CN-121996643-A - Data checking method, device, computer equipment and storage medium

CN121996643ACN 121996643 ACN121996643 ACN 121996643ACN-121996643-A

Abstract

The application belongs to the technical field of data processing, and relates to a data checking method, a device, computer equipment and a storage medium, wherein the method comprises the steps of inquiring credential row data in a state to be checked from a head row relation table if a triggered data checking task is received; if the number of the credential data is multiple, the credential data is subjected to slicing processing based on the slicing rule to obtain credential head slicing data, the credential head slicing data is subjected to checking processing based on the multithreading parallel checking strategy to obtain a first checking result, the credential head slicing data is subjected to item-by-item comparison processing of key fields based on the credential level checking strategy to obtain a second checking result, and a target checking result corresponding to the credential data is generated based on the first checking result and the second checking result. Furthermore, the target collation results of the present application may be stored in the blockchain. The application can be applied to the data checking scenes in the financial science and technology field and the digital medical field, and improves the processing efficiency and the processing accuracy of data checking.

Inventors

TIAN YUAN
ZHANG DI
QIU ZHIBING
HUANG XIUZHI
WU JIAXIONG
WEI YAODONG

Assignees

平安科技（深圳）有限公司

Dates

Publication Date: 20260508
Application Date: 20260106

Claims (10)

1. A data collation method characterized by comprising the steps of: Judging whether a triggered data checking task is received or not; If yes, inquiring the credential line data in the state to be checked from a preset head line relation table; If the number of the credential data is multiple, performing slicing processing on the credential data based on a preset slicing rule to obtain corresponding credential head slicing data; performing check processing on the credential head fragment data based on a preset multithreading parallel check strategy to obtain a corresponding first check result; Performing item-by-item comparison processing of key fields on the credential head fragment data based on a preset credential row level checking strategy to obtain a corresponding second checking result; And generating a target check result corresponding to the credential data based on the first check result and the second check result.
2. The data collation method according to claim 1, wherein the step of collating the credential header fragment data based on a preset multithreading parallel collation policy to obtain a corresponding first collation result comprises: Performing weight calculation processing on the credential head fragment data based on a preset dynamic weight calculation strategy to obtain a corresponding weight value; Calling a preset thread resource; Performing thread allocation processing on the credential head fragment data by using the thread resources based on the weight value to obtain a corresponding target thread; and carrying out checking processing on the credential head fragment data based on the target thread to obtain a specified checking result corresponding to the credential head fragment data.
3. The data checking method according to claim 2, wherein the step of performing weight calculation processing on the credential header slice data based on a preset dynamic weight calculation policy to obtain a corresponding weight value specifically includes: acquiring information of specified credential head slicing data based on preset weight influence factors to obtain corresponding relevant information, wherein the specified credential head slicing data is any group of data in all the credential head slicing data; acquiring a weight coefficient corresponding to the weight influence factor; Calling a preset weight calculation formula; calculating the related information and the weight coefficient based on the weight calculation formula to obtain a corresponding calculation result; And taking the calculation result as a specified weight value of the specified credential head fragment data.
4. The method for checking data according to claim 2, wherein the step of performing thread allocation processing on the credential header slice data using the thread resource based on the weight value to obtain a corresponding target thread specifically includes: acquiring a fragment allocation rule corresponding to the weight value; Based on the partition allocation rule, performing thread allocation processing on the credential head partition data by utilizing the thread resource to obtain a corresponding specified thread; verifying the appointed thread based on a preset verification strategy; And if the appointed thread passes the verification, the appointed thread is used as a target thread corresponding to the credential head fragmentation data.
5. The data collation method according to claim 1, wherein the step of generating a target collation result corresponding to the credential data based on the first collation result and the second collation result, specifically comprises: content analysis is carried out on the first checking result and the second checking result, and whether the result content of the first checking result and the result content of the second checking result pass through the checking is judged; if the content is the verification passing, generating a first target verification result with the content corresponding to the credential data as the verification passing; If the verification result is not passed, generating a second target verification result that the content corresponding to the credential line data is not passed.
6. The data collation method according to claim 1, further comprising, after the step of inquiring the credential line data in a state to be checked from a preset head line relation table: Acquiring relevant service information corresponding to the credential data; Calling a preset difference prediction model; Performing prediction processing on the credential data and the related service information based on the difference prediction model to obtain a corresponding difference prediction result; And outputting the difference prediction result.
7. The data collation method according to claim 1, further comprising, after the step of generating a target collation result corresponding to the credential data based on the first collation result and the second collation result: Calling a preset checking result table; Storing the first collation result, the second collation result and the target collation result into the collation result table; Inquiring a data record corresponding to the credential data from the head line relation table; And carrying out state updating processing on the data record based on the target checking result.
8. A data collation apparatus, characterized by comprising: the judging module is used for judging whether a triggered data checking task is received or not; The first query module is used for querying the credential row data in the state to be checked from a preset head row relation table if yes; The slicing module is used for slicing the credential line data based on a preset slicing rule to obtain corresponding credential head slicing data if the number of the credential line data is multiple; the first checking module is used for checking the credential head fragment data based on a preset multithreading parallel checking strategy to obtain a corresponding first checking result; the second checking module is used for carrying out item-by-item comparison processing on key fields of the credential head fragment data based on a preset credential row level checking strategy to obtain a corresponding second checking result; and the generation module is used for generating a target checking result corresponding to the credential data based on the first checking result and the second checking result.
9. A computer device comprising a memory and a processor, the memory having stored therein computer readable instructions which when executed by the processor implement the steps of the data collation method according to any one of claims 1 to 7.
10. A computer readable storage medium having stored thereon computer readable instructions which when executed by a processor perform the steps of the data collation method according to any one of claims 1 to 7.

Description

Data checking method, device, computer equipment and storage medium Technical Field The application relates to the technical field of data processing, and can be applied to the fields of financial science and technology, digital medical treatment and the like, in particular to a data checking method, a data checking device, computer equipment and a storage medium. Background In a traditional enterprise-level financial system, a financial ledger system serves as a financial system core hub and bears key functions such as accounting document processing, account book generation, financial statement establishment, account check and the like. As enterprise governance requirements rise, traditional "single-dimension" billing modes have difficulty meeting the dual requirements of management refinement and regulatory compliance. Therefore, the modern financial system adopts a 'management method separation' architecture design, namely, a management dimension and an accounting dimension two-dimensional credential are synchronously generated under the same business event, so that multi-view presentation and differentiated application of business data are realized. However, most current systems face "hard to check, difficult to trace, slow to correct errors" pain points in implementing tube separation. Because of the lack of unified business transaction identification, although management dimension data and accounting dimension data exist in parallel, strong association is difficult to establish, so that a system depends on a manual account checking mode of 'thick before thin', and gradually explores from a subject summarizing level to a credential detail, and the efficiency and the accuracy are low. For example, in the field of financial insurance, when an insurance company performs premium accounting by adopting a traditional financial system, the management dimension counts premium income according to business channels, and the accounting dimension records according to accounting subjects, so that the insurance company needs to manually check each account at the end of the month due to lack of uniform identification, and a great deal of time is consumed and errors are prone to occur. For example, in the medical field, a hospital adopts a traditional financial system to carry out balance management, the management dimension carries out statistics on income and expenditure according to departments, the accounting dimension carries out treatment according to accounting subjects, and the financial staff is difficult to quickly and accurately acquire relevant data when carrying out department balance analysis due to lack of uniform identification, so that the financial management efficiency and decision scientificity are affected. Accordingly, there is a need to provide an improved financial ledger system that solves the above-mentioned problems and improves the efficiency and accuracy of financial management. Disclosure of Invention The embodiment of the application aims to provide a data checking method, a device, computer equipment and a storage medium, which are used for solving the technical problems of low processing efficiency and low accuracy of the existing data checking method based on a manual checking method. In a first aspect, a data collation method is provided, including: Judging whether a triggered data checking task is received or not; If yes, inquiring the credential line data in the state to be checked from a preset head line relation table; If the number of the credential data is multiple, performing slicing processing on the credential data based on a preset slicing rule to obtain corresponding credential head slicing data; performing check processing on the credential head fragment data based on a preset multithreading parallel check strategy to obtain a corresponding first check result; Performing item-by-item comparison processing of key fields on the credential head fragment data based on a preset credential row level checking strategy to obtain a corresponding second checking result; And generating a target check result corresponding to the credential data based on the first check result and the second check result. In a second aspect, there is provided a data collation apparatus including: the judging module is used for judging whether a triggered data checking task is received or not; The first query module is used for querying the credential row data in the state to be checked from a preset head row relation table if yes; The slicing module is used for slicing the credential line data based on a preset slicing rule to obtain corresponding credential head slicing data if the number of the credential line data is multiple; the first checking module is used for checking the credential head fragment data based on a preset multithreading parallel checking strategy to obtain a corresponding first checking result; the second checking module is used for carrying out item-by-item comparison processing