Target object acquisition method and device

Abstract

One or more embodiments of the invention provide a target object acquisition method and device. Through the method, a first service platform can display an interactive interface, a first object identification list and feature information are determined in response to a selection operation of a user on a data source component, the first object identification list is used for indicating a first screening object stored locally, and the feature information is used for indicating a second screening object associated with a second service platform. The feature information comprises a processing type; determining a data processing mode corresponding to the feature information in response to a screening operation of the user on the processing type, and sending the data processing mode to a second service platform based on the generated request instruction to instruct the second service platform to obtain a second object identifier list of a second screening object based on the feature information; a processing result is generated through a data processing mode, the first object identification list and the second object identification list; and then the first service platform can determine a target object set based on the processing result of the second service platform.

Description

Technical Field

[0001] This specification relates to one or more embodiments in the field of computer technology, and more particularly to a method and apparatus for acquiring a target object. Background Technology

[0002] With the rapid development of digital marketing technologies, the selection and integration of target customer groups has become a crucial step in achieving precision marketing. Currently, various digital marketing platforms offer tools that can perform user analysis and target customer group selection based on enterprise data, meeting the needs of basic marketing scenarios.

[0003] As marketing scenarios become increasingly diverse, data sources are also diversifying, encompassing enterprise-owned data, partner data, and third-party data. A single data source often fails to comprehensively depict user characteristics, and relying solely on enterprise-owned data may result in incomplete or inaccurate audience targeting. While partner or third-party data (such as credit scores, cross-platform behavior, and industry tags) can effectively supplement user profiles, they typically cannot be directly imported into local data warehouses for customer segmentation due to privacy compliance, data security, and technical isolation concerns. To improve the breadth and richness of audience targeting, marketing systems need to efficiently integrate local and external data for joint calculations (such as intersection and filtering) to generate more precise target groups by incorporating multiple perspectives.

[0004] To achieve efficient collaborative computing of multi-source data and improve the accuracy and consistency of target customer group selection, a target object acquisition solution adapted to multi-source data collaborative computing scenarios is needed. Summary of the Invention

[0005] In order to achieve efficient collaborative computing of multi-source data and improve the accuracy and process consistency of target customer group selection, one or more embodiments of this specification provide a method and apparatus for obtaining target objects.

[0006] Firstly, one or more embodiments of this specification provide a method for obtaining a target object, applied to a first service platform, the method comprising: The interactive interface is provided with a data source component, which includes a local data source component and an external data source component. The external data source component is associated with the metadata of the second service platform, and the metadata includes the processing type. In response to a user's selection operation on the data source component, a first object identifier list and feature information are determined; wherein, the first object identifier list is used to indicate a first filter object stored locally, and the feature information is used to indicate a second filter object associated with the second service platform, and the feature information includes the processing type; In response to the user's filtering operation on the processing type, determine the data processing method corresponding to the feature information; A request instruction is generated based on the data processing method and sent to the second service platform to instruct the second service platform to obtain a second object identifier list of the second filtered object based on the feature information, and to generate a processing result based on the first object identifier list and the second object identifier list through the data processing method. The target object set is determined based on the processing results received from the second service platform.

[0007] In one possible implementation, determining the data processing method corresponding to the feature information in response to a user's filtering operation on the processing type includes: If the processing type included in the feature information is a privacy computing type, the data processing method is determined based on the user's filtering operation of the first preset processing method corresponding to the privacy computing type.

[0008] In one possible implementation, generating the request instruction based on the data processing method includes: Based on the preset encryption algorithm corresponding to the privacy computing type, the first object identifier list is encrypted to obtain the encrypted first object identifier list. The request instruction is generated based on the encrypted first object identifier list, the feature information, and the data processing method.

[0009] In one possible implementation, determining the target object set based on the processing result received from the second service platform includes: The processing result is used as the target object set.

[0010] In one possible implementation, determining the data processing method corresponding to the feature information in response to the user's filtering operation includes: When the processing type included in the feature information is an API interface type, the data processing method is determined based on the user's filtering operation of the second preset processing method corresponding to the API interface type.

[0011] In one possible implementation, generating the request instruction based on the preset processing method includes: The request instruction is generated based on the query conditions, the first object identifier list, and the feature information included in the data processing method.

[0012] In one possible implementation, determining the target object set based on the processing result received from the second service platform includes: Based on the processing result, the target object identifier that meets the query conditions is obtained from the first object identifier list; The target object set is determined based on the target object identifier.

[0013] In one possible implementation, before displaying the interactive interface, the method further includes: Obtain the metadata of the second service platform, wherein the metadata also includes the name, description, interface address and authentication information of the second service platform; The metadata is associated with the external data source component corresponding to the second service platform.

[0014] In one possible implementation, after determining the target object set, the method further includes: The target object collection is stored locally, generating a local data source component corresponding to the target object collection.

[0015] In one possible implementation, determining the first object identifier list and feature information in response to a user's selection operation on the data source component includes: In response to a user's selection operation for at least one of the local data source components, the first object identifier list is determined; The feature information is determined in response to a user's selection operation for at least one of the external data source components.

[0016] Secondly, one or more embodiments of this specification also provide a target object acquisition device, applied to a first service platform, the device comprising: The display module is used to display the interactive interface. The interactive interface is equipped with a data source component, which includes a local data source component and an external data source component. The external data source component is associated with the metadata of the second service platform, and the metadata includes the processing type. The first determining module is used to determine a first object identifier list and feature information in response to a user's selection operation on the data source component; wherein, the first object identifier list is used to indicate a first filter object stored locally, and the feature information is used to indicate a second filter object associated with the second service platform, and the feature information includes the processing type; The second determining module is used to determine the data processing method corresponding to the feature information in response to the user's filtering operation on the processing type. The generation module is used to generate a request instruction based on the data processing method and send the request instruction to the second service platform to instruct the second service platform to obtain a second object identifier list of the second filtered object based on the feature information, and to generate a processing result based on the first object identifier list and the second object identifier list through the data processing method. The third determining module is used to determine the target object set based on the processing result received from the second service platform.

[0017] In one possible implementation, the second determining module is configured to determine the data processing method corresponding to the feature information in response to a user's filtering operation on the processing type, including: If the processing type included in the feature information is a privacy computing type, the data processing method is determined based on the user's filtering operation of the first preset processing method corresponding to the privacy computing type.

[0018] In one possible implementation, the generation module is used to generate a request instruction based on the data processing method, including: The generation module is used to encrypt the first object identifier list based on a preset encryption algorithm corresponding to the privacy computing type to obtain an encrypted first object identifier list; and to generate the request instruction based on the encrypted first object identifier list, the feature information, and the data processing method.

[0019] In one possible implementation, the third determining module is used to determine the target object set based on the processing result received from the second service platform, including: The third determining module is used to use the processing result as the target object set.

[0020] In one possible implementation, the second determining module is configured to determine the data processing method corresponding to the feature information in response to a user's filtering operation on the processing type, including: The second determining module is used to determine the data processing method based on the user's filtering operation of the second preset processing method corresponding to the API interface type when the processing type included in the feature information is an API interface type.

[0021] In one possible implementation, the generation module is used to generate a request instruction based on the preset processing method, including: The generation module is used to generate the request instruction based on the query conditions included in the data processing method, the first object identifier list, and the feature information.

[0022] In one possible implementation, the third determining module is used to determine the target object set based on the processing result received from the second service platform, including: The third determining module is used to: obtain target object identifiers that meet the query conditions from the first object identifier list based on the processing result; and determine the target object set based on the target object identifiers.

[0023] In one possible implementation, the device further includes an acquisition module and an association module; The acquisition module is used to acquire the metadata of the second service platform, wherein the metadata includes the name, description information, interface address and authentication information of the second service platform; The association module is used to associate the metadata with the external data source component corresponding to the second service platform.

[0024] In one possible implementation, the device further includes a storage module. The storage module is used to store the target object set locally and generate a local data source component corresponding to the target object set.

[0025] In one possible implementation, the first determining module is configured to determine a first object identifier list and feature information in response to a user's selection operation on the data source component, including: The first determining module is configured to: determine the first object identifier list in response to a user's selection operation for at least one of the local data source components; and determine the feature information in response to a user's selection operation for at least one of the external data source components.

[0026] Thirdly, one or more embodiments of this specification also provide an electronic device, which includes a memory and a processor; the memory is used to store a computer program product; the processor is used to execute the computer program product stored in the memory, and when the computer program product is executed, it implements the target object acquisition method of the first aspect described above.

[0027] Fourthly, one or more embodiments of this specification also provide a computer-readable storage medium storing computer program instructions that, when executed, implement the target object acquisition method of the first aspect described above.

[0028] In summary, to achieve efficient collaborative processing of multi-source data and improve the accuracy and consistency of target customer group selection, one or more embodiments of this specification provide a method and apparatus for acquiring target objects applied to a first service platform. In this method, the first service platform can display an interactive interface with data source components, including a local data source component and an external data source component. The external data source component is associated with metadata of the second service platform, including processing types. In response to a user's selection operation on the data source component, the first service platform determines a first object identifier list and feature information. The first object identifier list indicates first filtered objects stored locally, and the feature information indicates second filtered objects associated with the second service platform, including processing types. Subsequently, in response to a user's filtering operation on processing types, the first service platform determines a data processing method corresponding to the feature information, generates a request instruction based on the data processing method, and sends the request instruction to the second service platform. This instructs the second service platform to acquire a second object identifier list of the second filtered objects based on the feature information, and to generate a processing result based on the first and second object identifier lists using the data processing method. Finally, the first service platform determines a target object set based on the processing result received from the second service platform.

[0029] In this way, by uniformly abstracting and encapsulating local and external data sources, both are transformed into standardized data source components under the same interactive interface of the first service platform. The external data source components are then associated with the metadata of the second service platform, constructing a data call chain between the local object identifier list and the external object characteristic information. This integrates the external data corresponding to the second service platform and the local data into the same target object acquisition process. In actual operation, the first service platform can respond to the user's selection of multi-source data and configuration of processing methods, generating corresponding request instructions and sending them to the external second service platform. This automatically completes secure collaborative computation of cross-platform data, ultimately obtaining the target object set based on the processing results of the external platform, achieving closed-loop integration of multi-source data. Throughout the entire process, marketers can complete multi-source data computation simply by dragging and selecting within the interactive interface of the first service platform, without switching operating scenarios. This effectively balances operational convenience and process continuity, achieving efficient collaborative computation of enterprise-owned data with external partners and third-party data in digital marketing scenarios. This can subsequently achieve precise and efficient streaming, improving the user experience. Attached Figure Description

[0030] To more clearly illustrate the technical solutions of one or more embodiments of this specification, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of one or more embodiments of this specification. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0031] Figure 1 This specification provides a schematic diagram of an application scenario for one or more embodiments. Figure 2 This is a schematic diagram illustrating another application scenario provided by one or more embodiments of this specification; Figure 3 A flowchart illustrating a method for obtaining a target object provided in one or more embodiments of this specification; Figure 4 A schematic diagram of an interactive interface provided for one or more embodiments of this specification; Figure 5 A schematic diagram of an interactive interface for one or more embodiments of this specification, illustrating an application scenario. Figure 6 A schematic diagram of an interactive interface for another application scenario provided by one or more embodiments of this specification. Figure 7 A schematic diagram illustrating yet another application scenario provided by one or more embodiments of this specification; Figure 8 A flowchart illustrating a method for obtaining a target object in a privacy computing scenario, provided for one or more embodiments of this specification; Figure 9 A flowchart illustrating a method for obtaining a target object in an API interface query scenario, provided for one or more embodiments of this specification; Figure 10 A structural block diagram of a target object acquisition device provided in one or more embodiments of this specification; Figure 11 This is a structural block diagram of an electronic device provided for one or more embodiments of this specification. Detailed Implementation

[0032] The present specification describes one or more embodiments in further detail below with reference to the accompanying drawings and examples. Through these descriptions, the features and advantages of one or more embodiments of the present specification will become clearer and more apparent.

[0033] The term “exemplary” as used herein means “serving as an example, embodiment, or illustration.” Any embodiment illustrated herein as “exemplary” is not necessarily to be construed as superior to or better than other embodiments. Although various aspects of embodiments are shown in the accompanying drawings, the drawings are not necessarily drawn to scale unless specifically indicated otherwise.

[0034] Furthermore, the technical features described below in one or more embodiments of this specification may be combined with each other as long as they do not conflict with each other.

[0035] To facilitate understanding, the application scenarios of the technical solutions provided in one or more embodiments of this specification will be described below.

[0036] With the rapid development of digital marketing technologies, the screening and integration of target customer groups has become a crucial step in achieving precision marketing. As marketing scenarios become increasingly diverse, data sources are also diversifying, encompassing enterprise-owned data, partner data, and third-party data. A single data source often fails to comprehensively depict user characteristics, and relying solely on enterprise-owned data may result in incomplete or inaccurate audience targeting. While partner or third-party data (such as credit scores, cross-platform behavior, and industry tags) can effectively supplement user profiles, they typically cannot be directly imported into local data warehouses for customer segmentation due to privacy compliance, data security, and technical isolation concerns. To improve the breadth and richness of audience targeting, marketing systems need to efficiently integrate local and external data for joint calculations (such as intersection and filtering) to generate a more precise target audience by incorporating multiple perspectives.

[0037] To help businesses conduct user analysis and target customer segmentation, and meet diverse marketing needs, digital marketing platforms have emerged. These platforms, such as Customer Data Platforms (CDPs) and Data Management Platforms (DMPs), are primarily used for collecting and processing data. Some platforms (like CDPs) mainly integrate a company's own first-party customer data to build a unified customer view for refined operations and marketing; others (like DMPs) focus on processing anonymized third-party advertising data for ad placement and audience targeting.

[0038] Digital marketing platforms, equipped with tools such as data canvases, can leverage data stored in local databases to select local target customer groups by translating user actions into Structured Query Language (SQL), thus meeting the needs of basic marketing scenarios.

[0039] The Data Canvas (also known as the Audience Canvas) is a visual interface adapted for digital marketing scenarios. Marketers can intuitively select and create target audiences by dragging and dropping and combining different user tags, behaviors, and other conditions. SQL is a standardized programming language for managing and manipulating relational databases. The Data Canvas parses and converts the marketer's visual operations into corresponding SQL statements, which are then executed by the local database to filter, statistically analyze, and perform calculations on the audience data, freeing marketers from understanding the underlying database storage logic. SQL is the core technology supporting the Data Canvas in selecting local target customer groups.

[0040] However, in practice, data sources are often diverse. Second-party and third-party data may be restricted (such as privacy compliance, data security, and technical isolation) and cannot usually be directly transferred offline to a local database or a locally accessible database. Existing SQL-based target customer segmentation methods can only handle physically accessible data (including data stored in local databases and data stored in locally accessible databases). When collaborative processing with external data sources is involved, SQL cannot directly reference external data in the data canvas, making it difficult to meet the complex cross-domain marketing target customer segmentation needs. Therefore, to achieve multi-source data processing, marketers can utilize multiple platforms for related operations.

[0041] See Figure 1 , Figure 1 This is a schematic diagram illustrating an application scenario provided for one or more embodiments of this specification. For example... Figure 1 As shown, this application scenario may include a local marketing platform 101, a computing platform 102, an external database 103, and a delivery platform 104.

[0042] For example, when marketers select target customer groups, they can first generate SQL statements in the local marketing platform 101 through visual operations to select local audiences (such as local audience 1 and local audience 2) and obtain audience combination 1. However, when it is necessary to combine external data to integrate multi-source audiences, audience combination 1 must be manually transferred to computing platform 102.

[0043] The computing platform 102 retrieves data from the external database 103 and performs cross-source calculations to obtain audience group 2. Afterwards, marketers need to manually send audience group 2 back to the local marketing platform 101 before they can export the target audience to the campaign platform 104 for marketing campaigns.

[0044] See Figure 2 , Figure 2This is a schematic diagram illustrating another application scenario provided by one or more embodiments of this specification. For example... Figure 2 As shown, this application scenario may include a local marketing platform 201, a conversion platform 202, a query platform 203, and a delivery platform 204.

[0045] For example, marketers can also generate SQL statements through visual operations in the data canvas of the local marketing platform 201 to select local audiences (such as local audience 3 and local audience 4) and obtain audience combination 3. If data from the external query platform 203 needs to be called, audience combination 3 must be manually transmitted to the conversion platform 202 for format conversion before a batch query is initiated. After the query results return audience combination 4, marketers must manually transmit audience combination 4 back to the local marketing platform 201 before it can be exported to the campaign platform 204 for marketing campaigns.

[0046] However, the target customer group selection process based on the aforementioned multi-source data requires marketers to switch between multiple platforms repeatedly. This cross-platform connection method, which relies on manual operation, has a high operational threshold and makes it difficult to efficiently complete the integration of the entire customer base.

[0047] Furthermore, the above methods suffer from issues such as process interruptions and inconsistent user experience during actual operation by marketers. Since data transmission and result feedback between platforms require manual execution, delays or data omissions in any step can lead to longer target customer group generation cycles or even data errors, failing to promptly support the precise targeting needs of marketing campaigns.

[0048] To address the issues of high operational barriers and process breakpoints in the multi-source data target customer group selection process, this specification provides a method and apparatus for obtaining target objects through one or more embodiments.

[0049] See Figure 3 , Figure 3 This is a flowchart illustrating a method for obtaining a target object according to one or more embodiments of this specification. This method can be applied to a first service platform or an electronic device running the first service platform. The following description uses an application to a first service platform as an example to illustrate the embodiments. Figure 3 As shown, the method may include steps S302 to S3010.

[0050] Step S302: Display the interactive interface, which has a data source component, including a local data source component and an external data source component; wherein, the external data source component is associated with the metadata of the second service platform, and the metadata includes the processing type, that is, the processing type used to limit the types of operations available on the second service platform.

[0051] Step S304: In response to the user's selection operation on the data source component, determine the first object identifier list and characteristic information; The first object identifier list is used to indicate the first filter object stored locally, and the feature information is used to indicate the second filter object associated with the second service platform. The feature information includes the processing type, which is used to indicate the interaction method of the second service platform and to clarify the collaborative operation mode of local data and external data, including privacy computing class, API interface class, etc.

[0052] Step S306: In response to the user's filtering operation on the processing type, determine the data processing method corresponding to the feature information; Different processing types (such as privacy computing type, API interface type, etc.) correspond to different data processing methods.

[0053] Step S308: Generate a request instruction based on the data processing method and send the request instruction to the second service platform to instruct the second service platform to obtain the second object identifier list of the second filter object based on the feature information, and generate a processing result based on the first object identifier list and the second object identifier list through the data processing method; Step S3010: Determine the target object set based on the processing results received from the second service platform.

[0054] This method unifies and abstracts local and external data sources, transforming them into standardized data source components within the same interactive interface of the first service platform. It then associates these external data source components with the metadata of the second service platform, constructing a data call chain between the local object identifier list and external object feature information. This integrates the external data from the second service platform with the local data into a single target object acquisition process. In practice, the first service platform responds to user selections and processing configurations for multi-source data, generating corresponding request commands and sending them to the external second service platform. This automatically completes secure collaborative computation of cross-platform data, ultimately yielding the target object set based on the processing results from the external platform, achieving closed-loop integration of multi-source data. Throughout the process, marketers can complete multi-source data computation simply by dragging and selecting elements within the first service platform's interactive interface, without switching operating scenarios. This effectively balances ease of operation with process continuity, enabling efficient collaborative computation of enterprise-owned data with external partners and third-party data in digital marketing scenarios. This leads to precise and efficient streaming, improving user experience.

[0055] The following describes an embodiment of the target object acquisition method provided in one or more embodiments of this specification.

[0056] In one possible implementation, the first service platform can be a local marketing platform configured with a local database, a visual interactive interface (such as a data canvas), an SQL translation engine, and a multi-party computation engine.

[0057] The local database primarily stores the enterprise's own first-party data, including user tags, behavioral data, and pre-defined object identifier lists. It can efficiently respond to query requests from the local user base, providing compliant data support for local data filtering and computation. The visually intuitive interface integrates data source components (including at least one local data source component and at least one external data source component), preset processing configuration components, and other functional components. This allows marketers to execute target object acquisition processes through point-and-click and drag-and-drop operations. The SQL translation engine accurately translates the marketers' actions on the interface into SQL query statements for the local database, driving the local database to complete data filtering and achieving seamless integration between visual operations and underlying data queries. The multi-party computation engine handles computation tasks involving external data sources. When marketers select an external data source component on the interface, the multi-party computation engine takes over the corresponding computation tasks, performing appropriate cross-platform computations or calls based on the type of the external data source component.

[0058] In this way, cross-platform toolchains that rely on manual operation can be automatically integrated into the first service platform, which then handles all the back-end work for the marketers.

[0059] In one possible implementation of step S302, see [reference]. Figure 4 , Figure 4 This is a schematic diagram illustrating an interactive interface provided for one or more embodiments of this specification. Figure 4 As shown, the interactive interface of this first service platform can include a functional component area on the left and an operation display area on the right. The functional component area includes a data source component and a preset processing method configuration component; the operation display area presents the selected components, their logical relationships, and processing methods based on the user's actions, allowing marketing personnel to intuitively view and verify the operation process.

[0060] Furthermore, such as Figure 4 As shown, the data source components in the functional component area mainly include local data source components and external data source components.

[0061] The local data source component is associated with a local database. Each local data source component corresponds to a set of locally stored filter objects (such as pre-selected local audiences, user tag combinations, etc.), which can be quickly accessed via SQL. Marketers can directly select local filter objects by clicking or dragging the local data source component.

[0062] The external data source component is used to associate external filtering objects corresponding to the second service platform. It serves as the visual representation of the external data source in the interactive interface. This external data source component can be a virtual audience component. A virtual audience refers to an external data source audience that is not stored in the local database and cannot be directly queried via SQL. It is not an actual user list, but rather metadata pointing to the external data source, serving as an access point to the external data. In this way, the external data source can be logically encapsulated through metadata, forming an external data source component on the canvas. Marketers can indirectly associate with the corresponding external filtering objects on the second service platform by selecting the external data source component, without switching operating platforms, thus achieving unified visual management of local and external data.

[0063] It should be noted that different virtual groups (external data source components) cannot directly perform collaborative calculations. This is because their corresponding data belong to different external platforms, and there are differences in the underlying architecture and security rules. Direct calculations would face problems such as complex technical integration and difficulty in establishing trust mechanisms.

[0064] In one possible implementation of step S302, before displaying the above-mentioned interactive interface, the first service platform needs to register the external data corresponding to the second service platform as an external data source component.

[0065] For example, the first service platform can obtain the metadata of the second service platform and associate the metadata with the external data source component corresponding to the second service platform.

[0066] The metadata may include the name, description, processing type, interface address, and authentication information of the second service platform. The name of the second service platform is used to identify the data source; the description describes the user characteristics or service attributes corresponding to the data source, such as "highly active user of a certain platform" or "third-party credit score query service"; the processing type indicates the interaction method with the external data source associated with the second service platform, clarifying the collaborative computing mode between local and external data, including privacy-preserving computation and API interface types; the interface address is the network address used to establish a communication connection between the first and second service platforms; and the authentication information verifies the access permissions of the first service platform, such as keys or tokens, to ensure the security and compliance of the data interaction process.

[0067] In one possible implementation, the first service platform would obtain the corresponding metadata based on the cooperation agreement and authorization with the second service platform.

[0068] The acquisition methods can include manual input and standardized access. For example, if the second service platform is a partner data platform, the staff of the first service platform can input metadata based on the information provided by the partner; if the second service platform is a third-party service provider, it can automatically obtain metadata in a standardized format through its open authorized channels.

[0069] Furthermore, the first service platform can create an external data source component corresponding to the second service platform on the interactive interface, and associate the acquired metadata with this component according to preset fields. For example, the "platform name" can be mapped to the component's display name, the "description information" can be associated with the component's floating tooltip text, and "processing type," "interface address," and "authentication information" can be stored as the component's backend configuration parameters. After the first service platform verifies the metadata, it can add the external data source component to the functional component area of ​​the interactive interface for marketers to select and use. In this way, all external data sources and their computing capabilities can be seamlessly embedded into the unified interactive interface of the "data canvas" that marketers are most familiar with, thereby automating complex technical processes.

[0070] The above is an explanation of step S302. The following is a further explanation of step S304.

[0071] In one possible implementation of step S304, the first service platform can respond to the marketer's new canvas operation, such as clicking function buttons like "Create New Targeting Task" or "Create Canvas" on the display interface, to quickly bring up and display the new canvas. Figure 4 The interactive interface shown.

[0072] Furthermore, the first service platform will determine a first object identifier list and characteristic information in response to the user's selection operation of the data source component. For example, in response to the user's selection operation of at least one local data source component, the first object identifier list will be determined; and in response to the user's selection operation of at least one external data source component, the characteristic information will be determined.

[0073] The first object identifier list is used to indicate the first filter object stored locally (i.e., the local filter object), and the feature information is used to indicate the second filter object associated with the second service platform (i.e., the external filter object).

[0074] For example, when a marketer selects one or more local data source components in the functional component area of ​​the interactive interface through click, drag, or other operations, the first service platform will determine the first object identifier list based on the attributes of the local data source components. For example, if the first filter object (local filter object) corresponding to the local data source component selected by the marketer is a pre-selected and stored audience package (such as previously created "highly active users," "newly registered users in October," etc.), the first filter object already has a structured object identifier list. After responding to the selection operation, the first service platform will directly call this structured object identifier list as the first object identifier list. If the first filter object corresponding to the local data source component selected by the marketer is an unfixed tag combination or filter condition (such as dynamic tags such as "users who have made purchases in the last 30 days" or "clicked on an ad for a certain activity"), after responding to the selection operation, the first service platform will query the local database using SQL statements based on the tag combination or filter condition of the first filter object, extract the identifiers (such as object ID, device number, etc.) of all objects that match the tag combination or filter condition, and integrate them to form the first object identifier list.

[0075] When marketers select an external data source component through clicks, drags, or other operations, the first service platform automatically retrieves the metadata associated with that component and extracts the feature information of the corresponding second-filter object (external filter object) based on this metadata. The feature information is essentially a summary of the core attributes of the second-filter object, such as the processing type of the second service platform associated with the second-filter object being privacy-computing, or the description information of the second-filter object being a user with a credit score ≥700, rather than actual user data.

[0076] The above is an explanation of step S304. The following is a further explanation of step S306.

[0077] In one possible implementation of step S306, if the processing type included in the feature information is a privacy computing type, the first service platform can determine the data processing method based on the user's filtering operation of the first preset processing method corresponding to the privacy computing type.

[0078] Among the feature information associated with the external data source component, when the processing type is privacy computing, the first preset processing method refers to the privacy computing method opened by the second service platform for the first computing platform, which may include set operations such as intersection, union, and difference.

[0079] For example, the second service platform will determine the privacy computing methods to be opened to the first computing platform based on its own data security policies and other factors. It may open only one privacy computing method (such as intersection operation) or open multiple privacy computing methods (such as supporting intersection, union and difference calculations at the same time, or supporting both intersection and union operations).

[0080] In one possible implementation, if the second service platform offers multiple privacy-preserving computation methods, the marketer's selection process for the first preset processing method of the external data source component includes two scenarios: First, during the process of the first service platform associating and binding the external data source component with the metadata of the second service platform, the marketer selects one of the multiple privacy-preserving computation methods offered by the second service platform as the first preset processing method for the external data source component based on specific needs, and there is no need to repeat the selection process each time the component is used subsequently; Second, the selection is dynamic during the specific operation stage, that is, after the marketer selects the external data source component in the interactive interface, the first service platform reads the feature information and displays the privacy-preserving computation method (first preset processing method) provided by the second service platform for the marketer to choose from.

[0081] See Figure 5 , Figure 5 This is a schematic diagram of an interactive interface for one or more embodiments of this specification, illustrating an application scenario. Figure 5 In the application scenario shown, the marketer selected local data source component 1 (consumers in the past 30 days) and external data source component 1 (members of platform A). The processing type corresponding to external data source component 1 is privacy calculation type, and the privacy calculation method (first preset processing method) includes intersection, union calculation and difference. The marketer can filter the first preset processing method by clicking, dragging and other operations. The first service platform responds to the aforementioned filtering operation and determines the data processing method according to the privacy calculation method selected by the marketer, which is used for subsequent privacy collaborative calculation between local and external filtered objects.

[0082] In one possible implementation of step S306, if the processing type included in the feature information is an API interface type, the first service platform can determine the data processing method based on the user's filtering operation of the second preset processing method corresponding to the API interface type.

[0083] Among the feature information associated with the external data source component, when the processing type is API interface type, the second preset processing method refers to the API interface query method opened by the second service platform for the first service platform, which may include preset query condition selection, custom query condition input, etc.

[0084] For example, if the second service platform uses a pre-defined query condition selection method for its API interface query to the first service platform, it will pre-set several fixed condition options (such as "credit score ≥ 650", "credit score ≥ 700", "recent 30-day consumption frequency ≥ 5 times") for the open query dimensions (such as credit score, consumption frequency, etc.). After associating metadata, the first service platform will synchronize these pre-defined conditions to the interactive interface for marketers to select, without the need for manual parameter input. If the second service platform uses a custom query condition input method for its API interface query to the first service platform, it will grant flexible configuration permissions for the query dimensions. The interactive interface of the first service platform will provide a condition configuration entry point, allowing marketers to independently select query dimensions (such as "average monthly consumption amount"), set calculation logic (such as ">", "≥", "range", etc.), and input custom thresholds (such as ">2000 yuan", "1000-5000 yuan", etc.) according to specific needs to meet personalized query requirements.

[0085] In addition, the second service platform can also open two API interface query methods at the same time, allowing marketers to flexibly choose according to actual scenarios.

[0086] See Figure 6 , Figure 6 This is a schematic diagram of an interactive interface for another application scenario provided by one or more embodiments of this specification. Figure 6 In the second application scenario shown, the marketer selected local data source component 2 (highly active users) and external data source component 2 (credit-compliant users). The processing type corresponding to external data source component 2 is API interface type, and the API interface query method (second preset processing method) is a preset query condition selection, including two preset query conditions: "650≤credit score≤700" and "credit score>700". The marketer can filter the second preset processing method by selecting these two preset query conditions. The first service platform responds to the aforementioned filtering operation and determines the data processing method according to the query conditions selected by the marketer, which is used to subsequently initiate query requests to the second service platform through the API interface.

[0087] The above is an explanation of step S306. The following is a further explanation of step S308.

[0088] In one possible implementation of step S308, the first service platform can encrypt the first object identifier list based on a preset encryption algorithm corresponding to the privacy computing type to obtain an encrypted first object identifier list; and generate a request instruction based on the encrypted first object identifier list, feature information and data processing method.

[0089] Taking the above application scenario one as an example, the marketer selects in the interactive interface Figure 5 The local data source component 1 (consumers in the past 30 days) and the external data source component 1 (members of platform A, processing type is privacy computation) are shown, and the data processing method is determined to be the intersection operation of the local data source component 1 and the external data source component 1. At this time, the multi-party computation engine of the first service platform is activated. After the multi-party computation engine parses the request as a privacy computation request, it calls the preset encryption algorithm to perform full encryption or digest processing on the first object identifier list to obtain the encrypted first object identifier list.

[0090] The preset encryption algorithm is typically a privacy-complementary algorithm pre-negotiated and determined by the first and second service platforms, such as a hash algorithm or asymmetric encryption algorithm adapted to the Privacy Set Intersection / Union (PSI / PSU) protocol. The PSI / PSU protocol is a cryptographic protocol that allows two or more participants holding their respective sets to compute the intersection / union of their sets without revealing any information about their sets other than the intersection / union. This avoids the leakage of the first object identifier list information and ensures local data privacy and security.

[0091] In practical applications, the PSII / PSU protocol can be adapted based on technologies such as Oblivious Transfer (OT) and obfuscated circuits. This solution does not limit the specific technology route and can be selected according to the specific needs of the scenario.

[0092] Furthermore, after the encryption process is completed, the multi-party computing engine will, according to the preset request instruction protocol format, structurally integrate the three types of information: the encrypted first object identifier list, the feature information of the second filtered object, and the data processing method, to generate a request instruction.

[0093] For example, the multi-party computing engine can first write the description information, interface address and authentication information of the second service platform in the feature information into the instruction header, then fill the encrypted first object identifier list into the data payload area of ​​the instruction, and finally convert the data processing method into standardized parameters and write them into the rule configuration area to form a collaborative computing request instruction that can be directly recognized and executed by the second service platform.

[0094] In one possible implementation of step S308, the first service platform can generate a request instruction based on the query conditions, the first object identifier list, and the feature information included in the data processing method.

[0095] Taking the second application scenario above as an example, the marketer selects in the interactive interface Figure 6The local data source component 2 (highly active users) and external data source component 2 (credit-compliant users, processing type is API interface type) are shown, and the data processing method includes the query condition of "credit score > 700" for users. At this time, the multi-party computing engine of the first service platform is activated. After parsing that this request is an API interface type query, the multi-party computing engine will retrieve the first object identifier list corresponding to the local data source component 1 in the local database and traverse the identifier of each object in the first object identifier list.

[0096] Furthermore, the multi-party computation engine can anonymize and desensitize the identifiers of each object in the first object identifier list, such as converting them into irreversible temporary identifiers, without carrying the original object's information. This prevents the leakage of information from the first object identifier list, ensuring local data privacy and security.

[0097] Furthermore, the multi-party computing engine will structure and integrate the three types of information—query conditions, anonymized first object identifier list, and feature information—in accordance with the request protocol format preset by the API interface of the second service platform, and generate request instructions.

[0098] For example, the multi-party computing engine can first write the description information, interface address, and authentication information of the second service platform from the feature information into the instruction header; then, it can fill the population scope parameter area of ​​the instruction with the anonymized first object identifier list according to the data format required by the interface, thus clarifying the base object set for this query; finally, it can convert the query conditions included in the data processing method into key-value pair parameters that the API interface can recognize (such as {"user_type":"high_active","credit_score":">700"}), and write them into the query rule parameter area of ​​the instruction, forming an API anonymous query request instruction that can be directly recognized and executed by the second service platform.

[0099] Furthermore, in one possible implementation of step S308, the first service platform sends the aforementioned request instruction to the second service platform to instruct the second service platform to obtain the second object identifier list of the second filtered object based on the feature information, and to generate a processing result based on the first object identifier list and the second object identifier list through data processing.

[0100] Taking the above application scenario one as an example, after the second service platform receives the collaborative computing request instruction sent by the first service platform, it will parse the request instruction and perform the following operations: Obtain the second object identifier list: Based on the feature information in the request instruction header, the second service platform matches the corresponding computing environment and retrieves the second object identifier list (i.e., the user identifier set of the second filter object) associated with the external data source component 1.

[0101] Performing privacy computation: The second service platform can use an encryption algorithm pre-negotiated with the first service platform to encrypt / digest the second object identifier list. Then, following the data processing method in the request instructions (performing an intersection operation on local data source component 1 and external data source component 1), it performs privacy computation on the encrypted object identifier list based on the PSI protocol. Alternatively, the second service platform can perform privacy computation on the encrypted first and second object identifier lists based on the PSI protocol, without exposing its full data, following the data processing method in the request instructions. Privacy computation refers to a series of information technologies that enable cross-platform data analysis and computation while protecting the data (i.e., the first and second object identifier lists) of both the first and second service platforms from external disclosure, ensuring that data is "usable but not visible" during transmission.

[0102] Generate processing result: The second service platform takes the object identifier list (encrypted state) which is the intersection of the first object identifier list and the second object identifier list obtained by calculation as the processing result and returns it to the first service platform.

[0103] The above is an explanation of the processing results generated by the second service platform for scenario one.

[0104] Taking the second application scenario above as an example, after the second service platform receives the API anonymous query request instruction sent by the first service platform, it will parse the request instruction and perform the following operations: Obtain the second object identifier list: The second service platform retrieves the second object identifier list associated with the external data source component 2 (i.e., the user identifier set of the second filter object, which is associated with external data such as credit score) based on the feature information (including API interface address, identity authentication information, etc.) in the request instruction header.

[0105] Perform anonymous query: The second service platform, according to the data processing method in the instruction (querying users with "credit score > 700" in "highly active users"), matches the anonymized first object identifier list (i.e., the list of highly active users) with the second object identifier list (i.e., the list of users with credit score > 700), and verifies whether the objects in the first object identifier list meet the query conditions.

[0106] Generate processing results: The second service platform returns the matching results (i.e. whether each object in the first object identifier list matches the query) as the processing results to the first service platform.

[0107] The above is an explanation of step S308. The following is a further explanation of step S3010.

[0108] In one possible implementation of step S3010, the first service platform can directly use the processing result as the target object set.

[0109] Taking the above application scenario as an example, after the second service platform returns the processing result (a list of intersection object identifiers in encrypted state) to the first service platform, the first service platform will call the decryption algorithm corresponding to the privacy computing protocol to decrypt the processing result, use the decrypted processing result as the target object set, and display the information of the target object set in the interactive interface. This may include the number of target objects (e.g., matching 10,000 intersection users) and feature tags (e.g., aggregate attributes such as "consumption in the last 30 days, member of platform A", etc.) for marketers to view.

[0110] In one possible implementation of step S3010, the first service platform can obtain the target object identifier that meets the query conditions from the first object identifier list based on the processing result, and determine the target object set based on the target object identifier.

[0111] Taking the above application scenario two as an example, after the second service platform returns the processing result (i.e. whether the objects in the first object identifier list meet the query conditions) to the first service platform, the first service platform will filter out the target object identifiers that meet the query conditions in the first object identifier list according to the processing result, and form a target object set based on these target object identifiers; at the same time, the information of the target object set can be displayed in the interactive interface, which may include the number of target objects (e.g., matching 8,000 users of the query conditions) and feature tags (e.g., aggregate attributes such as "high activity, credit score > 700") for marketers to view.

[0112] The above is an explanation of step S3010.

[0113] Furthermore, in one possible implementation, the first service platform can store the target object collection locally and generate a local data source component corresponding to the target object collection.

[0114] For example, after obtaining the target object set, the first service platform can automatically store the target object set as a new local filter object (the first filter object) in the local database. Furthermore, in the functional component area of ​​the interactive interface, a corresponding local data source component can be created for the new local filter object, allowing marketers to directly call it in the data canvas for subsequent operations such as target customer group selection, combination calculations, and marketing campaign outreach.

[0115] See Figure 7 , Figure 7 This is a schematic diagram illustrating another application scenario provided by one or more embodiments of this specification.

[0116] like Figure 7As shown, when marketers select target customer groups on the interactive interface of the first service platform, they can directly select local data source component 1 (corresponding to the first local filter object 1), local data source component 2 (corresponding to the first local filter object 2), and external data source component 3 (corresponding to the second filter object associated with the second service platform, with processing type either privacy computing or API interface), and select the processing method for the three data source components. At this time, the first service platform will first perform local operations (such as intersection / union operations) on local data source component 1 and local data source component 2 through the SQL translation engine to obtain a preliminary local object set 4; subsequently, the multi-party computing engine will generate a request instruction based on the feature information of local object set 4, external data source component 3, and preset processing method, and send it to the second service platform, instructing the second service platform to perform collaborative operations, obtain the processing result, and return it to the first service platform.

[0117] Specifically, if the processing type of the feature information of the second filtered object corresponding to the external data source component is privacy computation, the multi-party computation engine of the first service platform first encrypts the identifier list of the local object set 4, and then sends the encrypted identifier list to the second service platform through a request command, instructing the second service platform to perform privacy computation based on protocols such as PSI / PSU. If the processing type of the feature information of the second filtered object corresponding to the external data source component is API interface, the multi-party computation engine of the first service platform first anonymizes and desensitizes the identifier list of the local object set 4, and then sends the anonymized identifier list of the local object set 4 and the query conditions to the second service platform through a request command, instructing the second service platform to perform anonymous query.

[0118] Furthermore, after receiving the processing results returned by the second service platform, the first service platform can obtain a set of target objects for display, or store it in the local database as a new local filter object and generate a corresponding local data source component. In addition, the set of target objects can be directly exported to the delivery platform for marketing outreach, and can also be used as a new local data source component to continue to perform calculations with other components in the data canvas.

[0119] To present the interaction process between the first service platform and the second service platform in the two collaborative computing scenarios of privacy computing type and API interface type more clearly and intuitively, the specific execution process of the two scenarios is explained below with the help of a sequence diagram.

[0120] See Figure 8 , Figure 8 This specification provides a flowchart illustrating a method for obtaining a target object in a privacy computing scenario, as shown in one or more embodiments. It depicts the collaborative computation interaction between a first service platform and a second service platform when the processing type is privacy computing. Figure 8 As shown, it mainly includes the following steps S801 to S808.

[0121] Step S801: In response to the user's selection operation of the data source component, the first service platform determines the first object identifier list and feature information containing privacy computing type; Step S802: The first service platform responds to the user's filtering operation for privacy computing type and determines the corresponding PSI / PSU operation type data processing method; Step S803: The first service platform uses a preset encryption algorithm to encrypt the first object identifier list to obtain the encrypted first object identifier list; Step S804: The first service platform generates a request instruction based on the encrypted first object identifier list, feature information, and data processing method; Step S805: The first service platform sends the request instruction to the second service platform; Step S806: The second service platform receives the request instruction, obtains the second object identifier list based on the feature information, performs privacy calculations through the PSI / PSU protocol, and obtains the encrypted intersection result; Step S807: The second service platform returns the encrypted intersection result to the first service platform; Step S808: The first service platform decrypts the received intersection result to obtain the target object set.

[0122] See Figure 9 , Figure 9 This document provides a flowchart illustrating a method for obtaining a target object in an API interface query scenario, as shown in one or more embodiments of this specification. It depicts the collaborative computation and interaction process between a first service platform and a second service platform when the processing type is API interface. Figure 9 As shown, it mainly includes the following steps S901 to S908.

[0123] Step S901: In response to the user's selection operation of the data source component, the first service platform determines the first object identifier list and feature information containing the API interface type; Step S902: The first service platform responds to the user's filtering operation on the API interface type and determines the API query data processing method containing the query conditions; Step S903: The first service platform performs anonymization and desensitization processing on the first object identifier list to obtain the anonymized first object identifier list; Step S904: Generate a request instruction based on the anonymized first object identifier list, feature information, and data processing method containing query conditions; Step S905: The first service platform sends the request instruction to the second service platform; Step S906: The second service platform receives the request instruction, obtains the second object identifier list corresponding to the second filter object based on the feature information, and performs an anonymous matching query based on the anonymized first object identifier list and the second object identifier list through data processing to obtain a Boolean result; Step S907: The second service platform returns the Boolean result to the first service platform; Step S908: The first service platform filters out target object identifiers that meet the query conditions from the first object identifier list based on the Boolean result, and generates a target object set based on the identifier.

[0124] Using the aforementioned target audience acquisition method, marketers can easily select target customer groups through simple drag-and-drop and check-and-click operations on the interactive interface of the first service platform. This allows for collaborative computation of local and external data, quickly generating a set of target audiences that meets their needs. The entire process is completed through a visual interactive interface, eliminating the need for manual integration of scattered data across platforms, significantly lowering the technical barrier to target customer group selection. Furthermore, relying on pre-defined privacy computing protocols and anonymous query mechanisms, cross-platform data matching can be completed without exposing local or external data, ensuring data security and compliance while improving the efficiency and accuracy of target customer group selection.

[0125] The target object acquisition scheme provided in one or more embodiments of this specification can not only support direct calculations between local data sources, but also support collaborative calculations between local data sources and external data sources, thus meeting the diverse target customer group selection needs of marketers.

[0126] It's important to note that during multi-source data collaborative computation, even in scenarios involving one local data source and multiple external data sources, marketers don't need to perform complex operations. Instead, the multi-party computation engine of the first service platform automatically breaks down and executes the computation process. Specifically, the multi-party computation engine can first perform collaborative computation between local data and one of the external data sources, then use the generated intermediate results as new local data to continue computation with the next external data source until the final set of target objects is obtained. The entire process is completely transparent to the user of the front-end interface, allowing marketers to see both the computation process and the results, or simply display the final results.

[0127] Meanwhile, by storing the calculation results (target object set) of multi-source data in a local database, it can be ensured that all calculation results involving external data sources are stored as local data in the form of a clear object identifier list. This not only supports the customer group deployment needs of subsequent marketing activities, but also avoids the risk of secondary data leakage from the data flow link level, taking into account both the security of data processing and the feasibility of practical application.

[0128] Furthermore, for scenarios involving direct collaborative computation between multiple external data sources, it's crucial to understand that: since different external data sources belong to different data providers, their underlying technical architectures and data security domain rules differ significantly. Direct computation may face extremely high technical integration complexity and may also present challenges such as difficulty in establishing trust mechanisms among multiple data providers and managing data compliance risks. Therefore, direct collaborative computation between multiple external data sources is not feasible, a significant constraint on ensuring data security and computational feasibility. Based on this, this solution adopts a "local relay" model, using a local data source as a hub to complete indirect collaborative computation between multiple external data sources. Direct collaboration among multiple external data sources, if desired, must be implemented only after overcoming technical and procedural obstacles such as unifying multi-party protocols, establishing a secure trust system, and implementing compliance review mechanisms.

[0129] The above describes the target object acquisition scheme provided by one or more embodiments of this specification.

[0130] It is understood that the above embodiments are merely examples, and modifications can be made to the above embodiments in actual implementation. Those skilled in the art will understand that any modifications to the above embodiments that do not require creative effort fall within the protection scope of one or more embodiments of this specification, and will not be described again in the embodiments.

[0131] Based on the same inventive concept, one or more embodiments of this specification also provide a target object acquisition device. Since the principle of the target object acquisition device in solving the problem is similar to that of the aforementioned target object acquisition method, the implementation of the target object acquisition device can refer to the implementation of the aforementioned target object acquisition method, and the repeated parts will not be described again.

[0132] See Figure 10 , Figure 10 This is a structural block diagram of a target object acquisition device provided for one or more embodiments of this specification. Figure 10 As shown, the target object acquisition device 10 applied to the first service platform may include: a display module 11, a first determination module 12, a second determination module 13, a generation module 14, and a third determination module 15. Among them, The display module 11 is used to display the interactive interface. The interactive interface is equipped with a data source component, which includes a local data source component and an external data source component. The external data source component is associated with the metadata of the second service platform, and the metadata includes the processing type.

[0133] The first determining module 12 is used to determine a first object identifier list and feature information in response to the user's selection operation on the data source component; wherein, the first object identifier list is used to indicate a first filter object stored locally, and the feature information is used to indicate a second filter object associated with the second service platform, and the feature information includes processing type.

[0134] The second determining module 13 is used to determine the data processing method corresponding to the feature information in response to the user's filtering operation on the processing type.

[0135] The generation module 14 is used to generate a request instruction based on the data processing method and send the request instruction to the second service platform to instruct the second service platform to obtain the second object identifier list of the second filter object based on the feature information, and to generate a processing result based on the first object identifier list and the second object identifier list through the data processing method.

[0136] The third determining module 15 is used to determine the target object set based on the processing results received from the second service platform.

[0137] In one possible implementation, the second determining module 13 is used to determine the data processing method corresponding to the feature information in response to the user's filtering operation on the processing type, including: The second determining module 13 is used to determine the data processing method based on the user's filtering operation of the first preset processing method corresponding to the privacy computing type when the processing type included in the feature information is a privacy computing type.

[0138] In one possible implementation, the generation module 14 is used to generate request instructions based on the data processing method, including: The generation module 14 is used to encrypt the first object identifier list based on the preset encryption algorithm corresponding to the privacy computing type, so as to obtain the encrypted first object identifier list; and to generate a request instruction based on the encrypted first object identifier list, feature information and data processing method.

[0139] In one possible implementation, the third determining module 15 is used to determine the target object set based on the processing results received from the second service platform, including: The third determining module 15 is used to treat the processing results as a set of target objects.

[0140] In one possible implementation, the second determining module 13 is used to determine the data processing method corresponding to the feature information in response to the user's filtering operation on the processing type, including: The second determining module 13 is used to determine the data processing method based on the user's filtering operation of the second preset processing method corresponding to the API interface type when the processing type included in the feature information is an API interface type.

[0141] In one possible implementation, the generation module 14 is used to generate request instructions based on a preset processing method, including: The generation module 14 is used to generate a request instruction based on the query conditions, the first object identifier list, and the feature information included in the data processing method.

[0142] In one possible implementation, the third determining module 15 is used to determine the target object set based on the processing results received from the second service platform, including: The third determining module 15 is used to obtain target object identifiers that meet the query conditions from the first object identifier list based on the processing results; and to determine the target object set based on the target object identifiers.

[0143] One possible implementation is, such as Figure 10 As shown, the target object acquisition device 10 also includes an acquisition module 16 and an association module 17; The acquisition module 16 is used to acquire the metadata of the second service platform, wherein the metadata includes the name, description information, interface address and authentication information of the second service platform; The association module 17 is used to associate metadata with the external data source component corresponding to the second service platform.

[0144] One possible implementation is, such as Figure 8 As shown, the target object acquisition device 10 also includes a storage module 18; The storage module 18 is used to store the target object collection locally and generate a local data source component corresponding to the target object collection.

[0145] In one possible implementation, the first determining module 12 is used to determine a first object identifier list and characteristic information in response to a user's selection operation on a data source component, including: The first determining module 12 is used to determine a first object identifier list in response to a user's selection operation for at least one local data source component; and to determine feature information in response to a user's selection operation for at least one external data source component.

[0146] Based on the same inventive concept, one or more embodiments of this specification also provide an electronic device.

[0147] See Figure 11 , Figure 11 This is a structural block diagram of an electronic device provided for one or more embodiments of this specification. Figure 11 As shown, the electronic device 20 may include a processor 21 and a memory 22; the memory 22 may be coupled to the processor 21. It is worth noting that... Figure 11 This is an example; other types of structures can also be used to supplement or replace this structure to achieve telecommunications functions or other functions.

[0148] In one possible implementation, the functionality of the target object acquisition device 10 can be integrated into the processor 21. The processor 21 can be configured to perform the following operations: The interactive interface is displayed and includes a data source component, which includes a local data source component and an external data source component. The external data source component is associated with the metadata of the second service platform, and the metadata includes the processing type. In response to the user's selection operation of the data source component, a first object identifier list and feature information are determined; wherein, the first object identifier list is used to indicate the first filter object stored locally, and the feature information is used to indicate the second filter object associated with the second service platform, and the feature information includes the processing type; In response to the user's filtering operation on the processing type, determine the data processing method corresponding to the feature information; A request instruction is generated based on the data processing method and sent to the second service platform to instruct the second service platform to obtain the second object identifier list of the second filter object based on the feature information, and to generate a processing result based on the first object identifier list and the second object identifier list through the data processing method. The target object set is determined based on the processing results received from the second service platform.

[0149] In another possible implementation, the target object acquisition device 10 can be configured separately from the processor 21. For example, the target object acquisition device 10 can be configured as a chip connected to the processor 21, and the target object acquisition operation applied to the first service platform can be implemented through the control of the processor 21.

[0150] In one possible implementation, the plurality of processors 21 may be processors deployed on the same device. For example, the electronic device may be a high-performance device composed of multiple processors, and the plurality of processors 21 may be processors configured on the high-performance device.

[0151] In another possible implementation, the aforementioned multiple processors 21 may also be processors deployed on different devices. For example, the aforementioned electronic device may be a server cluster, and the aforementioned multiple processors 21 may be processors on different servers in the server cluster.

[0152] Furthermore, in some alternative implementations, the electronic device 20 may also include: a communication module, an input unit, an audio processor, a display, a power supply, etc. It is worth noting that the electronic device 20 is not necessarily required to include these components. Figure 11 All components shown; in addition, electronic device 20 may also include Figure 11 For components not shown, please refer to existing technologies.

[0153] In some alternative implementations, the processor 21, sometimes also referred to as a controller or operating control, may include a microprocessor or other processor device and / or logic device, which receives input and controls the operation of various components of the electronic device 20.

[0154] The memory 22 may be, for example, one or more of a cache, flash memory, hard drive, removable media, volatile memory, non-volatile memory, or other suitable devices. It may store information related to the target object acquisition device 10, and may also store programs for executing that information. The processor 21 may execute the program stored in the memory 22 to perform information storage or processing, etc.

[0155] An input unit can provide input to the processor 21. This input unit may be, for example, a button or touch input device. A power supply can be used to provide power to the electronic device 20. A display can be used to display images and text, etc. This display may be, for example, an LCD display, but is not limited to this.

[0156] The memory 22 can be a solid-state memory, such as a read-only memory (ROM), random access memory (RAM), a SIM card, etc. It can also be a memory that retains information even when power is off, can be selectively erased, and contains more data; examples of this type of memory are sometimes referred to as EPROMs, etc. The memory 22 can also be some other type of device. The memory 22 includes a buffer memory (sometimes called a buffer). The memory 22 may include an application / function storage unit for storing application programs and function programs or processes for executing the operation of the electronic device 20 via the processor 21.

[0157] The memory 22 may also include a data storage unit for storing data, such as contacts, digital data, pictures, sounds, and / or any other data used by the electronic device. The driver storage unit of the memory 22 may include various drivers for the computer device for communication functions and / or for performing other functions of the computer device (such as messaging applications, address book applications, etc.).

[0158] The communication module is a transmitter / receiver that sends and receives signals via an antenna. The communication module (transmitter / receiver) is coupled to the processor 21 to provide input signals and receive output signals, which can be the same as in a conventional mobile communication terminal.

[0159] Based on different communication technologies, multiple communication modules can be set up in the same computer device, such as cellular network module, Bluetooth module and / or wireless LAN module.

[0160] One or more embodiments of this specification also provide a computer-readable storage medium capable of implementing all steps of the target object acquisition method applied to the first service platform in the above embodiments. The computer-readable storage medium stores a computer program that, when executed by a processor, implements all steps of the target object acquisition method in the above embodiments. For example, when the processor executes the computer program, it implements the following steps: The interactive interface is displayed and includes a data source component, which includes a local data source component and an external data source component. The external data source component is associated with the metadata of the second service platform, and the metadata includes the processing type. In response to the user's selection operation of the data source component, a first object identifier list and feature information are determined; wherein, the first object identifier list is used to indicate the first filter object stored locally, and the feature information is used to indicate the second filter object associated with the second service platform, and the feature information includes the processing type; In response to the user's filtering operation on the processing type, determine the data processing method corresponding to the feature information; A request instruction is generated based on the data processing method and sent to the second service platform to instruct the second service platform to obtain the second object identifier list of the second filter object based on the feature information, and to generate a processing result based on the first object identifier list and the second object identifier list through the data processing method. The target object set is determined based on the processing results received from the second service platform.

[0161] While one or more embodiments of this specification provide the operational steps of the methods described in the embodiments or flowcharts, more or fewer operational steps may be included based on conventional or non-inventive labor. The order of steps listed in the embodiments is merely one possible execution order among many and does not represent the only possible execution order. In actual device or client product execution, the methods shown in the embodiments or drawings can be executed sequentially or in parallel (e.g., in a parallel processor or multi-threaded processing environment).

[0162] Those skilled in the art will understand that the embodiments of this specification can be provided as methods, apparatus, or computer program products. Therefore, the embodiments of this specification can take the form of entirely hardware embodiments, entirely software embodiments, or embodiments combining software and hardware aspects. Furthermore, one or more embodiments of this specification can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0163] This specification describes one or more embodiments of a method, apparatus, and computer program product according to one or more embodiments of this specification with reference to flowchart illustrations and / or block diagrams. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0164] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0165] These computer program instructions can also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0166] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the device and system embodiments are basically similar to the method embodiments, so the descriptions are relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0167] In this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, without necessarily requiring or implying any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Those skilled in the art will understand the specific meaning of the above terms in one or more embodiments of this specification, depending on the specific circumstances.

[0168] It should be noted that, unless otherwise specified, one or more embodiments and features thereof in this specification can be combined with each other. This specification is not limited to any single aspect, nor to any single embodiment, nor to any combination and / or substitution of such aspects and / or embodiments. Furthermore, each aspect and / or embodiment of one or more embodiments of this specification can be used alone or in combination with one or more other aspects and / or embodiments thereof.

[0169] The above embodiments are only used to illustrate the technical solutions of one or more embodiments of this specification, and are not intended to limit them. Although one or more embodiments of this specification have been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of one or more embodiments of this specification, and they should all be covered within the scope of the claims and the specification of one or more embodiments of this specification.

[0170] The foregoing description of one or more embodiments of this specification has been provided in conjunction with optional implementation methods. However, these embodiments are merely exemplary and serve only an illustrative purpose. Based on this, various substitutions and modifications can be made to one or more embodiments of this specification, all of which fall within the protection scope of one or more embodiments of this specification.

[0171] Finally, it should be noted that the information and data of the target customer groups involved in one or more embodiments of this specification are all processed in strict accordance with the requirements of laws and regulations, following the principles of legality, legitimacy, and necessity, based on the reasonable purpose of the application scenario, and are personal information actively provided by users or generated due to the use of products / services, as well as personal information obtained with user authorization.

[0172] This specification and one or more embodiments place great emphasis on the security of users' personal information and have adopted reasonable and feasible security protection measures that comply with industry standards to protect users' information and prevent unauthorized access, disclosure, use, modification, damage or loss of personal information.

Claims

1. A method for obtaining a target object, characterized in that, Applied to a first service platform, the method includes: The interactive interface is provided with a data source component, which includes a local data source component and an external data source component. The external data source component is associated with the metadata of the second service platform, and the metadata includes the processing type. In response to a user's selection operation on the data source component, a first object identifier list and feature information are determined; wherein, the first object identifier list is used to indicate a first filter object stored locally, and the feature information is used to indicate a second filter object associated with the second service platform, and the feature information includes the processing type; In response to the user's filtering operation on the processing type, determine the data processing method corresponding to the feature information; A request instruction is generated based on the data processing method and sent to the second service platform to instruct the second service platform to obtain a second object identifier list of the second filtered object based on the feature information, and to generate a processing result based on the first object identifier list and the second object identifier list through the data processing method. The target object set is determined based on the processing results received from the second service platform.

2. The target object acquisition method according to claim 1, characterized in that, The step of determining the data processing method corresponding to the feature information in response to the user's filtering operation on the processing type includes: If the processing type included in the feature information is a privacy computing type, the data processing method is determined based on the user's filtering operation of the first preset processing method corresponding to the privacy computing type.

3. The target object acquisition method according to claim 2, characterized in that, The generation of request instructions based on the data processing method includes: Based on the preset encryption algorithm corresponding to the privacy computing type, the first object identifier list is encrypted to obtain the encrypted first object identifier list. The request instruction is generated based on the encrypted first object identifier list, the feature information, and the data processing method.

4. The target object acquisition method according to claim 2, characterized in that, The step of determining the target object set based on the processing result received from the second service platform includes: The processing result is used as the target object set.

5. The target object acquisition method according to claim 1, characterized in that, The step of determining the data processing method corresponding to the feature information in response to the user's filtering operation on the processing type includes: When the processing type included in the feature information is an API interface type, the data processing method is determined based on the user's filtering operation of the second preset processing method corresponding to the API interface type.

6. The target object acquisition method according to claim 5, characterized in that, The generation of request instructions based on the preset processing method includes: The request instruction is generated based on the query conditions, the first object identifier list, and the feature information included in the data processing method.

7. The target object acquisition method according to claim 6, characterized in that, The step of determining the target object set based on the processing result received from the second service platform includes: Based on the processing result, the target object identifier that meets the query conditions is obtained from the first object identifier list; The target object set is determined based on the target object identifier.

8. The target object acquisition method according to claim 1, characterized in that, Before displaying the interactive interface, the method further includes: Obtain the metadata of the second service platform, wherein the metadata also includes the name, description, interface address and authentication information of the second service platform; The metadata is associated with the external data source component corresponding to the second service platform.

9. The target object acquisition method according to claim 1, characterized in that, After determining the target object set, the method further includes: The target object collection is stored locally, generating a local data source component corresponding to the target object collection.

10. An electronic device, characterized in that, The electronic device includes: Memory, used to store computer program products; A processor is configured to execute a computer program product stored in the memory, wherein when the computer program product is executed, it implements the target object acquisition method according to any one of claims 1 to 9.

Images