Bank flow data verification method and system

Abstract

The invention provides a bank flow data verification method and system, and the method comprises the steps: carrying out the preprocessing of to-be-verified bank flow data, so as to obtain processed flow data; performing first verification on the processing pipeline data based on the first feature data to obtain a first verification value; performing second verification on the processing pipeline data based on the second feature data to obtain a second verification value; performing third verification on the processing pipeline data based on the third feature data to obtain a third verification value; performing fourth verification on the processing pipeline data based on the fourth feature data to obtain a fourth verification value; and completing the verification of the processing flow data based on the first verification value, the second verification value, the third verification value and the fourth verification value. The bank flow data are verified for four times, so that the verification accuracy can be improved, and meanwhile, the supervision strength of the data can be improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of data verification, and in particular relates to a bank flow data verification method and system. Background Art

[0002] For the operation of an enterprise, quality management of bank transaction data is very necessary. For group-level financial personnel, the bank transaction data of each company under the entire group usually needs to be imported into the Kingdee system, and the auxiliary functions of the Kingdee system are combined with the financial personnel to manually complete the verification process of the bank transaction data. This leads to low verification efficiency on the one hand, and low verification accuracy on the other hand. Summary of the invention

[0003] In order to solve the above technical problems, the present invention provides a bank transaction data verification method and system for solving the technical problems in the prior art.

[0004] On the one hand, the present invention provides the following technical solution, a bank flow data verification method, comprising: Obtaining bank transaction data to be verified, and preprocessing the bank transaction data to be verified to obtain processed transaction data; Extracting first characteristic data of the processed flow data, and performing a first check on the processed flow data based on the first characteristic data to obtain a first check value; Extracting second characteristic data of the processed flow data, and performing a second check on the processed flow data based on the second characteristic data to obtain a second check value; Extracting third characteristic data of the processed flow data, and performing a third check on the processed flow data based on the third characteristic data to obtain a third check value; Extracting fourth characteristic data of the processed flow data, and performing a fourth check on the processed flow data based on the fourth characteristic data to obtain a fourth check value; The verification of the processed flow data is completed based on the first verification value, the second verification value, the third verification value, and the fourth verification value.

[0005] Compared with the prior art, the beneficial effects of the present invention are as follows: the present invention first obtains the bank transaction data to be verified, and pre-processes the bank transaction data to be verified to obtain the processed transaction data; extracts the first characteristic data of the processed transaction data, and performs a first check on the processed transaction data based on the first characteristic data to obtain a first check value; extracts the second characteristic data of the processed transaction data, and performs a second check on the processed transaction data based on the second characteristic data to obtain a second check value; extracts the third characteristic data of the processed transaction data, and performs a third check on the processed transaction data based on the third characteristic data to obtain a third check value; extracts the fourth characteristic data of the processed transaction data, and performs a fourth check on the processed transaction data based on the fourth characteristic data to obtain a fourth check value; completes the check of the processed transaction data based on the first check value, the second check value, the third check value, and the fourth check value. The present invention performs four check processes on the bank transaction data, thereby improving the accuracy of the check and facilitating the improvement of the supervision of the data.

[0006] Preferably, the step of obtaining the bank transaction data to be verified and preprocessing the bank transaction data to be verified to obtain the processed transaction data comprises: The bank transaction data to be verified is processed in sequence by removing null value data, removing duplicate data, and adjusting the format unit to obtain processed transaction data.

[0007] Preferably, the step of extracting the first characteristic data of the processed flow data and performing a first check on the processed flow data based on the first characteristic data to obtain a first check value comprises: Sequentially extract the processing flow data of the target company under the same data source to obtain the first feature data; Setting a data integrity rule based on the data standard of each group of the first characteristic data, and traversing and verifying all the first characteristic data based on the data integrity rule to obtain a traversal result; Calculate a first complete check value based on the traversal result : ; In the formula, Indicates the total number of data traversed by the data complete rule, Indicates the number of data that failed the data integrity rule traversal test; Extract the company set in the bank statement data to be verified, and calculate the second complete verification value based on the company set : ; In the formula, Indicates the first companies, represents the target company; Scan the zero-value attribute field in the first feature data, and calculate a third complete check value based on the zero-value attribute field : ; In the formula, , Indicates the number of data groups and attribute fields in the first feature data. Indicates There are zero-valued attribute fields; Comprehensive first complete checksum , the second complete check value , the third complete check value To obtain a first verification value.

[0008] Preferably, the step of extracting the second characteristic data of the processed flow data and performing a second check on the processed flow data based on the second characteristic data to obtain a second check value comprises: Sequentially extract the processing flow data of the target company under different data sources to obtain the second feature data; Obtaining the name of each data item under the second characteristic data, extracting the meaning of the data item name and performing a meaning comparison in a preset meaning library, unifying the data item names with the same meaning to obtain unified characteristic data; A set of data is randomly selected from the unified feature data as reference data, and the reference data and the remaining data in the unified feature data are hashed to obtain reference hash data and the remaining hash data; Calculate the similarity between the remaining hash data and the benchmark hash data. If the similarity between the remaining hash data and the benchmark hash data is not greater than the preset similarity, the unified feature data corresponding to the remaining hash data is removed. If the similarity between the remaining hash data and the benchmark hash data is greater than the preset similarity, the unified feature data corresponding to the remaining hash data is retained to obtain retained feature data. Calculate a first similarity check value based on the retained feature data : ; In the formula, represents the number of data groups in the unified feature data, Indicates the number of data groups in the retained feature data; Extracting the dependency relationship between each data item of the unified feature data, determining a dependency verification rule based on the dependency relationship, and traversing and verifying all unified feature data based on the dependency verification rule to obtain a dependency traversal result; Calculate a second similarity check value based on the dependency traversal result : ; In the formula, Indicates that The number of data covered by the dependent verification rules, Indicates that The number of data that has passed the verification of the dependent verification rules. Represents unified feature data; Comprehensive first similarity check value , the second similarity check value , to obtain a second verification value.

[0009] Preferably, the step of extracting the third characteristic data of the processed flow data and performing a third check on the processed flow data based on the third characteristic data to obtain a third check value comprises: Extracting the processing flow data of the target company within the same data source and preset time period to obtain the third feature data; Performing normalization processing on the third characteristic data to obtain normalized characteristic data; Performing normalization verification on the normalized feature data to obtain a verification box plot, and identifying and extracting first abnormal data in the verification box plot; Performing cluster analysis on the third characteristic data using a K clustering algorithm to obtain second abnormal data; Calculate a third check value based on the first abnormal data and the second abnormal data : ; In the formula, represents the number of data of the third characteristic data, , Represent the first abnormal data and the second abnormal data respectively.

[0010] Preferably, the step of extracting fourth characteristic data of the processed flow data and performing a fourth check on the processed flow data based on the fourth characteristic data to obtain a fourth check value comprises: Extracting single-dimensional data of the target company in different data periods to obtain the fourth characteristic data; Obtaining a timestamp of the fourth characteristic data, and determining a setting time requirement based on the timestamp; Determine a unit check value based on the set time requirement : ; In the formula, Indicates the total number of data per unit time. Indicates the total number of data that meets the set time requirements per unit time; Based on the unit calibration value Determine the fourth check value : ; In the formula, Indicates the total number of data cycles.

[0011] In a second aspect, the present invention provides the following technical solution, a bank flow data verification system, the system comprising: A processing module, used for obtaining bank transaction data to be verified, and preprocessing the bank transaction data to be verified to obtain processed transaction data; A first verification module, used for extracting first characteristic data of the processed flow data, and performing a first verification on the processed flow data based on the first characteristic data to obtain a first verification value; A second verification module, used for extracting second characteristic data of the processed flow data, and performing a second verification on the processed flow data based on the second characteristic data to obtain a second verification value; A third verification module, used for extracting third characteristic data of the processed flow data, and performing a third verification on the processed flow data based on the third characteristic data to obtain a third verification value; a fourth verification module, configured to extract fourth characteristic data of the processed flow data, and perform a fourth verification on the processed flow data based on the fourth characteristic data to obtain a fourth verification value; A final verification module is used to complete the verification of the processed flow data based on the first verification value, the second verification value, the third verification value, and the fourth verification value.

[0012] Preferably, the processing module is specifically used for: The bank transaction data to be verified is processed in sequence by removing null value data, removing duplicate data, and adjusting the format unit to obtain processed transaction data.

[0013] In a third aspect, the present invention provides the following technical solution: a computer comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the bank transaction data verification method as described above when executing the computer program.

[0014] In a fourth aspect, the present invention provides the following technical solution: a storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the bank transaction data verification method as described above. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0016] Figure 1 A flow chart of a bank statement data verification method provided in Embodiment 1 of the present invention; Figure 2 A structural block diagram of a bank transaction data verification system provided in Embodiment 2 of the present invention; Figure 3 A schematic diagram of the hardware structure of a computer provided in another embodiment of the present invention.

[0017] The embodiments of the present invention will be further described below with reference to the accompanying drawings. DETAILED DESCRIPTION

[0018] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the embodiments of the present invention, and should not be construed as limiting the present invention.

[0019] In the description of the embodiments of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside" and "outside" etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the embodiments of the present invention and simplifying the description, and do not indicate or imply that the referred device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present invention.

[0020] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of the embodiments of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0021] In the embodiments of the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements. For ordinary technicians in this field, the specific meanings of the above terms in the embodiments of the present invention can be understood according to specific circumstances.

[0022] Embodiment 1 In the first embodiment of the present invention, Figure 1 As shown, a bank transaction data verification method includes: S1. Obtain bank transaction data to be verified, and pre-process the bank transaction data to be verified to obtain processed transaction data; Wherein, the step S1 is specifically used for: The bank transaction data to be verified is processed by removing null value data, removing duplicate data, and adjusting the format unit in sequence to obtain processed transaction data; Specifically, the purpose of preprocessing is to remove blank values ​​and duplicate data in the data. At the same time, because data from different data sources may have different formats and units, it is necessary to unify the format units.

[0023] S2, extracting first characteristic data of the processed flow data, and performing a first check on the processed flow data based on the first characteristic data to obtain a first check value; Wherein, the step S2 comprises: S21, extracting the processing flow data of the target company under the same data source in sequence to obtain first feature data; S22, setting a data integrity rule based on the data standard of each group of the first characteristic data, and traversing and verifying all the first characteristic data based on the data integrity rule to obtain a traversal result; Specifically, assuming that complete data requires at least foreign keys, primary keys, field length, precision, type, attributes and other items, the data integrity rule is to determine whether the data includes all of the above items.

[0024] S23, calculating a first complete check value based on the traversal result : ; In the formula, Indicates the total number of data traversed by the data complete rule, Indicates the number of data that failed the data integrity rule traversal check.

[0025] S24, extracting a company set from the bank statement data to be verified, and calculating a second complete verification value based on the company set : ; In the formula, Indicates the first companies, Indicates the target company.

[0026] S24: Scan the zero-value attribute field in the first feature data, and calculate a third complete check value based on the zero-value attribute field. : ; In the formula, , Indicates the number of data groups and attribute fields in the first feature data. Indicates There are zero-valued attribute fields.

[0027] S26, comprehensive first complete check value , the second complete check value , the third complete check value To obtain a first check value; Specifically, the first verification process is specifically a data integrity check. The first full check value is used to determine the integrity of the metadata, the second full check value is used to determine the integrity of the data set, and the third full check value is used to determine the integrity of the data item. Therefore, in the actual first verification process, the first full check value can be used to determine the integrity of the data item. , the second complete check value , the third complete check value They can be verified independently or by weighted fusion verification.

[0028] S3, extracting second characteristic data of the processed flow data, and performing a second check on the processed flow data based on the second characteristic data to obtain a second check value; Wherein, the step S3 comprises: S31, sequentially extracting the processing flow data of the target company under different data sources to obtain second feature data; S32, obtaining the name of each data item under the second characteristic data, extracting the meaning of the data item name and performing a meaning comparison in a preset meaning library, unifying the data item names with the same meaning to obtain unified characteristic data; Specifically, in different data sources, different data items express the same meaning. For example, with respect to company numbers, some data sources directly use the company name as the company number, some use the social unified credit code as the company number, and some will number themselves according to their data source systems. Therefore, it is necessary to unify the data items that express the same meaning, so as to obtain the characteristic data corresponding to each company.

[0029] S33. Randomly select a group of data in the unified feature data as reference data, and perform hashing on the reference data and the remaining data in the unified feature data to obtain reference hash data and the remaining hash data.

[0030] S34. Calculate the similarity between the remaining hash data and the benchmark hash data. If the similarity between the remaining hash data and the benchmark hash data is not greater than the preset similarity, the unified feature data corresponding to the remaining hash data will be eliminated. If the similarity between the remaining hash data and the benchmark hash data is greater than the preset similarity, the unified feature data corresponding to the remaining hash data will be retained to obtain retained feature data.

[0031] S35: Calculate a first similarity check value based on the retained feature data : ; In the formula, represents the number of data groups in the unified feature data, Indicates the number of data groups in the retained feature data.

[0032] S36, extracting the dependency relationship between each data item of the unified feature data, determining a dependency verification rule based on the dependency relationship, and traversing and verifying all unified feature data based on the dependency verification rule to obtain a dependency traversal result; Specifically, for transaction data, there will be certain dependencies between different data items. For example, the current account balance is related to the account balance of the last deposit and withdrawal, the last deposit, and the last withdrawal. Therefore, the data is traversed by setting dependent verification rules to complete the second verification process.

[0033] S37: Calculate a second similarity check value based on the dependency traversal result. : ; In the formula, Indicates that The number of data covered by the dependent verification rules, Indicates that The number of data that has passed the verification of the dependent verification rules. Represents unified feature data; S38, comprehensive first similarity check value , the second similarity check value , to obtain a second check value; Specifically, the second verification process is a consistency verification process, the first similarity verification value is used to determine whether the data are equal, and the second similarity verification value is used to determine whether the data logic is consistent.

[0034] S4, extracting third characteristic data of the processed flow data, and performing a third check on the processed flow data based on the third characteristic data to obtain a third check value; Wherein, the step S4 comprises: S41. Extract the processing flow data of the target company within the same data source and preset time period to obtain third feature data.

[0035] S42: performing normalization processing on the third characteristic data to obtain normalized characteristic data.

[0036] S43, performing normalization verification on the normalized feature data to obtain a verification box plot, and identifying and extracting the first abnormal data in the verification box plot.

[0037] S44. Perform cluster analysis on the third characteristic data using a K clustering algorithm to obtain second abnormal data.

[0038] S45: Calculate a third check value based on the first abnormal data and the second abnormal data : ; In the formula, represents the number of data of the third characteristic data, , Respectively represent the first abnormal data and the second abnormal data; Specifically, the first abnormal data and the second abnormal data are outliers or abnormal values ​​existing in the feature data, and the third verification process is specifically a verification of data accuracy, and the third verification value is specifically used to determine the qualitative accuracy and quantitative accuracy of the data.

[0039] S5, extracting fourth characteristic data of the processed flow data, and performing a fourth check on the processed flow data based on the fourth characteristic data to obtain a fourth check value; Wherein, the step S5 comprises: S51. Extract single-dimensional data of the target company in different data periods to obtain fourth characteristic data.

[0040] S52: Obtain a timestamp of the fourth characteristic data, and determine a setting time requirement based on the timestamp.

[0041] S53: Determine a unit check value based on the set time requirement : ; In the formula, Indicates the total number of data per unit time. Indicates the total number of data that meets the set time requirements per unit time.

[0042] S54, based on the unit check value Determine the fourth check value : ; In the formula, Indicates the total number of data cycles; Specifically, for the timestamp of the data, the verification of the data needs to be timely, so the setting time requirement can be determined according to the verification requirement. Therefore, the fourth verification value is used to determine whether the data is within a preset time period, and the fourth verification is specifically a verification of the data time validity.

[0043] S6. Complete verification of the processed flow data based on the first verification value, the second verification value, the third verification value, and the fourth verification value; Specifically, after determining the first check value, the second check value, the third check value, and the fourth check value, a final check process may be performed according to the first check value, the second check value, the third check value, and the fourth check value, which may be divided into a separate check process and a fusion check process; When performing separate verification, corresponding thresholds can be set according to the first verification value, the second verification value, the third verification value, and the fourth verification value, respectively, and verification can be performed based on the comparison between the first verification value, the second verification value, the third verification value, and the fourth verification value and the threshold, and the data that fails the verification can be extracted. When performing fusion verification, the first verification value, the second verification value, the third verification value, and the fourth verification value can be weightedly fused, and then a fusion threshold is determined. By comparing with the fusion threshold, the entire batch of bank flow data can be verified. However, compared with separate verification, separate verification has higher accuracy.

[0044] The bank transaction data verification method provided in the first embodiment of the present invention first obtains the bank transaction data to be verified, pre-processes the bank transaction data to be verified to obtain the processed transaction data; extracts the first characteristic data of the processed transaction data, and performs a first verification on the processed transaction data based on the first characteristic data to obtain a first verification value; extracts the second characteristic data of the processed transaction data, and performs a second verification on the processed transaction data based on the second characteristic data to obtain a second verification value; extracts the third characteristic data of the processed transaction data, and performs a third verification on the processed transaction data based on the third characteristic data to obtain a third verification value; extracts the fourth characteristic data of the processed transaction data, and performs a fourth verification on the processed transaction data based on the fourth characteristic data to obtain a fourth verification value; and completes the verification of the processed transaction data based on the first verification value, the second verification value, the third verification value, and the fourth verification value. The present invention performs four verification processes on the bank transaction data, thereby improving the accuracy of the verification and facilitating the improvement of the supervision of the data.

[0045] Embodiment 2 like Figure 2 As shown, in the second embodiment of the present invention, a bank flow data verification system is provided, and the system includes: Processing module 1, used for obtaining bank transaction data to be verified, and preprocessing the bank transaction data to be verified to obtain processed transaction data; A first verification module 2, used for extracting first characteristic data of the processed flow data, and performing a first verification on the processed flow data based on the first characteristic data to obtain a first verification value; A second verification module 3, used for extracting second characteristic data of the processed flow data, and performing a second verification on the processed flow data based on the second characteristic data to obtain a second verification value; A third verification module 4 is used to extract third characteristic data of the processed flow data, and perform a third verification on the processed flow data based on the third characteristic data to obtain a third verification value; A fourth verification module 5, used for extracting fourth characteristic data of the processed flow data, and performing a fourth verification on the processed flow data based on the fourth characteristic data to obtain a fourth verification value; The final verification module 6 is used to complete the verification of the processed flow data based on the first verification value, the second verification value, the third verification value, and the fourth verification value.

[0046] The processing module 1 is specifically used for: The bank transaction data to be verified is processed in sequence by removing null value data, removing duplicate data, and adjusting the format unit to obtain processed transaction data.

[0047] The first verification module 2 includes: A first extraction submodule is used to sequentially extract the processing flow data of the target company under the same data source to obtain first feature data; A complete traversal submodule, used for setting a data completeness rule based on the data standard of each group of the first characteristic data, and traversing and verifying all the first characteristic data based on the data completeness rule to obtain a traversal result; A first complete check value calculation submodule is used to calculate a first complete check value based on the traversal result. : ; In the formula, Indicates the total number of data traversed by the data complete rule, Indicates the number of data that failed the data integrity rule traversal test; The second complete check value calculation submodule is used to extract the company set in the bank flow data to be verified, and calculate the second complete check value based on the company set : ; In the formula, Indicates the first companies, represents the target company; A third complete check value calculation submodule is used to scan the zero-value attribute field in the first feature data and calculate a third complete check value based on the zero-value attribute field. : ; In the formula, , Indicates the number of data groups and attribute fields in the first feature data. Indicates There are zero-valued attribute fields; The first check submodule is used to synthesize the first complete check value , the second complete check value , the third complete check value To obtain a first verification value.

[0048] The second verification module 3 includes: The second extraction submodule is used to sequentially extract the processing flow data of the target company under different data sources to obtain the second feature data; A comparison submodule, used to obtain the name of each data item under the second characteristic data, extract the meaning of the data item name and compare the meaning in a preset meaning library, unify the data item names with the same meaning to obtain unified characteristic data; A hash processing submodule, used for arbitrarily selecting a set of data in the unified feature data as reference data, and performing hash processing on the reference data and the remaining data in the unified feature data to obtain reference hash data and the remaining hash data; A judgment submodule is used to calculate the similarity between the remaining hash data and the benchmark hash data. If the similarity between the remaining hash data and the benchmark hash data is not greater than the preset similarity, the unified feature data corresponding to the remaining hash data is eliminated. If the similarity between the remaining hash data and the benchmark hash data is greater than the preset similarity, the unified feature data corresponding to the remaining hash data is retained to obtain retained feature data. A first similarity check value calculation submodule is used to calculate a first similarity check value based on the retained feature data : ; In the formula, represents the number of data groups in the unified feature data, Indicates the number of data groups in the retained feature data; A dependency traversal submodule, used for extracting the dependency relationship between each data item of the unified feature data, determining a dependency verification rule based on the dependency relationship, and traversing and verifying all unified feature data based on the dependency verification rule to obtain a dependency traversal result; A second similarity check value calculation submodule is used to calculate a second similarity check value based on the dependency traversal result. : ; In the formula, Indicates that The number of data covered by the dependent verification rules, Indicates that The number of data that has passed the verification of the dependent verification rules. Represents unified feature data; The second check submodule is used to synthesize the first similarity check value , the second similarity check value , to obtain a second verification value.

[0049] The third verification module 4 includes: The third extraction submodule is used to extract the processing flow data of the target company within the same data source and a preset time period to obtain third feature data; A normalization submodule, used for performing normalization processing on the third characteristic data to obtain normalized characteristic data; A graph output submodule, used for performing normalization verification on the normalized feature data to obtain a verification box graph, and identifying and extracting the first abnormal data in the verification box graph; A clustering submodule, used for performing clustering analysis on the third characteristic data by using a K clustering algorithm to obtain second abnormal data; A third check submodule is used to calculate a third check value based on the first abnormal data and the second abnormal data : ; In the formula, represents the number of data of the third characteristic data, , Represent the first abnormal data and the second abnormal data respectively.

[0050] The fourth verification module 5 includes: A fourth extraction submodule is used to extract single-dimensional data of the target company in different data periods to obtain fourth feature data; A time submodule, used for obtaining a timestamp of the fourth characteristic data, and determining a setting time requirement based on the timestamp; A unit check value calculation submodule is used to determine the unit check value based on the set time requirement. : ; In the formula, Indicates the total number of data per unit time. Indicates the total number of data that meets the set time requirements per unit time; A fourth check submodule, configured to check the value based on the unit Determine the fourth check value : ; In the formula, Indicates the total number of data cycles.

[0051] In some other embodiments of the present invention, the embodiments of the present invention provide the following technical solutions: a computer, comprising a memory 102, a processor 101, and a computer program stored in the memory 102 and executable on the processor 101; when the processor 101 executes the computer program, the bank transaction data verification method as described above is implemented.

[0052] Specifically, the processor 101 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiment of the present invention.

[0053] Among them, the memory 102 may include a large-capacity memory for data or instructions. By way of example and not limitation, the memory 102 may include a hard disk drive (HDD), a floppy disk drive, a solid state drive (SSD), a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a universal serial bus (USB) drive, or a combination of two or more of these. Where appropriate, the memory 102 may include a removable or non-removable (or fixed) medium. Where appropriate, the memory 102 may be inside or outside the data processing device. In a specific embodiment, the memory 102 is a non-volatile memory. In a specific embodiment, the memory 102 includes a read-only memory (ROM) and a random access memory (RAM). Where appropriate, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), an electrically alterable ROM (EAROM) or a flash memory (FLASH), or a combination of two or more of these. Under appropriate circumstances, the RAM can be a static random access memory (SRAM) or a dynamic random access memory (DRAM), wherein the DRAM can be a fast page mode dynamic random access memory (FPMDRAM), an extended data output dynamic random access memory (EDODRAM), a synchronous dynamic random access memory (SDRAM), etc.

[0054] The memory 102 may be used to store or cache various data files that need to be processed and / or used for communication, as well as possible computer program instructions executed by the processor 101 .

[0055] The processor 101 implements the above-mentioned bank transaction data verification method by reading and executing the computer program instructions stored in the memory 102.

[0056] In some embodiments, the computer may further include a communication interface 103 and a bus 100. Figure 3 As shown, the processor 101, the memory 102, and the communication interface 103 are connected via a bus 100 and communicate with each other.

[0057] The communication interface 103 is used to implement communication between the modules, devices, units and / or equipment in the embodiment of the present invention. The communication interface 103 can also implement data communication with other components such as: external devices, image / data acquisition equipment, databases, external storage, and image / data processing workstations.

[0058] The bus 100 includes hardware, software or both, and couples the components of the computer device to each other. The bus 100 includes but is not limited to at least one of the following: a data bus, an address bus, a control bus, an expansion bus, and a local bus. By way of example and not limitation, bus 100 may include an Accelerated Graphics Port (AGP) or other graphics bus, an Extended Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a Hyper Transport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an InfiniBand interconnect, a Low Pin Count (LPC) bus, a memory bus, a Micro Channel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local Bus (VLB) bus, or other suitable buses or a combination of two or more of these. Where appropriate, bus 100 may include one or more buses. Although embodiments of the present invention describe and illustrate a particular bus, the present invention contemplates any suitable bus or interconnect.

[0059] The computer can execute the bank transaction data verification method of the present invention based on the bank transaction data verification system obtained, thereby realizing bank transaction data verification.

[0060] In some further embodiments of the present invention, in combination with the above-mentioned bank transaction data verification method, the embodiments of the present invention provide the following technical solutions: a storage medium having a computer program stored thereon, and the computer program implements the above-mentioned bank transaction data verification method when executed by a processor.

[0061] Those skilled in the art will appreciate that the logic and / or steps represented in the flowchart or otherwise described herein, for example, may be considered as an ordered list of executable instructions for implementing logical functions, and may be specifically implemented in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For purposes of this specification, "computer-readable medium" may be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0062] More specific examples of readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0063] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or a combination thereof: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0064] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described 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 specification.

[0065] The above-mentioned embodiments only express several implementation methods of the present invention, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

Claims

1. A bank transaction data verification method, characterized in that: include: Obtaining bank transaction data to be verified, and preprocessing the bank transaction data to be verified to obtain processed transaction data; Extracting first characteristic data of the processed flow data, and performing a first check on the processed flow data based on the first characteristic data to obtain a first check value; Extracting second characteristic data of the processed flow data, and performing a second check on the processed flow data based on the second characteristic data to obtain a second check value; Extracting third characteristic data of the processed flow data, and performing a third check on the processed flow data based on the third characteristic data to obtain a third check value; Extracting fourth characteristic data of the processed flow data, and performing a fourth check on the processed flow data based on the fourth characteristic data to obtain a fourth check value; The verification of the processed flow data is completed based on the first verification value, the second verification value, the third verification value, and the fourth verification value.

2. The bank flow data verification method according to claim 1, characterized in that: The step of obtaining the bank transaction data to be verified and preprocessing the bank transaction data to be verified to obtain the processed transaction data comprises: The bank transaction data to be verified is processed in sequence by removing null value data, removing duplicate data, and adjusting the format unit to obtain processed transaction data.

3. The bank flow data verification method according to claim 1, characterized in that: The step of extracting the first characteristic data of the processed flow data and performing a first check on the processed flow data based on the first characteristic data to obtain a first check value comprises: Sequentially extract the processing flow data of the target company under the same data source to obtain the first feature data; Setting a data integrity rule based on the data standard of each group of the first characteristic data, and traversing and verifying all the first characteristic data based on the data integrity rule to obtain a traversal result; Calculate a first complete check value based on the traversal result : ； In the formula, Indicates the total number of data traversed by the data complete rule, Indicates the number of data that failed the data integrity rule traversal test; Extract the company set in the bank statement data to be verified, and calculate the second complete verification value based on the company set : ； In the formula, Indicates the first companies, represents the target company; Scan the zero-value attribute field in the first feature data, and calculate a third complete check value based on the zero-value attribute field : ； In the formula, , Indicates the number of data groups and attribute fields in the first feature data. Indicates There are zero-valued attribute fields; Comprehensive first complete checksum , the second complete check value , the third complete check value To obtain a first verification value.

4. The bank flow data verification method according to claim 1, characterized in that: The step of extracting the second characteristic data of the processed flow data and performing a second check on the processed flow data based on the second characteristic data to obtain a second check value comprises: Sequentially extract the processing flow data of the target company under different data sources to obtain the second feature data; Obtaining the name of each data item under the second characteristic data, extracting the meaning of the data item name and performing a meaning comparison in a preset meaning library, unifying the data item names with the same meaning to obtain unified characteristic data; A set of data is randomly selected from the unified feature data as reference data, and the reference data and the remaining data in the unified feature data are hashed to obtain reference hash data and the remaining hash data; Calculate the similarity between the remaining hash data and the benchmark hash data. If the similarity between the remaining hash data and the benchmark hash data is not greater than the preset similarity, the unified feature data corresponding to the remaining hash data is removed. If the similarity between the remaining hash data and the benchmark hash data is greater than the preset similarity, the unified feature data corresponding to the remaining hash data is retained to obtain retained feature data. Calculate a first similarity check value based on the retained feature data : ； In the formula, represents the number of data groups in the unified feature data, Indicates the number of data groups in the retained feature data; Extracting the dependency relationship between each data item of the unified feature data, determining a dependency verification rule based on the dependency relationship, and traversing and verifying all unified feature data based on the dependency verification rule to obtain a dependency traversal result; Calculate a second similarity check value based on the dependency traversal result : ； In the formula, Indicates that The number of data covered by the dependent verification rules, Indicates that The number of data that has passed the verification of the dependent verification rules. Represents unified feature data; Comprehensive first similarity check value , the second similarity check value , to obtain a second verification value.

5. The bank flow data verification method according to claim 1, characterized in that: The step of extracting the third characteristic data of the processed flow data and performing a third check on the processed flow data based on the third characteristic data to obtain a third check value comprises: Extracting the processing flow data of the target company within the same data source and preset time period to obtain the third feature data; Performing normalization processing on the third characteristic data to obtain normalized characteristic data; Performing normalization verification on the normalized feature data to obtain a verification box plot, and identifying and extracting first abnormal data in the verification box plot; Performing cluster analysis on the third characteristic data using a K clustering algorithm to obtain second abnormal data; Calculate a third check value based on the first abnormal data and the second abnormal data : ； In the formula, represents the number of data of the third characteristic data, , Represent the first abnormal data and the second abnormal data respectively.

6. The bank flow data verification method according to claim 1, characterized in that: The step of extracting the fourth characteristic data of the processed flow data and performing a fourth check on the processed flow data based on the fourth characteristic data to obtain a fourth check value comprises: Extracting single-dimensional data of the target company in different data periods to obtain the fourth characteristic data; Obtaining a timestamp of the fourth characteristic data, and determining a setting time requirement based on the timestamp; Determine a unit check value based on the set time requirement : ； In the formula, Indicates the total number of data per unit time. Indicates the total number of data that meets the set time requirements per unit time; Based on the unit calibration value Determine the fourth check value : ； In the formula, Indicates the total number of data cycles.

7. A bank transaction data verification system, characterized in that: The system comprises: A processing module, used for obtaining bank transaction data to be verified, and preprocessing the bank transaction data to be verified to obtain processed transaction data; A first verification module, used for extracting first characteristic data of the processed flow data, and performing a first verification on the processed flow data based on the first characteristic data to obtain a first verification value; A second verification module, used for extracting second characteristic data of the processed flow data, and performing a second verification on the processed flow data based on the second characteristic data to obtain a second verification value; A third verification module, used for extracting third characteristic data of the processed flow data, and performing a third verification on the processed flow data based on the third characteristic data to obtain a third verification value; a fourth verification module, configured to extract fourth characteristic data of the processed flow data, and perform a fourth verification on the processed flow data based on the fourth characteristic data to obtain a fourth verification value; A final verification module is used to complete the verification of the processed flow data based on the first verification value, the second verification value, the third verification value, and the fourth verification value.

8. The bank transaction data verification system according to claim 7, characterized in that: The processing module is specifically used for: The bank transaction data to be verified is processed in sequence by removing null value data, removing duplicate data, and adjusting the format unit to obtain processed transaction data.

9. A computer comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the bank transaction data verification method as described in any one of claims 1 to 6 is implemented.

10. A storage medium, characterized in that: The storage medium stores a computer program, which, when executed by a processor, implements the bank transaction data verification method as described in any one of claims 1 to 6.

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