User tag system test method and device, equipment, storage medium and program product

Abstract

The invention discloses a user tag system testing method and device, equipment, a storage medium and a program product. The method comprises the following steps: constructing and storing a label data set, and synchronizing the label data set to a big data label system; each piece of label data in the label data set takes a label identifier representing a label type and an enumeration value as indexes; simulating to obtain a first test case, and utilizing the first test case to obtain a first test result from the label data set; the first test case represents a circle selection rule of the user; based on a request sent to the big data label system, a second test result of the big data label system based on the request is obtained, and the request comprises first information related to the first test case; and comparing the first test result with the second test result, and determining whether the big data label system passes the user circle selection processing test according to the comparison result, thereby ensuring the accuracy of the big data label system on the user circle selection processing result.

Description

Technical Field

[0001] This invention relates to the field of artificial intelligence technology, specifically to a user tagging system testing method, apparatus, equipment, storage medium, and program product. Background Technology

[0002] A big data tagging system is a tool that uses big data technology to tag and classify data. It collects and integrates data from multiple channels and uses algorithms and data analysis models to tag the data to meet different business scenarios.

[0003] When verifying the correctness of a big data tagging system, the Structured Query Language (SQL) files within the system are usually checked manually. However, since user selection using a big data tagging system involves a large number and variety of tags, the workload of manually checking the SQL files is large, which can easily reduce testing efficiency and make the verification results inaccurate.

[0004] Furthermore, when verifying the accuracy of data selected by the big data tagging system, the method of extracting data and partial data is usually adopted. However, since the data is stored in the big data platform components, it makes the addition, deletion and modification of individual data complex. Moreover, the data extraction method has a certain probability of avoiding erroneous data, so the accuracy of the verification results cannot be guaranteed. Summary of the Invention

[0005] To address the existing technical problems, embodiments of the present invention provide a user tag system testing method, apparatus, device, storage medium, and program product, which can improve the accuracy of verification results and increase testing efficiency.

[0006] To achieve the above objectives, the technical solution of this invention is implemented as follows: In a first aspect, embodiments of the present invention provide a user tagging system testing method, the method comprising: A tag data set is constructed and stored, and the tag data set is synchronized to a big data tagging system; wherein, each tag data in the tag data set is indexed by a tag identifier representing the tag type and an enumeration value, and each tag data includes multiple user identifiers; A first test case is obtained through simulation, and a first test result is obtained from the tag data set using the first test case; the first test case represents the user selection rule, and the first test result includes a first user identifier set that matches the first test case; Based on a request sent to the big data tagging system, a second test result of the big data tagging system based on the request is obtained, wherein the request includes first information related to the first test case; the second test result includes a second set of user identifiers obtained based on the first information. Compare the first test result and the second test result, and determine whether the user selection process of the big data tagging system has passed the test based on the comparison result.

[0007] In the above scheme, constructing a tag data set includes: generating a first preset number of multiple user identifiers; dividing the multiple user identifiers based on a second preset number of tag types to obtain multiple first data sets, the number of the first data sets being the second preset number; for each first data set, copying at least one user identifier from at least one other first data set associated with each first data set into each first data set according to a first preset rule; one tag type corresponds to one first data set; based on the number of enumeration values ​​corresponding to each tag type, dividing the multiple user identifiers included in the first data set corresponding to each tag type to obtain tag data indexed by tag identifiers representing tag types and enumeration values.

[0008] In the above scheme, the simulation to obtain the first test case includes: selecting a third preset number of indices from the indices corresponding to all the tag data in the tag data set as multiple tags to be tested; setting the hierarchical relationship and / or logical operation relationship between the multiple tags; and generating the first test case based on the hierarchical relationship and / or logical operation relationship between the multiple tags.

[0009] In the above scheme, obtaining the first test result from the tag data set using the first test case includes: generating a first test file based on the first test case; traversing and parsing the first test file to obtain multiple tags and the hierarchical relationship and / or logical operation relationship between the multiple tags, wherein each tag includes a tag identifier representing the tag type and an enumeration value; determining the third user identifier set corresponding to each of the multiple tags from the tag data set; and performing logical operation processing on the multiple third user identifier sets corresponding to the multiple tags according to the hierarchical relationship and / or logical operation relationship between the multiple tags to obtain the first test result.

[0010] In the above scheme, obtaining the second test result of the big data tagging system based on the request sent to the big data tagging system includes: generating a first test file based on the first test case; traversing and parsing the first test file to obtain multiple tags and the hierarchical relationship and / or logical operation relationship between the multiple tags, each tag including a tag identifier representing the tag type and an enumeration value; sending a request carrying first information to the big data tagging system, the first information including the multiple tags and the hierarchical relationship and / or logical operation relationship between the multiple tags; and obtaining the second test result obtained by the big data tagging system based on the first information.

[0011] In the above scheme, determining whether the user selection processing test of the big data tagging system passes based on the comparison results includes: if the user identifiers in the first user identifier set and the user identifiers in the second user identifier set are the same, the first test case is determined to pass, and the user selection processing test of the big data tagging system passes; if the user identifiers in the first user identifier set and the user identifiers in the second user identifier set are different, the first test case is determined to fail, and the user selection processing test of the big data tagging system passes.

[0012] Secondly, embodiments of the present invention also provide a user tagging system testing device, the device comprising: a data construction and storage module, a simulation module, and a testing module; wherein, The data construction and storage module is used to construct and store a tag data set and synchronize the tag data set to the big data tag system; wherein, each tag data in the tag data set is indexed by a tag name representing the tag type and an enumerated value, and each tag data includes multiple user identifiers; The simulation module is configured to obtain a first test result from the tag data set using a first test case, wherein the first test result includes a first set of user identifiers matching the first test case; it is also configured to obtain a second test result from the big data tag system based on a request sent by the testing module to the big data tag system, wherein the request includes first information related to the first test case, and the second test result includes a second set of user identifiers obtained based on the first information; and it is further configured to compare the first test result and the second test result. The testing module is used to simulate and obtain a first test case, which represents the user's selection rules; it is also used to determine whether the selection process performed by the big data tagging system based on the first test case has passed based on the comparison results.

[0013] Thirdly, embodiments of the present invention also provide a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the method described in the embodiments of the present invention.

[0014] Fourthly, embodiments of the present invention also provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in the embodiments of the present invention.

[0015] Fifthly, embodiments of the present invention also provide a computer program product, including a computer program that, when executed by a processor, implements the method described in the embodiments of the present invention.

[0016] The present invention provides a user tag system testing method, apparatus, device, storage medium and program product. In the first aspect, a tag data set including multiple user identifiers is constructed locally, and each tag data in the tag data set is stored and synchronized to the big data tag system with a tag identifier representing the tag type and an enumeration value as an index. The storage method is decoupled from the big data tag system to be tested. Secondly, by simulating the selection rules of the big data tagging system to be tested, the first test case is obtained. Using the first test case obtained by simulation, the first test result matching the first test case is obtained from the stored tag data set, so as to realize the automated selection of users locally and improve the testing efficiency. Thirdly, by sending a request to the big data tagging system containing first information related to the first test case, a second test result based on the request is obtained. By comparing the first and second test results, it is determined whether the big data tagging system has passed the user selection process test. The big data tagging system under test is verified by comparing all data rather than sampling, thereby improving the accuracy of the verification results and increasing the testing efficiency. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating the user tagging system testing method according to an embodiment of the present invention; Figure 2 This is a schematic diagram illustrating the process of constructing a tag data set according to an embodiment of the present invention; Figure 3 The front-end interface for users to input selection rules in a big data tagging system; Figure 4 This is a schematic diagram of the composition structure of the user tag system testing device according to an embodiment of the present invention; Figure 5 This is an interactive schematic diagram illustrating the user tag system testing implemented by the user tag system testing device according to an embodiment of the present invention; Figure 6 This is a schematic diagram of the system architecture of the user tag system testing device according to an embodiment of the present invention; Figure 7 This is a schematic diagram of the hardware composition structure of a computer device provided in an embodiment of the present invention. Detailed Implementation

[0018] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments.

[0019] It should be understood that the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0020] The terms “first,” “second,” etc., used in the specification and claims of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented, for example, in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0021] Before further detailing the methods of the embodiments of the present invention, a brief description of the related technologies is necessary.

[0022] With the rapid development of big data technology, its applications are becoming increasingly widespread. Big data tagging systems (also known as big data user tagging systems) are a typical application of this technology. A complete big data tagging system is primarily responsible for data mining, tag construction, data storage in big data infrastructure components, and audience tag selection calculations. Given the aforementioned functions of a big data tagging system, ensuring data quality is crucial. Testing a big data tagging system can specifically include: data integrity testing, data consistency testing, and data accuracy testing.

[0023] Specifically, the functional testing methods currently used for data quality in big data tagging systems include the following: 1) Data constraint check: This check is used to verify whether the data type, data length, index, and primary key meet the requirements. All data types need to be covered during the test.

[0024] 2) Data storage check, used to assess whether it is necessary to store in the form of compressed files, whether the hive file (Hive table) type selection is reasonable, and whether the files and directories read and written in the code are correct.

[0025] 3) SQL file check: This checks for syntax problems in the SQL, specifically the correct use of SQL join methods, functions, aggregates, and keywords.

[0026] 4) Data processing logic verification, used to evaluate whether the verification process conforms to business logic, whether the use of operators and functions is correct, and whether the handling of outliers, "dirty" data, extreme values, and special data meets expectations.

[0027] 5) Scheduling task testing, which is a test of scheduling tasks in the big data platform, is used to evaluate whether the task itself supports "rerun", whether the dependent parent tasks are configured reasonably, whether the task dependency hierarchy is reasonable, and whether the task is completed within the specified time.

[0028] For audience segmentation tasks performed by big data tagging systems, manual SQL file checks are often used to verify system correctness. However, due to the large number and variety of tags involved, manual SQL file checks are labor-intensive, leading to low testing efficiency and inaccurate results. Furthermore, verifying the accuracy of segmentation results often involves data extraction and partial data modification. However, since the data is frequently stored within big data platform components, adding, deleting, and modifying individual data entries becomes complex, and data extraction methods cannot guarantee the accuracy of the verification results.

[0029] Therefore, how to automate the testing of big data tagging systems and improve the testing efficiency and accuracy of big data tagging systems has become an urgent problem to be solved.

[0030] Based on this, embodiments of the present invention provide a user tagging system testing method. Figure 1 This is a flowchart illustrating the user tagging system testing method according to an embodiment of the present invention; as shown below. Figure 1 As shown, the method includes: Step 101: Construct and store a tag data set, and synchronize the tag data set to the big data tag system; wherein, each tag data in the tag data set is indexed by a tag identifier representing the tag type and an enumeration value, and each tag data includes multiple user identifiers; Step 102: Simulate to obtain a first test case, and use the first test case to obtain a first test result from the tag data set; the first test case represents the user selection rule, and the first test result includes a first user identifier set that matches the first test case; Step 103: Based on the request sent to the big data tagging system, obtain the second test result of the big data tagging system based on the request, wherein the request includes first information related to the first test case; the second test result includes a second set of user identifiers obtained based on the first information; Step 104: Compare the first test result and the second test result, and determine whether the user selection process test performed by the big data tagging system has passed based on the comparison result.

[0031] In this embodiment, a tag data set including multiple tag data is constructed locally. Each tag data is indexed by a tag identifier representing the tag type and an enumeration value. Each tag data includes multiple user identifiers.

[0032] Here, a tag refers to a way of classifying and marking constructed user data; a tag type is a specific feature, attribute, and behavior of user data described or identified according to different business scenarios, that is, a specific tag corresponding to each user, such as a gender tag, age tag, salary tag, etc.; a tag identifier is a unique identifier corresponding to each tag type. In this embodiment of the invention, there are no specific restrictions on the tag identifier. For example, the tag identifier can be represented by numeric characters (such as the serial number of the tag type) or by text characters (such as the name of the tag type).

[0033] Enumerated values ​​refer to fixed or predefined types corresponding to each label type. For example, the enumerated values ​​in the gender label are fixed as male and female, and the enumerated values ​​in the age label are predefined as [0,10] years old, [11,20] years old, [21,30] years old, etc. For different label types, the corresponding enumerated value types and the number of enumerated values ​​may also be different.

[0034] The user identifier is a unique identifier corresponding to each user. This embodiment of the invention does not impose specific restrictions on the user identifier. For example, the user identifier can be represented by numeric characters, such as a mobile phone number.

[0035] It is understandable that the tag identifier corresponding to each tag type, and each enumeration value corresponding to that tag type, constitute the index of each tag data. In other words, each tag data includes the user identifier belonging to the enumeration value under that tag type.

[0036] In this embodiment, after the construction of the tag data set is completed, each tag data in the tag data set is stored in the form of a file. The file name is the index of each tag data, that is, the tag identifier and enumeration value corresponding to the tag type. Each file includes the user identifier of the enumeration value under the tag type.

[0037] For example, the filename corresponding to the tag data for users whose age is between 31 and 35 can be named: file-age-31_35; the filename corresponding to the tag data for users whose birthday is January 5th can be named: file-birthday-0105; and the filename corresponding to the tag data for users whose place of origin is Beijing can be named: file-province-beijing. In addition to storing the file corresponding to each tag data using the tag identifier and enumeration value as indexes, all user identifiers can also be stored in a file named: file-all-Y.

[0038] In this embodiment, after the construction and local storage of the tag data set are completed, the tag data set is synchronized to the big data tag system to be tested to ensure the consistency between the locally constructed tag data set and the big data tag system to be tested.

[0039] Specifically, after the tag data set is constructed, the local file storage system is traversed to read the file content corresponding to each tag data, and the data is written to big data platform components such as Hadoop Database (HBase), Hive, and Impala according to the system format of the big data tag system to be tested, so as to achieve data consistency between the local file storage system and the big data tag system to be tested.

[0040] In this embodiment, after the construction of the tag data set is completed, the selection rules input to the big data tag system to be tested will be simulated to obtain the first test case representing the user's selection rules. Using the first test case, the first test result is obtained from the locally stored tag data set.

[0041] In this context, selection rules refer to specific rules set during the user selection process based on a big data tagging system. These rules are related to tag types and enumeration values ​​and are used to filter users for specific business scenarios. Typically, users input selection rules into the big data tagging system through a web-based front-end interface.

[0042] The first test result includes a first set of user identifiers that match the first test case. In other words, the first set of user identifiers in the first test result includes user identifiers that conform to the selection rules of the first test case. That is, the first test result is the set of user identifiers determined by the local file storage system from the tag data set based on simulated selection rules.

[0043] In this embodiment, after obtaining the simulated first test case, a request including the first information is sent to the big data tagging system to be tested, and the second test result obtained by the big data tagging system based on the request is obtained.

[0044] The first information is related to the first test case, that is, the first information is related to the selection rules simulated locally. The specific content of the first information will be explained in detail below.

[0045] The second test result includes a second set of user identifiers obtained based on the first information related to the selection rules. In other words, the second test result is the set of user identifiers determined by the big data labeling system under test from the synchronized label data set based on the simulated selection rules.

[0046] In this embodiment, after obtaining the first test result including the first set of user identifiers locally and the second test result including the second set of user identifiers obtained through the big data tagging system to be tested, the first test result and the second test result are compared to determine whether the test of the first test case has passed based on the comparison result, thereby determining whether the user selection processing test of the big data tagging system has passed.

[0047] In the first aspect, by constructing a tag data set including multiple user identifiers locally, each tag data in the tag data set is stored and synchronized to the big data tag system using a tag identifier representing the tag type and an enumeration value as an index, thereby decoupling the storage method from the big data tag system to be tested. Secondly, by simulating the selection rules of the big data tagging system to be tested, the first test case is obtained. Using the first test case obtained by simulation, the first test result matching the first test case is obtained from the stored tag data set, so as to realize the automated selection of users locally and improve the testing efficiency. Thirdly, by sending a request to the big data tagging system containing first information related to the first test case, a second test result based on the request is obtained. By comparing the first and second test results, it is determined whether the big data tagging system has passed the user selection process test. The big data tagging system under test is verified by comparing all data rather than sampling, thereby improving the accuracy of the verification results and increasing the testing efficiency.

[0048] Figure 2 This is a schematic diagram illustrating the process of constructing a tag data set according to an embodiment of the present invention; wherein, the specific method for constructing the tag data set locally includes: Step 1011: Generate a first preset number of multiple user identifiers.

[0049] In this embodiment, during the construction of the tag data set, multiple user identifiers are first generated according to a first preset number, and the generated multiple user identifiers are stored.

[0050] The first preset quantity is the total number of user identifiers pre-set based on factors such as the hardware configuration of the test environment (e.g., the memory size of the local file storage system) and business scenario requirements (e.g., data volume requirements). For example, the first preset quantity is typically preset to hundreds of thousands. This embodiment of the invention does not impose specific limitations on the method of generating user identifiers; for example, a preset length of numeric characters can be randomly generated as user identifiers.

[0051] As an example, after generating a first preset number of user identifiers, all user identifiers are written to the file-all-Y file in the local file storage system for storage.

[0052] Step 1012: Divide the multiple user identifiers based on a second preset number of tag types to obtain multiple first data sets, the number of which is the second preset number.

[0053] In this embodiment, after generating a first preset number of user identifiers, they are divided according to preset tag types, and a tag type is assigned to each user identifier.

[0054] Specifically, the first preset number of user identifiers are divided according to the second preset number of tag types to obtain the second preset number of first data sets, and each first data set corresponds to a tag type.

[0055] As an example, all user identifiers are divided into N first data sets according to a second preset number N. Each first data set corresponds to a label type, and the user identifiers in the first data set belong to that label type.

[0056] Step 1013: For each first data set, according to a first preset rule, at least one user identifier from at least one other first data set associated with each first data set is copied into each first data set; one tag type corresponds to one first data set.

[0057] In this embodiment, after assigning a tag type to each user identifier, a portion of the user identifiers copied from other first data sets associated with the first data set will be added to each first data set according to the first preset rule. Each tag type corresponds to a first data set to simulate the situation in actual business scenarios where the same user has multiple tags.

[0058] For example, for each first data set, the first preset rule can be set to copy user identifiers from the first data set before and after it to the current first data set according to a preset step size, without limiting the step size corresponding to each first data set. For example, for first data set B, one user identifier is copied from first data set A every two user identifiers according to a preset step size 2 and added to first data set B every three user identifiers according to a preset step size 3, until all first data sets have been polled, so that at least some user identifiers correspond to multiple tag types.

[0059] Step 1014: Based on the number of enumeration values ​​corresponding to each tag type, divide the multiple user identifiers included in the first data set corresponding to each tag type to obtain tag data indexed by tag identifiers representing tag types and enumeration values.

[0060] In this embodiment, after simulating a scenario where the same user has multiple tags, each first data set is divided according to the enumeration value under the preset tag type, and an enumeration value is assigned to each user identifier to obtain a tag data set including tag data corresponding to the enumeration value of each tag type.

[0061] Specifically, the user identifiers of the first data set corresponding to each tag type are divided according to the number of enumeration values ​​for each tag type, resulting in tag data with the number of enumeration values ​​for each tag type, and each tag data corresponds to an enumeration value for a tag type.

[0062] As an example, based on the number M enumeration values ​​corresponding to each label type, all user identifiers in the corresponding first data set are divided into M label data. Each label data corresponds to an enumeration value, and the user identifier in each label data belongs to an enumeration value under that label type.

[0063] In some optional embodiments, the simulation to obtain the first test case includes: selecting a third preset number of indices from the indices corresponding to all the tag data in the tag data set as multiple tags to be tested; setting the hierarchical relationship and / or logical operation relationship between the multiple tags; and generating the first test case based on the hierarchical relationship and / or logical operation relationship between the multiple tags.

[0064] In this embodiment, the specific process of obtaining the first test case representing the selection rule by the user inputting the selection rule through the front-end interface in the simulated big data tagging system is as follows: according to the third preset number, the index is extracted from the candidate set composed of the indexes corresponding to all the tag data in the tag data set, that is, the third preset number of tag type enumeration values ​​are extracted as multiple tags to be tested in the selection rule, and the first test case is generated by setting the hierarchical relationship and / or logical operation relationship between the multiple tags to be tested.

[0065] The hierarchical relationship between multiple tags refers to the node position of each tag in the tag tree corresponding to the selection rule. The higher the level of the tag, the closer the node position in the corresponding tag tree is to the root node, that is, the higher the priority of selecting users according to the tag; conversely, the lower the level of the tag, the farther the node position in the corresponding tag tree is from the root node, that is, the lower the priority of selecting users according to the tag.

[0066] Logical operations between multiple tags refer to the operational relationships between sets of user identifiers corresponding to multiple tags, such as union (AND) and intersection (OR).

[0067] For example, Figure 3 For example, the front-end interface for users to input selection rules in a big data tagging system; Figure 3 As shown, through this front-end web interface, users first select the "Male" enumeration value under the "Gender" tag in the "Basic Attributes - Population Attributes" section of the "All Customers" list as the first-level tag. Next, they select the "Age" enumeration value under the "Age" tag in the "Basic Attributes - Population Attributes" list as the second-level tag. Simultaneously, they select the "Average Revenue Per User" (ARPU) value under the "Consumer Value - Big Network Business" tag as the second-level tag, where the enumeration value is not in the range of 10-100k. Furthermore, the "Age" tag and the "ARPU" tag are related by an "OR" relationship, while the first-level tags and the second-level tags are related by an "AND" relationship. This allows users to... Figure 3 The front-end interface of the big data tagging system shown here allows you to input the selection rules.

[0068] It should be noted that the embodiments of the present invention do not impose specific restrictions on the form of the simulated first test case. For example, the first test case can be stored in the form of an Extensible Markup Language (XML) file.

[0069] For example, as described above Figure 3Taking the selection rule input by the user through the front-end web interface of the big data tagging system as an example, the pseudocode corresponding to the XML file representing the first test case of the selection rule obtained in this embodiment of the invention is as follows:

[0070] In this embodiment of the invention, during the process of simulating the acquisition of the first test case, the "male" label with an enumeration value of "5000" under the gender label with the label "101001" is set as the first level, the age group label with an enumeration value of "[41,45]" under the age label with the label "101003" is set as the second level, and the label with an enumeration value of "30102001" that is not "10_100" under the ARPU value label that has a union operation relationship "OR" with the above age group label is also set as the second level. Furthermore, the labels of the first level and the labels of the second level have an intersection operation relationship "AND", thereby obtaining the XML file of the first test case obtained by simulating the above selection rules.

[0071] In some optional embodiments, obtaining the first test result from the tag data set using the first test case includes: generating a first test file based on the first test case; traversing and parsing the first test file to obtain multiple tags and the hierarchical relationship and / or logical operation relationship between the multiple tags, wherein each tag includes a tag identifier representing the tag type and an enumeration value; determining a set of third user identifiers corresponding to each of the multiple tags from the tag data set; and performing logical operation processing on the multiple sets of third user identifiers corresponding to the multiple tags according to the hierarchical relationship and / or logical operation relationship between the multiple tags to obtain the first test result.

[0072] In this embodiment, after simulating and obtaining the first test case, the specific process of obtaining the first test result from the tag data set using the first test case is as follows: a first test file is generated based on the first test case; the first test file is traversed; and multiple tags including tag identifiers representing tag types and enumeration values, as well as hierarchical relationships and / or logical operation relationships between multiple tags are parsed from the first test file; a third user identifier set corresponding to each tag is determined based on the parsed multiple tags; and the third user identifier set is processed according to the hierarchical relationships and / or logical operation relationships between the parsed multiple tags to obtain the first test result.

[0073] For example, after generating a first test file in XML format based on the first test case, the first test file is recursively traversed in a depth-first manner to parse and obtain the tags of each node (each level relationship) in the tag tree corresponding to the selection rule represented by the first test case, as well as the logical operation relationship (AND / OR relationship) between the user identifier sets corresponding to each tag. Based on the obtained tags of each node, the tag data stored in the local file storage system is read to obtain the parsed user identifier set corresponding to each tag. Based on the logical operation relationship between each tag, the user identifier set corresponding to each tag is subjected to AND / OR operation to obtain the final selected user identifier set, that is, the first test result including the first user identifier set matching the first test case is obtained, and the first test result is written to the local file storage system.

[0074] In this embodiment of the invention, a first test file in XML format generated by recursively traversing the first test case is used to parse and obtain multiple tags and the hierarchical relationships and / or logical operation relationships between the multiple tags. A set of third user identifiers corresponding to each tag in the multiple tags is determined from the tag data set. Logical operation processing is performed on the multiple sets of third user identifiers according to the hierarchical relationships and / or logical operation relationships between the multiple tags to obtain the first test result. The SQL calculation method of the big data tag system to be tested is simulated to obtain the data result without generating an SQL file. This makes the local user selection processing logic independent of SQL and fully decoupled from the business processing logic of the big data tag system to be tested, thereby improving the accuracy of the verification results.

[0075] In some optional embodiments, obtaining the second test result of the big data tagging system based on the request sent to the big data tagging system includes: generating a first test file based on the first test case; traversing and parsing the first test file to obtain multiple tags and the hierarchical relationships and / or logical operation relationships between the multiple tags, each tag including a tag identifier representing the tag type and an enumeration value; sending a request carrying first information to the big data tagging system, the first information including the multiple tags and the hierarchical relationships and / or logical operation relationships between the multiple tags; and obtaining the second test result obtained by the big data tagging system based on the first information.

[0076] In this embodiment, the specific process of obtaining the second test result of the big data tagging system based on the request is as follows: a first test file is generated based on the first test case; the first test file is traversed, and multiple tags including tag identifiers representing tag types and enumeration values, as well as hierarchical relationships and / or logical operation relationships between multiple tags are parsed from the first test file; first information in a format readable by the big data tagging system is generated based on the parsed information; then, a request carrying the first information is sent to the big data tagging system, and the big data tagging system performs a selection process based on the first information to obtain the second test result including the second set of user identifiers.

[0077] For example, after generating a first test file in XML format based on the first test case, the first test file is traversed recursively to parse and obtain the labels of each node (each level relationship) in the label tree corresponding to the selection rules represented by the first test case, as well as the logical operation relationship (AND / OR relationship) between the user identifier sets corresponding to each label. Based on the parsed information, JSON information that can be written to the big data label system is generated, and a request carrying the JSON information is sent to the big data label system. The big data label system reads the JSON information in the obtained request and performs internal processing to generate an SQL file and query the label data set synchronized to the big data platform component to obtain a second user identifier set that conforms to the selection rules represented by the JSON information, so that the local system obtains a second test result including the second user identifier set obtained by the big data label system.

[0078] It should be noted that the embodiments of the present invention do not impose specific limitations on the specific process of obtaining the second test result from the big data tagging system. For example, after obtaining the second test result including the second set of user identifiers, the big data tagging system can write the download path of the second test result and the unique test case identifier corresponding to the first test case into the database of the big data tagging system. Locally, it sends a download request carrying the unique test case identifier corresponding to the first test case to the big data tagging system, finds the download path of the corresponding second test result through the test case identifier, and downloads the second test result according to the download path.

[0079] In some optional embodiments, determining whether the user selection processing test of the big data tagging system passes based on the comparison result includes: if the user identifiers in the first user identifier set and the user identifiers in the second user identifier set are the same, determining that the first test case test passes and the user selection processing test of the big data tagging system passes; if the user identifiers in the first user identifier set and the user identifiers in the second user identifier set are different, determining that the first test case test fails and the user selection processing test of the big data tagging system passes.

[0080] In this embodiment, after obtaining a comparison result by comparing the first test result and the second test result, the specific process of determining whether the big data tag system has passed the user selection processing test based on the comparison result is as follows: if the user identifiers in the first user identifier set and the user identifiers in the second user identifier set are completely consistent, the first test case test is determined and marked as passed, and then the selection processing performed by the big data tag system based on the selection rules represented by the first test case is determined to be correct, that is, the big data tag system has passed the user selection processing test. If the user identifiers in the first user identifier set and the user identifiers in the second user identifier set are not completely consistent, the first test case is determined and marked as failing. This leads to the conclusion that the big data tagging system's selection process based on the selection rules represented by the first test case is incorrect, meaning that the big data tagging system's user selection process test has failed.

[0081] In this embodiment of the invention, corresponding test cases are obtained by simulating selection rules under different business scenarios or selection requirements of the big data tagging system. According to the parsed tags and the hierarchical relationship and / or logical operation relationship between tags, the tag dataset of the local file storage system is read and the intersection, union, complement and other operation relationships of the user identifier set corresponding to the tag are calculated. By comparing the local test results and the test results of the big data tagging system, the selection rules under more business scenarios are tested, while the user selection processing capability of the big data tagging system is tested.

[0082] The solution adopted in this invention employs a completely decoupled implementation method from the underlying tag data set storage method, the selection rule simulation, and the user selection processing based on test cases. By comparing the test results obtained locally with the test results obtained based on the big data tag system, it is determined whether the user selection processing test of the big data tag system has passed. The big data tag system under test is verified by comparing the full amount of data rather than sampling test, thereby improving the accuracy of the verification results and increasing the testing efficiency.

[0083] Based on the above embodiments, this invention also provides a user tagging system testing device. Figure 4 This is a schematic diagram of the composition structure of the user tag system testing device according to an embodiment of the present invention; as shown below. Figure 4 As shown, the device includes: a data construction and storage module 21, a testing module 22, and a simulation module 23; wherein, The data construction and storage module 21 is used to construct and store a tag data set and synchronize the tag data set to the big data tag system; wherein, each tag data in the tag data set is indexed by a tag name representing the tag type and an enumerated value, and each tag data includes multiple user identifiers; The testing module 22 is used to simulate obtaining a first test case, which represents the user's selection rules; it is also used to determine whether the selection process performed by the big data tagging system based on the first test case has passed based on the comparison results. The simulation module 23 is configured to obtain a first test result from the tag data set using a first test case, wherein the first test result includes a first set of user identifiers matching the first test case; it is also configured to obtain a second test result from the big data tag system based on a request sent by the test module 22 to the big data tag system, wherein the request includes first information related to the first test case, and the second test result includes a second set of user identifiers obtained based on the first information; and it is also configured to compare the first test result and the second test result.

[0084] In an optional embodiment of the present invention, the data construction and storage module 21 includes: a data construction module 211, configured to generate a first preset number of multiple user identifiers; divide the multiple user identifiers based on a second preset number of tag types to obtain multiple first data sets, wherein the number of the first data sets is the second preset number; for each first data set, according to a first preset rule, copy at least one user identifier from at least one other first data set associated with each first data set into each first data set; one tag type corresponds to one first data set; based on the number of enumeration values ​​corresponding to each tag type, divide the multiple user identifiers included in the first data set corresponding to each tag type to obtain tag data indexed by tag identifiers representing tag types and enumeration values.

[0085] In an optional embodiment of the present invention, the test module 22 is configured to select a third preset number of indices from the indices corresponding to all the tag data in the tag data set as multiple tags to be tested; set the hierarchical relationship and / or logical operation relationship between the multiple tags; and generate a first test case based on the hierarchical relationship and / or logical operation relationship between the multiple tags.

[0086] In an optional embodiment of the present invention, the testing module 22 is used to generate a first test file based on the first test case; the simulation module 23 is used to traverse and parse the first test file to obtain multiple tags and the hierarchical relationship and / or logical operation relationship between the multiple tags, each tag including a tag identifier representing the tag type and an enumeration value; determine the third user identifier set corresponding to each of the multiple tags from the tag data set; and perform logical operation processing on the multiple third user identifier sets corresponding to the multiple tags according to the hierarchical relationship and / or logical operation relationship between the multiple tags to obtain the first test result.

[0087] In an optional embodiment of the present invention, the testing module 22 is configured to generate a first test file based on the first test case, traverse and parse the first test file to obtain multiple tags and hierarchical relationships and / or logical operation relationships between the multiple tags, each tag including a tag identifier representing the tag type and an enumeration value; send a request carrying first information to the big data tagging system, the first information including the multiple tags and hierarchical relationships and / or logical operation relationships between the multiple tags; and the simulation module 23 is configured to obtain the second test result obtained by the big data tagging system based on the first information.

[0088] In an optional embodiment of the present invention, the testing module 22 is configured to determine that the first test case has passed and the user selection process of the big data tagging system has passed if the user identifiers in the first user identifier set and the user identifiers in the second user identifier set are the same; and to determine that the first test case has failed and the user selection process of the big data tagging system has passed if the user identifiers in the first user identifier set and the user identifiers in the second user identifier set are different.

[0089] In the implementation of this invention, the data construction and storage module 21, data construction module 211, test module 22 and simulation module 23 in the device can all be implemented by the central processing unit (CPU), digital signal processor (DSP), microcontroller unit (MCU) or field-programmable gate array (FPGA) in the device in practical applications.

[0090] It should be noted that the above-described user tag system testing device is only used as an example to illustrate the division of the above program modules when performing user tag system testing. In actual applications, the above processing can be assigned to different program modules as needed, that is, the internal structure of the device can be divided into different program modules to complete all or part of the processing described above. In addition, the user tag system testing device and the user tag system testing method embodiment provided in the above embodiments belong to the same concept, and the specific implementation process can be found in the method embodiment, which will not be repeated here.

[0091] As an example, Figure 5 This is an interactive schematic diagram illustrating the user tag system testing implemented by the user tag system testing device according to an embodiment of the present invention; as shown. Figure 5 As shown, the specific process of user selection processing test of the big data tagging system through the data construction and storage module 21, test module 22, simulation module 23 in the user tagging system test device 20 and the big data tagging system 30 is as follows: Step 401: Data construction and storage module 21 constructs and stores the tag data set.

[0092] Specifically, the data construction module 211 generates a first preset number of multiple user identifiers, divides the multiple user identifiers into multiple first data sets based on a second preset number of tag types, and adds some user identifiers from other first data sets associated with each first data set according to a first preset rule, so that the same user can correspond to multiple tags. Based on the number of enumeration values ​​corresponding to each tag type, the multiple user identifiers included in the first data set corresponding to each tag type are divided to obtain tag data indexed by tag identifiers representing tag types and enumeration values. The generated tag data sets are stored locally through the local storage module 212 in the data construction storage module 21.

[0093] Step 402: The data construction and storage module 21 synchronizes the tag data set to the big data tag system 30.

[0094] Specifically, after constructing the tag data set, the data construction and storage module 21 writes the tag data set into big data platform components such as HBase, Hive, and Impala according to the system format of the big data tag system to be tested.

[0095] Step 403: Test module 22 simulates and obtains the first test case.

[0096] Specifically, the test module 22 extracts a third preset number of enumerated values ​​of tag types from the candidate set composed of the indices corresponding to all tag data in the tag data set, as multiple tags to be tested in the selection rules, and generates the first test case in XML format by setting the hierarchical relationship and / or logical operation relationship between the multiple tags to be tested.

[0097] Step 404: Test module 22 sends a test request carrying the first test case to simulation module 23.

[0098] Specifically, after obtaining the first test case, the test module 22 sends the first test case to the simulation module 23 to request the simulation module 22 to perform tests based on the first test case.

[0099] Step 405: Based on the first test case, simulation module 23 obtains the first test result, which includes the first user identifier set, from the tag data set.

[0100] Specifically, the simulation module 23 traverses the first test file in XML format generated by the test module 22, and parses out multiple tags, including tag identifiers representing tag types and enumeration values, as well as hierarchical relationships and / or logical operation relationships between multiple tags, from the first test file. Based on the parsed multiple tags, it determines the third user identifier set corresponding to each tag, and processes the third user identifier set according to the hierarchical relationships and / or logical operation relationships between the parsed multiple tags to obtain the first test result including the first user identifier set.

[0101] Step 406: Test module 22 sends a request to big data tagging system 30 carrying information related to the first test case.

[0102] Specifically, after generating the first test file in XML format, the test module 22 traverses the first test file and parses it to obtain the labels of each node in the label tree corresponding to the selection rules represented by the first test case, as well as the logical operation relationship between the user identifier sets corresponding to each label. Based on the parsed information, it generates JSON information that can be written to the big data label system 30 and sends a request carrying the JSON information to the big data label system 30 to request the big data label system 30 to perform testing based on the first test case.

[0103] Step 407: The big data tagging system 30 obtains a second test result, including a second set of user identifiers, based on a request carrying information related to the first test case.

[0104] Specifically, the big data tagging system 30 reads the JSON information from the obtained request and processes it internally to generate an SQL file and query the tag data set synchronized to the big data platform component, thereby obtaining a second test result that includes a second set of user identifiers that conform to the selection rules represented by the JSON information.

[0105] Step 408: Test module 22 sends a download request to big data tagging system 30.

[0106] Specifically, the test module 22 sends a download request to the big data tagging system 30, carrying a unique test case identifier corresponding to the first test case, in order to request the download of the second test result obtained by the big data tagging system 30.

[0107] Step 409: Simulation module 23 obtains the second test result from big data labeling system 30.

[0108] Specifically, after determining the download address of the second test result stored in the database based on the test case identifier in the download request sent by the test module 22, the big data tagging system 30 sends the download address back to the simulation module 23 so that the simulation module 23 can download the second test result from the big data tagging system 30 based on the download address.

[0109] Step 410: Simulation module 23 compares the first test result and the second test result to obtain the comparison result.

[0110] Specifically, the simulation module 23 compares the user identifiers of the first user identifier set in the first test result with the user identifiers of the second user identifier set in the second test result to obtain the comparison result.

[0111] Step 411: The simulation module 23 sends the obtained comparison results to the test module 22. The test module 22 determines whether the user selection process test of the big data tagging system 30 passes based on the comparison results.

[0112] Specifically, if the user identifier in the first user identifier set and the user identifier in the second user identifier set are completely identical, the test module 22 determines and marks the first test case as passed, and then determines that the user selection process of the big data tagging system 30 has passed the test. If the user identifiers in the first user identifier set and the user identifiers in the second user identifier set are not completely consistent, the first test case is determined and marked as failing, and thus the user selection processing test performed by the big data tagging system 30 is determined to be failing.

[0113] Figure 6 This is a schematic diagram of the system architecture of the user tagging system testing device according to an embodiment of the present invention; as shown below. Figure 6 As shown, at the system architecture level, the user tag system testing device 20 of this embodiment can be divided into a data layer 51, a backend service layer 52, and a pytest test driver layer 53.

[0114] The data layer 51 includes a data construction module 211 and a local storage module 212, which have functions such as automated data construction, data writing, and data storage to ensure data consistency between the local system and the big data tagging system under test.

[0115] The backend service layer 52 includes a simulation module 23, which is used to simulate the big data tagging system under test. It has user selection, local selection calculation and data verification functions. The user selection function specifically includes automatic selection configuration, XML file generation, XML to JSON parameter generation and other functions. The local selection calculation function includes XML file traversal and parsing, user tag set calculation and other functions. The data verification function specifically includes obtaining the download path of the test results, downloading the test results of the system under test, and comparing the test results and other functions.

[0116] The Pytest test driver layer 53 includes a test module 22, which drives the entire system architecture to complete the test process. It has functions such as virtual environment construction and management (venv), test case generation and management, and test report generation and management.

[0117] This invention also provides a computer device. Figure 7 This is a schematic diagram of the hardware composition structure of a computer device provided in an embodiment of the present invention; such as... Figure 7 As shown, the computer device includes a memory 62, a processor 61, and a computer program stored on the memory 62 and executable on the processor 61.

[0118] Optionally, when the processor 61 executes the program, it implements the steps of the user tag system testing method of the present invention.

[0119] Optionally, the computer device also includes at least one communication component 64. The various components in the computer device can be coupled together via a bus system 63. It is understood that the bus system 63 is used to implement communication between these components. In addition to a data bus, the bus system 63 also includes a power bus, a control bus, and a status signal bus. However, for clarity, in… Figure 7 The general labeled all buses as Bus System 63.

[0120] It is understood that memory 62 can be volatile memory or non-volatile memory, or both. Non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), ferromagnetic random access memory (FRAM), flash memory, magnetic surface memory, optical disc, or compact disc read-only memory (CD-ROM); magnetic surface memory can be disk storage or magnetic tape storage. Volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of RAM are available, such as Static Random Access Memory (SRAM), Synchronous Static Random Access Memory (SSRAM), Dynamic Random Access Memory (DRAM), Synchronous Dynamic Random Access Memory (SDRAM), Double Data Rate Synchronous Dynamic Random Access Memory (DDRSDRAM), Enhanced Synchronous Dynamic Random Access Memory (ESDRAM), SyncLink Dynamic Random Access Memory (SLDRAM), and Direct Rambus Random Access Memory (DRRAM).The memory 62 described in this embodiment of the invention is intended to include, but is not limited to, these and any other suitable types of memory.

[0121] The methods disclosed in the above embodiments of the present invention can be applied to processor 61, or implemented by processor 61. Processor 61 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the integrated logic circuit of the hardware in processor 61 or by instructions in the form of software. The processor 61 may be a general-purpose processor, DSP, or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Processor 61 can implement or execute the methods, steps and logic block diagrams disclosed in the embodiments of the present invention. The general-purpose processor may be a microprocessor or any conventional processor, etc. The steps of the methods disclosed in the embodiments of the present invention can be directly manifested as being executed by a hardware decoding processor, or being executed by a combination of hardware and software modules in the decoding processor. The software modules may be located in a storage medium, which is located in memory 62. Processor 61 reads the information in memory 62 and completes the steps of the aforementioned method in combination with its hardware.

[0122] In an exemplary embodiment, the computer device may be implemented by one or more application-specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), complex programmable logic devices (CPLDs), FPGAs, general-purpose processors, controllers, MCUs, microprocessors, or other electronic components to perform the aforementioned methods.

[0123] This invention also provides a computer-readable storage medium having a computer program stored thereon.

[0124] Optionally, the computer-readable storage medium can be applied to the user tag system testing apparatus of the present invention; then, when the program is executed by the processor, it implements the steps of the user tag system testing method of the present invention.

[0125] This invention also provides a computer program product, including a computer program that can be executed by a processor 61 of a computer device to complete the steps of the user tag system testing method described in this invention.

[0126] The methods disclosed in the several method embodiments provided in this application can be arbitrarily combined without conflict to obtain new method embodiments.

[0127] The features disclosed in the several product embodiments provided in this application can be arbitrarily combined without conflict to obtain new product embodiments.

[0128] The features disclosed in the several method or device embodiments provided in this application can be arbitrarily combined without conflict to obtain new method or device embodiments.

[0129] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods, such as: multiple units or components can be combined, or integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the various components shown or discussed can be through some interfaces, and the indirect coupling or communication connection between devices or units can be electrical, mechanical, or other forms.

[0130] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the units may be selected to achieve the purpose of this embodiment according to actual needs.

[0131] In addition, in the various embodiments of the present invention, each functional unit can be integrated into one processing unit, or each unit can be a separate unit, or two or more units can be integrated into one unit; the integrated unit can be implemented in hardware or in the form of hardware plus software functional units.

[0132] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0133] Alternatively, if the integrated units of this invention are implemented as software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of this invention, or the parts that contribute to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, ROM, RAM, magnetic disks, or optical disks.

[0134] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A user tagging system testing method, characterized in that, The method includes: A tag data set is constructed and stored, and the tag data set is synchronized to a big data tagging system; wherein, each tag data in the tag data set is indexed by a tag identifier representing the tag type and an enumeration value, and each tag data includes multiple user identifiers; A first test case is obtained through simulation, and a first test result is obtained from the tag data set using the first test case; the first test case represents the user selection rule, and the first test result includes a first user identifier set that matches the first test case; Based on a request sent to the big data tagging system, a second test result of the big data tagging system based on the request is obtained, wherein the request includes first information related to the first test case; the second test result includes a second set of user identifiers obtained based on the first information. Compare the first test result and the second test result, and determine whether the user selection process of the big data tagging system has passed the test based on the comparison result.

2. The method according to claim 1, characterized in that, Construct a tag data set, including: Generate a first preset number of multiple user identifiers; The multiple user identifiers are divided based on a second preset number of tag types to obtain multiple first data sets, the number of which is the second preset number. For each first data set, according to a first preset rule, at least one user identifier from at least one other first data set associated with each first data set is copied into each first data set; one tag type corresponds to one first data set; Based on the number of enumeration values ​​corresponding to each tag type, the multiple user identifiers included in the first data set corresponding to each tag type are divided to obtain tag data indexed by tag identifiers representing tag types and enumeration values.

3. The method according to claim 1, characterized in that, The simulation yields the first test case, including: Select a third preset number of indices from all the indices corresponding to the tag data in the tag dataset as multiple tags to be tested; Set the hierarchical relationship and / or logical operation relationship between the multiple tags, and generate the first test case based on the hierarchical relationship and / or logical operation relationship between the multiple tags.

4. The method according to any one of claims 1 to 3, characterized in that, The step of obtaining a first test result from the tag dataset using the first test case includes: A first test file is generated based on the first test case. The first test file is traversed and parsed to obtain multiple tags and hierarchical relationships and / or logical operation relationships between the multiple tags. Each tag includes a tag identifier representing the tag type and an enumeration value. Determine the third user identifier set corresponding to each of the plurality of tags from the tag data set; Based on the hierarchical relationship and / or logical operation relationship between the multiple tags, perform logical operation processing on the multiple sets of third user identifiers corresponding to the multiple tags to obtain the first test result.

5. The method according to any one of claims 1 to 3, characterized in that, The step of obtaining a second test result from the big data tagging system based on the request sent to the big data tagging system includes: A first test file is generated based on the first test case. The first test file is traversed and parsed to obtain multiple tags and hierarchical relationships and / or logical operation relationships between the multiple tags. Each tag includes a tag identifier representing the tag type and an enumeration value. Send a request carrying first information to the big data tagging system, wherein the first information includes the plurality of tags and the hierarchical relationship and / or logical operation relationship between the plurality of tags; Obtain the second test result obtained by the big data tagging system based on the first information.

6. The method according to claim 1, characterized in that, The step of determining whether the user selection process of the big data tagging system passes the comparison result includes: If the user identifier in the first user identifier set is the same as the user identifier in the second user identifier set, then the first test case is deemed to have passed the test, and the user selection processing test of the big data tagging system is deemed to have passed the test. If the user identifiers in the first user identifier set and the user identifiers in the second user identifier set are different, the first test case is determined to fail, and the user selection processing test of the big data tagging system is passed.

7. A user tagging system testing device, characterized in that, The device includes: a data construction and storage module, a simulation module, and a testing module; wherein... The data construction and storage module is used to construct and store a tag data set and synchronize the tag data set to the big data tag system; wherein, each tag data in the tag data set is indexed by a tag name representing the tag type and an enumerated value, and each tag data includes multiple user identifiers; The simulation module is configured to obtain a first test result from the tag data set using a first test case, wherein the first test result includes a first set of user identifiers matching the first test case; it is also configured to obtain a second test result from the big data tag system based on a request sent by the testing module to the big data tag system, wherein the request includes first information related to the first test case, and the second test result includes a second set of user identifiers obtained based on the first information; and it is further configured to compare the first test result and the second test result. The testing module is used to simulate and obtain a first test case, which represents the user's selection rules; it is also used to determine whether the selection process performed by the big data tagging system based on the first test case has passed based on the comparison results.

8. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the program implements the steps of the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, The computer program, when executed by a processor, implements the method according to any one of claims 1 to 6.

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