CN112965981B - Data checking method, device, computer equipment and storage medium - Google Patents

Data checking method, device, computer equipment and storage medium Download PDF

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CN112965981B
CN112965981B CN202110276166.6A CN202110276166A CN112965981B CN 112965981 B CN112965981 B CN 112965981B CN 202110276166 A CN202110276166 A CN 202110276166A CN 112965981 B CN112965981 B CN 112965981B
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service data
business
checking
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CN112965981A (en
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刘泽平
时欢
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Ping An Property and Casualty Insurance Company of China Ltd
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Ping An Property and Casualty Insurance Company of China Ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/22—Indexing; Data structures therefor; Storage structures
    • G06F16/2282—Tablespace storage structures; Management thereof
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/27—Replication, distribution or synchronisation of data between databases or within a distributed database system; Distributed database system architectures therefor
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
    • G06F21/60—Protecting data
    • G06F21/602—Providing cryptographic facilities or services
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
    • G06F21/60—Protecting data
    • G06F21/64—Protecting data integrity, e.g. using checksums, certificates or signatures
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F40/00—Handling natural language data
    • G06F40/10—Text processing
    • G06F40/166—Editing, e.g. inserting or deleting
    • G06F40/186—Templates
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
    • G06Q40/08—Insurance
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/02—Agriculture; Fishing; Mining

Abstract

The application relates to the field of data processing, and provides a data checking method, a device, computer equipment and a storage medium, wherein the method comprises the following steps: acquiring the current time and a preset data checking time period; judging whether the current time reaches the starting end point time of the data checking time period or not; if yes, acquiring first list data of a first service system and second list data of a second service system; carrying out data statistics processing on the first list data based on a preset business data statistics rule to obtain first business statistics data; performing data statistics processing on the second list data to obtain second business statistics data; and carrying out data checking processing on the first business statistic data and the second business statistic data based on a preset data checking template to generate a data checking result. The data checking method and device can improve the speed of data checking and the accuracy of the data checking result. The method and the device can be applied to the field of blockchains, and the data such as the data checking result can be stored in the blockchain.

Description

Data checking method, device, computer equipment and storage medium
Technical Field
The present invention relates to the field of data processing technologies, and in particular, to a data checking method, a data checking device, a computer device, and a storage medium.
Background
Because the supervision department needs to supervise and count the data of the agricultural insurance claim settlement system, the claim settlement system and the information collection system need to check the data of the timing of the summary data of the claim settlement system list and the summary data of the information collection system to check whether the data difference exists in the summary data of the two lists or not. The existing method for checking the data of the list is that the claimant and the credit staff acquire the summarized list data at the same time, and then the two sides perform manual data checking together. However, when the number of the list data to be checked is large, the manual data checking method needs to occupy a large amount of manpower resources and spends a large amount of time, and can cause low efficiency of data checking, large checking data error and high error rate, thereby affecting the accuracy of the generated data checking result.
Disclosure of Invention
The main purpose of the present application is to provide a data checking method, device, computer equipment and storage medium, which aims to solve the technical problems that the existing data checking method for inventory data needs to occupy a great deal of manpower resources and spends a great deal of time, and can cause low efficiency of data checking, large checking data error and high error rate.
The application provides a data checking method, which comprises the following steps:
acquiring current time and acquiring a preset data checking time period;
judging whether the current time reaches the starting end point time of the data checking time period, wherein the data checking time period is a time period formed by a time interval contained by the starting end point time and the ending end point time;
if the current time reaches the starting end point time of the data checking time period, acquiring first list data of a first service system and second list data of a second service system;
performing data statistics processing on the first list data based on a preset service data statistics rule to obtain corresponding first service statistics data, wherein the first service statistics data comprise a first service data field and first service data information corresponding to the first service data field; the method comprises the steps of,
performing data statistics processing on the second list data to obtain corresponding second service statistics data, wherein the second service statistics data comprises a second service data field and second service data information corresponding to the second service data field;
And carrying out data checking processing on the first business statistic data and the second business statistic data based on a preset data checking template to generate a corresponding data checking result.
Optionally, the step of performing data checking processing on the first service statistics data and the second service statistics data based on a preset data checking template to generate corresponding data checking results includes:
filling the first business data field into the data check template;
filling the first business data information into a first corresponding position in the data check template based on the corresponding relation between the first business data field and the first business data information;
judging whether the second service data fields all contain designated service data fields which are respectively the same as the first service data fields;
if the second service data fields all contain the same appointed service data fields as the first service data fields, judging whether other service data fields except the appointed service data fields exist in the second service data fields;
if the second service data field does not contain other service data fields, screening specified service data information corresponding to the specified service data field from the second service data information;
Filling the specified service data information into a second corresponding position in the data check template based on the corresponding relation between the specified service data field and the first service data field;
checking first business data information contained in each first business data field in the data checking template with appointed business data information one by one to judge whether the two pieces of data information are identical;
if the two data information contained in each first service data field are the same, generating a first check result of successful check;
if the two data information contained in each first service data field are not the same, generating a second check result of failed check;
and acquiring a target service data field with difference between the first service data information and the appointed service data information, and adding an anomaly flag to the target service data field.
Optionally, after the step of obtaining the target service data field where the first service data information is different from the specified service data information and adding an anomaly flag to the target service data field, the method includes:
acquiring target service data information corresponding to the target service data field;
Generating corresponding first abnormal information based on the target service data field and the target service data information;
acquiring preset mail login information and acquiring a designated mail address;
logging in to a corresponding mail server based on the mail login information;
and sending the first abnormal information to the appointed mail address through the mail server.
Optionally, after the step of determining whether the second service data fields each include the same designated service data field as each of the first service data fields, the method includes:
if the second service data fields do not all contain the same appointed service data fields as the first service data fields, screening specific service data fields from the first service data fields, wherein the second service data fields do not contain the specific service data fields;
searching specific business data information corresponding to the specific business data field from the first business data information;
generating corresponding second abnormal information based on the specific service data field and the specific service data information;
And sending the second abnormal information to the appointed mail address through the mail server.
Optionally, after the step of determining whether the second service data field includes other service data fields than the specified service data field, the method includes:
if the other business data fields exist in the second business data field, other business data information corresponding to the other business data fields is found out from the second business data information;
generating corresponding third abnormal information based on the other business data fields and the other business data information;
and sending the third abnormal information to the appointed mail address through the mail server.
Optionally, before the step of obtaining the preset data checking period, the method includes:
acquiring the resource consumption of each appointed time period in each day in a first preset time period, wherein the appointed time period is a time period containing a preset time length;
carrying out statistical analysis on the first preset time period, the designated time period and the resource consumption amount to generate a corresponding resource consumption statistical table;
Screening out first time periods with the resource consumption less than a preset resource consumption threshold from all the designated time periods in each day in the first preset time period respectively based on the resource consumption statistical table;
screening out second time periods with the largest occurrence frequency from all the first time periods;
and taking the second time period as the data checking time period.
Optionally, before the step of obtaining the preset data checking period, the method includes:
dividing a time period of a day into a plurality of unit time periods based on a preset time division threshold;
based on prestored historical data processing record data, counting the total data processing amount of each unit time period in a second preset time period;
screening a first data processing total amount smaller than a preset data processing amount threshold value from all the data processing total amounts;
sequencing all the first data processing total amount according to the sequence from the small value to the large value of the first data processing total amount to obtain a corresponding sequencing result;
sequentially acquiring a specified number of second data processing total amount from the first data processing total amount ranked first in the sequencing result;
Acquiring a designated unit time period corresponding to the second data processing total amount from all the unit time periods;
the specified unit time period is taken as the data collation time period.
The application also provides a data collation apparatus including:
the first acquisition module is used for acquiring the current time and acquiring a preset data checking time period;
a judging module, configured to judge whether the current time reaches a start endpoint time of the data checking period, where the data checking period is a period formed by a time interval included by the start endpoint time and an end endpoint time;
the second acquisition module is used for acquiring first list data of the first service system and second list data of the second service system if the current time reaches the starting endpoint time of the data check time period;
the first processing module is used for carrying out data statistics processing on the first list data based on a preset business data statistics rule to obtain corresponding first business statistics data, wherein the first business statistics data comprises a first business data field and first business data information corresponding to the first business data field; the method comprises the steps of,
The second processing module is used for carrying out data statistics processing on the second list data to obtain corresponding second business statistics data, wherein the second business statistics data comprise a second business data field and second business data information corresponding to the second business data field;
and the checking module is used for performing data checking processing on the first business statistic data and the second business statistic data based on a preset data checking template to generate a corresponding data checking result.
The application also provides a computer device comprising a memory and a processor, the memory storing a computer program, the processor implementing the steps of the above method when executing the computer program.
The present application also provides a computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of the above method.
The data checking method, the data checking device, the computer equipment and the storage medium provided by the application have the following beneficial effects:
the data checking method, the device, the computer equipment and the storage medium provided by the application automatically acquire the first list data and the second list data when the current time reaches the starting end point time of the preset data checking time period, convert the first list data and the second list data into corresponding business statistical data, and then perform data checking processing on the first business statistical data and the second business statistical data based on a preset data checking template so as to generate a corresponding data checking result. The whole data checking process is automatically carried out without manual participation, and the data checking speed is high and the accuracy is high, so that a large amount of manpower resources and time can be saved, the checking data error can be reduced, the error rate is reduced, and the accuracy of the generated data checking result is effectively improved.
Drawings
FIG. 1 is a flow chart of a data collation method according to an embodiment of the present application;
fig. 2 is a schematic structural view of a data collation apparatus according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of a computer device according to an embodiment of the present application.
The realization, functional characteristics and advantages of the present application will be further described with reference to the embodiments, referring to the attached drawings.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application.
It will be understood by those skilled in the art that all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs unless defined otherwise. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
Referring to fig. 1, a data collation method according to an embodiment of the present application includes:
s1: acquiring current time and acquiring a preset data checking time period;
S2: judging whether the current time reaches the starting end point time of the data checking time period, wherein the data checking time period is a time period formed by a time interval contained by the starting end point time and the ending end point time;
s3: if the current time reaches the starting end point time of the data checking time period, acquiring first list data of a first service system and second list data of a second service system;
s4: performing data statistics processing on the first list data based on a preset service data statistics rule to obtain corresponding first service statistics data, wherein the first service statistics data comprise a first service data field and first service data information corresponding to the first service data field; the method comprises the steps of,
s5: performing data statistics processing on the second list data to obtain corresponding second service statistics data, wherein the second service statistics data comprises a second service data field and second service data information corresponding to the second service data field;
s6: and carrying out data checking processing on the first business statistic data and the second business statistic data based on a preset data checking template to generate a corresponding data checking result.
As described in steps S1 to S6, the execution subject of the embodiment of the method is a data collation apparatus. In practical applications, the data checking device may be implemented by a virtual device, for example, a software code, or may be implemented by an entity device in which related execution codes are written or integrated, and may perform man-machine interaction with a user through a keyboard, a mouse, a remote controller, a touch pad, or a voice control device. The data checking device in the embodiment can effectively improve the data checking speed and the accuracy of the generated data checking result. Specifically, the current time is first acquired, and a preset data collation period is acquired. The generation method of the data verification period is not particularly limited, and may be generated by a service idle period generated by analyzing system operation data of the device. For example, the historical resource consumption data of the data collation device may be subjected to analysis statistical processing, and then the service idle period of the device may be determined to be used as the data collation period based on the obtained analysis result. Or it is also possible to perform analysis processing on the total amount of data processing for each unit time period within a preset time period, and intelligently determine the service idle time period of the device based on the analysis result to be used as the data collation time period, and so on. And then judging whether the current time reaches the starting end point time of the data checking time period, wherein the data checking time period is a time period formed by a time interval contained by the starting end point time and the ending end point time. For example, if the data collation period is 2:00-3:00, the corresponding start endpoint time is 2:00, end point time is 3:00. if the current time reaches the starting end point time of the data checking time period, acquiring first list data of a first service system and second list data of a second service system. The data acquisition task for acquiring the first list data and the second list data can be preset, and the data acquisition task comprises a first data acquisition address of the first list data and a second data acquisition address of the second list data. After the current time reaches the initial endpoint time, the data acquisition task is started to acquire the first list data and the second list data from the service system. Specifically, the first list data may be obtained by sending a data acquisition request to the first data acquisition address, and the second list data may be obtained by sending a data acquisition request to the second data acquisition address. The first service system may be a claim settlement system, the first list data may be a claim settlement system list summary data, the second service system may be a credit system, and the second list data may be a credit system list summary data. The above-mentioned list data may be the list data in the last specified time period adjacent to the current time, and the specified time period is not limited, and may be set according to actual requirements, for example, may be set for one month. Then, carrying out data statistics processing on the first list data based on a preset service data statistics rule to obtain corresponding first service statistics data, wherein the first service statistics data comprises a first service data field and first service data information corresponding to the first service data field; and carrying out data statistics processing on the second list data to obtain corresponding second business statistics data, wherein the second business statistics data comprises a second business data field and second business data information corresponding to the second business data field. The service data statistics rule is rule information for performing data statistics on the list data, and specific content of the service statistics rule is not limited, and the service data statistics rule can be set according to actual requirements, for example, can include accumulation of amounts, calculation of difference of amounts, and the like. In addition, the specific fields included in the service data field are not limited, and may be set according to actual requirements, for example, may include: number of reimbursement, total amount of case establishment, total amount of case settlement, number of losses, number of killing, disaster area, absolute area, number of emergency users, common protection status and common protection share. And finally, carrying out data checking processing on the first business statistic data and the second business statistic data based on a preset data checking template to generate a corresponding data checking result. The first business statistical data and the second business statistical data are respectively filled in the corresponding positions of the data checking template, and then the first business statistical data and the second business statistical data are checked one by one, so that the data checking processing can be quickly and intelligently completed, and the corresponding data checking result can be obtained. When the current time reaches the starting end point time of the preset data checking time period, the embodiment automatically acquires the first list data and the second list data, converts the first list data and the second list data into corresponding business statistical data, and then performs data checking processing on the first business statistical data and the second business statistical data based on the preset data checking template, so as to generate a corresponding data checking result. The whole data checking process is automatically carried out without manual participation, and the data checking speed is high and the accuracy is high, so that a large amount of manpower resources and time can be saved, the checking data error can be reduced, the error rate is reduced, and the accuracy of the generated data checking result is effectively improved.
Further, in an embodiment of the present application, the step S6 includes:
s600: filling the first business data field into the data check template;
s601: filling the first business data information into a first corresponding position in the data check template based on the corresponding relation between the first business data field and the first business data information;
s602: judging whether the second service data fields all contain designated service data fields which are respectively the same as the first service data fields;
s603: if the second service data fields all contain the same appointed service data fields as the first service data fields, judging whether other service data fields except the appointed service data fields exist in the second service data fields;
s604: if the second service data field does not contain other service data fields, screening specified service data information corresponding to the specified service data field from the second service data information;
s605: filling the specified service data information into a second corresponding position in the data check template based on the corresponding relation between the specified service data field and the first service data field;
S606: checking first business data information contained in each first business data field in the data checking template with appointed business data information one by one to judge whether the two pieces of data information are identical;
s607: if the two data information contained in each first service data field are the same, generating a first check result of successful check;
s608: if the two data information contained in each first service data field are not the same, generating a second check result of failed check;
s609: and acquiring a target service data field with difference between the first service data information and the appointed service data information, and adding an anomaly flag to the target service data field.
As described in steps S600 to S609, the step of performing data checking processing on the first service statistics data and the second service statistics data based on the preset data checking template to generate corresponding data checking results may specifically include: the first service data field is filled into the data check template. The data verification template may be an Excel table that does not include data. And then filling the first business data information into a first corresponding position in the data check template based on the corresponding relation between the first business data field and the first business data information. And judging whether the second service data fields all contain the same appointed service data fields as the first service data fields. If the second service data fields all contain the same appointed service data fields as the first service data fields, further judging whether other service data fields except the appointed service data fields exist in the second service data fields. And if the other service data fields do not exist in the second service data field, screening the specified service data information corresponding to the specified service data field from the second service data information. And filling the specified service data information into a second corresponding position in the data check template based on the corresponding relation between the specified service data field and the first service data field. The method comprises the steps of filling first service data fields and corresponding first service data information into a data checking template, and filling specified service data information corresponding to the specified service data fields into the data checking template based on the corresponding relation between the specified service data fields and the first service data fields, so that each first service data field contains one data information corresponding to the first service data field in first service statistical data and one data information corresponding to the first service data field in second service statistical data. And subsequently, checking the first business data information contained in each first business data field in the data checking template with the appointed business data information one by one to judge whether the two pieces of data information are identical. And if the two data information contained in each of the first service data fields are the same, generating a first check result of successful check. And if the two data information contained in each of the first service data fields are not identical, generating a second checking result of failed checking. And after the second checking result is obtained, acquiring a target service data field with the difference between the first service data information and the appointed service data information, and adding an abnormal mark to the target service data field. The abnormal mark is not particularly limited, and may be, for example, a color mark, a text mark, or other forms of mark information. According to the embodiment, the first business statistical data and the second business statistical data are respectively filled in the corresponding positions of the data checking template, and then the first business statistical data and the second business statistical data are checked one by one, so that the data checking processing is quickly and intelligently completed, and the corresponding data checking result is obtained. The whole data checking process is automatically carried out without manual participation, and the data checking speed is high and the accuracy is high, so that a large amount of manpower resources and time can be saved, the checking data error can be reduced, the error rate is reduced, and the accuracy of the generated data checking result is effectively improved.
Further, in an embodiment of the present application, after the step S609, the method includes:
s6090: acquiring target service data information corresponding to the target service data field;
s6091: generating corresponding first abnormal information based on the target service data field and the target service data information;
s6092: acquiring preset mail login information and acquiring a designated mail address;
s6093: logging in to a corresponding mail server based on the mail login information;
s6094: and sending the first abnormal information to the appointed mail address through the mail server.
As described in the above steps S6090 to S6094, after the step of obtaining the target service data field in which the first service data information is different from the specified service data information and adding the anomaly flag to the target service data field, the method may further include a process of generating and sending the anomaly information corresponding to the target service data field. Specifically, first, target service data information corresponding to the target service data field is acquired. Wherein the target service data information includes first service data information and designated service data information corresponding to the target service data field. And then generating corresponding first abnormal information based on the target service data field and the target service data information. The first anomaly information at least comprises the target service data field and the target service data information, and the first anomaly information can be generated by filling the target service data field and the target service data information into a preset anomaly information template. In addition, the abnormal information template is not particularly limited, and may be, for example, a short message template set in advance according to a use requirement. And then acquiring preset mail login information and acquiring a designated mail address. After the mail login information is obtained, the mail login information is logged in a corresponding mail server based on the mail login information. And finally, the mail server sends the first abnormal information to the appointed mail address. According to the embodiment, the first abnormal information is sent to the appointed mail address by the mail server after the first abnormal information is obtained and the corresponding first abnormal information is generated, so that the appointed user corresponding to the appointed mail address can timely know the business data field with data abnormality and the corresponding business data information in the data verification processing process through the first abnormal information, and then the manual verification processing of the business data field with data abnormality can be timely adopted.
Further, in an embodiment of the present application, after the step S602, the method includes:
s6020: if the second service data fields do not all contain the same appointed service data fields as the first service data fields, screening specific service data fields from the first service data fields, wherein the second service data fields do not contain the specific service data fields;
s6021: searching specific business data information corresponding to the specific business data field from the first business data information;
s6022: generating corresponding second abnormal information based on the specific service data field and the specific service data information;
s6023: and sending the second abnormal information to the appointed mail address through the mail server.
As described in the above steps S6020 to S6023, there may be a case where the first service data field contains more field data than the second service data field, so that there is a data difference between the first service data information and the second service data information. After the step of determining whether the second service data fields all include the same designated service data fields as the first service data fields, the method may further include a process of generating and sending out abnormal information corresponding to the specific service data fields in the second service data fields that do not include the first service data fields. Specifically, if the second service data fields do not all include the same designated service data fields as the first service data fields, a specific service data field is selected from the first service data fields, where the second service data fields do not include the specific service data field. And then searching out the specific business data information corresponding to the specific business data field from the first business data information. And generating corresponding second abnormal information based on the specific service data field and the specific service data information. The second anomaly information at least comprises the specific service data field and the specific service data information, and can be generated by filling the specific service data field and the specific service data information into a preset anomaly information template. And finally, sending the second abnormal information to the appointed mail address through the mail server. According to the embodiment, the specific business data field is screened out from the first business data field, the corresponding second abnormal information is generated, the mail server is used for sending the second abnormal information to the appointed mail address, so that an appointed user corresponding to the appointed mail address can timely know the business data field with data abnormality and the corresponding business data information in the data checking process through the second abnormal information, and then manual checking processing on the business data field with data abnormality can be timely adopted.
Further, in an embodiment of the present application, after the step S603, the method includes:
s6030: if the other business data fields exist in the second business data field, other business data information corresponding to the other business data fields is found out from the second business data information;
s6031: generating corresponding third abnormal information based on the other business data fields and the other business data information;
s6032: and sending the third abnormal information to the appointed mail address through the mail server.
As described in the above steps S6030 to S6032, there may be a case where the second service data field contains more field data than the first service data field, so that there is a data difference between the second service data information and the first service data information. After the step of determining whether the second service data field has the other service data fields except the specified service data field, the method may further include generating and sending exception information corresponding to the second service data field having the other service data fields except the first service data field. Specifically, if the other service data field exists in the second service data field, other service data information corresponding to the other service data field is found out from the second service data information. And then generating corresponding third abnormal information based on the other service data fields and the other service data information. The third anomaly information at least includes the other service data field and the other service data information, and may be generated by filling the other service data field and the other service data information into a preset anomaly information template. And finally, sending the third abnormal information to the appointed mail address through the mail server. According to the embodiment, through obtaining other business data fields except the specified business data fields in the second business data fields and generating corresponding third abnormal information, the mail server is used to send the third abnormal information to the specified mail address, so that a specified user corresponding to the specified mail address can timely know the business data fields with data abnormality and corresponding business data information in the data checking process through the third abnormal information, and then manual checking processing on the business data fields with data abnormality can be timely adopted.
Further, in an embodiment of the present application, before the step S1, the method includes:
s100: acquiring the resource consumption of each appointed time period in each day in a first preset time period, wherein the appointed time period is a time period containing a preset time length;
s101: carrying out statistical analysis on the first preset time period, the designated time period and the resource consumption amount to generate a corresponding resource consumption statistical table;
s102: screening out first time periods with the resource consumption less than a preset resource consumption threshold from all the designated time periods in each day in the first preset time period respectively based on the resource consumption statistical table;
s103: screening out second time periods with the largest occurrence frequency from all the first time periods;
s104: and taking the second time period as the data checking time period.
As described in the above steps S100 to S104, the step of acquiring the preset data collation period may be preceded by a generation step of generating the data collation period. Specifically, the resource consumption of each specified time period in each day in a first preset time period is firstly obtained, wherein the specified time period is a time period including a preset time length, and the preset time length can be set according to actual requirements, for example, 1 hour can be used as the preset time length. In addition, the first preset time period is not particularly limited, and may be set according to actual requirements. For example, the first predetermined time period may be a last week adjacent to the current time. And then carrying out statistical analysis on the first preset time period, the appointed time period and the resource consumption amount to generate a corresponding resource consumption statistical table. The generating process of the resource consumption statistical table may include: filling the appointed time period to the position of a list head in a preset list template according to the sequence from small to large, dividing the first preset time period by taking each day as a unit, filling the appointed time period to the position of the list head in the list template according to the sequence from small to large, and filling the resource consumption corresponding to the list head and the list head into the cells of the list template in a one-to-one correspondence manner so as to generate the resource consumption statistical table. And then, based on the resource consumption statistical table, screening out first time periods of which the resource consumption is less than a preset resource consumption threshold from all the specified time periods in each day in the first preset time period respectively. The above-mentioned resource consumption threshold is not specifically limited, and may be set according to actual requirements, for example, may be set to 5, and if the resource consumption of the device is less than the resource consumption threshold, it indicates that the device is currently in a service idle state. And then screening out the second time periods with the largest occurrence times from all the first time periods. The second time period with the largest occurrence number in all the first time periods indicates that the second time period is the most common time period corresponding to the fact that the resource consumption of the device is smaller than the resource consumption threshold value in the first preset time period, that is, the device is usually in a service idle state in the second time period. And finally, taking the second time period as the data checking time period. According to the embodiment, the historical resource consumption data of the device are analyzed and counted, the service idle time period of the device is intelligently determined based on the analysis result, and the service idle time period is used as the data checking time period, so that the accuracy of the generated data checking time period is effectively improved. And the corresponding data checking process can be carried out in the data checking time period later, and the data checking process can not be carried out in the service peak period of the device, so that the normal use of a user can not be influenced, the reasonable utilization of system resources is ensured, and the processing speed and the processing efficiency of the data checking process are improved.
Further, in an embodiment of the present application, before the step S1, the method includes:
s110: dividing a time period of a day into a plurality of unit time periods based on a preset time division threshold;
s111: based on prestored historical data processing record data, counting the total data processing amount of each unit time period in a second preset time period;
s112: screening a first data processing total amount smaller than a preset data processing amount threshold value from all the data processing total amounts;
s113: sequencing all the first data processing total amount according to the sequence from the small value to the large value of the first data processing total amount to obtain a corresponding sequencing result;
s114: sequentially acquiring a specified number of second data processing total amount from the first data processing total amount ranked first in the sequencing result;
s115: acquiring a designated unit time period corresponding to the second data processing total amount from all the unit time periods;
s116: the specified unit time period is taken as the data collation time period.
As described in the above steps S110 to S116, the step of acquiring the preset data collation period may be preceded by a generating step of generating the data collation period. Specifically, the time period of one day is first divided into a plurality of unit time periods based on a preset time division threshold. The dividing manner of the unit time period is not particularly limited, and the time length included in each unit time period obtained by dividing may be set according to actual requirements, for example, 4 hours may be used as the dividing threshold, that is, the time length included in one unit time period, and then one day (24 hours) may be divided into 6 unit time periods from 0, that is, 0:00-4:00,4:00-8:00,8:00-12:00, 12:00-16:00, 16:00-20:00, 20:00-24:00. and then processing the recorded data based on the prestored historical data, and counting the total data processing amount of each unit time period in a second preset time period. The second preset time period is not specifically limited, and may be set according to actual requirements. For example, the second predetermined time period may be a last week adjacent to the current time. For example, if the unit time period is 12:00-16:00, the unit time period 12:00-16:00 total data processing in a week is unit time period 12 in the week: 00-16:00, the sum of the data processing amounts contained. And then screening a first data processing total amount smaller than a preset data processing amount threshold value from all the data processing total amounts. The data throughput threshold is not particularly limited, and may be set according to actual requirements. After the first data processing total amount is obtained, all the first data processing total amounts are ordered according to the order of the numerical values of the first data processing total amounts from small to large, and corresponding ordering results are obtained. And after the sequencing result is obtained, sequentially obtaining the second data processing total amount of the designated number from the first data processing total amount which is ranked first in the sequencing result. The specific number is not specifically limited, and may be set according to actual requirements, and it is only necessary to ensure that the specific number is not greater than the number of the first data processing total amount. And finally, acquiring a designated unit time period corresponding to the total amount of the second data processing from all the unit time periods, and taking the designated unit time period as the data checking time period. According to the embodiment, the data processing total amount of each unit time period in the second preset time period is analyzed and processed, the service idle time period of the device is intelligently determined based on the analysis result, and the service idle time period is used as the data checking time period, so that the accuracy of the generated data checking time period is effectively improved. And the corresponding data checking process can be carried out in the data checking time period later, and the data checking process can not be carried out in the service peak period of the device, so that the normal use of a user can not be influenced, the reasonable utilization of system resources is ensured, and the processing speed and the processing efficiency of the data checking process are improved.
The data checking method in the embodiment of the application can also be applied to the field of blockchains, such as storing the data such as the data checking result on the blockchain. By storing and managing the data collation results using the blockchain, the security and tamper-resistance of the data collation results can be effectively ensured.
The blockchain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, a consensus mechanism, an encryption algorithm and the like. The Blockchain (Blockchain), which is essentially a decentralised database, is a string of data blocks that are generated by cryptographic means in association, each data block containing a batch of information of network transactions for verifying the validity of the information (anti-counterfeiting) and generating the next block. The blockchain may include a blockchain underlying platform, a platform product services layer, an application services layer, and the like.
The blockchain underlying platform may include processing modules for user management, basic services, smart contracts, operation monitoring, and the like. The user management module is responsible for identity information management of all blockchain participants, including maintenance of public and private key generation (account management), key management, maintenance of corresponding relation between the real identity of the user and the blockchain address (authority management) and the like, and under the condition of authorization, supervision and audit of transaction conditions of certain real identities, and provision of rule configuration (wind control audit) of risk control; the basic service module is deployed on all block chain node devices, is used for verifying the validity of a service request, recording the service request on a storage after the effective request is identified, for a new service request, the basic service firstly analyzes interface adaptation and authenticates the interface adaptation, encrypts service information (identification management) through an identification algorithm, and transmits the encrypted service information to a shared account book (network communication) in a complete and consistent manner, and records and stores the service information; the intelligent contract module is responsible for registering and issuing contracts, triggering contracts and executing contracts, a developer can define contract logic through a certain programming language, issue the contract logic to a blockchain (contract registering), invoke keys or other event triggering execution according to the logic of contract clauses to complete the contract logic, and simultaneously provide a function of registering contract upgrading; the operation monitoring module is mainly responsible for deployment in the product release process, modification of configuration, contract setting, cloud adaptation and visual output of real-time states in product operation, for example: alarms, monitoring network conditions, monitoring node device health status, etc.
Referring to fig. 2, there is further provided in an embodiment of the present application a data collation apparatus including:
a first obtaining module 1, configured to obtain a current time and a preset data checking time period;
a judging module 2, configured to judge whether the current time reaches a start endpoint time of the data checking period, where the data checking period is a period formed by a time interval included by the start endpoint time and an end endpoint time;
a second obtaining module 3, configured to obtain first list data of a first service system and obtain second list data of a second service system if the current time reaches a start endpoint time of the data checking period;
the first processing module 4 is configured to perform data statistics processing on the first manifest data based on a preset service data statistics rule to obtain corresponding first service statistics data, where the first service statistics data includes a first service data field and first service data information corresponding to the first service data field; the method comprises the steps of,
a second processing module 5, configured to perform data statistics processing on the second manifest data to obtain corresponding second service statistics data, where the second service statistics data includes a second service data field and second service data information corresponding to the second service data field;
And the checking module 6 is used for performing data checking processing on the first business statistic data and the second business statistic data based on a preset data checking template to generate a corresponding data checking result.
In this embodiment, the implementation process of the functions and roles of the first acquiring module, the judging module, the second acquiring module, the first processing module, the second processing module and the checking module in the data checking device is specifically detailed in the implementation process corresponding to steps S1 to S6 in the data checking method, and will not be described herein.
Further, in an embodiment of the present application, the verification module includes:
the first filling unit is used for filling the first business data field into the data check template;
a second filling unit, configured to fill the first service data information to a first corresponding position in the data collation template based on a correspondence between the first service data field and the first service data information;
a first judging unit, configured to judge whether each of the second service data fields includes a designated service data field that is the same as each of the first service data fields;
a second judging unit, configured to judge whether other service data fields except for the specified service data fields exist in the second service data field if the specified service data fields which are the same as the first service data fields are included in the second service data fields;
A first screening unit, configured to screen, if the second service data field does not have the other service data field, specified service data information corresponding to the specified service data field from the second service data information;
a third filling unit, configured to fill the specified service data information to a second corresponding position in the data collation template based on a correspondence between the specified service data field and the first service data field;
a third judging unit, configured to check, one by one, first service data information and specified service data information included in each of the first service data fields in the data checking template to judge whether the two data information are identical;
a first generating unit, configured to generate a first verification result that is successful in verification if two data information included in each of the first service data fields are the same;
a second generating unit, configured to generate a second verification result of verification failure if two data information included in each of the first service data fields are not the same;
the first acquisition unit is used for acquiring a target service data field with difference between the first service data information and the appointed service data information, and adding an abnormal mark to the target service data field.
In this embodiment, the implementation processes of the functions and actions of the first filling unit, the second filling unit, the first judging unit, the second judging unit, the first screening unit, the third filling unit, the third judging unit, the first generating unit, the second generating unit and the first obtaining unit in the data checking device are specifically detailed in the implementation processes corresponding to steps S600 to S609 in the data checking method, and are not repeated here.
Further, in an embodiment of the present application, the verification module includes:
a second obtaining unit, configured to obtain target service data information corresponding to the target service data field;
a third generating unit, configured to generate corresponding first anomaly information based on the target service data field and the target service data information;
the third acquisition unit is used for acquiring preset mail login information and acquiring a designated mail address;
a login unit, configured to login to a corresponding mail server based on the mail login information;
and the first sending unit is used for sending the first abnormal information to the appointed mail address through the mail server.
In this embodiment, the implementation processes of the functions and actions of the second acquiring unit, the third generating unit, the third acquiring unit, the login unit and the first sending unit in the data checking device are specifically described in the implementation processes corresponding to steps S6090 to S6094 in the data checking method, which are not described herein.
Further, in an embodiment of the present application, the verification module includes:
a second screening unit, configured to screen a specific service data field from the first service data fields if the second service data fields do not all include the same designated service data fields as the first service data fields, where the second service data fields do not include the specific service data fields;
the first searching unit is used for searching specific business data information corresponding to the specific business data field from the first business data information;
a fourth generating unit, configured to generate corresponding second anomaly information based on the specific service data field and the specific service data information;
and a second sending unit configured to send, by the mail server, the second abnormality information to the specified mail address.
In this embodiment, the implementation process of the functions and actions of the second screening unit, the first searching unit, the fourth generating unit and the second sending unit in the data checking device is specifically described in the implementation process corresponding to steps S6020 to S6023 in the data checking method, which is not described herein.
Further, in an embodiment of the present application, the verification module includes:
a second searching unit, configured to, if the other service data field exists in the second service data field, search other service data information corresponding to the other service data field from the second service data information;
a fifth generating unit, configured to generate corresponding third abnormal information based on the other service data field and the other service data information;
and a third sending unit configured to send, by the mail server, the third abnormality information to the specified mail address.
In this embodiment, the implementation process of the functions and actions of the second search unit, the fifth generation unit and the third sending unit in the data checking device is specifically described in the implementation process corresponding to steps S6030 to S6032 in the data checking method, which is not described herein.
Further, in an embodiment of the present application, the data checking device includes:
a third obtaining module, configured to obtain a resource consumption amount of each specified time period in each day in a first preset time period, where the specified time period is a time period including a preset time length;
The generation module is used for carrying out statistical analysis on the first preset time period, the designated time period and the resource consumption amount to generate a corresponding resource consumption statistical table;
the first screening module is used for screening out first time periods of which the resource consumption is less than a preset resource consumption threshold value from all the designated time periods in each day in the first preset time period respectively based on the resource consumption statistical table;
the second screening module is used for screening out a second time period with the largest occurrence number from all the first time periods;
and the first determining module is used for taking the second time period as the data checking time period.
In this embodiment, the implementation process of the functions and roles of the third acquiring module, the generating module, the first screening module, the second screening module and the first determining module in the data checking device is specifically described in the implementation process corresponding to steps S100 to S104 in the data checking method, and will not be described herein.
Further, in an embodiment of the present application, the data checking device includes:
the dividing module is used for dividing the time period of one day into a plurality of unit time periods based on a preset time dividing threshold;
The statistics module is used for processing recorded data based on prestored historical data and counting the total data processing amount of each unit time period in a second preset time period;
the third screening module is used for screening a first data processing total amount smaller than a preset data processing amount threshold value from all the data processing total amounts;
the sorting module is used for sorting all the first data processing total amount according to the sequence from the smaller value to the larger value of the first data processing total amount to obtain a corresponding sorting result;
a fourth obtaining module, configured to sequentially obtain a specified number of second data processing total amounts from the first data processing total amounts ranked first in the ranking result;
a fifth acquisition module, configured to acquire a specified unit time period corresponding to the second data processing total amount from all the unit time periods;
and a second determining module configured to take the specified unit time period as the data collation time period.
In this embodiment, the implementation processes of the functions and roles of the dividing module, the counting module, the third screening module, the sorting module, the fourth obtaining module, the fifth obtaining module and the second determining module in the data checking device are specifically detailed in the implementation processes corresponding to steps S110 to S116 in the data checking method, and are not repeated here.
Referring to fig. 3, a computer device is further provided in the embodiment of the present application, where the computer device may be a server, and the internal structure of the computer device may be as shown in fig. 3. The computer device includes a processor, a memory, a network interface, a display screen, an input device, and a database connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a storage medium, an internal memory. The storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the storage media. The database of the computer device is used for storing data checking time period, list data, business data field, business data information, data checking template and data checking result. The network interface of the computer device is used for communicating with an external terminal through a network connection. The display screen of the computer equipment is an indispensable image-text output equipment in the computer and is used for converting digital signals into optical signals so that characters and graphics can be displayed on the screen of the display screen. The input device of the computer equipment is a main device for exchanging information between the computer and a user or other equipment, and is used for conveying data, instructions, certain sign information and the like into the computer. The computer program is executed by a processor to implement a data collation method.
The processor executes the steps of the data collation method:
acquiring current time and acquiring a preset data checking time period;
judging whether the current time reaches the starting end point time of the data checking time period, wherein the data checking time period is a time period formed by a time interval contained by the starting end point time and the ending end point time;
if the current time reaches the starting end point time of the data checking time period, acquiring first list data of a first service system and second list data of a second service system;
performing data statistics processing on the first list data based on a preset service data statistics rule to obtain corresponding first service statistics data, wherein the first service statistics data comprise a first service data field and first service data information corresponding to the first service data field; the method comprises the steps of,
performing data statistics processing on the second list data to obtain corresponding second service statistics data, wherein the second service statistics data comprises a second service data field and second service data information corresponding to the second service data field;
And carrying out data checking processing on the first business statistic data and the second business statistic data based on a preset data checking template to generate a corresponding data checking result.
Those skilled in the art will appreciate that the structures shown in fig. 3 are only block diagrams of portions of structures that may be associated with the aspects of the present application and are not intended to limit the scope of the apparatus, or computer devices on which the aspects of the present application may be implemented.
An embodiment of the present application further provides a computer readable storage medium having a computer program stored thereon, where the computer program when executed by a processor implements a data collation method, specifically:
acquiring current time and acquiring a preset data checking time period;
judging whether the current time reaches the starting end point time of the data checking time period, wherein the data checking time period is a time period formed by a time interval contained by the starting end point time and the ending end point time;
if the current time reaches the starting end point time of the data checking time period, acquiring first list data of a first service system and second list data of a second service system;
performing data statistics processing on the first list data based on a preset service data statistics rule to obtain corresponding first service statistics data, wherein the first service statistics data comprise a first service data field and first service data information corresponding to the first service data field; the method comprises the steps of,
Performing data statistics processing on the second list data to obtain corresponding second service statistics data, wherein the second service statistics data comprises a second service data field and second service data information corresponding to the second service data field;
and carrying out data checking processing on the first business statistic data and the second business statistic data based on a preset data checking template to generate a corresponding data checking result.
Those skilled in the art will appreciate that implementing all or part of the above-described embodiment methods may be accomplished by way of a computer program stored on a computer readable storage medium, which when executed, may comprise the steps of the above-described embodiment methods. Any reference to memory, storage, database, or other medium provided herein and used in embodiments may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual speed data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), memory bus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, apparatus, article, or method. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, apparatus, article or method that comprises the element.
The foregoing description is only of the preferred embodiments of the present application, and is not intended to limit the scope of the claims, and all equivalent structures or equivalent processes using the descriptions and drawings of the present application, or direct or indirect application in other related technical fields are included in the scope of the claims of the present application.

Claims (9)

1. A data collation method, characterized by comprising:
acquiring current time and acquiring a preset data checking time period;
judging whether the current time reaches the starting end point time of the data checking time period, wherein the data checking time period is a time period formed by a time interval contained by the starting end point time and the ending end point time;
If the current time reaches the starting end point time of the data checking time period, acquiring first list data of a first service system and second list data of a second service system;
performing data statistics processing on the first list data based on a preset service data statistics rule to obtain corresponding first service statistics data, wherein the first service statistics data comprise a first service data field and first service data information corresponding to the first service data field; the method comprises the steps of,
performing data statistics processing on the second list data to obtain corresponding second service statistics data, wherein the second service statistics data comprises a second service data field and second service data information corresponding to the second service data field;
performing data checking processing on the first business statistic data and the second business statistic data based on a preset data checking template to generate a corresponding data checking result;
the step of performing data checking processing on the first service statistical data and the second service statistical data based on a preset data checking template to generate a corresponding data checking result comprises the following steps:
Filling the first business data field into the data check template;
filling the first business data information into a first corresponding position in the data check template based on the corresponding relation between the first business data field and the first business data information;
judging whether the second service data fields all contain designated service data fields which are respectively the same as the first service data fields;
if the second service data fields all contain the same appointed service data fields as the first service data fields, judging whether other service data fields except the appointed service data fields exist in the second service data fields;
if the second service data field does not contain other service data fields, screening specified service data information corresponding to the specified service data field from the second service data information;
filling the specified service data information into a second corresponding position in the data check template based on the corresponding relation between the specified service data field and the first service data field;
checking first business data information contained in each first business data field in the data checking template with appointed business data information one by one to judge whether the two pieces of data information are identical;
If the two data information contained in each first service data field are the same, generating a first check result of successful check;
if the two data information contained in each first service data field are not the same, generating a second check result of failed check;
and acquiring a target service data field with difference between the first service data information and the appointed service data information, and adding an anomaly flag to the target service data field.
2. The data collation method according to claim 1, wherein after the step of acquiring a target service data field in which the first service data information differs from the specified service data information, and adding an abnormality flag to the target service data field, comprising:
acquiring target service data information corresponding to the target service data field;
generating corresponding first abnormal information based on the target service data field and the target service data information;
acquiring preset mail login information and acquiring a designated mail address;
logging in to a corresponding mail server based on the mail login information;
and sending the first abnormal information to the appointed mail address through the mail server.
3. The data collation method according to claim 2, wherein after the step of judging whether or not the second service data fields each contain the same designated service data field as each of the first service data fields, comprising:
if the second service data fields do not all contain the same appointed service data fields as the first service data fields, screening specific service data fields from the first service data fields, wherein the second service data fields do not contain the specific service data fields;
searching specific business data information corresponding to the specific business data field from the first business data information;
generating corresponding second abnormal information based on the specific service data field and the specific service data information;
and sending the second abnormal information to the appointed mail address through the mail server.
4. The data collation method according to claim 2, wherein after the step of judging whether or not there are other service data fields than the specified service data field in the second service data field, comprising:
If the other business data fields exist in the second business data field, other business data information corresponding to the other business data fields is found out from the second business data information;
generating corresponding third abnormal information based on the other business data fields and the other business data information;
and sending the third abnormal information to the appointed mail address through the mail server.
5. The data collation method according to claim 1, wherein before the step of acquiring the preset data collation period, comprising:
acquiring the resource consumption of each appointed time period in each day in a first preset time period, wherein the appointed time period is a time period containing a preset time length;
carrying out statistical analysis on the first preset time period, the designated time period and the resource consumption amount to generate a corresponding resource consumption statistical table;
screening out first time periods with the resource consumption less than a preset resource consumption threshold from all the designated time periods in each day in the first preset time period respectively based on the resource consumption statistical table;
Screening out second time periods with the largest occurrence frequency from all the first time periods;
and taking the second time period as the data checking time period.
6. The data collation method according to claim 1, wherein before the step of acquiring the preset data collation period, comprising:
dividing a time period of a day into a plurality of unit time periods based on a preset time division threshold;
based on prestored historical data processing record data, counting the total data processing amount of each unit time period in a second preset time period;
screening a first data processing total amount smaller than a preset data processing amount threshold value from all the data processing total amounts;
sequencing all the first data processing total amount according to the sequence from the small value to the large value of the first data processing total amount to obtain a corresponding sequencing result;
sequentially acquiring a specified number of second data processing total amount from the first data processing total amount ranked first in the sequencing result;
acquiring a designated unit time period corresponding to the second data processing total amount from all the unit time periods;
the specified unit time period is taken as the data collation time period.
7. A data collation apparatus for implementing the method according to any one of claims 1 to 6, characterized in that the apparatus comprises:
the first acquisition module is used for acquiring the current time and acquiring a preset data checking time period;
a judging module, configured to judge whether the current time reaches a start endpoint time of the data checking period, where the data checking period is a period formed by a time interval included by the start endpoint time and an end endpoint time;
the second acquisition module is used for acquiring first list data of the first service system and second list data of the second service system if the current time reaches the starting endpoint time of the data check time period;
the first processing module is used for carrying out data statistics processing on the first list data based on a preset business data statistics rule to obtain corresponding first business statistics data, wherein the first business statistics data comprises a first business data field and first business data information corresponding to the first business data field; the method comprises the steps of,
the second processing module is used for carrying out data statistics processing on the second list data to obtain corresponding second business statistics data, wherein the second business statistics data comprise a second business data field and second business data information corresponding to the second business data field;
And the checking module is used for performing data checking processing on the first business statistic data and the second business statistic data based on a preset data checking template to generate a corresponding data checking result.
8. A computer device comprising a memory and a processor, the memory having stored therein a computer program, characterized in that the processor, when executing the computer program, carries out the steps of the method according to any one of claims 1 to 6.
9. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 6.
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CN113342835B (en) * 2021-06-22 2023-06-20 中国平安财产保险股份有限公司 Method, device, equipment and medium for modifying text to be checked based on blockchain
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Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103136276A (en) * 2011-12-02 2013-06-05 阿里巴巴集团控股有限公司 System, method and device of verification of data
CN110147378A (en) * 2019-04-02 2019-08-20 平安科技（深圳）有限公司 Verification of data method, apparatus, computer equipment and storage medium
CN111061802A (en) * 2019-12-26 2020-04-24 宁波三星医疗电气股份有限公司 Power data management processing method and device and storage medium
WO2020207090A1 (en) * 2019-04-12 2020-10-15 创新先进技术有限公司 Blockchain-based data processing system and method, computing device and storage medium

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103136276A (en) * 2011-12-02 2013-06-05 阿里巴巴集团控股有限公司 System, method and device of verification of data
CN110147378A (en) * 2019-04-02 2019-08-20 平安科技（深圳）有限公司 Verification of data method, apparatus, computer equipment and storage medium
WO2020207090A1 (en) * 2019-04-12 2020-10-15 创新先进技术有限公司 Blockchain-based data processing system and method, computing device and storage medium
CN111061802A (en) * 2019-12-26 2020-04-24 宁波三星医疗电气股份有限公司 Power data management processing method and device and storage medium

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