Object label construction method, apparatus, medium, and device

Abstract

The application discloses a construction method, device, medium and equipment of object label, relates to the technical field of Internet, and the method comprises the following steps: acquiring basic attribute information of an object and interaction behavior information of the object, the interaction behavior information comprising interaction behavior sub-information corresponding to each business scene, the interaction behavior sub-information comprising interaction content of the object and behavior description sub-information for the interaction content; determining first object label information of the object based on the interaction behavior sub-information; performing semantic enhancement processing on the first object label information to determine second object label information of the object; performing extension processing on the second object label information based on the basic attribute information and the behavior description sub-information to determine third object label information of the object; and performing alignment processing on the third object label information to obtain fusion object label information in multiple business scenes. The application can improve the accuracy and richness of the object label and can better serve upper-layer applications.

Description

Technical Field

[0001] This application relates to the field of Internet technology, specifically to methods, apparatus, media, and devices for constructing object tags. Background Technology

[0002] In the field of internet applications, describing the characteristics of objects within an application by constructing object tags can better provide application services for those objects. However, in related technologies, the constructed object tags are from a single source and cannot accurately and completely characterize the object's features. Furthermore, the same object may exhibit different characteristics in different business scenarios, and related technologies cannot effectively distinguish between these differences. Summary of the Invention

[0003] To improve the accuracy and richness of object tags, this application provides a method, apparatus, medium, and device for constructing object tags. The technical solution is as follows:

[0004] Firstly, this application provides a method for constructing object tags, the method comprising:

[0005] Obtain the basic attribute information of the object and the interaction behavior information of the object. The interaction behavior information includes interaction behavior sub-information corresponding to each business scenario in multiple business scenarios. The interaction behavior sub-information includes the interaction content of the object in the corresponding business scenario and the behavior description sub-information of the object in response to the interaction content.

[0006] Based on the interaction behavior sub-information corresponding to each business scenario, the first object tag information of the object in each business scenario is determined; the first object tag information represents the object's interest preferences.

[0007] Based on the target context information associated with the interactive content, semantic enhancement processing is performed on the first object tag information to determine the second object tag information of the object in each business scenario.

[0008] Based on the basic attribute information and the behavior description sub-information, the second object tag information is extended to determine the third object tag information of the object in each business scenario;

[0009] Alignment processing is performed on the third object tag information of the object in each business scenario to obtain the fused object tag information of the object in multiple business scenarios.

[0010] In one embodiment of this application, the step of performing semantic enhancement processing on the first object tag information based on the target context information associated with the interactive content to determine the second object tag information of the object in each business scenario includes:

[0011] Obtain scene indication information for each business scenario, context information when the object interacts with the interactive content, and content summary information of the interactive content;

[0012] Based on the scene indication information, the context information, and the content summary information, determine the target context information associated with the interactive content;

[0013] The target context information and the first object label information are input into the first language model, so that the first language model performs semantic enhancement processing on the first object label information under the prompt of the target context information, so as to obtain the second object label information of the object in each business scenario.

[0014] In one embodiment of this application, the step of expanding the second object tag information based on the basic attribute information and the behavior description sub-information to determine the third object tag information of the object in each business scenario includes:

[0015] The short-term object tag information and the long-term object tag information are determined from the second object tag information;

[0016] The basic attribute information, the behavior descriptor information, and the short-term object label information are input into the second language model, so that the second language model expands the short-term object label information under the prompts of the basic attribute information and the behavior descriptor information to obtain the target short-term object label information;

[0017] The basic attribute information, the behavior descriptor information, and the long-term object label information are input into the second language model, so that the second language model expands the long-term object label information under the prompts of the basic attribute information and the behavior descriptor information to obtain the target long-term object label information;

[0018] The third object tag information is determined based on the target short-term object tag information and the target long-term object tag information.

[0019] In one embodiment of this application, aligning the third object tag information of the object in each business scenario to obtain the fused object tag information of the object in multiple business scenarios includes:

[0020] Retrieve scenario domain information for each business scenario from an external domain knowledge base;

[0021] The scenario domain information of each business scenario and the third object label information of the object in each business scenario are input into the third language model, so that the third language model performs alignment processing on the third object label information of the object in each business scenario with the prompt of the scenario domain information of each business scenario, so as to obtain the fused object label information of the object in multiple business scenarios.

[0022] In one embodiment of this application, the third language model includes a first sub-model module, a second sub-model module, and a third sub-model module. The step of inputting the scenario domain information of each business scenario and the third object label information of the object in each business scenario into the third language model, so that the third language model, prompted by the scenario domain information of each business scenario, performs alignment processing on the third object label information of the object in each business scenario to obtain the fused object label information of the object in multiple business scenarios, includes:

[0023] The scenario domain information of each business scenario and the third object label information of the object in each business scenario are input into the first sub-model module, and alignment processing based on the target label system is performed to obtain the first candidate fusion label information of the object in multiple business scenarios.

[0024] The scenario domain information of each business scenario and the first candidate fusion label information of the object in the multiple business scenarios are input into the second sub-model module, and the label semantic alignment processing is performed to obtain the second candidate fusion label information of the object in the multiple business scenarios.

[0025] The scenario domain information of each business scenario and the second candidate fusion label information of the object in the multiple business scenarios are input into the third sub-model module, and alignment processing for label weights is performed to obtain the fusion object label information of the object in the multiple business scenarios.

[0026] In one embodiment of this application, the method includes:

[0027] Obtain the content tag system corresponding to each business scenario: the content tag system indicates the organizational structure of the content tags;

[0028] Obtain the service requirement information of the service to be applied;

[0029] Based on the service requirement information of the service to be applied, the content tag system corresponding to each business scenario is fused to obtain the target tag system.

[0030] In one embodiment of this application, determining the first object tag information of the object in each business scenario based on the interaction behavior sub-information corresponding to each business scenario includes:

[0031] Based on the index identifier of the interactive content, a retrieval is performed from the content library of the corresponding business scenario to obtain the content tag of the interactive content in the content library of the corresponding business scenario;

[0032] Based on the content tags of the interactive content, the first candidate object tag information of the object in the corresponding business scenario is determined; the first candidate object tag information indicates the object's points of interest.

[0033] Based on the behavior description sub-information in the corresponding business scenario and the first candidate object tag information of the object in the corresponding business scenario, the first object tag information of the object in the corresponding business scenario is determined, and the first object tag sub-information indicates the object's preference for the point of interest.

[0034] Secondly, this application provides an apparatus for constructing object tags, the apparatus comprising:

[0035] The information acquisition module is used to acquire the basic attribute information of the object and the interaction behavior information of the object. The interaction behavior information includes the interaction behavior sub-information corresponding to each business scenario in multiple business scenarios. The interaction behavior sub-information includes the interaction content of the object in the corresponding business scenario and the behavior description sub-information of the object in response to the interaction content.

[0036] The first object tag information determination module is used to determine the first object tag information of the object in each business scenario based on the interaction behavior sub-information corresponding to each business scenario;

[0037] The second object tag information determination module is used to perform semantic enhancement processing on the first object tag information based on the target context information associated with the interactive content, and determine the second object tag information of the object in each business scenario.

[0038] The third object tag information determination module is used to perform extended processing on the second object tag information based on the basic attribute information and the behavior description sub-information to determine the third object tag information of the object in each business scenario;

[0039] The fusion object tag information determination module is used to align the third object tag information of the object in each business scenario to obtain the fusion object tag information of the object in multiple business scenarios.

[0040] Thirdly, this application provides a computer-readable storage medium storing at least one instruction or at least one program, which is loaded and executed by a processor to implement the object tag construction method as described in the first aspect.

[0041] Fourthly, this application provides a computer device including a processor and a memory, wherein the memory stores at least one instruction or at least one program, the at least one instruction or the at least one program being loaded and executed by the processor to implement the object tag construction method as described in the first aspect.

[0042] Fifthly, this application provides a computer program product comprising computer instructions that, when executed by a processor, implement the method for constructing object tags as described in the first aspect.

[0043] The method, apparatus, medium, and equipment for constructing object tags provided in this application have the following technical effects:

[0044] In the solution provided in this application, basic attribute information and interaction behavior information of an object are obtained. The interaction behavior information includes interaction behavior sub-information corresponding to each of multiple business scenarios. This sub-information includes the object's interaction content within the corresponding business scenario and a behavior description sub-information describing the object's actions in response to the interaction content. Based on the interaction behavior sub-information corresponding to each business scenario, a first object tag information for the object in each business scenario can be determined. This first object tag information represents the object's interests and preferences. Based on the target context information associated with the interaction content, semantic enhancement processing can be performed on the first object tag information to determine a second object tag information for the object in each business scenario. Based on the basic attribute information and behavior description sub-information, the second object tag information can be expanded to determine a third object tag information for the object in each business scenario. Alignment processing is performed on the third object tag information for the object in each business scenario to obtain fused object tag information for the object across multiple business scenarios. The solution provided in this application can comprehensively determine the first object label information, which can represent the object's interests and preferences, in each business scenario by using the interaction behavior sub-information corresponding to the object. By combining the target context information associated with the interaction, the semantic enhancement of the first object label information can provide a more specific and accurate description of the object's characteristics. The expansion of the second object label information by combining the object's basic attribute information and behavioral description sub-information can broaden the object's characteristics and achieve a higher degree of fit. Alignment processing of the third object label information in different business scenarios can eliminate ambiguity of the same label in different business scenarios and more accurately characterize the object's characteristics in different business scenarios. Based on a more accurate and richer characterization of object features, application services that better match the object's needs can be provided, improving the object's service experience.

[0045] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0046] To more clearly illustrate the technical solutions and advantages in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0047] Figure 1 This is a schematic diagram of the implementation environment for an object tag construction method provided in an embodiment of this application;

[0048] Figure 2This is a flowchart illustrating a method for constructing object tags according to an embodiment of this application;

[0049] Figure 3 This is a schematic diagram of a process for determining the tag information of a first object provided in an embodiment of this application;

[0050] Figure 4 This is a schematic diagram of a process for determining the tag information of a second object provided in an embodiment of this application;

[0051] Figure 5 This is a schematic diagram of a process for determining tag information of a third object provided in an embodiment of this application;

[0052] Figure 6 This is a schematic diagram of a process for aligning tag information of a third object provided in an embodiment of this application;

[0053] Figure 7 This is a service architecture diagram illustrating a method for constructing object tags provided in an embodiment of this application;

[0054] Figure 8 This is a schematic diagram of an object tag construction apparatus provided in an embodiment of this application;

[0055] Figure 9 This is a schematic diagram of the hardware structure of a device for implementing a method for constructing an object tag, provided in an embodiment of this application. Detailed Implementation

[0056] To improve the accuracy and richness of object tags, embodiments of this application provide methods, apparatus, media, and devices for constructing object tags. The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout.

[0057] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or server that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or devices.

[0058] It is understood that in the specific implementation of this application, data related to basic attribute information, namely, Oohoo behavior information, etc., are involved. When the above embodiments of this application are applied to specific products or technologies, user permission or consent is required, and the collection, use and processing of related data must comply with the relevant laws, regulations and standards of the relevant countries and regions.

[0059] Please see Figure 1 This is a schematic diagram illustrating the implementation environment of an object tag construction method provided in this application embodiment, such as... Figure 1 As shown, the implementation environment may include at least client 01 and server 02.

[0060] Specifically, the client 01 may include devices such as smartphones, desktop computers, tablets, laptops, in-vehicle terminals, digital assistants, smart wearable devices, and voice interaction devices. It may also include software running on the device, such as web pages provided to users by service providers, or applications provided by those service providers. Specifically, the client 01 can be used to provide multiple applications to an object, allowing the object to interact with various types of content in multiple business scenarios within these applications, and generate behavioral description sub-information about the interacted content.

[0061] Specifically, the server 02 can be an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN (Content Delivery Network), and big data and artificial intelligence platforms. The server 02 may include network communication units, processors, and memory, etc. The terminal and server can be directly or indirectly connected via wired or wireless communication, which is not limited herein. Specifically, the server 02 can obtain basic attribute information of an object and determine the object's interaction behavior information based on the interaction behavior generated by the object on the client 01. The interaction behavior information may include interaction behavior sub-information corresponding to each business scenario in multiple business scenarios. The interaction behavior sub-information includes the object's interaction content in the corresponding business scenario and the object's behavior description sub-information regarding the interaction content. The server 02 determines the first object tag information of the object in each business scenario based on the interaction behavior sub-information corresponding to each business scenario; the first object tag information represents the object's interest preferences; based on the target context information associated with the interaction content, the first object tag information is semantically enhanced to determine the second object tag information of the object in each business scenario; based on the basic attribute information and behavior description sub-information, the second object tag information is expanded to determine the third object tag information of the object in each business scenario; the third object tag information of the object in each business scenario is aligned to obtain the fused object tag information of the object in multiple business scenarios. The server 02 can comprehensively determine the first object tag information, which can represent the object's interests and preferences in each business scenario, by using the interaction behavior sub-information corresponding to the object in each business scenario. By combining the target context information associated with the interaction to semantically enhance the first object tag information, a more specific and accurate description of the object's characteristics can be achieved. By combining the object's basic attribute information and behavioral description sub-information to expand the second object tag information, the object's characteristics can be horizontally broadened, resulting in a higher degree of fit with the object. Aligning the third object tag information of the object in different business scenarios can eliminate ambiguity of the same tag in different business scenarios, more accurately depicting the object's characteristics in different business scenarios. Based on a more accurate and richer characterization of the object's features, application services that better match the object's needs can be provided, improving the object's service experience.

[0062] This application embodiment can also be implemented using cloud technology. Cloud technology refers to a hosting technology that unifies hardware, software, and network resources within a wide area network (WAN) or local area network (LAN) to achieve data computation, storage, processing, and sharing. It can also be understood as a general term for network technologies, information technologies, integration technologies, management platform technologies, and application technologies based on cloud computing business models. Cloud technology requires cloud computing as its support. Cloud computing is a computing model that distributes computing tasks across a resource pool composed of a large number of computers, enabling various application systems to obtain computing power, storage space, and information services as needed. The network providing these resources is called the "cloud." Specifically, the server 02 and the database are located in the cloud. The server 02 can be a physical machine or a virtualized machine.

[0063] The following describes a method for constructing object tags provided in this application. Figure 2 This is a flowchart illustrating a method for constructing object tags according to an embodiment of this application. This application provides the operational steps described in the embodiments or flowchart, but based on conventional or non-inventive methods, more or fewer operational steps may be included. The order of steps listed in the embodiments is merely one possible execution order among many and does not represent the only possible execution order. In actual system or server product execution, the methods shown in the embodiments or drawings can be executed sequentially or in parallel (e.g., in a parallel processor or multi-threaded processing environment). Please refer to... Figure 2 The method for constructing an object tag provided in this application embodiment may include the following steps:

[0064] S210: Obtain the basic attribute information and interaction behavior information of the object. The interaction behavior information includes the interaction behavior sub-information corresponding to each business scenario in multiple business scenarios. The interaction behavior sub-information includes the interaction content of the object in the corresponding business scenario and the behavior description sub-information of the object in response to the interaction content.

[0065] In this embodiment, the object can be a user or an account used by a user. The object's basic attribute information is used to characterize the object's basic identity attributes in the application, such as age, gender, or region.

[0066] In this embodiment, the interactive behavior information of an object can be determined based on the behavioral flow data generated by the object in various business scenarios. It is understood that the object can interact with various types of content in different business scenarios, such as content recommendation, local life channels, game centers, and e-reading, and generate behavioral description sub-information based on the interactive operations performed on the interactive content. The content can be media resources such as music, videos, images, and messages, or other objects such as friends, groups, and live streams. In addition to including the type of interactive operation performed on the interactive content, the behavioral description sub-information can also include data such as the frequency, timing, and duration of the interactive operation.

[0067] S220: Based on the interaction behavior sub-information corresponding to each business scenario, determine the first object label information of the object in each business scenario; the first object label information represents the object's interest preferences.

[0068] In this embodiment, based on the content features of the interactive content indicated by the interactive behavior sub-information generated by the object in each business scenario and the behavioral features of the interactive content, the first object tag information of the object in each scenario can be extracted more comprehensively. The first object tag information may include at least one tag, which may be a word, phrase, entity identifier, etc., which can determine the object's interest preferences in each business scenario.

[0069] In one embodiment of this application, such as Figure 3 As shown, step S220 may include:

[0070] S221: Based on the index identifier of the interactive content, retrieve the content tags of the interactive content in the content library of the corresponding business scenario.

[0071] It is feasible to build a content library corresponding to different business scenarios. The content library includes the content generated, transmitted, and interacted in the corresponding business scenario, and an index table is built based on the above content. The index table includes the index identifier of the content and the content tag. The content tag can be obtained by extracting features from the content.

[0072] It is feasible to retrieve the content tags of the object's interaction content from the content library of the corresponding business scenario based on the index identifier of the interaction content in each business scenario. It can be assumed that the object interacts with the content of the business scenario for reasons of interest; therefore, the content characteristics of the interaction content can indicate the object's points of interest.

[0073] S222: Based on the content tags of the interactive content, determine the first candidate object tag information of the object in the corresponding business scenario; the first candidate object tag information indicates the object's points of interest.

[0074] It is feasible to directly map the content tags of interactive content to the first candidate object tag information that can indicate the object's point of interest in the corresponding business scenario.

[0075] It is feasible to deduplicate the content tags of the interactive content to obtain the first candidate object tag information of the object in the corresponding business scenario.

[0076] S223: Based on the behavioral description sub-information in the corresponding business scenario and the first candidate object label information of the object in the corresponding business scenario, determine the first object label information of the object in the corresponding business scenario. The first object label sub-information indicates the object's preference for points of interest.

[0077] The behavioral descriptive information can include not only the type of interactive operation performed on the interactive content, but also data such as the frequency, timing, and duration of the interactive operation. Based on the behavioral descriptive information, the intensity of the interactive operation performed by the object on the interactive content can be determined. This intensity can quantitatively indicate the object's degree of interest in the interactive content, or the object's degree of interest in the points of interest corresponding to the content features of the interactive content, thereby obtaining the object's first object tag information in the corresponding business scenario. In one feasible implementation, the object's degree of interest in the interactive content can be represented by numerical data, which can be positive or negative. Negative numbers indicate the object's dislike of the points of interest, while positive numbers indicate the opposite.

[0078] In the above embodiments, based on the content library of each business scenario, the object's interest points in each business scenario can be determined by using the content tags of the interactive content. Furthermore, by combining the object's behavioral description information for the interactive content, the object's preference for its interest points in each business scenario can be quantified, thereby more accurately depicting the object's preferences in each business scenario.

[0079] S230: Based on the target context information associated with the interactive content, perform semantic enhancement processing on the first object label information to determine the second object label information of the object in each business scenario.

[0080] In this embodiment of the application, considering that semantic ambiguity may easily occur in the process of mapping the content tags of interactive content to the interest tags of objects, the semantic enhancement processing of the first object tag information can be performed by combining the target context information associated with the interactive content, so as to obtain the second object tag information that is more accurate and clear.

[0081] In this embodiment, the target context information associated with the interactive content may include content information of the interactive content itself, scene indication information of the corresponding business scenario, and interactive context information when the interactive content is generated, transmitted, or manipulated in the corresponding business scenario. The target context information can better help understand the semantics of the first object tag information.

[0082] In one embodiment of this application, such as Figure 4 As shown, step S230 may include:

[0083] S231: Obtain scene indication information, context information when objects interact with interactive content, and content summary information of interactive content for each business scenario.

[0084] The scenario indication information can include scenario identifiers, scenario descriptions, and scenario rules that indicate the corresponding business scenario. Contextual information can include the interaction environment, interaction method, and interaction participants. Content summary information can be textual information extracted from the interaction content.

[0085] S232: Based on scene indication information, context information, and content summary information, determine the target context information associated with the interactive content.

[0086] S233: Input the target context information and the first object label information into the first language model so that the first language model performs semantic enhancement processing on the first object label information with the prompt of the target context information to obtain the second object label information of the object in each business scenario.

[0087] It is feasible to pre-process the large language model based on LoRA (Low-Rank Adaptation of Large Language Models) to obtain a first language model. The training data consists of content labels of content samples, contextual information of content samples, and expanded descriptions of content labels of content samples while maintaining semantic consistency.

[0088] In the above embodiments, by utilizing the first language model and the target context information associated with the interactive content, efficient and accurate semantic enhancement processing of the first object label information can be achieved, thereby obtaining more accurate and clear second object label information.

[0089] S240: Based on the basic attribute information and behavior description sub-information, the second object label information is extended to determine the third object label information of the object in each business scenario.

[0090] Considering that mapping interactive content tags to object interest tags can easily lead to the problem of tag homogeneity, especially in many content-based services that use recommendation algorithms, the interactive content of objects is relatively sparse in terms of content type. This can also easily lead to the second object tag information indicating a relatively homogeneous interest point, making it impossible to comprehensively and accurately describe the object's true interest point.

[0091] In this embodiment, by utilizing basic attribute information and behavioral description sub-information, the second object tag information can be expanded horizontally or vertically to obtain richer third object tag information. Horizontal expansion can be an expansion of tags to other fields or types within the same level; for example, "liking swimming" can be expanded to "liking ball games." Vertical expansion can be an expansion of tags to different levels within the same field or type; for example, "liking swimming" can be expanded to "liking freestyle swimming," etc.

[0092] In one embodiment of this application, specifically, as Figure 5 As shown, step S240 may include:

[0093] S241: Determine short-term object label information and long-term object label information from the second object label information.

[0094] Yes, it's feasible. Building object tags is a periodic task, requiring periodic updates to the tags of the constructed objects. By combining historical object tag information, short-term and long-term object tag information can be determined within the second object tag information. Specifically, the object tags in the short-term object tag information are determined within the first time interval of the current period, while the object tags in the long-term object tag information are determined within the second time interval of the current period. The first time interval is shorter than the second time interval.

[0095] S242: Input the basic attribute information, behavioral descriptor information, and short-term object label information into the second language model so that the second language model can expand the short-term object label information with the prompts of the basic attribute information and behavioral descriptor information to obtain the target short-term object label information.

[0096] It is feasible to pre-process the large language model based on LoRA (Low-Rank Adaptation of Large Language Models) to obtain a second language model. The training data consists of the basic attribute information of the object sample, the behavioral descriptor information of the object sample, the short-term object label information of the object sample, and the extended labels for the short-term object label information of the object sample.

[0097] S243: Input the basic attribute information, behavioral descriptor information, and long-term object label information into the second language model so that the second language model can expand the long-term object label information with the prompts of the basic attribute information and behavioral descriptor information to obtain the target long-term object label information.

[0098] It is feasible to pre-process the large language model based on LoRA (Low-Rank Adaptation of Large Language Models) to obtain a second language model. The training data consists of the basic attribute information of the object sample, the behavioral descriptor information of the object sample, the long-term object label information of the object sample, and the extended labels for the long-term object label information of the object sample.

[0099] Understandably, object tags in long-term object tag information are retained for a longer period of time, reflecting more stable and persistent interests and preferences of the object, and can be further expanded to include richer object tags.

[0100] S244: Determine the third object label information based on the target short-term object label information and the target long-term object label information.

[0101] It is feasible to merge the target short-term object tag information and the target long-term object tag information after deduplication to obtain the third object tag information. In addition, for object tags that appear in both the target short-term object tag information and the target long-term object tag information, the intensity data of the above object tags can be re-determined.

[0102] In the above embodiments, by using the second language model, basic attribute information, and behavioral description sub-information to expand the short-term object label information and long-term object label information in the second object label information, a richer third object label information that better meets the actual and potential needs of the object can be obtained.

[0103] S250: Align the third object tag information of the object in each business scenario to obtain the fused object tag information of the object in multiple business scenarios.

[0104] In this application embodiment, the tagging systems constructed for different business scenarios will be different. The category, level, and weight value of the same tag in different tagging systems will also differ, and even the meaning of the same tag will differ in different business scenarios. Due to the need to obtain object tags that can comprehensively measure the object's interest preferences from the third object tag information of the object in each business scenario, it is necessary to align the third object tag information of the object in each business scenario, rather than simply merging them.

[0105] In one embodiment of this application, step S250 may include:

[0106] S251: Retrieve scenario domain information for each business scenario from an external domain knowledge base.

[0107] External domain knowledge bases record the characteristics of various domains from a professional perspective. The scenario domain information retrieved from external domain knowledge bases can effectively explain and distinguish the domains involved in each business scenario, thereby helping to understand the true meaning, tag level, and importance of the same tag in different business scenarios.

[0108] S252: Input the scenario domain information of each business scenario and the third object label information of the object in each business scenario into the third language model, so that the third language model can perform alignment processing on the third object label information of the object in each business scenario with the prompt of the scenario domain information of each business scenario, and obtain the fused object label information of the object in multiple business scenarios.

[0109] It is feasible to pre-process the large language model based on LoRA (Low-Rank Adaptation of Large Language Models) to obtain a third language model. The training data can include multiple labeled samples, the true meaning of multiple labeled samples in different business scenarios, the interrelationships between multiple labeled samples, and the scenario domain information of different business scenarios.

[0110] In the above embodiments, the use of external domain knowledge bases and large language models can effectively align the third object tag information of objects in various business scenarios, eliminate possible ambiguities, and describe object tags in a unified tag system.

[0111] In one embodiment of this application, the third language model includes a first sub-model module, a second sub-model module, and a third sub-model module, such as... Figure 6 As shown, step S252 may include:

[0112] S310: Input the scenario domain information of each business scenario and the third object label information of the object in each business scenario into the first sub-model module, perform alignment processing based on the target label system, and obtain the first candidate fusion label information of the object in multiple business scenarios.

[0113] The target tagging system can be constructed by integrating tagging systems from various business scenarios, or it can be constructed guided by the service requirements of the upper-layer applications to be served. Based on the target tagging system, the third-party object tag information of objects in each business scenario is reorganized, which can more accurately reflect the relationships between object tags.

[0114] S320: Input the scenario domain information of each business scenario and the first candidate fusion label information of the object in multiple business scenarios into the second sub-model module, perform semantic alignment processing for the labels, and obtain the second candidate fusion label information of the object in multiple business scenarios.

[0115] By aligning the semantics of tags, we can effectively eliminate the problem that the same object tag may have different meanings in different business scenarios, and improve the accuracy of the object tag meaning.

[0116] S330: Input the scenario domain information of each business scenario and the second candidate fusion label information of the object in multiple business scenarios into the third sub-model module, perform alignment processing for label weights, and obtain the fusion object label information of the object in multiple business scenarios.

[0117] By aligning the label weights, we can more accurately determine the importance of an object label among all object labels, and also provide effective service guidance for subsequent upper-layer applications.

[0118] In one feasible implementation, the target labeling system can be determined in the following manner:

[0119] S311: Obtain the content tag system corresponding to each business scenario; the content tag system indicates the organizational structure of the content tags.

[0120] In this application embodiment, object tags are determined based on content tags of interactive content. Therefore, an object-oriented tag system can also be determined based on the content tag system of each business scenario.

[0121] S312: Obtain the service requirement information of the service to be applied.

[0122] The service to be applied can be a higher-level service of the application object tag, such as recommendation service or operation service. Service requirement information can indicate the service type, service rules, service goals, etc.

[0123] S313: Based on the service requirement information of the service to be applied, the content tag system corresponding to each business scenario is integrated to obtain the target tag system.

[0124] It is feasible to first represent the content tag system in a tree structure, and then, guided by the service requirements information of the service to be applied, perform processing such as merging branches, adding branches, and adjusting levels on the trees corresponding to each content tag system to obtain the target tag system.

[0125] In the above embodiments, the target tag system is constructed based on the service requirements of the upper layer services. Thus, the fused object tag information aligned with the target tag system can be better used by the application services and the service experience of the object can be optimized.

[0126] Figure 7 A service architecture diagram illustrating a method for constructing object tags is shown, such as... Figure 7 As shown, at the data layer, the interaction behavior flow data of objects in various business scenarios is collected, and the interaction behavior information of the objects is calculated, as well as the basic attribute information of the objects are obtained. At the calculation layer, based on the interaction behavior information, the first object tag information of the object in each business scenario can be calculated. The object tags may also contain indicator data that can indicate the degree of preference, such as... Figure 7 The examples shown include interest scores, intensity scores, and confidence scores. In the enhancement and extension layer, semantic enhancement processing can be performed on the first object tag information based on the target context information associated with the interactive content to determine the second object tag information for the object in each business scenario. Then, based on basic attribute information and behavioral description sub-information, the second object tag information is extended to determine the third object tag information for the object in each business scenario. In the alignment layer, the third object tag information for the object in each business scenario is aligned to obtain the fused object tag information for the object across multiple business scenarios. The specific implementation process can be referred to the aforementioned embodiments, and will not be elaborated here.

[0127] As can be seen from the above embodiments, in the method provided in this application, basic attribute information and interaction behavior information of the object are obtained. The interaction behavior information includes interaction behavior sub-information corresponding to each business scenario in multiple business scenarios. The interaction behavior sub-information includes the interaction content of the object in the corresponding business scenario and the behavior description sub-information of the object in response to the interaction content. Based on the interaction behavior sub-information corresponding to each business scenario, a first object tag information of the object in each business scenario can be determined. The first object tag information represents the object's interests and preferences. Based on the target context information associated with the interaction content, semantic enhancement processing can be performed on the first object tag information to determine the second object tag information of the object in each business scenario. Based on the basic attribute information and behavior description sub-information, the second object tag information can be extended to determine the third object tag information of the object in each business scenario. Alignment processing is performed on the third object tag information of the object in each business scenario to obtain the fused object tag information of the object in multiple business scenarios. The solution provided in this application can comprehensively determine the first object label information, which can represent the object's interests and preferences, in each business scenario by using the interaction behavior sub-information corresponding to the object. By combining the target context information associated with the interaction, the semantic enhancement of the first object label information can provide a more specific and accurate description of the object's characteristics. The expansion of the second object label information by combining the object's basic attribute information and behavioral description sub-information can horizontally broaden the object's characteristics and achieve a higher degree of fit with the object. Alignment processing of the third object label information in different business scenarios can eliminate ambiguity of the same label in different business scenarios and more accurately characterize the object's characteristics in different business scenarios. Based on a more accurate and richer characterization of object features, application services that better match the object's needs can be provided, improving the object's service experience.

[0128] This application embodiment also provides an object tag construction apparatus 800, such as... Figure 8 As shown, the device may include:

[0129] The information acquisition module 810 is used to acquire the basic attribute information of the object and the interaction behavior information of the object. The interaction behavior information includes the interaction behavior sub-information corresponding to each business scenario in multiple business scenarios. The interaction behavior sub-information includes the interaction content of the object in the corresponding business scenario and the behavior description sub-information of the object for the interaction content.

[0130] The first object tag information determination module 820 is used to determine the first object tag information of the object in each business scenario based on the interaction behavior sub-information corresponding to each business scenario;

[0131] The second object tag information determination module 830 is used to perform semantic enhancement processing on the first object tag information based on the target context information associated with the interactive content, and determine the second object tag information of the object in each business scenario.

[0132] The third object tag information determination module 840 is used to perform extended processing on the second object tag information based on the basic attribute information and the behavior description sub-information to determine the third object tag information of the object in each business scenario;

[0133] The fusion object tag information determination module 850 is used to align the third object tag information of the object in each business scenario to obtain the fusion object tag information of the object in multiple business scenarios.

[0134] In one embodiment of this application, the second object tag information determination module 830 includes:

[0135] The information acquisition unit is used to acquire scene indication information for each business scenario, context information when the object interacts with the interactive content, and content summary information of the interactive content.

[0136] The target context information determination unit is used to determine the target context information associated with the interactive content based on the scene indication information, the context information, and the content summary information;

[0137] The second object label information determination unit is used to input the target context information and the first object label information into the first language model, so that the first language model performs semantic enhancement processing on the first object label information under the prompt of the target context information to obtain the second object label information of the object in each business scenario.

[0138] In one embodiment of this application, the third object tag information determination module 840 includes:

[0139] The tag information segmentation unit is used to determine short-term object tag information and long-term object tag information from the second object tag information;

[0140] The first extension unit is used to input the basic attribute information, the behavior descriptor information and the short-term object label information into the second language model, so that the second language model can extend the short-term object label information under the prompts of the basic attribute information and the behavior descriptor information to obtain the target short-term object label information;

[0141] The second extension unit is used to input the basic attribute information, the behavior descriptor information, and the long-term object label information into the second language model, so that the second language model can extend the long-term object label information under the prompts of the basic attribute information and the behavior descriptor information to obtain the target long-term object label information;

[0142] The third object tag information determination unit is used to determine the third object tag information based on the target short-term object tag information and the target long-term object tag information.

[0143] In one embodiment of this application, the fusion object tag information determination module 850 includes:

[0144] The domain knowledge retrieval submodule is used to retrieve scenario domain information for each business scenario from an external domain knowledge base.

[0145] The model alignment submodule is used to input the scenario domain information of each business scenario and the third object label information of the object in each business scenario into the third language model, so that the third language model performs alignment processing on the third object label information of the object in each business scenario under the prompt of the scenario domain information of each business scenario, and obtains the fused object label information of the object in multiple business scenarios.

[0146] In one embodiment of this application, the third language model includes a first sub-model module, a second sub-model module, and a third sub-model module, and the model alignment sub-module includes:

[0147] The first alignment unit is used to input the scenario domain information of each business scenario and the third object label information of the object in each business scenario into the first sub-model module, and perform alignment processing based on the target label system to obtain the first candidate fusion label information of the object in multiple business scenarios;

[0148] The second alignment unit is used to input the scenario domain information of each business scenario and the first candidate fusion label information of the object in the multiple business scenarios into the second sub-model module, and perform semantic alignment processing for the label to obtain the second candidate fusion label information of the object in the multiple business scenarios.

[0149] The third alignment unit is used to input the scenario domain information of each business scenario and the second candidate fusion label information of the object in the multiple business scenarios into the third sub-model module, and perform alignment processing based on label weights to obtain the fusion object label information of the object in the multiple business scenarios.

[0150] In one embodiment of this application, the device 800 further includes:

[0151] The content tag system acquisition subunit is used to acquire the content tag system corresponding to each business scenario: the content tag system indicates the organizational structure of the content tags;

[0152] The service requirement information acquisition subunit is used to acquire the service requirement information of the service to be applied.

[0153] The tag system fusion subunit is used to fuse the content tag system corresponding to each business scenario based on the service requirement information of the service to be applied, so as to obtain the target tag system.

[0154] In one embodiment of this application, the first object tag information determination module 820 includes:

[0155] The content retrieval unit is used to retrieve content tags of the interactive content from the content library of the corresponding business scenario based on the index identifier of the interactive content;

[0156] The first candidate object tag information determination unit is used to determine the first candidate object tag information of the object in the corresponding business scenario based on the content tags of the interactive content; the first candidate object tag information indicates the object's points of interest;

[0157] The first object tag information determination unit is used to determine the first object tag information of the object in the corresponding business scenario based on the behavior description sub-information in the corresponding business scenario and the first candidate object tag information of the object in the corresponding business scenario. The first object tag sub-information indicates the object's preference for the point of interest.

[0158] In this application embodiment, the terms "module" or "unit" refer to a computer program or part of a computer program that has a predetermined function and works with other related parts to achieve a predetermined goal, and can be implemented wholly or partially using software, hardware (such as processing circuitry or memory), or a combination thereof. Similarly, a processor (or multiple processors or memory) can be used to implement one or more modules or units. Furthermore, each module or unit can be part of an overall module or unit that includes the functionality of that module or unit.

[0159] It should be noted that the apparatus provided in the above embodiments is only illustrated by the division of the above functional modules when implementing its functions. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the apparatus and method embodiments provided in the above embodiments belong to the same concept, and the specific implementation process can be found in the method embodiments, which will not be repeated here.

[0160] This application provides a computer device including a processor and a memory. The memory stores at least one instruction or at least one program, which is loaded and executed by the processor to implement a method for constructing an object tag as provided in the above method embodiments.

[0161] Figure 9 A schematic diagram of the hardware structure of an apparatus for implementing a method provided in an embodiment of this application is shown. This apparatus may constitute or include the device or system provided in the embodiment of this application. Figure 9 As shown, device 10 may include one or more processors 1002 (shown as 1002a, 1002b, ..., 1002n in the figure) 1002 (processor 1002 may include, but is not limited to, a microprocessor MCU or a programmable logic device FPGA, etc.), a memory 1004 for storing data, and a transmission device 1006 for communication functions. In addition, it may also include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of the I / O interface), a network interface, a power supply, and / or a camera. Those skilled in the art will understand that... Figure 9 The structure shown is for illustrative purposes only and does not limit the structure of the electronic device described above. For example, device 10 may also include a... Figure 9 The more or fewer components shown, or having the same Figure 9 The different configurations shown.

[0162] It should be noted that the aforementioned one or more processors 1002 and / or other data processing circuits are generally referred to herein as "data processing circuits". These data processing circuits may be embodied, in whole or in part, in software, hardware, firmware, or any other combination thereof. Furthermore, the data processing circuits may be a single, independent processing module, or may be wholly or partially integrated into any other element within device 10 (or mobile device). As involved in the embodiments of this application, the data processing circuits serve as a processor control mechanism (e.g., selection of a variable resistor termination path connected to an interface).

[0163] The memory 1004 can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the method described in the embodiments of this application. The processor 1002 executes various functional applications and data processing by running the software programs and modules stored in the memory 1004, thereby realizing the above-described method for constructing object tags. The memory 1004 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 1004 may further include memory remotely located relative to the processor 1002, and these remote memories can be connected to the device 10 via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0164] The transmission device 1006 is used to receive or send data via a network. Specific examples of the network described above may include a wireless network provided by the communication provider of device 10. In one example, the transmission device 1006 includes a Network Interface Controller (NIC), which can connect to other network devices via a base station to communicate with the Internet. In another example, the transmission device 1006 may be a Radio Frequency (RF) module, used for wireless communication with the Internet.

[0165] The display may be, for example, a touchscreen liquid crystal display (LCD) that allows a user to interact with the user interface of device 10 (or a mobile device).

[0166] This application embodiment also provides a computer-readable storage medium, which can be disposed in a server to store at least one instruction or at least one program related to implementing an object tag construction method in the method embodiment. The at least one instruction or the at least one program is loaded and executed by the processor to implement the object tag construction method provided in the above method embodiment.

[0167] Optionally, in this embodiment, the storage medium may be located at at least one of the multiple network servers in a computer network. Optionally, in this embodiment, the storage medium may include, but is not limited to, various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0168] This invention also provides a computer program product or computer program, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform a method for constructing an object tag provided in the various optional embodiments described above.

[0169] As can be seen from the embodiments of the object tag construction method, apparatus, medium and device provided in this application above,

[0170] In the solution provided in this application, basic attribute information and interaction behavior information of an object are obtained. The interaction behavior information includes interaction behavior sub-information corresponding to each of multiple business scenarios. This sub-information includes the object's interaction content within the corresponding business scenario and a behavior description sub-information describing the object's actions in response to the interaction content. Based on the interaction behavior sub-information corresponding to each business scenario, a first object tag information for the object in each business scenario can be determined. This first object tag information represents the object's interests and preferences. Based on the target context information associated with the interaction content, semantic enhancement processing can be performed on the first object tag information to determine a second object tag information for the object in each business scenario. Based on the basic attribute information and behavior description sub-information, the second object tag information can be expanded to determine a third object tag information for the object in each business scenario. Alignment processing is performed on the third object tag information for the object in each business scenario to obtain fused object tag information for the object across multiple business scenarios. The solution provided in this application can comprehensively determine the first object label information, which can represent the object's interests and preferences, in each business scenario by using the interaction behavior sub-information corresponding to the object. By combining the target context information associated with the interaction, the semantic enhancement of the first object label information can provide a more specific and accurate description of the object's characteristics. The expansion of the second object label information by combining the object's basic attribute information and behavioral description sub-information can horizontally broaden the object's characteristics and achieve a higher degree of fit with the object. Alignment processing of the third object label information in different business scenarios can eliminate ambiguity of the same label in different business scenarios and more accurately characterize the object's characteristics in different business scenarios. Based on a more accurate and richer characterization of object features, application services that better match the object's needs can be provided, improving the object's service experience.

[0171] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this application. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps described in the claims can be performed in a different order than that shown in the embodiments and still achieve the desired results. Additionally, the processes depicted in the drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are also possible or may be advantageous.

[0172] The various embodiments in this application are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the device, equipment, and storage medium embodiments are basically similar to the method embodiments, so the descriptions are relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0173] Those skilled in the art will understand that all or part of the steps of the above embodiments can be implemented by hardware or by a program instructing related hardware. The program can be stored in a computer-readable storage medium, such as a read-only memory, a disk, or an optical disk.

[0174] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A method for constructing object tags, characterized in that, The method includes: Obtain the basic attribute information of the object and the interaction behavior information of the object. The interaction behavior information includes interaction behavior sub-information corresponding to each business scenario in multiple business scenarios. The interaction behavior sub-information includes the interaction content of the object in the corresponding business scenario and the behavior description sub-information of the object in response to the interaction content. Based on the interaction behavior sub-information corresponding to each business scenario, the first object tag information of the object in each business scenario is determined; the first object tag information represents the object's interest preferences. Based on the target context information associated with the interactive content, semantic enhancement processing is performed on the first object tag information to determine the second object tag information of the object in each business scenario. Based on the basic attribute information and the behavior description sub-information, the second object tag information is extended to determine the third object tag information of the object in each business scenario; Alignment processing is performed on the third object tag information of the object in each business scenario to obtain the fused object tag information of the object in multiple business scenarios.

2. The method according to claim 1, characterized in that, The step of performing semantic enhancement processing on the first object tag information based on the target context information associated with the interactive content, and determining the second object tag information of the object in each business scenario, includes: Obtain scene indication information for each business scenario, context information when the object interacts with the interactive content, and content summary information of the interactive content; Based on the scene indication information, the context information, and the content summary information, determine the target context information associated with the interactive content; The target context information and the first object label information are input into the first language model, so that the first language model performs semantic enhancement processing on the first object label information under the prompt of the target context information, so as to obtain the second object label information of the object in each business scenario.

3. The method according to claim 1, characterized in that, The step of extending the second object tag information based on the basic attribute information and the behavior description sub-information to determine the third object tag information of the object in each business scenario includes: The short-term object tag information and the long-term object tag information are determined from the second object tag information; The basic attribute information, the behavior descriptor information, and the short-term object label information are input into the second language model, so that the second language model expands the short-term object label information under the prompts of the basic attribute information and the behavior descriptor information to obtain the target short-term object label information; The basic attribute information, the behavior descriptor information, and the long-term object label information are input into the second language model, so that the second language model expands the long-term object label information under the prompts of the basic attribute information and the behavior descriptor information to obtain the target long-term object label information; The third object tag information is determined based on the target short-term object tag information and the target long-term object tag information.

4. The method according to claim 1, characterized in that, The step of aligning the third object tag information of the object in each business scenario to obtain the fused object tag information of the object in multiple business scenarios includes: Retrieve scenario domain information for each business scenario from an external domain knowledge base; The scenario domain information of each business scenario and the third object label information of the object in each business scenario are input into the third language model, so that the third language model performs alignment processing on the third object label information of the object in each business scenario with the prompt of the scenario domain information of each business scenario, so as to obtain the fused object label information of the object in multiple business scenarios.

5. The method according to claim 4, characterized in that, The third language model includes a first sub-model module, a second sub-model module, and a third sub-model module. The process involves inputting the scenario domain information of each business scenario and the third object label information of the object in each business scenario into the third language model. This allows the third language model to align the third object label information of the object in each business scenario, guided by the scenario domain information, to obtain the fused object label information of the object across multiple business scenarios. This includes: The scenario domain information of each business scenario and the third object label information of the object in each business scenario are input into the first sub-model module, and alignment processing based on the target label system is performed to obtain the first candidate fusion label information of the object in multiple business scenarios. The scenario domain information of each business scenario and the first candidate fusion label information of the object in the multiple business scenarios are input into the second sub-model module, and the label semantic alignment processing is performed to obtain the second candidate fusion label information of the object in the multiple business scenarios. The scenario domain information of each business scenario and the second candidate fusion label information of the object in the multiple business scenarios are input into the third sub-model module, and alignment processing for label weights is performed to obtain the fusion object label information of the object in the multiple business scenarios.

6. The method according to claim 5, characterized in that, The method includes: Obtain the content tag system corresponding to each business scenario: the content tag system indicates the organizational structure of the content tags; Obtain the service requirement information of the service to be applied; Based on the service requirement information of the service to be applied, the content tag system corresponding to each business scenario is fused to obtain the target tag system.

7. The method according to claim 1, characterized in that, The step of determining the first object tag information of the object in each business scenario based on the interaction behavior sub-information corresponding to each business scenario includes: Based on the index identifier of the interactive content, a retrieval is performed from the content library of the corresponding business scenario to obtain the content tag of the interactive content in the content library of the corresponding business scenario; Based on the content tags of the interactive content, the first candidate object tag information of the object in the corresponding business scenario is determined; the first candidate object tag information indicates the object's points of interest. Based on the behavior description sub-information in the corresponding business scenario and the first candidate object tag information of the object in the corresponding business scenario, the first object tag information of the object in the corresponding business scenario is determined, and the first object tag sub-information indicates the object's preference for the point of interest.

8. An object tag construction apparatus, characterized in that, The device includes: The information acquisition module is used to acquire the basic attribute information of the object and the interaction behavior information of the object. The interaction behavior information includes the interaction behavior sub-information corresponding to each business scenario in multiple business scenarios. The interaction behavior sub-information includes the interaction content of the object in the corresponding business scenario and the behavior description sub-information of the object in response to the interaction content. The first object tag information determination module is used to determine the first object tag information of the object in each business scenario based on the interaction behavior sub-information corresponding to each business scenario; The second object tag information determination module is used to perform semantic enhancement processing on the first object tag information based on the target context information associated with the interactive content, and determine the second object tag information of the object in each business scenario. The third object tag information determination module is used to perform extended processing on the second object tag information based on the basic attribute information and the behavior description sub-information to determine the third object tag information of the object in each business scenario; The fusion object tag information determination module is used to align the third object tag information of the object in each business scenario to obtain the fusion object tag information of the object in multiple business scenarios.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores at least one instruction or at least one program segment, which is loaded and executed by a processor to implement the method for constructing object tags as described in any one of claims 1 to 7.

10. A computer device, characterized in that, The computer device includes a processor and a memory, the memory storing at least one instruction or at least one program, the at least one instruction or the at least one program being loaded and executed by the processor to implement the method for constructing object tags as described in any one of claims 1 to 7.

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