Task execution method and device, electronic device, storage medium and program product

Abstract

The present disclosure provides a task execution method and device, electronic device, storage medium and program product, which relate to the field of data processing technology, especially to technical fields such as user profiling and intelligent recommendation, and can be used in application scenarios such as generative search, intelligent assistant, and intelligent e-commerce. The method includes: responding to a task execution request for a target scenario, according to the target dimension indicated by the task execution request, determining at least one target label subset in the label set for the target scenario, the label set including at least one candidate label subset for each candidate dimension, each target label subset characterizing the mapping relationship between the candidate label and multiple candidate configuration items, and the candidate label characterizing the characteristics of the object; in response to detecting that at least one target configuration item in the candidate configuration items is selected, executing the task execution request according to the at least one target configuration item and the target label for each of the at least one target configuration item, and obtaining the task execution result.

Description

Technical Field

[0001] The present disclosure relates to the field of data processing technology, in particular to technical fields such as user profiling and intelligent recommendation, and can be used in application scenarios such as generative search, intelligent assistants, and intelligent e-commerce. More specifically, it relates to a task execution method and device, electronic equipment, storage medium, and program product. Background Art

[0002] With the popularization of the Internet and the convenience of mobile payment, e-commerce has rapidly emerged around the world and has become an important part of modern business. Summary of the Invention

[0003] The present disclosure provides a task execution method and device, an electronic device, a storage medium, and a program product.

[0004] According to one aspect of the present disclosure, a task execution method is provided, comprising: in response to a task execution request for a target scenario, determining at least one target label subset in a label set for the target scenario according to the target dimension indicated by the task execution request, wherein the label set includes a candidate label subset for each of at least one candidate dimension, each of the target label subsets represents a mapping relationship between a candidate label and a plurality of candidate configuration items, and the candidate labels represent characteristics of an object; and, in response to detecting that at least one target configuration item among the candidate configuration items is selected, executing the task execution request according to the at least one target configuration item and the target label for each of the at least one target configuration item to obtain a task execution result.

[0005] According to another aspect of the present disclosure, a task execution device is provided, including: a determination module for, in response to a task execution request for a target scenario, determining, according to the target dimension indicated by the task execution request, at least one target label subset in a label set for the target scenario, wherein the label set includes a candidate label subset for each of at least one candidate dimension, each of the target label subsets characterizing a mapping relationship between a candidate label and a plurality of candidate configuration items, and the candidate labels characterizing features of an object; and an execution module for, in response to detecting that at least one target configuration item among the candidate configuration items is selected, executing the task execution request according to the at least one target configuration item and the target label for each of the at least one target configuration item to obtain a task execution result.

[0006] According to another aspect of the present disclosure, an electronic device is provided, comprising: one or more processors; and a memory for storing one or more computer programs, wherein the one or more processors execute the one or more computer programs to implement the steps of the above method.

[0007] According to another aspect of the present disclosure, a computer-readable storage medium is provided, on which a computer program or instructions are stored. When the computer program or instructions are executed by a processor, the steps of the above method are implemented.

[0008] According to another aspect of the present disclosure, a computer program product is provided, comprising a computer program or instructions, which implement the steps of the above method when executed by a processor.

[0009] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present disclosure, nor are they intended to limit the scope of the present disclosure. Other features of the present disclosure will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] The above and other objects, features and advantages of the present disclosure will become more apparent through the following description of the embodiments of the present disclosure with reference to the accompanying drawings, in which:

[0011] Figure 1 The system architecture to which the task execution method according to an embodiment of the present disclosure can be applied is schematically shown;

[0012] Figure 2 The following schematically shows a flowchart of a task execution method according to an embodiment of the present disclosure;

[0013] Figure 3A An example diagram schematically illustrates a process for determining a tag set for a target scene according to an embodiment of the present disclosure;

[0014] Figure 3B An example schematic diagram schematically illustrates at least one target tag subset of an object behavior dimension according to an embodiment of the present disclosure;

[0015] Figure 3C An example schematic diagram schematically illustrates at least one target tag subset of an object value dimension according to an embodiment of the present disclosure;

[0016] Figure 3D An example schematic diagram schematically illustrates at least one target tag subset of an object preference dimension according to an embodiment of the present disclosure;

[0017] Figure 4 Schematically illustrates an example of a process of determining at least one target tag subset in a tag set for a target scenario according to a target dimension indicated by a task execution request according to an embodiment of the present disclosure;

[0018] Figure 5 An example diagram schematically illustrates a process of creating a new tag according to an embodiment of the present disclosure;

[0019] Figure 6A Schematically illustrates an example process of executing a task execution request and obtaining a task execution result when the task execution request is used to perform quality assessment on a tag set according to an embodiment of the present disclosure;

[0020] Figure 6B Schematically illustrates an example process of executing a task execution request and obtaining a task execution result when the task execution request is used to use a tag set according to an embodiment of the present disclosure;

[0021] Figure 7 A block diagram schematically shows a task execution device according to an embodiment of the present disclosure; and

[0022] Figure 8 A block diagram of an electronic device suitable for implementing a task execution method according to an embodiment of the present disclosure is schematically shown. DETAILED DESCRIPTION

[0023] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are merely exemplary and are not intended to limit the scope of the present disclosure. In the detailed description below, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of the embodiments of the present disclosure. However, it is apparent that one or more embodiments may also be implemented without these specific details. In addition, in the following description, descriptions of well-known structures and technologies are omitted to avoid unnecessary confusion of the concepts of the present disclosure.

[0024] The terms used herein are only for describing specific embodiments and are not intended to limit the present disclosure. The terms "comprise," "include," etc. used herein indicate the presence of the features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0025] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those skilled in the art unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification and should not be interpreted in an idealized or overly rigid manner.

[0026] When expressions such as "at least one of A, B, and C, etc." are used, they should generally be interpreted in accordance with the meaning commonly understood by those skilled in the art (for example, "a system having at least one of A, B, and C" should include but is not limited to a system having A alone, B alone, C alone, A and B, A and C, B and C, and / or A, B, C, etc.).

[0027] With the rapid development of e-commerce platforms, consumers' online shopping behaviors are becoming increasingly complex, and their demands are becoming more diverse and personalized. Consumers are no longer satisfied with simple commodity purchases, but are beginning to seek diverse and personalized goods and services. This requires e-commerce platforms to develop more refined marketing strategies to more accurately meet consumer needs.

[0028] In one example, user profiles can be constructed by collecting and analyzing user behavior data to depict user characteristics and behavioral patterns, such as interests, preferences, and shopping habits. By building user profiles, e-commerce platforms can gain a deeper understanding of consumers, enabling them to provide more personalized recommendations and refined marketing strategies. User profiles can be constructed using at least one of the following methods: based on user behavior data, based on manual rules, or based on machine learning or deep learning.

[0029] Construction based on user behavior data involves collecting behavioral data such as user purchases, browsing history, search keywords, and click-through rates, and then assigning different labels to users through simple tag classification methods. This method is the most basic and can, to a certain extent, reflect basic user needs. Construction based on manual rules involves extracting user characteristics through a series of rules established by business experts or data analysts based on their understanding and judgment of user behavior. For example, user groups can be categorized and labeled based on behavioral data such as purchase behavior and active time periods.

[0030] However, the dimensions of the above two methods are often too simplistic and cannot fully depict user characteristics. For example, they only focus on users' purchasing behavior or browsing history, but ignore the diversity of users in social aspects, interests and hobbies, etc., and cannot fully reflect users' diverse needs, which in turn affects marketing effectiveness and user experience. In addition, the labels of the above two methods are not updated in a timely manner. Users' interests and needs may change over time, and methods based on historical user behavior and manually set rules often only reflect users' past needs, but are difficult to predict users' current and future needs. This leads to insufficient timeliness of the labeling system. In addition, the timeliness issues of the above two methods may also cause accuracy issues.

[0031] Machine learning or deep learning-based construction involves leveraging machine learning algorithms, such as decision trees, clustering, and association rules, to automatically learn hidden patterns and relationships within user data, thereby generating more accurate user labels. This approach can handle large-scale, complex data and is highly adaptable. Deep learning, a type of machine learning, can automatically extract underlying features from user behavior data, building richer and more detailed user profiles. Deep learning models are capable of processing high-dimensional data, such as images, speech, and text.

[0032] However, these approaches are subject to the quality of training data and feature engineering. If the training data is biased or noisy, or if the features are improperly selected, model performance will degrade. Deep learning models may also suffer from overfitting or underfitting, affecting the accuracy of the image.

[0033] In summary, since the construction and maintenance of a tag system is a complex process that requires consideration of multiple links such as tag selection, updating, and deletion, the various methods mentioned above often cannot effectively deal with these problems. For example, how to update tags in a timely manner according to changes in user behavior, how to delete invalid or outdated tags, and how to deal with redundancy and conflicts between tags, etc., make it difficult to continuously optimize and expand the tag system.

[0034] To this end, an embodiment of the present disclosure proposes a task execution scheme. For example, in response to a task execution request for a target scenario, at least one target label subset is determined in a label set for the target scenario according to the target dimension indicated by the task execution request, wherein the label set includes a candidate label subset for each of at least one candidate dimension, each target label subset represents a mapping relationship between a candidate label and a plurality of candidate configuration items, and the candidate label represents a feature of an object; and in response to detecting that at least one target configuration item among the candidate configuration items is selected, the task execution request is executed according to the at least one target configuration item and the target label for each of the at least one target configuration item to obtain a task execution result.

[0035] According to an embodiment of the present disclosure, by determining at least one target tag subset from a tag set based on the target dimension indicated by the task execution request, the targeted execution of the task is ensured. Because each target tag subset represents the mapping relationship between candidate tags and multiple candidate configuration items, when at least one target configuration item among the candidate configuration items is detected to be selected, the task execution request can be executed based on the target configuration item and the corresponding target tag, thereby obtaining an accurate task execution result, which not only improves the accuracy of task execution, but also improves the efficiency of task execution.

[0036] In the technical solution of the present invention, the collection, storage, use, processing, transmission, provision and disclosure of user personal information involved comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0037] In the technical solution of the present invention, the user's authorization or consent is obtained before obtaining or collecting the user's personal information.

[0038] Figure 1 The system architecture to which the task execution method according to the embodiment of the present disclosure can be applied is schematically shown. Figure 1The examples shown are merely examples of system architectures to which the embodiments of the present disclosure may be applied, to help those skilled in the art understand the technical content of the present disclosure, but do not mean that the embodiments of the present disclosure may not be used in other devices, systems, environments or scenarios.

[0039] like Figure 1 As shown, the system architecture 100 according to this embodiment may include a first terminal device 101, a second terminal device 102, a third terminal device 103, a network 104, and a server 105. The network 104 is used as a medium for providing a communication link between the first terminal device 101, the second terminal device 102, the third terminal device 103, and the server 105. The network 104 may include various connection types, such as wired or wireless communication links or fiber optic cables.

[0040] The user may use at least one of the first terminal device 101, the second terminal device 102, and the third terminal device 103 to interact with the server 105 via the network 104 to receive or send messages, etc. Various communication client applications may be installed on the first terminal device 101, the second terminal device 102, and the third terminal device 103, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, social platform software, etc. (only as examples).

[0041] The first terminal device 101 , the second terminal device 102 , and the third terminal device 103 may be various electronic devices having display screens and supporting web browsing, including but not limited to smart phones, tablet computers, laptop computers, desktop computers, and the like.

[0042] The server 105 may be a server that provides various services, such as a background management server (for example only) that supports websites browsed by users using the first terminal device 101, the second terminal device 102, and the third terminal device 103. The background management server may analyze and process received data such as user requests, and feed back processing results (e.g., web pages, information, or data obtained or generated based on user requests) to the terminal devices.

[0043] It should be noted that the task execution method provided in the embodiment of the present disclosure can generally be executed by the server 105. Accordingly, the task execution device provided in the embodiment of the present disclosure can generally be set in the server 105. The task execution method provided in the embodiment of the present disclosure can also be executed by a server or server cluster that is different from the server 105 and can communicate with the first terminal device 101, the second terminal device 102, the third terminal device 103 and / or the server 105. Accordingly, the task execution device provided in the embodiment of the present disclosure can also be set in a server or server cluster that is different from the server 105 and can communicate with the first terminal device 101, the second terminal device 102, the third terminal device 103 and / or the server 105.

[0044] Alternatively, the task execution method provided in the embodiment of the present disclosure may also be executed by the first terminal device 101, the second terminal device 102, or the third terminal device 103, or may also be executed by another terminal device different from the first terminal device 101, the second terminal device 102, or the third terminal device 103. Accordingly, the task execution apparatus provided in the embodiment of the present disclosure may also be provided in the first terminal device 101, the second terminal device 102, or the third terminal device 103, or may be provided in another terminal device different from the first terminal device 101, the second terminal device 102, or the third terminal device 103.

[0045] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is merely illustrative. Any number of terminal devices, networks and servers may be provided as required.

[0046] It should be noted that the sequence numbers of the operations in the following method are only used to indicate the operation for the purpose of description, and should not be regarded as indicating the order in which the operations should be performed. Unless explicitly stated, the method does not need to be performed in the order shown.

[0047] Figure 2 The flowchart of the task execution method according to the embodiment of the present disclosure is schematically shown.

[0048] like Figure 2 As shown, the task execution method 200 includes operations S210 to S220.

[0049] In operation S210, in response to a task execution request for a target scenario, at least one target label subset is determined in a label set for the target scenario based on a target dimension indicated by the task execution request, wherein the label set includes a candidate label subset for each of at least one candidate dimension, each target label subset represents a mapping relationship between a candidate label and multiple candidate configuration items, and the candidate label represents a feature of an object.

[0050] In operation S220 , in response to detecting that at least one target configuration item among the candidate configuration items is selected, a task execution request is executed according to the at least one target configuration item and a target tag for each of the at least one target configuration item to obtain a task execution result.

[0051] A target scenario refers to a specific application scenario or business domain. Before executing the task execution method 200 provided herein, a candidate tag set for each candidate scenario can be pre-built based on the relevant data of the candidate scenario. This candidate tag set for each candidate scenario can serve as the basic data for the task execution method.

[0052] After receiving a task execution request for a target scenario, the target scenario is matched against each candidate scenario to determine the candidate tag set corresponding to the candidate scenario that matches the target scenario as the tag set for the target scenario. A tag set, or tag system, can be understood as categorizing the various tags required for the target scenario and defining tag attributes to facilitate tag management and maintenance.

[0053] A label set can include at least one candidate dimension and a subset of candidate labels for each candidate dimension. A candidate dimension is a set of dimensional options that can be used to filter or process data. A candidate label subset is a set of candidate labels for each candidate dimension. Each candidate label represents a different characteristic of an object and can be used to further refine data filtering or processing.

[0054] Each candidate tag subset represents a mapping relationship between candidate tags of at least one level and multiple candidate configuration items. The mapping relationship can be understood as a corresponding relationship between candidate tags and candidate configuration items. Candidate configuration items refer to optional configuration options for configuring the candidate tags corresponding thereto. In one example, the task execution request may indicate a target dimension. In this case, each candidate dimension can be matched according to the target dimension to determine the candidate tag subset corresponding to the candidate dimension that matches the target dimension as the target tag subset.

[0055] After determining the target tag subset, the target configuration item can be determined from at least one candidate configuration item included in the target tag subset. The target configuration item refers to a configuration item selected from the candidate configuration items for executing the business processing request. The method for determining the target configuration item can be configured according to actual business needs and is not limited here. For example, the at least one candidate configuration item can be displayed on the front-end interface, and the target configuration item can be determined based on the user's selection operation on the front-end interface. Alternatively, the task execution request may include requirement information for the target scenario. In this case, the requirement information can be matched with at least one candidate configuration item to determine the target configuration item.

[0056] After obtaining the target configuration item, for each target configuration item, a target tag corresponding to the target configuration item can be determined from at least one candidate tag based on a mapping relationship. For example, the candidate tag subset includes candidate tag A and candidate tag B, and the mapping relationship includes that candidate tag A corresponds to candidate configuration item 1, candidate configuration item 2, and candidate configuration item 3, and candidate tag B corresponds to candidate configuration item 4 and candidate configuration item 5. If the target configuration item is candidate configuration item 2, the target tag corresponding to candidate configuration item 2 can be determined to be candidate tag A based on the mapping relationship.

[0057] After determining at least one target configuration item and the target tag for each of the at least one target configuration item, a task execution request can be executed based on the at least one target configuration item and the target tag for each of the at least one target configuration item to obtain a task execution result. The specific content of the task execution request can be configured based on actual business needs and is not limited here.

[0058] For example, when the task execution request is used to perform a quality assessment on a tag set, the task execution result may include a first prompt message indicating at least one abnormal tag. Alternatively, when the task execution request is used to perform a usage assessment on a tag set, the task execution result may include a second prompt message indicating at least one abnormal tag. Alternatively, when the task execution request is used to manage a tag set, the task execution result may include a change operation for the tag, and the change operation may include a new operation, a delete operation, and a modification operation.

[0059] In embodiments of the present disclosure, the user's consent or authorization may be obtained before obtaining the user's information. For example, before executing the task execution method provided by the present disclosure, a request to obtain the user's information may be issued to the user. If the user consents or authorizes the acquisition of the user's information, the task execution method provided by the present disclosure is executed.

[0060] In embodiments of the present disclosure, a corresponding operation portal can be provided for the user to choose to agree or reject the automated decision result. That is, before processing user information, an instruction to agree or reject the processing can be obtained from the user through the corresponding operation portal. If the user agrees to the processing, the user information is processed, that is, the task execution method provided by the present disclosure is executed. If the user rejects the processing, the expert decision process is entered.

[0061] According to an embodiment of the present disclosure, by determining at least one target tag subset from a tag set based on the target dimension indicated by the task execution request, the targeted execution of the task is ensured. Because each target tag subset represents the mapping relationship between candidate tags and multiple candidate configuration items, when at least one target configuration item among the candidate configuration items is detected to be selected, the task execution request can be executed based on the target configuration item and the corresponding target tag, thereby obtaining an accurate task execution result, which not only improves the accuracy of task execution, but also improves the efficiency of task execution.

[0062] Figure 3A The following schematically illustrates an example of a process for determining a tag set for a target scene according to an embodiment of the present disclosure.

[0063] like Figure 3A As shown in Figure 300A, object data 303 within a historical period for a target scenario 302 can be obtained from data source 301. The user is aware of and consents to the acquisition and use of object data 303, and the acquisition and use of object data 303 comply with relevant laws and regulations and do not violate public order and good morals. For example, if the target scenario is an e-commerce scenario, data source 301 can be a data warehouse containing users who browse and place orders across the entire e-commerce site.

[0064] The data source 301 based on the user portrait has been pre-processed and connected to the system. The data warehouse may include a data dimension table layer, a data middle layer, a data service layer, and a data application layer. The data dimension table layer can be used to store basic dimensional data closely related to the user portrait, such as core dimensions such as object information tables and commodity strategy category tables. The data middle layer is used to perform complex data calculation and processing tasks, including but not limited to data aggregation, feature extraction, and modeling. The data service layer relies on a pre-built tag set and can build user portrait theme wide tables based on the hierarchical structure of different tags. These wide tables can integrate multi-dimensional tag data to facilitate subsequent data analysis and use. The data application layer is used to extract and calculate the required data from the above layers based on specific business needs, and ultimately generate business-oriented data analysis reports.

[0065] After obtaining the object data 303, feature extraction can be performed on the object data 303 to obtain data features 304. Based on the data features 304, division is performed at the dimension level and the label level to obtain a label set 305. The label set 305 may include at least one candidate dimension and a respective candidate label subset for each candidate dimension. The candidate dimension may refer to a first-level hierarchy, and each candidate dimension contains a number of second- and third-level labels, namely candidate labels. The candidate label subset represents the mapping relationship between the candidate labels of at least one level and multiple candidate configuration items. The above-mentioned candidate dimensions and the multi-level labels under each candidate dimension can accurately characterize the characteristics of e-commerce users through permutations and combinations.

[0066] These candidate scenarios can be explored from various perspectives, including product, operations, and strategy. For example, from a product perspective, candidate tag sets can serve as a basis for understanding user needs and optimizing product features. By analyzing user profiles determined based on candidate tag sets, product managers can identify the characteristics and preferences of different user groups, thereby designing product features that better meet market needs and enhance the user experience. Furthermore, user profiles determined based on candidate tag sets can help product managers predict market trends, providing data support for product iteration and innovation.

[0067] Alternatively, from an operational perspective, the candidate tag set can be used as a basis for improving operational efficiency. By analyzing user behavior data and preferences, operations managers can develop targeted operational strategies, such as personalized recommendations and precision marketing, to increase user activity and retention.

[0068] Alternatively, from a strategic perspective, user profiles determined based on candidate tag sets can serve as a basis for market analysis and decision-making. By integrating and analyzing user profile data, policy managers can fully grasp market dynamics and evolving user needs, thereby formulating corporate strategies that better align with market trends and user needs. Furthermore, policy managers can use user profile feedback to promptly adjust policy direction and optimize policy model configuration.

[0069] According to the embodiments of the present disclosure, by pre-building a set of labels for target scenarios, a unified and standard labeling system is formed, which simplifies the complexity and statistical difficulty of the data, can quickly, accurately and comprehensively realize the extraction of user features, and help improve the efficiency of data development, analysis and utilization.

[0070] In one example, the candidate dimensions may include at least one of the following: a basic attribute dimension, an object value dimension, an object group dimension, an object preference dimension, and an object behavior dimension. The basic attribute dimension refers to a dimension that describes the basic characteristics of an object. For example, the basic attribute dimension may include basic information such as age, gender, and geographic location. The object value dimension refers to a dimension that evaluates the value of an object. For example, the object value dimension may include the user's purchasing power, consumption frequency, average order amount, etc. The object group dimension refers to a dimension used to group objects. For example, the object group dimension may include the object's behavior pattern, purchase preference, and active time period. The object preference dimension refers to a dimension that describes the object's preference. For example, the object preference dimension may include the object's preference for product categories, brand preference, and price sensitivity. The object behavior dimension refers to a dimension that describes the object's behavior. For example, the object behavior dimension may include the object's browsing history, purchase record, and evaluation feedback.

[0071] According to the embodiments of the present disclosure, in e-commerce scenarios, by introducing a comprehensive multi-dimensional, multi-level tag management system, it is possible to understand object characteristics in more detail, improve the diversity of data, and achieve refined characterization and efficient management of object characteristics. This not only improves the efficiency and accuracy of tag management, but also optimizes the task processing process, allowing e-commerce to manage and apply object data more effectively.

[0072] Figure 3B An example schematic diagram of at least one target tag subset of the object behavior dimension according to an embodiment of the present disclosure is schematically shown.

[0073] like Figure 3B As shown, for the object behavior dimension, the secondary tags may include search, video, live broadcast, and order. In 300B, with the secondary tag "video", its corresponding tertiary tags may include play volume, effective play volume, completed play volume, fast scrolling volume, play duration, and click count.

[0074] It should be noted that although the third-level labels of the remaining secondary labels except "Video" are not shown in 300B, each secondary label can have a corresponding third-level label. For example, for the secondary label "Search", its corresponding third-level label may include the frequency of product keyword searches. Alternatively, for the secondary label "Live Broadcast", its corresponding third-level label may include exposure, clicks, viewing time, effective viewing time, product card clicks, order volume, number of likes, number of views, number of comments, number of shares, number of shopping cart clicks, number of explanation card bubble clicks, number of flash sale bubble card clicks and number of hook product clicks, etc. Alternatively, for the secondary label "Order", its corresponding third-level label may include field, dimension and purchase information, etc.

[0075] Figure 3CAn example schematic diagram of at least one target tag subset of an object value dimension according to an embodiment of the present disclosure is schematically shown.

[0076] like Figure 3C As shown in the figure, for the object value dimension, the secondary tags can include new and old users, activity stratification, life cycle, RFM (Recency Frequency Monetary, customer relationship management) user value stratification and consumption level stratification.

[0077] Figure 3D An example schematic diagram of at least one target tag subset of the object preference dimension according to an embodiment of the present disclosure is schematically shown.

[0078] like Figure 3D As shown, for the object preference dimension, the secondary tags may include product preference, field preference, content preference, marketing tool preference, industry preference, and preference for merchants in the same industry.

[0079] After the tag set is constructed, data processing can be optimized based on the characteristics of the tag set data construction. For example, for each candidate tag, it can be determined whether the candidate tag has a target attribute. In response to the candidate tag having the target attribute, the candidate tag data is adjusted based on the target attribute. The target attribute can include at least one of an update attribute, a statistical attribute, and an extended attribute.

[0080] In one example, since the update cycles of different candidate tags vary due to their characteristics, when the target attribute is an update attribute, the candidate tags can be updated based on the update cycle. For example, candidate tags that are not easy to change in the basic attribute dimensions such as gender and age can be updated on a weekly or monthly basis. Alternatively, since candidate tags with predictive types and candidate tags with feature types may affect real-time business decisions, real-time updates at the minute or hour level are required. By splitting processing tasks with different update cycles based on the update cycle, it is possible to save computing resources, improve timeliness, and ensure the accuracy and practicality of the data while ensuring it.

[0081] In another example, since the same candidate tag may have different statistical periods, and the statistical periods of different candidate tags are more likely to be different, if different candidate tags with different statistical periods or the same candidate tags with different statistical periods are simply placed in the same task, it is easy to waste computing resources, and it is also likely to cause task delays and affect the timeliness of the candidate tags. Therefore, when the target attribute is a statistical attribute, the candidate tags can be counted based on the statistical period. By splitting the processing tasks of different statistical periods based on the statistical period, the accuracy and timeliness of the candidate tags can be guaranteed.

[0082] In another example, during the construction of the tag set, some candidate tags need to be frequently updated and expanded. For example, taking targeted crowd tags as an example, as the business develops, new crowd types such as men's clothing, clothing, shoes and bags need to be gradually added. Therefore, when the target attribute is an extended attribute, an extended field can be added to the candidate tag based on the field format. The field format can be "JSON format" or "XML format" to ensure that the extended field is compatible with the existing data structure.

[0083] Figure 4 The following schematically illustrates an example process of determining at least one target tag subset in a tag set for a target scenario according to a target dimension indicated by a task execution request according to an embodiment of the present disclosure.

[0084] like Figure 4 As shown, in 400 , when the task execution request is used to manage a tag set, the target tag subset may be determined by interacting with the front-end interface.

[0085] The tag management interface 410 can be part of the user interface for managing tag collections. This management can include functions such as creating, updating, deleting, categorizing, searching, and managing permissions for tags. Furthermore, the tag management interface 410 can also provide a tag version control mechanism to ensure the traceability of changes, thereby improving the efficiency and accuracy of tag management.

[0086] The tag management interface 410 may include a tag selection control 401 , which is used to trigger a tag selection process and jump to a tag selection interface 420 , so that the user can select dimensions and tags.

[0087] In one example, in response to detecting that the tag selection control 401 in the tag management interface 410 is triggered, at least one candidate dimension is displayed in the tag selection interface 420. For example, the candidate dimensions may include the aforementioned basic attribute dimension, object value dimension, object group dimension, object preference dimension, and object behavior dimension.

[0088] In response to detecting that a target dimension is selected in at least one candidate dimension of the tag selection interface 420, at least one target tag subset for the target dimension may be displayed. For example, in response to detecting that the object behavior dimension 402 is selected, taking the third-level tag "play volume 404" under the second-level tag "video" as an example, the target tag subset 403 for the object behavior dimension 402 characterizes the mapping relationship between the play volume 404 and multiple candidate configuration items 405. The candidate configuration items 405 may, for example, include "nearly 7 days", "nearly 14 days", "nearly 30 days", "nearly 60 days", "nearly 90 days", "nearly 180 days", "nearly 365 days" and "more than 365 days".

[0089] According to an embodiment of the present disclosure, when a task execution request is used to manage a tag collection, in response to detecting that a tag selection control in a tag management interface has been triggered, at least one candidate dimension is displayed in the tag selection interface, thereby ensuring the smoothness and intuitiveness of user operations. Furthermore, when a user selects a target dimension from at least one candidate dimension in the tag selection interface, at least one target tag subset for that target dimension is displayed. This not only improves the accuracy of tag selection but also optimizes the user's selection path, enabling the user to quickly locate and select the desired tag, thereby enhancing interactivity and user satisfaction.

[0090] Figure 5 The following schematic diagram schematically illustrates an example of a process for creating a new tag according to an embodiment of the present disclosure.

[0091] like Figure 5 As shown in 500, the tag management interface 510 may further include a tag creation control 501. The tag creation control 501 is used to trigger the tag creation process and jump to the tag creation interface 520 so that the user can create a tag.

[0092] In one example, in response to the label creation control 501 being triggered, a label creation interface 520 is displayed. For example, the label creation interface 520 may include a label name input box, an expiration time input box, and a periodic update selection control 502. The label name input box can be used to enter a label name, the expiration time input box can be used to enter an expiration time, and the periodic update selection control 502 can be used to set whether the label has an update attribute.

[0093] Alternatively, the label creation interface 520 may further include a label selection control 503, which is used to trigger the label selection process and jump to the label selection interface to facilitate the user to select dimensions and labels. After the user completes the label selection operation, the selected label display box of the label creation interface 520 may display at least one label involved in the label selection operation.

[0094] Alternatively, the tag creation interface 520 may further include a tag exclusion control 504, which is used to trigger the tag exclusion process and jump to the tag exclusion interface to facilitate the user to exclude tags. After the user completes the tag exclusion operation, the excluded tag display box in the tag creation interface 520 can display at least one tag involved in the tag exclusion operation.

[0095] A new tag is created by inputting information obtained through the tag name input box, the expiration time input box, the periodic update selection control 502, the tag selection control 503 and the tag exclusion control 504.

[0096] According to the embodiments of the present disclosure, by triggering the tag creation button provided in the tag management interface, the tag creation interface can be quickly accessed, simplifying user operations and improving the user-friendliness of the interface. In the tag creation interface, users can conveniently enter the required information, so that new tags are automatically created based on the entered information. This achieves automated and personalized tag management, allowing users to flexibly create tags according to their specific needs, thereby optimizing the flexibility of task execution processes and data management.

[0097] Figure 6A The following schematically illustrates an example process of executing a task execution request and obtaining a task execution result when the task execution request is used to perform quality assessment on a tag set according to an embodiment of the present disclosure.

[0098] like Figure 6A As shown, in 600A, data 603 to be evaluated for at least one target configuration item 602 can be obtained from a data source 601. The user is aware of and agrees to the acquisition and use of the data 603 to be evaluated, and the acquisition and use of the data 603 are in compliance with relevant laws and regulations and do not violate public order and good morals.

[0099] When the task execution request is used to perform a quality assessment on a tag set, the predetermined assessment conditions may include a predetermined fluctuation range and a predetermined assessment threshold for each target configuration item 602. Based on this, the assessment process for the data to be assessed 603 may include an assessment of the fluctuation range and a value range.

[0100] During the numerical range evaluation phase, operation S610 may be performed for the data to be evaluated 603. In operation S610, it is determined whether the data to be evaluated 603 meets a predetermined evaluation threshold. If not, an evaluation result 605 indicating that the data to be evaluated 603 does not meet the predetermined evaluation threshold may be determined. If so, operation S620 may be performed.

[0101] During the fluctuation range evaluation phase, i.e., operation S620 , it is determined whether the actual fluctuation range 604 of the data to be evaluated 603 meets the predetermined fluctuation range. If not, an evaluation result 606 indicating that the actual fluctuation range 604 does not meet the predetermined fluctuation range can be determined. If so, an evaluation result 607 indicating that the data to be evaluated 603 meets the predetermined evaluation threshold and the actual fluctuation range 604 meets the predetermined fluctuation range can be determined.

[0102] Based on evaluation results 605 and 606, prompt information 608 can be determined, indicating at least one abnormal quality label. This prompt information 608 can be used for monitoring and alarming to ensure the accuracy and completeness of the label set. By utilizing the task execution method to implement timely monitoring and regular alarm mechanisms, data deviations can be promptly discovered and corrected, effectively identifying and prompting abnormal labels, thereby optimizing the accuracy and efficiency of task execution and ensuring the credibility of user profiles constructed based on the label set.

[0103] Figure 6B The following schematically illustrates an example process of executing a task execution request and obtaining a task execution result when the task execution request is used to use a tag set according to an embodiment of the present disclosure.

[0104] like Figure 6B As shown, in 600B, data 611 to be evaluated for each of at least one target configuration item 610 can be obtained from a data source 609. The user is aware of and agrees to the acquisition and use of the data 611 to be evaluated, and the acquisition and use of the data 611 are in compliance with relevant laws and regulations and do not violate public order and good morals.

[0105] When the task execution request is used to evaluate the usage of a tag set, the predetermined evaluation conditions may include a usage popularity threshold, a usage frequency threshold, and a contribution threshold for each target configuration item 610. Based on this, the evaluation process of the data to be evaluated 611 may include an evaluation of usage popularity, an evaluation of usage frequency, and an evaluation of contribution.

[0106] During the usage popularity evaluation phase, the actual usage popularity 612 of the data to be evaluated 611 can be determined, and operation S630 can be executed. In operation S630, it is determined whether the actual usage popularity 612 meets the usage popularity threshold. If not, an evaluation result 615 indicating that the actual usage popularity 612 does not meet the usage popularity threshold can be determined. If so, operation S640 can be executed.

[0107] During the frequency of use evaluation phase, the actual frequency of use 613 of the data to be evaluated 611 can be determined, and operation S640 is performed. In operation S640, it is determined whether the actual frequency of use 613 meets the frequency of use threshold. If not, an evaluation result 616 indicating that the actual frequency of use 613 does not meet the frequency of use threshold can be determined. If so, operation S650 can be performed.

[0108] During the contribution evaluation phase, for the data to be evaluated 611, the actual contribution 614 of the data to be evaluated 611 can be determined, and operation S650 is performed. In operation S650, is it determined whether the actual contribution 614 meets the contribution threshold? If not, an evaluation result 617 can be determined indicating that the actual contribution 614 does not meet the contribution threshold. If so, an evaluation result 618 can be determined indicating that the actual usage heat meets the usage heat threshold, the actual usage frequency 613 meets the usage frequency threshold, and the actual contribution 614 meets the contribution threshold.

[0109] Based on evaluation results 615, 616, and 617, prompt information 619 can be determined for an abnormal tag indicating at least one abnormal usage situation. Such abnormal tags may include tags with no popularity, low popularity, or tags with no contribution rate for a long period of time. These abnormal tags can be removed from service or replaced to reduce the decision-making cost of using user profile tags, effectively identify and prompt abnormal tags, and thus optimize the accuracy and efficiency of task execution.

[0110] The above are merely exemplary embodiments, but are not limited thereto. Other task execution methods known in the art may also be included, as long as a unified and standard labeling system can be formed to improve the efficiency and accuracy of task execution.

[0111] Figure 7 The block diagram of a task execution device according to an embodiment of the present disclosure is schematically shown.

[0112] like Figure 7 As shown, the task execution device 700 may include a determination module 710 and an execution module 720 .

[0113] The determination module 710 is used to determine, in response to a task execution request for a target scenario, at least one target label subset in the label set for the target scenario according to the target dimension indicated by the task execution request, wherein the label set includes a candidate label subset for each of at least one candidate dimension, each target label subset represents a mapping relationship between a candidate label and multiple candidate configuration items, and the candidate label represents a feature of an object.

[0114] The execution module 720 is configured to, in response to detecting that at least one target configuration item among the candidate configuration items is selected, execute a task execution request according to the at least one target configuration item and a target tag for each of the at least one target configuration item to obtain a task execution result.

[0115] According to an embodiment of the present disclosure, a label set for a target scenario is obtained in the following manner: object data within a historical period under the target scenario is obtained from a data source; and, based on the data characteristics of the object data, the data is divided into dimension levels and label levels to obtain at least one candidate dimension and a candidate label subset for each candidate dimension, wherein the candidate label subset represents a mapping relationship between candidate labels of at least one level and multiple candidate configuration items.

[0116] According to an embodiment of the present disclosure, for each candidate tag, the task execution device 700 may further include an adjustment module.

[0117] The adjustment module is configured to adjust the candidate tag according to the target attribute in response to the candidate tag having the target attribute.

[0118] According to an embodiment of the present disclosure, the target attribute includes at least one of an update attribute, a statistical attribute and an extended attribute. The update attribute is used to update the candidate tag based on an update period, the statistical attribute is used to perform statistics on the candidate tag based on a statistical period, and the extended attribute is used to add an extended field to the candidate tag according to the field format.

[0119] According to an embodiment of the present disclosure, when the task execution request is used to perform quality evaluation or usage evaluation on a tag set, the task execution result includes prompt information for indicating at least one abnormal tag.

[0120] According to an embodiment of the present disclosure, the execution module 720 includes an acquisition unit, an evaluation unit, and an output unit.

[0121] The acquiring unit is configured to acquire the to-be-evaluated data of at least one target configuration item from a data source.

[0122] The evaluation unit is used to evaluate each data to be evaluated according to a predetermined evaluation condition to obtain an evaluation result for each target configuration item.

[0123] The output unit is used to output prompt information according to the evaluation results of each target configuration item, wherein the abnormal label is a label used for the evaluation result to indicate that the target configuration item does not meet the predetermined evaluation conditions.

[0124] According to an embodiment of the present disclosure, when a task execution request is used to perform quality evaluation on a tag set, the predetermined evaluation conditions include a predetermined fluctuation range and a predetermined evaluation threshold for each target configuration item, and the evaluation result represents whether the actual fluctuation range of the data to be evaluated conforms to the predetermined fluctuation range, and whether the data to be evaluated conforms to the predetermined evaluation threshold.

[0125] According to an embodiment of the present disclosure, when a task execution request is used to perform usage evaluation on a tag set, the predetermined evaluation conditions include a usage heat threshold, a usage frequency threshold, and a contribution threshold of the target configuration item, and the evaluation result characterizes whether the actual usage heat of the target configuration item meets the usage heat threshold, whether the actual usage frequency meets the usage frequency threshold, and whether the actual contribution meets the contribution threshold.

[0126] According to an embodiment of the present disclosure, when the task execution request is used to manage a tag set, the determination module 710 includes a first display unit and a second display unit.

[0127] The first display unit is configured to display at least one candidate dimension on the tag selection interface in response to detecting that a tag selection control in the tag management interface is triggered.

[0128] The second display unit is configured to display at least one target tag subset for the target dimension in response to detecting that a target dimension is selected from at least one candidate dimension in the tag selection interface.

[0129] According to an embodiment of the present disclosure, the tag management interface further includes a tag creation control.

[0130] According to an embodiment of the present disclosure, the task execution device 700 may further include a presentation module and a creation module.

[0131] The display module is used to display the label creation interface in response to the label creation control being triggered.

[0132] The creation module is used to create new tags based on the input information obtained through the tag creation interface.

[0133] According to an embodiment of the present disclosure, the target scenario includes an e-commerce scenario, and the candidate dimensions include at least one of the following: a basic attribute dimension, an object value dimension, an object group dimension, an object preference dimension, and an object behavior dimension.

[0134] Figure 8 A block diagram of an electronic device suitable for implementing a task execution method according to an embodiment of the present disclosure is schematically shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0135] like Figure 8As shown, device 800 includes a computing unit 801, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 802 or a computer program loaded from a storage unit 808 into a random access memory (RAM) 803. RAM 803 may also store various programs and data required for the operation of device 800. Computing unit 801, ROM 802, and RAM 803 are interconnected via a bus 804. An input / output (I / O) interface 805 is also connected to bus 804.

[0136] Various components in device 800 are connected to I / O interface 805, including an input unit 806, such as a keyboard, mouse, etc.; an output unit 807, such as various types of displays, speakers, etc.; a storage unit 808, such as a magnetic disk, optical disk, etc.; and a communication unit 809, such as a network card, modem, wireless communication transceiver, etc. The communication unit 809 allows device 800 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0137] The computing unit 801 can be any general-purpose and / or specialized processing component with processing and computing capabilities. Some examples of the computing unit 801 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 801 performs the various methods and processes described above, such as the task execution method. For example, in some embodiments, the task execution method may be implemented as a computer software program tangibly embodied in a machine-readable medium, such as the storage unit 808. In some embodiments, part or all of the computer program may be loaded and / or installed onto the device 800 via the ROM 802 and / or the communication unit 809. When the computer program is loaded into the RAM 803 and executed by the computing unit 801, one or more steps of the task execution method described above may be performed. Alternatively, in other embodiments, the computing unit 801 may be configured to perform the task execution method via any other suitable means (e.g., via firmware).

[0138] Various embodiments of the systems and techniques described above can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system-on-a-chip systems (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0139] The program code for implementing the method of the present disclosure can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device so that when the program code is executed by the processor or controller, the functions / operations specified in the flow chart and / or block diagram are implemented. The program code can be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0140] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of machine-readable storage media may include an electrical connection based on one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), optical fibers, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0141] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0142] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), and the Internet.

[0143] A computer system may include a client and a server. The client and server are generally remote from each other and typically interact through a communication network. The client-server relationship arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server may be a cloud server, a server in a distributed system, or a server integrated with a blockchain.

[0144] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in this disclosure can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved. This is not a limitation herein.

[0145] The above specific embodiments do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure shall be included within the scope of protection of this disclosure.

Claims

1. A task execution method, comprising: In response to a task execution request for a target scenario, determining at least one target label subset in a label set for the target scenario according to a target dimension indicated by the task execution request, wherein the label set includes a candidate label subset for each of at least one candidate dimension, each target label subset represents a mapping relationship between a candidate label and a plurality of candidate configuration items, the candidate label represents a feature of an object, and the candidate configuration item refers to an optional configuration option used to configure the corresponding candidate label; and In response to detecting that at least one target configuration item among the candidate configuration items is selected, executing the task execution request according to the at least one target configuration item and the target tag for each of the at least one target configuration item, and obtaining a task execution result, wherein, when the task execution request is used to perform a quality assessment or a usage assessment on the tag set, the task execution result includes prompt information for indicating at least one abnormal tag; The executing the task execution request according to the at least one target configuration item and the target tag for each of the at least one target configuration item to obtain the task execution result includes: Obtaining the to-be-evaluated data of the at least one target configuration item from a data source; According to predetermined evaluation conditions, each of the data to be evaluated is evaluated separately to obtain an evaluation result for each of the target configuration items; and The prompt information is output according to the evaluation result of each target configuration item, wherein the abnormal label is a label used for the evaluation result to indicate that the target configuration item does not meet the predetermined evaluation condition.

2. The method according to claim 1, wherein The tag set for the target scene is obtained in the following way: Obtaining object data within a historical period under the target scenario from a data source; and According to the data characteristics of the object data, a division is performed at the dimension level and the label level to obtain the at least one candidate dimension and a respective candidate label subset of each candidate dimension, wherein the candidate label subset represents a mapping relationship between the respective candidate labels of at least one level and the plurality of candidate configuration items.

3. The method according to claim 2, wherein: For each candidate tag, the method further includes: In response to the candidate tag having a target attribute, adjusting the candidate tag according to the target attribute; Among them, the target attribute includes at least one of an update attribute, a statistical attribute and an extended attribute. The update attribute is used to update the candidate tag based on an update period, the statistical attribute is used to count the candidate tag based on a statistical period, and the extended attribute is used to add an extended field to the candidate tag according to the field format.

4. The method according to claim 1, wherein In a case where the task execution request is used to perform a quality assessment on the tag set, the predetermined assessment condition includes a predetermined fluctuation range and a predetermined assessment threshold for each target configuration item, and the assessment result indicates whether the actual fluctuation range of the data to be assessed meets the predetermined fluctuation range and whether the data to be assessed meets the predetermined assessment threshold; as well as In the case where the task execution request is used to perform usage evaluation on the tag set, the predetermined evaluation conditions include the usage heat threshold, usage frequency threshold and contribution threshold of the target configuration item, and the evaluation result represents whether the actual usage heat of the target configuration item meets the usage heat threshold, whether the actual usage frequency meets the usage frequency threshold, and whether the actual contribution meets the contribution threshold.

5. The method according to any one of claims 1 to 3, wherein In a case where the task execution request is used to manage the tag set, determining at least one target tag subset in the tag set for the target scenario according to the target dimension indicated by the task execution request includes: In response to detecting that a tag selection control in the tag management interface is triggered, displaying the at least one candidate dimension in the tag selection interface; and In response to detecting that the target dimension is selected from at least one candidate dimension of the tag selection interface, at least one target tag subset for the target dimension is displayed.

6. The method according to claim 5, wherein: The tag management interface also includes a tag creation control; The method further comprises: In response to the label creation control being triggered, displaying a label creation interface; and Create a new tag based on the input information obtained through the tag creation interface.

7. The method according to claim 1, wherein The target scenario includes an e-commerce scenario, and the candidate dimensions include at least one of the following: a basic attribute dimension, an object value dimension, an object group dimension, an object preference dimension, and an object behavior dimension.

8. A task execution device, comprising: a determination module, configured to, in response to a task execution request for a target scenario, determine, based on a target dimension indicated by the task execution request, at least one target label subset in a label set for the target scenario, wherein the label set includes a candidate label subset for each of at least one candidate dimension, each target label subset representing a mapping relationship between a candidate label and a plurality of candidate configuration items, the candidate label representing a feature of an object, and the candidate configuration item being an optional configuration option used to configure the corresponding candidate label; and an execution module, configured to, in response to detecting that at least one target configuration item among the candidate configuration items is selected, execute the task execution request according to the at least one target configuration item and the target tag for each of the at least one target configuration item, and obtain a task execution result, wherein, when the task execution request is for performing a quality assessment or a usage assessment on the tag set, the task execution result includes prompt information for indicating at least one abnormal tag; The execution module includes: an acquiring unit, configured to acquire the data to be evaluated of each of the at least one target configuration item from a data source; an evaluation unit, configured to evaluate each of the to-be-evaluated data according to predetermined evaluation conditions, and obtain an evaluation result for each of the target configuration items; and An output unit is configured to output the prompt information according to the evaluation result of each target configuration item, wherein the abnormal label is a label used for the evaluation result to indicate that the target configuration item does not meet the predetermined evaluation condition.

9. The device according to claim 8, wherein The tag set for the target scene is obtained in the following way: Obtaining object data within a historical period under the target scenario from a data source; as well as According to the data characteristics of the object data, a division is performed at the dimension level and the label level to obtain the at least one candidate dimension and a respective candidate label subset of each candidate dimension, wherein the candidate label subset represents a mapping relationship between the respective candidate labels of at least one level and the plurality of candidate configuration items.

10. The device according to claim 9, wherein For each candidate tag, the apparatus further includes: an adjusting module, configured to adjust the candidate tag according to the target attribute in response to the candidate tag having the target attribute; Among them, the target attribute includes at least one of an update attribute, a statistical attribute and an extended attribute. The update attribute is used to update the candidate tag based on an update period, the statistical attribute is used to count the candidate tag based on a statistical period, and the extended attribute is used to add an extended field to the candidate tag according to the field format.

11. The device according to claim 8, wherein In a case where the task execution request is used to perform a quality assessment on the tag set, the predetermined assessment condition includes a predetermined fluctuation range and a predetermined assessment threshold for each target configuration item, and the assessment result indicates whether the actual fluctuation range of the data to be assessed meets the predetermined fluctuation range and whether the data to be assessed meets the predetermined assessment threshold; as well as In the case where the task execution request is used to perform usage evaluation on the tag set, the predetermined evaluation conditions include the usage heat threshold, usage frequency threshold and contribution threshold of the target configuration item, and the evaluation result represents whether the actual usage heat of the target configuration item meets the usage heat threshold, whether the actual usage frequency meets the usage frequency threshold, and whether the actual contribution meets the contribution threshold.

12. The device according to any one of claims 8 to 10, wherein In a case where the task execution request is used to manage the tag set, the determining module includes: A first display unit is configured to display the at least one candidate dimension on the tag selection interface in response to detecting that a tag selection control in the tag management interface is triggered; and The second display unit is configured to display at least one target tag subset for the target dimension in response to detecting that the target dimension is selected from at least one candidate dimension in the tag selection interface.

13. The device according to claim 12, wherein The tag management interface also includes a tag creation control; The device further comprises: a display module, configured to display a label creation interface in response to the label creation control being triggered; and The creation module is used to create a new tag according to the input information obtained through the tag creation interface.

14. The device according to claim 8, wherein The target scenario includes an e-commerce scenario, and the candidate dimensions include at least one of the following: a basic attribute dimension, an object value dimension, an object group dimension, an object preference dimension, and an object behavior dimension.

15. An electronic device comprising: one or more processors; a memory for storing one or more computer programs, It is characterized in that the one or more processors execute the one or more computer programs to implement the steps of the method according to any one of claims 1 to 7.

16. A computer-readable storage medium having a computer program or instruction stored thereon, characterized in that: When the computer program or instruction is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

17. A computer program product comprising a computer program or instructions, characterized in that When the computer program or instruction is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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