Attribute data updating method and device, terminal, electronic equipment and storage medium

Abstract

The invention relates to an attribute data updating method and device, a terminal, electronic equipment and a storage medium, and the method comprises the steps that an attribute configuration page is displayed, and the attribute page is used for configuring attribute information; under the condition that the attribute configuration page receives a configuration operation for the target attribute, displaying an attribute group configuration page which is used for configuring an attribute group to which the target attribute belongs; under the condition that the configuration operation for the target attribute group is started, an attribute editing page corresponding to the target article is displayed, the attribute editing page is used for displaying an attribute editing template corresponding to the article category of the target article, and the target attribute group is an attribute group to which the target attribute belongs; the target article is any article conforming to the article category to which the target attribute group belongs; and under the condition that the attribute editing page receives an editing operation for the target attribute in the target attribute group, updating attribute data corresponding to the target article. According to the invention, attribute data updating can be accurately and quickly carried out.

Description

Technical Field

[0001] This disclosure relates to the field of computer technology, and in particular to an attribute data updating method, apparatus, terminal, electronic device, and storage medium. Background Technology

[0002] In scenarios involving electronic interaction of goods, the goods interaction platform needs to generate attribute editing templates for the goods and publish these templates to the goods editing platform. Goods providers can input data into these attribute editing templates on the goods editing platform, allowing the goods interaction platform to obtain the goods' attribute data. By publishing this attribute data, the goods interaction platform can attract user attention and encourage participation in the electronic interaction of the goods, thereby promoting their circulation. Clearly, the quality of the goods' attribute data has a significant impact on the circulation capacity of the goods.

[0003] In many cases, there are no fixed standards for the attributes of items. For example, items in the fields of food, medical care, and cultural tourism do not have fixed attribute standards. This requires the item interaction platform to customize and develop attribute editing templates and iterate on the development of attribute editing templates according to actual conditions, and publish the results of the iteration. This process is very cumbersome. In addition, there are a large number of items without fixed standards, covering many fields, which puts a heavy burden on the management of item attribute editing templates, puts great pressure on the updating of item attribute data, and reduces the circulation capacity of items. Summary of the Invention

[0004] This disclosure provides a method, apparatus, terminal, electronic device, and storage medium for updating attribute data to solve problems in related technologies. The technical solution of this disclosure is as follows: According to a first aspect of the present disclosure, an attribute data updating method is provided, the method comprising: Display the attribute configuration page, which is used to configure attribute information; When the attribute configuration page receives a configuration operation for the target attribute, the attribute group configuration page is displayed. The attribute group configuration page is used to configure the attribute group to which the target attribute belongs. When the configuration operation for the target attribute group is enabled, the attribute editing page corresponding to the target item is displayed. The attribute editing interface is used to display the attribute editing template corresponding to the item category of the target item. The target attribute group is the attribute group to which the target attribute belongs, and the target item is any item that matches the item category to which the target attribute group belongs. When the attribute editing page receives an edit operation for the target attribute under the target attribute group, the attribute data corresponding to the target item is updated.

[0005] In one exemplary embodiment, the attribute information includes an attribute identifier and a corresponding attribute format, and the attribute group includes the behavior pattern attribute of each attribute within the attribute group and the item category to which the attribute group belongs; The attribute editing page for displaying the target item includes: an attribute group editing control for displaying the target attribute group; The method further includes: When the operation received by the attribute group editing control matches the behavior pattern attribute corresponding to the target attribute, the editing control corresponding to the target attribute is displayed in the attribute group editing control. The editing control corresponding to the target attribute is used to receive the editing operation for the target attribute under the target attribute group.

[0006] In one exemplary embodiment, the method further includes: Display a category configuration page, which is used to configure at least one item category to which the target attribute group belongs; When the configuration operation for the category configuration page is enabled, if the item category to which the target item belongs belongs to the at least one item category, the attribute editing template corresponding to the item category of the target item includes the attribute group editing control corresponding to the target attribute group.

[0007] In one exemplary embodiment, the method further includes: If the configuration of any preset attribute is updated, the preset attribute editing template is updated. The preset attribute editing template is the attribute editing template corresponding to any item category to which the preset attribute group belongs, and the preset attribute group is any attribute group to which the preset attribute belongs.

[0008] In one exemplary embodiment, after updating the preset attribute editing template, the method further includes: Display the attribute diagnostic information and suggested update information corresponding to the preset item. The preset item is any item under any item category to which the preset attribute group belongs. The attribute diagnostic information indicates the matching status of the existing attribute data of the preset item with the updated preset attribute editing template. The suggested update information is the attribute update suggestion corresponding to the attribute diagnostic information. Upon receiving a request to edit the attributes of the preset item, the attribute editing interface corresponding to the preset item is displayed, and the attribute editing interface corresponding to the preset item includes the preset attribute editing template.

[0009] In one exemplary embodiment, after updating the preset attribute editing template, the method further includes: Display updated recommendation information and a recommendation adoption control, wherein the updated recommendation information includes the attribute values ​​of the attribute to be updated recommended based on the attribute diagnostic information; When the recommendation adoption control is triggered, the existing attribute data of the preset item is updated based on the updated recommendation information.

[0010] In one exemplary embodiment, the method further includes: Display property auto-update control; When the attribute auto-update control is triggered, if the configuration of the preset attribute is updated, the existing attribute data of the preset item will be updated accordingly. The updated attributes include text-type attributes or image-type attributes.

[0011] In one exemplary embodiment, the method further includes: Based on the updated preset attribute editing template, determine the attributes to be updated; Extract the attribute reference information corresponding to the preset item; By inputting the attribute reference information into the attribute collaborative recommendation workflow, the attribute collaborative recommendation workflow is triggered to predict the attribute value of the attribute to be updated. The attribute collaborative recommendation workflow is used to coordinate and schedule the attribute recommendation workflows corresponding to multiple attribute categories. Each attribute recommendation workflow is used to predict the attribute value of the attribute to be updated under its corresponding attribute category. Each attribute and each attribute group has a corresponding attribute category.

[0012] In one exemplary implementation, the attribute recommendation workflow corresponding to each of the multiple attribute categories includes an item basic information recommendation workflow, a combined attribute information recommendation workflow, a service item recommendation workflow, and a basic attribute recommendation workflow. The basic information recommendation workflow for items is used to recommend item titles, item categories, and item types. The combined attribute information recommendation workflow is used to recommend relevant attributes under the attribute category of combined attributes; The service item recommendation workflow is used to recommend relevant attributes under the attribute category of service items. The basic attribute recommendation workflow is used to recommend other attributes, including tiled attribute sets and attribute groups.

[0013] In one exemplary implementation, for any recommendation workflow, the data used in attribute prediction is prioritized from high to low as follows: existing attributes corresponding to the preset item, attribute reference information from standard products, attribute reference information from the same product, and attribute reference information from similar products from the same merchant. The existing attributes corresponding to the preset item, the attribute reference information from standard products, the attribute reference information from the same product, and the attribute reference information from similar products from the same merchant all belong to the attribute reference information corresponding to the preset item.

[0014] In one exemplary implementation, the item basic information recommendation workflow performs the following operations: Obtain the corresponding item category tree based on the merchant identifier corresponding to the preset item; Obtain the key category corresponding to the merchant identifier; Extract the first text data related to the product text from the attribute reference information; Extract the second text data from the corresponding image based on the image address in the attribute reference information; The item category tree, the key category, the first text data, and the second text data are input into the basic information recommendation model, which triggers the basic information recommendation model to predict the item category and the item name corresponding to the preset item. Retrieve the list of product types under the specified item category; Attribute recommendations are made based on the item category corresponding to the preset item, the item name corresponding to the preset item, and the list of product types.

[0015] In one exemplary implementation, the combined attribute information recommendation workflow performs the following operations: The combined attribute structure of the preset item is analyzed to obtain the combined attribute structure analysis result; Extract the first text data related to the product text from the attribute reference information; Extract the second text data from the corresponding image based on the image address in the attribute reference information; The combined attribute structure parsing result, the first text data and the second text data are input into the combined attribute information recommendation model to trigger the combined attribute information recommendation model to predict the combined attribute group name and the price of the inventory holding unit. The individual item structure associated with the preset item is analyzed to obtain the individual item structure analysis result; The product structure analysis results, the combined attribute group name, and the price of the inventory holding unit are input into the combined attribute product information recommendation model, which triggers the combined attribute product recommendation model to predict attributes related to the combined attribute details.

[0016] In one exemplary implementation, the service recommendation workflow performs the following operations: Obtain the set of service items corresponding to the preset item, wherein the set of service items includes general service items or customized service items; Extract the project template corresponding to each of the aforementioned service items; Extract the first text data related to the product text from the attribute reference information; Extract the second text data from the corresponding image based on the image address in the attribute reference information; The project template, the first text data and the second text data corresponding to each service item are input into the first service item recommendation model, which triggers the first service item recommendation model to predict the project group type, service project group and corresponding project structure. The project group type, the service project group, and the corresponding project structure are input into the second service project recommendation model, which then triggers the second service project recommendation model to predict the attributes under the service project attribute category.

[0017] In one exemplary implementation, the basic attribute recommendation workflow performs the following operations: If the attribute to be predicted belongs to the tiled attribute set, the basic attribute recommendation workflow calls the tiled attribute workflow. When the attribute to be predicted belongs to an attribute group, the basic attribute recommendation workflow calls the attribute group workflow.

[0018] In one exemplary implementation, the tiling attribute workflow performs the following operations: Generate an attribute structure based on the general and custom attributes of the preset item; Extract the first text data related to the product text from the attribute reference information; Extract the second text data from the corresponding image based on the image address in the attribute reference information; The attribute structure, the first text data, and the second text data are input into the tiled attribute recommendation model, which triggers the tiled attribute recommendation model to predict attributes that conform to the attribute structure.

[0019] In one exemplary implementation, the attribute group workflow performs the following operations: Obtain the attribute group structure corresponding to the preset item; Extract the attribute layers from the attribute group structure sequentially from the outside in. If the attribute layer belongs to a tiled attribute, invoke the tiled attribute workflow; if the attribute layer belongs to an attribute group, invoke the attribute group workflow.

[0020] According to a second aspect of the present disclosure, an attribute data updating apparatus is provided, the apparatus comprising: The first configuration module is configured to execute and display the attribute configuration page, which is used to configure attribute information. The second configuration module is configured to display an attribute group configuration page when the attribute configuration page receives a configuration operation for the target attribute. The attribute group configuration page is used to configure the attribute group to which the target attribute belongs. The attribute editing module is configured to display the attribute editing page corresponding to the target item when the configuration operation for the target attribute group is enabled. The attribute editing interface is used to display the attribute editing template corresponding to the item category of the target item. The target attribute group is the attribute group to which the target attribute belongs, and the target item is any item that conforms to the item category to which the target attribute group belongs. The update module is configured to update the attribute data corresponding to the target item when the attribute editing page receives an edit operation for the target attribute under the target attribute group.

[0021] In one exemplary embodiment, the attribute information includes an attribute identifier and a corresponding attribute format, and the attribute group includes the behavior pattern attribute of each attribute within the attribute group and the item category to which the attribute group belongs; The attribute editing module is configured to execute and display the attribute group editing control corresponding to the target attribute group; When the operation received by the attribute group editing control matches the behavior pattern attribute corresponding to the target attribute, the editing control corresponding to the target attribute is displayed in the attribute group editing control. The editing control corresponding to the target attribute is used to receive the editing operation for the target attribute under the target attribute group.

[0022] In one exemplary implementation, the attribute editing module is configured to perform: Display a category configuration page, which is used to configure at least one item category to which the target attribute group belongs; When the configuration operation for the category configuration page is enabled, if the item category to which the target item belongs belongs to the at least one item category, the attribute editing template corresponding to the item category of the target item includes the attribute group editing control corresponding to the target attribute group.

[0023] In one exemplary implementation, the update module is configured to perform: If the configuration of any preset attribute is updated, the preset attribute editing template is updated. The preset attribute editing template is the attribute editing template corresponding to any item category to which the preset attribute group belongs, and the preset attribute group is any attribute group to which the preset attribute belongs.

[0024] In one exemplary embodiment, after updating the preset attribute editing template, the method further includes: Display the attribute diagnostic information and suggested update information corresponding to the preset item. The preset item is any item under any item category to which the preset attribute group belongs. The attribute diagnostic information indicates the matching status of the existing attribute data of the preset item with the updated preset attribute editing template. The suggested update information is the attribute update suggestion corresponding to the attribute diagnostic information. Upon receiving a request to edit the attributes of the preset item, the attribute editing interface corresponding to the preset item is displayed, and the attribute editing interface corresponding to the preset item includes the preset attribute editing template.

[0025] In one exemplary implementation, the update module is configured to perform: Display updated recommendation information and a recommendation adoption control, wherein the updated recommendation information includes the attribute values ​​of the attribute to be updated recommended based on the attribute diagnostic information; When the recommendation adoption control is triggered, the existing attribute data of the preset item is updated based on the updated recommendation information.

[0026] In one exemplary implementation, the update module is configured to perform: Display property auto-update control; When the attribute auto-update control is triggered, if the configuration of the preset attribute is updated, the existing attribute data of the preset item will be updated accordingly. The updated attributes include text-type attributes or image-type attributes.

[0027] In one exemplary implementation, the update module is configured to perform: Based on the updated preset attribute editing template, determine the attributes to be updated; Extract the attribute reference information corresponding to the preset item; By inputting the attribute reference information into the attribute collaborative recommendation workflow, the attribute collaborative recommendation workflow is triggered to predict the attribute value of the attribute to be updated. The attribute collaborative recommendation workflow is used to coordinate and schedule the attribute recommendation workflows corresponding to multiple attribute categories. Each attribute recommendation workflow is used to predict the attribute value of the attribute to be updated under its corresponding attribute category. Each attribute and each attribute group has a corresponding attribute category.

[0028] In one exemplary implementation, the attribute recommendation workflow corresponding to each of the multiple attribute categories includes an item basic information recommendation workflow, a combined attribute information recommendation workflow, a service item recommendation workflow, and a basic attribute recommendation workflow. The basic information recommendation workflow for items is used to recommend item titles, item categories, and item types. The combined attribute information recommendation workflow is used to recommend relevant attributes under the attribute category of combined attributes; The service item recommendation workflow is used to recommend relevant attributes under the attribute category of service items. The basic attribute recommendation workflow is used to recommend other attributes, including tiled attribute sets and attribute groups.

[0029] In one exemplary implementation, for any recommendation workflow, the data used in attribute prediction is prioritized from high to low as follows: existing attributes corresponding to the preset item, attribute reference information from standard products, attribute reference information from the same product, and attribute reference information from similar products from the same merchant. The existing attributes corresponding to the preset item, the attribute reference information from standard products, the attribute reference information from the same product, and the attribute reference information from similar products from the same merchant all belong to the attribute reference information corresponding to the preset item.

[0030] In one exemplary embodiment, the update module is configured to perform the following operations: Obtain the corresponding item category tree based on the merchant identifier corresponding to the preset item; Obtain the key category corresponding to the merchant identifier; Extract the first text data related to the product text from the attribute reference information; Extract the second text data from the corresponding image based on the image address in the attribute reference information; The item category tree, the key category, the first text data, and the second text data are input into the basic information recommendation model, which triggers the basic information recommendation model to predict the item category and the item name corresponding to the preset item. Retrieve the list of product types under the specified item category; Attribute recommendations are made based on the item category corresponding to the preset item, the item name corresponding to the preset item, and the list of product types.

[0031] In one exemplary embodiment, the update module is configured to perform the following operations: The combined attribute structure of the preset item is analyzed to obtain the combined attribute structure analysis result; Extract the first text data related to the product text from the attribute reference information; Extract the second text data from the corresponding image based on the image address in the attribute reference information; The combined attribute structure parsing result, the first text data and the second text data are input into the combined attribute information recommendation model to trigger the combined attribute information recommendation model to predict the combined attribute group name and the price of the inventory holding unit. The individual item structure associated with the preset item is analyzed to obtain the individual item structure analysis result; The product structure analysis results, the combined attribute group name, and the price of the inventory holding unit are input into the combined attribute product information recommendation model, which triggers the combined attribute product recommendation model to predict attributes related to the combined attribute details.

[0032] In one exemplary embodiment, the update module is configured to perform the following operations: Obtain the set of service items corresponding to the preset item, wherein the set of service items includes general service items or customized service items; Extract the project template corresponding to each of the aforementioned service items; Extract the first text data related to the product text from the attribute reference information; Extract the second text data from the corresponding image based on the image address in the attribute reference information; The project template, the first text data and the second text data corresponding to each service item are input into the first service item recommendation model, which triggers the first service item recommendation model to predict the project group type, service project group and corresponding project structure. The project group type, the service project group, and the corresponding project structure are input into the second service project recommendation model, which then triggers the second service project recommendation model to predict the attributes under the service project attribute category.

[0033] In one exemplary embodiment, the update module is configured to perform the following operations: If the attribute to be predicted belongs to the tiled attribute set, the basic attribute recommendation workflow calls the tiled attribute workflow. When the attribute to be predicted belongs to an attribute group, the basic attribute recommendation workflow calls the attribute group workflow.

[0034] In one exemplary embodiment, the update module is configured to perform the following operations: Generate an attribute structure based on the general and custom attributes of the preset item; Extract the first text data related to the product text from the attribute reference information; Extract the second text data from the corresponding image based on the image address in the attribute reference information; The attribute structure, the first text data, and the second text data are input into the tiled attribute recommendation model, which triggers the tiled attribute recommendation model to predict attributes that conform to the attribute structure.

[0035] In one exemplary embodiment, the update module is configured to perform the following operations: Obtain the attribute group structure corresponding to the preset item; Extract the attribute layers from the attribute group structure sequentially from the outside in. If the attribute layer belongs to a tiled attribute, invoke the tiled attribute workflow; if the attribute layer belongs to an attribute group, invoke the attribute group workflow.

[0036] According to a third aspect of the present disclosure, an electronic device is provided, comprising: processor; Memory used to store the processor's executable instructions; The processor is configured to execute the instructions to implement the attribute data update method as described in any of the above embodiments.

[0037] According to a fourth aspect of the present disclosure, a computer storage medium is provided, which, when instructions in the computer storage medium are executed by a processor of an electronic device, causes the electronic device to perform the attribute data update method described in any of the above embodiments.

[0038] According to a fifth aspect of the present disclosure, a computer program product is provided, the computer program product including a computer program that, when executed by a processor, implements the attribute data update method described in any of the above embodiments.

[0039] The technical solutions provided by the embodiments of this disclosure have at least the following beneficial effects: This disclosure provides an attribute data update method that enables "configuration-to-generation" of attribute editing templates. The method uniformly models the structured information in the attribute editing template as categories, attribute groups under categories, and individual attributes under attribute groups. By configuring the records corresponding to attributes and attribute groups in a visual interface, the structured information required for the attribute editing template is managed in a record-managing manner. Then, the attribute editing templates corresponding to these records can be directly displayed to the user. This process only requires storing category records, attribute group records, and attribute records at the data level, and the attribute editing templates can be directly displayed on the front end. It eliminates the need for separate customization and iterative development for each attribute editing template, significantly improving the update efficiency of attribute editing templates.

[0040] Moreover, each attribute record can belong to multiple attribute groups, and each attribute group can belong to multiple categories. By assembling attributes and attribute groups, attribute editing templates corresponding to multiple categories can be generated, realizing the reuse of attributes and attribute groups. This significantly reduces the workload of developing attribute editing templates. By flexibly adjusting the relationship between attributes, attribute groups, and categories, adding, deleting, or modifying attributes, or adding, deleting, or modifying attribute groups, changes in business needs can be responded to quickly, making the updates of attribute editing templates more agile.

[0041] If the attribute editing template is updated, the updated attribute editing template can be displayed directly without developing code. Then, attribute data can be updated by editing the attribute editing template, making attribute data updates more agile and improving the circulation of related items.

[0042] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0043] The accompanying drawings, which are incorporated in and form part of this disclosure, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure, and are not intended to unduly limit this disclosure.

[0044] Figure 1 This is a flowchart illustrating an attribute data update method according to an exemplary embodiment; Figure 2 This is a schematic diagram of an attribute configuration page according to an exemplary embodiment; Figure 3 This is a schematic diagram of an attribute group configuration page according to an exemplary embodiment; Figure 4 This is a schematic diagram of the linkage rule configuration page corresponding to the attribute group, according to an exemplary embodiment. Figure 5 This is a schematic diagram of an attribute editing interface according to an exemplary embodiment; Figure 6 This is a schematic diagram of a diagnostic page according to an exemplary embodiment; Figure 7 This is a schematic diagram of an attribute-based intelligent recommendation page according to an exemplary embodiment; Figure 8 This is a schematic diagram illustrating the automatic property update of the interface where the control is located, according to an exemplary embodiment. Figure 9 This is a schematic diagram of an attribute value prediction method according to an exemplary embodiment; Figure 10 This is a schematic diagram illustrating collaborative operation according to an exemplary embodiment; Figure 11 This is a schematic diagram illustrating the workflow of recommending basic item information according to an exemplary embodiment; Figure 12 This is a schematic diagram of the workflow for recommending basic information about items, according to an exemplary embodiment. Figure 13 This is a schematic diagram illustrating the workflow of a combined attribute information recommendation process according to an exemplary embodiment; Figure 14 This is a schematic diagram of the working framework of a combined attribute information recommendation workflow according to an exemplary embodiment; Figure 15 This is a schematic diagram illustrating the workflow of a service item recommendation process according to an exemplary embodiment; Figure 16 This is a schematic diagram of the working framework of a service item recommendation workflow according to an exemplary embodiment; Figure 17 This is a schematic diagram illustrating the execution process of a basic attribute recommendation workflow according to an exemplary embodiment; Figure 18 This is a schematic diagram illustrating the workflow of tiling attributes according to an exemplary embodiment; Figure 19 This is a schematic diagram of the workflow framework for a tiled attribute workflow according to an exemplary embodiment; Figure 20 This is a schematic diagram of the working framework of the attribute group workflow according to an exemplary embodiment; Figure 21 This is a block diagram of an attribute data updating device according to an exemplary embodiment; Figure 22 This is a structural block diagram of a computer device according to an exemplary embodiment. Figure 1 ; Figure 23 This is a structural block diagram of a computer device according to an exemplary embodiment. Figure 2 . Detailed Implementation

[0045] To enable those skilled in the art to better understand the technical solutions of this disclosure, the technical solutions in the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings.

[0046] It should be noted that the terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this disclosure 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 disclosure described herein can be implemented in orders other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this disclosure. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this disclosure as detailed in the appended claims.

[0047] Figure 1 This is a flowchart illustrating an attribute data update method according to an exemplary embodiment. The attribute data update method can be applied to an electronic device, which can be implemented independently by a server or a terminal, or collaboratively by a terminal and a server. The terminal can be, but is not limited to, physical devices such as smartphones, tablets, laptops, desktop computers, smart speakers, smart wearable devices, digital assistants, augmented reality devices, and virtual reality devices, and can also include software such as applications running on the physical device. The server can be, but is not limited to, a standalone 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 storage, network services, cloud communication, middleware services, domain name services, security services, and big data and artificial intelligence platforms, etc. (Refer to...) Figure 1 As shown, the method includes the following steps.

[0048] In step S110, an attribute configuration page is displayed, which is used to configure attribute information.

[0049] The attribute information in this disclosure may include attribute identifiers and corresponding attribute formats. Attribute identifiers indicate attribute uniqueness, while attribute formats are used to converge attributes from a format perspective and can be used to automatically determine the controls required for entering the corresponding attribute values. Attribute identifiers can be strings, numbers, or combinations thereof, and can indicate an attribute ID or attribute name, ensuring attribute uniqueness. Attribute formats may include information such as data type, value range, length limits, and encoding methods to standardize the attributes.

[0050] Please refer to Figure 2This diagram illustrates the attribute configuration page in this disclosure. The attribute configuration page allows for the retrieval, addition, deletion, and modification of attributes. Configurable attribute information on this page includes attribute ID, attribute name, attribute key, attribute format, attribute value, attribute status, attribute updater, and attribute update time. Specifically, the attribute ID uniquely identifies an attribute, ensuring no duplicate attribute identifiers appear in the system. The attribute name provides a clear description of the attribute, facilitating user understanding and differentiation, and is also displayed in the attribute editing template. The attribute key is the attribute's key value, typically used for quick retrieval and location of attributes within the program. The attribute format defines the data type, value range, length limits, and encoding method of the attribute value to ensure data consistency and standardization. The attribute value is the actual content stored by the attribute, which can be a string, number, or other data format. The attribute status indicates whether the attribute is currently available, such as enabled, disabled, or deleted. If the attribute is enabled, it is allowed to be displayed in the associated attribute editing template. If an attribute is disabled, it will be removed from the attribute editing template and users will no longer be able to view it, but its record will still be retained for later restoration. Deleted attributes will be marked as invisible. The attribute updater record records the user information of the last user who modified the attribute to trace the source of the operation. The attribute update time records the timestamp of the last modification to the attribute, providing a basis for version management. These configuration items together constitute the core content of the attribute configuration page, providing users with a comprehensive and flexible attribute management tool.

[0051] In step S120, when the attribute configuration page receives a configuration operation for the target attribute, the attribute group configuration page is displayed. The attribute group configuration page is used to configure the attribute group to which the target attribute belongs.

[0052] The target attribute refers to the attribute configured by the user on the attribute configuration page after it is displayed in S110. After configuring the target attribute, it is also necessary to manage the attribute groups to which the target attribute belongs. In this case, the attribute group configuration page is displayed. For example, if the target attribute belongs to attribute group 1, attribute group 2, and attribute group 3, and category 1 includes attribute group 1, category 2 includes attribute group 2, and category 3 and category 4 all include attribute group 3, and the target attribute, attribute group 1, attribute group 2, and attribute group 3 are all enabled, then the attribute editing templates for items under category 1, category 2, category 3, and category 4 will all include the target attribute. For items under category 1, category 2, category 3, and category 4, the target attribute needs to be edited to obtain the corresponding attribute value, thereby improving the management of these items.

[0053] The attribute group page configuration can include an attribute group identifier, attribute group status, behavioral pattern attributes of each attribute within the attribute group, and the item category to which the attribute group belongs. The attribute group identifier uniquely identifies the attribute group, ensuring accurate differentiation between different attribute groups within the system. The attribute group status indicates whether the attribute group is currently enabled, disabled, or in another specific state, allowing control over its availability in different scenarios. If an attribute group is enabled, it is allowed to be displayed in the corresponding attribute editing template; however, whether it is ultimately displayed and how many attributes from the attribute group are displayed depends on whether the specific attribute is configured to be enabled. If an attribute group is disabled, all its subordinate attributes will be removed from the attribute editing template, and users will no longer be able to view it, but its records will still be retained for later restoration. For deleted attribute groups, the system will mark them as invisible.

[0054] The behavior pattern attributes of each attribute within an attribute group define the operational rules or constraints of these attributes under specific conditions, such as whether they are editable, required, whether their display depends on the editing results of other attributes in the same attribute group, and whether their display depends on the editing results of other attributes in other attribute groups. The item category to which the attribute group belongs clarifies the scope of item categories to which the attribute group applies.

[0055] Please refer to Figure 3 This diagram illustrates the attribute group configuration page of this disclosure. This page allows for the searching, adding, deleting, and modifying of attribute groups. The information that can be configured on this page includes the attribute group ID, attribute group name, attribute content, attribute group status, attribute group updater, and attribute group update time. Specifically, the attribute group ID uniquely identifies an attribute group, ensuring that no duplicate attribute group identifiers appear in the system. The attribute group name is a brief description of the attribute group and can be displayed in the corresponding attribute editing template. The attribute group name can include the names of the attributes belonging to the group. The attribute content details all relevant attributes within the group, such as attribute name, type, and default value, providing users with comprehensive reference information. The attribute group status reflects the current status of the attribute group in real time, such as whether it is enabled or disabled, helping administrators flexibly control its availability in the system. Furthermore, the attribute group updater records the information of the person who last modified the attribute group, facilitating subsequent traceability and accountability. The attribute group update time marks the last modification, ensuring data timeliness and traceability. These configuration items together form a complete and efficient attribute group management page, improving system usability and management efficiency.

[0056] In one exemplary implementation, the behavior pattern attribute of each attribute within an attribute group may include linkage rules for the attributes within the attribute group. For example, when the value of a certain attribute changes, other related attributes can automatically adjust their values ​​or states according to preset linkage rules, such as being displayed or hidden. In this way, the attributes within the attribute group are no longer isolated individuals, but form an organic whole, jointly serving the specific application scenario requirements.

[0057] Please refer to Figure 4 This diagram illustrates the configuration page for the linkage rules corresponding to the attribute group in this disclosure. When configuring an attribute group, this page allows configuration of linkage rules for the attributes within that group. These rules include the linkage type, the controlled attribute, the triggering condition, and the logical relationships between multiple conditions. The linkage type defines the way attributes are linked, such as show-hide linkage. The controlled attribute specifies the attribute that needs to be controlled in the linkage rule. The triggering condition clarifies the prerequisite for the linkage rule to take effect; for example, the rule will only be triggered when an attribute value reaches a specific threshold or meets a certain state. The logical relationships between multiple conditions are used to handle complex scenarios and can be set to logical operators such as "AND" and "OR" to achieve more flexible rule combinations. Through this design, administrators can customize the behavior patterns of attribute groups according to actual needs, ensuring the system operates efficiently in different application scenarios.

[0058] In step S130, when the configuration operation for the target attribute group is enabled, the attribute editing page corresponding to the target item is displayed. The attribute editing interface is used to display the attribute editing template corresponding to the item category of the target item. The target attribute group is the attribute group to which the target attribute belongs, and the target item is any item that conforms to the item category to which the target attribute group belongs.

[0059] A target attribute can belong to multiple attribute groups, and any one of these attribute groups can serve as the target attribute group. A target attribute group can belong to multiple item categories, and any item within any of these categories can serve as the target item. Once the target attribute and target attribute group are configured, the corresponding records can be managed on the attribute page and attribute group page, and the configuration results for the target attribute and target attribute group can be displayed in the target item's attribute editing template. In this case, there is no need to develop an attribute editing template; the display results of the attribute editing template automatically reflect the configuration results of the target attribute and target attribute group. That is, the attribute editing page corresponding to the target item can display controls showing the configuration results of the target attribute and target attribute group, and the behavior patterns of these controls conform to the configured linkage rules and the behavior patterns of each attribute within the attribute group. The display of the relevant controls for the target attribute and target attribute group facilitates the user's input of the relevant attribute values.

[0060] In one exemplary embodiment, the attribute editing page displaying the target item includes an attribute group editing control displaying the target attribute group. If the target attribute group is configured to be enabled, when the attribute editing page of the target item is displayed, the attribute group editing control corresponding to the target attribute group can be displayed if the target item belongs to the item category to which the target attribute group belongs, allowing the user to edit the target attribute group. Obviously, in this case, developers do not need to develop an attribute editing interface, nor do they need to specifically lay out, select, and set the controls. It is sufficient that the records corresponding to the target attribute group have been configured in advance on the attribute group configuration page. This is because each attribute in the target attribute group has been configured on the attribute configuration page, and its attribute information includes the attribute format, and some even include optional attribute values. Therefore, the system can automatically assign appropriate controls and automatically lay out the page according to the attribute groups under the category attributes of the target item, eliminating the need for developers to perform front-end development for the attribute editing interface.

[0061] The method further includes: when the operation received by the attribute group editing control matches the behavior pattern attribute corresponding to the target attribute, displaying the editing control corresponding to the target attribute in the attribute group editing control. The editing control corresponding to the target attribute is used to receive the editing operation for the target attribute under the target attribute group. For example, the behavior pattern attribute corresponding to the target attribute "delivery address" stipulates that the editing control for "delivery address" will only be displayed when its associated attribute "delivery method" is set to "door-to-door delivery". This design effectively reduces the complexity of the user interface while ensuring that the editing options of related attributes are only displayed when necessary, thereby improving user experience and operational efficiency. In addition, the system dynamically adjusts the visibility and state of the control according to the behavior pattern of the target attribute, avoiding invalid or irrelevant operations from interfering with the user's editing process. This intelligent control management method not only simplifies the user's operation path but also significantly reduces the risk of data errors caused by misoperation.

[0062] In step S140, when the attribute editing page receives an editing operation for the target attribute under the target attribute group, the attribute data corresponding to the target item is updated.

[0063] This disclosure achieves "configuration-as-generation" of attribute editing templates through attribute configuration and attribute group configuration. This allows for rapid and agile updates to attribute editing templates, enabling users to quickly and efficiently update attribute values ​​on the updated templates. This, in turn, rapidly updates attribute data and enhances the circulation capabilities of related items. Specifically, this disclosure models the structured information in attribute editing templates as categories, attribute groups under categories, and individual attributes under attribute groups. By configuring records corresponding to attributes and attribute groups in a visual interface, the structured information required for attribute editing templates is managed in a record-managing manner. The system then directly displays the attribute editing templates corresponding to these records to the user. This process only requires storing category records, attribute group records, and attribute records at the data level, and the attribute editing templates can be directly displayed on the front end. This eliminates the need for separate customization and iterative development for each attribute editing template, significantly improving the update efficiency of attribute editing templates.

[0064] Moreover, each attribute record can belong to multiple attribute groups, and each attribute group can belong to multiple categories. By assembling attributes and attribute groups, attribute editing templates corresponding to multiple categories can be generated, realizing the reuse of attributes and attribute groups. This significantly reduces the workload of developing attribute editing templates. By flexibly adjusting the relationship between attributes, attribute groups, and categories, adding, deleting, or modifying attributes, or adding, deleting, or modifying attribute groups, changes in business needs can be responded to quickly, making the updates of attribute editing templates more agile.

[0065] If the attribute editing template is updated, the updated attribute editing template can be displayed directly without developing code. Then, attribute data can be updated by editing the attribute editing template, making attribute data updates more agile and improving the circulation of related items.

[0066] In one exemplary embodiment, the method further includes: displaying a category configuration page, the category configuration page being used to configure at least one item category to which the target attribute group belongs; when configuration operations for the category configuration page are enabled, if the item category to which the target item belongs belongs to the at least one item category, the attribute editing template corresponding to the item category of the target item includes an attribute group editing control corresponding to the target attribute group. Based on this design, users can intuitively see the association between the target attribute group and the item category when configuring item categories. This process not only improves the transparency of configuration but also reduces erroneous operations caused by information asymmetry. In addition, through the association between attribute groups and categories, attribute group editing controls of associated attribute groups can be dynamically loaded into the attribute editing template of a specific category. The system can flexibly adjust the template content according to the user's actual needs, achieving rapid and agile template iteration and attribute group reuse.

[0067] Please refer to Figure 5 The diagram illustrates the attribute editing interface of this disclosure. This attribute editing interface is specifically designed for items categorized as "food," or more specifically, for items under the category of "food / local cuisine / Sichuan cuisine." The interface includes numerous attributes such as product type, product name, product image, product cover video, product code, and product remarks. These attributes can be configured through attribute page configuration, attribute group configuration, and establishing the association between attribute groups and item categories. The configuration is then presented visually. Figure 5 The attribute values ​​are displayed in a way that allows users to enter specific attribute values.

[0068] In one exemplary embodiment, the method further includes: updating a preset attribute editing template when the configuration of any preset attribute is updated, wherein the preset attribute editing template is an attribute editing template corresponding to any item category to which the preset attribute group belongs, and the preset attribute group is any attribute group to which the preset attribute belongs.

[0069] The preset attribute can be any attribute managed by the system, and any attribute in any attribute record can be used as the preset attribute. If the preset attribute is updated, the attribute editing template including that preset attribute can be updated accordingly. Therefore, this disclosure does not require iterative development of each attribute editing template including the preset attribute; it only needs to trigger an automated adjustment mechanism for the associated attribute editing templates when the preset attribute is updated. This mechanism ensures that the system can respond efficiently to attribute changes without manual intervention or repetitive development. In this way, not only is the system's flexibility and maintainability improved, but development costs and time consumption are also significantly reduced. In addition, this method supports dynamic expansion, allowing for rapid adaptation to changes in business needs when adding new attributes or adjusting existing attributes.

[0070] In one exemplary embodiment, after updating the preset attribute editing template, the method further includes: displaying attribute diagnostic information and suggested update information corresponding to a preset item, wherein the preset item is an item under any item category to which the preset attribute group belongs, the attribute diagnostic information indicates the matching status of the existing attribute data of the preset item with the updated preset attribute editing template, and the suggested update information is an attribute update suggestion corresponding to the attribute diagnostic information; and upon receiving an attribute editing request for the preset item, displaying an attribute editing interface corresponding to the preset item, wherein the attribute editing interface corresponding to the preset item includes the preset attribute editing template.

[0071] After the preset attribute editing template is updated, the existing attributes of preset items entered based on the previous template may no longer meet the requirements. For example, the entered content may not match the template's requirements, or some content may be missing. In such cases, it's necessary to perform a diagnostic test on each item whose attributes were entered using the preset attribute editing template, using the preset item as an example. This test will display attribute diagnostic information and suggested update information. By displaying these diagnostic and update information, users can effectively identify and correct problems in existing attribute data, improving data accuracy and completeness. During the diagnostic process, the system automatically analyzes the existing attribute data of the preset item and compares it with the updated template requirements, generating a detailed diagnostic report. Simultaneously, the suggested update information provides specific modification guidance, including fields that need to be added or adjusted and their recommended values. This approach not only simplifies the user's workflow but also reduces the probability of human error, thereby further optimizing overall data management efficiency.

[0072] Please refer to Figure 6 The diagram illustrates the diagnostic page of this disclosure. This diagnostic page can display attribute diagnostic information and suggested update information. Specifically, if a preset attribute editing template is updated, then the attribute data corresponding to each item whose attributes were entered using that preset attribute editing template needs to participate in the diagnostic process, and the corresponding attribute diagnostic information and suggested update information will be output.

[0073] In one exemplary embodiment, after updating the preset attribute editing template, the method further includes: displaying updated recommendation information and a recommendation adoption control, wherein the updated recommendation information includes attribute values ​​of the attributes to be updated recommended based on the attribute diagnostic information; and, when the recommendation adoption control is triggered, updating the existing attribute data of the preset item based on the updated recommendation information.

[0074] In this implementation, not only can attribute diagnostic information and suggested update information be displayed, but the system can also automatically predict the attribute values ​​of the attributes to be updated based on the attribute diagnostic information and item-related information, and make recommendations. Users only need to trigger the recommendation adoption control, and the system will automatically update the existing attribute data of the preset item based on the updated recommendation information. This process eliminates the need for users to manually update attribute values, improving the efficiency of attribute data updates. At the same time, this method can effectively avoid data omissions or errors caused by human negligence, ensuring that attribute data always remains up-to-date and consistent.

[0075] Please refer to Figure 7The illustration shows a schematic diagram of the attribute intelligent recommendation page disclosed herein. This attribute intelligent recommendation page can display the attribute values ​​to be updated automatically predicted by the system. These attribute values ​​can include added attribute values, modified attribute values, and optimized attribute values ​​(such as image clarity). Users only need to click the "One-Click Adoption" control to achieve automatic updates of these attribute values, eliminating the need for manual entry or correction.

[0076] In one exemplary embodiment, the method further includes: displaying an automatic attribute update control; when the automatic attribute update control is triggered, if the configuration of the preset attribute is updated, the existing attribute data of the preset item is updated accordingly, and the updated attributes include text-type attributes or image-type attributes. In this embodiment, if the automatic attribute update control is triggered, the system will automatically perform synchronous prediction (recommendation) and update based on the prediction results of the existing attribute data of the preset item whenever the configuration of the preset attribute is updated. This process requires no additional user operation, significantly reducing the time cost of manual intervention, allowing the update of item attributes to be independent of user operation, enabling rapid attribute updates and display of the latest attribute data, thereby greatly improving the circulation capacity of items.

[0077] Please refer to Figure 8 The diagram illustrates the interface of the automatic attribute update control disclosed herein. This interface allows for automatic updates of four attributes: required attributes, key attributes, image size, and image clarity. Users can enable or disable the automatic update function for a specific attribute by selecting the corresponding options. For example, if a user only wants to optimize image clarity while keeping other attributes unchanged, they can select the corresponding function module individually. By providing fine-grained control over the automatic update of specific attributes, users can flexibly manage the update strategy for item attributes, meeting personalized needs in different scenarios.

[0078] In one exemplary embodiment, this disclosure proposes to recommend attributes by constructing multiple workflows. This allows the system to automatically and intelligently predict the accurate attribute values ​​that need to be updated quickly, eliminating the need for manual attribute updates by the user. Please refer to... Figure 9 The diagram illustrates a flowchart of the attribute value prediction method of this disclosure. The method includes: S910. Based on the updated preset attribute editing template, determine the attributes to be updated.

[0079] This disclosure does not limit the method for determining attributes to be updated. For example, if an attribute of a preset item changes after the template is updated, such as adjusting the image clarity requirement from normal to higher clarity, then that attribute is an attribute to be updated. Also, if an attribute is added after the template is updated, then that attribute is also an attribute to be updated.

[0080] S920. Extract the attribute reference information corresponding to the preset item.

[0081] This disclosure does not limit the scope of attribute reference information; it can broadly refer to various information used to infer the attribute values ​​of the attribute to be updated. For example, the attribute reference information may include images, or text information obtained through OCR recognition of images related to a preset item, as well as documents related to the preset item, such as instructions, item descriptions, or notes. Furthermore, it may include existing attributes of the item and the results of unstructured item information parsing. The unstructured item information parsing results refer to the attribute information related to the item extracted from various unstructured data sources. These data sources may include web page descriptions, social media comments, user reviews, etc. By parsing this unstructured information, the system can obtain more potential information about the item for inferring attribute values, thus providing a more comprehensive and accurate reference for attribute updates. In addition, unstructured item information parsing can be combined with natural language processing technology to further mine the semantic information hidden in the text, assisting in inferring attribute values ​​that better meet actual needs. This method not only improves the efficiency of attribute updates but also significantly enhances the accuracy and practicality of the recommendation results.

[0082] S930. By inputting the attribute reference information into the attribute collaborative recommendation workflow, the attribute collaborative recommendation workflow is triggered to predict the attribute value of the attribute to be updated. The attribute collaborative recommendation workflow is used to coordinate and schedule the attribute recommendation workflows corresponding to multiple attribute categories. Each attribute recommendation workflow is used to predict the attribute value of the attribute to be updated under its corresponding attribute category. Each attribute and each attribute group has a corresponding attribute category.

[0083] In this disclosure, each attribute and attribute group has a corresponding attribute category, and different attribute categories have their own designed workflows. This design ensures that each attribute category is processed specifically, thereby improving the accuracy and efficiency of predictions. By collaboratively scheduling multiple attribute recommendation workflows, the system can generate more reasonable and accurate attribute value prediction results based on a comprehensive consideration of the differences and correlations between different attribute categories. Furthermore, this method supports dynamically adjusting workflow parameters and models to adapt to constantly changing data environments and business needs. In practical applications, this mechanism can significantly reduce manual intervention, improve the automation level of attribute updates, and reduce the error rate caused by human factors. Moreover, different workflows can work in parallel when working collaboratively, improving prediction efficiency.

[0084] In one exemplary implementation, the attribute recommendation workflow corresponding to each of the multiple attribute categories includes an item basic information recommendation workflow, a combined attribute information recommendation workflow, a service item recommendation workflow, and a basic attribute recommendation workflow. The basic information recommendation workflow for items is used to recommend item titles, item categories, and item types. The combined attribute information recommendation workflow is used to recommend relevant attributes under the attribute category of combined attributes; The service item recommendation workflow is used to recommend relevant attributes under the attribute category of service items. The basic attribute recommendation workflow is used to recommend other attributes, including tiled attribute sets and attribute groups.

[0085] Tiled attribute sets and attribute groups are two different types of attribute collections. In a tiled attribute set, each attribute is displayed flat and independent of the others. However, the attributes in an attribute group may have a hierarchical relationship. For example, if an attribute group contains attribute 1, attribute 2, and attribute 3, attributes 2 and 3 will only be displayed if the value of attribute 1 meets a certain condition. This hierarchical relationship is a characteristic of attribute groups.

[0086] The attribute classification and recommendation workflow designed in this disclosure can classify and model the attributes of various non-standard items (items without fixed standards). This allows for a unified attribute classification across different non-standard items, despite their varying and frequently changing attributes. This enables a unified workflow for deriving attributes from the perspective of attribute classification. The technical effect of this design is a significant improvement in attribute prediction efficiency. By unifying the attribute classification model for non-standard items and setting corresponding attribute recommendation workflows for each classification, the attribute prediction of various non-standard items is incorporated into the same framework, enabling attribute prediction for a wide range of non-standard items. Furthermore, this design can flexibly adapt to attribute changes in different scenarios, giving the system strong scalability. When facing complex business requirements, this method can quickly respond and generate suitable attribute recommendation solutions, thereby significantly reducing maintenance costs and improving overall work efficiency.

[0087] In this disclosure, "workflow" refers to an Agent. In the field of computer science, an Agent can be understood as a software entity capable of autonomously performing specific tasks. It possesses the ability to perceive the environment, analyze data, make decisions, and take actions, thereby achieving efficient task processing and resource management in complex systems. An Agent can dynamically adjust its behavior patterns based on preset rules or learning algorithms to adapt to constantly changing needs and environmental conditions. This flexibility allows the Agent to intelligently select the optimal data source and computation method during attribute data updates, improving the overall system's responsiveness and accuracy. Furthermore, Agents can collaborate with other Agents to form a distributed workflow network, further enhancing the system's scalability and robustness. Please refer to [reference needed]. Figure 10 This diagram illustrates the collaborative workflow in this disclosure. The input to this attribute-based collaborative recommendation workflow is the attribute reference information corresponding to the preset item. Taking the preset item as a target product to be sold as an example, the attribute reference information may include merchant ID, product text, product image, etc. By classifying the attributes to be updated, the attribute categories covered by these attributes can be obtained, thereby determining the collaborative scheduling method between the attribute recommendation workflows corresponding to each attribute category.

[0088] For example, if the attributes to be updated cover basic item information, service items, and basic attributes, attribute prediction can be performed by coordinating the basic item information recommendation workflow (product basic agent), the service item recommendation workflow (service item agent), and the basic attribute recommendation workflow (product attribute agent). Specifically, the product basic agent can be scheduled first, and then the service item agent and product attribute agent can be scheduled in parallel. The predicted attribute values ​​include the product information output by the product basic agent, the product attributes output by the product attribute agent, and the attribute values ​​of the attributes to be updated in the product service items output by the service item agent.

[0089] For example, if the attributes to be updated cover basic item information, combined attribute information, and basic attributes, then attribute prediction can be performed by coordinating the basic item information recommendation workflow (product basic agent), the combined attribute information recommendation workflow (product combined attribute agent), and the basic attribute recommendation workflow (product attribute agent). Specifically, the product basic agent can be scheduled first, followed by the product combined attribute agent and the product attribute agent in parallel. The predicted attribute values ​​include the product information output by the product basic agent, the product attributes output by the product attribute agent, and the attribute values ​​of the attributes to be updated in the product combined attributes output by the product combined attribute agent. In some cases, combined attributes can be used to describe meal packages.

[0090] For example, if the attribute to be updated covers both service items and basic attributes, attribute prediction can be performed by coordinating the service item recommendation workflow (service item agent) and the basic attribute recommendation workflow (product attribute agent). Specifically, the service item agent and the product attribute agent can be scheduled in parallel. The predicted attribute values ​​include the product attributes output by the product attribute agent and the attribute values ​​of the attributes to be updated in the product service items output by the service item agent.

[0091] In one exemplary implementation, for any recommendation workflow, the data used in attribute prediction is prioritized from high to low as follows: existing attributes corresponding to the preset item, attribute reference information from standard products, attribute reference information from the same product, and attribute reference information from similar products from the same merchant. The existing attributes corresponding to the preset item, the attribute reference information from standard products, the attribute reference information from the same product, and the attribute reference information from similar products from the same merchant all belong to the attribute reference information corresponding to the preset item.

[0092] The "standardized products" disclosed herein refer to standardized goods, a concept contrasted with non-standardized products. Standardized products lack fixed attributes; many attributes originate from established e-commerce platforms or are governed by national or international standards. Their attributes are typically comprehensive and authoritative. "Similar products" can be goods from other systems or platforms that possess the same or similar attributes to the pre-defined item. The attribute information of these similar products can serve as supplementary references, aiding in attribute prediction when information on non-standardized products is lacking.

[0093] Taking an arbitrary recommendation workflow as an example, if the attribute value to be updated can be derived using the existing attributes corresponding to the preset item, then there is no need to use attribute reference information from standardized products, attribute reference information from the same product, or attribute reference information from similar products from the same merchant. If the existing attributes corresponding to the preset item are insufficient to derive the attribute value to be updated, then the system will sequentially attempt to use attribute reference information from standardized products, attribute reference information from the same product, and attribute reference information from similar products from the same merchant. This hierarchical data usage method ensures the accuracy and efficiency of prediction, while also fully utilizing the correlation between attribute information from different sources. This approach effectively reduces unnecessary waste of computational resources and improves the overall reliability of attribute prediction. In practical applications, this method can also flexibly adjust the priority order according to specific business needs, thereby adapting to different scenarios and data environments.

[0094] In one exemplary implementation, please refer to Figure 11 This diagram illustrates the workflow for recommending basic item information in this disclosure. The workflow performs the following operations: S1110. Obtain the corresponding item category tree based on the merchant identifier corresponding to the preset item.

[0095] Taking a pre-defined item as a product intended for sale (the target product) as an example, this item category tree refers to a structured classification system used to systematically organize and manage the products sold by a merchant. This category tree is typically presented hierarchically, expanding from broad categories to subcategories, clearly reflecting the relationships and attributes between products. Through this category tree, the category node containing the target product can be quickly located, and standardized attribute information related to that node can be obtained, providing a foundation for subsequent attribute data updates.

[0096] S1120. Obtain the key category corresponding to the merchant identifier.

[0097] This key category can be understood as a fallback category for businesses, meaning a classification node that is of significant importance in the business's operations. Key categories typically reflect a business's main business scope or key product categories and serve as an important reference when updating attribute data.

[0098] S1130. Extract the first text data related to the product text from the attribute reference information.

[0099] The first type of text data consists of textual information related to the target product.

[0100] S1140. Extract the second text data from the corresponding image based on the image address in the attribute reference information.

[0101] The second text data consists of the recognition results of image-related information about the target product.

[0102] S1150. Input the item category tree, the key category, the first text data and the second text data into the basic information recommendation model, and trigger the basic information recommendation model to predict the item category and the item name corresponding to the preset item.

[0103] The basic information recommendation model can be a large model designed or trained for basic information prediction. It can be an existing model or designed according to actual conditions, and does not pose an obstacle to implementation.

[0104] S1160. Obtain the list of product types under the item category.

[0105] The product type list records different product types and their related attribute information, providing crucial data for subsequent product management and attribute updates. The product type list typically includes key fields such as product name, specifications, brand, and price range to comprehensively describe product characteristics. Analyzing this list can further optimize the accuracy of product classification and improve the efficiency of attribute matching.

[0106] S1170. Attribute recommendations are made based on the item category corresponding to the preset item, the item name corresponding to the preset item, and the product type list.

[0107] Recommendation results can include attribute values ​​from product titles, product categories, and product types. This disclosure does not limit the recommendation method used in S1170; a pre-designed or trained large model can be used. This model can be an existing model or designed according to actual conditions, and does not constitute an obstacle to implementation. Through the design of the recommendation workflow for basic item information, fully automated and highly accurate prediction of attribute values ​​in the basic information of various non-standard products can be achieved, improving prediction efficiency, reducing development and iteration difficulty, and realizing agile development, agile iteration, and agile prediction.

[0108] Taking this preset item as the target product as an example, please refer to... Figure 12This diagram illustrates the workflow framework for recommending basic item information in this disclosure. The input parameters of the basic item information recommendation workflow include merchant ID, product text, and product image. The workflow uses RPC calls to obtain the category tree (item category tree) based on the merchant ID, and also obtains the merchant's catch-all category, product text (first text data), and text information (second text data). This information is then input into the basic information recommendation model for prediction. The model then outputs the product category (the item category corresponding to the preset item) and product name (the item name corresponding to the preset item). Finally, an RPC call is used to obtain a list of product types, which, along with the product category and product name, are fed into the larger model for prediction, yielding the attribute values ​​for the product title, product category, and product type. RPC calls refer to a remote procedure call protocol that allows a program on one computer to call a subroutine or method on another computer without requiring the programmer to explicitly write the underlying communication details. This mechanism makes interaction between different systems more efficient and flexible, especially in distributed systems, where RPC calls can significantly simplify cross-service data transfer and functional collaboration.

[0109] In one exemplary implementation, please refer to Figure 13 This diagram illustrates the workflow of the combined attribute information recommendation process in this disclosure. The combined attribute information recommendation workflow performs the following operations: S1310. Parse the combined attribute structure of the preset item to obtain the combined attribute structure parsing result.

[0110] Taking the preset item as a target product for sale as an example, the combined attribute structure analysis result refers to the structured information of the preset item under different combinations or pairings, including but not limited to the components of the combined attributes, the relationships between the components, and the corresponding attribute descriptions. This analysis result can clarify the role and function of each item in the combined attributes, providing basic data support for subsequent recommendation workflows. Through analysis, the core features of the combined attributes can be further extracted to achieve more accurate matching and optimization in the recommendation process.

[0111] S1320. Extract the first text data related to the product text from the attribute reference information.

[0112] The first type of text data consists of textual information related to the target product.

[0113] S1330. Extract the second text data from the corresponding image based on the image address in the attribute reference information.

[0114] The second text data consists of the recognition results of image-related information about the target product.

[0115] S1340. Input the combined attribute structure parsing result, the first text data and the second text data into the combined attribute information recommendation model, and trigger the combined attribute information recommendation model to predict the combined attribute group name and the price (SKU) of the inventory holding unit.

[0116] The combined attribute information recommendation model disclosed herein can be a large model designed or trained for combined attribute information prediction. It can be an existing model or designed according to actual conditions, and does not constitute an obstacle to implementation.

[0117] S1350. Parse the individual item structure associated with the preset item to obtain the individual item structure parsing result.

[0118] The single-item structure analysis result refers to the structured information of each independent entity in the combined attributes, covering its basic attributes, feature descriptions, and relationships with other items. This analysis result can clearly reflect the performance and functional positioning of a single item in different scenarios, providing an important basis for subsequent data updates and optimizations.

[0119] S1360. Input the single-item structure analysis result, the combined attribute group name, and the price of the inventory holding unit into the combined attribute single-item information recommendation model, and trigger the combined attribute single-item recommendation model to predict attributes related to the combined attribute details.

[0120] The recommendation results may include the attribute values ​​from the combined attribute details. This disclosure does not limit the recommendation method used in S1360; a pre-designed or trained large model can be used. This model can be an existing model or designed according to actual conditions, without posing an implementation obstacle. Through the design of the combined attribute information recommendation workflow, the attribute values ​​from the combined attribute details of various non-standard products can be predicted automatically and with high accuracy, improving prediction efficiency, reducing development and iteration difficulty, and enabling agile development, agile iteration, and agile prediction.

[0121] Taking this preset item as the target product as an example, please refer to... Figure 14 This diagram illustrates the workflow framework for the combined attribute information recommendation process disclosed herein. The input parameters of the combined attribute information recommendation workflow include product text, product type, product category, merchant ID, product text, and product image. The workflow processes the combined attribute structure and inputs the parsed results of this structure, along with the product text (first text data) and text information (second text data), into the combined attribute information recommendation model for prediction. The model then outputs the combined attribute group name and SKU price. Finally, by processing the individual product structure, the processing results, combined attribute group name, and SKU price are input into another large model for prediction, yielding the attribute values ​​for the attributes in the combined attribute details.

[0122] In one exemplary implementation, please refer to Figure 15 This diagram illustrates the workflow of the service recommendation process in this disclosure. The service recommendation workflow performs the following operations: S1510. Obtain the service item set corresponding to the preset item, wherein the service item set includes general service items or customized service items.

[0123] Taking the target product intended for sale as an example, the service package includes general and customized services related to the target product, such as after-sales service, installation service, or repair service. General services may include standard delivery, return guarantee, and basic consultation, while customized services may cover value-added services such as personalized packaging, dedicated customer service, or scheduled delivery.

[0124] S1520. Extract the project template corresponding to each of the aforementioned service items.

[0125] Whether it's a general service project or a customized service project, there are corresponding project templates available. These templates include a detailed description of the service project, its scope of application, execution process, and relevant parameters. These templates can be flexibly adjusted to suit the service requirements of different scenarios.

[0126] S1530. Extract the first text data related to the product text from the attribute reference information.

[0127] The first type of text data consists of textual information related to the target product.

[0128] S1540. Extract the second text data from the corresponding image based on the image address in the attribute reference information.

[0129] The second text data consists of the recognition results of image-related information about the target product.

[0130] S1550. Input the project template, the first text data and the second text data corresponding to each service item into the first service item recommendation model, and trigger the first service item recommendation model to predict the project group type, service project group and corresponding project structure.

[0131] In this disclosure, the first service item recommendation model can be a large model designed or trained for service item prediction. It can be an existing model or designed according to actual conditions, and does not constitute an obstacle to implementation.

[0132] S1560. Input the project group type, the service project group and the corresponding project structure into the second service project recommendation model, and trigger the second service project recommendation model to predict the attributes under the service project attribute category.

[0133] The recommendation results can include the attribute values ​​of each attribute under the service item category. This disclosure does not limit the second service item used in S1560; a pre-designed or trained large model can be used. This model can be an existing model or designed according to actual conditions, and does not constitute an implementation obstacle. Through the design of the service item recommendation workflow, fully automated and highly accurate prediction of attribute values ​​in various non-standard service items can be achieved, improving prediction efficiency, reducing development and iteration difficulty, and realizing agile development, agile iteration, and agile prediction.

[0134] Taking this preset item as the target product as an example, please refer to... Figure 16 This diagram illustrates the workflow framework for service item recommendation in this disclosure. The input parameters of the service item recommendation workflow include product text, product template, product type, product category, merchant ID, product text, and product image. The workflow processes the product template to obtain a set of service items, and then inputs the extracted item templates (product templates), product text (first text data), and text information (second text data) into a first service item recommendation model for prediction. This model then outputs the service item group and item group type. Finally, the processed item content structure, combined attribute group name, and SKU price are input into another large model (the second service item recommendation model) for prediction, obtaining the attribute values ​​for each attribute under the service item attribute category.

[0135] In one exemplary implementation, please refer to Figure 17 This diagram illustrates the execution process of the basic attribute recommendation workflow. The basic attribute recommendation workflow performs the following operations: S1710. When the attribute to be predicted belongs to the tiled attribute set, the basic attribute recommendation workflow calls the tiled attribute workflow; S1720. When the attribute to be predicted belongs to an attribute group, the basic attribute recommendation workflow calls the attribute group workflow.

[0136] This design enables effective and accurate prediction of different types of attributes, enhancing the flexibility and adaptability of the overall prediction process. The tiled attribute workflow focuses on handling single, independent attribute value prediction tasks, generating results quickly; while the attribute group workflow excels at handling correlation analysis and comprehensive prediction of complex attribute sets, ensuring the consistency and accuracy of the output results. Through their collaboration, various attribute values ​​belonging to basic attributes can be predicted.

[0137] In one exemplary implementation, please refer to Figure 18 This diagram illustrates the workflow of the tiling attribute workflow in this disclosure. The tiling attribute workflow performs the following operations: S1810. Generate an attribute structure based on the general and custom attributes of the preset item.

[0138] S1820. Extract the first text data related to the product text from the attribute reference information.

[0139] The first type of text data consists of textual information related to the target product.

[0140] S1830. Extract the second text data from the corresponding image based on the image address in the attribute reference information.

[0141] The second text data consists of the recognition results of image-related information about the target product.

[0142] S1840. Input the attribute structure, the first text data, and the second text data into the tiled attribute recommendation model, and trigger the tiled attribute recommendation model to predict attributes that conform to the attribute structure.

[0143] The tiled attribute recommendation model disclosed herein can be a large model designed or trained for tiled attribute prediction. It can be an existing model or designed according to actual conditions, and does not pose an obstacle to implementation. By designing the tiled attribute recommendation workflow, the attribute values ​​of various non-standard products can be predicted automatically and with high accuracy, improving prediction efficiency, reducing development and iteration difficulty, and realizing agile development, agile iteration, and agile prediction.

[0144] Taking this preset item as the target product as an example, please refer to... Figure 19 This diagram illustrates the workflow framework of the tiled attribute workflow in this disclosure. The input parameters of the tiled attribute workflow are processed to obtain the attribute structures of general attributes and customized attributes. Based on these attribute structures, product text (first text data) and text information (second text data), these are input into the tiled attribute recommendation model for prediction, and then the model is triggered to output attributes that conform to the attribute structures.

[0145] In one exemplary implementation, the attribute group workflow performs the following operations: Obtain the attribute group structure corresponding to the preset item; Extract the attribute layers from the attribute group structure sequentially from the outside in. If the attribute layer belongs to a tiled attribute, invoke the tiled attribute workflow; if the attribute layer belongs to an attribute group, invoke the attribute group workflow.

[0146] An attribute layer refers to a hierarchical data structure based on attribute groups. This hierarchy clarifies the dependency and association characteristics between attributes. By parsing the attribute layers layer by layer, the attribute relationships at each layer are obtained. If these attributes are independent of each other, they form a set of attributes; otherwise, they form an attribute group. This design enables fully automated and highly accurate prediction of attribute values ​​in attribute groups for various non-standard products, improving prediction efficiency, reducing development and iteration difficulty, and achieving agile development, agile iteration, and agile prediction.

[0147] Taking this preset item as the target product as an example, please refer to... Figure 20 This diagram illustrates the workflow framework of the attribute group process in this disclosure. Attribute layers are broken down layer by layer from the outside in; the closer to the outside, the easier the sequential layers are to display. The outermost attribute layer consists of the attributes that are directly displayed. By iterating through the attribute layers, the attribute information for each layer can be obtained. If it is a tiled attribute, the tiled attribute workflow is invoked for processing; if it is an attribute group, the attribute group workflow is invoked for further analysis. This approach ensures that attributes at each level are accurately identified and processed, thereby achieving comprehensive analysis of complex attribute structures. Through this recursive processing logic, the system can efficiently handle different types of attribute combinations, regardless of their structural complexity.

[0148] Figure 21 This is a block diagram illustrating an attribute data updating apparatus according to an exemplary embodiment. The apparatus includes: The first configuration module 2110 is configured to execute the display attribute configuration page, the attribute page being used to configure attribute information; The second configuration module 2120 is configured to display an attribute group configuration page when the attribute configuration page receives a configuration operation for a target attribute. The attribute group configuration page is used to configure the attribute group to which the target attribute belongs. The attribute editing module 2130 is configured to display the attribute editing page corresponding to the target item when the configuration operation for the target attribute group is enabled. The attribute editing interface is used to display the attribute editing template corresponding to the item category of the target item. The target attribute group is the attribute group to which the target attribute belongs, and the target item is any item that conforms to the item category to which the target attribute group belongs. The update module 2140 is configured to update the attribute data corresponding to the target item when the attribute editing page receives an editing operation for the target attribute under the target attribute group.

[0149] In one exemplary embodiment, the attribute information includes an attribute identifier and a corresponding attribute format, and the attribute group includes the behavior pattern attribute of each attribute within the attribute group and the item category to which the attribute group belongs; The attribute editing module 2130 is configured to execute and display the attribute group editing control corresponding to the target attribute group; When the operation received by the attribute group editing control matches the behavior pattern attribute corresponding to the target attribute, the editing control corresponding to the target attribute is displayed in the attribute group editing control. The editing control corresponding to the target attribute is used to receive the editing operation for the target attribute under the target attribute group.

[0150] In one exemplary embodiment, the attribute editing module 2130 is configured to perform: Display a category configuration page, which is used to configure at least one item category to which the target attribute group belongs; When the configuration operation for the category configuration page is enabled, if the item category to which the target item belongs belongs to the at least one item category, the attribute editing template corresponding to the item category of the target item includes the attribute group editing control corresponding to the target attribute group.

[0151] In one exemplary implementation, the update module 2140 is configured to perform: If the configuration of any preset attribute is updated, the preset attribute editing template is updated. The preset attribute editing template is the attribute editing template corresponding to any item category to which the preset attribute group belongs, and the preset attribute group is any attribute group to which the preset attribute belongs.

[0152] In one exemplary embodiment, after updating the preset attribute editing template, the method further includes: Display the attribute diagnostic information and suggested update information corresponding to the preset item. The preset item is any item under any item category to which the preset attribute group belongs. The attribute diagnostic information indicates the matching status of the existing attribute data of the preset item with the updated preset attribute editing template. The suggested update information is the attribute update suggestion corresponding to the attribute diagnostic information. Upon receiving a request to edit the attributes of the preset item, the attribute editing interface corresponding to the preset item is displayed, and the attribute editing interface corresponding to the preset item includes the preset attribute editing template.

[0153] In one exemplary implementation, the update module 2140 is configured to perform: Display updated recommendation information and a recommendation adoption control, wherein the updated recommendation information includes the attribute values ​​of the attribute to be updated recommended based on the attribute diagnostic information; When the recommendation adoption control is triggered, the existing attribute data of the preset item is updated based on the updated recommendation information.

[0154] In one exemplary implementation, the update module 2140 is configured to perform: Display property auto-update control; When the attribute auto-update control is triggered, if the configuration of the preset attribute is updated, the existing attribute data of the preset item will be updated accordingly. The updated attributes include text-type attributes or image-type attributes.

[0155] In one exemplary implementation, the update module 2140 is configured to perform: Based on the updated preset attribute editing template, determine the attributes to be updated; Extract the attribute reference information corresponding to the preset item; By inputting the attribute reference information into the attribute collaborative recommendation workflow, the attribute collaborative recommendation workflow is triggered to predict the attribute value of the attribute to be updated. The attribute collaborative recommendation workflow is used to coordinate and schedule the attribute recommendation workflows corresponding to multiple attribute categories. Each attribute recommendation workflow is used to predict the attribute value of the attribute to be updated under its corresponding attribute category. Each attribute and each attribute group has a corresponding attribute category.

[0156] In one exemplary implementation, the attribute recommendation workflow corresponding to each of the multiple attribute categories includes an item basic information recommendation workflow, a combined attribute information recommendation workflow, a service item recommendation workflow, and a basic attribute recommendation workflow. The basic information recommendation workflow for items is used to recommend item titles, item categories, and item types. The combined attribute information recommendation workflow is used to recommend relevant attributes under the attribute category of combined attributes; The service item recommendation workflow is used to recommend relevant attributes under the attribute category of service items. The basic attribute recommendation workflow is used to recommend other attributes, including tiled attribute sets and attribute groups.

[0157] In one exemplary implementation, for any recommendation workflow, the data used in attribute prediction is prioritized from high to low as follows: existing attributes corresponding to the preset item, attribute reference information from standard products, attribute reference information from the same product, and attribute reference information from similar products from the same merchant. The existing attributes corresponding to the preset item, the attribute reference information from standard products, the attribute reference information from the same product, and the attribute reference information from similar products from the same merchant all belong to the attribute reference information corresponding to the preset item.

[0158] In one exemplary embodiment, the update module 2140 is configured to perform the following operations: Obtain the corresponding item category tree based on the merchant identifier corresponding to the preset item; Obtain the key category corresponding to the merchant identifier; Extract the first text data related to the product text from the attribute reference information; Extract the second text data from the corresponding image based on the image address in the attribute reference information; The item category tree, the key category, the first text data, and the second text data are input into the basic information recommendation model, which triggers the basic information recommendation model to predict the item category and the item name corresponding to the preset item. Retrieve the list of product types under the specified item category; Attribute recommendations are made based on the item category corresponding to the preset item, the item name corresponding to the preset item, and the list of product types.

[0159] In one exemplary embodiment, the update module 2140 is configured to perform the following operations: The combined attribute structure of the preset item is analyzed to obtain the combined attribute structure analysis result; Extract the first text data related to the product text from the attribute reference information; Extract the second text data from the corresponding image based on the image address in the attribute reference information; The combined attribute structure parsing result, the first text data and the second text data are input into the combined attribute information recommendation model to trigger the combined attribute information recommendation model to predict the combined attribute group name and the price of the inventory holding unit. The individual item structure associated with the preset item is analyzed to obtain the individual item structure analysis result; The product structure analysis results, the combined attribute group name, and the price of the inventory holding unit are input into the combined attribute product information recommendation model, which triggers the combined attribute product recommendation model to predict attributes related to the combined attribute details.

[0160] In one exemplary embodiment, the update module 2140 is configured to perform the following operations: Obtain the set of service items corresponding to the preset item, wherein the set of service items includes general service items or customized service items; Extract the project template corresponding to each of the aforementioned service items; Extract the first text data related to the product text from the attribute reference information; Extract the second text data from the corresponding image based on the image address in the attribute reference information; The project template, the first text data and the second text data corresponding to each service item are input into the first service item recommendation model, which triggers the first service item recommendation model to predict the project group type, service project group and corresponding project structure. The project group type, the service project group, and the corresponding project structure are input into the second service project recommendation model, which then triggers the second service project recommendation model to predict the attributes under the service project attribute category.

[0161] In one exemplary embodiment, the update module 2140 is configured to perform the following operations: If the attribute to be predicted belongs to the tiled attribute set, the basic attribute recommendation workflow calls the tiled attribute workflow. When the attribute to be predicted belongs to an attribute group, the basic attribute recommendation workflow calls the attribute group workflow.

[0162] In one exemplary embodiment, the update module 2140 is configured to perform the following operations: Generate an attribute structure based on the general and custom attributes of the preset item; Extract the first text data related to the product text from the attribute reference information; Extract the second text data from the corresponding image based on the image address in the attribute reference information; The attribute structure, the first text data, and the second text data are input into the tiled attribute recommendation model, which triggers the tiled attribute recommendation model to predict attributes that conform to the attribute structure.

[0163] In one exemplary embodiment, the update module 2140 is configured to perform the following operations: Obtain the attribute group structure corresponding to the preset item; Extract the attribute layers from the attribute group structure sequentially from the outside in. If the attribute layer belongs to a tiled attribute, invoke the tiled attribute workflow; if the attribute layer belongs to an attribute group, invoke the attribute group workflow.

[0164] Regarding the apparatus in the above embodiments, the specific manner of each step has been described in detail in the embodiments of the foregoing method, and will not be elaborated here.

[0165] Please refer to Figure 22 It illustrates the structural block of a computer device provided in an exemplary embodiment of this disclosure. Figure 1 The computer device may be a terminal. This computer device is used to implement the attribute data update method provided in the above embodiments. Specifically: Typically, computer device 2200 includes a processor 2201 and a memory 2202.

[0166] Processor 2201 may include one or more processing cores, such as a quad-core processor, an octa-core processor, etc. Processor 2201 may be implemented using at least one hardware form selected from DSP (Digital Signal Processing), FPGA (Field Programmable Gate Array), and PLA (Programmable Logic Array). Processor 2201 may also include a main processor and a coprocessor. The main processor, also known as a CPU (Central Processing Unit), is used to process data in the wake-up state; the coprocessor is a low-power processor used to process data in the standby state. In an exemplary embodiment, processor 2201 may integrate a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content to be displayed on the screen. In an exemplary embodiment, processor 2201 may also include an AI (Artificial Intelligence) processor, which is used to handle computational operations related to machine learning.

[0167] The memory 2202 may include one or more computer-readable storage media, which may be non-transitory. The memory 2202 may also include high-speed random access memory and non-volatile memory, such as one or more disk storage devices or flash memory devices. In an exemplary embodiment, the non-transitory computer-readable storage medium in the memory 2202 is used to store at least one instruction, at least one program, code set, or instruction set, configured to be executed by one or more processors to implement the attribute data update method described above.

[0168] In one exemplary embodiment, the computer device 2200 may optionally include a peripheral device interface 2203 and at least one peripheral device. The processor 2201, memory 2202, and peripheral device interface 2203 can be connected via a bus or signal line. Each peripheral device can be connected to the peripheral device interface 2203 via a bus, signal line, or circuit board. Specifically, the peripheral device includes at least one of the following: a radio frequency circuit 2204, a touch display screen 2205, a camera assembly 2206, an audio circuit 2207, a positioning assembly 2208, and a power supply 2209.

[0169] Those skilled in the art will understand that Figure 22 The structure shown does not constitute a limitation on computer device 2200 and may include more or fewer components than shown, or combine certain components, or use different component arrangements.

[0170] Please refer to Figure 23 It illustrates the structural block of a computer device provided in another exemplary embodiment of this disclosure. Figure 2 The computer device can be a server for executing the aforementioned attribute data update method. Specifically: Computer device 2300 includes a central processing unit (CPU) 2301, a system memory 2304 including random access memory (RAM) 2302 and read-only memory (ROM) 2303, and a system bus 2305 connecting the system memory 2304 and the CPU 2301. Computer device 2300 also includes a basic input / output system (I / O system) 2306 that facilitates information transfer between various devices within the computer, and a mass storage device 2307 for storing the operating system 2313, application programs 2314, and other program modules 2311.

[0171] The basic input / output system 2306 includes a display 2308 for displaying information and an input device 2309 for user input, such as a mouse or keyboard. Both the display 2308 and the input device 2309 are connected to the central processing unit 2301 via an input / output controller 1230 connected to the system bus 2305. The basic input / output system 2306 may also include the input / output controller 1230 for receiving and processing input from multiple other devices such as a keyboard, mouse, or electronic stylus. Similarly, the input / output controller 1230 also provides output to a display screen, printer, or other types of output devices.

[0172] Mass storage device 2307 is connected to central processing unit 2301 via a mass storage controller (not shown) connected to system bus 2305. Mass storage device 2307 and its associated computer-readable media provide non-volatile storage for computer device 2300. That is, mass storage device 2307 may include computer-readable media (not shown) such as hard disk or CD-ROM (Compact Disc Read-Only Memory) drive.

[0173] Without loss of generality, computer-readable media can include computer storage media and communication media. Computer storage media includes volatile and non-volatile, removable and non-removable media implemented using any method or technology for storing information such as computer-readable instructions, data structures, program modules, or other data. Computer storage media includes RAM, ROM, EPROM (Erasable Programmable Read Only Memory), EEPROM (Electrically Erasable Programmable Read Only Memory), flash memory or other solid-state storage technologies, CD-ROM, DVD (Digital Video Disc) or other optical storage, magnetic tape cassettes, magnetic tape, disk storage, or other magnetic storage devices. Of course, those skilled in the art will recognize that computer storage media are not limited to the above-mentioned types. The system memory 2304 and mass storage device 2307 described above can be collectively referred to as memory.

[0174] According to various embodiments of this disclosure, the computer device 2300 can also be connected to a remote computer on a network, such as the Internet. That is, the computer device 2300 can be connected to the network 2312 via the network interface unit 2311 connected to the system bus 2305, or the network interface unit 2311 can be used to connect to other types of networks or remote computer systems (not shown).

[0175] The aforementioned memory also includes a computer program stored in the memory and configured to be executed by one or more processors to implement the aforementioned attribute data update method.

[0176] In an exemplary embodiment, a computer-readable storage medium is also provided, which stores at least one instruction, at least one program, code set, or instruction set, wherein the at least one instruction, at least one program, code set, or instruction set is executed by a processor to implement the attribute data update method.

[0177] Optionally, the computer-readable storage medium may include: ROM (Read Only Memory), RAM (Random Access Memory), SSD (Solid State Drives), or optical disc, etc. The random access memory may include ReRAM (Resistance Random Access Memory) and DRAM (Dynamic Random Access Memory).

[0178] In an exemplary embodiment, a computer-readable storage medium including program code is also provided, such as a memory including program code, which can be executed by a processor to complete the aforementioned attribute data update method. Optionally, the computer-readable storage medium may be read-only memory (ROM), random access memory (RAM), compact-disc read-only memory (CD-ROM), magnetic tape, floppy disk, and optical data storage device, etc.

[0179] In an exemplary embodiment, a computer program product is also provided, including a computer program that, when executed by a processor, implements the attribute data update method described above.

[0180] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This disclosure is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the following claims.

[0181] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is limited only by the appended claims.

Claims

1. An attribute data updating method characterized by comprising: The method comprises: displaying an attribute configuration page, the attribute page being used for configuring attribute information; in the case where a configuration operation for a target attribute is received on the attribute configuration page, an attribute group configuration page is displayed, the attribute group configuration page being used for configuring an attribute group to which the target attribute belongs; in the case where a configuration operation for a target attribute group is enabled, an attribute editing page corresponding to a target item is displayed, the attribute editing interface being used for displaying an attribute editing template corresponding to an item category of the target item, the target attribute group being an attribute group to which the target attribute belongs, and the target item being any item conforming to an item category to which the target attribute group belongs; in the case where an editing operation for the target attribute under the target attribute group is received on the attribute editing page, attribute data corresponding to the target item is updated.

2. The method of claim 1, wherein, The attribute information comprises an attribute identifier and a corresponding attribute format, and the attribute group comprises a behavior mode attribute of each attribute in the attribute group and an item category to which the attribute group belongs; The method further comprises: in the case where an operation received on the attribute group editing control conforms to the behavior mode attribute corresponding to the target attribute, an editing control corresponding to the target attribute is displayed in the attribute group editing control, the editing control corresponding to the target attribute being used for receiving the editing operation for the target attribute under the target attribute group. The method further comprises:

3. The method according to claim 1 or 2, characterized in that, displaying a category configuration page, the category configuration page being used for configuring at least one item category to which the target attribute group belongs; in the case where a configuration operation for the category configuration page is enabled, if an item category to which the target item belongs belongs to the at least one item category, an attribute group editing control corresponding to the target attribute group is included in an attribute editing template corresponding to the item category of the target item. The method further comprises:

4. The method of claim 1, wherein, in the case where configuration of any preset attribute is updated, a preset attribute editing template is updated, the preset attribute editing template being an attribute editing template corresponding to any item category to which a preset attribute group belongs, and the preset attribute group being any attribute group to which a preset attribute belongs. After the preset attribute editing template is updated, the method further comprises:

5. The method of claim 4, wherein, displaying attribute diagnosis information and suggestion update information corresponding to a preset item, the preset item being an item under any item category to which the preset attribute group belongs, the attribute diagnosis information indicating a matching condition between existing attribute data of the preset item and the updated preset attribute editing template, and the suggestion update information being attribute update suggestions corresponding to the attribute diagnosis information; in the case where an attribute editing request for the preset item is received, an attribute editing interface corresponding to the preset item is displayed, the attribute editing interface corresponding to the preset item comprising the preset attribute editing template. After the preset attribute editing template is updated, the method further comprises:

6. The method of claim 5, wherein, ​ The update recommendation information includes attribute values of to-be-updated attributes recommended based on the attribute diagnosis information; In a case where the recommendation adoption control is triggered, the existing attribute data of the preset item is updated based on the update recommendation information.

7. The method of claim 5, wherein, The method further includes: displaying an attribute automatic update control; In a case where the attribute automatic update control is triggered, if the configuration of the preset attribute is updated, the existing attribute data of the preset item is updated accordingly, and the updated attribute includes a text type attribute or an image type attribute.

8. The method according to claim 6 or 7, characterized in that, The method further includes: determining to-be-updated attributes according to the updated preset attribute editing template; extracting attribute reference information corresponding to the preset item; triggering the attribute collaborative recommendation workflow to predict attribute values of the to-be-updated attributes by inputting the attribute reference information into the attribute collaborative recommendation workflow, the attribute collaborative recommendation workflow being used for collaboratively scheduling attribute recommendation workflows corresponding to multiple attribute categories, each attribute recommendation workflow being used for predicting attribute values of to-be-updated attributes in an attribute category corresponding to the attribute recommendation workflow, each attribute and each attribute group having a corresponding attribute category.

9. The method of claim 8, wherein, The attribute recommendation workflows corresponding to the multiple attribute categories include an item basic information recommendation workflow, a combined attribute information recommendation workflow, a service item recommendation workflow, and a basic attribute recommendation workflow; The item basic information recommendation workflow is used for recommending an item title, an item category, and an item type. The combined attribute information recommendation workflow is used for recommending attributes in a combined attribute category. The service item recommendation workflow is used for recommending attributes in a service item category. The basic attribute recommendation workflow is used for recommending other attributes, and the other attributes include a flat attribute set and an attribute group.

10. The method of claim 9, wherein, For any attribute recommendation workflow, the priority of data used for attribute prediction from high to low is: existing attributes corresponding to the preset item, attribute reference information from a standard item, attribute reference information from a same item, and attribute reference information from a similar item of a same merchant, wherein the existing attributes corresponding to the preset item, the attribute reference information from the standard item, the attribute reference information from the same item, and the attribute reference information from the similar item of the same merchant all belong to attribute reference information corresponding to the preset item.

11. The method according to claim 9 or 10, characterized in that, The item basic information recommendation workflow performs the following operations: obtaining an item category tree corresponding to a merchant identifier of the preset item; obtaining a key category corresponding to the merchant identifier; extracting first text data related to a product text in the attribute reference information; extracting second text data in a corresponding picture based on a picture address in the attribute reference information; inputting the item category tree, the key category, the first text data, and the second text data into a basic information recommendation model, and triggering the basic information recommendation model to predict an item category corresponding to the preset item and an item name corresponding to the preset item; obtaining a product type list under the item category; recommend an attribute based on the item category corresponding to the preset item, the item name corresponding to the preset item, and the list of product types.

12. The method according to claim 9 or 10, characterized in that, The combined attribute information recommendation workflow performs the following operations: parsing a combined attribute structure of the preset item to obtain a combined attribute structure parsing result; extracting first text data related to product text in the attribute reference information; extracting second text data in corresponding pictures based on picture addresses in the attribute reference information; inputting the combined attribute structure parsing result, the first text data, and the second text data into a combined attribute information recommendation model to trigger the combined attribute information recommendation model to predict a combined attribute group name and a price of a stock keeping unit; parsing a single product structure associated with the preset item to obtain a single product structure parsing result; inputting the single product structure parsing result, the combined attribute group name, and the price of the stock keeping unit into a combined attribute single product information recommendation model to trigger the combined attribute single product recommendation model to predict an attribute related to a combined attribute detail.

13. The method of claim 9 or 10, wherein, The service item recommendation workflow performs the following operations: obtaining a service item set corresponding to the preset item, the service item set including a general service item or a customized service item; extracting a project template corresponding to each service item; extracting first text data related to product text in the attribute reference information; extracting second text data in corresponding pictures based on picture addresses in the attribute reference information; inputting the project template corresponding to each service item, the first text data, and the second text data into a first service item recommendation model to trigger the first service item recommendation model to predict a project group type, a service item group, and a corresponding project structure; inputting the project group type, the service item group, and the corresponding project structure into a second service item recommendation model to trigger the second service item recommendation model to predict each attribute under the attribute classification of the service item.

14. The method of claim 9 or 10, wherein, The basic attribute recommendation workflow performs the following operations: in a case where the attribute to be predicted belongs to a flat attribute set, the basic attribute recommendation workflow calls a flat attribute workflow; in a case where the attribute to be predicted belongs to an attribute group, the basic attribute recommendation workflow calls an attribute group workflow.

15. The method of claim 9 or 10, wherein, The flat attribute workflow performs the following operations: generating an attribute structure according to general attributes and customized attributes of the preset item; extracting first text data related to product text in the attribute reference information; extracting second text data in corresponding pictures based on picture addresses in the attribute reference information; inputting the attribute structure, the first text data, and the second text data into a flat attribute recommendation model to trigger the flat attribute recommendation model to predict an attribute conforming to the attribute structure.

16. The method of claim 15, wherein, The attribute group workflow performs the following operations: obtaining an attribute group structure corresponding to the preset item; extracting attribute layers in the attribute group structure from outside to inside in sequence, in a case where the attribute layer belongs to a flat attribute, calling the flat attribute workflow; in a case where the attribute layer belongs to an attribute group, calling the attribute group workflow.

17. An attribute data updating apparatus characterized by comprising: The apparatus comprises: The first configuration module is configured to execute a property configuration page, the property page being used for configuring property information; The second configuration module is configured to execute a property group configuration page for configuring a property group to which the target property belongs when the configuration operation for the target property is received on the property configuration page; The property editing module is configured to execute a property editing page corresponding to a target item when a configuration operation for a target property group is enabled, the property editing interface being used for displaying a property editing template corresponding to an item category of the target item, the target property group being a property group to which the target property belongs, and the target item being any item that meets an item category to which the target property group belongs; The updating module is configured to update property data corresponding to the target item when an editing operation for the target property under the target property group is received on the property editing page.

18. An electronic device, comprising: Comprise: A processor; A memory for storing instructions executable by the processor; Wherein the processor is configured to execute the instructions to implement the property data updating method of any one of claims 1-16.

19. A computer-readable storage medium, characterized in that, When the instructions in the computer readable storage medium are executed by the processor of the electronic device, the electronic device executes the property data updating method of any one of claims 1-16.

20. A computer program product, characterised in that, The computer program product comprises a computer program stored in a readable storage medium, and at least one processor of a computer device reads and executes the computer program from the readable storage medium, so that the device executes the property data updating method of any one of claims 1-16.

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