CN113742329A - Data checking method, device, equipment and storage medium - Google Patents
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Abstract
The invention relates to the field of data processing technology and artificial intelligence, and discloses a data checking method, a data checking device, data checking equipment and a storage medium. The method comprises the following steps: obtaining original data to be checked and original reference data through the acquired task execution data; performing structure conversion on the original data to be checked and the original reference data to obtain target data to be checked and target reference data; verifying the target data to be verified based on a verification rule corresponding to the target data to be verified acquired from a preset configuration table to obtain a verification result; and when the verification result is that the target data to be verified conforms to the verification rule, selecting standard data from the target reference data and the target data to be verified for verification according to the target data to be verified and the product codes corresponding to the target data to be verified, and obtaining the verification result. By solving the prepositive work of policy contract grouping and making a corresponding basis for the contract grouping of the policy behind the project, abnormal data can be quickly found, and the data checking efficiency is improved.
Description
Technical Field
The present invention relates to the field of data processing technology and artificial intelligence, and in particular, to a data checking method, apparatus, device, and storage medium.
Background
The data storage system can generate a large amount of operation data in the operation process, and the parameter maintainers can check the generated operation data with the standard data to determine whether the operation of the data storage system is normal.
However, the data generated in the data storage system is of a large variety and quantity, and parameter maintainers select all data and check the data simultaneously in the process of comprehensively checking the data. Due to the fact that the data quantity is large, the data is complex in variety, errors are prone to occur, the checking speed is low, the preposed work of policy contract grouping cannot be solved, a corresponding basis is made for the contract grouping of the policy behind the project, automatic checking of mass data cannot be achieved, and the data checking efficiency is low. Therefore, how to realize the automatic checking of mass data and improve the data checking efficiency becomes a technical problem to be solved by the technical personnel in the field.
Disclosure of Invention
The invention mainly aims to solve the prepositive work of policy contract grouping and make a corresponding basis for the contract grouping of the policy behind a project, thereby quickly finding abnormal data and improving the data checking efficiency.
The first aspect of the present invention provides a data collation method, including: acquiring task execution data, wherein the task execution information carries contents to be checked; acquiring original data to be checked and original reference data according to the task execution data; performing structure conversion on the original data to be checked and the original reference data to obtain target data to be checked and target reference data; acquiring a check rule corresponding to the target data to be checked from a preset configuration table, and checking the target data to be checked based on the check rule to obtain a check result; if yes, selecting standard data from the target reference data and the target data to be checked to check according to the target data to be checked and the product codes corresponding to the target data to be checked, and obtaining a checking result.
Optionally, in a first implementation manner of the first aspect of the present invention, before the acquiring the task execution data, the method further includes: constructing a configuration table in a database based on the category of the data checking rule; storing product codes and data check rules of products into the configuration table in a classified manner according to a service configuration instruction; and when the content to be checked is checked, acquiring the product code of the purchased product.
Optionally, in a second implementation manner of the first aspect of the present invention, after the acquiring the task execution data, the method further includes: judging whether a check data table exists or not; and if the check data table does not exist, generating a target check table according to the data to be checked.
Optionally, in a third implementation manner of the first aspect of the present invention, the generating a target verification table according to the data to be verified includes: acquiring data fields and data attributes in the data to be checked; and defining a field corresponding to each line of data in the target check list and the attribute of each line of data according to the data field and the data attribute, and correspondingly generating the target check list.
Optionally, in a fourth implementation manner of the first aspect of the present invention, the performing structure transformation on the original data to be checked and the original reference data to obtain target data to be checked and target reference data includes: acquiring a data structure of each data in the original data to be checked, and performing data structure conversion on each data in the original data to be checked according to a preset first conversion relation to obtain target data to be checked; and acquiring a data structure of each datum in the original datum data, and performing data structure conversion on each datum in the original datum data according to a preset second conversion relation to obtain target datum data, wherein the data to be checked and the datum data have the same structure.
Optionally, in a fifth implementation manner of the first aspect of the present invention, the obtaining, from a preset configuration table, a collation rule corresponding to the target data to be collated includes: acquiring field information corresponding to target data to be checked of the target data to be checked; and determining a checking rule corresponding to the target data to be checked of the target data to be checked according to the field information.
Optionally, in a sixth implementation manner of the first aspect of the present invention, the selecting, according to the target data to be checked and the product code corresponding to the target data to be checked, standard data from the target standard data to be checked with the target data to be checked, and obtaining a checking result includes: selecting target data to be checked from the data to be checked in the target checking table according to the repeated item checking content in the content to be checked; inquiring data which is the same as the target data to be checked in the data to be checked of the target checking table to obtain the number of repetition of the target data to be checked; if the number of the repeated data is larger than the preset number, the checking result is that the target data to be checked is a repeated record item of the data to be checked; and when target reference data is selected from the target checking table for checking, selecting the target reference data from the target checking table for checking according to the content to be checked to obtain a checking result.
A second aspect of the present invention provides a data collation apparatus comprising: the system comprises a first acquisition module, a second acquisition module and a verification module, wherein the first acquisition module is used for acquiring task execution data, and the task execution information carries contents to be verified; the second acquisition module is used for acquiring original data to be checked and original reference data according to the task execution data; the structure conversion module is used for carrying out structure conversion on the original data to be checked and the original reference data to obtain target data to be checked and target reference data; the verification module is used for acquiring a verification rule corresponding to the target data to be verified from a preset configuration table, and verifying the target data to be verified based on the verification rule to obtain a verification result; and the checking module is used for selecting standard data from the target reference data to be checked and checking the target data to be checked according to the target data to be checked and the product codes corresponding to the target data to be checked when the target data to be checked accords with the checking rule to obtain a checking result.
Optionally, in a first implementation manner of the second aspect of the present invention, the data checking apparatus further includes: the construction module is used for constructing a configuration table in the database based on the category of the data checking rule; the storage module is used for storing the product codes and the data check rules of the products into the configuration table in a classified manner according to the service configuration instructions; and the third acquisition module is used for acquiring the product code of the purchased product when the content to be checked is checked.
Optionally, in a second implementation manner of the second aspect of the present invention, the data checking apparatus further includes: the judging module is used for judging whether the check data table exists or not; and the generating module is used for generating a target verification table according to the data to be verified if the verification data table does not exist.
Optionally, in a third implementation manner of the second aspect of the present invention, the generating module is specifically configured to: acquiring data fields and data attributes in the data to be checked; and defining a field corresponding to each line of data in the target check list and the attribute of each line of data according to the data field and the data attribute, and correspondingly generating the target check list.
Optionally, in a fourth implementation manner of the second aspect of the present invention, the structure conversion module is specifically configured to: acquiring a data structure of each data in the original data to be checked, and performing data structure conversion on each data in the original data to be checked according to a preset first conversion relation to obtain target data to be checked; and acquiring a data structure of each datum in the original datum data, and performing data structure conversion on each datum in the original datum data according to a preset second conversion relation to obtain target datum data, wherein the data to be checked and the datum data have the same structure.
Optionally, in a fifth implementation manner of the second aspect of the present invention, the verification module includes a module specifically configured to: an acquisition unit configured to acquire field information corresponding to target to-be-checked data of the target to-be-checked data; and the determining unit is used for determining a checking rule corresponding to the target data to be checked of the target data to be checked according to the field information.
Optionally, in a sixth implementation manner of the second aspect of the present invention, the checking module is specifically configured to: selecting target data to be checked from the data to be checked in the target checking table according to the repeated item checking content in the content to be checked; inquiring data which is the same as the target data to be checked in the data to be checked of the target checking table to obtain the number of repetition of the target data to be checked; if the number of the repeated data is larger than the preset number, the checking result is that the target data to be checked is a repeated record item of the data to be checked; and when target reference data is selected from the target checking table for checking, selecting the target reference data from the target checking table for checking according to the content to be checked to obtain a checking result.
A third aspect of the present invention provides a data collation apparatus comprising: a memory having instructions stored therein and at least one processor, the memory and the at least one processor interconnected by a line;
the at least one processor invokes the instructions in the memory to cause the data collation apparatus to perform the steps of the data collation method described above.
A fourth aspect of the present invention provides a computer-readable storage medium having stored therein instructions, which, when run on a computer, cause the computer to perform the steps of the data collation method described above.
According to the technical scheme provided by the invention, original data to be checked and original reference data are obtained through acquired task execution data; performing structure conversion on the original data to be checked and the original reference data to obtain target data to be checked and target reference data; verifying the target data to be verified based on a verification rule corresponding to the target data to be verified acquired from a preset configuration table to obtain a verification result; if yes, selecting standard data from the target reference data and the target data to be checked for checking according to the target data to be checked and the product codes corresponding to the target data to be checked, and obtaining a checking result. By solving the prepositive work of policy contract grouping and making a corresponding basis for the contract grouping of the policy behind the project, abnormal data can be quickly found, and the data checking efficiency is improved.
Drawings
FIG. 1 is a schematic diagram of a first embodiment of the data collation method of the present invention;
FIG. 2 is a schematic diagram of a second embodiment of the data verification method according to the present invention;
FIG. 3 is a schematic diagram of a third embodiment of the data collation method of the present invention;
FIG. 4 is a schematic diagram of a fourth embodiment of the data collation method of the present invention;
FIG. 5 is a schematic diagram of a fifth embodiment of the data collation method of the present invention;
FIG. 6 is a schematic view of a first embodiment of the data collation apparatus of the present invention;
FIG. 7 is a schematic view of a second embodiment of the data collation apparatus according to the present invention;
fig. 8 is a schematic view of an embodiment of the data collation apparatus of the present invention.
Detailed Description
The embodiment of the invention provides a data checking method, a device, equipment and a storage medium, wherein task execution data is acquired firstly to obtain original data to be checked and original reference data; performing structure conversion on the original data to be checked and the original reference data to obtain target data to be checked and target reference data; verifying the target data to be verified based on a verification rule corresponding to the target data to be verified acquired from a preset configuration table to obtain a verification result; and when the verification result is that the target data to be verified conforms to the verification rule, selecting standard data from the target reference data and the target data to be verified for verification according to the target data to be verified and the product codes corresponding to the target data to be verified, and obtaining the verification result. By solving the prepositive work of policy contract grouping and making a corresponding basis for the contract grouping of the policy behind the project, abnormal data can be quickly found, and the data checking efficiency is improved.
The terms "first," "second," "third," "fourth," and the like in the description and in the claims, as well as in the drawings, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It will be appreciated that the data so used may be interchanged under appropriate circumstances such that the embodiments described herein may be practiced otherwise than as specifically illustrated or described herein. Furthermore, the terms "comprises," "comprising," or "having," and any variations thereof, are intended to cover non-exclusive inclusions, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
For convenience of understanding, a detailed flow of an embodiment of the present invention is described below, and referring to fig. 1, a first embodiment of a data checking method according to an embodiment of the present invention includes:
101. acquiring task execution data, wherein the task execution information carries contents to be checked;
in this embodiment, the insurance company data storage system generates a large amount of policy data, and in order to facilitate the overall detection of the policy data in the data storage system, the policy data generated by the data storage system may be defined in a data range. For example, the policy data generated by the data storage system is divided according to the location of the insurance company or the time zone in which the insurance company is located to obtain corresponding different areas, and the policy data generated by the policy data storage system of the insurance company located in the different areas is corresponding to the divided different areas.
In addition, the policy data generated by the data storage system can be divided according to different products, and the policy data obtained by the policy data storage system of the insurance company belonging to a smaller area can be divided together to obtain the policy data of the corresponding product. For example, policy data of an insurance company belonging to a product may be divided into data of products of the product insurance company. Further, policy data may be categorized according to business type, for example, policy data may be divided according to life insurance, car insurance, heavy duty insurance, children insurance, and the like.
Before checking, task execution information is obtained, and the task execution information comprises a target checking task. The task execution information includes data of this time of collation and information related to the collation method, and the task execution information may include task execution time, data range of this time of collation, and the like. The target matching task is basic information for instructing the current matching, and may specifically include a matching data type, a matching method, and the like. The task execution information can be preset, and can be triggered manually or automatically by setting a trigger time.
102. Acquiring original data to be checked and original reference data according to task execution data;
in this embodiment, data structure conversion is performed on the original data to be checked and the original reference data, respectively, to obtain the data to be checked and the reference data. And acquiring original data to be checked and original reference data according to the task execution information. It is understood that the task execution information includes information such as a range of the data to be collated this time, and the data to be collated this time can be specified from the task execution information.
The acquisition of the original data to be checked and the original reference data can be performed from a server configuration table storing data according to the task execution information. The original data to be checked is operation data obtained by operating a data storage system of the insurance company, and the original data to be checked may be stored in an FTP (File Transfer Protocol) server. The original datum data are template data for data comparison by parameter maintainers, are reference operation data when the data storage system of the insurance company operates normally, and can be uploaded to the server.
103. Performing structure conversion on the original data to be checked and the original reference data to obtain target data to be checked and target reference data;
in this embodiment, it can be understood that the original data to be checked is data directly generated by the operation of the data storage system of the insurance company, the original reference data is template data for checking by the parameter maintainer, data structures of the original data to be checked and the original reference data may be different, and before the data to be checked and the original reference data are imported into the target checking table, data structure conversion needs to be performed on the original data to be checked and the original reference data respectively, so as to obtain the data to be checked and the reference data with the consistent data structures.
104. Acquiring a check rule corresponding to the target data to be checked from a preset configuration table, and checking the target data to be checked based on the check rule to obtain a check result;
in this embodiment, the server stores the checking rules corresponding to each type of target data to be checked in advance, that is, after the document data is split according to the field, the corresponding target data to be checked is classified according to the field, and then the corresponding checking rules are summarized for each class and stored in the server, so that the automatic checking is facilitated. Specifically, the server stores a correspondence between field information and a collation rule in advance, divides the document data into a plurality of target data to be collated according to fields, acquires the field information corresponding to each target data to be collated, and then acquires the corresponding collation rule according to the field information corresponding to the target data to be collated.
105. And when the verification result is that the target data to be verified conforms to the verification rule, selecting standard data from the target reference data and the target data to be verified for verification according to the target data to be verified and the product codes corresponding to the target data to be verified, and obtaining the verification result.
In this embodiment, according to the target verification content, the data to be verified is selected for verification, and a verification result is obtained. The target collation content may have the number of significant digits of data collation, a field of collated data, an attribute of data, and the like. According to the target verification content, the columns participating in verification in the target verification table and the main key column can be determined.
When the data in the target verification table is verified, verification between the data to be verified and the reference data and comparison between the data to be verified or the reference data themselves may be included. Specifically, there may be duplicated data in the data to be checked or in the reference data, and at this time, it is necessary to select the target data to be checked or the target reference data according to the target checking content to check whether there is duplicated data. The target collation content has duplicate entry collation content, and the duplicate entry collation content has information of duplicate data to be collated, for example, a field and an attribute of collated data.
In the embodiment of the invention, original data to be checked and original reference data are obtained through the acquired task execution data; performing structure conversion on the original data to be checked and the original reference data to obtain target data to be checked and target reference data; acquiring a check rule corresponding to the target data to be checked from a preset configuration table, and checking the target data to be checked based on the check rule to obtain a check result; and when the verification result is that the target data to be verified conforms to the verification rule, selecting standard data from the target reference data and the target data to be verified for verification according to the target data to be verified and the product codes corresponding to the target data to be verified, and obtaining the verification result. By solving the prepositive work of policy contract grouping and making a corresponding basis for the contract grouping of the policy behind the project, abnormal data in the database can be quickly found, and the data checking efficiency is improved.
Referring to fig. 2, a second embodiment of the data checking method according to the embodiment of the present invention includes:
201. constructing a configuration table in a database based on the category of the data checking rule;
in this embodiment, the data check rule is a reference for data check, and taking the data check rule of the insurance company as an example, the check objects of the data check rule include, but are not limited to, premium number, number of copies, payment method, occupation, age, and the like; taking the insurance as an example, most products of the life insurance have the rule requirements of which the insurance cannot be lower than the insurance or the rule requirements of which the insurance is multiple. The embodiment of the invention classifies the data checking rules based on different checking objects, and then constructs the configuration tables corresponding to different classes in the database according to the classes of the underwriting rules. Each data checking rule category corresponds to at least one configuration table, so that the data checking rules are classified and stored through the configuration tables.
202. Storing product codes and data check rules of products into a configuration table in a classified manner according to the service configuration instruction;
in this embodiment, the configuration table is used to record the product code of the product and the data checking rule related to the product type. Here, one insurance product corresponds to one product code, which is identification information of the insurance product for distinguishing different products.
After the configuration table is constructed by the database, the data checking rules of the insurance products are classified and stored. Here, the data collation rules may be entered in a coded manner on the background server by the background developer, or may be entered in a selected manner on the front-end device by the front-end service person. For the existing data check rule, class division is completed by service personnel in the early stage, then the data check rule is handed to developers for code entry, and the background server acquires the data check rule of the insurance product based on the code information and stores the data check rule into a corresponding configuration table in a classified manner. For the newly added data check rule of the service requirement, a service person can input a service configuration instruction on the front-end equipment through an operation management interface, and the background server acquires the data check rule of the insurance product based on the service configuration instruction and stores the data check rule into a corresponding configuration table in a classified manner. And the management interface loads and displays the content according to the content recorded by the configuration table.
Compared with the prior art which adopts a hard code support mode, the logic realized by the embodiment of the invention is mainly realized in oracle and is realized by calling the corresponding sql statement by a java end. Because the partitioning logic of the metering unit is performed according to the full amount of data, the performance requirement on the data is higher. Later performance optimization in this respect can also be done.
203. When the content to be checked is checked, acquiring a product code of a purchased product;
in the embodiment, when data verification and verification are carried out on the insurance policy data of the customer, the security policy data acquisition method and the security policy data acquisition system acquire the security policy codes according to the product types of the products purchased by the customer. Optionally, for a new customer, the customer insurance policy data can be acquired according to the input operation of the service personnel, and for an old customer, the customer insurance policy data can be called out from the database according to the selection instruction input by the service personnel on the management page.
204. Acquiring task execution data, wherein the task execution information carries contents to be checked;
205. acquiring original data to be checked and original reference data according to task execution data;
206. performing structure conversion on the original data to be checked and the original reference data to obtain target data to be checked and target reference data;
207. acquiring a check rule corresponding to the target data to be checked from a preset configuration table, and checking the target data to be checked based on the check rule to obtain a check result;
208. and when the verification result is that the target data to be verified conforms to the verification rule, selecting standard data from the target reference data and the target data to be verified for verification according to the target data to be verified and the product codes corresponding to the target data to be verified, and obtaining the verification result.
The steps 204-208 in this embodiment are similar to the steps 101-105 in the first embodiment, and are not described herein again.
In the embodiment of the invention, the original data to be checked and the original reference data are obtained through the acquired task execution data; performing structure conversion on the original data to be checked and the original reference data to obtain target data to be checked and target reference data; verifying the target data to be verified based on a verification rule corresponding to the target data to be verified acquired from a preset configuration table to obtain a verification result; if yes, selecting standard data from the target reference data and the target data to be checked for checking according to the target data to be checked and the product codes corresponding to the target data to be checked, and obtaining a checking result. By solving the prepositive work of policy contract grouping and making a corresponding basis for the contract grouping of the policy behind the project, abnormal data can be quickly found, and the data checking efficiency is improved.
Referring to fig. 3, a third embodiment of the data checking method according to the embodiment of the present invention includes:
301. acquiring task execution data, wherein the task execution information carries contents to be checked;
302. judging whether a check data table exists or not;
in this embodiment, the check data table is a table for data check, and may specifically be a check table in a database. And after the task execution information is acquired, starting the checking task. Since there may be repeated periodic collation tasks in performing collation of the operation data of the data storage system of the insurance company and the collation data table is created based on the historical object collation contents for performing the historical collation tasks, it is possible to determine whether the collation data table is usable by judging whether the collation data table exists and whether the object collation contents are identical to the historical object collation contents.
303. Acquiring data fields and data attributes in data to be checked;
in this embodiment, the data field refers to a column of data in the target check table, and the data attribute refers to an attribute of the data of the column of data; the data attribute refers to attribute information of data, and is an attribute marked for a field in advance, that is, attribute information corresponding to the field is acquired according to the field corresponding to the target data to be checked, that is, attribute information of the target data to be checked. The attribute information includes the type of field, the complexity of the field, and the level of the field. For example, the types of the fields are classified into Chinese, English and numeric; the complexity of the field is divided into simple, general and complex; the field is classified into a primary level, a middle level and a high level. For example, the attribute information of the "date of birth" field is labeled as "numeric, simple, junior", and the attribute of the "identification card information" field is labeled as "numeric, normal, intermediate".
304. According to the data fields and the data attributes, defining fields corresponding to each line of data in the target check list and the attributes of each line of data, and correspondingly generating the target check list;
in this embodiment, the data field and the data attribute in the target verification content are acquired, a field corresponding to each line of data in the target verification table and an attribute of each line of data are defined according to the data field and the data attribute, and the target verification table is correspondingly generated.
It will be appreciated that the data fields and data attributes are the basis upon which the data is partitioned. The characteristics of a column of data can be determined according to the data field and the data attribute, and the characteristics of each column of data in the target verification table can be determined according to the data field and the data attribute in the target verification content, so that the data can be conveniently imported. Thereby, a target collation table corresponding to the target collation content can be created.
305. Acquiring original data to be checked and original reference data according to task execution data;
306. performing structure conversion on the original data to be checked and the original reference data to obtain target data to be checked and target reference data;
307. acquiring a check rule corresponding to the target data to be checked from a preset configuration table, and checking the target data to be checked based on the check rule to obtain a check result;
308. and when the verification result is that the target data to be verified conforms to the verification rule, selecting standard data from the target reference data and the target data to be verified for verification according to the target data to be verified and the product codes corresponding to the target data to be verified, and obtaining the verification result.
In the embodiment of the invention, the original data to be checked and the original reference data are obtained through the acquired task execution data; performing structure conversion on the original data to be checked and the original reference data to obtain target data to be checked and target reference data; verifying the target data to be verified based on a verification rule corresponding to the target data to be verified acquired from a preset configuration table to obtain a verification result; if yes, selecting standard data from the target reference data and the target data to be checked for checking according to the target data to be checked and the product codes corresponding to the target data to be checked, and obtaining a checking result. By solving the prepositive work of policy contract grouping and making a corresponding basis for the contract grouping of the policy behind the project, abnormal data can be quickly found, and the data checking efficiency is improved.
Referring to fig. 4, a fourth embodiment of the data checking method according to the embodiment of the present invention includes:
401. acquiring task execution data, wherein the task execution information carries contents to be checked;
402. acquiring original data to be checked and original reference data according to task execution data;
403. acquiring a data structure of each data in the original data to be checked, and performing data structure conversion on each data in the original data to be checked according to a preset first conversion relation to obtain the data to be checked;
in this embodiment, a data structure of each piece of data in the original data to be checked is obtained, and data structure conversion is performed on each piece of data in the original data to be checked according to a preset first conversion relationship, so as to obtain the data to be checked. The first conversion relation is a conversion relation according to which the original data to be checked is converted into the data to be checked. The first conversion relationship may be specifically determined according to a data structure of the original data to be checked and a data structure that the target check table can read.
404. Acquiring a data structure of each datum in the original datum data, and performing data structure conversion on each datum in the original datum data according to a preset second conversion relation to obtain datum data, wherein the data to be checked and the datum data have the same structure;
in this embodiment, data structure conversion (data structure conversion) is conversion from one form of data structure to another, so that two different data can be identified and compatible with each other. The second conversion relationship is a conversion relationship according to which the original reference data is converted into the reference data.
405. Acquiring field information corresponding to target data to be checked of the target data to be checked;
in this embodiment, after the server divides the document data into a plurality of types of target data to be checked according to fields, first, field information corresponding to each type of target data to be checked is acquired, where the field information is a field including a certain topic information. Since the target data to be collated is divided according to the field, the field information represents the characteristics of the type of the target data to be collated.
406. Determining a checking rule corresponding to the target data to be checked of the target data to be checked according to the field information;
in this embodiment, the corresponding collation rules are summarized in advance based on the characteristics of the same field information, and the collation rules are stored in the server in advance. After the server acquires the field information corresponding to each sub-data to be checked, the check rule corresponding to each sub-data to be checked is determined according to the field information. The automatic checking is carried out according to the checking rule, so that the automatic checking by the system is realized, and the checking efficiency is improved.
407. And when the verification result is that the target data to be verified conforms to the verification rule, selecting standard data from the target reference data and the target data to be verified for verification according to the target data to be verified and the product codes corresponding to the target data to be verified, and obtaining the verification result.
The steps 401-402 and 407 in the present embodiment are similar to the steps 101-102 and 105 in the first embodiment, and are not described herein again.
In the embodiment of the invention, the original data to be checked and the original reference data are obtained through the acquired task execution data; performing structure conversion on the original data to be checked and the original reference data to obtain target data to be checked and target reference data; verifying the target data to be verified based on a verification rule corresponding to the target data to be verified acquired from a preset configuration table to obtain a verification result; if yes, selecting standard data from the target reference data and the target data to be checked for checking according to the target data to be checked and the product codes corresponding to the target data to be checked, and obtaining a checking result. By solving the prepositive work of policy contract grouping and making a corresponding basis for the contract grouping of the policy behind the project, abnormal data can be quickly found, and the data checking efficiency is improved.
Referring to fig. 5, a fifth embodiment of the data checking method according to the present invention includes:
501. acquiring task execution data, wherein the task execution information carries contents to be checked;
502. acquiring original data to be checked and original reference data according to task execution data;
503. performing structure conversion on the original data to be checked and the original reference data to obtain target data to be checked and target reference data;
504. acquiring a check rule corresponding to the target data to be checked from a preset configuration table, and checking the target data to be checked based on the check rule to obtain a check result;
505. selecting target data to be checked from the data to be checked in the target checking table according to the repeated item checking content in the content to be checked;
in this embodiment, according to the content of the duplicate item check, first target data to be checked or first target reference data is selected from the target check table, and the number of duplicates is checked.
Specifically, the data to be checked which is the same as the data to be checked of the first target may be queried in other data to be checked according to the selected data to be checked of the first target, and the number of the data to be checked which is the same, that is, the first number of repetitions, may be recorded. It is understood that, among the data to be collated, it is possible that a part of the data is repeatedly recorded. Accordingly, a first preset number of data repeatedly recorded may be set in advance. If the first target data to be checked has the first preset number, comparing the first repeated number with the first preset number, and if the first repeated number is larger than the first preset number, indicating that abnormal repeated records exist in the first target data to be checked. The corresponding collation result is that the first target reference data is the reference data duplicate entry.
506. Inquiring data which is the same as the target data to be checked in the data to be checked of the target checking table to obtain the number of repetition of the target data to be checked;
in this embodiment, the data to be checked needs to be checked according to the reference data, and at this time, the target data to be checked and the target reference data need to be selected according to the target check content for checking. When the target data to be checked and the target reference data are selected from the target checking table for checking, according to the target checking content, the target data to be checked and the target reference data are selected from the target checking table for checking, and a checking result is obtained, wherein the checking result comprises the following steps: selecting second target data to be checked from the target checking table according to the comparison checking content in the target checking content; inquiring whether second target reference data corresponding to the second target data to be checked exists in the target checking table; if not, the checking result is that the second target data to be checked exists and the second target reference data does not exist; if yes, comparing whether the second target data to be checked is different from the second target reference data or not; if the difference exists, the checking result shows that the data has the difference.
507. When the number of the repetition is larger than the preset number, the checking result is that the target data to be checked is the repeated record item of the data to be checked;
in this embodiment, second target reference data is selected from the target verification table according to the comparison and verification content in the target verification content; inquiring whether second target data to be checked corresponding to the second target reference data exists in the target checking table; if not, the checking result is that the second target reference data exists and the second target data to be checked does not exist; if yes, comparing whether the second target reference data and the second target data to be checked are different; if the difference exists, the checking result shows that the data has the difference.
508. And determining a product code corresponding to the target data to be checked, selecting standard data from the target reference data and checking the standard data with the target data to be checked based on the target data to be checked and the product code to obtain a checking result.
In this embodiment, when the reference data is used to check the data to be checked, the data to be checked or the reference data may have data loss, omission and errors. The data to be checked of the second target can be selected as the basis for inquiring whether the data has the corresponding reference data of the second target. If so, comparing whether the second target data to be checked is different from the second target reference data, if so, comparing the data to be checked with the reference data, and if so, determining that the data to be checked is different from the reference data and the corresponding checking result is that the data is different. If the corresponding second target reference data is not available, the data to be checked is recorded incorrectly or the reference data is lost, the corresponding checking result is that the second target data to be checked exists and the second target reference data does not exist.
By using the second target data to be checked as the basis for inquiry, the problems of error record of the data to be checked, difference between the data to be checked and the reference data and loss of the reference data can be checked, and the problem of loss of the data to be checked cannot be checked. Correspondingly, the second target reference data can be used as a query basis to query the corresponding data to be checked, and the problems of data loss to be checked, recording errors and the like can be checked. In addition, the obtained checking result can be stored so as to be checked by a parameter maintenance personnel.
The steps 501-504 in this embodiment are similar to the steps 101-104 in the first embodiment, and are not described herein again.
In the embodiment of the invention, the original data to be checked and the original reference data are obtained through the acquired task execution data; performing structure conversion on the original data to be checked and the original reference data to obtain target data to be checked and target reference data; verifying the target data to be verified based on a verification rule corresponding to the target data to be verified acquired from a preset configuration table to obtain a verification result; and when the verification result is that the target data to be verified conforms to the verification rule, selecting standard data from the target reference data and the target data to be verified for verification according to the target data to be verified and the product codes corresponding to the target data to be verified, and obtaining the verification result. By solving the prepositive work of policy contract grouping and making a corresponding basis for the contract grouping of the policy behind the project, abnormal data can be quickly found, and the data checking efficiency is improved.
With reference to fig. 6, the data verification method in the embodiment of the present invention is described above, and a data verification apparatus in the embodiment of the present invention is described below, where a first embodiment of the data verification apparatus in the embodiment of the present invention includes:
a first obtaining module 601, configured to obtain task execution data, where the task execution information carries content to be checked;
a second obtaining module 602, configured to obtain original data to be checked and original reference data according to the task execution data;
a structure conversion module 603, configured to perform structure conversion on the original data to be checked and the original reference data to obtain target data to be checked and target reference data;
the verification module 604 is configured to obtain a verification rule corresponding to the target data to be verified from a preset configuration table, and verify the target data to be verified based on the verification rule to obtain a verification result;
and the checking module 605 is configured to, if the checking result is that the target data to be checked meets the checking rule, select standard data from the target reference data to be checked and check the target data to be checked according to the target data to be checked and the product code corresponding to the target data to be checked to obtain a checking result.
In the embodiment of the invention, the original data to be checked and the original reference data are obtained through the acquired task execution data; performing structure conversion on the original data to be checked and the original reference data to obtain target data to be checked and target reference data; verifying the target data to be verified based on a verification rule corresponding to the target data to be verified acquired from a preset configuration table to obtain a verification result; and when the verification result is that the target data to be verified conforms to the verification rule, selecting standard data from the target reference data and the target data to be verified for verification according to the target data to be verified and the product codes corresponding to the target data to be verified, and obtaining the verification result. By solving the prepositive work of policy contract grouping and making a corresponding basis for the contract grouping of the policy behind the project, abnormal data can be quickly found, and the data checking efficiency is improved.
Referring to fig. 7, a second embodiment of the data checking apparatus according to the embodiment of the present invention specifically includes:
a first obtaining module 601, configured to obtain task execution data, where the task execution information carries content to be checked;
a second obtaining module 602, configured to obtain original data to be checked and original reference data according to the task execution data;
a structure conversion module 603, configured to perform structure conversion on the original data to be checked and the original reference data to obtain target data to be checked and target reference data;
the verification module 604 is configured to obtain a verification rule corresponding to the target data to be verified from a preset configuration table, and verify the target data to be verified based on the verification rule to obtain a verification result;
and the checking module 605 is configured to, if the checking result is that the target data to be checked meets the checking rule, select standard data from the target reference data to be checked and check the target data to be checked according to the target data to be checked and the product code corresponding to the target data to be checked to obtain a checking result.
In this embodiment, the data collation apparatus further includes:
a building module 606, configured to build a configuration table in the database based on the category of the data checking rule;
the storage module 607 is used for storing the product codes and the data check rules of the products into the configuration table in a classified manner according to the service configuration instruction;
the third obtaining module 608 is configured to obtain a product code of the purchased product when the content to be checked is checked.
In this embodiment, the data collation apparatus further includes:
a judging module 609, configured to judge whether the check data table exists;
a generating module 610, configured to generate a target verification table according to the to-be-verified data if the verification data table does not exist.
In this embodiment, the generating module 610 is specifically configured to:
acquiring data fields and data attributes in the data to be checked;
and defining a field corresponding to each line of data in the target check list and the attribute of each line of data according to the data field and the data attribute, and correspondingly generating the target check list.
In this embodiment, the structure conversion module 603 is specifically configured to:
acquiring a data structure of each data in the original data to be checked, and performing data structure conversion on each data in the original data to be checked according to a preset first conversion relation to obtain target data to be checked;
and acquiring a data structure of each datum in the original datum data, and performing data structure conversion on each datum in the original datum data according to a preset second conversion relation to obtain target datum data, wherein the data to be checked and the datum data have the same structure.
In this embodiment, the verification module 604 is specifically configured to:
an acquisition unit 6041 configured to acquire field information corresponding to target to-be-collated data of the target to-be-collated data;
a determining unit 6042 configured to determine a collation rule corresponding to target to-be-collated data of the target to-be-collated data, from the field information.
In this embodiment, the checking module 605 is specifically configured to:
selecting target data to be checked from the data to be checked in the target checking table according to the repeated item checking content in the content to be checked;
inquiring data which is the same as the target data to be checked in the data to be checked of the target checking table to obtain the number of repetition of the target data to be checked;
if the number of the repeated data is larger than the preset number, the checking result is that the target data to be checked is a repeated record item of the data to be checked;
and when target reference data is selected from the target checking table for checking, selecting the target reference data from the target checking table for checking according to the content to be checked to obtain a checking result.
In the embodiment of the invention, the original data to be checked and the original reference data are obtained through the acquired task execution data; performing structure conversion on the original data to be checked and the original reference data to obtain target data to be checked and target reference data; verifying the target data to be verified based on a verification rule corresponding to the target data to be verified acquired from a preset configuration table to obtain a verification result; and when the verification result is that the target data to be verified conforms to the verification rule, selecting standard data from the target reference data and the target data to be verified for verification according to the target data to be verified and the product codes corresponding to the target data to be verified, and obtaining the verification result. By solving the prepositive work of policy contract grouping and making a corresponding basis for the contract grouping of the policy behind the project, abnormal data can be quickly found, and the data checking efficiency is improved.
Fig. 6 and 7 describe the data collating device in the embodiment of the present invention in detail from the perspective of the modular functional entity, and the data collating apparatus in the embodiment of the present invention is described in detail from the perspective of the hardware processing.
Fig. 8 is a schematic structural diagram of a data verification apparatus according to an embodiment of the present invention, where the data verification apparatus 800 may generate relatively large differences due to different configurations or performances, and may include one or more processors (CPUs) 810 (e.g., one or more processors) and a memory 820, and one or more storage media 830 (e.g., one or more mass storage devices) storing an application 833 or data 832. Memory 820 and storage medium 830 may be, among other things, transient or persistent storage. The program stored in storage medium 830 may include one or more modules (not shown), each of which may include a series of instructions operating on data collation apparatus 800. Still further, processor 810 may be configured to communicate with storage medium 830 and execute a series of instruction operations in storage medium 830 on data collating apparatus 800 to implement the steps of the data collating method provided by the various method embodiments described above.
The present invention also provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium, and may also be a volatile computer-readable storage medium, having stored therein instructions, which, when executed on a computer, cause the computer to perform the steps of the above-mentioned data collation method.
In the embodiment of the present application, the data processing implemented by the above method steps may be implemented based on an artificial intelligence technology, and the related data is acquired and processed based on the artificial intelligence technology. Among them, Artificial Intelligence (AI) is a theory, method, technique and application system that simulates, extends and expands human Intelligence using a digital computer or a machine controlled by a digital computer, senses the environment, acquires knowledge and uses the knowledge to obtain the best result.
The artificial intelligence infrastructure generally includes technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, operation/interaction systems, mechatronics, and the like. The artificial intelligence software technology mainly comprises a computer vision technology, a robot technology, a biological recognition technology, a voice processing technology, a natural language processing technology, machine learning/deep learning and the like.
It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a read-only memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A data collation method, characterized in that said data collation method comprises:
acquiring task execution data, wherein the task execution information carries contents to be checked;
acquiring original data to be checked and original reference data according to the task execution data;
performing structure conversion on the original data to be checked and the original reference data to obtain target data to be checked and target reference data, wherein the target data to be checked carries corresponding product codes;
acquiring a check rule corresponding to the target data to be checked from a preset configuration table, and checking the target data to be checked based on the check rule to obtain a check result;
and if the verification result is that the target data to be verified accords with the verification rule, selecting standard data from the target reference data and verifying the target data to be verified according to the target data to be verified and the product code corresponding to the target data to be verified to obtain a verification result.
2. The data collation method according to claim 1, wherein before said task execution data acquisition, further comprising:
constructing a configuration table in a database based on the category of the data checking rule;
storing product codes and data check rules of products into the configuration table in a classified manner according to a service configuration instruction;
and when the content to be checked is checked, acquiring the product code of the purchased product.
3. The data collation method according to claim 2, wherein after said acquisition task executes data, further comprising:
judging whether a check data table exists or not;
and if the check data table does not exist, generating a target check table according to the data to be checked.
4. The data collation method according to claim 3, wherein said generating a target collation table based on said data to be collated includes:
acquiring data fields and data attributes in the data to be checked;
and defining a field corresponding to each line of data in the target check list and the attribute of each line of data according to the data field and the data attribute, and correspondingly generating the target check list.
5. The data collation method according to claim 1, wherein said performing structure conversion on said original data to be collated and said original reference data to obtain target data to be collated and target reference data includes:
acquiring a data structure of each data in the original data to be checked, and performing data structure conversion on each data in the original data to be checked according to a preset first conversion relation to obtain target data to be checked;
and acquiring a data structure of each datum in the original datum data, and performing data structure conversion on each datum in the original datum data according to a preset second conversion relation to obtain target datum data, wherein the data to be checked and the datum data have the same structure.
6. The data verification method according to claim 1, wherein the obtaining of the verification rule corresponding to the target data to be verified from a preset configuration table comprises:
acquiring field information corresponding to target data to be checked of the target data to be checked;
and determining a checking rule corresponding to the target data to be checked of the target data to be checked according to the field information.
7. The data verification method according to claim 1, wherein the obtaining of the verification result by selecting standard data from the target reference data and verifying the standard data with the target data to be verified according to the target data to be verified and the product code corresponding to the target data to be verified comprises:
selecting target data to be checked from the data to be checked in the target checking table according to the repeated item checking content in the content to be checked;
inquiring data which is the same as the target data to be checked in the data to be checked of the target checking table to obtain the number of repetition of the target data to be checked;
if the number of the repeated data is larger than the preset number, the target data to be checked is a repeated record item of the data to be checked;
and determining a product code corresponding to the target data to be checked, and selecting standard data from the target reference data to be checked with the target data to be checked based on the target data to be checked and the product code to obtain a checking result.
8. A data collation apparatus, characterized in that said data collation apparatus comprises:
the system comprises a first acquisition module, a second acquisition module and a verification module, wherein the first acquisition module is used for acquiring task execution data, and the task execution information carries contents to be verified;
the second acquisition module is used for acquiring original data to be checked and original reference data according to the task execution data;
the structure conversion module is used for carrying out structure conversion on the original data to be checked and the original reference data to obtain target data to be checked and target reference data;
the verification module is used for acquiring a verification rule corresponding to the target data to be verified from a preset configuration table, and verifying the target data to be verified based on the verification rule to obtain a verification result;
and the checking module is used for selecting standard data from the target reference data to be checked and checking the target data to be checked according to the target data to be checked and the product codes corresponding to the target data to be checked if the checking result is that the target data to be checked accords with the checking rule, so as to obtain a checking result.
9. A data collation apparatus characterized in that said data collation apparatus comprises: a memory having instructions stored therein and at least one processor, the memory and the at least one processor interconnected by a line;
the at least one processor invoking the instructions in the memory to cause the data collation apparatus to perform the steps of the data collation method according to any one of claims 1 to 7.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the data collation method according to any one of claims 1 to 7.
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