exception type, in other words, the method description statements may be determined according to the exception types. The specific form of the method description statement may be determined according to the type of the target database, for example, if the target database is a MySQL database, the method description statement may be an sql statement. It should be noted that the rule description file may be encrypted in advance, for example, the rule description file may be encrypted by the third party terminal 14 in fig. 1, and after the rule description file is obtained, the rule description file may be decrypted first.
In a specific implementation of step S202, data in the target data table may be read from the target database. Further, whether the data in the target data table has an exception of the storage structure can be identified.
Referring to FIG. 3, FIG. 3 is a partial flow diagram illustrating another method for identifying financial data anomalies according to an embodiment of the present invention. Another method for identifying financial data anomalies, illustrated in FIG. 3, may include the steps of:
step S301: reading preset storage structure information of the target data table;
step S302: judging whether the field attribute of the data in the target data table is consistent with the standard field attribute according to the preset storage structure information to obtain a judgment result;
step S303: and sending the judgment result to the user terminal.
It should be noted that steps S301 to S303 may be executed after step S202 and before step S203.
In the implementation of step S301, the preset storage structure information may be obtained from the outside, for example, from the user terminal 11 in fig. 1, or may be stored in a local storage device in advance.
The preset storage structure information may be used to describe a standard storage structure of data in the target data table. Specifically, the preset storage structure information may include standard field attributes of the target data table, where the standard field attributes may include, but are not limited to, a standard field name, a standard field type, a standard field length, and the like. The standard field name is used to indicate the type of information to which each column of data in the target data table belongs, and for example, the standard field name may be: time, transaction amount, customer account number, and the like. The standard field type is a type of data belonging to the field, and may be, for example, a binary data type, a character data type, a Unicode data type, a currency data type, and the like, but is not limited thereto. The standard field length is used to indicate the size of the storage space occupied in the database by the data belonging to the field. And the standard field name, the standard field length and the standard field type have corresponding relation.
Further, the preset storage structure information may further include a standard index and a preset padding value of the target data table, where the preset padding value is a padding value preset at a data missing position in the target data table.
In the specific implementation of step S302, it may be determined whether the field attribute of the data in the target data table is consistent with the standard field attribute. Specifically, it may be determined whether each field name of the data in the target data table is consistent with the standard field name, in other words, it may be determined whether each of the plurality of standard field names can be found in the target data table, and if not, the standard field name that cannot be found in the target data table may be used as a part of the determination result.
Further, for each standard field name found in the target data table, it may be determined whether the type of the data belonging to the standard field name is the standard field type corresponding to the standard field name, and whether the length of the data is consistent with the standard field length corresponding to the standard field name, and the position of the data with the type inconsistent with the standard field type and the financial data with the length inconsistent with the standard field length in the target data table is used as a part of the determination result.
Further, whether the actual index of the target data table is consistent with the standard index can be judged. Specifically, a plurality of standard indexes may be looked up in the index information of the target data table, in other words, it may be determined whether the actual index of the target data table includes a plurality of standard indexes, and if not, the standard index that is not included may be taken as part of the determination result. It should be noted that, the embodiment of the present invention does not limit the order of judging the field attribute and the index.
Further, after determining whether the field attribute of the data is consistent with the standard field attribute, it may also be determined whether data missing exists in the data whose field attribute of the data in the target data table is consistent with the standard field attribute, if so, it is determined whether an actual padding value at the missing position is consistent with a preset padding value, and if the actual padding value is not consistent with the preset padding value, a position of the actual padding value in the target data table may be used as a part of the determination result.
In a specific implementation of step S303, the determination result may be sent to the user terminal, so that the user terminal corrects the data that does not satisfy the preset storage result information.
Further, update data may be acquired from the user terminal, the update data being determined according to the determination result, and then the data in the target data table may be updated according to the update data, in other words, the data in the target data table may be modified according to the update data, so that the data in the target data table meets the requirement of the preset storage structure information. For example, the actual padding value at the data missing position may be modified to a preset padding value or the like, but is not limited thereto.
Further, whether the financial data in the target data table meets the requirement of the preset storage structure information may be judged again, if yes, step S203 may be executed, otherwise, the judged result of the second judgment may be sent to the user terminal, so that the user terminal revises the data of the target data table again. In other words, in the solution of the embodiment of the present invention, it may be determined whether the data in the target data table meets the requirement of the preset storage structure information multiple times, until the data in the target data table meets the requirement of the preset storage structure information, and then step S203 is executed.
In a specific example, after the identification request is obtained, a plurality of data tables in the target database may be read from the target database, and whether the storage structure information of each data table meets the requirement of the preset storage structure information is determined according to the preset storage structure information of the data table, and if not, the determination result of the data table is sent to the user terminal, so that the user terminal performs modification. If the storage structure information of each data table meets the requirement of the preset storage structure information, the rule description file can be obtained, and step S203 is executed according to the rule description file. The specific content for determining whether the storage structure information of each data table meets the requirement of the preset storage structure information of the data table may refer to the above related content of the storage structure information of the determination target data table, and is not described herein again.
In another specific example, the rule description file may further include identification information of an associated data table of the target data table, where the associated data table is a data table in which data having an association relationship with the data to be identified is located. For example, the target data table is a transaction data table, and the associated data table may be an account information table, etc., but is not limited thereto. And preset storage structure information of the associated data table can be acquired and recorded as associated preset storage structure information, then whether the data in the associated data table meets the associated preset storage structure information or not can be judged, and the data in the associated data table is corrected according to the judgment result until the data in the associated data table meets the associated preset storage structure information. More contents about associating the preset storage structure information and determining whether the data in the associated data table satisfies the associated preset storage structure information may refer to the above description about determining whether the data in the target data table satisfies the preset storage structure information, and are not described herein again.
With continued reference to fig. 2, in a specific implementation of step S203, the data in the target data table may be subjected to exception identification according to the method description statement.
In a first specific example, the method description statement may include a first key field, and a plurality of data belonging to the first key field in the target data table may be determined and may be denoted as a plurality of first key data. Further, whether a plurality of first key data exist in the associated data table or not can be judged, if not, the first key data which do not exist in the associated data table can be used as abnormal data, and the position of the first key data which do not exist in the associated data table in the target data table can be used as a part of the identification result. For example, the target data table is a transaction data table, the first key field is account identifiers of both transaction parties, the plurality of first key data are account identifiers of both transaction parties in each transaction data, the associated data table is an account information table, whether the account identifier of each transaction data can be found in the account information table can be judged, and if not, the account identifier which cannot be found in the account information table can be used as abnormal data.
In a second specific example, the method description statement may further include a second key field, and it may be determined whether there is data missing in the data belonging to the second key field in the target data table. Specifically, it may be determined that a preset padding value is searched for in each financial data in a column corresponding to the second key field in the target data table, and a position of the preset padding value is used as a position of the abnormal data in the target data table. The first key field and the second key field may be the same or different, and the embodiment of the present invention does not limit this. For example, the target data table is a transaction data table, the second key field is account identifiers of both parties of the transaction, and it can be determined in the transaction data table whether data are missing in a column corresponding to the account identifiers of both parties of the transaction, that is, whether the account identifiers in each piece of transaction data are missing is determined, and if so, the missing part can be used as a position of abnormal data in the target data table.
In a third specific example, the method description statement may further include a uniqueness field, and it may be determined whether there is a duplication of the financial data belonging to the uniqueness field in the target data table. For example, if the target data table is an account information table and the unique field is an account identifier, it may be determined whether multiple account identifiers exist in the multiple account identifiers in the account information table, and if so, the multiple account identifiers may be used as the abnormal data. For another example, the target data table is a transaction data table, and the unique field may include transaction time and account information of the transaction party, so that it may be determined whether there is transaction data in each transaction data in the transaction data table, where the transaction time is the same and the account information of the transaction party is the same, and if so, the transaction data in the transaction time is the same and the account information of the transaction party is the same, which may be used as abnormal data.
In a fourth specific example, the method description statement may further include an identifier type and an identifier statement, where the identifier type and the identifier statement are in one-to-one correspondence, and each identifier statement is used to describe a standard format of data of the corresponding identifier type. Further, the identity type to which the identity data in the target data table belongs can be determined, whether the identity data is consistent with the standard format required by the identity type is judged according to the identity statement, and if not, the identity data which is inconsistent with the standard format can be used as abnormal data.
In a fifth specific example, the target data table is a transaction data table, the transaction data table includes a plurality of transaction records, the association data table is an account information table, and the transaction time of each transaction record and the account information of the transaction party can be determined; further, the account creation time of the transaction party may be looked up in the account information table, and it is determined whether the account creation time is after the transaction time, and if so, the transaction record is taken as the abnormal data.
Further, the identification result can be sent to the user terminal, and since the identification result includes the position of the abnormal data in the target data table, the user can correct the abnormal data according to the identification result, so that the financial data in the target data table meets the requirement of the method description statement.
Referring to fig. 4, fig. 4 is a schematic structural diagram of an apparatus for identifying financial data anomalies according to an embodiment of the present invention. The apparatus may include:
an obtaining module 41, configured to obtain an identification request and a rule description file, where the identification request includes address information of a target database, and the rule description information includes: the method comprises a target data table, an exception type and a method description statement, wherein the target data table is a data table to which data to be identified belongs in the target database, the method description statement is used for describing a method for identifying an exception, and the method description statement is determined according to the exception type;
a reading module 42, configured to read data in the target data table from the target database;
and the identifying module 43 is configured to perform exception identification on the data in the target data table according to the method description statement to obtain an identification result, where the identification result includes a position of the exception data in the target data table.
In a specific implementation, the device for identifying the financial data abnormality may correspond to a chip having a function of identifying the financial data abnormality in the terminal, or correspond to a chip module having a function of identifying the financial data abnormality in the terminal, or correspond to the terminal.
For more contents of the working principle, the working mode, the beneficial effects, and the like of the device for identifying financial data anomalies shown in fig. 4, reference may be made to the above description of the method for identifying financial data anomalies, and details are not repeated here.
Embodiments of the present invention further provide a storage medium, on which a computer program is stored, where the computer program, when executed by a processor, performs the steps of the above method for identifying financial data anomalies. The storage medium may include ROM, RAM, magnetic or optical disks, etc. The storage medium may further include a non-volatile memory (non-volatile) or a non-transitory memory (non-transient), and the like.
The embodiment of the present invention further provides a computing device, which includes a memory and a processor, where the memory stores a computer program operable on the processor, and the processor executes the steps of the above method for identifying financial data anomalies when executing the computer program. The computing device includes, but is not limited to, a mobile phone, a computer, a tablet computer, and other terminal devices. In one particular example, the computing device may be, but is not limited to, computing platform 12 of FIG. 1.
It should be understood that, in the embodiment of the present application, the processor may be a Central Processing Unit (CPU), and the processor may also be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, and the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
It will also be appreciated that the memory in the embodiments of the subject application can be either volatile memory or nonvolatile memory, or can include both volatile and nonvolatile memory. The nonvolatile memory may be a read-only memory (ROM), a Programmable ROM (PROM), an Erasable PROM (EPROM), an Electrically Erasable PROM (EEPROM), or a flash memory. Volatile memory can be Random Access Memory (RAM), which acts as external cache memory. By way of example and not limitation, many forms of Random Access Memory (RAM) are available, such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (enhanced SDRAM), SDRAM (SLDRAM), synchlink DRAM (SLDRAM), and direct bus RAM (DR RAM).
The above embodiments may be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented in software, the above-described embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product comprises one or more computer instructions or computer programs. The procedures or functions according to the embodiments of the present application are wholly or partially generated when the computer instructions or the computer program are loaded or executed on a computer. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer program may be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another computer readable storage medium, for example, the computer program may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wire or wirelessly.
In the several embodiments provided in the present application, it should be understood that the disclosed method, apparatus and system may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative; for example, the division of the unit is only a logic function division, and there may be another division manner in actual implementation; for example, various elements or components may be combined or may be integrated into another system, or some features may be omitted, or not implemented. The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be physically included alone, or two or more units may be integrated into one unit. The integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional unit. For example, for each device or product applied to or integrated into a chip, each module/unit included in the device or product may be implemented by hardware such as a circuit, or at least a part of the module/unit may be implemented by a software program running on a processor integrated within the chip, and the rest (if any) part of the module/unit may be implemented by hardware such as a circuit; for each device or product applied to or integrated with the chip module, each module/unit included in the device or product may be implemented by using hardware such as a circuit, and different modules/units may be located in the same component (e.g., a chip, a circuit module, etc.) or different components of the chip module, or at least some of the modules/units may be implemented by using a software program running on a processor integrated within the chip module, and the rest (if any) of the modules/units may be implemented by using hardware such as a circuit; for each device and product applied to or integrated in the terminal, each module/unit included in the device and product may be implemented by using hardware such as a circuit, and different modules/units may be located in the same component (e.g., a chip, a circuit module, etc.) or different components in the terminal, or at least part of the modules/units may be implemented by using a software program running on a processor integrated in the terminal, and the rest (if any) part of the modules/units may be implemented by using hardware such as a circuit.
It should be understood that the term "and/or" herein is merely one type of association relationship that describes an associated object, meaning that three relationships may exist, e.g., a and/or B may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the character "/" in this document indicates that the former and latter related objects are in an "or" relationship.
The "plurality" appearing in the embodiments of the present application means two or more.
The descriptions of the first, second, etc. appearing in the embodiments of the present application are only for illustrating and differentiating the objects, and do not represent the order or the particular limitation of the number of the devices in the embodiments of the present application, and do not constitute any limitation to the embodiments of the present application.
Although the present invention is disclosed above, the present invention is not limited thereto. Various changes and modifications may be effected therein by one skilled in the art without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (11)
1. A method of identifying financial data anomalies, the method comprising:
acquiring an identification request and a rule description file, wherein the identification request comprises address information of a target database, and the rule description information comprises: identification information, an exception type and a method description statement of a target data table, wherein the target data table is a data table to which data to be identified belongs in the target database, the method description statement is used for describing a method for identifying an exception, and the method description statement is determined according to the exception type;
reading data in the target data table from the target database;
and performing exception identification on the data in the target data table according to the method description statement to obtain an identification result, wherein the identification result comprises the position of the exception data in the target data table.
2. The method of identifying financial data anomalies according to claim 1, wherein prior to identifying anomalies in data in the target data table according to the method description statement, the method further comprises:
reading preset storage structure information of the target data table, wherein the preset storage structure information comprises a standard field attribute of the target data table;
judging whether the field attribute of the data in the target data table is consistent with the standard field attribute according to the preset storage structure information to obtain a judgment result;
and sending the judgment result to a user terminal, wherein the judgment result comprises: a location of data in the target data table where the field attribute is inconsistent with the standard field attribute.
3. The method of claim 2, wherein the preset storage structure information further includes a standard index of the target data table, and before the determination result is sent to the user terminal, the method further comprises:
and judging whether the actual index of the target data table comprises the standard index, and if not, taking the standard index which is not included as a part of the judgment result.
4. The method of claim 2, wherein the preset storage structure information further includes a preset padding value, and after determining whether the field attribute of the data in the target data table is consistent with the standard field attribute, before sending the determination result to the user terminal, the method further comprises:
and judging whether data with the field attribute of the data in the target data table consistent with the standard field attribute has data missing, if so, judging whether an actual filling value at the missing part is consistent with the preset filling value, and if not, taking the position of the actual filling value in the target data table as a part of the judgment result.
5. The method of identifying financial data anomalies according to claim 4, wherein identifying anomalies in the data in the target data table according to the method description statements includes:
and searching the preset filling value in the target data table, and taking the position of the preset filling value as the position of the abnormal data in the target data table.
6. The method of identifying financial data anomalies according to claim 2, wherein prior to identifying anomalies in data in the target data table according to the method description statement, the method further comprises:
acquiring updating data from the user terminal, wherein the updating data is determined according to the judgment result;
updating the data of the target data table according to the updating data;
and judging whether the data in the target data table meets the requirement of the preset storage structure information, if so, performing exception identification on the data in the target data table according to the method description statement.
7. The method of identifying financial data anomalies according to claim 1, wherein the method description statement includes a first key field, and identifying anomalies in data in the target data table according to the method description statement includes:
determining an associated data table of the target data table, wherein the associated data table is a data table where data having an association relation with the data to be identified are located;
determining a plurality of data belonging to the first key field in the target data table, and recording the data as a plurality of first key data;
and judging whether the plurality of first key data exist in the associated data table, if not, taking the first key data which do not exist in the associated data table as the abnormal data.
8. The method for identifying financial data anomalies according to claim 7, wherein the target data table is a transaction data table, the transaction data table includes a plurality of transaction records, the associated data table is an account information table, and identifying anomalies in the data in the target data table according to the method description statement includes:
determining the transaction time of each transaction record and the account information of a transaction party;
and looking up the account creating time of the transaction party in the account information table, judging whether the account creating time is after the transaction time, and if so, taking the transaction record as the abnormal data.
9. An apparatus for identifying financial data anomalies, the apparatus comprising:
an obtaining module, configured to obtain an identification request and a rule description file, where the identification request includes address information of a target database, and the rule description information includes: identification information, an exception type and a method description statement of a target data table, wherein the target data table is a data table to which data to be identified belongs in the target database, the method description statement is used for describing a method for identifying an exception, and the method description statement is determined according to the exception type;
the reading module is used for reading the data in the target data table from the target database;
and the identification module is used for carrying out exception identification on the data in the target data table according to the method description statement to obtain an identification result, wherein the identification result comprises the position of the exception data in the target data table.
10. A storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, performs the steps of the method of identifying financial data anomalies according to any one of claims 1 to 8.
11. A computing device comprising a memory and a processor, the memory having stored thereon a computer program operable on the processor, wherein the processor, when executing the computer program, performs the steps of the method of identifying financial data anomalies of any one of claims 1 to 8.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2024124706A1 (en) * | 2022-12-15 | 2024-06-20 | 上海观安信息技术股份有限公司 | Database traffic identification method and apparatus, storage medium and computer device |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2024124706A1 (en) * | 2022-12-15 | 2024-06-20 | 上海观安信息技术股份有限公司 | Database traffic identification method and apparatus, storage medium and computer device |
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