Quality inspection methods, devices, terminal equipment, and media for tag data in user profiles

Abstract

This application relates to the field of data quality analysis technology, and particularly to a method, apparatus, terminal device, and medium for quality inspection of tag data in user profiles. The method retrieves the unique identifiers of all users under a target tag and the total number of users from a tag database, determines the sub-attribute tags under the target tag, and retrieves the unique identifiers of users corresponding to each sub-attribute tag from the tag database. It then matches the unique identifiers of users corresponding to each sub-attribute tag with the unique identifiers of all users under the target tag to determine the number of users whose unique identifiers match under the corresponding sub-attribute tag. The number of users whose unique identifiers match under all sub-attribute tags is summed. If the summation result is greater than half of the total number of users, the quality inspection result of the target tag is determined to be passed. The credibility of the tag is determined using the concentration ratio of the sub-attribute tag in the tag, i.e., the summation result. This achieves full-coverage quality inspection and improves the accuracy of tag analysis and verification.

Description

Technical Field

[0001] This application relates to the field of data quality analysis technology, and in particular to a method, apparatus, terminal equipment and medium for quality inspection of tag data in user profiles. Background Technology

[0002] Currently, data tags, as a product of metadata processing, are not ideal for enterprises to use in production planning due to the diverse sources and inconsistent quality of business data. For predicted tag data generated by models in user profiles, quality checks are typically performed using precision and recall rates. However, calculating precision and recall requires obtaining the actual true values ​​of the prediction results. Therefore, this method has limitations; it may not fully cover all tag sub-attributes, and when checking the quality of preference attributes under predicted tags, precision and recall cannot evaluate the quality of high, medium, and low stratified results. Therefore, how to perform comprehensive quality checks on all tag sub-attributes to improve the accuracy of tag data quality checks has become an urgent problem to be solved. Summary of the Invention

[0003] In view of this, embodiments of this application provide a method, apparatus, terminal device and medium for quality inspection of tag data in user profiles, in order to solve the problem of how to perform full-coverage quality inspection of the sub-attributes of tags in order to improve the accuracy of tag data quality inspection.

[0004] In a first aspect, embodiments of this application provide a quality inspection method for tag data in a user profile, the quality inspection method comprising:

[0005] Retrieve the unique identifiers of all users under the target tag and the total number of users from the tag database;

[0006] Identify the sub-attribute tags under the target tag, and obtain the unique identifier of the user corresponding to each sub-attribute tag from the tag database;

[0007] Match the unique identifier of the user corresponding to each sub-attribute tag with the unique identifiers of all users under the target tag to determine the number of users whose unique identifiers match under the corresponding sub-attribute tag;

[0008] Sum the number of users whose unique identifiers match under all sub-attribute tags. If the summation result is greater than half of the total number of users, then the quality inspection result of the target tag is determined to be passed.

[0009] Secondly, embodiments of this application provide a quality inspection device for tag data in user profiles, the quality inspection device comprising:

[0010] The user data acquisition module is used to obtain the unique identifiers of all users under the target tag and the total number of users from the tag database.

[0011] The sub-attribute data acquisition module is used to determine the sub-attribute tags under the target tag and obtain the unique identifier of the user corresponding to each sub-attribute tag from the tag database;

[0012] The matching module is used to match the unique identifier of the user corresponding to each sub-attribute tag with the unique identifiers of all users under the target tag, and determine the number of users whose unique identifiers match under the corresponding sub-attribute tag;

[0013] The first quality inspection module is used to sum the number of users whose unique identifiers match under all sub-attribute tags. If the summation result is greater than half of the total number of users, the quality inspection result of the target tag is determined to be passed.

[0014] Thirdly, embodiments of this application provide a terminal device, the terminal device including a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the quality inspection method as described in the first aspect.

[0015] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the quality inspection method as described in the first aspect.

[0016] The beneficial effects of this application embodiment compared with the prior art are as follows: This application obtains the unique identifiers of all users under the target tag and the total number of users from the tag database, determines the sub-attribute tags under the target tag, and obtains the unique identifiers of users corresponding to each sub-attribute tag from the tag database. It matches the unique identifiers of users corresponding to each sub-attribute tag with the unique identifiers of all users under the target tag to determine the number of users whose unique identifiers match under the corresponding sub-attribute tag. It sums up the number of users whose unique identifiers match under all sub-attribute tags. If the summation result is greater than half of the total number of users, the quality inspection result of the target tag is determined to be passed. It matches the users of the target tag with the users of its sub-attribute tags to determine the concentration ratio of the sub-attribute tags in the target tag, i.e., the summation result. And the credibility of the target tag is determined based on the concentration ratio. This achieves full-coverage quality inspection and improves the accuracy, flexibility, applicability and convenience of tag analysis and verification. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a schematic diagram of an application environment for a quality inspection method for tag data in a user profile provided in Embodiment 1 of this application;

[0019] Figure 2 This is a flowchart illustrating a quality inspection method for tag data in a user profile, provided in Embodiment 2 of this application.

[0020] Figure 3 This is a flowchart illustrating a quality inspection method for tag data in a user profile, provided in Embodiment 3 of this application.

[0021] Figure 4 This is a schematic diagram of the structure of a quality inspection device for tag data in a user profile provided in Embodiment 4 of this application;

[0022] Figure 5 This is a schematic diagram of the structure of a terminal device provided in Embodiment 5 of this application. Detailed Implementation

[0023] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0024] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0025] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0026] As used in this application specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if detected [the described condition or event]" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once detected [the described condition or event]," or "in response to detection [the described condition or event]."

[0027] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0028] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0029] It should be understood that the sequence number of each step in the following embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0030] To illustrate the technical solution of this application, specific embodiments are described below.

[0031] The quality inspection method for tag data in user profiles provided in Embodiment 1 of this application can be applied to, for example... Figure 1 In this application environment, the client and server communicate with each other. Clients include, but are not limited to, handheld computers, desktop computers, laptops, ultra-mobile personal computers (UMPCs), netbooks, cloud terminal devices, and personal digital assistants (PDAs). The server can be implemented using a standalone server or a server cluster consisting of multiple servers.

[0032] See Figure 2This is a flowchart illustrating a quality inspection method for tag data in a user profile, provided in Embodiment 2 of this application. The aforementioned quality inspection method can be applied to... Figure 1 The server-side component connects to the database to retrieve the corresponding tag data. For example... Figure 2 As shown, this quality inspection method may include the following steps:

[0033] Step S201: Obtain the unique identifiers of all users under the target tag and the total number of users from the tag database.

[0034] In this application, the terminal device of the aforementioned server connects to the corresponding tag database to obtain the corresponding target tag and the tag data corresponding to the target tag. The tag data stored in the tag database may include tag name, tag number and tag value, etc. In addition, the tag data also includes the unique identifier of the user to which the tag data belongs, wherein the unique identifier may be the user identity document (ID) or MAC code, etc.

[0035] For a given tag, there may be multiple tag data entries. Each tag data entry corresponds to a user. The number of users under that tag, i.e., the total number of users, can be determined based on the user's unique identifier.

[0036] In one implementation, the target tags include multiple tags, each corresponding to a unique identifier for all users and the total number of users. In subsequent steps, each tag is processed one by one until all tags are traversed.

[0037] Step S202: Determine the sub-attribute tags under the target tag, and obtain the unique identifier of the user corresponding to each sub-attribute tag from the tag database.

[0038] In this application, a sub-attribute tag can be a tag belonging to an attribute of a corresponding tag. For a target tag, there may be multiple attributes, and each attribute corresponds to a tag, i.e., a sub-attribute tag. For example, if the target tag is "maternal and infant preference group", the corresponding sub-attribute could be "highly active in purchasing maternal and infant products" or "highly active in watching maternal and infant movies and TV shows", meaning that there are highly active shoppers and highly active movie and TV show viewers among the maternal and infant preference group.

[0039] The tag data under the target tag is sorted out to determine the sub-attribute tags under the target tag. Specifically, the type corresponding to each data object in each tag data of the target tag is extracted, and each type is a sub-attribute tag.

[0040] The aforementioned tag database stores tag data corresponding to each sub-attribute tag. The tag data contains the user corresponding to the data and their unique identifier. For any sub-attribute tag, the unique identifier of all users corresponding to the sub-attribute tag can be determined. By traversing all sub-attribute tags, the unique identifier of the user corresponding to each sub-attribute tag can be determined.

[0041] Step S203: Match the unique identifier of the user corresponding to each sub-attribute tag with the unique identifiers of all users under the target tag to determine the number of users whose unique identifiers match under the corresponding sub-attribute tag.

[0042] In this application, matching can refer to comparing unique identifiers, such as character matching, similarity matching, etc., to determine whether a user's unique identifier is the unique identifier of a user under the target tag.

[0043] Matching unique identifiers under corresponding sub-attribute tags can mean that the unique identifier of a user in the sub-attribute tag is the same as the unique identifier of a user in the target tag.

[0044] For example, the target tag is tag A, which contains sub-attribute tags B, C, and D. Tag A contains User 1 (ID: 1001), User 2 (ID: 1002), and User 3 (ID: 1003). Tag B contains User 1 (ID: 1001), User 3 (ID: 1003), and User 4 (ID: 1004). Tag C contains User 2 (ID: 1002), User 3 (ID: 1003), and User 4 (ID: 1004). Under the tag D, there are users 1 (ID: 1001), 2 (ID: 1002), and 3 (ID: 1003). For users 1 and 3 whose unique identifiers match the tag B, the total number of users whose unique identifiers match the tag B is 2. For users 2 and 3 whose unique identifiers match the tag C, the total number of users whose unique identifiers match the tag C is 2. For users 1, 2, and 3 whose unique identifiers match the tag D, the total number of users whose unique identifiers match the tag D is 3.

[0045] Step S204: Sum the number of users whose unique identifiers match under all sub-attribute tags. If the summation result is greater than half of the total number of users, then the quality inspection result of the target tag is determined to be passed.

[0046] In this application, in the above steps, the number of users matching the unique identification numbers under each sub-attribute label can be obtained. The numbers of users corresponding to all sub-attribute labels are summed up to obtain a summation result. This summation result can be used to represent the concentration ratio of the corresponding sub-attribute label, and the concentration ratio indicates the proportion of the sub-attribute label in the target label. For example, the number of users matching the unique identification number under label B is 2, the number of users matching the unique identification number under label C is 2, and the number of users matching the unique identification number under label D is 3, and the summation result is 7.

[0047] In the above step S201, the total number of users corresponding to the target label has been obtained. It is judged whether the summation result is greater than half of the total number of users. If the summation result is greater than half of the total number of users, it is determined that the quality inspection result of the target label is passed. If the summation result is not greater than half of the total number of users, it is determined that the quality inspection result of the target label is not passed.

[0048] For example, the target label is A, the corresponding total number of users is z, the sub-attribute labels are B and C respectively, and the numbers of users matching the unique identification numbers are b and c;

[0049] P(b) = b / z, P(c) = c / z;

[0050] Among them, P(b) and P(c) are the concentration ratios of the sub-attributes to the label respectively;

[0051] If P(b) + P(c) > P - (P(b) + P(c)), then the summation result is greater than half of the total number of users;

[0052] If P(b) + P(c) < P - (P(b) + P(c)), then the summation result is not greater than half of the total number of users.

[0053] Optionally, after determining that the quality inspection result of the target label is passed, it further includes:

[0054] Calculate the ratio of the summation result to the total number of users, and determine that the ratio result is the credibility of the target label.

[0055] Among them, the credibility is used to represent the trustworthy degree of the target label. When the credibility is greater than a certain value, the label data corresponding to the target label is available. When the credibility is less than a certain value, the portrait effect of using the label data corresponding to the target label for user portrait is poor. Referring to the above example, the credibility is (P(b) + P(c)) / P, and the credibility is (P(b) + P(c)) / P.

[0056] Optionally, after summing up the numbers of users matching the unique identification numbers under all sub-attribute labels, it further includes:

[0057] If the summation result is not greater than half of the total number of users, then the quality inspection result of the target tag is determined to be unsuccessful.

[0058] If the summation result is not greater than half of the total number of users, it indicates that the sub-attribute tags in the target tag do not meet the level of credibility. Therefore, the quality inspection result of the target tag is not passed.

[0059] This application embodiment obtains the unique identifiers of all users under the target tag and the total number of users from the tag database, determines the sub-attribute tags under the target tag, and obtains the unique identifiers of users corresponding to each sub-attribute tag from the tag database. It then matches the unique identifiers of users corresponding to each sub-attribute tag with the unique identifiers of all users under the target tag to determine the number of users whose unique identifiers match under the corresponding sub-attribute tag. The sum of the number of users whose unique identifiers match under all sub-attribute tags is calculated. If the sum is greater than half of the total number of users, the quality inspection result of the target tag is determined to be passed. The users of the target tag are matched with the users of its sub-attribute tags to determine the concentration ratio of the sub-attribute tags in the target tag, i.e., the summation result. The credibility of the target tag is determined based on the concentration ratio, achieving full-coverage quality inspection and improving the accuracy, flexibility, applicability, and convenience of tag analysis and verification.

[0060] See Figure 3 This is a flowchart illustrating a quality inspection method for tag data in a user profile, as provided in Embodiment 3 of this application. Figure 3 As shown, this quality inspection method may include the following steps:

[0061] Step S301: Obtain the label data of N labels to be inspected, and check whether the data of each label conforms to the preset inspection rules.

[0062] In this application, N is an integer greater than zero. Pre-processing is performed on one or more labels to be inspected. This pre-processing is used to detect the label data to determine whether the label data meets the preset detection conditions, thereby improving the accuracy of subsequent quality inspection of the target label.

[0063] Because the sources of tag data are diverse, there may be some unusable data in the data, such as empty data, out-of-range data, and uncontrollable data. Therefore, the preset detection rules are constructed based on the judgment conditions for how to determine whether tag data is usable data.

[0064] Optionally, checking whether each tag data conforms to preset detection rules includes:

[0065] Extract the tag value for each tag data and determine whether the tag value exceeds the threshold;

[0066] If the label value does not exceed the threshold, the corresponding label data is determined to be label data that conforms to the preset detection rules; otherwise, if the label value exceeds the threshold, the corresponding label data is determined to be label data that does not conform to the preset detection rules.

[0067] In determining the label value in the label data, the label value is generally compared with a threshold. If the label value exceeds the threshold, it means that the label value is unusable data and does not meet the preset detection rules; otherwise, it means that the label value is usable data and meets the preset detection rules.

[0068] This threshold can be a range; if the label value in the label data is not within this range, it is considered to exceed the threshold.

[0069] Optionally, checking whether each tag data conforms to preset detection rules includes:

[0070] Extract the tag value type for each tag data and determine whether the tag value type matches the preset type;

[0071] If the tag value type matches the preset type, the corresponding tag data is determined to be tag data that meets the preset detection rules; otherwise, if the tag value type does not match the preset type, the corresponding tag data is determined to be tag data that does not meet the preset detection rules.

[0072] The tag value type can refer to data types such as string, enumeration, boolean, date, and number. The preset types can include one or more data types. It is determined whether the tag value type of each tag data belongs to any of the preset types. If it does, it means that the corresponding tag data conforms to the preset type, the corresponding tag value is usable data, and it meets the preset detection rules; otherwise, it means that the tag data does not conform to the preset type, the corresponding tag value is unusable data, and it does not meet the preset detection rules.

[0073] Optionally, checking whether each tag data conforms to preset detection rules includes:

[0074] Extract the data structure of each tag and determine whether the data structure falls within the preset range of data structure variations;

[0075] If the data structure falls within the range of data structure changes, the corresponding label data is determined to be label data that conforms to the preset detection rules; otherwise, if the data structure does not fall within the range of data structure changes, the corresponding label data is determined to be label data that does not conform to the preset detection rules.

[0076] The data structure can refer to structures such as arrays, stacks, queues, linked lists, trees, hash tables, heaps, and graphs. The preset range of data structure variations can include one or more data structures. The system determines whether the data structure of each tag belongs to any data structure within the preset range of data structure variations. If it does, the corresponding tag data is usable and meets the preset detection rules; otherwise, the tag data is unusable and does not meet the preset detection rules.

[0077] Step S302: Store the detected label data that conforms to the preset detection rules locally, and determine the label to be inspected corresponding to the locally stored label data as the target label.

[0078] In this application, the tag data that conforms to the detection rules is the target data. Storing the target data locally can help with subsequent quality inspection of the target data. The tag corresponding to the target data is the target tag. There may be one or more target data stored locally, and there may be one or more tags corresponding to the target data. Therefore, when performing quality inspection on the target tags, it is necessary to perform quality inspection on each tag separately until all tags within the target tag are traversed.

[0079] Step S303: Obtain the unique identifiers of all users under the target tag and the total number of users from the tag database.

[0080] Step S304: Determine the sub-attribute tags under the target tag, and obtain the unique identifier of the user corresponding to each sub-attribute tag from the tag database.

[0081] Step S305: Match the unique identifier of the user corresponding to each sub-attribute tag with the unique identifiers of all users under the target tag to determine the number of users whose unique identifiers match under the corresponding sub-attribute tag.

[0082] Step S306: Sum the number of users whose unique identifiers match under all sub-attribute tags. If the summation result is greater than half of the total number of users, then the quality inspection result of the target tag is determined to be passed.

[0083] Steps S303 to S306 are the same as steps S201 to S204 above, and can be referred to the description of steps S201 to S204, which will not be repeated here.

[0084] Before conducting quality inspection on the labels, this application embodiment also screens the labels to be inspected to determine the target labels, thereby eliminating non-compliant labels and label data, which helps to improve the accuracy of subsequent label quality inspection.

[0085] Corresponding to the quality inspection method in the above embodiments, Figure 4This diagram illustrates the structural block diagram of a quality inspection device for tag data in user profiles provided in Embodiment 4 of this application. The aforementioned quality inspection device can be applied to… Figure 1 The server in the diagram connects to the database to obtain the corresponding tag data. For ease of explanation, only the parts relevant to the embodiments of this application are shown.

[0086] See Figure 4 The quality inspection device includes:

[0087] User data acquisition module 41 is used to obtain the unique identifiers of all users under the target tag and the total number of users from the tag database;

[0088] The sub-attribute data acquisition module 42 is used to determine the sub-attribute tags under the target tag and obtain the unique identifier of the user corresponding to each sub-attribute tag from the tag database;

[0089] Matching module 43 is used to match the unique identifier of the user corresponding to each sub-attribute tag with the unique identifiers of all users under the target tag, and determine the number of users whose unique identifiers match under the corresponding sub-attribute tag;

[0090] The first quality inspection module 44 is used to sum the number of users whose unique identifiers match under all sub-attribute tags. If the summation result is greater than half of the total number of users, the quality inspection result of the target tag is determined to be passed.

[0091] Optionally, the quality inspection device also includes:

[0092] The tag data detection module is used to obtain tag data of N tags to be inspected before obtaining the unique identifiers of all users under the target tag and the total number of users from the tag database, and to detect whether each tag data conforms to the preset detection rules, where N is an integer greater than zero;

[0093] The target label determination module is used to store the detected label data that conforms to the preset detection rules locally, and determine the label to be inspected corresponding to the locally stored label data as the target label.

[0094] Optionally, the above-mentioned label data detection module includes:

[0095] The tag value extraction unit is used to extract the tag value of each tag data and determine whether the tag value exceeds the threshold.

[0096] The first determination unit is used to determine that if the label value does not exceed the threshold, the corresponding label data is label data that conforms to the preset detection rules, or if the label value exceeds the threshold, the corresponding label data is label data that does not conform to the preset detection rules.

[0097] Optionally, the above-mentioned label data detection module includes:

[0098] The type extraction unit is used to extract the tag value type of each tag data and determine whether the tag value type conforms to the preset type;

[0099] The second determination unit is used to determine that if the label value type conforms to the preset type, the corresponding label data is label data that conforms to the preset detection rules; or if the label value type does not conform to the preset type, the corresponding label data is label data that does not conform to the preset detection rules.

[0100] Optionally, the above-mentioned label data detection module includes:

[0101] The structure extraction unit is used to extract the data structure of each tag data and determine whether the data structure belongs to the preset data structure variation range.

[0102] The third determination unit is used to determine that if the data structure falls within the range of data structure changes, the corresponding label data is label data that conforms to the preset detection rules; or if the data structure does not fall within the range of data structure changes, the corresponding label data is label data that does not conform to the preset detection rules.

[0103] Optionally, the quality inspection device also includes:

[0104] The credibility determination module is used to calculate the ratio of the summation result to the total number of users after determining that the quality inspection result of the target tag is passed, and to determine the credibility of the target tag based on the ratio result.

[0105] Optionally, the quality inspection device may also include:

[0106] The second quality inspection module is used to sum the number of users whose unique identifiers match under all sub-attribute tags. If the summation result is greater than half of the total number of users, the quality inspection result of the target tag is determined to be unsuccessful.

[0107] It should be noted that the information interaction and execution process between the above modules are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method embodiments section, which will not be repeated here.

[0108] Figure 5 This is a schematic diagram of the structure of a terminal device provided in Embodiment 5 of this application. Figure 5 As shown, the terminal device of this embodiment includes: at least one processor ( Figure 5 Only one is shown in the diagram), a memory, and a computer program stored in the memory and executable on at least one processor, which, when executed by the processor, implements the steps in any of the above-described quality inspection method embodiments.

[0109] The terminal device may include, but is not limited to, a processor and memory. Those skilled in the art will understand that... Figure 5 This is merely an example of a terminal device and does not constitute a limitation on the terminal device. A terminal device may include more or fewer components than shown in the figure, or a combination of certain components, or different components, such as network interfaces, displays, and input devices.

[0110] The processor referred to can be a CPU, but it can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor.

[0111] The memory includes readable storage media, internal memory, etc., wherein the internal memory can be the main memory of the terminal device, and the internal memory provides an environment for the operation of the operating system and computer-readable instructions stored in the readable storage media. The readable storage media can be the hard drive of the terminal device, or in some embodiments, it can be an external storage device of the terminal device, such as a plug-in hard drive, a Smart Media Card (SMC), a Secure Digital Card (SD), or a Flash Card. Furthermore, the memory can include both internal storage units and external storage devices of the terminal device. The memory is used to store the operating system, applications, bootloader, data, and other programs, such as program code for computer programs. The memory can also be used to temporarily store data that has been output or will be output.

[0112] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above device can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here. If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of this application can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the above method embodiments. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. A computer-readable medium can include at least: any entity or device capable of carrying computer program code, a recording medium, a computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media. Examples include USB flash drives, portable hard drives, magnetic disks, or optical disks. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electrical carrier signals or telecommunication signals.

[0113] The implementation of all or part of the processes in the methods of the above embodiments can also be accomplished by a computer program product. When the computer program product is run on a terminal device, the terminal device executes the steps in the above method embodiments.

[0114] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0115] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0116] In the embodiments provided in this application, it should be understood that the disclosed apparatus / terminal devices and methods can be implemented in other ways. For example, the apparatus / terminal device embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0117] The units described 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 can be selected to achieve the purpose of this embodiment according to actual needs.

[0118] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A quality inspection method for tag data in user profiles, characterized in that, The quality inspection methods include: Retrieve the unique identifiers of all users under the target tag and the total number of users from the tag database; Identify the sub-attribute tags under the target tag, and obtain the unique identifier of the user corresponding to each sub-attribute tag from the tag database; The unique identifier of each user in each sub-attribute tag is matched with the unique identifier of all users under the target tag to determine the number of users whose unique identifier matches under the corresponding sub-attribute tag. The matching of unique identifiers under the corresponding sub-attribute tag means that the unique identifier of a user in the corresponding sub-attribute tag is the same as the unique identifier of a user in the target tag. Sum the number of users whose unique identifiers match under all sub-attribute tags. If the summation result is greater than half of the total number of users, then the quality inspection result of the target tag is determined to be passed.

2. The quality inspection method according to claim 1, characterized in that, Before retrieving the unique identifiers of all users under the target tag and the total number of users from the tag database, the process also includes: Obtain the label data of N labels to be inspected, and check whether each label data meets the preset inspection rules, where N is an integer greater than zero; The detected label data that conforms to the preset detection rules is stored locally, and the label to be inspected corresponding to the locally stored label data is determined as the target label.

3. The quality inspection method according to claim 2, characterized in that, Checking whether each tag data conforms to the preset detection rules includes: Extract the tag value for each tag data and determine whether the tag value exceeds the threshold; If the label value does not exceed the threshold, the corresponding label data is determined to be label data that conforms to the preset detection rules; or if the label value exceeds the threshold, the corresponding label data is determined to be label data that does not conform to the preset detection rules.

4. The quality inspection method according to claim 2, characterized in that, Checking whether each tag data conforms to the preset detection rules includes: Extract the tag value type for each tag data and determine whether the tag value type matches the preset type; If the tag value type matches the preset type, the corresponding tag data is determined to be tag data that meets the preset detection rules; otherwise, if the tag value type does not match the preset type, the corresponding tag data is determined to be tag data that does not meet the preset detection rules.

5. The quality inspection method according to claim 2, characterized in that, Checking whether each tag data conforms to the preset detection rules includes: Extract the data structure of each tag and determine whether the data structure falls within the preset range of data structure variations; If the data structure falls within the range of data structure changes, the corresponding tag data is determined to be tag data that conforms to the preset detection rules; otherwise, if the data structure does not fall within the range of data structure changes, the corresponding tag data is determined to be tag data that does not conform to the preset detection rules.

6. The quality inspection method according to claim 1, characterized in that, After determining that the quality inspection result of the target label is passed, the process also includes: Calculate the ratio of the summation result to the total number of users, and determine the ratio result as the credibility of the target tag.

7. The quality inspection method according to any one of claims 1 to 6, characterized in that, After summing the number of users whose unique identifiers match under all sub-attribute tags, the following is also included: If the summation result is not greater than half of the total number of users, then the quality inspection result of the target tag is determined to be unsuccessful.

8. A quality inspection device for tag data in user profiles, characterized in that, The quality inspection device includes: The user data acquisition module is used to obtain the unique identifiers of all users under the target tag and the total number of users from the tag database. The sub-attribute data acquisition module is used to determine the sub-attribute tags under the target tag and obtain the unique identifier of the user corresponding to each sub-attribute tag from the tag database; The matching module is used to perform similarity matching between the unique identifier of each user in each sub-attribute tag and the unique identifier of all users under the target tag, and to determine the number of users whose unique identifiers match under the corresponding sub-attribute tag. The matching of unique identifiers under the corresponding sub-attribute tag means that the unique identifier of a user in the corresponding sub-attribute tag is the same as the unique identifier of a user in the target tag. The first quality inspection module is used to sum the number of users whose unique identifiers match under all sub-attribute tags. If the summation result is greater than half of the total number of users, the quality inspection result of the target tag is determined to be passed.

9. A terminal device, characterized in that, The terminal device includes a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the quality inspection method as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the quality inspection method as described in any one of claims 1 to 7.

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