CN113886242A

CN113886242A - Data processing method, device, terminal and storage medium

Info

Publication number: CN113886242A
Application number: CN202111152915.0A
Authority: CN
Inventors: 刘亚梅
Original assignee: Ping An Bank Co Ltd
Current assignee: Ping An Bank Co Ltd
Priority date: 2021-09-29
Filing date: 2021-09-29
Publication date: 2022-01-04

Abstract

The embodiment of the application provides a data processing method, a data processing device, a terminal and a storage medium, wherein the method comprises the following steps: acquiring demand information of first test data; determining a target data type of the first test data according to the requirement information; determining a target data construction template according to the target data type; constructing a template and the requirement information according to the target data, and constructing first test data; verifying the validity of the first test data according to a verification database to obtain a verification result; if the verification result indicates that the first test data is valid data, the unmanaged activation system is tested according to the first test data to obtain a test result, the efficiency of constructing the test data can be improved, and therefore the efficiency of testing the unmanaged activation system is improved.

Description

Data processing method, device, terminal and storage medium

Technical Field

The present application relates to the field of data processing technologies, and in particular, to a data processing method, an apparatus, a terminal, and a storage medium.

Background

The method is characterized in that a management-releasing activation system is a credit card core system, a list of clients to be managed and activated is also a key business project to be triggered before first swiping, because of numerous channels and associated parties in current butt joint, data of the management-releasing activation system is usually constructed when the management-releasing activation system is tested at present, and when the data is constructed at present, dependence parties and a large number of calling interfaces are needed, so that the efficiency is low when the data is constructed, and the efficiency is high when the management-releasing activation system is tested.

Disclosure of Invention

The embodiment of the application provides a data processing method, a data processing device, a terminal and a storage medium, which can improve the efficiency of constructing test data, thereby improving the efficiency of testing an unmanaged activation system.

A first aspect of an embodiment of the present application provides a data processing method, where the method includes:

acquiring demand information of first test data;

determining the target data type of the test data according to the requirement information;

determining a target data construction template according to the target data type;

according to the target data construction template and the requirement information, constructing first test data

Verifying the validity of the first test data according to a verification database to obtain a verification result;

and if the verification result indicates that the first test data is valid data, testing the unmanaged activation system according to the first test data to obtain a test result.

With reference to the first aspect, in a possible implementation manner, the determining a target data type of the test data according to the requirement information includes:

extracting keywords from the demand information to obtain a keyword group;

acquiring types of keywords in the keyword group to obtain K keyword types;

determining K reference data types according to the K keyword types and keywords corresponding to each keyword type in the K keyword types;

and determining the target data type according to the K reference data types.

With reference to the first aspect, in a possible implementation manner, the determining a target data construction template according to the target data type includes:

determining at least one reference data construction template according to the target data type, wherein the reference data construction template comprises a plurality of sub-modules;

determining at least one sub-module from each reference data construction template of the at least one reference data construction template according to the requirement information to obtain a sub-module set;

acquiring a first incidence relation of sub-modules in the sub-module set;

and combining the sub-modules in the sub-module set according to the first incidence relation to obtain the target data construction template.

With reference to the first aspect, in a possible implementation manner, the obtaining a first association relationship of sub-modules in the sub-module set includes:

obtaining module description information of each sub-module in the sub-module set;

determining the functional information of each sub-module according to the module description information;

and determining a first association relation of the sub-modules in the sub-module set according to the functional information of each sub-module.

With reference to the first aspect, in one possible implementation manner, the method further includes:

obtaining the accuracy of the test result;

if the accuracy of the test result is lower than a preset threshold, determining the use information of each sub-module according to the description information of each sub-module;

determining incidence relation correction information according to the use information of each sub-module;

determining a second incidence relation according to the incidence relation correction information and the first incidence relation;

combining the sub-modules in the sub-module set according to the second incidence relation to obtain the modified target data construction template;

and constructing a template and the requirement information according to the corrected target data, and constructing second test data.

acquiring the current data volume of the first test data;

determining a first moment for constructing the first test data according to the current data volume and the consumption rate of the first test data;

at the first time, the first test data is constructed.

With reference to the first aspect, in a possible implementation manner, the verifying validity of the first test data according to the verification database to obtain a verification result includes:

comparing the first test data with the verification data in the verification database to obtain a comparison result;

judging whether the check data in the check database is similar to the first test data or not according to the comparison result so as to obtain a similar judgment result;

and determining the similar judgment result as the verification result.

A second aspect of an embodiment of the present application provides a data processing apparatus, including:

the acquisition unit is used for acquiring the demand information of the first test data;

the first determining unit is used for determining the target data type of the test data according to the requirement information;

the second determining unit is used for determining a target data construction template according to the target data type;

the construction unit is used for constructing a template and the requirement information according to the target data and constructing first test data;

the verification unit is used for verifying the validity of the first test data according to a verification database to obtain a verification result;

and the test unit is used for testing the unmanaged activation system according to the first test data to obtain a test result if the verification result indicates that the first test data is valid data.

With reference to the second aspect, in one possible implementation manner, the first determining unit is configured to:

extracting keywords from the demand information to obtain a keyword group;

acquiring types of keywords in the keyword group to obtain K keyword types;

and determining the target data type according to the K reference data types.

With reference to the second aspect, in one possible implementation manner, the second determining unit is configured to:

acquiring a first incidence relation of sub-modules in the sub-module set;

With reference to the second aspect, in a possible implementation manner, in the aspect of obtaining the first association relationship of the sub-modules in the sub-module set, the second determining unit is configured to:

With reference to the second aspect, in one possible implementation manner, the apparatus is further configured to:

obtaining the accuracy of the test result;

acquiring the current data volume of the first test data;

at the first time, the first test data is constructed.

With reference to the second aspect, in one possible implementation manner, the verification unit is configured to:

and determining the similar judgment result as the verification result.

A third aspect of the embodiments of the present application provides a terminal, including a processor, an input device, an output device, and a memory, where the processor, the input device, the output device, and the memory are connected to each other, where the memory is used to store a computer program, and the computer program includes program instructions, and the processor is configured to call the program instructions to execute the step instructions in the first aspect of the embodiments of the present application.

A fourth aspect of embodiments of the present application provides a computer-readable storage medium, where the computer-readable storage medium stores a computer program for electronic data exchange, where the computer program makes a computer perform part or all of the steps as described in the first aspect of embodiments of the present application.

A fifth aspect of embodiments of the present application provides a computer program product, wherein the computer program product comprises a non-transitory computer readable storage medium storing a computer program operable to cause a computer to perform some or all of the steps as described in the first aspect of embodiments of the present application. The computer program product may be a software installation package.

The embodiment of the application has at least the following beneficial effects:

the method comprises the steps of obtaining demand information of first test data, determining a target data type of the test data according to the demand information, determining a target data construction template according to the target data type, constructing a template and the demand information according to the target data, constructing first test data, carrying out validity verification on the first test data according to a verification database to obtain a verification result, testing a pipe release activation system according to the first test data to obtain a test result if the verification result indicates that the first test data is valid data, and automatically constructing the first test data according to the demand information of a pipe release activation service and testing the pipe release activation system according to the first test data, so that the efficiency of testing the pipe release activation system is improved.

Drawings

In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.

Fig. 1 is a schematic flow chart of a data processing method according to an embodiment of the present application;

FIG. 2 is a schematic flow chart diagram illustrating another data processing method according to an embodiment of the present application;

FIG. 3 is a schematic flow chart diagram illustrating another data processing method according to an embodiment of the present application;

fig. 4 is a schematic structural diagram of a terminal according to an embodiment of the present application;

fig. 5 is a schematic structural diagram of a data processing apparatus according to an embodiment of the present application.

Detailed Description

The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.

The terms "first," "second," and the like in the description and claims of the present application and in the above-described drawings are used for distinguishing between different objects and not for describing a particular order. Furthermore, the terms "include" and "have," as well as any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements listed, but may alternatively include other steps or elements not listed, or inherent to such process, method, article, or apparatus.

Reference in the specification to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the specification. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.

In order to better understand the data processing method provided in the embodiments of the present application, a brief description of a unmanaged activation system to which the data processing method is applied is first provided below. The method comprises the steps that a management-releasing activation system is a credit card core system, a list of clients to be managed and activated is also a key business project to be triggered before first swiping, at present, channels and related parties for butting the management-releasing activation system are numerous, the management-releasing activation system needs to be tested after the management-releasing activation system is successfully built, and due to the fact that the capacity of the system is large, a large amount of needed test data are built, and therefore the test requirements are met. The data processing method provided by the embodiment of the application is applied to terminal equipment, the terminal equipment can be a computer, a server, a tablet computer, a mobile terminal and the like, the terminal equipment can determine the target data type of the test data according to the requirement information of the unmanaged activation service in the unmanaged activation system, the terminal equipment determines the target data construction template according to the target data type of the test data, and the terminal equipment constructs the test data according to the target data construction template and the requirement information, so that the first test data can be automatically constructed, and the efficiency of constructing the first test data is improved.

Referring to fig. 1, fig. 1 is a schematic flow chart illustrating a data processing method according to an embodiment of the present disclosure. As shown in fig. 1, the method is applied to a terminal device, and includes the following steps:

101. and acquiring the requirement information of the first test data.

The first test data is test data corresponding to the unmanaged activation service, and if the service scenes of the unmanaged activation service are different, the requirement information of the first test data is also different. For example, the business scenario is a credit card activation scenario for adults in a common salary family, and so on.

The method for acquiring the demand information may be that the demand information is acquired by a user input mode, or the demand information is acquired from a server side, or the demand information is acquired by a management person input mode.

102. And determining the target data type of the first test data according to the requirement information.

The method can extract keywords from the required information to obtain a corresponding keyword group, and determine the target data type according to the type of the keywords in the keyword group and the information carried by the keywords.

103. And determining a target data construction template according to the target data type.

At least one reference data construction template can be determined from the database according to the type of the target data, and the target data construction template can be determined according to the reference data construction template.

For example, at least one reference data construction template is determined from the database in a matching mode according to the target data type. Or, according to the similarity between the target data type and the data type of the data construction template in the database, at least one reference data construction template is determined from the database, for example, the data construction template corresponding to the data type with the similarity higher than a preset similarity threshold may be determined as the reference data construction template. The preset similarity threshold is set by empirical values or historical data.

104. And constructing a template and the requirement information according to the target data to construct first test data.

The characteristic information of the first test data can be determined according to the requirement information, and the first test data can be constructed according to the characteristic information and the target data construction template.

The method for determining the feature information according to the demand information may be: the characteristic information may be, for example, a quantity characteristic, a quantity requirement characteristic, and the like, the quantity characteristic may be understood as the quantity of the first test data, the quantity requirement characteristic may be understood as adjustment information of the first test data in different regions, for example, in an urban region, the adjustment information may be an increase in the quantity of the first test data and an increase in the data amount in different urban regions, in a rural region, the adjustment information may be a decrease in the quantity of the first test data and a decrease in the data amount in a rural region, and the like.

105. And verifying the validity of the first test data according to a verification database to obtain a verification result.

106. And if the verification result indicates that the first test data is valid data, testing the unmanaged activation system according to the first test data to obtain a test result.

The verification database may be a third-party trusted database, which may be a database authenticated by a trusted certificate authority, or the like.

In this example, the method includes obtaining demand information of first test data, determining a target data type of the test data according to the demand information, determining a target data construction template according to the target data type, constructing a first test data according to the target data construction template and the demand information, performing validity verification on the first test data according to a verification database to obtain a verification result, and if the verification result indicates that the first test data is valid data, testing a release management activation system according to the first test data to obtain a test result.

In one possible implementation, a possible method for determining a target data type of the first test data according to the requirement information includes:

a1, extracting keywords from the demand information to obtain a keyword group;

a2, obtaining the types of the keywords in the keyword group to obtain K keyword types;

a3, determining K reference data types according to the K keyword types and keywords corresponding to each keyword type in the K keyword types;

a4, determining the target data type according to the K reference data types.

The method can adopt a general keyword extraction algorithm to extract keywords from the demand information to obtain a keyword group, wherein the keywords in the keyword group are keywords related to the data type.

The keywords correspond to the types of the related key words, and the types of the keywords can be identified, so that the types of the keywords are obtained. For example, the keyword a, the keyword B, and the keyword C may be subjected to category identification, so as to obtain a keyword type 1 corresponding to the keyword a, a keyword type 2 corresponding to the keyword B, and a keyword type 1 corresponding to the keyword C, respectively, where K is a positive integer less than or equal to the total number of the keywords in the keyword group because the types corresponding to the keywords may be the same.

The semantic information of the keyword and the keyword type may be combined to obtain combined information, which is information for determining with reference to the data type. The method for combining the semantic information and the keyword types of the keywords can be to number the semantic information by adopting a preset numbering processing method to obtain a number corresponding to the semantic information; and combining the number with the keyword type to obtain combined information. And then determining a reference data type corresponding to the combined information according to the mapping relation between the combined information and the data type.

The K reference data types may be combined as subtypes of the target data type to arrive at the target data type. For example, the K reference data types may be understood as child data types included by the target data type. The target data types are different after different reference data types are combined.

In this example, a keyword group is obtained by extracting keywords from demand information, types of keywords in the keyword group are obtained to obtain K keyword types, K reference data types are determined according to the K keyword types and keywords corresponding to each keyword type in the K keyword types, and the target data type is determined according to the K reference data types, so that the target data type is determined according to the K reference data types, and accuracy in determining the target data type is improved.

In one possible implementation, a possible determining a target data construction template according to the target data type includes:

b1, determining at least one reference data construction template according to the target data type, wherein the reference data construction template comprises a plurality of sub-modules;

b2, determining at least one sub-module from each reference data construction template of the at least one reference data construction template according to the requirement information to obtain a sub-module set;

b3, acquiring a first incidence relation of the sub-modules in the sub-module set;

and B4, combining the sub-modules in the sub-module set according to the first incidence relation to obtain the target data construction template.

The reference data construction template may be a manually preset template for constructing test data, the reference data template may include parameters for constructing a plurality of test data, and since the same data type may correspond to a plurality of similar scenes or similar scenes, the target data type may correspond to a plurality of reference data construction templates. Each reference data construction template comprises a plurality of sub-modules, and different reference data construction templates can have the same sub-module or different sub-modules. The performance parameters of the same sub-modules are the same, for example, the description information of the sub-modules is the same, and the usage information of the sub-modules is the same. The description information of the sub-module can be understood as the description of the performance of the sub-module, and the use information of the sub-module can be understood as the use frequency of the sub-module when the sub-module is used, etc.

Because the at least one reference data construction template can be a reference data construction template corresponding to a scene with similar demand information, at least one sub-module adapted to the demand information can be determined from a plurality of sub-modules of each reference data construction template according to the demand information, so that a sub-module set is obtained. The method for determining at least one sub-module adapted to the requirement information from the plurality of sub-modules of the reference data construction template according to the requirement information may be: multiple demand directions in the demand information can be obtained, and at least one submodule is determined from multiple submodules of the reference data construction template according to the demand directions. The demand direction may be understood as a demand footfall, for example, area demand, age bracket of the user, income information of the user, asset information of the user, and the like.

The first association relationship can be determined according to description information of the sub-modules in the sub-module set, the first association relationship can be understood as the compactness between the sub-modules, and the closer the association relationship is, the closer the setting distance between the sub-modules is, and the farther the association relationship is, the farther the setting distance between the sub-modules is.

And combining the sub-modules according to the closeness degree of the first association relation to obtain a target data construction template, wherein the closer the association relation between the sub-modules is, the closer the distance between the sub-modules is, and the farther the association relation between the sub-modules is, the farther the distance between the sub-modules is.

In this example, at least one reference data construction template is determined according to the type of the target data, at least one sub-module is determined from each reference data construction template of the at least one reference data construction template according to the requirement information, a sub-module set is obtained, and sub-modules in the sub-module set are combined according to the first association relationship between the sub-modules to obtain the target data construction template.

In a possible implementation manner, a possible obtaining of the first association relationship of the sub-modules in the sub-module set includes:

c1, obtaining module description information of each sub-module in the sub-module set;

c2, determining the function information of each sub-module according to the module description information;

and C3, determining a first association relation of the sub-modules in the sub-module set according to the function information of each sub-module.

The module description information of each sub-module may be determined from the module description information set stored in the database, for example, the module description information of the sub-module may be determined from the module description information and the module description information of the sub-module through the sub-module identifier, or the module description information may be directly obtained from the attribute information if the attribute information of each sub-module carries the module description information. Of course, the module description information of the sub-module may be obtained in other manners. The module description information includes function information, use information, creation information, and the like.

The function information corresponding to the sub-module can be extracted from the module description information. The functional information of the sub-modules can be compared to obtain the similarity, and the first association relationship is determined according to the similarity. The method specifically comprises the following steps: the higher the similarity between the function information of the two sub-modules is, the more compact the first association relationship between the two sub-modules is, and the lower the similarity between the function information of the two sub-modules is, the more distant the first association relationship between the two sub-modules is.

In this example, the first association relationship is determined according to the functional information in the module description information by obtaining the module description information of each sub-module, so that the accuracy of determining the association relationship is improved.

In a possible implementation manner, when testing the unmanaged activation system, there may be a case that the test data has a deviation, resulting in that if the accuracy of the test result is poor, the test data may also be corrected to improve the accuracy of the test, specifically as follows:

d1, obtaining the accuracy of the test result;

d2, if the accuracy of the test result is lower than a preset threshold, determining the use information of each sub-module according to the description information of each sub-module;

d3, determining incidence relation correction information according to the use information of each sub-module;

d4, determining a second incidence relation according to the incidence relation correction information and the first incidence relation;

d5, combining the sub-modules in the sub-module set according to the second incidence relation to obtain the modified target data construction template;

d6, constructing a template and the requirement information according to the corrected target data, and constructing second test data.

The preset threshold may be set by an empirical value or historical data. The usage information may include the usage frequency and usage duration of the sub-module, etc.

The association relationship correction information may be determined according to the use frequency and the use duration, and specifically may be: the smaller the difference between the service durations of the two sub-modules, the more compact the association relationship, the smaller the difference between the service frequencies, the more compact the association relationship, the larger the difference between the service durations of the two sub-modules, the more distant the association relationship, the larger the difference between the service frequencies, the more distant the association relationship, and the correlation correction value can be determined according to the relationship.

Of course, the frequency of use can also be understood as the frequency with which two modules are used in association, and the higher the frequency of use in association, the more compact the association relationship, and the lower the frequency of use in association, the more distant the association relationship.

The first association relationship may be adjusted according to the association relationship correction information, for example, the closer or further the first association relationship between the two sub-modules is adjusted, the adjusted first association relationship may be determined as the second association relationship.

In this example, the first incidence relation is corrected through the acquired incidence relation correction information to obtain a second incidence relation, and the sub-modules in the sub-module set are combined according to the second incidence relation to obtain a corrected target data construction template, so that the second test data is finally constructed, and the accuracy of the second test data in the test is improved.

In a possible implementation manner, the first test data may also be automatically generated, specifically:

e1, acquiring the current data volume of the first test data;

e2, determining a first moment for constructing the first test data according to the current data volume and the consumption rate of the first test data;

e3, constructing the first test data at the first time.

The test data is disposable and can not be reused after being used, so that the first moment for constructing the first test data is determined according to the current quantity and the consumption rate, the test data is constructed at the first moment, and the stability during testing is improved.

In a possible implementation manner, a possible method for performing validity verification on the first test data according to a verification database to obtain a verification result includes:

f1, comparing the first test data with the check data in the check database to obtain a comparison result;

f2, judging whether the check data in the check database is similar to the first test data according to the comparison result to obtain a similar judgment result;

f3, determining the similar judgment result as the verification result.

When the first test data and the verification data are compared, a similarity comparison method can be adopted to obtain a comparison result, wherein the comparison result is the similarity between the first test data and the verification data.

The similarity in the comparison result may be compared with a preset similarity threshold, if the similarity is higher than the similarity threshold, it is determined that the first test data is similar to the verification data, and if the similarity is lower than the similarity threshold, it is determined that the first test data is not similar to the verification data.

The unmanaged activation system provided by the embodiment of the application can also be used for conducting visual management understanding on management data through a unified interface, so that a tester can obtain and use data information more efficiently; the system functions of managing a white list, business opportunity pushing, an analog-to-digital converter and the like are integrated, and the construction of different scene data is flexible and customized.

Referring to fig. 2, fig. 2 is a schematic flow chart illustrating another data processing method according to an embodiment of the present application. As shown in fig. 2, the method is applied to a terminal device, and includes the following steps:

201. acquiring demand information of first test data;

202. extracting keywords from the demand information to obtain a keyword group;

203. acquiring types of keywords in the keyword group to obtain K keyword types;

204. determining K reference data types according to the K keyword types and keywords corresponding to each keyword type in the K keyword types;

205. determining the target data type according to the K reference data types;

206. determining a target data construction template according to the target data type;

207. and constructing a template and the requirement information according to the target data to construct first test data.

Referring to fig. 3, fig. 3 is a schematic flow chart illustrating another data processing method according to an embodiment of the present application. As shown in fig. 3, the method is applied to a terminal device, and includes the following steps:

301. acquiring demand information of first test data;

302. determining a target data type of the first test data according to the requirement information;

303. determining at least one reference data construction template according to the target data type, wherein the reference data construction template comprises a plurality of sub-modules;

304. determining at least one sub-module from each reference data construction template of the at least one reference data construction template according to the requirement information to obtain a sub-module set;

305. obtaining module description information of each sub-module in the sub-module set;

306. determining the functional information of each sub-module according to the module description information;

307. determining a first association relation of the sub-modules in the sub-module set according to the functional information of each sub-module;

308. combining the sub-modules in the sub-module set according to the first incidence relation to obtain the target data construction template;

309. and constructing a template and the requirement information according to the target data to construct first test data.

In accordance with the foregoing embodiments, please refer to fig. 4, where fig. 4 is a schematic structural diagram of a terminal provided in an embodiment of the present application, and as shown in the figure, the terminal includes a processor, an input device, an output device, and a memory, where the processor, the input device, the output device, and the memory are connected to each other, where the memory is used to store a computer program, the computer program includes program instructions, the processor is configured to call the program instructions, and the program includes instructions for performing the following steps;

acquiring demand information of first test data;

determining a target data type of the first test data according to the requirement information;

constructing a template and the requirement information according to the target data, and constructing first test data;

The above description has introduced the solution of the embodiment of the present application mainly from the perspective of the method-side implementation process. It is understood that the terminal includes corresponding hardware structures and/or software modules for performing the respective functions in order to implement the above-described functions. Those of skill in the art will readily appreciate that the present application is capable of hardware or a combination of hardware and computer software implementing the various illustrative elements and algorithm steps described in connection with the embodiments provided herein. Whether a function is performed as hardware or computer software drives hardware depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.

In the embodiment of the present application, the terminal may be divided into the functional units according to the above method example, for example, each functional unit may be divided corresponding to each function, or two or more functions may be integrated into one processing unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit. It should be noted that the division of the unit in the embodiment of the present application is schematic, and is only a logic function division, and there may be another division manner in actual implementation.

In accordance with the above, please refer to fig. 5, and fig. 5 is a schematic structural diagram of a data processing apparatus according to an embodiment of the present application. As shown in fig. 5, the apparatus includes:

an obtaining unit 501, configured to obtain requirement information of first test data;

a first determining unit 502, configured to determine a target data type of the test data according to the requirement information;

a second determining unit 503, configured to determine a target data construction template according to the target data type;

a constructing unit 504, configured to construct a template and the requirement information according to the target data, and construct first test data;

a verification unit 505, configured to perform validity verification on the first test data according to a verification database to obtain a verification result;

a testing unit 506, configured to test the unmanaged activation system according to the first test data to obtain a test result if the verification result indicates that the first test data is valid data.

In one possible implementation manner, the first determining unit 502 is configured to:

extracting keywords from the demand information to obtain a keyword group;

acquiring types of keywords in the keyword group to obtain K keyword types;

and determining the target data type according to the K reference data types.

In one possible implementation manner, the second determining unit 503 is configured to:

acquiring a first incidence relation of sub-modules in the sub-module set;

In a possible implementation manner, in terms of obtaining the first association relationship of the sub-modules in the sub-module set, the second determining unit 503 is configured to:

In one possible implementation, the apparatus is further configured to:

obtaining the accuracy of the test result;

In one possible implementation, the apparatus is further configured to:

acquiring the current data volume of the first test data;

at the first time, the first test data is constructed.

In one possible implementation, the verification unit is configured to 506:

and determining the similar judgment result as the verification result.

Embodiments of the present application also provide a computer storage medium, wherein the computer storage medium stores a computer program for electronic data exchange, and the computer program enables a computer to execute part or all of the steps of any one of the data processing methods as described in the above method embodiments.

Embodiments of the present application also provide a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program causes a computer to execute part or all of the steps of any one of the data processing methods as described in the above method embodiments.

It should be noted that, for simplicity of description, the above-mentioned method embodiments are described as a series of acts or combination of acts, but those skilled in the art will recognize that the present application is not limited by the order of acts described, as some steps may occur in other orders or concurrently depending on the application. Further, those skilled in the art should also appreciate that the embodiments described in the specification are preferred embodiments and that the acts and modules referred to are not necessarily required in this application.

In the foregoing embodiments, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.

In the embodiments provided in the present application, it should be understood that the disclosed apparatus may be implemented in other manners. For example, the above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one type of division of logical functions, and there may be other divisions when actually implementing, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not implemented. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of some interfaces, devices or units, and may be an electric or other form.

The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.

In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit may be implemented in the form of hardware, or may be implemented in the form of a software program module.

The integrated units, if implemented in the form of software program modules and sold or used as stand-alone products, may be stored in a computer readable memory. Based on such understanding, the technical solution of the present application may be substantially implemented or a part of or all or part of the technical solution contributing to the prior art may be embodied in the form of a software product stored in a memory, and including several 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 described in the embodiments of the present application. And the aforementioned memory comprises: various media capable of storing program codes, such as a usb disk, a read-only memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and the like.

Those skilled in the art will appreciate that all or part of the steps in the methods of the above embodiments may be implemented by associated hardware instructed by a program, which may be stored in a computer-readable memory, which may include: flash memory disks, read-only memory, random access memory, magnetic or optical disks, and the like.

The foregoing detailed description of the embodiments of the present application has been presented to illustrate the principles and implementations of the present application, and the above description of the embodiments is only provided to help understand the method and the core concept of the present application; meanwhile, for a person skilled in the art, according to the idea of the present application, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present application.

Claims

1. A method of data processing, the method comprising:

acquiring demand information of first test data;

2. The method of claim 1, wherein determining the target data type of the first test data according to the requirement information comprises:

extracting keywords from the demand information to obtain a keyword group;

acquiring types of keywords in the keyword group to obtain K keyword types;

and determining the target data type according to the K reference data types.

3. The method of claim 1 or 2, wherein determining a target data construction template according to the target data type comprises:

acquiring a first incidence relation of sub-modules in the sub-module set;

4. The method of claim 3, wherein the obtaining the first association relationship of the sub-modules in the sub-module set comprises:

5. The method of claim 4, further comprising:

obtaining the accuracy of the test result;

6. The method according to claim 4 or 5, characterized in that the method further comprises:

acquiring the current data volume of the first test data;

at the first time, the first test data is constructed.

7. The method of claim 6, wherein the validating the first test data against the verification database to obtain a validation result comprises:

and determining the similar judgment result as the verification result.

8. A data processing apparatus, characterized in that the apparatus comprises:

9. A terminal, comprising a processor, an input device, an output device, and a memory, the processor, the input device, the output device, and the memory being interconnected, wherein the memory is configured to store a computer program comprising program instructions, the processor being configured to invoke the program instructions to perform the method of any of claims 1-7.

10. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program comprising program instructions that, when executed by a processor, cause the processor to carry out the method according to any one of claims 1-7.