A data auditing method and system, an electronic device, and a storage medium

Abstract

This application relates to the field of data processing technology, and more specifically, to a data auditing method, system, electronic device, and storage medium. The method includes: obtaining a subject to be audited; matching the subject to be audited with subjects in a dynamic auditing subject set; wherein the dynamic auditing subject set corresponds to a preset auditing tag, and is obtained by filtering historical data of the subjects according to preset filtering rules of the auditing tags, and updating based on the filtered target subjects; when a match is found, obtaining the auditing tag corresponding to the dynamic auditing subject set; obtaining a preset auditing strategy based on the corresponding auditing tag, and performing data auditing on the subject to be audited based on the auditing strategy. This application aims to improve the flexibility of data auditing.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and more specifically, to a data auditing method, system, electronic device, and storage medium. Background Technology

[0002] With the increasing diversity of internet content formats, various audio, video, text, and real-time interactive content have become core components of platform ecosystems. To ensure content security and maintain a healthy platform environment, platforms must conduct efficient and accurate data review of massive amounts of content. However, traditional review solutions often employ hard-coded logic and fragmented, temporary configuration items. While this architecture can support basic review processes, it lacks overall flexibility and scalability. When faced with rapidly changing review requirements and the need for refined operational review, this rigid approach struggles to respond agilely—whether in real-time scenarios like live streaming or non-real-time content such as short videos and text. Failure to intervene promptly in cases of violations or accounts can lead to a lack of timeliness and reliability in handling such violations or abnormal accounts, potentially causing business disruptions from a technical perspective. Summary of the Invention

[0003] This invention provides a data auditing method, system, electronic device, and storage medium to improve the flexibility of data auditing.

[0004] According to a first aspect of this application, a data auditing method is provided, the method comprising: Obtain the entity to be reviewed; The pending review entities are matched with the target review entities in the dynamic review entity set; wherein the dynamic review entity set corresponds to a preset review tag, and is obtained by filtering the historical data of the review entities according to the preset filtering rules of the review tag, and updating based on the filtered target review entities; When a match is found, the audit tag corresponding to the dynamic audit subject set is obtained; The preset review strategy is obtained based on the corresponding review tag, and the data of the subject to be reviewed is reviewed based on the review strategy.

[0005] Understandably, by constructing a dynamic set of audit subjects and pre-setting filtering rules and corresponding audit strategies for each audit tag, and by filtering and updating the target audit subjects in the dynamic set of audit subjects based on their historical data, dynamic adaptation and precise execution of audit rules are achieved. In practical applications, for audit subjects requiring special handling, this application can effectively distinguish them from ordinary audit subjects by setting corresponding audit tags, dynamic sets of audit subjects, and audit strategies, avoiding the execution of conventional strategies due to misjudgment by audit personnel. The filtering rules for audit tags in this application support on-demand customization, allowing audit strategies to be quickly adjusted according to changes in business or regulatory needs without modifying the underlying code, achieving high reliability, low latency, and refined operational capabilities for data auditing.

[0006] Optionally, the dynamic review subject set corresponds to a preset review tag, and is obtained by filtering historical data of review subjects according to the preset filtering rules of the review tags, and updating based on the filtered target review subjects, including: Pre-set review tags and set update cycles and filtering rules for the review tags, wherein the review tags correspond to the dynamic review subject set; Based on the update cycle and filtering rules of the review tags, the current historical data of the review subject is obtained periodically, and the current historical data is filtered based on the filtering rules of the review tags to obtain the filtered target review subject. Update the dynamic audit subject set corresponding to the audit tag by the selected target audit subjects.

[0007] Understandably, the review tags introduce a configurable update cycle. By periodically obtaining the current historical data of the review subject and executing the filtering rules, the dynamic review subject set is periodically updated. This ensures that the target review subject in the dynamic review subject set is always synchronized with the actual business status, avoiding the data lag and manual maintenance costs caused by static configuration. Thus, while meeting the requirements of rapid regulatory changes, it significantly improves the accuracy and response efficiency of data review.

[0008] Optionally, the filtering rules include a data source type and rule content; The step of filtering the historical data of the audit subject according to the preset filtering rules of the audit label includes: Based on the data source type in the filtering rules of the aforementioned review tags, historical data of the review subject is obtained; The historical data is filtered based on the rules in the filtering rules of the aforementioned review tags.

[0009] Understandably, by configuring the data source type and rule content for the filtering rules, flexible acquisition and refined filtering of historical data of the auditing entity are achieved. Historical data can be retrieved from a database of a specified data source type as needed, and the historical data of the auditing entity can be filtered in combination with the rule content. This accurately identifies the target auditing entities that need to be included in the dynamic auditing entity set, greatly improving the adaptability of the dynamic auditing entity set and the scalability of the filtering rules. This enables the dynamic auditing entity set to iterate efficiently with changes in business scenarios, effectively reducing manual intervention and enhancing the accuracy of auditing strategy execution.

[0010] Optionally, the historical data of the auditing entity is stored in a first database and / or a second database and / or an external database, and the data source type includes an original database acquisition type corresponding to the first database, a transfer unit acquisition type corresponding to the second database, and a storage interface acquisition type corresponding to the external database. The historical data of the review subject obtained from the data source type in the filtering rules based on the review tags includes: When the data source type in the filtering rules of the review label is the original database acquisition type, the historical data of the review subject is obtained from the first database; When the data source type in the filtering rules of the audit tag is the transfer unit acquisition type, the historical data of the audit subject is obtained from the transfer unit, wherein the historical data of the audit subject in the transfer unit is obtained by transferring the historical data of the audit subject in the second database; When the data source type in the filtering rules of the audit tag is the storage interface acquisition type, the historical data of the audit subject is obtained from the external database through the storage interface.

[0011] Understandably, by distinguishing between three data source types—the original database, the transfer unit, and the external storage interface—it supports on-demand selection of direct query, query after transfer, or external query, effectively balancing the real-time nature of data acquisition, scalability, and the frequency limitations of external interface calls, thereby significantly improving the construction efficiency of dynamic audit subject sets and the overall robustness of data audit.

[0012] Optionally, prior to the step of obtaining the subject to be reviewed, the method further includes: The dynamic review subject set is stored in the third database, and the mapping relationship between the dynamic review subject set and the review tag is stored in the fourth database. The step of matching the pending review subject with the target review subject in the dynamic review subject set includes: matching the pending review subject with the target review subject in the dynamic review subject set in the third database; The step of obtaining the audit tags corresponding to the dynamic audit subject set includes: obtaining the audit tags corresponding to the dynamic audit subject set based on the fourth database.

[0013] Understandably, storing the dynamic review subject set in the third database and the mapping relationship between the dynamic review subject set and the review tag in the fourth database allows the matching operation to be performed efficiently directly based on the third database, while the review tag is quickly located through the fourth database. This decouples and separates the review subject matching and review tag query, significantly reducing the access pressure and data redundancy of a single database, and improving the concurrent response speed and data review maintainability in the review process.

[0014] Optionally, the review strategy includes review operations; The data review of the subject to be reviewed based on the review strategy includes: Based on the audit operations in the audit strategy, data audits are conducted on the entities to be audited.

[0015] Understandably, the review strategy includes review operations, which allow the corresponding review operations to be invoked directly after a review tag is matched to perform data review. This simplifies the intermediate steps in the review execution chain, improves the certainty of the review response and execution efficiency, and supports flexible configuration of differentiated review operation behaviors for different review subjects, thereby enhancing the programmability and reusability of data review.

[0016] Optionally, the audit operation includes an audit execution object label; The data audit of the subject to be audited, based on the audit operation in the audit strategy, includes: The subject to be reviewed is assigned to the audit execution object specified by the audit execution object label, so that the audit execution object performs data audit on the subject to be reviewed based on the audit operation in the audit strategy.

[0017] Understandably, by introducing audit execution object tags into the audit operation, the precise assignment of pending audit subjects is achieved. Different audit tasks can be automatically assigned to the audit execution objects specified by the audit execution object tags, which significantly improves the automation level and flexibility of audit task distribution. It supports differentiated scheduling of audit execution objects according to the characteristics of pending audit subjects, thereby optimizing the utilization rate of audit resources and shortening the response time of audit tasks.

[0018] According to a second aspect of this application, a data auditing system is provided, the system comprising: The acquisition module is used to acquire entities pending review. The matching module is used to match the pending review subjects with the target review subjects in the dynamic review subject set; wherein the dynamic review subject set corresponds to a preset review tag, and is obtained by filtering the historical data of the review subjects according to the preset filtering rules of the review tag, and updating based on the filtered target review subjects; The review tag acquisition module is used to acquire the review tags corresponding to the dynamic review subject set when a match is found. The review module is used to obtain the preset review strategy based on the corresponding review tag, and to conduct data review on the subject to be reviewed based on the review strategy.

[0019] According to a third aspect of this application, an electronic device is provided, comprising: Memory, used to store one or more computer programs; A processor, when the one or more computer programs are executed by the processor, implements the data auditing method described in the first aspect above.

[0020] According to a fourth aspect of this application, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the data auditing method described in the first aspect above.

[0021] Based on any of the above aspects, the data auditing method, system, electronic device, and storage medium provided in this application embodiment can achieve the following technical effects: (1) By constructing a dynamic audit subject set and pre-setting filtering rules and corresponding audit strategies for each audit tag, and by filtering and updating the target audit subjects in the dynamic audit subject set based on the historical data of the audit subjects, dynamic adaptation and precise execution of audit rules are achieved. In practical applications, for audit subjects that require special handling, this application can effectively distinguish them from ordinary audit subjects by setting corresponding audit tags, dynamic audit subject sets, and audit strategies, avoiding the execution of conventional strategies due to misjudgment by audit personnel; the filtering rules of the audit tags in this application support on-demand customization, so that the audit strategy can be quickly adjusted according to changes in business or regulatory needs without modifying the underlying code, achieving high reliability, low latency, and refined operation capabilities for data auditing.

[0022] (2) The review tags introduce a configurable update cycle. By periodically obtaining the current historical data of the review subject and executing the filtering rules, the periodic self-updating of the dynamic review subject set is realized. This ensures that the target review subject in the dynamic review subject set is always synchronized with the actual business status, avoiding the data lag and manual maintenance costs caused by static configuration. Thus, while meeting the requirements of rapid regulatory changes, the accuracy and response efficiency of data review are significantly improved.

[0023] (3) By configuring the data source type and rule content for the filtering rules, the historical data of the audit subject can be flexibly obtained and finely filtered. Historical data can be obtained from the database of the specified data source type as needed, and the historical data of the audit subject can be filtered in combination with the rule content. This accurately identifies the target audit subjects that need to be included in the dynamic audit subject set, greatly improving the adaptability of the dynamic audit subject set and the scalability of the filtering rules. This enables the dynamic audit subject set to iterate efficiently with changes in business scenarios, effectively reducing manual intervention and enhancing the accuracy of the audit strategy execution.

[0024] (4) By distinguishing between three types of data sources, namely the original database, the transfer unit and the external storage interface, it supports direct query, query after transfer or external query as needed, effectively taking into account the real-time nature of data acquisition, scalability and the frequency limit of external interface calls, thereby significantly improving the construction efficiency of dynamic audit subject set and the overall robustness of data audit.

[0025] (5) The dynamic review subject set is stored in the third database, and the mapping relationship between the dynamic review subject set and the review tag is stored in the fourth database. This allows the matching operation to be performed efficiently based on the third database, while the review tag is quickly located through the fourth database. This achieves the decoupling and separate storage of review subject matching and review tag query, significantly reducing the access pressure and data redundancy of a single database, and improving the concurrent response speed and data review maintainability in the review process.

[0026] (6) The audit strategy has audit operations, which enable the corresponding audit operations to be called directly after the audit tag is matched to perform data audit. This simplifies the intermediate links of the audit execution chain, improves the certainty of the audit response and the execution efficiency, and supports flexible configuration of differentiated audit operation behaviors for different audit subjects, thereby enhancing the programmability and reusability of data audit.

[0027] (7) By introducing audit execution object tags in the audit operation, the precise assignment of pending audit subjects is realized. Different audit tasks can be automatically assigned to the audit execution objects specified by the audit execution object tags, which significantly improves the automation level and flexibility of audit task distribution. It supports differentiated scheduling of audit execution objects according to the characteristics of pending audit subjects, thereby optimizing the utilization rate of audit resources and shortening the response time of audit tasks. Attached Figure Description

[0028] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying 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.

[0029] Figure 1 This is an illustrative application scenario diagram of a data auditing method provided in this embodiment.

[0030] Figure 2 This is a flowchart of a data auditing method provided in this embodiment.

[0031] Figure 3 This is a flowchart of the updated dynamic review subject set provided in this embodiment.

[0032] Figure 4 This is a flowchart for filtering historical data provided in this embodiment.

[0033] Figure 5 This is a schematic diagram of the functional modules of a data auditing system provided in this embodiment.

[0034] Figure 6 This is a schematic diagram of the structure of the electronic device provided in this embodiment. Detailed Implementation

[0035] The accompanying drawings are for illustrative purposes only and should not be construed as limiting the scope of this application. To better illustrate the following embodiments, some components in the drawings may be omitted, enlarged, or reduced, and do not represent the actual dimensions of the product; it is understandable to those skilled in the art that some well-known structures and their descriptions may be omitted in the drawings.

[0036] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0037] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings 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 in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover 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.

[0038] Traditional data moderation solutions rely on hard-coded and fragmented configurations, resulting in rigid architectures and a lack of scalability, making it difficult to cope with rapidly changing moderation needs. Whether in real-time scenarios like live streaming or non-real-time content, this inflexibility leads to violations or accounts not being dealt with promptly. Ultimately, this technological lag can trigger business disruption risks, threatening platform content security and the health of the ecosystem.

[0039] This embodiment provides a technical solution that can solve the above problems. The specific implementation of this application will be described in detail below with reference to the accompanying drawings.

[0040] An exemplary diagram illustrating an application scenario of a data auditing method provided in this application embodiment is shown below. Figure 1 As shown, the application scenario includes at least a server 100 and a terminal 200 that can communicate with the server 100.

[0041] Understandably, the server 100 can be an independent electronic device or a cluster of multiple electronic devices; the terminal 200 can be a smartphone terminal, personal computer, tablet computer, vehicle terminal, etc., but is not limited to these.

[0042] In one feasible implementation, server 100 and terminal 200 may each execute a data auditing method provided in the embodiments of this application. Alternatively, the data auditing method provided in the embodiments of this application may be partially executed in server 100 and partially executed in terminal 200.

[0043] like Figure 2 As shown, this embodiment provides a data auditing method, which can be further divided into the following steps: S100, Obtain the subject of pending review; Understandably, to create a rich interactive environment, platforms typically allow users to upload interactive content such as audio, video, and text. For example, a live streamer can start a live broadcast on a live streaming platform and upload real-time video for other users to watch or interact with. However, to maintain a healthy platform environment and ensure that all presented content meets review requirements, the platform must review uploaded content and handle non-compliant content. In this embodiment, the subject to be reviewed refers to candidate subjects on the platform that need to be reviewed, including but not limited to live streamers who have just started broadcasting on the live streaming platform and users participating in the live stream interaction.

[0044] Preferably, the entity awaiting review is assigned a unique identifier. For example, the entity awaiting review receives a unique identifier during platform registration, allowing for quick location of the entity during review or other operations. In this embodiment, obtaining the entity awaiting review allows for the acquisition of its identifier, providing a convenient query index for subsequent matching and review operations, thus improving review efficiency. For example, in a live streaming platform, the user identifier of a newly started broadcaster can be obtained; in a text and image platform, the user identifier of a user who has just uploaded text and images can be obtained.

[0045] S200. Match the pending review subjects with the target review subjects in the dynamic review subject set; wherein the dynamic review subject set corresponds to a preset review tag, and is obtained by filtering the historical data of the review subjects according to the preset filtering rules of the review tag, and updating based on the filtered target review subjects; S300. When a match is found, obtain the audit tag corresponding to the dynamic audit subject set; In this embodiment, the review tag serves as a simple tool for identifying and linking filtering rules, enabling the review tag to be associated with specific filtering rules. The review tag automatically filters target review subjects that meet the preset filtering rules from the historical data of the review subjects, and maintains a dynamically updated set of review subjects; subsequently, this dynamic set of review subjects is associated with the review tags corresponding to the filtering rules.

[0046] Specifically, the historical data of the auditing entity is filtered according to the filtering rules of the auditing tags. When there is data in the historical data that meets the filtering rules, the auditing entity corresponding to the historical data is taken as the target auditing entity.

[0047] In this embodiment, the filtering rules associated with the review tag are a set of preset conditions or logical expressions used to filter review entities that meet specific conditions from the historical data of the review entities. For example, these filtering rules can be defined based on dimensions such as the review entity's historical behavior (e.g., number of violations, content type), historical attributes (e.g., registration duration, number of followers), or historical content features (e.g., keywords, image tags), thereby accurately identifying the target review entities that need to be included in the dynamic review entity set corresponding to the review tag.

[0048] In this embodiment, the dynamic review subject set is obtained by filtering the historical data of review subjects according to the review tag filtering rules, and is used to record the target review subjects that need to be reviewed. It is understood that the review subjects can be all registered users on the platform. These registered users have performed certain operations on the platform in the past, such as uploading audio, video, or text content; receiving rewards or reports from other users for their uploaded content; possessing corresponding traffic; or having been subject to platform review penalties. This data is considered the historical data of the registered user. The historical data of the review subjects can be filtered according to the review tag filtering rules to identify review subjects that meet the filtering rules and construct a dynamic review subject set. When a review subject performs an upload operation again, the review subject becomes a pending review subject. The pending review subject is matched with the target review subjects in the dynamic review subject set. When a match is found, it indicates that the pending review subject needs to be reviewed, thus quickly locating the pending review subjects that need review to complete the subsequent data review work.

[0049] Preferably, each reviewing entity also has a corresponding unique identifier. By filtering historical data of reviewing entities according to the review tag filtering rules, target reviewing entities can be found, their identifiers can be obtained, and recorded in the dynamic reviewing entity set. That is, the dynamic reviewing entity set is actually an identifier table identifying target reviewing entities, saving storage space. Furthermore, when matching a pending reviewing entity with the dynamic reviewing entity set, it can be directly determined whether the identifier of the pending reviewing entity falls into the identifier table of the dynamic reviewing entity set, thereby determining whether the pending reviewing entity needs review and improving review efficiency.

[0050] Specifically, such as Figure 3 As shown, the dynamic review subject set corresponds to preset review tags, and is obtained by filtering historical data of review subjects according to preset filtering rules of the review tags, and updating based on the filtered target review subjects. This may include the following steps: A1. Preset review tags and set update cycles and filtering rules for the review tags, wherein the review tags correspond to the dynamic review subject set; In this embodiment, the review label has an update cycle, which provides a cyclical time interval for the screening process, thereby enabling periodic screening. For example, the cycle can be set to once per hour or once per day, and can be adjusted as needed based on actual circumstances.

[0051] A2. Based on the update cycle and filtering rules of the review tags, the current historical data of the review subject is obtained periodically, and the current historical data is filtered based on the filtering rules of the review tags to obtain the filtered target review subject; Understandably, new registered users are constantly being added to the platform, while existing users are also constantly updating their activities, such as uploading new audio and video content, increasing their traffic, and changing the number of times they receive tips. All of these factors influence the platform's historical user data and overall user dynamics. Therefore, it is necessary to periodically obtain the current historical data of the review entities and filter this data based on the aforementioned review tag filtering rules to obtain the selected target review entities, ensuring compatibility with the constantly updated review entities and their historical data on the platform.

[0052] A3. Update the dynamic audit subject set corresponding to the audit tag with the selected target audit subjects.

[0053] In this embodiment, the selected target review subjects can preferably be further identified by their identifiers, so that the identifier table of the dynamic review subject set can be updated to cope with the increasingly changing data of review subjects in the platform.

[0054] Specifically, the filtering rules include a data source type and rule content; like Figure 4 As shown, filtering the historical data of the review subject according to the preset filtering rules of the review label may include the following steps: B1. Based on the data source type in the filtering rules of the aforementioned review tags, obtain the historical data of the review subject; In this embodiment, because the historical data of the reviewing entity has different business characteristics, different types of historical data may be stored in different locations or databases. Therefore, the filtering rules include data source types. When a filtering rule needs to retrieve corresponding historical data for filtering, the corresponding retrieval method can be used based on the data source type in the filtering rules to retrieve the historical data, thereby avoiding situations such as historical data retrieval failure, data omission, or inaccurate filtering results. For example, if a filtering rule needs to retrieve the number of times a reviewing entity has received rewards and the amount of traffic it has generated, and the reward records and traffic records of the reviewing entity are stored in different databases, then the storage path for retrieving historical data needs to be determined based on the data source type in order to accurately retrieve the required historical data.

[0055] B2. Based on the rules in the filtering rules of the audit tags, the historical data is filtered.

[0056] In this embodiment, the filtering rules also include rule content. For example, the rule content may be that the number of times the review subject has received rewards is greater than or equal to a first threshold, and / or that the review subject's traffic value is greater than or equal to a second threshold. When the review subject meets one or more rule contents, it is determined that the review subject meets the filtering rules, and the review subject can be marked as the target review subject corresponding to the review tag and recorded in the dynamic review subject set. Preferably, the identifier of the target review subject is recorded in the dynamic review subject set. When a review subject uploads audio, video, or text content, it can be determined whether it falls into the dynamic review subject set based on the review subject's identifier and the identifier record of the target review subject in the dynamic review subject set.

[0057] Specifically, the historical data of the auditing entity is stored in the first database and / or the second database and / or the external database. The data source types include the original database acquisition type corresponding to the first database, the transfer unit acquisition type corresponding to the second database, and the storage interface acquisition type corresponding to the external database. The historical data of the review subject obtained from the data source type in the filtering rules based on the review tags includes: When the data source type in the filtering rules of the review label is the original database acquisition type, the historical data of the review subject is obtained from the first database; When the data source type in the filtering rules of the audit tag is the transfer unit acquisition type, the historical data of the audit subject is obtained from the transfer unit, wherein the historical data of the audit subject in the transfer unit is obtained by transferring the historical data of the audit subject in the second database; When the data source type in the filtering rules of the audit tag is the storage interface acquisition type, the historical data of the audit subject is obtained from the external database through the storage interface.

[0058] In this embodiment, the historical data of the auditing entity is stored in a first database and / or a second database and / or an external database. The first database is the original database for storing historical data. It is understood that the historical data required by the filtering rules can be obtained directly by accessing the original database where the historical data is stored. Furthermore, accessing the original database does not require complex verification methods or the verification methods are relatively simple, making it convenient and saving transfer space. Therefore, when the historical data required by the filtering rules is suitable to be obtained directly from the original database, the data source type of the filtering rules can be set to the original database retrieval type. When historical data needs to be obtained based on this filtering rule, the original database can be accessed directly to obtain the historical data according to the original database retrieval type.

[0059] In this embodiment, the second database is also a raw database used for storing historical data. However, accessing the historical data stored in the second database directly may be complex and inefficient. Therefore, it is necessary to complete the preparatory work for accessing the second database in advance by transferring the historical data to a transfer unit. This allows the historical data to be directly retrieved from the transfer unit when the filtering rules require it. Since the database selected as the transfer unit is one that can be accessed and retrieved quickly, obtaining historical data from the transfer unit is more efficient. Another scenario involves the historical data in the second database being raw datasets. The historical data required by the filtering rules may require complex processing such as aggregation operations on the raw datasets. Furthermore, the historical data required by the filtering rules may be aggregated data from raw datasets stored in different second databases that have undergone extensive and complex aggregation processing. Directly accessing the raw datasets of multiple second databases may not allow for extensive and complex aggregation processing, nor can it quickly obtain the required aggregated data. Therefore, it is necessary to transfer the required raw datasets from each second database to a transfer unit and perform data aggregation processing within the transfer unit to easily obtain the historical data required by the filtering rules. These aggregated historical data are stored in the transfer unit. When the filtering rule needs to retrieve historical data, it can directly access the transfer unit to retrieve the historical data based on the retrieval type of the transfer unit.

[0060] In this embodiment, historical data may also be stored in an external database, making it impossible to directly access the external database to obtain historical data. Therefore, the data source type of the filtering rule can be set to the storage interface retrieval type corresponding to the external database, and the corresponding storage interface can be specified. When the filtering rule needs to retrieve historical data, it can retrieve the historical data of the review subject from the external database through the storage interface retrieval type.

[0061] Specifically, prior to the step of obtaining the subject to be reviewed, the method further includes: The dynamic review subject set is stored in the third database, and the mapping relationship between the dynamic review subject set and the review tag is stored in the fourth database. The step of matching the pending review subject with the target review subject in the dynamic review subject set includes: matching the pending review subject with the target review subject in the dynamic review subject set in the third database; The step of obtaining the audit tags corresponding to the dynamic audit subject set includes: obtaining the audit tags corresponding to the dynamic audit subject set based on the fourth database.

[0062] In this embodiment, the dynamic review subject set can be an identifier table identifying the target review subject, which can be directly stored in a third database. Preferably, it can be stored in comma-separated values ​​(CSV) format, and can be adjusted appropriately according to actual needs. In the step of matching the pending review subject with the target review subject in the dynamic review subject set, the dynamic review subject set in the third database can be directly obtained as the data basis for matching to complete the matching work, thereby improving the efficiency of matching.

[0063] In this embodiment, the mapping relationship between the dynamic review subject set and the review tags also needs to be stored in a fourth database. Preferably, this can be stored as a mapping table, and can be adjusted appropriately according to actual needs. After a pending review subject matches the dynamic review subject set, the mapping table in the fourth database can be directly accessed to obtain the review tags corresponding to the dynamic review subject set.

[0064] S400. Obtain the preset review strategy according to the corresponding review tag, and conduct data review on the subject to be reviewed based on the review strategy.

[0065] In this embodiment, the review strategy refers to the specific review actions and handling rules executed against the target review entity or its published content. It can guide subsequent review and processing measures, such as blocking, demotion, and banning, thereby achieving differentiated and refined review management for review entities with different risk levels.

[0066] In this embodiment, a corresponding review strategy needs to be preset for the review tag, enabling data review of the pending review subjects belonging to that review tag based on the review strategy. It is understood that the review tag automatically filters target review subjects that meet the preset filtering rules from the historical data of the review subjects, and maintains a dynamically updated set of review subjects. Subsequently, this dynamic set of review subjects is associated with the review tags corresponding to the filtering rules; and each review tag is associated with one or more review strategies, so that subsequent pending review subjects matching the target review subjects in the dynamic set of review subjects can find the corresponding review strategy for data review. Therefore, the review tag is both a classification identifier for the pending review subjects and a core link for achieving differentiated and automated review management.

[0067] Specifically, the audit strategy includes audit operations; The data review of the subject to be reviewed based on the review strategy includes: Based on the audit operations in the audit strategy, data audits are conducted on the entities to be audited.

[0068] In this embodiment, the review strategy needs to include review operations to enable data review of matched entities awaiting review based on the review operations within the review strategy using review tags. For example, the review operations include, but are not limited to, masking specific areas of the target entity, blocking the target entity, and penalizing the target entity. These review operations can be set by administrators or review execution targets based on actual review needs.

[0069] Specifically, the audit operation is equipped with an audit execution object label; The data audit of the subject to be audited, based on the audit operation in the audit strategy, includes: The subject to be reviewed is assigned to the audit execution object specified by the audit execution object label, so that the audit execution object performs data audit on the subject to be reviewed based on the audit operation in the audit strategy.

[0070] In this embodiment, the review operation also requires a review execution object tag. This tag specifies a corresponding review execution object, allowing that object to review the subject to be reviewed. It is understood that the categories of subjects to be reviewed received on the platform are diverse. For different categories, the review execution object may need to possess review knowledge of the corresponding category or domain. Furthermore, it is understood that review execution objects with accumulated experience in reviewing this category of subjects can perform their review work more effectively and efficiently, thereby reducing review errors. Therefore, it is necessary to classify the review execution objects and assign them corresponding tags. When a subject to be reviewed is matched and located to the corresponding review strategy, the subject can be dispatched to the designated review execution object based on the tag in the review strategy. This allows the review execution object to perform data review on the subject based on the review operation in the review strategy, significantly improving the efficiency and accuracy of data review. For example, the review execution object tag includes, but is not limited to, show-type review, game-type review, etc. In the screening process based on filtering rules, high-risk live-streaming platforms and relatively low-risk game platforms are separated for pending review, and corresponding review strategies are matched to them. This allows pending review entities to be assigned to relevant review execution targets. At the same time, review execution targets are pre-classified, distinguishing between those skilled in detecting violations in live-streaming platforms and those skilled in detecting violations in game platforms. This ensures that the review process for pending review entities can be accurately assigned to the appropriate review execution targets, thereby improving review efficiency.

[0071] Preferably, the review strategy can also include review scenarios, which may include primary review, secondary review, and preferred review. Rules that meet the criteria for primary, secondary, or preferred review are added to the filtering rules. When a review subject matches the rules corresponding to the filtering rules, it is recorded in the corresponding dynamic review subject set. When a pending review subject matches a target review subject in the dynamic review subject set, the corresponding review strategy can be obtained based on the review tags corresponding to that dynamic review subject set. This review strategy has specific settings for review scenarios, enabling the review execution object, upon receiving a review task from a pending review subject, to perform a review operation on the pending review subject according to the review scenario specified by the review strategy, following the primary, secondary, or preferred review scenario.

[0072] like Figure 5 As shown in the illustration, this application also provides a data auditing system. Optionally, the system includes: The modules are: Acquisition module 511, Matching module 512, Tag Acquisition module 513, and Review module 514, among which: Module 511 is used to obtain entities pending review; In this embodiment, the acquisition module 511 can be used to perform... Figure 2 For a detailed description of the acquisition module 511, please refer to the description of step S100 shown.

[0073] The matching module 512 is used to match the pending review subjects with the target review subjects in the dynamic review subject set; wherein the dynamic review subject set corresponds to a preset review tag, and is obtained by filtering the historical data of the review subjects according to the preset filtering rules of the review tag, and updating based on the filtered target review subjects; In this embodiment, the matching module 512 can be used to perform... Figure 2 For a detailed description of the matching module 512, please refer to the description of step S200 shown.

[0074] The matching module 512 is further configured to preset review tags and set update cycles and filtering rules for the review tags, wherein the review tags correspond to the dynamic review subject set; based on the update cycle and filtering rules of the review tags, periodically obtain the current historical data of the review subjects, and filter the current historical data based on the filtering rules of the review tags to obtain the filtered target review subjects; update the dynamic review subject set corresponding to the review tags with the filtered target review subjects.

[0075] In this embodiment, the matching module 512 is further configured to perform Figure 3 For a more detailed description of the matching module 512, please refer to the description of steps A1-A3 shown in the figure.

[0076] The filtering rules in the matching module 512 include a data source type and rule content; the matching module 512 is also used to obtain historical data of the auditing entity based on the data source type in the filtering rules of the auditing tag; and to filter the historical data based on the rule content in the filtering rules of the auditing tag.

[0077] In this embodiment, the matching module 512 can be used to perform... Figure 4 For a more detailed description of the matching module 512, see steps B1-B2 shown below.

[0078] The review tag acquisition module 513 is used to acquire the review tag corresponding to the dynamic review subject set when a match is found. In this embodiment, the audit tag acquisition module 513 can be used to perform... Figure 2 For a detailed description of the audit label acquisition module 513, please refer to the description of step S300 shown.

[0079] The review module 514 is used to obtain the preset review strategy according to the corresponding review tag, and to conduct data review on the subject to be reviewed based on the review strategy.

[0080] In this embodiment, the audit module 514 can be used to perform... Figure 2 For a detailed description of the audit module 514, please refer to the description of step S400 shown.

[0081] This application also provides an electronic device, the structure of which is as follows: Figure 6 As shown, the electronic device includes a memory 611, a processor 612, a communication module 613, and an input / output interface 614, etc. Optionally, the memory 611, the processor 612, the communication module 613, and the input / output interface 614 can be connected and communicate with each other through a bus 615.

[0082] The memory 611 is used to store one or more computer programs and to transfer the code of the computer programs to the processor 612; when the one or more computer programs are executed by the processor 612, a data auditing method in this application embodiment is implemented.

[0083] Optionally, the electronic device can be connected to a network via communication module 613 to communicate with other devices, such as terminals or servers, to achieve data interaction. The electronic device can be various forms of digital computers, exemplarily such as desktop computers, servers, workbenches, mainframes, or other types of computers. The electronic device can also be various forms of mobile terminals, exemplarily such as smartphones, tablets, wearable devices (such as helmets, glasses, watches, etc.), and other similar mobile terminals.

[0084] Optionally, the electronic device can connect to desired input / output devices, such as a keyboard or display device, via the input / output interface 614. The electronic device itself may have a display device, and other display devices can be connected externally via the input / output interface 64. Optionally, a storage device, such as a hard disk, can also be connected via the input / output interface 614 to store data from the electronic device, read data from the storage device, or store data from the storage device in the memory 611. It is understood that the input / output interface 614 can be a wired interface or a wireless interface. Depending on the actual application scenario, the device connected to the input / output interface 614 can be a component of the electronic device or an external device connected to the electronic device when needed.

[0085] Optionally, the memory 611 may be a volatile memory and / or a non-volatile memory. The volatile memory may be a random access memory, etc., and the non-volatile memory may be a read-only memory, a programmable read-only memory, an erasable programmable read-only memory, an electrically erasable programmable read-only memory, or a flash memory, etc.

[0086] Optionally, the computer program stored in the memory 611 can be divided into one or more modules, which are stored in the memory 611 and executed by the processor 612 to perform the method provided in this embodiment. The one or more modules can be a series of computer program instruction segments capable of performing specific functions, which describe the execution process of the computer program in the electronic device.

[0087] Optionally, the processor 612 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the processor 612 include, but are not limited to, a central processing unit, a graphics processing unit, a digital signal processor, various special-purpose artificial intelligence computing chips, various processors running machine learning model algorithms, and can also be any suitable controller, microcontroller, processor, etc. The processor 612 executes the various methods and processes of this embodiment, exemplarily, such as a data auditing method according to an embodiment of this application.

[0088] Optionally, the bus 615 may include a path for transmitting information. Depending on its function, the bus 615 may be divided into an address bus, a data bus, a control bus, etc.

[0089] In an optional implementation, this application embodiment also provides a computer storage medium storing a computer program thereon. When executed by a computer, the computer program enables the computer to perform the methods described in the above-described method embodiments. Part or all of the computer program can be loaded and / or installed on the memory 611 of an electronic device. When the computer program is executed by the processor 612, one or more steps of a data auditing method according to an embodiment of this application can be performed.

[0090] Optionally, the computer-readable storage medium may be a random access memory, a read-only memory, a programmable read-only memory, an erasable programmable read-only memory, an electrically erasable programmable read-only memory, etc.

[0091] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the technical solution of the present invention, and are not intended to limit the specific implementation of the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the claims of the present invention should be included within the protection scope of the claims of the present invention.

Claims

1. A data auditing method, characterized in that, The method includes: Obtain the entity to be reviewed; The pending review entities are matched with the target review entities in the dynamic review entity set; wherein the dynamic review entity set corresponds to a preset review tag, and is obtained by filtering the historical data of the review entities according to the preset filtering rules of the review tag, and updating based on the filtered target review entities; When a match is found, the audit tag corresponding to the dynamic audit subject set is obtained; The preset review strategy is obtained based on the corresponding review tag, and the data of the subject to be reviewed is reviewed based on the review strategy.

2. The method according to claim 1, characterized in that, The dynamic review subject set corresponds to preset review tags, and is obtained by filtering historical data of review subjects according to preset filtering rules of the review tags, and updating based on the filtered target review subjects, including: Pre-set review tags and set update cycles and filtering rules for the review tags, wherein the review tags correspond to the dynamic review subject set; Based on the update cycle and filtering rules of the review tags, the current historical data of the review subject is obtained periodically, and the current historical data is filtered based on the filtering rules of the review tags to obtain the filtered target review subject. Update the dynamic audit subject set corresponding to the audit tag by the selected target audit subjects.

3. The method according to claim 1, characterized in that, The filtering rules specify the data source type and rule content; The step of filtering the historical data of the audit subject according to the preset filtering rules of the audit label includes: Based on the data source type in the filtering rules of the aforementioned review tags, historical data of the review subject is obtained; The historical data is filtered based on the rules in the filtering rules of the aforementioned review tags.

4. The method according to claim 3, characterized in that, The historical data of the auditing entity is stored in the first database and / or the second database and / or the external database. The data source type includes the original database acquisition type corresponding to the first database, the transfer unit acquisition type corresponding to the second database, and the storage interface acquisition type corresponding to the external database. The historical data of the review subject obtained from the data source type in the filtering rules based on the review tags includes: When the data source type in the filtering rules of the review label is the original database acquisition type, the historical data of the review subject is obtained from the first database; When the data source type in the filtering rules of the audit tag is the transfer unit acquisition type, the historical data of the audit subject is obtained from the transfer unit, wherein the historical data of the audit subject in the transfer unit is obtained by transferring the historical data of the audit subject in the second database; When the data source type in the filtering rules of the audit tag is the storage interface acquisition type, the historical data of the audit subject is obtained from the external database through the storage interface.

5. The method according to any one of claims 1-3, characterized in that, Prior to the step of obtaining the subject to be reviewed, the method further includes: The dynamic review subject set is stored in the third database, and the mapping relationship between the dynamic review subject set and the review tag is stored in the fourth database. The step of matching the pending review subject with the target review subject in the dynamic review subject set includes: matching the pending review subject with the target review subject in the dynamic review subject set in the third database; The step of obtaining the audit tags corresponding to the dynamic audit subject set includes: obtaining the audit tags corresponding to the dynamic audit subject set based on the fourth database.

6. The method according to any one of claims 1-3, characterized in that, The audit strategy includes audit operations; The data review of the subject to be reviewed based on the review strategy includes: Based on the audit operations in the audit strategy, data audits are conducted on the entities to be audited.

7. The method according to claim 6, characterized in that, The audit operation is labeled with the audit execution object; The data audit of the subject to be audited, based on the audit operation in the audit strategy, includes: The subject to be reviewed is assigned to the audit execution object specified by the audit execution object label, so that the audit execution object performs data audit on the subject to be reviewed based on the audit operation in the audit strategy.

8. A data auditing system, characterized in that, The system includes: The acquisition module is used to acquire entities pending review. The matching module is used to match the pending review subjects with the target review subjects in the dynamic review subject set; wherein the dynamic review subject set corresponds to a preset review tag, and is obtained by filtering the historical data of the review subjects according to the preset filtering rules of the review tag, and updating based on the filtered target review subjects; The review tag acquisition module is used to acquire the review tags corresponding to the dynamic review subject set when a match is found. The review module is used to obtain the preset review strategy based on the corresponding review tag, and to conduct data review on the subject to be reviewed based on the review strategy.

9. An electronic device, characterized in that, include: Memory, used to store one or more computer programs; A processor, when the one or more computer programs are executed by the processor, implements a data auditing method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute a data auditing method as described in any one of claims 1-7.

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