Transaction data checking method and device, equipment and medium
An automated comparison method, constructed using measurement matrix back-calculation and compressed sensing theory, solves the problem of low efficiency in transaction data verification in multi-channel, multi-protocol testing environments, and achieves efficient and accurate transaction data verification.
Patent Information
- Application Number
- CN202411985830.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-31
- Publication Date
- 2026-01-13
AI Technical Summary
In test environments with multiple channels, protocols, and business scenarios, existing technologies rely on manual verification of transaction application data and confirmation data one by one, resulting in low efficiency, easy omissions, and high costs.
The expected transaction application data is generated by reverse engineering the measurement matrix and compared with the actual transaction application data. The measurement matrix is constructed using compressed sensing theory for automated comparison, and key data fields are screened for dimensionality reduction.
It enables automated verification of transaction data, saving manpower, improving verification efficiency, avoiding human error, and quickly locating inconsistencies.
Smart Images
Figure CN121329632A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to the technical field of financial technology, and in particular to a transaction data checking method and device, equipment and medium. BACKGROUND
[0002] With the multi-channel and multi-system expansion of financial product agency sales business, the business scenarios for testing are more and more, and in the limited test period, the test scenario data of various sales channels and various business types need to be covered. Since the completeness and accuracy of the test scenario data often need to be checked manually, the work efficiency of processing the test data is low. Taking the field of financial product agency sales as an example, the sales channels cover the parent bank personal channel, the parent bank private bank channel, the parent bank personal pension channel, the parent bank three pillar pension channel, the parent bank legal person channel, the parent bank legal person pension channel, the third party agency sales channel, and the direct sales channel. The business standard data exchange protocol involves the bank login protocol, the constant protocol, and the in-house APIP service of the central data exchange platform.
[0003] For multi-channel, multi-protocol, and multi-business scenario, it is necessary to implement the non-missing checking of the transaction application data of the transaction applicant and the transaction confirmation data of the transaction confirmer, so as to ensure the accurate connection in the whole link joint test of the upstream and downstream applications. In order to improve the accuracy of the checking, the current test mainly relies on manual checking of the consistency of the test application data and the test confirmation data, which consumes a lot of manpower, is easy to overlook, and has a high test time cost. SUMMARY
[0004] The present application provides a transaction data checking method, device, equipment and medium to improve the checking efficiency of transaction application and confirmation data.
[0005] According to an aspect of the present application, a transaction data checking method is provided, comprising:
[0006] The expected transaction application data required for the transaction confirmation data to be checked is generated by backstepping the measurement matrix;
[0007] The expected transaction application data and the actual transaction application data to be checked are compared for consistency;
[0008] If the expected transaction application data and the actual transaction application data are consistent, it is determined that the actual transaction application data passes the checking.
[0009] According to another aspect of the present application, a transaction data checking device is provided, comprising:
[0010] The backstepping module is configured to generate the expected transaction application data required for the transaction confirmation data to be checked by backstepping the measurement matrix;
[0011] a comparison module, configured to compare the expected transaction application data with actual transaction application data to be checked for consistency;
[0012] a passing module, configured to determine that the actual transaction application data passes the check if the expected transaction application data and the actual transaction application data are consistent.
[0013] According to another aspect of the present application, there is provided a computer program product comprising a computer program which, when executed by a processor, implements the transaction data checking method according to any embodiment of the present application.
[0014] According to another aspect of the present application, there is provided an electronic device comprising at least one processor, and a memory connected to the at least one processor in communication; wherein the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to perform the transaction data checking method according to any embodiment of the present application.
[0015] According to another aspect of the present application, there is provided a computer readable storage medium storing computer instructions for causing a processor to perform the transaction data checking method according to any embodiment of the present application when executed by the processor.
[0016] The embodiments of the present application achieve automatic comparison of transaction confirmation data and transaction application data through measurement matrix, without human intervention in data processing operation, saving human resources, improving checking efficiency and avoiding errors.
[0017] It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the present application or to limit the scope of the present application. Other features of the present application will become apparent from the following description. BRIEF DESCRIPTION OF DRAWINGS
[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings needed in the embodiments description. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative effort based on these drawings.
[0019] Figure 1 is a flowchart of a transaction data checking method according to an embodiment of the present application;
[0020] Figure 2Ais a flow chart of a transaction data checking method according to another embodiment of the present application;
[0021] Figure 2B is a schematic diagram of a consistency comparison process according to another embodiment of the present application;
[0022] Figure 3 is a structural schematic diagram of a transaction data checking device according to another embodiment of the present application;
[0023] Figure 4 is a structural schematic diagram of an electronic device implementing an embodiment of the present application. DETAILED DESCRIPTION
[0024] In order to enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative efforts should fall within the scope of the present application.
[0025] It should be noted that the terms "first", "second", and the like in the specification and claims of the present application and the above-described drawings are used to distinguish similar objects, and do not necessarily indicate a specific order or a chronological sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product, or device that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but can include other steps or units that are not clearly listed or inherent to the process, method, product, or device.
[0026] Figure 1 is a flow chart of a transaction data checking method according to an embodiment of the present application. The present embodiment can be applied to a case where paired transaction confirmation data and transaction application data need to be checked. The method can be performed by a transaction data checking device, which can be implemented in the form of hardware and / or software, and can be configured in an electronic device with corresponding data processing capability, such as a checking system. As shown in the figure, the method comprises the following steps. Figure 1
[0027] S110, the expected transaction application data required for generating the transaction confirmation data to be checked is obtained by inverse deduction based on the measurement matrix.
[0028] S120, the expected transaction application data and the actual transaction application data to be checked are compared for consistency.
[0029] S130, if the expected transaction application data and the actual transaction application data are consistent, it is determined that the actual transaction application data passes the check.
[0030] Wherein, the transaction application data refers to the data submitted by one party (such as a buyer, a seller or a financial institution) before the transaction is completed or confirmed, and the transaction application data usually contains the identity information of the transaction parties, the transaction amount, the currency type, the payment method, the description of goods or services, and any additional terms or conditions that may be required. The transaction confirmation data is the final and unchangeable transaction record, which proves that the transaction has been successfully executed according to the predetermined conditions, and usually contains a unique transaction ID, a timestamp, a participant confirmation, a transaction status (such as success, failure), and possible transaction fees or exchange rates.
[0031] Specifically, based on the compression sensing theory, each transaction application data can obtain a transaction confirmation data through the measurement matrix Φ. If the transaction confirmation data is known, the corresponding transaction application data x0 can be deduced, provided that a suitable measurement matrix is established. After the measurement matrix is established and tested, when there are paired transaction confirmation data and actual transaction application data to be checked, the transaction application data required to generate the transaction confirmation data is generated by the measurement matrix and is determined as the expected transaction application data. The expected transaction application data and the actual transaction application data are compared, and if they are consistent, it means that the actual transaction application data corresponds to the transaction after completion, and the transaction confirmation data can be obtained, and the check passes. If they are not consistent, it means that the actual transaction application data corresponds to the transaction after completion, and the transaction confirmation data obtained is other transaction confirmation data, not the current transaction confirmation data to be checked, and the check fails.
[0032] The embodiment of the application realizes the automatic comparison of the transaction confirmation data and the transaction application data through the measurement matrix, without the need for human intervention in the data processing operation, saving human resources, and improving the work efficiency of the data to be processed and avoiding errors.
[0033] Figure 2A A flowchart of a transaction data checking method provided by another embodiment of the application is shown in FIG. 8. The embodiment is optimized and improved on the basis of the above-mentioned embodiment. As shown in FIG. 8, the method comprises the following steps. Figure 2A
[0034] S210, the transaction confirmation data to be checked is compressed and sampled to obtain a transaction confirmation matrix, and the measurement matrix is converted to obtain an expected transaction application matrix.
[0035] S220. Compress and sample the actual transaction application data to be verified to obtain the actual transaction application matrix; perform a consistency comparison between the expected transaction application matrix and the actual transaction application matrix.
[0036] Specifically, such as Figure 2B As shown, the transaction confirmation data to be verified and the actual transaction confirmation data are compressed and sampled to obtain a transaction confirmation matrix and an actual transaction application matrix. Matrix operations are then performed on the measurement matrix and the transaction confirmation matrix to obtain the expected transaction application matrix. The expected transaction application matrix is identical to the actual transaction application matrix in terms of the number of rows and columns. If the matrix elements in both are the same at all positions, it indicates that the actual transaction application data is consistent with the expected transaction application data; if the matrix elements in both are different at a certain position, it indicates that the actual transaction application data is inconsistent with the expected transaction application data. This matrix-based comparison method improves the comparison effect and makes it easier to identify specific inconsistencies. Furthermore, by comparing matrices, it is also possible to quickly and accurately locate missing and redundant data.
[0037] Based on the above embodiments, optionally, the step of compressing and sampling the transaction confirmation data to be verified to obtain the transaction confirmation matrix includes:
[0038] The transaction confirmation data is filtered by dimensionality reduction according to the standard business data exchange protocol to obtain the key data fields in the transaction confirmation data.
[0039] Construct a transaction confirmation matrix based on the key data fields.
[0040] Key data fields include those that are helpful for reverse engineering, such as business code, product code, customer account, transaction amount, and transaction share.
[0041] Specifically, transaction data (application or confirmation) exchanged between different systems follows the standard business data exchange protocol, and all fields in the protocol are effectively assigned values. The matrix construction process mainly involves effectively filtering the fields in the (application or confirmation) transaction data according to the standard business data exchange protocol, retaining only key data fields such as business code, product code, customer account, transaction amount, and transaction share, while filtering out the rest. This achieves the goal of streamlining and reducing the workload of subsequent reverse engineering and consistency comparison.
[0042] Based on the above embodiments, optionally, the key data fields include business code fields used to represent business scenarios.
[0043] Specifically, each transaction data has a business code field, which represents the business scenario corresponding to the current transaction application / confirmation data, such as subscription application (020), subscription application (022), redemption application (024), subscription confirmation (130), subscription confirmation (122), and redemption confirmation (124). As a key data field, the business code field can make the constructed transaction confirmation / application matrix also contain the application scenario information of the transaction, improve the information richness of the matrix, and reduce the data distortion in the dimensionality reduction screening process.
[0044] On the basis of the above embodiment, optionally, the training process of the measurement matrix is as follows:
[0045] The historical transaction confirmation data is taken as the observation value of compressed sensing, and the historical transaction application data is taken as the original signal of compressed sensing.
[0046] The measurement matrix of compressed sensing is trained according to the original signal and the observation value.
[0047] Specifically, the measurement matrix is a key component in compressed sensing. In the traditional signal processing process, it is used to map the high-dimensional original signal to a low-dimensional space to obtain low-dimensional observation values. By referring to the theory of compressed sensing, the historical transaction confirmation data is regarded as the observation value of compressed sensing, and the historical transaction application data is regarded as the original signal of compressed sensing. The measurement matrix is constructed, trained and tested. By referring to mature compressed sensing algorithms, the construction efficiency of the measurement matrix is improved.
[0048] Exemplarily, the transaction application data is compressed and sampled into the application confirmation matrix x’ (x1, x2, x3…), and the transaction confirmation data is compressed and sampled into the application confirmation matrix y’ (y1, y2, y3…). The variable data in the matrix is the key data field such as application amount, financial product, and financial transaction account number. After multiple optimizations of the existing inventory data as input x’ (x1, x2, x3…) and output y’ (y1, y2, y3…), a relatively appropriate measurement matrix T can be obtained as follows:
[0049]
[0050] Wherein a is a key information, such as a1 is a business code, a2 is a product code, a3 is a customer account number, a4 is a transaction amount, and a5 is a transaction share. The first row data of the sensing matrix T is (busincode020 prod_code TA_acco balance shares navalue), the second row data is (busincode022 prod_code TA_acco balance shares navalue), and so on.
[0051] If x1 is a subscription data, then x1 is (022 20GS901 GY10002000100 0)T, x2 is a redemption data, then x2 is (024 20GS902 GY100020010 200)T,
[0052] After multiple optimizations of the inventory data as input x'(x1, x2, x3...) and output y'(y1, y2, y3...), the correlation calculation of the matrix key field is adjusted, and the sensing matrix construction is completed.
[0053] The measurement matrix needs to be constantly trained and improved after preliminary construction. For example, by multiple calculations, the key field application number is added to maintain unique data. In addition, for product expiration transactions, the original application data will not be sent to the expiration application, but the confirmation data will have product expiration confirmation, which will cause the inconsistency between the front and back, and the sensing matrix needs to be adjusted in the continuous optimization.
[0054] S230, if the expected transaction application data and the actual transaction application data are consistent, it is determined that the actual transaction application data passes the check.
[0055] On the basis of the above embodiment, optionally, after the consistency comparison of the expected transaction application data and the actual transaction application data to be checked, it further includes:
[0056] If the expected transaction application data and the actual transaction application data are inconsistent, an exception report is generated according to the expected transaction application data, the actual transaction application data and the transaction confirmation data, and the exception report is pushed to the review personnel.
[0057] Specifically, when the check fails, in addition to outputting the check result of the check failure, the data used and generated in this check (the expected transaction application data, the actual transaction application data and the transaction confirmation data) need to be sorted and filled into the template data according to the preset format to obtain an exception report. The exception report is actively pushed to the subsequent review personnel. The exception report can improve the efficiency of the review personnel and quickly determine whether this check is wrong.
[0058] The embodiment of the application takes the matrix as the comparison unit to compare the expected transaction application data and the actual transaction application data, which can improve the comparison effect and more easily determine the specific inconsistency.
[0059] Figure 3 A structure diagram of a transaction data checking device provided by another embodiment of the application is shown in FIG. 2. Figure 3 As shown in the figure, the device includes:
[0060] The reverse pushing module 310 is configured to generate expected transaction application data required for the transaction confirmation data to be checked by measurement matrix reverse pushing;
[0061] The comparison module 320 is configured to compare the expected transaction application data and the actual transaction application data to be checked for consistency.
[0062] The passing module 330 is configured to determine that the actual transaction application data passes the check if the expected transaction application data and the actual transaction application data are consistent.
[0063] The transaction data checking device provided by the embodiments of the present application can perform the transaction data checking method provided by any of the embodiments of the present application, and has the corresponding function modules and beneficial effects of the execution method.
[0064] Optionally, the reverse pushing module 310 comprises:
[0065] The first sampling unit is configured to compressively sample the transaction confirmation data to be checked to obtain a transaction confirmation matrix.
[0066] The conversion unit is configured to convert the transaction confirmation matrix by using a measurement matrix to obtain an expected transaction application matrix.
[0067] Correspondingly, the comparison module 320 comprises:
[0068] The second sampling unit is configured to compressively sample the actual transaction application data to be checked to obtain an actual transaction application matrix.
[0069] The comparison unit is configured to compare the expected transaction application matrix and the actual transaction application matrix for consistency.
[0070] Optionally, the first sampling unit is specifically configured to: perform dimension reduction screening on the transaction confirmation data according to a standard business data exchange protocol to obtain key data fields in the transaction confirmation data; and construct the transaction confirmation matrix according to the key data fields.
[0071] Optionally, the training process of the measurement matrix is as follows:
[0072] The historical transaction confirmation data is taken as an observation value of compressive sensing, and the historical transaction application data is taken as an original signal of compressive sensing.
[0073] The measurement matrix of compressive sensing is trained according to the original signal and the observation value.
[0074] Optionally, the device further comprises a feedback module configured to generate an exception report according to the expected transaction application data, the actual transaction application data and the transaction confirmation data, and push the exception report to a reviewer, if the expected transaction application data and the actual transaction application data are inconsistent.
[0075] Optionally, the key data field comprises a business code field for representing a business scenario.
[0076] The transaction data checking device further described herein can also perform the transaction data checking method provided by any of the embodiments of the present application, and has the corresponding function modules and beneficial effects of performing the method.
[0077] According to embodiments of the present disclosure, the present application also provides an electronic device, a readable storage medium and a computer program product.
[0078] Figure 4 A structural schematic diagram of an electronic device 40 that can be used to implement embodiments of the present application is shown. The electronic device is intended to represent various forms of digital computers, such as laptops, desktops, tablets, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular telephones, smart phones, wearable devices (e.g., headsets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the present application described and / or claimed in this document.
[0079] As shown in Figure 4 The electronic device 40 includes at least one processor 41, and a memory, such as a read-only memory (ROM) 42, a random access memory (RAM) 43, etc., which is communicatively connected to the at least one processor 41, wherein the memory stores a computer program that can be executed by the at least one processor. The processor 41 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 42 or the computer program loaded from the storage unit 48 into the random access memory (RAM) 43. In the RAM 43, various programs and data required for the operation of the electronic device 40 can also be stored. The processor 41, the ROM 42, and the RAM 43 are connected to each other through a bus 44. An input / output (I / O) interface 45 is also connected to the bus 44.
[0080] A plurality of components in the electronic device 40 are connected to the I / O interface 45, including: an input unit 46, such as a keyboard, a mouse, etc.; an output unit 47, such as various types of displays, speakers, etc.; a storage unit 48, such as a magnetic disk, an optical disk, etc.; and a communication unit 49, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 49 allows the electronic device 40 to exchange information / data with other devices through a computer network, such as the Internet, and / or various telecommunication networks.
[0081] The processor 41 can be various general and / or special purpose processing components with processing and computing capabilities. Some examples of the processor 41 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 41 performs various methods and processes described above, such as the transaction data reconciliation method.
[0082] In some embodiments, the transaction data reconciliation method can be implemented as a computer program tangibly embodied in a computer readable storage medium, such as the storage unit 48. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device 40 via the ROM 42 and / or the communication unit 49. When the computer program is loaded onto the RAM 43 and executed by the processor 41, one or more steps of the transaction data reconciliation method described above can be performed. Alternatively, in other embodiments, the processor 41 can be configured to perform the transaction data reconciliation method by any other appropriate means, such as by means of firmware.
[0083] Various implementations of the systems and techniques described above can be realized in digital electronic circuitry, integrated circuitry, a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), a system on a chip (SOC), a programmable logic device (PLD), a computer hardware, firmware, software, and / or combinations thereof. These various implementations can include implementation in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.
[0084] Computer programs for implementing the methods of the present application can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer program, when executed, enables the functions / acts specified in the flowcharts and / or block diagrams to be implemented. The computer program can be executed entirely on a machine, partially on a machine, partially on a machine as a standalone software package and partially on a remote machine or entirely on a remote machine or server.
[0085] In the context of the present application, a computer-readable storage medium can be a tangible medium that can contain or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. A computer-readable storage medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium can be a machine-readable signal medium. More specific examples of a machine-readable storage medium will include one or more lines of a program of instructions in a transitory signal, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
[0086] To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the electronic device. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input.
[0087] The systems and techniques described herein can be implemented in a computing system that includes a back end component, e.g., as a data server, or that includes a middleware component, e.g., an application server, or that includes a front end component, e.g., a user computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described herein, or any combination of such back end, middleware, or front end components. The components of the system can be interconnected by any form or medium of digital data communication, e.g., a communication network. Examples of communication networks include a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.
[0088] The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. A server can be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system, to solve the defects of large management difficulty and weak business scalability in traditional physical host and VPS service.
[0089] It should be understood that the various forms of flow shown above can be re-ordered, added to, or deleted from without departing from the scope of the present disclosure. For example, the steps recited in the present disclosure can be performed in parallel, in series, or in a different order, and the present disclosure is not limited in this regard.
[0090] The specific embodiments described above are not intended to limit the scope of the present disclosure. Those skilled in the art will understand that various modifications, combinations, sub-combinations, and alternatives can be made to the specific embodiments without departing from the spirit and principles of the present disclosure. Any further modifications, equivalents, and / or alternatives come within the scope of the present disclosure as recited by the claims.
Claims
1. A method of collating transaction data, characterized by, The method comprises: generating expected transaction application data required for the transaction confirmation data to be checked by means of a measurement matrix; performing consistency comparison between the expected transaction application data and actual transaction application data to be checked; if the expected transaction application data and the actual transaction application data are consistent, determining that the actual transaction application data passes the check.
2. The method of claim 1, wherein, The generating of the expected transaction application data required for the transaction confirmation data to be checked by means of a measurement matrix comprises: performing compressed sampling on the transaction confirmation data to be checked to obtain a transaction confirmation matrix; performing conversion on the confirmation matrix by means of a measurement matrix to obtain an expected transaction application matrix; Correspondingly, the consistency comparison between the expected transaction application data and actual transaction application data to be checked comprises: performing compressed sampling on the actual transaction application data to be checked to obtain an actual transaction application matrix; performing consistency comparison between the expected transaction application matrix and the actual transaction application matrix.
3. The method of claim 2, wherein, The performing of the compressed sampling on the transaction confirmation data to be checked to obtain a transaction confirmation matrix comprises: performing dimension reduction screening on the transaction confirmation data according to a standard business data exchange protocol to obtain key data fields in the transaction confirmation data; constructing a transaction confirmation matrix according to the key data fields.
4. The method of claim 3, wherein, The key data fields comprise a business code field used for representing a business scenario.
5. The method of claim 1, wherein, The training process of the measurement matrix is as follows: taking historical transaction confirmation data as observation values of compressed sensing and taking historical transaction application data as original signals of compressed sensing; training a measurement matrix of compressed sensing according to the original signals and the observation values.
6. The method of claim 1, wherein, After the consistency comparison between the expected transaction application data and actual transaction application data to be checked, the method further comprises: if the expected transaction application data and the actual transaction application data are inconsistent, generating an exception report according to the expected transaction application data, the actual transaction application data and the transaction confirmation data, and pushing the exception report to a review personnel.
7. A transaction data collating apparatus characterized by comprising: The device comprises: a backstepping module configured to generate expected transaction application data required for transaction confirmation data to be checked by means of a measurement matrix; a comparison module configured to perform consistency comparison between the expected transaction application data and actual transaction application data to be checked; a passing module configured to determine that the actual transaction application data passes the check if the expected transaction application data and the actual transaction application data are consistent.
8. An electronic device, comprising: The electronic device comprises: at least one processor; and a memory connected with the at least one processor in communication; wherein the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to execute the transaction data checking method in any one of claims 1-6.
9. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer instructions, and the computer instructions are used to enable the processor to implement the transaction data checking method in any one of claims 1-6 when executed.
10. A computer program product, characterised in that, A computer program including a program that, when executed by a processor, implements the transaction data collation method of any one of claims 1-6.