Financial data processing method and device, electronic equipment and storage medium

Abstract

The invention relates to the technical field of data processing, and discloses a financial data processing method and device, electronic equipment and a storage medium. The method comprises the steps of collecting to-be-processed financial data from at least two financial data sources through a preset data collection interface, and storing the to-be-processed financial data to a preset big data processing tool; performing data cleaning, data conversion and data standardization processing on the to-be-processed financial data in sequence through a big data processing tool to obtain to-be-detected financial data; sending the to-be-detected financial data to a preset financial data detection model to obtain a financial data consistency detection result; if the financial data consistency detection result has abnormal financial data, modifying the abnormal financial data according to a preset financial data consistency correction rule to obtain corrected financial data; and synchronizing the corrected financial data to a corresponding financial data source through a big data processing tool. The method solves the problem of inconsistent financial data, and improves the financial management efficiency.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a financial data processing method, device, electronic device and storage medium. Background Art

[0002] In corporate financial management, due to the diversity of financial data sources, complex processing procedures, and data incompatibility between different systems, the same financial data often appear different in multiple channels or different systems, which in turn leads to wrong financial decisions, increased audit risks, and reduced corporate operating efficiency. However, traditional technology has many shortcomings in solving the above problems, such as high labor costs, long time consumption, and prone to errors when reconciling accounts manually, as well as lack of real-time performance and low degree of automation. Summary of the invention

[0003] The main technical problem solved by the implementation methods of the present application is that traditional technologies have low efficiency and accuracy when solving the consistency of financial data.

[0004] To solve the above technical problems, the first technical solution adopted in the implementation mode of the present application is: to provide a financial data processing method, comprising: through a preset data acquisition interface, collecting financial data to be processed from at least two financial data sources, and storing the financial data to be processed in a preset big data processing tool; through the big data processing tool, sequentially performing data cleaning, data conversion and data standardization processing on the financial data to be processed to obtain the financial data to be tested; sending the financial data to be tested to a preset financial data detection model to obtain a financial data consistency detection result, wherein the financial data detection model includes at least one financial data consistency detection algorithm, and / or at least one financial data consistency detection rule; if there is abnormal financial data in the financial data consistency detection result, modifying the abnormal financial data according to the preset financial data consistency correction rule to obtain corrected financial data; through the big data processing tool, synchronizing the corrected financial data to the corresponding financial data source.

[0005] Optionally, after the step of collecting the financial data to be processed from at least two financial data sources through a preset data collection interface, the method further includes: setting the priority of the financial data source; after the step of if there is abnormal financial data in the financial data consistency detection result, the method further includes: according to the priority of the financial data source, correcting the abnormal financial data to the financial data corresponding to the financial data source with the highest priority.

[0006] Optionally, the step of modifying the abnormal financial data according to a preset financial data consistency correction rule includes: detecting whether there is a corresponding financial data calculation formula for the abnormal financial data; if there is a corresponding financial data calculation formula, determining whether the variables of the financial data calculation formula are abnormal financial data; if it is not abnormal financial data, obtaining target financial data corresponding to the variables of the financial data calculation formula; and calculating and processing the target financial data according to the financial data to obtain the corrected financial data.

[0007] Optionally, the step of modifying the abnormal financial data according to a preset financial data consistency correction rule includes: detecting whether the exception handling rule corresponding to the abnormal financial data is a speculative correction; if it is a speculative correction, calculating historical mean data based on the historical data corresponding to the abnormal financial data, or processing the historical data through a preset data change trend calculation model to obtain a trend change speculation value; setting the historical mean data or the trend change speculation value as the corrected financial data.

[0008] Optionally, after the step of collecting the financial data to be processed from at least two financial data sources, it also includes: obtaining the original hash value of the financial data to be processed; calculating the verification hash value of the financial data to be processed according to a preset hash function; if the original hash value is not equal to the verification hash value, deleting the financial data to be processed corresponding to the original hash value.

[0009] Optionally, after the step of synchronizing the revised financial data to the corresponding financial data source, it also includes: monitoring whether the revised financial data changes again through a distributed transaction processing tool; if it changes again, obtaining the changed financial data, and synchronizing the changed financial data to the corresponding financial data source through the distributed transaction tool.

[0010] Optionally, if the financial data consistency check result contains abnormal financial data, it also includes: calculating the data difference of the abnormal financial data from different financial data sources; if the data difference exceeds a preset data difference threshold range, generating corresponding abnormal financial data warning information, wherein the abnormal financial data warning information includes the abnormal financial data and the corresponding financial data source; and sending the financial data warning information to financial management personnel with corresponding authority.

[0011] In order to solve the above technical problems, the second technical solution adopted by the implementation mode of the present application is: to provide a financial data processing device, including: a financial data acquisition module, used to collect the financial data to be processed from at least two financial data sources through a preset data acquisition interface, and store the financial data to be processed in a preset big data processing tool; a financial data preprocessing module, used to perform data cleaning, data conversion and data standardization processing on the financial data to be processed in sequence through the big data processing tool to obtain the financial data to be detected; a financial data detection module, used to send the financial data to be detected to a preset financial data detection model to obtain a financial data consistency detection result, wherein the financial data detection model includes at least one financial data consistency detection algorithm and / or at least one financial data consistency detection rule; a financial data correction module, used to modify the abnormal financial data according to a preset financial data consistency correction rule to obtain corrected financial data if there is abnormal financial data in the financial data consistency detection result; a financial data synchronization module, used to synchronize the corrected financial data to the corresponding financial data source through the big data processing tool.

[0012] To solve the above technical problems, the third technical solution adopted in the implementation mode of the present application is: to provide an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the financial data processing method as described above.

[0013] In order to solve the above technical problems, the fourth technical solution adopted in the implementation mode of the present application is: providing a non-volatile computer-readable storage medium, wherein the non-volatile computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are executed by an electronic device, the electronic device executes the financial data processing method as described above.

[0014] Different from the related technologies, this application uses specific financial data consistency detection algorithms and / or rules to perform consistency detection, and automatically takes corresponding conflict resolution measures. At the same time, it establishes a data synchronization and update mechanism to ensure that data between different financial systems and / or software are updated in a timely manner and remain consistent. It can effectively solve the problem of inconsistent financial data, improve the accuracy of corporate financial data, and improve financial management efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] One or more embodiments are exemplarily described by corresponding drawings, which do not constitute limitations on the embodiments. Elements with the same reference numerals in the drawings represent similar elements, and the figures in the drawings do not constitute proportional limitations unless otherwise stated.

[0016] Figure 1 It is a schematic diagram of the operating environment of the financial data processing method provided in the embodiment of the present application.

[0017] Figure 2 It is a schematic diagram of the execution flow of the financial data processing method provided in the embodiment of the present application.

[0018] Figure 3 It is a schematic diagram of the execution flow of correcting abnormal financial data according to a calculation formula in the financial data processing method provided in an embodiment of the present application.

[0019] Figure 4 It is a schematic diagram of the execution flow of correcting abnormal financial data based on speculation in the financial data processing method provided in an embodiment of the present application.

[0020] Figure 5 It is a schematic diagram of the execution flow of data verification in the financial data processing method provided in the embodiment of the present application.

[0021] Figure 6 It is a schematic diagram of the execution flow of sending financial data warning information in the financial data processing method provided in an embodiment of the present application.

[0022] Figure 7 It is a schematic diagram of the system structure of the financial data processing device provided in an embodiment of the present application.

[0023] Figure 8 It is a schematic diagram of the hardware structure of an electronic device for executing the financial data processing method provided in an embodiment of the present application. DETAILED DESCRIPTION

[0024] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0025] It should be noted that, if there is no conflict, the various features in the embodiments of the present application can be combined with each other, all within the scope of protection of the present application. In addition, although the functional modules are divided in the device schematic diagram and the logical order is shown in the flow chart, in some cases, the steps shown or described can be performed in a different order than the module division in the device schematic diagram or the order in the flow chart.

[0026] Unless otherwise defined, all technical and scientific terms used in this specification have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used in the specification of this application are only for the purpose of describing specific embodiments and are not intended to limit this application. The term "and / or" used in this specification includes any and all combinations of one or more of the related listed items.

[0027] To facilitate understanding of this embodiment, a financial data processing method disclosed in the embodiment of this application is first described in detail. Figure 1 , Figure 1 is a schematic diagram of the operating environment of the financial data processing method provided in the embodiment of the present application, such as Figure 1 As shown, the execution subject of the financial data processing method provided in the embodiment of the present application is generally an electronic device with certain computing capabilities, such as a computer device. In some possible implementations, the financial data processing method can be implemented by a processor calling a computer-readable instruction stored in a memory. Figure 1 The computer device in the term can be a server. The server can be an independent server or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (CDN), and big data and artificial intelligence platforms. It can be understood that Figure 1 The number of computer devices in the figure is only for reference and can be expanded to any number according to actual needs.

[0028] Please continue reading Figure 2 , Figure 2 is a schematic diagram of the execution flow of the financial data processing method provided in the embodiment of the present application, such as Figure 2 As shown, it includes steps S1 to S5.

[0029] S1. Collect the financial data to be processed from at least two financial data sources through a preset data collection interface, and store the financial data to be processed in a preset big data processing tool.

[0030] The above-mentioned financial data sources may include but are not limited to: tax systems, financial reporting systems, accounting software, purchase and sales systems, etc. For example, within an insurance institution, it is necessary not only to deploy the corresponding insurance business system, but also to deploy the corresponding financial reporting system, connect to the tax system, and install the accounting software used by accountants, etc., and these systems or software related to finance contain at least some financial data with the same attributes. It is very important to ensure the consistency of the same financial data in different systems or software, so as to avoid errors in financial decision-making, financial auditing, etc. caused by inconsistent financial data.

[0031] S2. Use big data processing tools to perform data cleaning, data conversion and data standardization on the financial data to be processed in turn to obtain the financial data to be tested.

[0032] Among them, the systems or software corresponding to the current financial data sources generally use traditional relational databases, such as MySQL, Oracle, etc., and with the development of technology, traditional structured databases cannot bear the association processing and multi-dimensional data analysis of massive data levels, which leads to the inability to quickly calculate whether the financial data is consistent. On the other hand, due to the different data sources of financial data and the need to ensure the timeliness of financial data, traditional relational databases can no longer meet the requirements, so it is necessary to use the computing power of big data to improve computing power and data processing capabilities.

[0033] For example, by establishing a data link from financial data, Kafka cluster, Flink, and Hudi table in sequence, data storage, data cleaning, data conversion, data standardization, and subsequent data analysis and data warning are performed in this data link. This can achieve real-time writing of data to big data tables, and with the help of big data computing power, calculation of massive data can be achieved, meeting the needs of real-time reconciliation and ensuring the accuracy of financial data.

[0034] S3. Send the financial data to be tested to a preset financial data testing model to obtain a financial data consistency test result, wherein the financial data testing model includes at least one financial data consistency testing algorithm and / or at least one financial data consistency testing rule.

[0035] Among them, the above-mentioned financial data detection model can set corresponding financial data consistency detection algorithms and / or rules according to the financial rules, financial data calculation methods, etc. commonly used in the field of financial technology, and can also customize corresponding customized consistency detection algorithms and / or rules according to business needs and financial needs.

[0036] For example, in the insurance business, for the insurance data and claim data of the same policy, the consistency is determined by comparing the amount, timestamp, and corresponding business note information.

[0037] S4. If there is abnormal financial data in the financial data consistency detection result, the abnormal financial data is modified according to a preset financial data consistency correction rule to obtain corrected financial data.

[0038] As an optional implementation, please continue to refer to Figure 3 , Figure 3 is a schematic diagram of an execution flow of correcting abnormal financial data according to a calculation formula in a financial data processing method provided in an embodiment of the present application, such as Figure 3 As shown, the following steps S401 to S404 may be included.

[0039] S401, detecting whether there is a corresponding financial data calculation formula for the abnormal financial data.

[0040] For example, gross profit = (sales revenue - cost) / sales revenue * 100%, return on assets = net income / total assets.

[0041] S402: If there is a corresponding financial data calculation formula, determine whether the variables of the financial data calculation formula are abnormal financial data.

[0042] Among them, if the variables of the financial data calculation formula are also abnormal financial data, the abnormal financial data cannot be used for calculation, and the calculation formula whose variables are not abnormal financial data needs to be processed first.

[0043] S403: If the financial data is not abnormal, then the target financial data corresponding to the variables of the financial data calculation formula is obtained.

[0044] Among them, if the variables of the financial data calculation formula are not obtained in the above data acquisition process, it is also necessary to obtain the corresponding variables from the above financial data source, or other channels, financial data sources, etc., or calculate the corresponding variable values ​​first according to the existing financial data processing method.

[0045] S404. Calculate and process target financial data according to the financial data to obtain revised financial data.

[0046] As another optional implementation, please continue to refer to Figure 4 , Figure 4 is a schematic diagram of an execution flow of correcting abnormal financial data according to speculation in the financial data processing method provided in an embodiment of the present application, such as Figure 4 As shown, the following steps S411 to S413 may be included.

[0047] S411. Detect whether the abnormality handling rule corresponding to the abnormal financial data is a speculative correction.

[0048] S412: If it is a speculative correction, the historical mean data is calculated based on the historical data corresponding to the abnormal financial data, or the historical data is processed by a preset data change trend calculation model to obtain a trend change speculative value.

[0049] For example, if the system settings allow or the manual settings allow, certain financial data can be speculated. The above-mentioned speculation calculation method can be used to obtain historical average data or trend change speculation values, and then use historical average data or difference change speculation values ​​for correction. However, with the development of technology, there is also a need for speculation in financial systems, such as estimating next month's sales, predicting annual financial data, etc. If this type of financial data has consistency issues in different financial data sources, it is also necessary to solve the consistency problem, so as to avoid making wrong financial decisions based on wrong predicted financial data.

[0050] S413. Set the historical average data or the estimated value of trend change as the revised financial data.

[0051] S5. Use big data processing tools to synchronize the corrected financial data to the corresponding financial data source.

[0052] As an optional implementation, please continue to refer to Figure 5 , Figure 5 : is a schematic diagram of the execution flow of data verification in the financial data processing method provided in the embodiment of the present application, such as Figure 5 As shown, the following steps S11 to S13 may be included.

[0053] S11. Obtain the original hash value of the financial data to be processed, for example, the original hash value of the financial data to be processed in the original financial data source.

[0054] S12, calculating the verification hash value of the financial data to be processed according to a preset hash function, that is, calculating the hash value again after obtaining the financial data to be processed, and the hash function used in the calculation is consistent with the hash function used in the original financial data source.

[0055] S13. If the original hash value is not equal to the verification hash value, the pending financial data corresponding to the original hash value is deleted. This means that the pending financial data has been tampered with or damaged, and there is a certain data security risk, so the pending financial data is deleted.

[0056] As a preferred implementation, after the above step S5, the distributed transaction processing tool can be used to monitor whether the revised financial data changes again. If it changes again, the changed financial data is obtained, and the changed financial data is synchronized to the corresponding financial data source through the distributed transaction tool. For example, when the monthly sales amount changes in the insurance sales system, the monthly sales data can be synchronized to other systems or software, such as a financial reporting system, a tax system, etc.

[0057] As another optional implementation, please continue to refer to Figure 6 , Figure 6 : is a schematic diagram of the execution flow of sending financial data warning information in the financial data processing method provided in the embodiment of the present application, such as Figure 6 As shown, the following steps S421 to S423 may be included.

[0058] S421. Calculate the data difference of abnormal financial data from different financial data sources.

[0059] S422. If the data difference exceeds a preset data difference threshold range, corresponding abnormal financial data warning information is generated, wherein the abnormal financial data warning information includes the abnormal financial data and the corresponding financial data source.

[0060] S423. Send financial data warning information to financial management personnel with corresponding authority.

[0061] Among them, in the field of financial technology, there is also a data tolerance for certain data errors. When the errors of some financial data are within a certain error range, corresponding processing may not be performed. However, if the errors of the financial data are large, in addition to correction, the corresponding financial management personnel can be informed through the above-mentioned abnormal financial data warning form, so that the corresponding financial management personnel can respond to the warning information in a timely manner, manually intervene in the abnormal financial data, and achieve the effect of timely solving the abnormal financial data problem.

[0062] The financial data processing method provided in the embodiment of the present application performs consistency detection by using specific financial data consistency detection algorithms and / or rules, and automatically takes corresponding conflict resolution measures. At the same time, a data synchronization and update mechanism is established to ensure that data between different financial systems and / or software are updated in a timely manner and remain consistent. This can effectively solve the problem of inconsistent financial data and improve the accuracy of corporate financial data and financial management efficiency.

[0063] Please continue reading Figure 7 , Figure 7 is a schematic diagram of the system structure of the financial data processing device provided in the embodiment of the present application, such as Figure 7As shown, the device 70 includes: a financial data acquisition module 71, a financial data preprocessing module 72, a financial data detection module 73, a financial data correction module 74 and a financial data synchronization module 75.

[0064] The financial data collection module 71 is used to collect the financial data to be processed from at least two financial data sources through a preset data collection interface, and store the financial data to be processed in a preset big data processing tool;

[0065] The financial data preprocessing module 72 is used to perform data cleaning, data conversion and data standardization on the financial data to be processed in sequence through the big data processing tool to obtain the financial data to be detected;

[0066] The financial data detection module 73 is used to send the financial data to be detected to a preset financial data detection model to obtain a financial data consistency detection result, wherein the financial data detection model includes at least one financial data consistency detection algorithm and / or at least one financial data consistency detection rule;

[0067] The financial data correction module 74 is used to modify the abnormal financial data according to the preset financial data consistency correction rules to obtain the corrected financial data if there is abnormal financial data in the financial data consistency detection result;

[0068] The financial data synchronization module 75 is used to synchronize the corrected financial data to the corresponding financial data source through the big data processing tool.

[0069] As an optional implementation, the priority of the financial data source in the financial data collection module 71; the financial data collection module 71 is also used to correct the abnormal financial data to the financial data corresponding to the financial data source with the highest priority according to the priority of the financial data source.

[0070] As an optional implementation, the financial data correction module 74 is also used to detect whether there is a corresponding financial data calculation formula for the abnormal financial data; if there is a corresponding financial data calculation formula, it is determined whether the variables of the financial data calculation formula are abnormal financial data; if it is not abnormal financial data, the target financial data corresponding to the variables of the financial data calculation formula is obtained; the target financial data is processed according to the financial data calculation to obtain the corrected financial data.

[0071] As an optional implementation, the financial data correction module 74 is also used to detect whether the exception handling rule corresponding to the abnormal financial data is a speculative correction; if it is a speculative correction, the historical mean data is calculated based on the historical data corresponding to the abnormal financial data, or the historical data is processed by a preset data change trend calculation model to obtain a trend change speculation value; the historical mean data or the trend change speculation value is set as the corrected financial data.

[0072] As an optional implementation, the financial data collection module 71 is also used to obtain the original hash value of the financial data to be processed; calculate the verification hash value of the financial data to be processed according to a preset hash function; if the original hash value is not equal to the verification hash value, delete the financial data to be processed corresponding to the original hash value.

[0073] As an optional implementation, the financial data synchronization module 75 is also used to monitor whether the revised financial data changes again through a distributed transaction processing tool; if it changes again, the changed financial data is obtained and synchronized to the corresponding financial data source through the distributed transaction tool.

[0074] As an optional implementation, the financial data correction module 74 is also used to calculate the data difference of the abnormal financial data from different financial data sources; if the data difference exceeds a preset data difference threshold range, a corresponding abnormal financial data warning information is generated, wherein the abnormal financial data warning information includes the abnormal financial data and the corresponding financial data source; and the financial data warning information is sent to financial management personnel with corresponding authority.

[0075] It should be noted that the above-mentioned financial data processing device can execute the financial data processing method provided in the embodiment of the present application, and has the functional modules and beneficial effects corresponding to the execution method. For technical details not fully described in the embodiment of the financial data processing device, please refer to the financial data processing method provided in the embodiment of the present application.

[0076] Please continue reading Figure 8 , Figure 8 8 is a schematic diagram of the hardware structure of an electronic device 800 for executing a financial data processing method provided in an embodiment of the present application. Figure 8 As shown, the electronic device 800 includes:

[0077] One or more processors 810 and memory 820, Figure 8 A processor 810 is taken as an example.

[0078] The processor 810 and the memory 820 may be connected via a bus or other means. Figure 8The example of connecting through bus is taken in the following.

[0079] The memory 820 is a non-volatile computer-readable storage medium that can be used to store non-volatile software programs, non-volatile computer executable programs and modules, such as program instructions / modules corresponding to the financial data processing method in the embodiment of the present application. The processor 810 executes various functional applications and data processing of the server by running the non-volatile software programs, instructions and modules stored in the memory 820, that is, the financial data processing method in the above method embodiment is implemented.

[0080] The memory 820 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and an application required by at least one function; the data storage area may store data created according to the use of the financial data processing device, etc. In addition, the memory 820 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some embodiments, the memory 820 may optionally include a memory remotely arranged relative to the processor 810, and these remote memories may be connected to the financial data processing device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0081] The one or more modules are stored in the memory 820, and when executed by the one or more processors 810, perform the financial data processing method in any of the above method embodiments, for example, perform the above described Figure 2 Steps S1 to S5 of the method, Figure 3 Steps S401 to S404 of the method, Figure 4 Steps S411 to S413 of the method, Figure 5 Steps S11 to S13 of the method, Figure 6 Steps S421 to S423 of the method are implemented Figure 7 The functions of modules 71-75 in.

[0082] The above-mentioned product can execute the method provided in the embodiment of the present application, and has the functional modules and beneficial effects corresponding to the execution method. For technical details not fully described in this embodiment, please refer to the method provided in the embodiment of the present application.

[0083] The embodiment of the present application provides a non-volatile computer-readable storage medium, wherein the non-volatile computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions are executed by one or more processors, such as Figure 8A processor 810 in the embodiment may enable the one or more processors to execute the financial data processing method in any of the above method embodiments, for example, to execute the above described Figure 2 Steps S1 to S5 of the method, Figure 3 Steps S401 to S404 of the method, Figure 4 Steps S411 to S413 of the method, Figure 5 Steps S11 to S13 of the method, Figure 6 Steps S421 to S423 of the method are implemented Figure 7 The functions of modules 71-75 in.

[0084] The present application provides a computer program product, which includes a computer program stored on a non-volatile computer-readable storage medium, wherein the computer program includes program instructions. When the program instructions are executed by the electronic device, the electronic device is enabled to execute the financial data processing method in any of the above method embodiments, for example, to execute the above described Figure 2 Steps S1 to S5 of the method, Figure 3 Steps S401 to S404 of the method, Figure 4 Steps S411 to S413 of the method, Figure 5 Steps S11 to S13 of the method, Figure 6 Steps S421 to S423 of the method are implemented Figure 7 The functions of modules 71-75 in.

[0085] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0086] Through the description of the above implementation methods, ordinary technicians in this field can clearly understand that each implementation method can be implemented by means of software plus a general hardware platform, and of course, it can also be implemented by hardware. Ordinary technicians in this field can understand that all or part of the processes in the above-mentioned embodiment method can be completed by instructing related hardware through a computer program, and the program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, the storage medium can be a disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM), etc.

[0087] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Under the concept of the present application, the technical features in the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other changes in different aspects of the present application as described above, which are not provided in detail for the sake of simplicity. Although the present application has been described in detail with reference to the aforementioned embodiments, a person of ordinary skill in the art should understand that the technical solutions described in the aforementioned embodiments can still be modified, or some of the technical features can be replaced by equivalents. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for processing financial data, characterized in that: include: Collecting the financial data to be processed from at least two financial data sources through a preset data collection interface, and storing the financial data to be processed in a preset big data processing tool; By using the big data processing tool, the financial data to be processed are sequentially cleaned, converted and standardized to obtain the financial data to be tested; Sending the financial data to be tested to a preset financial data testing model to obtain a financial data consistency testing result, wherein the financial data testing model includes at least one financial data consistency testing algorithm and / or at least one financial data consistency testing rule; If the financial data consistency detection result contains abnormal financial data, the abnormal financial data is modified according to a preset financial data consistency correction rule to obtain corrected financial data; The corrected financial data is synchronized to the corresponding financial data source through the big data processing tool.

2. The financial data processing method according to claim 1, characterized in that: After the step of collecting the financial data to be processed from at least two financial data sources through the preset data collection interface, the method further includes: Setting the priority of the financial data source; After the step of if the financial data consistency detection result contains abnormal financial data, the method further includes: According to the priorities of the financial data sources, the abnormal financial data is corrected to be the financial data corresponding to the financial data source with the highest priority.

3. The financial data processing method according to claim 1, characterized in that: The step of modifying the abnormal financial data according to the preset financial data consistency correction rule includes: Detecting whether the abnormal financial data has a corresponding financial data calculation formula; If there is a corresponding financial data calculation formula, determining whether the variable of the financial data calculation formula is abnormal financial data; If the financial data is not abnormal, then obtaining the target financial data corresponding to the variables of the financial data calculation formula; The target financial data is calculated and processed according to the financial data to obtain the revised financial data.

4. The financial data processing method according to claim 1, characterized in that: The step of modifying the abnormal financial data according to the preset financial data consistency correction rule includes: Detecting whether the abnormality handling rule corresponding to the abnormal financial data is a speculative correction; If it is a speculative correction, the historical mean data is calculated according to the historical data corresponding to the abnormal financial data, or the historical data is processed by a preset data change trend calculation model to obtain a trend change speculative value; The historical average data or the trend change estimated value is set as the revised financial data.

5. The financial data processing method according to claim 1, characterized in that: After the step of collecting the financial data to be processed from at least two financial data sources, the method further includes: Obtaining the original hash value of the financial data to be processed; Calculate the verification hash value of the financial data to be processed according to a preset hash function; If the original hash value is not equal to the verification hash value, the to-be-processed financial data corresponding to the original hash value is deleted.

6. The financial data processing method according to claim 1, characterized in that: After the step of synchronizing the corrected financial data to the corresponding financial data source, the method further includes: Monitor whether the revised financial data changes again through a distributed transaction processing tool; If a change occurs again, the changed financial data is obtained, and the changed financial data is synchronized to the corresponding financial data source through the distributed transaction tool.

7. The financial data processing method according to claim 1, characterized in that: If the financial data consistency test result contains abnormal financial data, the method further includes: Calculating data differences of the abnormal financial data from different financial data sources; If the data difference exceeds a preset data difference threshold range, corresponding abnormal financial data warning information is generated, wherein the abnormal financial data warning information includes the abnormal financial data and the corresponding financial data source; The financial data warning information is sent to financial management personnel with corresponding authority.

8. A financial data processing device, characterized in that: include: A financial data collection module, used to collect the financial data to be processed from at least two financial data sources through a preset data collection interface, and store the financial data to be processed in a preset big data processing tool; A financial data preprocessing module, used to perform data cleaning, data conversion and data standardization on the financial data to be processed in sequence through the big data processing tool to obtain the financial data to be tested; A financial data detection module, used for sending the financial data to be detected to a preset financial data detection model to obtain a financial data consistency detection result, wherein the financial data detection model includes at least one financial data consistency detection algorithm and / or at least one financial data consistency detection rule; A financial data correction module, used to modify the abnormal financial data according to a preset financial data consistency correction rule to obtain corrected financial data if there is abnormal financial data in the financial data consistency detection result; The financial data synchronization module is used to synchronize the corrected financial data to the corresponding financial data source through the big data processing tool.

9. An electronic device, characterized in that: include: at least one processor; as well as, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the financial data processing method described in any one of claims 1-7.

10. A non-volatile computer-readable storage medium, characterized in that: The non-volatile computer-readable storage medium stores computer-executable instructions. When the computer-executable instructions are executed by an electronic device, the electronic device executes the financial data processing method according to any one of claims 1 to 7.

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