Data verification method, computer program product and electronic equipment

Abstract

The invention discloses a data verification method, a computer program product and electronic equipment. The method comprises the steps that data are obtained, and the data comprise a plurality of data variables; based on the grouping type to which the data belongs and the life cycle to which the data belongs, a verification template matched with the data is searched, and the verification template comprises a verification strategy used for verifying the data variable; and verifying the data variables in the data by using the verification strategy in the verification template. The technical problem of poor data verification accuracy caused by a fixed and single data verification mode is solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of data processing, in particular to a data verification method, a computer program product and an electronic device. BACKGROUND

[0002] With the intensification of market competition and the diversification of customer demand, the channels for obtaining electric sales data are increasing, resulting in uneven quality of the obtained data.

[0003] In order to verify the validity of the data, most electric sales systems often use preset verification rules to verify when processing data. However, these rules are usually defined uniformly at the initial stage of system design, and rarely adjusted according to changes in external environment or business requirements.

[0004] In view of the above problems, no effective solution has been proposed so far. SUMMARY

[0005] The embodiments of the present application provide a data verification method, a computer program product and an electronic device to at least solve the technical problem of poor data verification accuracy caused by fixed and single data verification method.

[0006] According to an aspect of the embodiments of the present application, a data verification method is provided, comprising: obtaining data, wherein the data includes a plurality of data variables; based on the grouping type to which the data belongs and the life cycle in which the data is located, searching for a verification template matched with the data, wherein the verification template includes a verification strategy for verifying the data variables; and verifying the data variables in the data by using the verification strategy in the verification template.

[0007] According to another aspect of the embodiments of the present application, a data verification device is also provided, comprising: an obtaining unit configured to obtain data, wherein the data includes a plurality of data variables; a searching unit configured to search for a verification template matched with the data based on the grouping type to which the data belongs and the life cycle in which the data belongs, wherein the verification template includes a verification strategy for verifying the data variables; and a verification unit configured to verify the data variables in the data by using the verification strategy in the verification template.

[0008] According to still another aspect of the embodiments of the present application, a computer readable storage medium is also provided, which stores a computer program, wherein the computer program is configured to execute the above-mentioned data verification method when running.

[0009] According to still another aspect of the embodiments of the present application, a computer program product or computer program is provided, which comprises computer instructions stored in a computer readable storage medium. A processor of a computer device reads the computer instructions from the computer readable storage medium, and the processor executes the computer instructions to cause the computer device to perform the data verification method as above.

[0010] According to still another aspect of the embodiments of the present application, an electronic device is also provided, which comprises a memory and a processor, wherein the memory stores a computer program, and the processor is configured to execute the data verification method as above by using the computer program.

[0011] In the embodiments of the present application, the data is acquired, wherein the data comprises a plurality of data variables; a verification template matched with the data is found based on a group type to which the data belongs and a life cycle to which the data belongs, and the data variables in the data are verified by using a verification strategy included in the verification template for verifying the data variables, so that the purpose of determining the verification template matched with the data in combination with the group type to which the data belongs and the life cycle in which the data is located is achieved, the effect of verifying the data according to the real-time data state of the data is achieved, the effect of dynamically adjusting the data verification mode is achieved, the flexibility of the data verification mode and the accuracy of the data verification are improved, and the technical problem of poor data verification accuracy caused by the fixed and single data verification mode is solved. BRIEF DESCRIPTION OF DRAWINGS

[0012] The accompanying drawings, which are included to provide a further understanding of the present application, constitute a part of the present application and illustrate the illustrative embodiments of the present application and its description, and do not constitute improper limitations to the present application. In the drawings:

[0013] Figure 1 is a schematic diagram of an application environment of an optional data verification method according to the embodiments of the present application;

[0014] Figure 2 is a flowchart of an optional data verification method according to the embodiments of the present application;

[0015] Figure 3 is a schematic diagram of an optional data verification method according to the embodiments of the present application;

[0016] Figure 4 is a flowchart of another optional data verification method according to the embodiments of the present application;

[0017] Figure 5 is a flowchart of still another optional data verification method according to the embodiments of the present application;

[0018] Figure 6 is a flow chart of still another optional data verification model according to an embodiment of the application;

[0019] Figure 7 is a structural schematic diagram of an optional data verification model according to an embodiment of the application;

[0020] Figure 8 is a flow chart of still another optional data verification method according to an embodiment of the application;

[0021] Figure 9 is a structural schematic diagram of an optional data verification apparatus according to an embodiment of the application;

[0022] Figure 10 is a structural schematic diagram of an optional electronic device according to an embodiment of the application. DETAILED DESCRIPTION

[0023] In order to enable persons skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by persons skilled in the art without creative work should fall within the protection scope of the present application.

[0024] It should be noted that the terms "first", "second", and the like in the specification and claims of the present application and the above-described drawings are used to distinguish similar objects, and do not necessarily indicate a specific order or a chronological sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product, or device that includes a series of steps or units does not necessarily have to be limited to only those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to the process, method, product, or device.

[0025] At present, most of the electric dialing systems often only use preset verification rules to verify data when importing data when verifying data, but ignore the effectiveness changes of data in the whole life cycle. In addition, the preset verification rules are often defined in advance before data verification, and it is difficult to change with the changes of the state of data or the changes of actual application requirements. In other words, the data verification method provided by the related art has the problem that only fixed and unchanged data verification rules are used to verify data, resulting in poor flexibility of the data verification method, and further resulting in poor data verification accuracy.

[0026] To solve the above problems, the embodiment of the present application provides a data verification method. After obtaining data, a verification template matched with the data is found based on a group type to which the data belongs and a life cycle to which the data belongs. The data includes a plurality of data variables, and the verification template includes a verification strategy for verifying the data variables. The data variables in the data are verified by using the verification strategy in the verification template. Therefore, the verification template matched with the data can be determined in combination with the group type to which the data belongs and the life cycle to which the data belongs. Then, the effect of dynamically adjusting the data verification template based on the state information and the current state of the data is realized. The flexibility of the data verification method is improved. The data variables in the data can be verified in different life cycles to improve the accuracy of data verification. The problem that the data is verified by using fixed and unchanged data verification rules, thereby resulting in poor flexibility of the data verification method and poor accuracy of data verification is solved.

[0027] Optionally, in the embodiment, the data verification method can be applied in various data processing scenarios to verify the data of different users. For example, the data verification method can be applied in the field of financial services to verify the data such as user information and credit status of a user, so as to provide financial services for the user based on valid user credit information and reduce the risk of an enterprise when providing financial services. For another example, the data verification method can be applied in the scenario of social welfare services. Before a social welfare subsidy matching the economic status of a recipient is issued to the recipient, the information such as the economic status, residence address and health status of the recipient is verified to improve the fairness of social welfare issuance and avoid the problem of waste of social welfare resources caused by misissuance or wrong issuance of social welfare. For another example, the data verification method can also be applied in the scenario of user analysis in the field of e-commerce. When the transaction status and activity of a user are tracked and analyzed, the information such as the transaction status, transaction preference and latest transaction status of the user is verified to enable user transaction behavior analysis based on accurate and valid user transaction information and improve the accuracy of user transaction behavior analysis.

[0028] In the embodiment of the present application, the data verification method can be applied in the field of e-commerce, the field of social welfare services, the field of financial services, the field of user analysis, and the like. Figure 1In the data verification system in the hardware environment shown. The data verification system may include but is not limited to a terminal device 102, a data verification server 104, a first database 106, and a second database 108. As shown in step S102 and step S1042 to step S1046, the terminal device 102 sends data to the data verification server 104; the data verification server 104 obtains the data and searches for a verification template that matches the data based on the group type to which the data belongs and the life cycle to which the data belongs, and the verification template includes a verification strategy for verifying multiple data variables to be verified included in the data; then, the data verification server 104 verifies the data variables in the data using the verification strategy in the verification template. Further, as shown in step S106 to step S108, after the data is verified, the data verification server sends the data that has passed the verification to the first database 106, and sends the data that has failed the verification to the second database 108, so as to achieve data storage.

[0029] Optionally, in this embodiment, the terminal device may be a terminal device configured with a first client, which may include but is not limited to at least one of the following: a mobile phone (such as an Android phone, an iOS phone, etc.), a laptop computer, a tablet computer, a PDA, an MID (Mobile Internet Device), a PAD, a desktop computer, a smart TV, etc. The first client may be a video client, an instant messaging client, a browser client, an education client, etc. The server may be a single server, a server cluster consisting of multiple servers, or a cloud server. The above is merely an example and is not limited in this embodiment.

[0030] Alternatively, as an optional implementation, Figure 2 As shown, the above data verification method includes:

[0031] S202, acquiring data, wherein the data includes a plurality of data variables;

[0032] Optionally, in this embodiment, the above data may include, but is not limited to, user data of multiple users, where the users may come from the same application or from multiple different applications. The user data here may include, but is not limited to, different types of user information, and the type of user information here may be determined based on how the user information is generated, the source of the information, the purpose of the information, and whether the information changes over time. The above data variables may include, but are not limited to, the above different types of user information. For example, Figure 3As shown, according to the use of user information, the user information is divided into user basic information, user behavior analysis, user credit investigation, etc., and each type of user information includes multiple data variables, wherein the user basic information is used to indicate the personal state of the user, the user behavior analysis is used to indicate the application usage state of the user, and the user credit investigation is used to indicate the credit status of the user. As shown in Figure 3 As shown, the user basic information includes the gender, name, age, mobile phone number, etc. of the user, the user behavior analysis includes the latest login time, the latest withdrawal time, the latest transaction time, the latest transaction amount, etc. of the user, and the user credit investigation includes the credit inquiry times, overdue times, overdue days, user credit points, etc. of the user.

[0033] It should be noted that the user information (including but not limited to user equipment information, user personal information, etc.) and data (including but not limited to data for display or for analysis) involved in the present application are information and data authorized by the user or authorized by all parties, and the collection, storage, use, processing, transmission, provision, application, disclosure, etc. of the above user information and data comply with relevant laws, regulations and standards, necessary security measures are taken, do not violate public order and good customs, and provide corresponding operation portal for user to choose to agree or refuse automatic decision result; if the user chooses to refuse, enter the expert decision process.

[0034] S204, based on the grouping type to which the data belongs and the life cycle to which the data belongs, find a verification template matched with the data, wherein the verification template includes a verification strategy for verifying the data variables;

[0035] Optionally, in the present embodiment, the above grouping type can but is not limited to indicate the grouping type of the grouping obtained by grouping the above plurality of users, and the user grouping can but is based on the user credit status, user basic information, service usage status of the user, etc. The above life cycle can include but is not limited to a plurality of different data processing stages, such as the data receiving stage, the inventory stage, the outbound stage, etc. It should be noted that the plurality of data processing stages can but are not limited to have a chronological order, for example, the data receiving stage is located before the data outbound stage. The above verification template can but is not limited to have a mapping relationship with the grouping type and the life cycle. The above verification strategy can include but is not limited to a plurality of verification rules for verifying the data.

[0036] For example, the mapping relationship between the above verification template and the grouping type and the life cycle can be as shown in Table 1:

[0037]

[0038] Table 1

[0039] The interval step length can be used to indicate the frequency of inventory stage data verification, as shown in Table 1, the data belonging to group B is in the inventory stage, and the time interval between adjacent two data verifications is 20 minutes. Different group types can correspond to different verification templates in different stages of the life cycle, and the verification template includes a set of verification strategies for verifying data variables, each set of verification strategies can include one or more verification rule templates, and each verification rule template corresponds to a different priority.

[0040] S206, verifying the data variables in the data by using the verification strategies in the verification template.

[0041] Optionally, in the embodiment, the verification strategy can include but is not limited to a plurality of verification rule templates, and the verification rule template can include but is not limited to a plurality of verification rule groups, each verification rule group includes data verification rules corresponding to different data variables, and the priority matched with the data verification rules.

[0042] For example, the verification rule template can be as shown in Table 2:

[0043]

[0044] Table 2

[0045] The verification rule template can be but not limited to classified according to the type of data variable, each verification rule group is used to verify different data variables, and each verification rule group corresponds to a different priority. Optionally, the verification rule group can be as shown in Table 3:

[0046]

[0047] Table 3

[0048] As shown in Table 3, each verification rule group includes one or more verification rules for verifying the data variables matched with the verification rule group, and each verification rule corresponds to a different priority.

[0049] According to the embodiments provided in the present application, data is acquired, wherein the data includes a plurality of data variables; based on the group type to which the data belongs and the life cycle to which the data belongs, a verification template matched with the data is searched, and the data variables in the data are verified by using the verification strategy included in the verification template for verifying the data variables, so that the purpose of determining the verification template matched with the data in combination with the group type to which the data belongs and the life cycle in which the data is located is achieved, the effect of verifying the data variables in the data according to the real-time data state of the data is achieved, and the effect of dynamically adjusting the data verification mode is achieved, the flexibility of the data verification mode and the accuracy of the data verification are improved, and the technical problem of poor data verification accuracy caused by the fixed and single data verification mode is solved.

[0050] Optionally, as an optional implementation, the life cycle to which the data belongs includes at least two stages, different stages are configured with different verification strategy sets, and based on the group type to which the data belongs and the life cycle to which the data belongs, the verification template matched with the data is searched, including:

[0051] The life cycle in which the data is located when the data is acquired is determined.

[0052] Optionally, in the embodiment, the at least two stages can be, but are not limited to, different stages included in the life cycle and having a time sequence relationship, for example, the different stages can be the receiving stage and the inventory stage included in the life cycle, and the inventory stage is located after the receiving stage. Further, the verification strategy set can be, but is not limited to, the plurality of verification rule templates, and the verification strategy sets corresponding to different life cycles of the data of the same group type are different.

[0053] In the verification strategy set corresponding to the life cycle, the verification template matched with the group type is searched out.

[0054] Optionally, in the embodiment, the verification templates matched with different group types under the same life cycle can be the same or different. The verification template can be, but is not limited to, searched by using the life cycle and the group type according to the mapping relationship between each verification template and the group type and the life cycle.

[0055] According to the embodiments provided in the present application, after determining the life cycle in which the data is acquired, a verification template matching the group type to which the data belongs is found in the verification policy set corresponding to the life cycle, thereby achieving the effect of determining the verification template matching the group type under different life cycles, and further enabling the effectiveness of the data to be continuously verified and maintained in the entire data life cycle, thereby avoiding the problem that the data is verified only when the data is imported, the verification times are too few, the change of data effectiveness is difficult to be discovered in time, and the data verification accuracy is poor.

[0056] As an optional implementation, finding the verification template matching the group type in the verification policy set corresponding to the life cycle includes:

[0057] In the verification policy set, a candidate verification template set corresponding to the group type to which the data belongs is found, wherein the candidate verification template set contains candidate verification templates configured with different template verification priorities.

[0058] Optionally, in the present embodiment, the group type may, but is not limited to, be obtained by classifying different users according to their credit levels, and the credit level of each user can be determined by the credit score interval in which the credit score of each user is located. For example, level 1: credit score 0-100; level 2: credit score 101-500; level 3: credit score 501-1000; the credit score of user 1 is 300, and the credit level of user 1 can be obtained from the credit score interval in which the credit score of user 1 is located, i.e., the credit level of user 1 is level 2. The candidate verification template set may, but is not limited to, include the above-mentioned multiple verification rule templates, and the candidate verification template may, but is not limited to, correspond to each verification rule template. Optionally, in the present embodiment, the template verification priority may, but is not limited to, be used to indicate the execution sequence of each candidate verification template.

[0059] Further, the template verification priority may, but is not limited to, be used to indicate the severity of the failure of each candidate verification template. For example, as shown in Table 1 above, when the verification result indicates that the data fails to pass the verification, the verification process of the data is immediately terminated if the verification rule template has a priority of P0; when the verification result indicates that the data fails to pass the verification, the verification process of the data is immediately suspended and the data is repaired if the verification rule template has a priority of P1; when the verification result indicates that the data fails to pass the verification, the data verification process of the data continues, and the data variable that fails to pass the verification in the data is repaired after all data variables in the data complete the data verification if the verification rule template has a priority of P2.

[0060] According to the template check priority, the candidate check templates in the candidate check template set are determined as the check templates matched with the group type respectively.

[0061] It should be noted that the multiple candidate check templates corresponding to the same template check priority can be concurrently checked to improve the check efficiency of the data.

[0062] According to the embodiments provided in the present application, in the check policy set, the candidate check template set corresponding to the group type to which the data belongs is found out, and according to the template check priority configured by the different candidate check templates, the candidate check templates in the candidate check template set are determined as the check templates matched with the group type respectively, so that the data can be checked based on the candidate check templates in turn according to the different priorities, and the data check is optimized, and the unnecessary resource waste is reduced.

[0063] As an optional implementation, the checking of the data variable in the data by using the check policy in the check template includes:

[0064] In the case that the life cycle is the first acquisition stage, the first type of check policy is acquired from the check policy, and the data variable in the data is checked, wherein the first type of check policy is used to check whether the data variable in the data is a null variable.

[0065] Optionally, in the present embodiment, the above-mentioned first acquisition stage can but is not limited to refer to the receiving stage of the data. The above-mentioned first type of check policy can include but is not limited to the candidate check template with higher template check priority. The above-mentioned null variable can but is not limited to refer to the content of the data variable being empty.

[0066] In the case that the life cycle is not the first acquisition stage, the second type of check policy is acquired from the check policy, and the data variable in the data is checked, wherein the second type of check policy is used to check the change amount of the data variable contained in the data relative to the data variable corresponding to the first time period.

[0067] Optionally, in the embodiment, in the case that the life cycle is not the first acquisition stage, the life cycle can be the inventory stage, the outbound call stage, etc. The second type of verification strategy can include, but is not limited to, a candidate verification template with a lower template verification priority. Optionally, the first time period can be a time period before the current time period, and the first time period can be a time period adjacent to the current time period or a time period not adjacent to the current time period. Further, the second type of verification strategy can include a candidate verification template for verifying a change amount of a data variable in a same time period (such as a same day, a same month, a same quarter) in different years relative to a data variable corresponding to the first time period, a candidate verification template for verifying a change amount of a data variable in adjacent two time periods (such as adjacent two days, adjacent two months, adjacent two quarters) relative to a data variable corresponding to the first time period, etc.

[0068] It should be noted that in different life cycles, different types of data can be selectively verified. For example, for offline variables whose content does not change in real time with time, verification can be performed only in the first acquisition stage; for online variables whose content changes in real time with time, verification needs to be performed in both the first acquisition stage and the non-first acquisition stage. For example, for single-use data variables (such as verification codes), verification can be performed only in the first acquisition stage; for continuously used data variables, verification needs to be performed in both the first acquisition stage and the non-first acquisition stage.

[0069] Through the embodiments provided in the present application, in the case that the life cycle is the first acquisition stage, the first type of verification strategy is acquired from the verification strategy, and the data variable in the data is verified; in the case that the life cycle is not the first acquisition stage, the second type of verification strategy is acquired from the verification strategy, and the data variable in the data is verified, so that different types of verification strategies can be preferentially acquired in different life cycles, thereby achieving the effect of data verification in different stages with different verification strategies, and enabling the data verification resources to be used scientifically in different life cycles, so as to improve the data verification efficiency and reduce the running burden of the data verification server.

[0070] As an optional implementation, after the data variable in the data is verified by using the verification strategy in the verification template, the method further includes:

[0071] In the case that the result of the verification indicates that the data variable in the data passes the verification, the data is stored into the first database;

[0072] Optionally, in the embodiment, the first database can be used to store data, and the data variable in the data stored in the first database passes the check. In the case that the life cycle of the data is in the first acquisition stage, after the data is stored in the first database, the life cycle of the data can become the inventory stage. The data variable stored in the first database can be dynamically changed. For example, after the data passing the check is stored in the first database, the life cycle of the data becomes the inventory stage; in the inventory stage, when the data check result in the first database indicates that the data does not pass the check, the data not passing the check is moved to the second database.

[0073] In the case that the check result indicates that the data variable in the data does not pass the check, the data is stored in the second database; the number of check exceptions of the data is determined; in the case that the number of check exceptions reaches a target threshold, the data is returned to the upstream data allocation system, or the data is stored in the third database.

[0074] Optionally, the second database can be used to store data, and the data variable in the data stored in the second database does not pass the check. In the embodiment, after the data is stored in the second database, the data can be subjected to data repair processing. It should be noted that, in the case that the data repair in the second database is successful, the repaired data can be subjected to re-checking; in the case that the re-checking result indicates that the data passes the check, the data can be moved to the first database. The number of check exceptions can be the number of times that the re-checking result indicates that the data does not pass the data check after each repair process of the data in the second database is completed. The third database can be used to store the data whose number of check exceptions reaches the target threshold. The returning operation of the data can be to return the data whose number of check exceptions reaches the threshold to the client of the user.

[0075] As an optional solution, as shown in steps S402 to S406 in Figure 4 After the check of the data is completed, it is determined whether the current check result indicates that the data passes the check. In the case that the current check result indicates that the data passes the check, the data is stored in the first database, and the life cycle of the data becomes the inventory stage; in the inventory stage, the data in the first database is subjected to data check again after a period of time, and the data check result is updated; in the case that the updated check result indicates that the data does not pass the check, the data is moved to the second database.

[0076] Further, as shown in steps S402 to S406 in Figure 4In the case where the current check result indicates that the data does not pass the check, the data is stored in the second database, as shown in steps S408 to S412; after the data is repaired successfully, the repaired data is checked, and the number of check exceptions of the data that does not pass the check is determined, until the check result indicates that the data passes the check, or the number of check exceptions reaches a target threshold; in the case where the number of check exceptions reaches the target threshold, the data is processed, such as being returned to the upstream data allocation system, or being stored in the third database.

[0077] According to the embodiments provided in the present application, in the case where the check result indicates that the data variable in the data passes the check, the data is stored in the first database; in the case where the check result indicates that the data variable in the data does not pass the check, the data is stored in the second database, and the number of check exceptions of the data is determined; in the case where the number of check exceptions reaches the target threshold, the data is returned to the upstream data allocation system, or is stored in the third database, so that the data can be stored in the database matched with the check result according to the check result of the data variable in the data, the effective data and the invalid data are stored separately, the problem of mutual interference of the data stored in the same database is avoided, and the security of data storage is improved.

[0078] As an optional implementation, after the data is stored in the second database, the method further includes:

[0079] The data variable with the check exception in the data is repaired by calling the completion interface corresponding to the data variable with the check exception;

[0080] Optionally, in the present embodiment, the check exception can but is not limited to refer to the case where the data variable does not pass the check. The completion interface can but is not limited to be determined according to the data type of the data variable, and the completion interfaces of the data variables of the same type can be the same. The data type here can but is not limited to be classified based on the data source or generation mode of the data variable. The completion repair process can but is not limited to refer to the process that the data query request matched with the data variable with the check exception is sent to the completion interface corresponding to the data variable with the check exception, and after the data variable for completion repair fed back by the completion interface is received, the data variable with the check exception is replaced with the data variable fed back by the completion interface.

[0081] In the case where the repair by calling the completion interface is successful, the repaired data is migrated to the first database;

[0082] Optionally, in the present embodiment, the repair success case can but is not limited to refer to the case where the data variable after the completion repair passes the check after being checked again.

[0083] In the case that the call of the completion interface fails and the life cycle is the first acquisition stage, the data variable with the verification exception is repaired by completion when the life cycle reaches the in-library stage.

[0084] Optionally, in the embodiment, the repair failure can refer to, but is not limited to, the case that the completion interface is unavailable or the completion interface does not feed back the data variable for the completion repair, or the case that the data variable after the completion repair fails the verification again.

[0085] In the case that the call of the completion interface fails and the life cycle is the in-library stage in the first acquisition stage, the data variable with the verification exception is repaired by completion using the data variable corresponding to the second time period.

[0086] Optionally, in the embodiment, the second time period can be a time period before the current time period. The data variable corresponding to the second time period can refer to, but is not limited to, the data variable content when the data variable passes the verification in the verification process of the data variable in the second time period. The completion repair process can refer to, but is not limited to, the process of replacing the data variable with the repair failure with the data variable corresponding to the second time period.

[0087] As an optional solution, as shown in steps S502 to S512 in Figure 5 After the data is stored in the second database, the completion interface corresponding to the data variable with the verification exception in the data is called to repair the data variable with the verification exception in the second database by completion, and it is determined whether the data variable with the verification exception is repaired successfully. In the case that the call of the completion interface is successful, the repaired data is migrated to the first database; in the case that the call of the completion interface fails, it is determined whether the life cycle of the data variable is the first acquisition stage. In the case that the life cycle of the data variable is the first acquisition stage, the data variable with the verification exception is repaired by completion when the life cycle reaches the in-library stage; in the case that the life cycle is the in-library stage in the first acquisition stage, the data variable with the verification exception is repaired by completion using the data variable corresponding to the second time period, where the second time period is before the current time period.

[0088] By the embodiments provided in the present application, the data variable with the check exception in the calling data is repaired by calling the completion interface corresponding to the data variable with the check exception, and the repaired data is migrated to the first database when the repair by calling the completion interface is successful; when the repair by calling the completion interface fails and the life cycle is the first acquisition stage, the data variable with the check exception is repaired again when the life cycle reaches the in-database stage; when the repair by calling the completion interface fails and the life cycle is the in-database stage in the first acquisition stage, the data variable with the check exception is repaired by using the data variable of the second time period, so that the data variable with the check exception can be automatically repaired by using the completion interface or the data variable of the second time period, the data variable repair method is enriched, and thus the repair success rate of the data variable is improved, and the loss of the data variable is reduced.

[0089] As an optional implementation, when the life cycle is the out-call stage in the first acquisition stage, after the result of the check indicates that the data variable in the data does not pass the check, the method further includes:

[0090] Based on the exception type to which the data variable with the check exception in the data belongs, a repair strategy is determined.

[0091] Optionally, in the present embodiment, the exception type to which the data variable belongs and the repair strategy can have a mapping relationship with each other, and the repair strategy can be, but is not limited to, repairing the data variable with the check exception by using the data variable corresponding to the time period before the current time period, or manually repairing the data variable with the check exception.

[0092] The data variable with the check exception is repaired according to the repair strategy.

[0093] It should be noted that after the data repair is completed, when the data repair result indicates that the data repair fails, the repair strategy can be determined again, and the data variable is repaired again by using the newly determined repair strategy.

[0094] As an optional solution, as Figure 6In step S602 to step S614, after determining the abnormal type to which the data variable with the check abnormality in the data belongs, a first repair strategy is determined based on the abnormal type, and the data variable with the check abnormality is repaired according to the first repair strategy. After the repair process of the data variable is completed, it is determined whether the data variable is repaired successfully. In the case that the data variable is repaired successfully, the out-call processing can be performed on the repaired data variable. In the case that the data variable is not repaired successfully, a second repair strategy is determined, and the data variable with the check abnormality is repaired according to the second repair strategy. The second repair strategy is different from the first repair strategy, for example, the first repair strategy is to use the data variable corresponding to the period before the current period for data repair, and the second repair strategy can be to use the manual repair method to repair the data variable.

[0095] According to the embodiments provided in the present application, in the case that the life cycle is not the out-call stage in the first acquisition stage, after the result of the check indicates that the data variable in the data does not pass the check, the repair strategy is determined based on the abnormal type to which the data variable with the check abnormality in the data belongs, and the data variable with the check abnormality is repaired according to the repair strategy. Therefore, the repair strategy matched with the data variable can be determined according to the data type of the data variable, and the robustness of the data repair process is improved.

[0096] As an optional implementation, after the data is stored in the third database, the invalid data in the third database can also be subjected to feature learning to obtain a check strategy optimization parameter, and the check strategy in each check template is adjusted based on the check strategy optimization parameter to obtain an optimized check strategy, so as to achieve the effect of continuously updating and optimizing the check strategy in the check template based on the invalid data. This not only can reduce the generation of future invalid data, but also can improve the design quality and execution efficiency of the check rule.

[0097] Optionally, the feature learning process can be implemented through a pre-trained network model, and the check strategy optimization parameter can be used to optimize a plurality of check rules for data check. For example, the structure of the invalid data feature learning model used to implement the feature learning process can be as follows Figure 7As shown, the invalid data feature learning model can include an input layer 702, a feature extraction layer 704, a nonlinear conversion layer 706, and an output layer 708. The input layer 702 is configured to receive invalid data in the third database and perform format conversion on invalid data variables in the invalid data to unify the formats of the invalid data variables. The feature extraction layer 704 is configured to receive the format-converted invalid data variables and perform feature extraction on the format-converted invalid data variables to generate a feature vector indicating the correlation between the invalid data variables. The nonlinear conversion layer 706 is configured to perform nonlinear conversion on the feature vector to further learn the mutual relationship between the invalid data variables. The output layer 708 is configured to perform format conversion on the nonlinearly converted feature vector to determine the verification strategy optimization parameter.

[0098] Further, the verification strategy optimization parameter can be a parameter related to the data variable, such as an extreme value, an average value, an interval endpoint value, etc. of the data variable, and the adjustment process of the verification strategy can refer to a process of adjusting the verification rule configuration of each candidate verification template based on the verification strategy optimization parameter. For example, for a group of invalid data of the “age” variable, the verification strategy optimization parameter is the average value of the group of invalid data, which is 45 years old, and the interval endpoint value is 20 and 60, and the corresponding verification template is “age greater than 20 and less than 40 is normal”. Based on the verification strategy optimization parameter, the verification template can be adjusted to “age greater than 20 and less than 50 is normal”.

[0099] As an optional implementation, after the data variables in the data are verified by the verification strategy in the verification template, the verification strategy in the verification template can be adjusted according to the variable verification result of the data variables in the data, such as adjusting the strategy verification priority of the verification strategy in the verification template, adjusting the verification frequency of the verification strategy in the verification template, or adjusting the verification execution ratio of the verification strategy in the verification template. This not only enables dynamic adjustment of the allocation of data verification resources based on real-time verification results of the data variables, but also avoids unnecessary repeated data verification and waste of data verification resources.

[0100] Optionally, the variable verification result of the data variable can be used to indicate whether the data variable passes the verification, and the strategy verification priority can refer to the priority of the candidate verification template included in the verification strategy. The verification frequency can refer to the length of the time interval between adjacent two data verifications. The verification execution ratio can refer to the ratio of the data being verified to the total data when the data is verified.

[0101] Optionally, the adjustment process of the above-mentioned verification strategy can refer to the data verification pass rate determined according to the variable verification result of the data to adjust the policy verification priority, verification frequency, etc. of the verification strategy. For example, for data variables with a higher data verification pass rate, the verification frequency can be reduced, and for data variables with a lower data verification pass rate, the verification frequency can be increased. For example, for data variables with a higher data verification pass rate, the verification execution ratio can be reduced. For example, for data variables with a higher data verification pass rate, the policy verification priority of the verification strategy for verifying whether the data variable is a null variable can be reduced, and the policy verification priority of the verification strategy for verifying the change amount of the data variable contained in the data relative to the data variable corresponding to the first time period can be increased, where the first time period is located before the current time period.

[0102] As an optional implementation, in the process of verifying the data variables in the data by using the verification strategies in the verification template, the verification process can be suspended in time in the case of an abnormality in the verification process, so as to avoid waste of verification resources and maintain stable data verification process. For example, in the case where the cumulative number of abnormality occurrences after the execution of the target verification strategy in the verification strategy reaches a preset threshold, the other verification strategies in the verification strategy that have not been executed can be suspended.

[0103] Optionally, the above-mentioned target verification strategy can be a candidate verification template included in the above-mentioned verification strategy, and the above-mentioned case of abnormality after verification can refer to the case where the verification failure rate of the data variables in the data reaches a preset threshold when the data is verified by using the above-mentioned target verification strategy. The preset threshold corresponding to different verification strategies can be different. It should be noted that the data verification processes of data belonging to different grouping types are independent of each other, and the suspension operations of the verification strategies in the data verification processes of data of different grouping types do not affect each other. For example, in the data verification process of grouping A, when the suspension condition of the verification strategy occurs, the data verification process of grouping B does not need to be suspended along with the suspension of the verification strategy corresponding to grouping A.

[0104] As an optional implementation, after the data variables in the data are verified by using the verification strategies in the verification template, the following operations are further included:

[0105] According to the variable information and real-time storage state of the data variables in the data, at least one of the following adjustment operations is performed on the candidate verification template set corresponding to the grouping type:

[0106] The template verification priority of the candidate verification template included in the candidate verification template set is adjusted;

[0107] The verification interval step of the candidate verification template included in the candidate verification template set is adjusted;

[0108] The template checking execution proportion of the candidate checking template included in the candidate checking template set is adjusted.

[0109] Optionally, in the embodiment, the template checking priority can be, but is not limited to, the execution priority of each candidate checking template. The checking interval step length can be, but is not limited to, the length of the time interval between adjacent two data checks. The template checking execution proportion can be, but is not limited to, the proportion of the data variable to be checked in all data variables when checking the data variable in the data.

[0110] As an optional solution, the variable information of the data variable in the data can include, but is not limited to, the user attribute information of the user to which the data variable belongs, the attribute information of the data variable, etc. For example, the variable information of the data variable can be the credit level of the customer to which the data variable in the data belongs, the checking pass rate of the data variable in the data. The real-time storage state can be, but is not limited to, used to indicate the storage duration of the data variable in the first database or the second database.

[0111] Optionally, the adjustment operation can be, but is not limited to, implemented in combination with the user attribute information of the user to which the data variable belongs, the attribute information of the data variable, and the storage duration of the data variable in the first database or the second database. For example, the checking interval step length can be reduced for the data variable in the first database with a high checking pass rate. The checking interval step length can be increased for the data variable stored in the second database for a long time. For example, the template checking execution proportion of the data variable can be reduced as the storage duration of the data variable in the first database increases, for the data variable in the first database with a high credit level of the customer to which the data variable belongs. For example, the template checking priority of the candidate checking template can be dynamically adjusted as the storage duration of the data variable in the first database increases, for the data variable in the first database with a high credit level of the customer to which the data variable belongs. For example, the template checking priority of the candidate checking template for checking whether the data variable in the data is a null variable can be reduced, and the template checking priority of the candidate checking template for checking the change amount of the data variable included in the data relative to the data variable corresponding to the first time period before the current time period can be increased.

[0112] According to the embodiments provided in the present application, the template checking priority or the checking interval step length, or the template checking execution proportion of the candidate checking template included in the candidate checking template set corresponding to the grouping type is adjusted according to the variable information of the data variable in the data and the real-time storage state, so that the checking resources of different candidate checking templates can be dynamically allocated in combination with the attribute information and the real-time state of the data, and the data checking process can meet the actual data checking demand and the data checking server performance, thereby improving the robustness of the data checking.

[0113] As an optional implementation, the reference data can also be acquired before the data is acquired, and a reference grouping type to which the reference data belongs and a reference life cycle in which the reference data is located are determined, so as to generate a plurality of verification templates based on the reference grouping type and the reference life cycle, and configure initial verification control information for the plurality of verification templates. The initial verification control information here can include template verification priority, verification interval step, template verification execution ratio, verification exception repair strategy, etc. This can pre-configure the verification templates corresponding to different grouping types and different life cycles before data verification, so as to improve the data verification efficiency.

[0114] Optionally, the reference data can include a plurality of reference data variables, and the reference data variables here can be user data corresponding to different applications or a plurality of users of the same application. The user data can include but is not limited to different types of user information, and the types of the user information can be determined according to the generation mode of the user information, the information source, the information use, and whether the information will change over time.

[0115] Optionally, the reference grouping type can be a grouping type of a grouping of users to which each data variable in the reference data belongs, and the grouping type here can be but not limited to used to indicate user reputation, user basic information, service usage of the user, etc. The life cycle can be but not limited to different stages corresponding to the reference data, such as a receiving stage, a stock stage, and an outbound stage of the reference data.

[0116] Optionally, the generation process can be but not limited to a process of configuring a corresponding verification template for the reference grouping type and the reference life cycle. As shown in Table 1 above, the generation process can be a process of establishing a mapping relationship between the verification template and the grouping type and the life cycle. The template verification priority can be a priority corresponding to different candidate verification templates in the verification template. The template verification execution ratio can be a ratio of data variables to be verified selected from the reference data variables to all data variables in the reference data. The verification exception repair strategy can be a strategy of adjusting each candidate verification template in the verification strategy in the case of an exception in the execution of each verification strategy, or a strategy of repairing the data variables in the case of a verification result indicating that the data variables to be verified in the reference data variables do not pass the verification.

[0117] Specifically, the user data verification process to which the embodiments of the present application are applied in actual applications will be described in combination with Figure 8

[0118] ​S802, configure a group type and a check template; obtain user data corresponding to a plurality of users using different applications to obtain reference data; determine a reference group type to which the reference data belongs and a reference life cycle in which the reference data is located, configure a corresponding check template for the reference group type and the reference life cycle, and configure initial check control information for each check template respectively.

[0119] S804, obtain data; the data includes user data corresponding to a plurality of users using different applications to be checked, and the user data of each user includes different types of data variables.

[0120] S806, according to a group type to which the data belongs and a life cycle to which the data belongs, find a check template matched with the data, and check the data variables in the data by using the check template; determine the life cycle in which the data is located, and find a candidate check template set corresponding to the group type in a check strategy set corresponding to the life cycle; according to a template check priority corresponding to each candidate check template, determine the candidate check templates included in the candidate check template set as check templates matched with the group type respectively, and check the data variables in the data by using the check templates.

[0121] S808, judge whether the result of the check indicates that the data variables in the data pass the check; in a case where the data variables in the data do not pass the check, perform steps S810 to S814; in a case where the data variables in the data pass the check, perform steps S816 to S820.

[0122] S810, store the data into a second database; after the data is stored into the second database, call a completion interface corresponding to the data variables to complete and repair the data variables.

[0123] S812, determine a check exception number of the data; after the completion and repair of the data variables are completed, check the repaired data variables, and update the number of times that the data does not pass the check according to a check result.

[0124] S814, in a case where the check exception number reaches a target threshold value, return the data to an upstream data allocation system, or store the data into a third database.

[0125] S816, store the data into a first database.

[0126] S818, in the case of receiving the data acquisition request, checking the data variable in the data matched by the data acquisition request; determining that the life cycle where the data is located is the outbound stage, and determining a candidate check template set corresponding to the group type to which the group where the data is located belongs in the check policy set corresponding to the outbound stage, determining each candidate check template included in the candidate check template set as a check template matched with the group type, and checking the data variable in the data by using the check template.

[0127] S820, in the case of the check result indicating that the data variable in the data matched by the data acquisition request passes the check, sending the data matched by the data acquisition request.

[0128] It should be noted that, for the foregoing method embodiments, in order to simply describe, they are all described as a series of action combinations, but those skilled in the art should know that the present application is not limited to the action sequence described, because according to the present application, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should know that the embodiments described in the specification all belong to preferred embodiments, and the actions and modules involved are not necessarily necessary for the present application.

[0129] According to another aspect of the embodiments of the present application, a data checking device for implementing the above-mentioned data checking method is also provided. As shown in the figure, the device comprises: Figure 9

[0130] The first acquisition unit 902 is configured to acquire data, wherein the data comprises a plurality of data variables;

[0131] The finding unit 904 is configured to find a check template matched with the data based on the group type to which the data belongs and the life cycle where the data is located, wherein the check template comprises a check policy for checking the data variable;

[0132] The checking unit 906 is configured to check the data variable in the data by using the check policy in the check template.

[0133] Optionally, the embodiments in the present solution can refer to the method embodiments, but are not limited thereto, which will not be described here.

[0134] As an optional solution, the finding unit 904 further comprises:

[0135] The determining module is configured to determine the life cycle where the data is located when the data is acquired;

[0136] The finding module is configured to find the check template matched with the group type in the check policy set corresponding to the life cycle.

[0137] ​Optionally, the embodiments in the present solution can be, but are not limited to, reference method embodiments, which will not be repeated here.

[0138] As an optional solution, the above finding module comprises:

[0139] The finding sub-module is configured to find, in the verification policy set, a candidate verification template set corresponding to the group type to which the data belongs, wherein the candidate verification template set contains candidate verification templates configured with different template verification priorities.

[0140] The determining sub-module is configured to determine the candidate verification templates contained in the candidate verification template set as verification templates matched with the group type according to the template verification priorities.

[0141] Optionally, the embodiments in the present solution can be, but are not limited to, reference method embodiments, which will not be repeated here.

[0142] As an optional solution, the above verification unit 906 comprises:

[0143] The first processing module is configured to acquire a first type of verification policy from the verification policy in a case where the life cycle is a first acquisition stage, and perform verification on the data variable in the data, wherein the first type of verification policy is used to verify whether the data variable in the data is a null variable.

[0144] The second processing module is configured to acquire a second type of verification policy from the verification policy in a case where the life cycle is not the first acquisition stage, and perform verification on the data variable in the data, wherein the second type of verification policy is used to verify a change amount of the data variable in the data relative to a data variable corresponding to a first time period.

[0145] Optionally, the embodiments in the present solution can be, but are not limited to, reference method embodiments, which will not be repeated here.

[0146] As an optional solution, the above device further comprises:

[0147] The storage unit is configured to store the data into the first database in a case where the result of the verification indicates that the data variable in the data passes the verification.

[0148] The first processing unit is configured to store the data into the second database in a case where the result of the verification indicates that the data variable in the data does not pass the verification, determine a verification exception number of the data, and return the data to an upstream data allocation system or store the data into a third database in a case where the verification exception number reaches a target threshold.

[0149] Optionally, the embodiments in the present solution can be, but are not limited to, reference method embodiments, which will not be repeated here.

[0150] As an optional solution, the first processing unit comprises:

[0151] The first repair module is configured to call a completion interface corresponding to the data variable with the verification exception in the data, and complete and repair the data variable with the verification exception in the second database.

[0152] The migration module is configured to, in a case where the completion interface is successfully repaired, migrate the repaired data to the first database.

[0153] The second repair module is configured to, in a case where the completion interface is unsuccessfully repaired and the life cycle is the first acquisition stage, complete and repair the data variable with the verification exception when reaching the in-database stage.

[0154] The third repair module is configured to, in a case where the completion interface is unsuccessfully repaired and the life cycle is the in-database stage which is not the first acquisition stage, complete and repair the data variable with the verification exception by using the data variable corresponding to the second time period.

[0155] Optionally, the embodiments in the present solution can refer to the method embodiments but are not limited thereto, and details are not repeated here.

[0156] As an optional solution, the device further comprises:

[0157] The determination unit is configured to determine a repair strategy based on an exception type to which the data variable with the verification exception belongs.

[0158] The repair unit is configured to repair the data variable with the verification exception according to the repair strategy.

[0159] Optionally, the embodiments in the present solution can refer to the method embodiments but are not limited thereto, and details are not repeated here.

[0160] As an optional solution, the processing unit further comprises:

[0161] The learning module is configured to perform feature learning on the invalid data in the third database to obtain a verification strategy optimization parameter.

[0162] The adjustment module is configured to adjust the verification strategy in each verification template based on the verification strategy optimization parameter to obtain an optimized verification strategy.

[0163] Optionally, the embodiments in the present solution can refer to the method embodiments but are not limited thereto, and details are not repeated here.

[0164] As an optional solution, the device further comprises:

[0165] The first adjusting unit is configured to perform at least one of the following adjustment operations according to the variable check result of the data variable in the data: adjusting a strategy check priority of the check strategy in the check template; adjusting a check frequency of the check strategy in the check template; and adjusting a check execution ratio of the check strategy in the check template.

[0166] Optionally, the embodiments in the present solution can be, but are not limited to, reference method embodiments, which will not be described here.

[0167] As an optional solution, the check unit 906 further includes:

[0168] The suspension module is configured to suspend other check strategies in the check strategies which have not been executed for checking in a case where the cumulative number of exceptions after the target check strategy in the check strategies is executed for checking reaches a preset threshold.

[0169] Optionally, the embodiments in the present solution can be, but are not limited to, reference method embodiments, which will not be described here.

[0170] As an optional solution, the device further includes:

[0171] The second adjusting unit is configured to perform at least one of the following adjustment operations on the candidate check template set corresponding to the grouping type according to the variable information and the real-time storage state of the data variable in the data: adjusting a template check priority of a candidate check template included in the candidate check template set; adjusting a check interval step of the candidate check template included in the candidate check template set; and adjusting a template check execution ratio of the candidate check template included in the candidate check template set.

[0172] Optionally, the embodiments in the present solution can be, but are not limited to, reference method embodiments, which will not be described here.

[0173] As an optional solution, the device further includes:

[0174] The second obtaining unit is configured to obtain reference data.

[0175] The second determining unit is configured to determine a reference grouping type to which the reference data belongs and a reference life cycle in which the reference data is located.

[0176] The second processing unit is configured to generate a plurality of check templates based on the reference grouping type and the reference life cycle, and configure initial check control information for the plurality of check templates respectively, wherein the initial check control information includes: a template check priority, a check interval step, a template check execution ratio, and a check exception repair strategy.

[0177] Optionally, the embodiments in the present solution can be, but are not limited to, reference method embodiments, which will not be described here.

[0178] According to an aspect of the present application, a computer readable storage medium is provided, from which a processor of a computer device reads computer instructions, and the processor executes the computer instructions, so that the computer device executes the method provided in any of the optional implementation manners of the voice recognition method.

[0179] Optionally, in the embodiment, the computer readable storage medium can be configured to store a computer program for executing the following steps:

[0180] S1, obtaining data, wherein the data comprises a plurality of data variables;

[0181] S2, searching for a verification template matched with the data based on a group type to which the data belongs and a life cycle to which the data belongs, wherein the verification template comprises a verification strategy for verifying the data variables;

[0182] S3, verifying the data variables in the data by using the verification strategy in the verification template.

[0183] According to an aspect of the present application, a computer program product is provided, which comprises computer program / instructions containing program codes for executing the data verification method.

[0184] According to an aspect of the present application, a computer program product is provided, which comprises computer program / instructions containing program codes for executing the data verification method.

[0185] According to another aspect of the embodiments of the present application, an electronic device for implementing the data verification method is also provided, which can be Figure 1 the terminal device or the server as shown. The present embodiment takes the electronic device as the server as an example for illustration. As shown in the figure, the electronic device comprises a memory 1002 and a processor 1004, the memory 1002 stores a computer program, and the processor 1004 is configured to execute the steps in any of the method embodiments by using the computer program. Figure 10

[0186] Optionally, in the embodiment, the electronic device can be located in at least one of the network devices in the computer network.

[0187] Optionally, in the embodiment, the processor can be configured to execute the following steps by using the computer program:

[0188] S1, obtaining data, wherein the data comprises a plurality of data variables;

[0189] ​S2, based on the group type to which the data belongs and the life cycle to which the data belongs, searching for a check template matched with the data, wherein the check template comprises a check strategy for checking the data variable;

[0190] S3, checking the data variable in the data by using the check strategy in the check template.

[0191] Optionally, those skilled in the art can understand that, Figure 10 The structure shown is only schematic, and the electronic device can also be a smart phone (such as an Android phone, an iOS phone, etc.), a tablet computer, a palm computer, a Mobile Internet Device (MID), a PAD, or the like. Figure 10 The structure of the electronic device is not limited above. For example, the electronic device can further include more or less components (such as a network interface, etc.) than those shown in the figure, or have a different configuration from that shown in the figure. Figure 10 Figure 10

[0192] The memory 1002 can be used to store software programs and modules, such as program instructions / modules corresponding to the data check method and device in the embodiments of the present application. The processor 1004 executes various functions and data processing by running the software programs and modules stored in the memory 1002, that is, implements the data check method described above. The memory 1002 can include a high-speed random access memory, and can also include a non-volatile memory, such as one or more magnetic storage devices, flash memories, or other non-volatile solid-state memories. In some examples, the memory 1002 can further include a memory remotely arranged with respect to the processor 1004, which can be connected to the terminal through a network. Examples of the network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof. Specifically, the memory 1002 can be used to store the data variable, but is not limited to this. As an example, as shown in the figure, the memory 1002 can include but is not limited to the first acquisition unit 902, the searching unit 904, and the checking unit 906 in the data check device described above. In addition, the memory 1002 can also include but is not limited to other module units in the data check device described above, which will not be described in detail in this example. Figure 10

[0193] ​​​Optionally, the transmission device 1006 is configured to receive or send data via a network. Examples of the network can include a wired network and a wireless network. In an example, the transmission device 1006 includes a network interface controller (NIC) which can be connected to other network devices and routers through a network cable so as to communicate with the Internet or a local area network. In an example, the transmission device 1006 is a radio frequency (RF) module which is configured to communicate with the Internet in a wireless manner.

[0194] In addition, the electronic device further includes a display 1008 configured to display the checking result of the data variable in the data, and a connection bus 1010 configured to connect various module components in the electronic device.

[0195] In other embodiments, the terminal device or the server can be a node in a distributed system, and the distributed system can be a blockchain system. The blockchain system can be a distributed system formed by the plurality of nodes communicating with each other via a network. The nodes can form a point-to-point network, and any computing device such as a server or a terminal can become a node in the blockchain system by joining the point-to-point network.

[0196] Optionally, in the embodiments of the present application, the term "module" or "unit" refers to a computer program or a part of a computer program with a predetermined function, and works together with other related parts to achieve a predetermined target, and can be implemented entirely or partially by using software, hardware (such as a processing circuit or a memory) or a combination thereof. Similarly, one processor (or multiple processors or memories) can be used to implement one or more modules or units. In addition, each module or unit can be a part of an integral module or unit that includes the functions of the module or unit.

[0197] Optionally, in the embodiments, a person of ordinary skill in the art can understand that all or part of the steps of the various methods in the above embodiments can be completed by instructing the hardware related to the terminal device by a program, and the program can be stored in a computer readable storage medium, and the storage medium can include a flash disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0198] The integrated units in the above embodiments, if implemented in the form of software function units and sold or used as independent products, can be stored in the above computer-readable storage medium. Based on such understanding, the technical solutions of the present application, essentially or in other words, the part that contributes to the prior art or the whole or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium, and includes a number of instructions to make one or more computer devices (which can be personal computers, servers or network devices, etc.) execute all or part of the steps of the methods described in the various embodiments of the present application.

[0199] In the above embodiments of the present application, the description of each embodiment has its own focus, and the parts not described in detail in a certain embodiment can be referred to the relevant description of other embodiments.

[0200] In the several embodiments provided by the present application, it should be understood that the disclosed client can be implemented in other ways. Of course, the above device embodiment is only illustrative, and for example, the division of the units is only a logical function division, and there can be another division manner in actual implementation, for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units shown or discussed can be indirect coupling or communication connection through some interface, unit or module, and can be electrical or other forms.

[0201] The units described as separate components can or can not be physically separate, and the components shown as units can or can not be physical units, i.e., they can be located in one place or distributed on multiple network units. Some or all of the units can be selected to achieve the purpose of the embodiment according to actual needs.

[0202] In addition, each functional unit in each embodiment of the present application can be integrated in one processing unit, or each unit can exist physically, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of a software function unit.

[0203] The above is only the preferred embodiment of the present application, and it should be pointed out that for those skilled in the art, without departing from the principles of the present application, a number of improvements and refinements can be made, which should be considered as the protection scope of the present application.

Claims

1. A data checking method, characterized by, The method comprises: acquiring data, wherein the data comprises a plurality of data variables; based on a group type to which the data belongs and a life cycle to which the data belongs, searching for a verification template matched with the data, wherein the verification template comprises a verification strategy for verifying the data variables; verifying the data variables in the data by using the verification strategy in the verification template.

2. The method of claim 1, wherein, The life cycle to which the data belongs comprises at least two stages, and different stages are configured with different sets of verification strategies. The searching for the verification template matched with the data based on the group type to which the data belongs and the life cycle to which the data belongs comprises: determining the life cycle to which the data belongs when the data is acquired; in the set of verification strategies corresponding to the life cycle, searching for the verification template matched with the group type.

3. The method of claim 2, wherein, The searching for the verification template matched with the group type in the set of verification strategies corresponding to the life cycle comprises: in the set of verification strategies, searching for a set of candidate verification templates corresponding to the group type to which the data belongs, wherein the set of candidate verification templates comprises candidate verification templates configured with different template verification priorities; according to the template verification priorities, determining the candidate verification templates in the set of candidate verification templates as the verification templates matched with the group type, respectively.

4. The method of claim 2, wherein, The verifying the data variables in the data by using the verification strategy in the verification template comprises: in the case that the life cycle is a first acquisition stage, acquiring a first type of verification strategy from the verification strategy to verify the data variables in the data, wherein the first type of verification strategy is used to verify whether the data variables in the data are null variables; in the case that the life cycle is not the first acquisition stage, acquiring a second type of verification strategy from the verification strategy to verify the data variables in the data, wherein the second type of verification strategy is used to verify a change amount of the data variables corresponding to a first time period in the data variables contained in the data.

5. The method of claim 1, wherein, After the verifying the data variables in the data by using the verification strategy in the verification template, the method further comprises: in the case that a result of the verification indicates that the data variables in the data pass the verification, storing the data into a first database; in the case that the result of the verification indicates that the data variables in the data do not pass the verification, storing the data into a second database; determining a verification exception number of the data; in the case that the verification exception number reaches a target threshold, returning the data to an upstream data allocation system or storing the data into a third database.

6. The method of claim 5, wherein, After the storing the data into the second database, the method further comprises: calling a completion interface corresponding to the data variable with the verification exception in the data to complete and repair the data variable with the verification exception in the second database; In a case that the repair succeeds after the call of the repair interface, the repaired data is migrated to the first database; In a case that the repair fails after the call of the repair interface and the life cycle is the first acquisition stage, the repair of the data variable with the check exception is performed after the library stage is reached; In a case that the repair fails after the call of the repair interface and the life cycle is the library stage in the non-first acquisition stage, the repair of the data variable with the check exception is performed by using the data variable corresponding to the second time period.

7. The method of claim 5, wherein, In a case that the life cycle is the outbound stage in the non-first acquisition stage, after the result of the check indicates that the data variable in the data does not pass the check, the method further comprises: determining a repair strategy based on the type of the data variable with the check exception in the data; repairing the data variable with the check exception according to the repair strategy.

8. The method according to any one of claims 1 to 7, characterized in that, After the data variable in the data is checked according to the check strategy in the check template, the method further comprises: According to the variable information and the real-time storage state of the data variable in the data, at least one of the following adjustment operations is performed on the candidate check template set corresponding to the grouping type: adjusting the template check priority of the candidate check template included in the candidate check template set; adjusting the check interval step of the candidate check template included in the candidate check template set; adjusting the template check execution proportion of the candidate check template included in the candidate check template set.

9. A computer program product comprising computer programs / instructions, characterized in that, The computer program / instruction is executed by the processor to realize the steps of the method in any one of claims 1 to 8.

10. An electronic device comprising a memory and a processor, characterized in that, The memory stores a computer program, and the processor is configured to execute the method in any one of claims 1 to 8 by using the computer program.

Images