Data review method, device, electronic device and storage medium

Abstract

The embodiments of the present application disclose a data review method, device, electronic device and storage medium, the method comprising: obtaining content indicators of data to be processed; matching the content indicators of the data to be processed with review rules to determine target data to be reviewed from the data to be processed; allocating the target data to a review platform so that the reviewer can review the target data after receiving the order for the target data through the review platform, so that the reviewer can receive the order for the target data through the review platform and review the target data. It can be seen that screening the data content based on the review rules can greatly reduce the workload of manual review by the reviewer, improve the efficiency of the entire content review process, and enhance the review effect.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular to a data review method, device, electronic device, and storage medium. Background Art

[0002] With the development of information technology, the information industry is becoming increasingly diverse, and the amount of information output is also increasing. This exponentially increasing amount of information is flooding the online environment. However, much of this information may contain vulgar content or even content that endangers public safety. Therefore, to maintain network security and create a positive online environment, it is necessary to monitor the information posted by users and promptly address any abnormal content to prevent it from spreading and causing further harm.

[0003] However, current data review methods mostly rely on manual inspections. Faced with massive amounts of information, limited human resources, and prohibitively high labor costs, it's difficult to effectively detect numerous anomalies within the data. This is costly and inefficient. Random inspections can only assess the proportion of anomalous content on the external network relative to the total sample size, but lack the ability to detect anomalous content. Therefore, there's an urgent need to improve information review methods to better handle anomalous content posted by users. Summary of the Invention

[0004] The embodiments of the present application provide a data review method, device, electronic device and storage medium, which can improve the efficiency of data review and optimize the review effect.

[0005] In a first aspect, a data review method is applied to a server, the method comprising:

[0006] Get the content indicator of the data to be processed.

[0007] The content indicators of the data to be processed are matched with the review rules to determine the target data to be reviewed from the data to be processed.

[0008] The target data is distributed to a review platform so that a reviewer can review the target data after receiving the target data through the review platform.

[0009] In a second aspect, a data review device is provided, comprising a communication unit and a processing unit, wherein:

[0010] The processing unit is used to obtain content indicators of the data to be processed.

[0011] The processing unit is further configured to match the content indicators of the data to be processed with review rules to determine target data to be reviewed from the data to be processed.

[0012] The communication unit is used to distribute the target data to the review platform, so that the reviewer can review the target data after receiving the target data through the review platform.

[0013] In a third aspect, an embodiment of the present application provides an electronic device comprising a processor, a memory, a communication interface, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the processor, and the program comprises instructions for executing the steps in the first aspect of the embodiment of the present application.

[0014] In a fourth aspect, an embodiment of the present application provides a chip comprising a processor and a data interface, wherein the processor reads instructions stored in a memory through the data interface and executes the methods of the first to third aspects and any optional implementation method described above.

[0015] In a fifth aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the above-mentioned computer-readable storage medium stores a computer program for electronic data exchange, wherein the above-mentioned computer program enables a computer to execute some or all of the steps described in the first aspect of the embodiment of the present application.

[0016] In a sixth aspect, embodiments of the present application provide a computer program product, wherein the computer program product includes a non-transitory computer-readable storage medium storing a computer program, wherein the computer program is operable to cause a computer to execute some or all of the steps described in the first aspect of the embodiments of the present application. The computer program product may be a software installation package.

[0017] It can be seen that in the embodiment of the present application, the server uses pre-configured review rules to determine whether the data to be processed matches the review rules to detect whether there are abnormal indicators in the external network content, and when the data to be processed matches the review rules, the data to be processed is then allocated to the review platform as the target data to be reviewed, so that the reviewers can receive the target data through the review platform and review the target data. Screening the data content based on the review rules can greatly reduce the workload of manual review by the reviewers, improve the efficiency of the entire content review process, and enhance the review effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0019] Figure 1A This is a structural diagram of a data review system provided in an embodiment of the present application;

[0020] Figure 1B This is a schematic diagram of the structure of a review service software system provided in an embodiment of the present application;

[0021] Figure 2 This is a flow chart of a data review method provided in an embodiment of the present application;

[0022] Figure 3A This is a flow chart of another data review method provided in an embodiment of the present application;

[0023] Figure 3B This is a schematic diagram of an order receiving interface provided in an embodiment of the present application;

[0024] Figure 3C This is a schematic diagram of an interface of a review content details page provided in an embodiment of the present application;

[0025] Figure 3D This is a schematic diagram of an interface for review rules provided in an embodiment of the present application;

[0026] Figure 3E This is a schematic diagram of a review rule configuration interface provided in an embodiment of the present application;

[0027] Figure 3F This is a schematic diagram of an interface of a review result list provided in an embodiment of the present application;

[0028] Figure 3G This is a schematic diagram of a data review report provided in an embodiment of the present application;

[0029] Figure 3H This is a schematic diagram of another data review report provided in an embodiment of the present application;

[0030] Figure 4 This is a schematic diagram of the functional units of a data review device provided in an embodiment of the present application;

[0031] Figure 5 This is a structural diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0032] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0033] The terms "first," "second," and the like in the specification and claims of this application and the accompanying drawings are used to distinguish between different objects, not to describe a particular order. Furthermore, the terms "including," "having," and any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus comprising a series of steps or elements is not limited to the listed steps or elements but may optionally include steps or elements not listed, or may optionally include other steps or elements inherent to the process, method, product, or apparatus.

[0034] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute an independent or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0035] Currently, data review is mostly done manually. Faced with massive amounts of information, limited human resources, and prohibitively high labor costs, it's difficult to effectively detect numerous anomalies within the data. This is costly and inefficient. Random checks can only assess the proportion of anomalous content on the external network relative to the total sample size, but lack the ability to detect anomalous content. Therefore, there's an urgent need to improve information review methods to better handle anomalous content posted by users.

[0036] To address the above-mentioned issues, the present invention provides a data review method and related device, which are applied to a server and are described in detail below with reference to the accompanying drawings.

[0037] First, see Figure 1A The structural diagram of the data review system 100 shown includes a client 110 , a server 120 , and a review terminal 130 .

[0038] When users browse videos, articles and other data products on the Internet through the client 110, if they find that the data products have vulgar content, or the title and text do not match, or the video is incomplete, the users will report the problems with the data products. The server 120 collects the data reported by users. These data have at least one content indicator, and can regularly store these data in the distributed multi-user full-text search engine ElasticSearch. The storage can support querying other information of this content according to any indicator as an index. The server 120 then determines whether the data reported by the user matches the review rules according to the customized review rules. The server 120 also distributes the target data that matches the review rules to the review platform. The reviewer uses the review terminal 130 to receive the target data to be reviewed on the review platform, and then reviews the target data to be reviewed.

[0039] The client 110 includes but is not limited to devices with communication functions, such as distributed storage servers, traditional servers, large storage systems, desktop computers, laptops, tablet computers, PDAs, smart phones, portable digital players, smart watches and smart bracelets, etc.

[0040] The server 120 includes but is not limited to distributed storage servers, traditional servers, large storage systems, desktop computers, laptop computers, tablet computers, PDAs, smart phones, etc.

[0041] The server 120 may include Figure 1BThe review service software system 200 shown includes a review scheduling service, which is used to periodically query configured review rules to match content that meets the review requirements and submit it for review; a rule configuration service, which is used to manage the configuration information of the review rules and allow others to view the effective review configuration, providing a web interface to support operations such as adding, modifying, and delisting; a content indicator management service, which is used to receive usage data from external users as data to be processed, and aggregate and store the content indicator information together with existing content indicators using ElasticSearch. Content indicators include but are not limited to biu count, feed clicks, feed exposure, puin account, title, and the number of low-quality model filters; and a submission service, which is used to receive target data for review from the review scheduling service, supplement the public information of the review content, such as classification and submission time, and submit it for review to the designated channel according to the review configuration information. The review service displays the content provided by the submission service on the review interface of the review platform. Reviewers can use the review platform to select the data for the request form according to classification, time, and other conditions within the designated review channel. Each time content is received, orders are collected, and reviewed, information such as the number of unreviewed content, received but unreviewed content, and reviewed content is counted and reported for statistical analysis. If reviewers determine that content is abnormal, this service can be used to crack down on, remove, or delete the content. The data collection service receives key data reported by the review scheduling service throughout the content lifecycle, including matching review rules, matching time, submission time, order collection time, review time, and review results. Data department colleagues then interpret this information to generate data review reports for system monitoring and statistical purposes.

[0042] In addition, the above-mentioned review service can be replaced with asynchronous distributed message middleware, such as Zookeeper, Hippo, etc., which can reduce real-time requirements and perform data peak shaving, making the downstream load more reasonable and the distribution of target data to be reviewed more scientific and reasonable.

[0043] The review scheduling service currently uses scheduled scanning to match content indicators and review rules for pending data. Alternatively, the server can provide external interfaces for other devices to access the review rules and pending data stored on the server. Once the data is identified as target data for review, the other device can proactively push the data to the server, which then allocates the target data to a review channel.

[0044] The above-mentioned review terminal 130 includes but is not limited to distributed storage servers, traditional servers, large storage systems, desktop computers, laptop computers, tablet computers, PDAs, smart phones, etc.

[0045] The technical solution of the embodiment of the present application can be based on Figure 1AThe communication system of the illustrated architecture or its modified architecture is specifically implemented.

[0046] See also Figure 2 , Figure 2 This is a flow chart of a data review method provided in an embodiment of the present application. This method may include but is not limited to the following steps:

[0047] 201. Obtain content indicators of the data to be processed.

[0048] Specifically, the data to be processed is data published by various network platforms, such as articles, videos, audios, etc. The server obtains the content indicators of the data to be processed. These data have at least one content indicator, such as the number of negative feedbacks, the number of negative comments, etc. It can be understood that after the server receives these data to be processed, it stores these data in a distributed multi-user full-text search engine ElasticSearch in a timely or real-time manner. The storage can support querying other information of this content according to any indicator as an index. For example, based on a content indicator of the data to be processed, other content indicators are queried, or new content indicators are calculated. For example, based on the number of negative feedbacks and the reading time, the number of valid negative feedbacks is calculated. For example, negative feedback with a reading time lower than the time threshold of 10s is marked as invalid negative feedback. Valid negative feedback number = negative feedback number - invalid negative feedback number.

[0049] In a possible example, the method further includes: receiving content indicators of the data to be processed sent by the user terminal, wherein the content indicators include at least one of the following: source, classification identifier, popularity data, negative feedback data, effective negative feedback data, and user browsing data; storing the content indicators in a full-text search engine; wherein obtaining the content indicators of the data to be processed includes: obtaining the content indicators of the data to be processed from the full-text search engine.

[0050] Specifically, when users browse videos, articles and other data products on the Internet through the client, if they find that the data products have vulgar content, or the title and text do not match, or the video is incomplete, the users will report the problems with the data products. The server stores the data reported by users in the distributed multi-user full-text search engine ElasticSearch. The storage can support querying other information of this content according to any indicator as an index. The storage method can be either timed storage, such as according to a certain storage period, or real-time storage. These user-reported data stored in ElasticSearch are the content indicators of the data to be processed. For example, the source of an article, the number of likes, the number of shares, the number of comments, the number of effective negative feedback, etc. Therefore, when it is necessary to obtain the content indicators of these data to be processed, the content indicators of the data to be processed are obtained from ElasticSearch. The content indicators of the data may include sources, such as Kandian, Express, browser, etc.; classification identifiers, such as first-level classification ID, first-level classification name, second-level classification ID, second-level classification name, third-level classification ID, third-level classification name, etc.; popularity data, such as biu number, feeds clicks, feeds exposure clicks, likes, shares, whether it is a hot article, comment likes, comments, exposure, related recommendation clicks; negative feedback data, low-quality model filtering number of negative feedback, number of negative feedback for rumors, number of negative comments, posterior comprehensive low-quality score, report_suspected plagiarism, number of rumor reports, report Total volume, content quality score, number of manual inspection and deletions, suspected rumor status, similar low-quality content; effective negative feedback data, including effective negative feedback rate, number of effective negative feedback, effective negative feedback as rumor, main feed negative feedback, main feed negative feedback count as rumor, main feed negative feedback count as quality-related negative feedback, main feed effective negative feedback, main feed effective negative feedback count as quality-related negative feedback; user browsing data, such as average scrolling speed, average reading time, average reading completion rate, whether enabled, reading time, number of reading times, reading completion rate, number of reading completion rates, and number of characters in the article. Additionally, the aforementioned content metrics may include ASN boss review time, PUIN account number, data title, and joint media ID.

[0051] 202. Match the content index of the data to be processed with the review rules to determine the target data to be reviewed from the data to be processed.

[0052] Specifically, the server obtains data to be processed, such as an article or a video. The server matches the pre-configured review rules with the content indicators of the data to be processed. For example, the content indicators of the article include the source of "Viewpoints", the first-level classification name "Hotspots", the number of readings of 100,000 times, the second-level classification name "Number of Effective Negative Feedbacks", which is 500 times, etc. The server matches the pre-set review rules based on the above content indicators, and when the content indicators of the article match the review rules, the article is determined as the target data to be reviewed. It can be understood that the data to be processed is a large number of data, and the review rules may also be many. It can be pre-set for a specific data to be processed, and at least one review rule among the many review rules can be matched, so that the data to be processed can be determined as the target data to be reviewed, thereby determining all target data that need to be reviewed.

[0053] 203. Allocate the target data to a review platform, so that a reviewer reviews the target data after receiving a form for the target data through the review platform.

[0054] Specifically, after the server identifies the target data to be reviewed, it assigns one or more of these data points to the review platform. This platform is a managed cloud platform, and the server can assign the target data to it. Reviewers can also access the platform through a review terminal to retrieve the target data. They can then review the data, determine if any violations exist, and take appropriate action, such as removing or deleting any such data.

[0055] It can be seen that in the embodiment of the present application, the server uses pre-configured review rules to determine whether the data to be processed matches the review rules to detect whether there are abnormal indicators in the external network content, and when the data to be processed matches the review rules, the data to be processed is then allocated to the review platform as the target data to be reviewed, so that the reviewers can receive the target data through the review platform and review the target data. Screening the data content based on the review rules can greatly reduce the workload of manual review by the reviewers, improve the efficiency of the entire content review process, and enhance the review effect.

[0056] With the above Figure 2 For details on the embodiment shown, please refer to Figure 3A , Figure 3A Another data review method provided in the embodiments of the present application includes:

[0057] 301. Obtain content indicators of the data to be processed.

[0058] 302. Match the content index of the data to be processed with the review rules to determine the target data to be reviewed from the data to be processed.

[0059] The above steps 301-302 refer to steps 201-202 and are not repeated here.

[0060] 303. Obtain a review rule that matches the content indicator of the target data.

[0061] Specifically, there are several pre-configured review rules, such as Figure 3B As shown, the existing POP (abnormal review) rules include rule ID, rule type, etc., as well as rule name. In addition, they may include creator, creation time, rule status, etc. For example, when the source of the content indicator of the target data is a browser, the classification identifier is video-hotspot, and the negative feedback data is 50. It can be determined that the review rule with rule ID 288 is of type video, and the rule name is video-browser-abrupt stop high VV. The video corresponding to this review rule has high clicks or views but the video is incomplete. And obtain the negative feedback interval by viewing the details button to determine the content indicator of the target data that the rule matches.

[0062] 304. Obtain the order priority and review channel of the target data according to the matched review rule.

[0063] Specifically, as mentioned above, after the server determines the review rule that matches the content indicator of the target data, for example, the review rule with rule ID is 288, the order priority of the review rule 288 is P0, and the review channel is video-browser, then the order priority of the target data matching 288 is also P0, and the review channel is video-browser.

[0064] 305. Allocate the target data, the order priority of the target data, and the review channel to the review platform, so that the reviewer reviews the target data after receiving the order of the target data through the review platform according to the order priority and the review channel of the target data.

[0065] Specifically, it can be understood that when there are multiple target data, such as the first target data and the second target data, the priority of the first target data is P0, and the priority of the second target data is P1, and the priority of P0 is higher than P1. The review channels of the two are the same or similar, such as both are video-browser, or the other is video-viewing point, then when the reviewer receives the target data in the same review channel or in a similar review channel, the first target data will be taken in first. Figure 3BIn the order interface shown, the reviewer can choose to select a specific review channel through the review channel, i.e. the monitoring dimension, such as negative review - browser, etc. In addition, it can be understood that the order priority is the server's allocation of target data and the allocation of target data according to the order priority. The reviewer can review the target data as follows Figure 3C As shown on the review content details page, review the specific content of the target data or the content indicators of the target data.

[0066] It can be seen that in the embodiment of the present application, the server uses the pre-configured review rules to determine whether the data to be processed matches the review rules to detect whether there are abnormal indicators in the external network content, and when the data to be processed matches the review rules, the order priority and review channel of the target data are obtained, and then the data to be processed is used as the target data to be reviewed together with the order priority and review channel of the target data to be allocated to the review platform. The data with abnormal indicators in the external network is detected more comprehensively, reducing the possibility of missed attacks. And based on the pre-judgment of the server and the configuration of the review rules, the workload of manual review by the review personnel is greatly reduced, and the setting of the order priority and review channel makes it easier for the review personnel to receive the target data through the review platform, thereby improving the efficiency of the entire review process and improving the review effect.

[0067] In one possible example, matching the content indicators of the data to be processed with the review rules to determine the target data to be reviewed from the data to be processed includes: obtaining at least one pre-configured review rule; matching the content indicators of first data with the at least one review rule, where the first data is any one of the data to be processed; if there is a review rule in the at least one review rule that matches the content indicator of the first data, then using the first data as the target data to be reviewed.

[0068] Specifically, it can be understood that the data to be processed includes multiple items, such as multiple articles or multiple videos, or both. The first data is any one of the data to be processed, such as an article. There is also at least one pre-configured review rule. The content indicator of the first data is matched with at least one review rule, and if at least one composite rule matches the content indicator of the first data, the first data is used as the target data to be reviewed.

[0069] As can be seen, for a specific data item among multiple pending data items and multiple review rules, it can only be determined as the target data for review if it meets at least one review rule. This effectively controls the number of manual reviews and ensures review efficiency.

[0070] In a possible example, the review rule includes at least one of the following configuration items: number of reviews, review channels, sorting rules, review content source, order priority, review cycle, number of single reviews, and filtering rules.

[0071] Specifically, such as Figure 3D As shown in the review rule configuration interface, when a new rule needs to be added, the review rule configuration interface will appear. The review rules include at least one of the following configuration items: Number of reviews, namely the POP review module: optional one-time review (the same content can only be reviewed once, and when the content is reviewed again after being reviewed, the content will be ignored to save labor costs), two-time review (when a content has been reviewed, and the content meets the review conditions again, it is allowed to be reviewed again). Review channel, namely the POP review channel: According to the source dimensions of express / highlights / browser, hot spots / negative feedback / low quality and other feature dimensions, different channels for receiving orders are divided, such as hot article review-highlights, negative review-browser, suspected low quality-content, etc., to make the manpower allocation of reviewers more reasonable. Sorting rules, such as topfilter, sort content that meets the review rules according to a specified method. "single-field" indicates sorting by a single metric, such as the number of likes or the number of valid negative feedbacks. "expression" calculates the metric using an addition, subtraction, or multiplication expression and then sorts by the result. "divisor / dividend" calculates the metric using a division expression and then sorts by the result. For example, "valid negative feedback / negative feedback" represents the ratio of valid negative feedback to negative feedback, and can be sorted in descending order. topfilter expression: This configuration item appears when "expression" is selected for topfilter.

[0072] The sources of review content refer to three sources: for example, a video being reviewed may originate from a website, a newsletter, or a browser. The order priority can be set as P0, P1, or P2, indicating the order of priority within the same review channel. P0 can be prioritized over P1, and vice versa. The number of single reviews refers to the scheduled POP number: for example, the target data submitted for review can be set to 50 or 100 at a time to limit the number of submissions per review, preventing excessive review labor costs from being incurred due to excessive submissions. The review cycle refers to the scheduled POP interval in hours: this limits the waiting time between reviews and determines the target data to be reviewed according to the preset cycle. Filtering rules can include expression: an expression configured with addition, subtraction, or multiplication operations to filter by indicators. For example, the addition operation expression means that several rules in the expression are met at the same time; keyword filter: set the keywords contained in a certain indicator, and the review and adjustment will be met only when these keywords exist, such as "content title" contains "doctor"; match filtering: set the content of a certain indicator to be equal to the configured value, such as "first-level classification" equal to "entertainment", "games", etc.; exclusion filtering: set the content of a certain indicator not equal to the configured value, such as "first-level classification" not equal to "entertainment", "games", etc.; range interval: set the value range of a certain indicator content, such as "number of likes" is greater than 10 and less than 100, etc.

[0073] In addition, the review rule configuration page can also include the POP rule name: a name that describes this review rule; sampling source: if sampling is required, configure the highlights, express reports, and browser sources to set the sampling method; business scenarios: such as sampling, comments, and others.

[0074] It can be seen that the server configures the review rules through the review rule configuration interface. These review rules act as "and" conditions for each other, requiring that the content indicators of the data to be reviewed are met at the same time before the review rule is considered matched, thereby controlling the number of reviews, submission frequency, content screening, order collection channels, content sources, order collection priority, etc.

[0075] In one possible example, the method further includes: receiving a review rule configuration instruction input by a user; displaying a review rule configuration interface in response to the review rule configuration instruction, the review rule configuration interface including at least one configuration item; obtaining configuration parameters input by the user for the at least one configuration item; and generating configuration rules based on the configuration parameters.

[0076] Specifically, such as Figure 3D As shown, the server can not only display the configured review rules through the rule management interface, and can modify, delete, disable, etc. at any time, but also add new review rules. Figure 3DWhen the user clicks on the Add Text Rule button on the interface, the server receives the review rule configuration instruction input by the user and displays the following information: Figure 3E The review rule configuration interface shown includes at least one configuration item. The server obtains the configuration parameters entered by the user for the at least one configuration item. For example, the configuration parameter entered by the user for the POP review module is "one review", the configuration parameter entered for the POP review is "highlights", and the POP rule name is "video - number of negative feedbacks - high reading volume". Items marked with an * are required, while items without an * are optional. Furthermore, after detecting that the user clicks the Save button, the server generates a configuration rule based on the detected configuration parameters entered by the user.

[0077] It can be seen that by providing a review rule configuration interface, it is convenient to manage review rules and improve review efficiency.

[0078] In one possible example, the method further includes: obtaining the review results submitted by the reviewer for the target data; generating a data review report based on the review progress, review results and review rules matching the content indicators of the target data, wherein the data review report includes at least one of the following: a review statistics report and a review process report.

[0079] Specifically, after reviewing the target data, the reviewer will submit the review results on the review interface, such as Figure 3F As shown in the review result list, the review result is "failed". In addition, the server will generate a data review report based on the review progress of the entire review process, the review results, and the review rules that match the review results submitted by the reviewer for the target data and the content indicators of the target data obtained by the server, wherein the data review report includes at least one of the following: a review statistical report and a review process report. The review statistical report is used to report the review data in a statistical sense, such as the recall ratio, etc.; another review process report is used to display the progress, results and matching review rules of the review. For example, Figure 3G As shown in the data review report, the data review report counts the specific details of the rules under different review rules, such as the number of TOP likes under the hot article review-highlights, the number of articles that are determined to be reviewed at a time is 2, and the cumulative number of views in the past seven days, the number of review submissions that are determined to be target data is 1, as well as the number of review removals, the proportion of recalled content activated, etc. In addition, the data review report can also be as follows Figure 3HAs shown, statistics are collected for specific target data review related information, such as the client reporting time, the review method (single review), the submission time, the review channel source, and the review results. A value of "1" can be set to indicate a passed review, allowing the target data to continue to be displayed on various online platforms or apps. Otherwise, the target data will be removed, deleted, or recalled.

[0080] As can be seen, the types of indicators and channels involved in the review are very diverse, making it very difficult to track the status of a target data throughout the entire review process, from matching, submission for review, receipt of the order, review, and crackdown. By sorting out the key data of the review process and generating a data review report based on the matching review rules, matching time, submission for review time, receipt of the order time, review time, and review results, it facilitates subsequent query and further analysis and processing, thereby further optimizing the review process.

[0081] See also Figure 4 , is a functional unit diagram of a data review device 400 according to an embodiment of the present invention. The data review device 40 according to the embodiment of the present invention may be a built-in device of the server 120 or an external device of the server 120.

[0082] In one implementation of the apparatus according to an embodiment of the present invention, the apparatus 400 includes the following structure:

[0083] The processing unit 420 is configured to obtain content indicators of the data to be processed.

[0084] The processing unit 420 is further configured to match the content index of the data to be processed with a review rule to determine target data to be reviewed from the data to be processed.

[0085] The communication unit 410 is used to distribute the target data to the review platform, so that the reviewer can review the target data after receiving the target data through the review platform.

[0086] In one possible example, in terms of matching the content indicators of the data to be processed with the review rules to determine the target data to be reviewed from the data to be processed, the processing unit 420 is specifically used to obtain at least one pre-configured review rule; match the content indicator of first data with the at least one review rule, where the first data is any one of the data to be processed; if there is a review rule in the at least one review rule that matches the content indicator of the first data, then the first data is used as the target data to be reviewed.

[0087] In a possible example, the review rule includes at least one of the following configuration items: number of reviews, review channels, sorting rules, review content source, order priority, review cycle, number of single reviews, and filtering rules.

[0088] In one possible example, the processing unit 420 is also used to receive a review rule configuration instruction input by a user; display a review rule configuration interface in response to the review rule configuration instruction, wherein the review rule configuration interface includes at least one configuration item; obtain the configuration parameters input by the user for the at least one configuration item; and generate configuration rules based on the configuration parameters.

[0089] In one possible example, in terms of allocating the target data to a review platform so that the reviewer reviews the target data after receiving the target data through the review platform, the processing unit 420 is specifically used to obtain a review rule that matches the content indicator of the target data; obtain the order priority and review channel of the target data according to the matching review rule; and allocate the target data, the order priority and review channel of the target data to the review platform so that the reviewer reviews the target data after receiving the target data through the review platform according to the order priority and review channel of the target data.

[0090] In one possible example, the processing unit 420 is also used to obtain the review results submitted by the reviewer for the target data; generate a data review report based on the review progress, review results and review rules matching the content indicators of the target data, wherein the data review report includes at least one of the following: a review statistical report and a review process report.

[0091] In a possible example, the processing unit 420 is further used to receive content indicators of the data to be processed sent by the user terminal, wherein the content indicators include at least one of the following: source, classification identification, popularity data, negative feedback data, effective negative feedback data, and user browsing data; and store the content indicators in a full-text search engine; wherein, in terms of obtaining the content indicators of the data to be processed, the processing unit 420 is specifically used to obtain the content indicators of the data to be processed from the full-text search engine.

[0092] See also Figure 5 , is a schematic diagram of the structure of an electronic device according to an embodiment of the present invention. The electronic device according to the embodiment of the present invention corresponds to the server described above. The electronic device includes a power supply module and other structures, and includes a processor 501, a storage device 502, and a communication interface 503. The processor 501, the storage device 502, and the communication interface 503 can exchange data.

[0093] The storage device 502 may include a volatile memory, such as a random-access memory (RAM); the storage device 502 may also include a non-volatile memory, such as a flash memory, a solid-state drive (SSD), etc.; the storage device 502 may also include a combination of the above types of memory.

[0094] The processor 501 may be a central processing unit (CPU) 501. In one embodiment, the processor 501 may also be a graphics processing unit (GPU) 501. The processor 501 may also be a combination of a CPU and a GPU. In one embodiment, the storage device 502 is used to store program instructions. The processor 501 may call the program instructions and execute the following steps:

[0095] Get the content indicator of the data to be processed.

[0096] The content indicators of the data to be processed are matched with the review rules to determine the target data to be reviewed from the data to be processed.

[0097] The target data is distributed to a review platform so that a reviewer can review the target data after receiving the target data through the review platform.

[0098] In a possible example, the processor 501 is specifically configured to:

[0099] Get at least one pre-configured review rule.

[0100] Matching a content indicator of first data with the at least one review rule, wherein the first data is any one of the data to be processed.

[0101] If there is a review rule in the at least one review rule that matches the content indicator of the first data, the first data is used as target data to be reviewed.

[0102] In a possible example, the review rule includes at least one of the following configuration items: number of reviews, review channels, sorting rules, review content source, order priority, review cycle, number of single reviews, and filtering rules.

[0103] In a possible example, the processor 501 is further configured to:

[0104] Receive the review rule configuration instructions input by the user.

[0105] In response to the review rule configuration instruction, a review rule configuration interface is displayed, where the review rule configuration interface includes at least one configuration item.

[0106] Acquire configuration parameters input by the user for the at least one configuration item.

[0107] A configuration rule is generated according to the configuration parameters.

[0108] In a possible example, the processor 501 is specifically configured to:

[0109] A review rule matching the content indicator of the target data is obtained.

[0110] The order priority and review channel of the target data are obtained according to the matched review rules.

[0111] The target data, the order priority of the target data and the review channel are allocated to the review platform, so that the reviewer can review the target data after receiving the order of the target data through the review platform according to the order priority of the target data and the review channel.

[0112] In a possible example, the processor 501 is further configured to:

[0113] Obtain the review results submitted by the reviewer for the target data.

[0114] A data review report is generated according to the review progress, review results and review rules matching the content indicators of the target data, wherein the data review report includes at least one of the following: a review statistics report and a review process report.

[0115] In one possible example, the processor 501 is further used to receive content indicators of the data to be processed sent by the user terminal, wherein the content indicators include at least one of the following: source, classification identification, popularity data, negative feedback data, effective negative feedback data, and user browsing data; and store the content indicators in a full-text search engine; wherein, in terms of obtaining the content indicators of the data to be processed, the processor 501 is specifically used to obtain the content indicators of the data to be processed from the full-text search engine.

[0116] In a specific implementation, the processor 501, the storage device 502 and the communication interface 503 described in the embodiment of the present invention can execute the embodiment of the present invention. Figure 2 or Figure 3AThe implementation described in the relevant embodiments of the data review method provided can also be used to implement the embodiments of the present invention. Figure 4 The implementation methods described in the relevant embodiments of the provided data review device will not be repeated here.

[0117] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing related hardware through a computer program. The program can be stored in a computer-readable storage medium, and when executed, the program can include the processes in the above-described method embodiments. The storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0118] The above disclosure is only part of the embodiments of the present invention, which certainly cannot be used to limit the scope of the rights of the present invention. Ordinary technicians in this field can understand that implementing all or part of the processes of the above embodiments and making equivalent changes in accordance with the claims of the present invention still fall within the scope of the invention.

Claims

1. A data review method, characterized in that: Applied to a server, the method includes: Obtaining content indicators of data to be processed; wherein the data to be processed includes multiple data, each of which includes any one of articles, videos, and audios; Obtain at least one pre-configured review rule; wherein the review rule includes at least one of the following configuration items: number of reviews, review channel, sorting rule, review content source, order priority, review cycle, number of single reviews, and filtering rule; Matching the content indicator of each data in the to-be-processed data with the at least one review rule respectively; If there is a review rule in the at least one review rule that matches the content indicator of each data, then each data is used as target data to be reviewed; The target data is distributed to a review platform so that a reviewer can review the target data after receiving the target data through the review platform.

2. The method according to claim 1, characterized in that The method further comprises: Receive review rule configuration instructions input by the user; Displaying a review rule configuration interface in response to the review rule configuration instruction, wherein the review rule configuration interface includes at least one configuration item; Acquiring configuration parameters input by the user for the at least one configuration item; A configuration rule is generated according to the configuration parameters.

3. The method according to claim 1, characterized in that The allocating the target data to the review platform so that the reviewer reviews the target data after receiving the target data through the review platform includes: Acquiring a review rule that matches the content indicator of the target data; Obtaining the order priority and review channel of the target data according to the matched review rules; The target data, the order priority of the target data and the review channel are allocated to the review platform, so that the reviewer can review the target data after receiving the order of the target data through the review platform according to the order priority of the target data and the review channel.

4. The method according to claim 1, wherein The method further comprises: Obtaining the review results submitted by the reviewer for the target data; A data review report is generated according to the review progress, review results and review rules matching the content indicators of the target data, wherein the data review report includes at least one of the following: a review statistics report and a review process report.

5. The method according to claim 1, wherein The method further comprises: Receiving content indicators of data to be processed sent by a user terminal, wherein the content indicators include at least one of the following: source, classification identifier, popularity data, negative feedback data, effective negative feedback data, and user browsing data; Storing content metrics in a full-text search engine; The step of obtaining content indicators of the data to be processed includes: Obtain content indicators of the data to be processed from the full-text search engine.

6. A data review device, characterized in that: The data review device includes a communication unit and a processing unit, wherein: The processing unit is configured to obtain content indicators of data to be processed; wherein the data to be processed includes a plurality of data, each of which includes any one of articles, videos, and audios; The processing unit is further configured to obtain at least one pre-configured review rule; match the content indicator of each data item in the data to be processed with the at least one review rule; if there is a review rule in the at least one review rule that matches the content indicator of each data item, then use the each data item as the target data to be reviewed; wherein the review rule includes at least one of the following configuration items: number of reviews, review channel, sorting rule, review content source, order priority, review cycle, number of single reviews, and filtering rule; The communication unit is used to distribute the target data to the review platform, so that the reviewer can review the target data after receiving the target data through the review platform.

7. An electronic device, characterized in that: The system comprises a processor, a memory, a communication interface, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the processor as claimed in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that A computer program for electronic data exchange is stored, wherein the computer program enables a computer to execute the method according to any one of claims 1 to 5.

9. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 5 is implemented.

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