Data processing method, device, equipment and storage medium

Abstract

The embodiments of the present application relate to the field of computer technology and disclose a data processing method, apparatus, device and storage medium, the method comprising: in response to a trigger event for analyzing a sharing operation performed on a target resource link, displaying an analysis setting interface, and obtaining a data identifier of source data of the target resource link, and an event identifier corresponding to the sharing operation performed on the target resource link; obtaining the source data of the target resource link according to the data identifier, and performing data screening processing on the obtained source data according to the event identifier to obtain analysis reference data of the target resource link; analyzing and processing the analysis reference data to obtain multiple user sets; obtaining feedback information generated after sharing the target resource link, the feedback information being used to determine a target user set from the multiple user sets when sharing other resource links to be shared, and quickly disseminating other resource links with reference to the dissemination of the target resource link.

Description

Technical Field

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

[0002] In daily life, by analyzing and processing different data, we can understand the patterns or connections between them. For example, in scenarios such as event planning or product sales, we hope to analyze the intensity and effectiveness of the event or product's dissemination, so that in the subsequent event planning or product sales process, we can adjust the dissemination strategy based on the analysis results to maximize the dissemination effect. However, the current analysis of the dissemination of a particular event or product is limited to a simple statistics of data such as the total number of shares of the target resource link of the event or product and the total number of users who shared it. This only provides a simple analysis of the dissemination of the current event or product, and has a very limited beneficial impact on the dissemination of subsequent events or products. Summary of the Invention

[0003] The embodiments of the present application provide a data processing method, apparatus, device, and storage medium that can understand the propagation status of target resource links and quickly propagate other resource links.

[0004] In one aspect, an embodiment of the present application provides a data processing method, comprising:

[0005] In response to a trigger event for analyzing a sharing operation performed on a target resource link, displaying an analysis setting interface, and obtaining from the analysis setting interface a data identifier of source data of the target resource link and an event identifier corresponding to the sharing operation performed on the target resource link;

[0006] Acquiring source data of the target resource link according to the data identifier, and performing data screening processing on the acquired source data according to the event identifier to obtain analytical reference data of the target resource link, wherein the analytical reference data records multiple user identifiers and sharing relationships between different users regarding the target resource link;

[0007] Analyzing and processing the analysis reference data to divide users corresponding to user identifiers recorded in the analysis reference data into different user sets to obtain a plurality of user sets;

[0008] Acquire feedback information generated by each user who shares the target resource link after sharing the target resource link, and use the feedback information to determine a target user set from the multiple user sets when sharing other resource links to be shared, wherein the other resource links and the target resource link indicate the same resource type.

[0009] In one aspect, an embodiment of the present application provides a data processing device, comprising:

[0010] a display unit, configured to display an analysis setting interface in response to a triggering event for analyzing a sharing operation performed on a target resource link;

[0011] an acquiring unit, configured to acquire, from the analysis setting interface, a data identifier of the source data of the target resource link and an event identifier corresponding to a sharing operation performed on the target resource link;

[0012] The acquisition unit is further configured to acquire the source data of the target resource link according to the data identifier;

[0013] a processing unit configured to perform data screening processing on the acquired source data according to the event identifier to obtain analytical reference data of the target resource link, wherein the analytical reference data records multiple user identifiers and sharing relationships between different users regarding the target resource link;

[0014] The processing unit is further configured to analyze and process the analysis reference data to divide users corresponding to user identifiers recorded in the analysis reference data into different user sets to obtain a plurality of user sets;

[0015] The acquisition unit is further used to obtain feedback information generated by each user who shares the target resource link after sharing the target resource link, and the feedback information is used to determine the target user set when sharing other resource links to be shared from the multiple user sets, wherein the other resource links and the target resource link indicate the same resource type.

[0016] In one aspect, an embodiment of the present application provides a data processing device, characterized in that the data processing device includes an input interface and an output interface, and further includes:

[0017] a processor adapted to implement one or more instructions; and

[0018] A computer storage medium stores one or more instructions, wherein the one or more instructions are suitable for being loaded by the processor and executing the above-mentioned data processing method.

[0019] On the one hand, an embodiment of the present application provides a computer storage medium, characterized in that computer program instructions are stored in the computer storage medium, and when the computer program instructions are executed by a processor, they are used to execute the above-mentioned data processing method.

[0020] On the one hand, an embodiment of the present application provides a computer program product or a computer program, wherein the computer program product or the computer program includes computer instructions, and the computer instructions are stored in a computer-readable storage medium; a processor of a data processing device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions. When the computer instructions are executed by the processor, they are used to execute the above-mentioned data processing method.

[0021] In an embodiment of the present application, the data processing device first obtains the data identifier of the source data of the target resource link and the event identifier corresponding to the sharing operation performed on the target resource link in the analysis setting interface; then the source data of the target resource link is obtained according to the data identifier, and the obtained source data is filtered and processed according to the event identifier to obtain analysis reference data of the target resource link, and the analysis reference data records multiple user identifiers and the sharing relationship between different users regarding the target resource link; further, the data processing device analyzes and processes the analysis reference data to divide the users corresponding to the user identifiers recorded in the analysis reference data into different user sets to obtain multiple user sets; thereafter, the data processing device obtains feedback information generated by each user who shares the target resource link after sharing the target resource link, and the feedback information is used to collect information from multiple users. A target user set is determined in the set when sharing other resource links to be shared, wherein the resource types indicated by the other resource links and the target resource link are the same; by obtaining the source data of the target resource link, analysis reference data of the target resource link can be further obtained, and the analysis reference data includes multiple user identifiers and sharing relationships between different users regarding the target resource link; and based on the analysis reference data, users corresponding to the user identifiers recorded in the analysis reference data are divided into multiple user sets; finally, feedback information on the target resource link can be obtained, so that users can understand the dissemination status of the target resource link; at the same time, the target user set can also be obtained from multiple user sets based on the feedback information, so that other resource links of the same type that need to be quickly disseminated can be quickly disseminated through the target user set, thereby achieving a good publicity effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0023] Figure 1 It is a data table of source data of a target resource link provided by an embodiment of the present application;

[0024] Figure 2 This is a flow chart of a data processing method provided in an embodiment of the present application;

[0025] Figure 3a This is an interface diagram of an analysis setting interface provided in an embodiment of the present application;

[0026] Figure 3b Schematic diagram of a switching analysis setting interface provided in an embodiment of the present application;

[0027] Figure 4a This is a schematic diagram of a trigger display analysis setting interface provided in an embodiment of the present application;

[0028] Figure 4b Schematic diagram of another trigger display analysis setting interface provided in an embodiment of the present application;

[0029] Figure 5 This is a schematic diagram of a method for obtaining multiple user sets through division provided in an embodiment of the present application;

[0030] Figure 6 This is a schematic diagram of processing source data linked to a target resource provided by an embodiment of the present application;

[0031] Figure 7 This is a flow chart of another data processing method provided in an embodiment of the present application;

[0032] Figure 8a This is a schematic diagram of the change of feedback information of a target resource link over time provided by an embodiment of the present application;

[0033] Figure 8b This is a comparison diagram of feedback information of different resource links provided in an embodiment of the present application;

[0034] Figure 9 This is an interface diagram of a collection query interface provided in an embodiment of the present application;

[0035] Figure 10a This is a schematic diagram of triggering the display of user information of a target user set provided by an embodiment of the present application;

[0036] Figure 10b This is a schematic diagram of displaying a target user identification sharing relationship in a sharing relationship display area provided by an embodiment of the present application;

[0037] Figure 11a This is a schematic diagram of an imported target identifier set provided in an embodiment of the present application;

[0038] Figure 11bThis is a schematic diagram of triggering user profile analysis of users corresponding to a target identification set provided by an embodiment of the present application;

[0039] Figure 12 is a structural diagram of a data processing device provided in an embodiment of the present application;

[0040] Figure 13 It is a structural diagram of a data processing device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0041] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0042] Artificial Intelligence (AI) refers to the theories, methods, techniques, and application systems that use digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, to perceive the environment, acquire knowledge, and use that knowledge to achieve optimal results. In other words, AI is a comprehensive technology within computer science that seeks to understand the essence of intelligence and produce new intelligent machines that can respond in a manner similar to human intelligence. AI also involves studying the design principles and implementation methods of various intelligent machines, enabling them to possess the capabilities of perception, reasoning, and decision-making.

[0043] Artificial intelligence technology is an interdisciplinary subject that covers a wide range of fields, including both hardware-level and software-level technologies. Basic artificial intelligence technologies generally include sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technology, operation / interaction systems, mechatronics and other technologies. Artificial intelligence software technology mainly includes computer vision technology, speech processing technology, natural language processing technology and machine learning (ML) / deep learning and other major directions. The embodiments of this application mainly relate to machine learning in artificial intelligence. Machine learning specializes in how computers simulate or implement human learning behavior to acquire new knowledge or skills and reorganize existing knowledge structures to continuously improve their own performance. Based on this, an embodiment of the present application proposes a data processing method, which enables a data processing device to obtain the source data of a target resource link and further obtain analysis reference data of the target resource link, wherein the analysis reference data includes multiple user identifiers and sharing relationships between different users regarding the target resource link; thereby, the data processing device can divide the users corresponding to the user identifiers recorded in the analysis reference data into multiple user sets according to the analysis reference data, and finally obtain feedback information on the target resource link so that users can understand the dissemination status of the target resource link; at the same time, the target user set can also be obtained from multiple user sets based on the feedback information, so that other resource links of the same type that need to be disseminated quickly can be quickly disseminated through the target user set, thereby achieving a good publicity effect.

[0044] Among them, the target resource link is a link pointing to the target resource page, that is, the user can access the target resource page through the target resource link. When the target resource link points to the target web page, the user can access the target web page through the target resource link. When the target resource link points to a page in the application, the page in the application can be accessed through the target resource link. In one embodiment, the data processing device can be a terminal device, which can be any one or more of a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart car-mounted device, and a smart wearable device; in another embodiment, the data processing device can also be a server, which can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms. The data processing device and the server can be directly or indirectly connected in a wired or wireless communication manner, and this application does not limit this.

[0045] In one embodiment, a user can share a target resource link with other users so that other users can access the target resource page through the target resource link. If other users share the target resource link again after receiving it, and share it with other users, then more users will be able to access the target resource page through the target resource link. If different users continue to share the same target resource link to attract more users to visit the target resource page, then the effect of sharing fission can be achieved, and then the target resource page can be quickly spread. For example, if user A shares the target resource link with user B and user C; user B and user C visit the target resource page through the target resource link, and user B shares the target resource link with user D and user E, and user C shares the target resource link with user F and user G, and so on, then the target resource link will spread rapidly among different users at an exponential rate, bringing a large number of visits to the target resource page. Among them, users can share the target resource link based on certain channels. For example, they can share the target resource link with WeChat friends or QQ friends based on WeChat, QQ and other sharing channels in the social ecosystem of WeChat, QQ, etc., or they can share the target resource link through social platforms such as WeChat groups, QQ groups, WeChat Moments, and QQ Space.

[0046] In one embodiment, the source data of the target resource link may include relevant parameters of the access event generated by accessing the target resource page corresponding to the target resource link, relevant parameters of the sharing event generated by sharing the target resource link, relevant parameters of the click target resource link event (or so-called sharing landing page exposure event) generated by accessing the target resource page through the target resource link, relevant parameters of the sharing landing page click event generated by subsequent clicks on the sharing landing page, and other relevant parameters, wherein the sharing landing page is the target resource page entered through the target resource link. The source data of the above-mentioned target resource link includes different related parameters of different events, and the parameter types of different related parameters are different, which can be string type, number type, single-precision floating point type, double-precision floating point type, integer type, etc.; the related parameters of the events corresponding to the source data of the target resource link in different application scenarios may also be different; for example, if in an activity that only wants to carry out promotion, it is not important whether the user accessing the target resource page is a paying user. In this application scenario, the related parameters of the event may not include the related parameter of whether the user is a paying user; but if it is in an activity for selling goods, it will be important whether the user accessing the target resource page is a paying user. In this application scenario, the related parameters of the event will include the related parameter of whether the user is a paying user. The embodiment of the present application uses this application scenario as an example.

[0047] like Figure 1 As shown, a data table of source data of a target resource link provided by an embodiment of the present application, wherein the relevant parameters of the access event include user identification ( Figure 1 Specifically displayed as user id), event time, user attributes, channel type, channel identifier ( Figure 1 Specifically displayed as channel id), sharing user ID_sharing time ( Figure 1 Specifically displayed as sharing user id_sharing time). Among them, the user ID in the access event corresponds to the user who accessed the target resource page. The user ID is an ID used to uniquely mark the user. It can be a phone number or a business number, etc. The sharing user ID_sharing time is the user ID and sharing time of the user shared with him when the user in the access event accesses the target resource page through the target resource link shared to him. The user attributes may include whether it is a new user, whether it is a paid user, whether it is a new paid user, etc. The channel type and sharing user ID_sharing time can be empty; the relevant parameters of the sharing event include user ID, event occurrence time, user attributes, channel type, channel ID, scene, page name, page module, content type, content subtype, content ID ( Figure 1 Specifically displayed as content id), link and extension fields.

[0048] Among them, the user corresponding to the user ID in the sharing event is the user who performed the sharing operation on the target resource link. The user attributes may include whether it is a new user, whether it is a paid user, whether it is a new payment, etc. The user content type can indicate the type of content of the target resource link, such as a product, a store or a group store type. The content identifier is the product identifier, the store identifier or the group store identifier, etc. The channel type, scene, page name, page module, content type, content subtype, content identifier, link and extension fields can be empty; the relevant parameters of the target resource link click event include user ID, event occurrence time, user attributes, sharing user ID_sharing time, channel type, channel identifier Identification, scenario, page name, page module, content type, content subtype, content identifier, link and extension fields, among which, the user corresponding to the user identifier in the target resource link click event is the user who accesses the target resource page by clicking the target resource link shared to him, the shared user identifier_sharing time is the user identifier and sharing time of the user shared to him, user attributes may include whether it is a new user, whether it is a paid user, whether it is a new paid user, etc., the channel type, scenario, page name, page module, content type, content subtype, content identifier, link and extension fields may be empty; the relevant parameters of the sharing landing page click event are the same as the relevant parameters of the target resource link click event. Among them, the relevant parameters of the source data of the target resource link are obtained by burying data at different nodes of the target resource link.

[0049] In one embodiment, an analysis user who wants to analyze the spread of a target resource link can, after obtaining the source data of the target resource link, upload the source data of the target resource link through the analysis setting interface of the data processing device. The data processing device can obtain the data identifier of the source data of the target resource link and the event identifier of the event corresponding to the sharing operation performed on the target resource link from the analysis setting interface. Among them, the event identifier of the event corresponding to the sharing operation performed on the target resource link is the event identifier of the corresponding different events in the source data of the uploaded target resource link; then, the data processing device obtains the source data of the target resource link based on the data identifier, and performs data screening processing on the obtained source data based on the event identifier to obtain the analysis reference data of the target resource link, which includes multiple user identifiers and the sharing relationship between different users regarding the target resource link. The data processing device analyzes and processes the analysis reference data, and divides the users corresponding to the user identifiers recorded in the analysis reference data into multiple user sets; then, the data processing device obtains feedback information on the target resource link, and can display the feedback information in the data processing device so that the analysis user can understand the dissemination status of the target resource link; the target user set can also be obtained from multiple user sets through the feedback information, so that other resource links of the same type that need to be quickly disseminated can be quickly disseminated through the target user set, thereby achieving a good publicity effect.

[0050] In one embodiment, after obtaining the source data and event identifier of the target resource link, the data processing device can upload the source data and event identifier of the target resource to the server, so that the server performs subsequent operations and sends the obtained multiple user sets, feedback information and related information of the target user set to the data processing device, so that the data processing device displays them; or after obtaining the analysis reference data, the analysis reference data can be uploaded to the server, so that the server performs subsequent operations and sends the obtained multiple user sets, feedback information and related information of the target user set to the data processing device, so that the feedback information is displayed in the data processing device, and other resource links of the same type that need to be quickly disseminated are quickly disseminated through the target user set.

[0051] It should be noted that the relevant data involved in the data processing process of this application (such as target resource links, user portrait data, user identification and feedback information, etc.). When the above embodiments of this application are applied to specific products or technologies, user permission or consent must be obtained, and the relevant data collection, use and processing processes must comply with relevant local laws, regulations and standards, and comply with the principles of legality, legitimacy and necessity, and do not involve obtaining data types prohibited or restricted by laws and regulations. In some optional embodiments, the relevant data involved in the embodiments of this application are obtained after separate authorization by the user. In addition, when obtaining separate authorization from the user, the purpose of the relevant data involved is indicated to the user.

[0052] Based on the above data processing system, the present application embodiment provides a data processing method. Figure 2 , which is a flow chart of a data processing method provided in an embodiment of the present application. Figure 2 The data processing method shown can be executed by the above-mentioned data processing device. Figure 2 The data processing method shown may include the following steps:

[0053] S201 : In response to a triggering event for analyzing a sharing operation performed on a target resource link, an analysis setting interface is displayed.

[0054] Among them, the target resource link is a link pointing to the target resource page, that is, the user can access the target resource page through the target resource link. When the target resource link points to the target web page, the user can access the target web page through the target resource link. When the target resource link points to a page in the application, the user can access the page in the application through the target resource link.

[0055] In one embodiment, in response to a triggering event for analyzing a sharing operation performed by an analysis user on a target resource link, the data processing device displays an analysis setting interface for enabling the analysis user to upload source data of the target resource link. Figure 3aAs shown, an interface diagram of an analysis setting interface provided in an embodiment of the present application is shown. The analysis setting interface includes an operation prompt area as marked 301, an analysis category option as marked 302, a data import area as marked 303, and a progress component as marked 304. The operation prompt area is used to prompt the analysis user to upload the source data of the target resource link. Specifically, the prompt message displayed in the operation prompt area may be: "1. Select data resources, events, fields; 2. Select global filter fields; 3. Submit". The analysis user can select the type of analysis to be performed in the analysis category option marked 302. When the analysis user wants to perform a propagation analysis on the target resource link, the "sharing fission" category can be selected. When the analysis category option is empty, that is, the analysis user has not selected the category to be analyzed in the analysis category option, a prompt message will be displayed on the analysis setting interface to prompt the analysis user to select the analysis category. For example, the prompt message may be "Please select an analysis category". Optionally, the prompt message can be displayed in the form of a pop-up window in the analysis setting interface. The embodiment of the present invention does not impose specific restrictions on the content of the prompt message or the display method of the prompt message.

[0056] If the "sharing fission" category is selected, the data import template corresponding to the "sharing fission" category can be displayed in the data import area marked as 303. The analysis user can select the source data of the target resource link through the data import area, select the source data of the target resource link including access events, sharing events, click events on the target resource link, and sharing landing page click events, and select related parameters of different events. If the source data of the target resource link is selected, the data identifier of the source data of the target resource link will be displayed in the analysis setting interface. If different events are selected, the event identifiers of different events will be displayed in the analysis setting interface; optionally, after selecting the source data of the target resource link, the data processing device will automatically match the filling of different events and related parameters of different events according to the data import template; the progress component marked as 304 can include a cancel component and a next step component. If the cancel component is triggered, the upload of the source data of the target resource link will be canceled. If the next step component is triggered, the analysis setting interface will display the content corresponding to "Select global filter field". Optionally, the analysis setting interface may also include an add real-time data source component as marked 305, which is used to add real-time data of the target resource link, and the real-time data of the target resource link is consistent with the events and related parameters of the events included in the source data of the target resource link.

[0057] In one embodiment, if the next step component is triggered, the analysis setting interface displays the content corresponding to "Select global filter field", such as Figure 3bAs shown, it is a schematic diagram of a switching analysis setting interface provided by the present embodiment. As shown by the mark 311, it is an analysis setting interface when displaying the content corresponding to the "global filter field". At this time, the analysis setting interface includes field filtering options, sharing time window and data source options. Among them, the field filtering option is used to filter the relevant parameters commonly shared by the sharing event, the target resource link click event and the sharing landing page click event, such as filtering the content type, content identifier and user attributes and other relevant parameters contained in the sharing event, the target resource link click event and the sharing landing page event. The sharing time window is used to set the time interval from the target resource link being shared to the target resource link being clicked. Time intervals exceeding this time interval will not be calculated. The data source option is used to set whether the source data of the target resource link is an offline data source. Specifically, the field filtering option can be shown as marked 312, which is specifically displayed as "Select fields for filtering", the sharing time window can be shown as marked 313, and the data source option can be shown as marked 314, which is specifically expressed as "Is the data source offline?"; the progress component at this time is shown as marked 315, which specifically includes a cancel component and a submit component. If the cancel component is triggered, the field filtering option, the sharing time window, and the content in the data source option are canceled, and the upload of the source data of the target resource link is canceled. If the submit component is triggered, the source data of the target resource link is uploaded.

[0058] In one embodiment, the trigger event for analyzing the sharing operation performed on the target resource link may include: the data processing device displays the application open market interface in response to the trigger operation on the application market component, and the application open market interface includes function options, and the function options include analysis model options; if the analysis model option is selected, the analysis model interface including the sharing fission module is displayed, and the sharing fission module is a module for analyzing the propagation of the target resource link, and the sharing fission module includes a module introduction entrance and a module analysis entrance. If the module introduction entrance is triggered, the module introduction interface is displayed, and the module introduction interface is used to introduce the sharing fission module, and the module introduction interface includes an analysis setting entrance; if the analysis setting entrance is triggered, the following is displayed. Figure 3a The analysis setting interface shown in the figure is as follows; if the module analysis entry is triggered for the first time, the following Figure 3a The analysis setting interface shown; if the module analysis entry is triggered, and it is not the first time it is triggered, the analysis dashboard interface is displayed, which is used to display the analysis results of the source data linked to the target resource.

[0059] like Figure 4aAs shown, it is a schematic diagram of a trigger display analysis setting interface provided by an embodiment of the present application. As shown by the mark 401, if the application market component marked by 402 is triggered and the analysis model option is selected, the analysis model interface marked by 403 is displayed, wherein the sharing fission module is shown by the mark 404, the module introduction entrance is shown by the mark 405, specifically displayed as "Details", the module analysis entrance is shown by 406, specifically displayed as "Enter", if the module analysis entrance is triggered and it is the first time, that is, the "Enter" entrance is triggered for the first time, then the analysis setting interface marked by 407 is displayed; Figure 4b As shown, it is a schematic diagram of another trigger display analysis setting interface provided by an embodiment of the present application. If the analysis model interface is as shown by mark 411, when the module introduction entrance is triggered, that is, the "Details" entrance is triggered, the module introduction interface as shown by mark 412 is displayed, wherein the analysis setting entrance included in the module introduction interface can be as shown by mark 413, specifically displayed as "Configuration", if the analysis configuration entrance is triggered, that is, the "Configuration" entrance is triggered, then the Figure 3a The analysis settings interface is shown.

[0060] S202: Acquire, from the analysis setting interface, a data identifier of the source data of the target resource link and an event identifier corresponding to the sharing operation performed on the target resource link.

[0061] S203: Acquire source data of the target resource link according to the data identifier.

[0062] S204: performing data screening processing on the acquired source data according to the event identifier to obtain analysis reference data of the target resource link.

[0063] In steps S202-S204, after the analysis user triggers the submission component in the analysis setting interface, the data processing device obtains the data identifier of the source data of the target resource link selected by the analysis user from the analysis setting interface, as well as the event identifier corresponding to the sharing operation performed on the target resource link, including the event identifier of the access event, the event identifier of the sharing event, the event identifier of the target resource link click event, and the event identifier of the sharing landing page click event; and obtains the source data of the target resource link based on the data identifier, and performs data screening processing on the obtained source data based on the event identifier to obtain the analysis reference data of the target resource link. Among them, the analysis reference data records multiple user identifiers and the sharing relationship between different users regarding the target resource link.

[0064] Specifically, the sharing relationship between different users regarding the target resource link is obtained from the sharing user ID_sharing time in the relevant parameters of the target resource link click event in the source data. The sharing user ID_sharing time can be used to find out which user shared the target resource link through which the user who accessed the target resource page through the target resource link accessed the target resource page, thereby finding the sharing relationship. For example, if user A accesses the target resource page through the target resource link sent to him by user B, then there is a sharing relationship between user A and user B. Among them, there can be strong sharing relationships and weak sharing relationships between users with sharing relationships. For example, if the proportion of user A clicking on the links sent by user B to user A is higher than a certain threshold, it can be considered that user A and user B have a strong sharing relationship. If it is lower than a certain threshold, it can be considered that user A and user B have a weak sharing relationship. Optionally, there may be a strong or weak relationship (or pan-relationship) between users with a sharing relationship. For example, WeChat users naturally have strong friendships, and their friendships are strong relationships. For example, there is no direct relationship between accounts such as groups, WeChat public accounts, and mini-programs, so they are weak relationships. Relationships generated through indirect behaviors such as common group memberships and mini-program forwarding relationships are also weak relationships.

[0065] The data processing device may analyze and process the analysis reference data to divide the users corresponding to the user identifiers recorded in the analysis reference data into different user sets to obtain multiple user sets, and then proceed to step S205.

[0066] S205 : Analyze and process the analysis reference data to divide users corresponding to the user identifiers recorded in the analysis reference data into different user sets to obtain multiple user sets.

[0067] Among them, the multiple user sets obtained by dividing the users corresponding to the user identifiers are intended to ensure that users within the user set have a strong sharing relationship, and users between user sets have a weak sharing relationship, so that other resource links to be shared can be quickly spread within the user set.

[0068] In one embodiment, the analysis reference data is a knowledge graph constructed using multiple nodes, wherein the knowledge graph includes a node for recording a user identifier, and the knowledge graph includes an edge for indicating the sharing relationship between users corresponding to the corresponding user identifier. If the analysis reference data does not include weights between users, the data processing device traverses each node in the knowledge graph, determines at least one adjacent node of the current node traversed, and calculates the node association between the current node and each adjacent node; based on the node association, clustering operations are performed on each node in the knowledge graph, and when the set association of the node set obtained by clustering reaches a maximum value, the clustering operation on the knowledge graph is stopped to obtain multiple node sets; wherein a node set is used to indicate a user set, and the user identifier recorded by each node included in a node set is the user identifier corresponding to the user in the corresponding user set.

[0069] Among them, the above-mentioned clustering operation is performed on each node in the knowledge graph according to the node correlation, specifically including: clustering the current node and any adjacent node to obtain multiple reference node sets, and the set correlation corresponding to each reference node set; selecting the maximum set correlation from the set correlation corresponding to each reference node set, and using the reference node set indicated by the maximum set correlation as the node set to which the current node is clustered.

[0070] In a specific implementation, the data processing device traverses each node and calculates the node association between the current node and each adjacent node, wherein the adjacent node of the current node is a node that has an edge connection with the current node in the knowledge graph, that is, there is a sharing relationship between the user corresponding to the adjacent node and the user corresponding to the current node; the node association between the current node and an adjacent node can be the weight between the current node and an adjacent node, corresponding to the weight between the user corresponding to the current node and the user corresponding to an adjacent node, and the weight can be determined based on the sharing relationship between the user corresponding to the current node and the user corresponding to an adjacent node, or based on the sharing relationship and strong or weak relationship between the user corresponding to the current node and the user corresponding to an adjacent node.

[0071] Based on the node association degree of the current node, a clustering operation is performed on the current node. Specifically, the current node and any adjacent node are clustered to obtain multiple reference node sets and the set association degree corresponding to each reference node set; wherein a reference node set includes the current node and an adjacent node. The maximum set association degree is selected from the set association degrees corresponding to each reference node set. Because the clustering operation on the current node is to ensure that the user corresponding to the current node after clustering has a strong sharing relationship with the user corresponding to the target adjacent node, wherein the target adjacent node is the adjacent node in the reference node set corresponding to the selected maximum set association degree, it is necessary to determine whether the maximum set association degree is greater than the sum of the set association degrees of the node set corresponding to the current node and the node set corresponding to the target adjacent node before clustering. wherein the node set corresponding to the current node only includes the current node, and the node set corresponding to the target adjacent node only includes the target adjacent node. If this condition is met, the reference node set indicated by the maximum set association degree is used as the node set to which the current node is clustered; if this condition is not met, the node set corresponding to the current node is used as the node set to which the current node is clustered. Traverse each node, perform clustering operations on each node, obtain the set of nodes that are clustered corresponding to each node, and then obtain multiple sets of nodes that are clustered, wherein multiple nodes can correspond to the same set of nodes that are clustered. Optionally, the node association degree between the current node and each adjacent node can be calculated, and the current node can be clustered based on the node association degree corresponding to the current node; for the next node, the node association degree between the next node and each adjacent node can be calculated, and the next node can be clustered based on the node association degree of the next node, until all nodes are clustered; it is also possible to traverse all nodes, calculate the node association degree between all nodes and their adjacent nodes, and then perform clustering operations on all nodes in turn.

[0072] The set association degree can be expressed by the set modularity, and the specific formula can be shown by the following formula (1):

[0073]

[0074] Where c is any node set whose set association degree is to be calculated, m is the sum of the weights of all edges, ∑in is the sum of the weights of the edges in node set c, and ∑tot is the total weight of the incident node set c, that is, the sum of the weights of all edges connected to the nodes in node set c. If a clustering operation is performed on node i, and node set c is the node set of the target adjacent nodes in the reference node set corresponding to the maximum set association degree, then when it is necessary to determine whether the maximum set association degree obtained after clustering is greater than the sum of the set association degrees of the node set corresponding to node i before clustering and the node set c corresponding to the target adjacent nodes, the difference in set association degree, that is, the difference in set modularity, can be used to represent this condition. This can be specifically shown by formula (2):

[0075]

[0076] Among them, m is the sum of the weights of all edges, k i,in is the sum of the weights of the nodes incident from node i into node set c, ∑tot is the total weight of the incident node set c, that is, the sum of the weights of all edges connected to the nodes in node set c, k i is the sum of the weights of the edges connected to node i.

[0077] Each clustered node set is treated as a new node, and each new node is traversed. A clustering operation is performed on each new node, and when the set correlation degree of the clustered node set reaches a maximum value, the clustering operation is stopped to obtain multiple node sets, wherein the multiple node sets include at least one new node.

[0078] For example, if Figure 5 As shown, a schematic diagram of a plurality of user sets obtained by dividing provided by an embodiment of the present application, assuming that there are 16 nodes, different nodes are numbered from 0 to 15 for easy distinction, the edge between two nodes indicates the existence of a sharing relationship between the users corresponding to the two nodes, assuming that the node association (i.e., weight) of each node and its adjacent node calculated after traversing each node is 1; assuming that the knowledge graph between the 16 nodes is shown as marked 501, because it is assumed that the weight of each node and its adjacent node is 1, the weight value is not marked. First, according to the node association corresponding to the current node, that is, according to the weight between the current node and its adjacent nodes, the current node and each of its adjacent nodes are clustered, assuming that the current node is node 0, and the weight between it and node 2, node 3, node 4 and node 5 is 1, then node 0 is clustered with node 2, node 3, node 4 and node 5 to obtain multiple reference node sets, namely the first reference node set {0, 2}, the second reference node set {0, 3}, the third reference node set {0, 4} and the fourth reference node set {0, 5}.

[0079] Calculate the set association degree of each reference node set. If the set association degree of the second reference node set {0, 2} among the above four reference node sets is the maximum set association degree, then determine whether the set association degree of the second reference node set {0, 2} is greater than the sum of the set association degrees of the node set {0} corresponding to the current node and the node set {2} corresponding to the target adjacent node before clustering. If the condition is met, the second reference node set {0, 2} is used as the node set to which node 0 is clustered. If the condition is not met, the node set {0} corresponding to the current node is used as the node set to which node 0 is clustered.

[0080] Then, a clustering operation as shown in node 0 is performed on each node to obtain the node set corresponding to each node, and then multiple node sets are obtained. Assume that 4 node sets are obtained as marked 502 at this time, and the 4 node sets are respectively used as new nodes. At this time, the weight between each new node can be as shown in 503. The clustering operation as shown in node 0 is repeated for each new node, and when the set association degree of the node set obtained by clustering reaches the maximum value, the clustering operation is stopped to obtain multiple node sets. Finally, the multiple node sets can be as shown in 504, where one node set includes node 0, node 1, node 2, node 4, node 5, node 3, node 6 and node 7, and the other node set includes node 8, node 9, node 10, node 11, node 12, node 13, node 14 and node 15.

[0081] In one embodiment, each node in the knowledge graph can be clustered by setting a label for each node, so that multiple user sets can be obtained. Specifically, first, an independent label can be set for each node in the knowledge graph, that is, each node has a label; each node is clustered in an iterative manner, that is, each iteration changes the label of the current node to the label that appears most frequently in the adjacent nodes of the current node. If the adjacent nodes include multiple labels that appear most frequently, the label of the current node is randomly changed to one of the multiple labels that appear most frequently; until the label of each node is the same as the label that appears most frequently in its adjacent nodes, the iteration is stopped. At this point, multiple node sets with different labels can be obtained, where the nodes in a node set have the same label, and a node set corresponds to a user set.

[0082] S206: Acquire feedback information generated by each user who shares the target resource link after sharing the target resource link.

[0083] The feedback information can be used to measure the dissemination effect of the target resource link, and can also be used to determine the target user set from multiple user sets when sharing other resource links to be shared, wherein the resource types indicated by the other resource links and the target resource link are the same.

[0084] In one embodiment, the feedback information may include one or more of the following: the total number of times the target resource link is shared, the total number of users who share the target resource link with other users, the total number of users who access the shared target resource link, the total amount of resource transfer generated by resource transfer based on the shared target resource link, and the sharing propagation coefficient of the target resource link; wherein the sharing propagation coefficient of the target resource link is used to measure the speed of propagation of the target resource link, for example, it can be a viral propagation coefficient. In the scenario of commodity sales, the total amount of resource transfer generated by resource transfer based on the shared target resource link can be the total transaction volume (Gross Merchandise Volume, GMV) brought by the shared target resource link. The propagation effect of the target resource link can be measured based on one of the above feedback information, or the propagation effect of the target resource link can be measured in multiple dimensions based on multiple of the above feedback information.

[0085] In one embodiment, the feedback information generated by a user who shares a target resource link after sharing the target resource link may include: the total number of times the user has shared the target resource link, and the total number of users who have accessed the target resource link based on the target resource link shared by the user. Optionally, it may also include the total amount of resource transfers generated by resource transfers based on the shared target resource link, which may specifically include the total transaction amount brought about by the target resource link shared by the user and the number of orders brought about by the target resource link shared by the user. Optionally, after obtaining the feedback information generated by each user who shares the target resource link after sharing the target resource link, the data processing device may display the user information of the user, which may include the feedback information generated by each user after sharing the target resource link, so that the analysis user can understand which users have a better dissemination effect on the target resource link.

[0086] In one embodiment, the data processing device can perform data screening on the source data of the target resource link to obtain analysis reference data, and the data processing device can obtain feedback information generated by each user who shares the target resource link after sharing the target resource link, all of which are obtained based on the source data of the target resource link; wherein, when the data processing device performs data screening on the source data of the target resource link to obtain analysis reference data, it can also determine the weight between the two users based on the sharing relationship between the users corresponding to the two user identifiers, or determine the weight between the two users based on the sharing relationship and the strong relationship or weak relationship between the users corresponding to the two user identifiers. At this time, the analysis reference data also includes the weight between the two users.

[0087] like Figure 6 As shown, a schematic diagram of processing the source data of the target resource link provided by an embodiment of the present application, as shown by the mark 601, the data processing device can obtain feedback information from the source data of the target resource link, such as the data displayed in the statistical result table of the user who initiated the sharing, and the relevant information of the user who performed the sharing operation on the target resource link, such as user attributes (displayed as user_type), etc., and statistics are performed on all users, that is, the total number of users corresponding to sharing the target resource link with other users can be obtained, and other indicators such as sharing penetration rate can be obtained, wherein the sharing penetration rate represents the proportion of new users who share the target resource link among the new users who access the target resource page through the target resource link; for example, the user statistical results brought about by sharing The data shown in the table corresponds to the relevant information of users who access the target resource page based on the target resource link, and may also include user attributes, etc. By counting all users, the total number of users who access the target resource link based on the shared link can be obtained, and the viral transmission coefficient can be obtained, where the viral transmission coefficient is the number of new users brought by sharing / the number of users who initiate sharing, that is, the number of new users among the users who access the target resource page based on the target resource link / the total number of users who share the target resource link with other users; as shown in the 602 mark, the data processing device can perform data screening and processing on the source data of the target resource link to obtain analysis reference data, where the analysis reference data includes user identification (displayed as id) and the weight between two users (displayed as weight), etc.

[0088] In an embodiment of the present application, the data processing device first obtains the data identifier of the source data of the target resource link and the event identifier corresponding to the sharing operation performed on the target resource link in the analysis setting interface; then the source data of the target resource link is obtained according to the data identifier, and the obtained source data is filtered and processed according to the event identifier to obtain analysis reference data of the target resource link, and the analysis reference data records multiple user identifiers and the sharing relationship between different users regarding the target resource link; further, the data processing device analyzes and processes the analysis reference data to divide the users corresponding to the user identifiers recorded in the analysis reference data into different user sets to obtain multiple user sets; thereafter, the data processing device obtains feedback information generated by each user who shares the target resource link after sharing the target resource link, and the feedback information is used to collect information from multiple users. A target user set is determined in the set when sharing other resource links to be shared, wherein the resource types indicated by the other resource links and the target resource link are the same; by obtaining the source data of the target resource link, analysis reference data of the target resource link can be further obtained, and the analysis reference data includes multiple user identifiers and sharing relationships between different users regarding the target resource link; and based on the analysis reference data, users corresponding to the user identifiers recorded in the analysis reference data are divided into multiple user sets; finally, feedback information on the target resource link can be obtained, so that users can understand the dissemination status of the target resource link; at the same time, the target user set can also be obtained from multiple user sets based on the feedback information, so that other resource links of the same type that need to be quickly disseminated can be quickly disseminated through the target user set, thereby achieving a good publicity effect.

[0089] Based on the above method embodiment, the present application embodiment provides another data processing method, see Figure 7 , which is a flow chart of another data processing method provided in an embodiment of the present application. Figure 7 The data processing method shown can be executed by a data processing device. Figure 7 The data processing method shown may include the following steps:

[0090] S701 : In response to a triggering event for analyzing a sharing operation performed on a target resource link, an analysis setting interface is displayed.

[0091] S702: Acquire the data identifier of the source data of the target resource link and the event identifier corresponding to the sharing operation performed on the target resource link from the analysis setting interface.

[0092] S703: Acquire source data of the target resource link according to the data identifier.

[0093] S704: Perform data screening on the acquired source data according to the event identifier to obtain analysis reference data linked to the target resource.

[0094] S705 : Analyze and process the analysis reference data to divide users corresponding to the user identifiers recorded in the analysis reference data into different user sets to obtain multiple user sets.

[0095] S706: Obtain feedback information generated by each user who shares the target resource link after sharing the target resource link.

[0096] Steps S701-S705 are consistent with steps S201-S205 and will not be repeated here.

[0097] In one embodiment, after a data processing device obtains feedback information generated by each user who shares a target resource link after sharing the target resource link, it can determine, from multiple user sets, a target user set for sharing other resource links to be shared, according to the feedback information, wherein the resource types indicated by the other resource links and the target resource link are the same.

[0098] In one embodiment, the user with the best feedback information can be determined as the target user, and the user set to which the target user belongs can be determined as the target user set. In a specific implementation, the data processing device can determine the sharing user identifier that performed a sharing operation on the target resource link from multiple user identifiers analyzed in the reference data based on the sharing relationship between users, and the user indicated by the sharing user identifier is the user who shared the target resource link; then determine the feedback information generated by each user indicated by the sharing user identifier after sharing the target resource link, and determine the target feedback information from the feedback information of the user indicated by each sharing user identifier; determine the target user indicated by the user identifier corresponding to the target feedback information, and use the user set to which the target user belongs as the target user set. The target feedback information can include one or more of the following: the maximum number of times the target resource link has been shared, the maximum total number of users corresponding to sharing the target resource link with other users, the maximum total number of users accessing the shared target resource link, the maximum total amount of resource transfer corresponding to resource acquisition based on the shared target resource link, and the maximum sharing propagation coefficient of the target resource link. The target user can be determined based on one of the above target feedback information, or based on multiple of the above target feedback information.

[0099] For example, if you are only interested in promotion, you can focus only on the maximum total number of users who have accessed the target resource link shared by the user corresponding to each shared user ID, that is, the maximum number of users who have accessed the link shared by users corresponding to different shared user IDs; but if you are in an activity to sell goods, you may also need to pay attention to the value of the maximum total amount of resource transfer corresponding to resource acquisition based on the shared target resource link. For example, if the maximum total number of users who have accessed the target resource link shared by user A is 50, the maximum total number of users who have accessed the target resource link shared by user B is 35, and the maximum total number of users who have accessed the target resource link shared by user C is 15, then the user set corresponding to user A is determined as the target user set.

[0100] In one embodiment, the feedback information generated by the users included in each user set can be counted to obtain collective feedback information, and the user set with the best collective feedback information can be determined as the target user set. In a specific implementation, the collective feedback information generated by each user who shared the target resource link after sharing the target resource link can be determined; the target collective feedback information can be determined from the collective feedback information corresponding to each user set, and the user set corresponding to the target collective feedback information can be used as the target user set. For example, if the collective feedback information of user set 1 is higher than that of user set 2 and higher than that of user set 3, user set 1 will be determined as the target user set.

[0101] In one embodiment, the feedback information generated by each user who shared the target resource link after sharing the target resource link can be obtained, and the feedback information can be counted, so that the feedback information of the target resource link can be obtained, and then the feedback information of the target resource link can be displayed in the data processing device. Optionally, the feedback information generated by the user who shared the target resource link after sharing the target resource link and the time information of the feedback information can be obtained, and the feedback information can be counted at different time nodes according to the time information to obtain the feedback information of the target resource link at different time nodes, and can be displayed in the data processing device. Figure 8a As shown, it is a schematic diagram of the feedback information of a target resource link provided by an embodiment of the present application changing over time, which corresponds to the interface when the sharing history overview in the analysis dashboard interface is selected, wherein the target resource link is specifically a product page link of a target product, wherein the feedback information includes: the total number of times the target resource link is shared (in Figure 8a The total number of users who share the target resource link with other users (in Figure 8aThe total number of users who have shared resources is shown in the table below), and the total number of users who have accessed the shared resource link (in Figure 8a Specifically displayed is the cumulative number of users brought by sharing and forwarding), the total amount of resource transfer generated by resource transfer based on the shared target resource link (in Figure 8a Specifically displayed in the figure is the cumulative GMV brought by sharing), and the sharing propagation coefficient of the target resource link (in Figure 8a Specifically shown as the sharing virus coefficient K), optional, such as Figure 8a The diagram showing the change in feedback information of target resource links over time also includes other parameters for measuring the dissemination effect, such as sharing penetration rate.

[0102] In one embodiment, the source data of multiple comparison resource links can be analyzed to obtain feedback information of multiple comparison resource links, and compared with the feedback information of the target resource link to obtain the resource link with the best feedback information. Figure 8b As shown, it is a feedback information comparison diagram of different resource links provided by an embodiment of the present application. Assuming that the user wants to analyze the dissemination of the products in the same activity interface, so as to understand which products have good dissemination and which products have poor dissemination, and what types of products the user prefers, so as to avoid using products that the user does not like for the next activity, and select products that the user likes to improve the effect of the activity. Assuming that the activity interface shown by the 801 mark includes links to product a, product b and product c, the dissemination effects of the links to product a, product b and product c can be analyzed to know the results of the activity. The spread of some products, assuming that as shown in 802, the cumulative number of users sharing product a, product b, and product c are 354, 35, and 651 respectively; the cumulative number of times product a, product b, and product c are shared are 443, 38, and 919 respectively; the cumulative number of users brought by sharing and forwarding product a, product b, and product c are 1438, 84, and 3211 respectively; the cumulative GMV brought by sharing product a, product b, and product c are 2442243, 111748, and 1565108 respectively, and the sharing virus coefficient k of product a, product b, and product c are 4, 2.4, and 4.9 respectively, then it can be known that product c has the best spread effect.

[0103] S707: In response to the query trigger operation on the user set, the obtained set information of each user set is displayed in the set query interface.

[0104] Among them, the collection query interface includes collection information of each user collection, and any collection information includes one or more of the following: collection identifier, the total number of times the target resource link is shared by any collection, and the total amount of resource transfer generated by the sharing of the target resource link by any collection.

[0105] In one embodiment, the data processing device displays a collection query interface in response to the query trigger operation of the analysis user on the user collection. Specifically, when the sharing fission analysis in the analysis dashboard interface is selected, the collection query interface can be displayed. Figure 9 As shown, it is an interface diagram of a collection query interface provided by an embodiment of the present application, wherein the collection query interface includes an analysis and filtering area such as marked 901, a user collection filtering area such as marked 902, a download component such as marked 903, and a collection information display area such as marked 904. In the analysis and filtering area, users filter the sharing channels, user attributes, query date range, content type, content identification, etc. of the target resource link. For example, the sharing channel can be a WeChat applet or a web page (H5 page), and the user attributes can filter indicators such as new visiting users, old visiting users, new customers, and old customers (wherein old customers and new customers can be displayed as old customers and new customers on the page), and the content type is Figure 9 The sharing type displayed in the figure can be used to filter the type of target resource link, such as product type, store type, etc.; the user set filtering area marked with 902 can filter the user set according to the number of users in the user set (in Figure 9 The download component marked as 903 can be used to download the user identifiers of users included in the user set displayed in the set information display area marked as 904, such as the information of the user set displayed in the set information display area marked as 904 under the filtering conditions of the user set filtering area, such as filtering the collection information of the top 5 user sets ranked by the number of users.

[0106] Among them, the collection information may include a collection identifier, the total number of times the collection has shared the target resource link (displayed as the cumulative number of times the collection has shared), and the total amount of resource transfers generated by the collection's sharing of the target resource link, wherein the total amount of resource transfers may be the cumulative transaction amount of the collection; optionally, the collection information display area may also display information such as the number of collection users, the average number of times the collection has shared per person, the cumulative number of collection transactions, the average number of collection transactions per person, and the average transaction amount per person. Among them, it can be known that the user collection with the user collection identifier of 301 includes the largest number of users, the user collection with the user collection identifier of 210 has the highest cumulative collection transaction amount, and the highest average collection transaction amount, so the user collection with the user collection identifier of 210 is a high-conversion head user collection. Optionally, the collection query interface may also include a selection identifier for querying users included in any user collection. The selection identifier may be as shown in the 905 mark. Figure 9In the query interface, it is displayed as "View Details". This selection identifier is used to trigger the display of user information of the user set associated with the selection identifier. In one embodiment, if the set query interface also includes a selection identifier for querying users included in any user set, then when the target selection identifier is triggered, the user information of each user included in the target user set associated with the target selection identifier is displayed, and based on the user information of each user, the key communication users in the target user set are determined, wherein the user information includes one or more of the following: the user identifier of the corresponding user, and the feedback information generated after sharing the target resource link.

[0107] In specific implementation, such as Figure 10a As shown, it is a schematic diagram of a method of triggering the display of user information of a target user set provided by an embodiment of the present application. If the user set with the highest cumulative transaction amount is selected, that is, the selection identifier corresponding to the user set with the user set identifier 210, the user information of the users included in the user set corresponding to the user set identifier 210 can be displayed in the user information display area marked as 1001. At this time, the interface also includes a user screening area marked as 1002, a return component marked as 1003, a download component marked as 1004, and a sharing relationship display area marked as 1005. Among them, the user screening area marked as 1002 can filter users according to the feedback information of different users, and specifically can filter users based on the feedback information. The total number of users who have accessed the target resource link based on sharing can be filtered, that is, the users who bring the top few visiting users can be filtered. The return component shown by the mark 1003 can return the collection query interface showing the collection information. The download component shown by the mark 1004 can be used to download the user identifiers of the users included in the user collection. The sharing relationship display area shown by the mark 1005 displays the sharing relationship information between the users included in the user collection, wherein each dot represents a user identifier, which corresponds to a user, and the line between the two dots indicates that there is a sharing relationship between the users corresponding to the two user identifiers, and the sharing relationship corresponding to the user identifier can be displayed by triggering the dots.

[0108] Among them, such as Figure 10a The user information shown includes: the user's user ID, feedback information generated after sharing the target resource link, wherein the feedback information may specifically include: the total number of times the target resource link is shared (in Figure 10a The total number of users who accessed the shared target resource link ( Figure 10a The total amount of resource transfers generated by resource transfer based on the shared target resource link, wherein the total amount of resource transfers may include the number of orders brought by the shared target resource link (in Figure 10aThe information is displayed as the number of orders brought and the number of new orders brought); optionally, the user information may also include the user nickname, the number of first-time visiting users brought, the number of user purchases, and the user transaction amount. If the top 100 users with the most visiting users are screened, the information display area marked with 1001 exemplarily displays the information of the top 5 users, then it can be known that the user with user ID 4464357 brings the most visiting users, the most orders, and the most new orders. Therefore, the user with user ID 4464357 is determined to be a key dissemination user. Figure 10b As shown, this is a schematic diagram of displaying the sharing relationship of the target user identifier in the sharing relationship display area provided in an embodiment of the present application. As shown by the mark 1011, the sharing relationship is displayed when the dot with the target user identifier 4464357 is triggered. As shown by the mark 1012, the sharing relationship is displayed when the dot with the target user identifier 4499782 is triggered.

[0109] Optionally, a key communication user set can be established from multiple key communication users determined from multiple user sets. Then, by sharing other resource links to be shared with multiple key communication users and using the key communication users to share again, the communication effect can be greatly improved. Optionally, if there is a user set with data anomalies or a key communication user with data anomalies in the multiple user sets obtained, the user set with data anomalies or the key communication user with data anomalies can be processed. For example, if it is found that the data of a key communication user in a user set is anomaly, the user set can be determined to be a user set with data anomalies. By processing the user set with data anomalies, multiple users with data anomalies who are closely related to the key communication user with data anomalies can be easily processed, thereby easily determining whether the user set is a wool group or a black market group.

[0110] In one embodiment, a selection operation on a download component is detected, and user identifiers of users included in a target user set are downloaded to obtain a target identifier set; a data processing device displays a user management interface in response to a display trigger operation of the analysis user for user management analysis, and the user management interface includes a user identifier adding component; a selection operation on the user identifier adding component is detected, the target identifier set is imported, and an analysis component for performing user portrait analysis on users corresponding to each user identifier in the target identifier set is displayed; when the analysis component is selected, user portrait analysis is performed on users corresponding to the user identifiers included in the target identifier set, and the corresponding user portrait analysis results are displayed.

[0111] In specific implementation, such as Figure 11aAs shown, it is a schematic diagram of an import target identification set provided by an embodiment of the present application, wherein the displayed user management interface is shown as marked as 1101, and the user identification adding component is shown as marked as 1102 (specifically displayed as a "create group" component in the user management interface), wherein the user management interface corresponds to the interface displayed when the "user manager" component is selected, that is, the display trigger operation of the data processing device in response to the analysis of the user for the user management analysis is equivalent to the data processing device responding to the selection operation of the "user manager" component; when the selection operation of the user identification adding component is detected, a prompt window for adding the user identification is displayed in the user management interface, and the prompt window can be shown as marked as 1103. Specifically, the entry for adding the user identification is specifically displayed as "local ID list"; if the entry for adding the user identification is selected, the user identification adding interface shown as 1111 is displayed, and the target identification set downloaded to the local storage can be selected through the user identification uploading component shown as 1112, and the target identification set can be uploaded to the local storage through the universal Internet number (Universal Internet Number) shown as 1113. Number, UIN) type selection area selects the type of user identification in the target identification set, for example, it can be a mobile phone number, business account number, etc., and the collection identification of the target identification set can be added through the information adding area as shown in 1114, for example, it can be a collection name, etc., and the target identification set can be imported by clicking the identification import component as shown in 1115, where (the identification import component can be displayed as a "generate population" component).

[0112] If the target identification set is imported, the set identification of the target identification set will be displayed on the user management interface, and the analysis component for performing user portrait analysis on the users corresponding to each user identification in the target identification set will be displayed; when the analysis component is selected, user portrait analysis will be performed on the users corresponding to the user identifications included in the target identification set, and the corresponding user portrait analysis results will be displayed. Figure 11bAs shown, it is a schematic diagram of triggering a user portrait analysis of users corresponding to a target identification set provided by an embodiment of the present application. As shown by the mark 1121, it is a user management interface that displays the set identifier of the target identification set. If the set identifier of the target identification set is set to set 210, it is displayed as set 210 in the user management interface. The analysis component can be specifically displayed as a "portrait" component as shown by the mark 1122; if the analysis component corresponding to the target identification set is triggered, a user portrait analysis is performed on the users corresponding to the user identifiers included in the target identification set, and the user portrait analysis results shown by the mark 1131 are displayed. The user portrait analysis results can display the analysis results of the age distribution, gender distribution, city distribution, industry distribution and status distribution of the users corresponding to the target identification set, wherein the status distribution identifies what kind of life status the user is in, such as marital status, etc. The user portrait analysis results shown by the mark 1131 can be concluded that the ages of users in set 210 are concentrated between 20 and 35, and are mainly users in second-, third- and fourth-tier cities. Optionally, the same user portrait analysis may be performed on identification sets corresponding to user identifications of users included in multiple user sets, thereby comparing user portraits of different user sets.

[0113] In an embodiment of the present application, after the data processing device obtains the feedback information generated by each user who shares the target resource link after sharing the target resource link, it can determine the target user set when sharing other resource links to be shared from multiple user sets based on the feedback information, wherein the resource types indicated by the other resource links and the target resource link are the same, which is conducive to the rapid dissemination of the other resource links to be shared in the target user set; and the collection information of the multiple user sets obtained by division can be displayed, the user information of the target user set can be displayed, the user portrait analysis of the users included in the target user set can be performed, the key dissemination users of the target user set can be determined, and the dissemination situation of the target resource link can be comprehensively analyzed, and the key dissemination users can be used to quickly disseminate other resource links to be shared, thereby improving the dissemination efficiency.

[0114] Based on the above data processing method embodiment, the present application embodiment provides a data processing device. Figure 12 , is a structural diagram of a data processing device provided in an embodiment of the present application. The data processing device 120 may include a display unit 1201, an acquisition unit 1202 and a processing unit 1203. Figure 12 The data processing device 120 shown can run the following units:

[0115] The display unit 1201 is configured to display an analysis setting interface in response to a triggering event for analyzing a sharing operation performed on a target resource link;

[0116] An acquiring unit 1202 is configured to acquire, from the analysis setting interface, a data identifier of the source data of the target resource link and an event identifier corresponding to a sharing operation performed on the target resource link;

[0117] The acquisition unit 1202 is further configured to acquire the source data of the target resource link according to the data identifier;

[0118] The processing unit 1203 is configured to perform data screening processing on the acquired source data according to the event identifier to obtain analytical reference data of the target resource link, wherein the analytical reference data records multiple user identifiers and sharing relationships between different users regarding the target resource link;

[0119] The processing unit 1203 is further configured to analyze and process the analysis reference data to divide users corresponding to user identifiers recorded in the analysis reference data into different user sets to obtain multiple user sets;

[0120] The acquisition unit 1202 is further used to obtain feedback information generated by each user who shares the target resource link after sharing the target resource link, and the feedback information is used to determine the target user set when sharing other resource links to be shared from the multiple user sets, wherein the resource type indicated by the other resource links and the target resource link is the same.

[0121] In one embodiment, the analysis reference data is a knowledge graph constructed using multiple nodes, wherein a node included in the knowledge graph is used to record a user identifier, and an edge included in the knowledge graph is used to indicate a sharing relationship between users corresponding to the corresponding user identifiers; when the processing unit 1203 analyzes and processes the analysis reference data to divide users corresponding to the user identifiers recorded in the analysis reference data into different user sets, and obtains multiple user sets, it specifically performs the following operations:

[0122] Traversing each node in the knowledge graph, determining at least one adjacent node of the traversed current node, and calculating the node association degree between the current node and each adjacent node;

[0123] performing a clustering operation on each node in the knowledge graph according to the node association degree, and stopping the clustering operation on the knowledge graph when the set association degree of the node set obtained by clustering reaches a maximum value, thereby obtaining a plurality of node sets;

[0124] A node set is used to indicate a user set, and the user identifier of each node record included in a node set is the user identifier corresponding to the user in the corresponding user set.

[0125] In one embodiment, when the processing unit 1203 performs clustering operations on the nodes in the knowledge graph according to the node association, the processing unit 1203 specifically performs the following operations:

[0126] Performing a clustering operation on the current node and any adjacent node to obtain multiple reference node sets and a set association degree corresponding to each reference node set;

[0127] A maximum set association degree is selected from the set association degrees corresponding to each reference node set, and the reference node set indicated by the maximum set association degree is used as the node set to which the current node is clustered.

[0128] In one embodiment, the processing unit 1203 is further configured to:

[0129] Determining, based on the sharing relationship, a sharing user identifier that has performed a sharing operation on the target resource link from a plurality of user identifiers in the analysis reference data, wherein the user indicated by the sharing user identifier is the user who has shared the target resource link;

[0130] Determining feedback information generated by each user indicated by the sharing user identifier after sharing the target resource link, and determining target feedback information from the feedback information of each user indicated by the sharing user identifier;

[0131] A target user indicated by the user identifier corresponding to the target feedback information is determined, and a user set including the target user is used as a target user set.

[0132] In one embodiment, the processing unit 1203 is further configured to:

[0133] Determining aggregate feedback information generated by each user group after sharing the target resource link based on feedback information generated by each user who shared the target resource link after sharing the target resource link;

[0134] Target set feedback information is determined from the set feedback information corresponding to each user set, and the user set corresponding to the target set feedback information is used as the target user set.

[0135] In one embodiment, the feedback information includes one or more of the following: the total number of times the target resource link is shared, the total number of users who share the target resource link with other users, the total number of users who access the target resource link based on the shared target resource link, the total amount of resource transfers generated by resource transfers based on the shared target resource link, and the sharing propagation coefficient of the target resource link;

[0136] The target feedback information includes one or more of the following: the maximum number of times the target resource link is shared, the maximum total number of users corresponding to sharing the target resource link with other users, the maximum total number of users accessing the shared target resource link, the maximum total amount of resource transfer corresponding to resource acquisition based on the shared target resource link, and the maximum sharing propagation coefficient of the target resource link.

[0137] In one embodiment, the display unit 1201 is further configured to:

[0138] In response to a query trigger operation on a user set, the obtained set information of each user set is displayed in the set query interface;

[0139] Among them, the collection query interface includes collection information of each user collection, and any collection information includes one or more of the following: collection identifier, the total number of times the target resource link is shared by any collection, and the total amount of resource transfer generated by the sharing of the target resource link by any collection.

[0140] In one embodiment, the set query interface further includes a selection indicator for querying users included in any user set;

[0141] The display unit 1201 is further configured to display user information of each user included in the target user set associated with the target selection identifier when the target selection identifier is triggered, wherein the user information includes one or more of the following: a user identifier of the corresponding user, and feedback information generated after sharing the target resource link;

[0142] The processing unit 1203 is further configured to determine key propagation users in the target user set based on the user information of each user.

[0143] In one embodiment, the collection query interface further includes a download component;

[0144] The processing unit 1203 is further configured to detect a selection operation on the download component, download user identifiers of users included in the target user set, and obtain a target identifier set;

[0145] The display unit 1201 is further configured to display a user management interface in response to a display triggering operation for user management analysis, wherein the user management interface includes a user identification adding component;

[0146] The processing unit 1203 is further configured to detect a selection operation on the user identification adding component and import the target identification set. The display unit 1201 is further configured to display an analysis component for performing user profile analysis on users corresponding to each user identification in the target identification set.

[0147] The processing unit 1203 is further configured to perform user portrait analysis on the user corresponding to the user identifier included in the target identifier set when the analysis component is selected, and the display unit 1201 is further configured to display the corresponding user portrait analysis result.

[0148] According to one embodiment of the present application, Figure 2 as well as Figure 7 The steps involved in the data processing method shown can be Figure 12 The data processing unit 120 shown in FIG. Figure 2 Step S201 shown can be performed by Figure 12 The display unit 1201 in the data processing device 120 shown in FIG. 1 is used to execute the steps S202-S203 and S206. Figure 12 The data processing device 120 shown in FIG. 1 is used to obtain the data. The steps S204-S205 can be performed by the acquisition unit 1202 in the data processing device 120 shown in FIG. Figure 12 The processing unit 1203 in the data processing device 120 shown in FIG. 1 is executed; for example, Figure 7 Steps S701 and S707 shown can be performed by Figure 12 The display unit 1201 in the data processing device 120 shown in FIG. 1 is used to execute the steps S702-S703 and S706. Figure 12 The data processing device 120 shown in FIG. 1 is used to obtain the data. The steps S704-S705 can be performed by the acquisition unit 1202 in the data processing device 120 shown in FIG. Figure 12 The processing unit 1203 in the data processing device 120 shown is executed.

[0149] According to another embodiment of the present application, Figure 12 The various units in the data processing device 120 shown can be separately or all combined into one or several other units to constitute, or one (some) of the units can also be further split into multiple smaller units in function to constitute, which can achieve the same operation without affecting the realization of the technical effects of the embodiments of the present application. The above-mentioned units are divided based on logical functions. In actual applications, the functions of one unit can also be implemented by multiple units, or the functions of multiple units can be implemented by one unit. In other embodiments of the present application, the data processing device 120 divided based on logical functions can also include other units. In actual applications, these functions can also be implemented with the assistance of other units, and can be implemented by the collaboration of multiple units.

[0150] According to another embodiment of the present application, the program can be executed by running on a general computing device such as a computer including a central processing unit (CPU), a random access memory (RAM), a read-only memory (ROM) and other processing elements and storage elements. Figure 2 as well as Figure 7 A computer program (including program code) for each step of the corresponding method shown in FIG. Figure 12 The data processing device 120 shown in the figure can be used to implement the data processing method of the embodiment of the present application. The computer program can be recorded on a computer-readable storage medium, for example, and loaded into the above-mentioned computing device through the computer-readable storage medium and run therein.

[0151] In an embodiment of the present application, the data processing device 120 first obtains the data identifier of the source data of the target resource link and the event identifier corresponding to the sharing operation performed on the target resource link in the analysis setting interface; then, the source data of the target resource link is obtained according to the data identifier, and the obtained source data is filtered and processed according to the event identifier to obtain analysis reference data of the target resource link, and the analysis reference data records multiple user identifiers and the sharing relationship between different users regarding the target resource link; further, the data processing device 120 analyzes and processes the analysis reference data to divide the users corresponding to the user identifiers recorded in the analysis reference data into different user sets to obtain multiple user sets; thereafter, the data processing device 120 obtains feedback information generated by each user who shares the target resource link after sharing the target resource link, and the feedback information is used to obtain the target resource link from the user. A target user set is determined from multiple user sets when sharing other resource links to be shared, wherein the resource types indicated by the other resource links and the target resource link are the same; by obtaining the source data of the target resource link, analysis reference data of the target resource link can be further obtained, and the analysis reference data includes multiple user identifiers and sharing relationships between different users regarding the target resource link; and based on the analysis reference data, users corresponding to the user identifiers recorded in the analysis reference data are divided into multiple user sets; finally, feedback information on the target resource link can be obtained, so that users can understand the dissemination status of the target resource link; at the same time, the target user set can also be obtained from multiple user sets based on the feedback information, so that other resource links of the same type that need to be quickly disseminated can be quickly disseminated through the target user set, thereby achieving a good publicity effect.

[0152] Based on the above method embodiment and apparatus embodiment, the present application also provides a data processing device. Figure 13 , is a structural diagram of a data processing device provided in an embodiment of the present application. Figure 13 The data processing device 130 shown may include at least a processor 1301, an input interface 1302, an output interface 1303, and a computer storage medium 1304. The processor 1301, the input interface 1302, the output interface 1303, and the computer storage medium 1304 may be connected via a bus or other means.

[0153] Computer storage medium 1304 may be stored in memory 1305 of data processing device 130. Computer storage medium 1304 is used to store computer programs, which include program instructions. Processor 1301 is used to execute the program instructions stored in computer storage medium 1304. Processor 1301 (or CPU (Central Processing Unit)) is the computing core and control core of data processing device 130. It is suitable for implementing one or more instructions, and is specifically suitable for loading and executing one or more instructions to implement the above-mentioned data processing method flow or corresponding functions.

[0154] The embodiment of the present application also provides a computer storage medium (Memory), which is a memory device in the data processing device 130 for storing programs and data. It is understandable that the computer storage medium here can include both the built-in storage medium in the terminal and, of course, the extended storage medium supported by the terminal. The computer storage medium provides a storage space that stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor 1301 are also stored in the storage space. These instructions can be one or more computer programs (including program codes). It should be noted that the computer storage medium here can be a high-speed random access memory (RAM) memory, or a non-volatile memory (non-volatile memory), such as at least one disk storage; optionally, it can also be at least one computer storage medium located away from the aforementioned processor.

[0155] In one embodiment, the processor 1301 may load and execute one or more instructions stored in a computer storage medium to implement the above-mentioned Figure 2 as well as Figure 7 In the corresponding steps of the method in the data processing method embodiment, in a specific implementation, one or more instructions in the computer storage medium are loaded by the processor 1301 and the following steps are executed:

[0156] In response to a trigger event for analyzing a sharing operation performed on a target resource link, displaying an analysis setting interface, and obtaining from the analysis setting interface a data identifier of source data of the target resource link and an event identifier corresponding to the sharing operation performed on the target resource link;

[0157] Acquiring source data of the target resource link according to the data identifier, and performing data screening processing on the acquired source data according to the event identifier to obtain analytical reference data of the target resource link, wherein the analytical reference data records multiple user identifiers and sharing relationships between different users regarding the target resource link;

[0158] Analyzing and processing the analysis reference data to divide users corresponding to user identifiers recorded in the analysis reference data into different user sets to obtain a plurality of user sets;

[0159] Acquire feedback information generated by each user who shares the target resource link after sharing the target resource link, and use the feedback information to determine a target user set from the multiple user sets when sharing other resource links to be shared, wherein the other resource links and the target resource link indicate the same resource type.

[0160] In one embodiment, the analysis reference data is a knowledge graph constructed using multiple nodes, wherein a node included in the knowledge graph is used to record a user identifier, and an edge included in the knowledge graph is used to indicate a sharing relationship between users corresponding to the corresponding user identifiers; when the processor 1301 analyzes and processes the analysis reference data to divide users corresponding to the user identifiers recorded in the analysis reference data into different user sets, and obtains multiple user sets, the processor 1301 specifically performs the following operations:

[0161] Traversing each node in the knowledge graph, determining at least one adjacent node of the traversed current node, and calculating the node association degree between the current node and each adjacent node;

[0162] performing a clustering operation on each node in the knowledge graph according to the node association degree, and stopping the clustering operation on the knowledge graph when the set association degree of the node set obtained by clustering reaches a maximum value, thereby obtaining a plurality of node sets;

[0163] A node set is used to indicate a user set, and the user identifier of each node record included in a node set is the user identifier corresponding to the user in the corresponding user set.

[0164] In one embodiment, when the processing unit 1301 performs a clustering operation on each node in the knowledge graph according to the node association, the processing unit 1301 specifically performs the following operations:

[0165] Performing a clustering operation on the current node and any adjacent node to obtain multiple reference node sets and a set association degree corresponding to each reference node set;

[0166] A maximum set association degree is selected from the set association degrees corresponding to each reference node set, and the reference node set indicated by the maximum set association degree is used as the node set to which the current node is clustered.

[0167] In one embodiment, the processor 1301 is further configured to:

[0168] Determining, based on the sharing relationship, a sharing user identifier that has performed a sharing operation on the target resource link from a plurality of user identifiers in the analysis reference data, wherein the user indicated by the sharing user identifier is the user who has shared the target resource link;

[0169] Determining feedback information generated by each user indicated by the sharing user identifier after sharing the target resource link, and determining target feedback information from the feedback information of each user indicated by the sharing user identifier;

[0170] A target user indicated by the user identifier corresponding to the target feedback information is determined, and a user set including the target user is used as a target user set.

[0171] In one embodiment, the processor 1301 is further configured to:

[0172] Determining aggregate feedback information generated by each user group after sharing the target resource link based on feedback information generated by each user who shared the target resource link after sharing the target resource link;

[0173] Target set feedback information is determined from the set feedback information corresponding to each user set, and the user set corresponding to the target set feedback information is used as the target user set.

[0174] In one embodiment, the feedback information includes one or more of the following: the total number of times the target resource link is shared, the total number of users who share the target resource link with other users, the total number of users who access the target resource link based on the shared target resource link, the total amount of resource transfers generated by resource transfers based on the shared target resource link, and the sharing propagation coefficient of the target resource link;

[0175] The target feedback information includes one or more of the following: the maximum number of times the target resource link is shared, the maximum total number of users corresponding to sharing the target resource link with other users, the maximum total number of users accessing the shared target resource link, the maximum total amount of resource transfer corresponding to resource acquisition based on the shared target resource link, and the maximum sharing propagation coefficient of the target resource link.

[0176] In one embodiment, the processor 1301 is further configured to:

[0177] In response to a query trigger operation on a user set, the obtained set information of each user set is displayed in the set query interface;

[0178] Among them, the collection query interface includes collection information of each user collection, and any collection information includes one or more of the following: collection identifier, the total number of times the target resource link is shared by any collection, and the total amount of resource transfer generated by the sharing of the target resource link by any collection.

[0179] In one embodiment, the set query interface further includes a selection indicator for querying users included in any user set; the processor 1301 is further configured to:

[0180] When the target selection identifier is triggered, user information of each user included in the target user set associated with the target selection identifier is displayed, the user information including one or more of the following: a user identifier of the corresponding user, and feedback information generated after sharing the target resource link;

[0181] According to the user information of each user, key propagation users in the target user set are determined.

[0182] In one embodiment, the collection query interface further includes a download component; the processor 1301 is further configured to:

[0183] detecting a selection operation on the download component, downloading user identifiers of users included in the target user set, and obtaining a target identifier set;

[0184] In response to a display triggering operation for user management analysis, displaying a user management interface, the user management interface including a user identification adding component;

[0185] Detecting an operation of selecting the user identifier adding component, importing the target identifier set, and displaying an analysis component for performing user profile analysis on users corresponding to each user identifier in the target identifier set;

[0186] When the analysis component is selected, a user portrait analysis is performed on the user corresponding to the user identifier included in the target identifier set, and the corresponding user portrait analysis result is displayed.

[0187] The embodiment of the present application provides a computer program product or computer program, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device performs the above-mentioned Figure 2 or Figure 7The computer-readable storage medium may be a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0188] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. A data processing method, characterized in that: include: In response to a triggering event for analyzing a sharing operation performed on a target resource link, displaying an analysis setting interface, and obtaining from the analysis setting interface a data identifier of source data of the target resource link and an event identifier corresponding to the sharing operation performed on the target resource link; Acquiring source data of the target resource link according to the data identifier, and performing data screening processing on the acquired source data according to the event identifier to obtain analytical reference data of the target resource link, wherein the analytical reference data records multiple user identifiers and sharing relationships between different users regarding the target resource link; Analyzing and processing the analysis reference data to divide users corresponding to user identifiers recorded in the analysis reference data into different user sets to obtain a plurality of user sets; Obtain feedback information generated by each user who shares the target resource link after sharing the target resource link, the feedback information is used to determine, from the multiple user sets, a target user set when sharing other resource links to be shared, wherein the resource types indicated by the other resource links and the target resource link are the same; a method for determining the target user set includes: according to the sharing relationship, determining, from the multiple user identifiers of the analysis reference data, a sharing user identifier who has performed a sharing operation on the target resource link, the user indicated by the sharing user identifier being the user who shared the target resource link; determining feedback information generated by the user indicated by each sharing user identifier after sharing the target resource link, and determining target feedback information from the feedback information of the user indicated by each sharing user identifier; determining the target user indicated by the user identifier corresponding to the target feedback information, and taking the user set to which the target user belongs as the target user set; The target feedback information includes one or more of the following: the maximum number of times the target resource link is shared, the maximum total number of users corresponding to sharing the target resource link with other users, the maximum total number of users accessing the shared target resource link, the maximum total amount of resource transfer corresponding to resource acquisition based on the shared target resource link, and the maximum sharing propagation coefficient of the target resource link.

2. The method according to claim 1, characterized in that The analysis reference data is a knowledge graph constructed using multiple nodes, wherein a node included in the knowledge graph is used to record a user identifier, and an edge included in the knowledge graph is used to indicate a sharing relationship between users corresponding to the corresponding user identifiers; the analysis reference data is analyzed and processed to divide users corresponding to the user identifiers recorded in the analysis reference data into different user sets, thereby obtaining multiple user sets, including: Traversing each node in the knowledge graph, determining at least one adjacent node of the traversed current node, and calculating the node association degree between the current node and each adjacent node; performing a clustering operation on each node in the knowledge graph according to the node association degree, and stopping the clustering operation on the knowledge graph when the set association degree of the node set obtained by clustering reaches a maximum value, thereby obtaining a plurality of node sets; A node set is used to indicate a user set, and the user identifier of each node record included in a node set is the user identifier corresponding to the user in the corresponding user set.

3. The method according to claim 2, characterized in that The clustering operation is performed on each node in the knowledge graph according to the node association, including: Performing a clustering operation on the current node and any adjacent node to obtain multiple reference node sets and a set association degree corresponding to each reference node set; A maximum set association degree is selected from the set association degrees corresponding to each reference node set, and the reference node set indicated by the maximum set association degree is used as the node set to which the current node is clustered.

4. The method according to claim 1, wherein The method further comprises: Determining aggregate feedback information generated by each user group after sharing the target resource link based on feedback information generated by each user who shared the target resource link after sharing the target resource link; Target set feedback information is determined from the set feedback information corresponding to each user set, and the user set corresponding to the target set feedback information is used as the target user set.

5. The method according to claim 1, wherein The feedback information includes one or more of the following: the total number of times the target resource link is shared, the total number of users who share the target resource link with other users, the total number of users who access the shared target resource link, the total amount of resource transfer generated by resource transfer based on the shared target resource link, and the sharing propagation coefficient of the target resource link.

6. The method according to claim 1, characterized in that The method further comprises: In response to a query trigger operation on a user set, the obtained set information of each user set is displayed in the set query interface; Among them, the collection query interface includes collection information of each user collection, and any collection information includes one or more of the following: collection identifier, the total number of times the target resource link is shared by any collection, and the total amount of resource transfer generated by the sharing of the target resource link by any collection.

7. The method according to claim 6, characterized in that The set query interface also includes a selection indicator for querying users included in any user set; the method further includes: When the target selection identifier is triggered, user information of each user included in the target user set associated with the target selection identifier is displayed, the user information including one or more of the following: a user identifier of the corresponding user, and feedback information generated after sharing the target resource link; According to the user information of each user, key propagation users in the target user set are determined.

8. The method according to claim 6, characterized in that The collection query interface further includes a download component; the method further includes: detecting a selection operation on the download component, downloading user identifiers of users included in the target user set, and obtaining a target identifier set; In response to a display triggering operation for user management analysis, displaying a user management interface, the user management interface including a user identification adding component; Detecting an operation of selecting the user identifier adding component, importing the target identifier set, and displaying an analysis component for performing user profile analysis on users corresponding to each user identifier in the target identifier set; When the analysis component is selected, a user portrait analysis is performed on the user corresponding to the user identifier included in the target identifier set, and the corresponding user portrait analysis result is displayed.

9. A data processing device, characterized in that: include: a display unit, configured to display an analysis setting interface in response to a triggering event for analyzing a sharing operation performed on a target resource link; an acquiring unit, configured to acquire, from the analysis setting interface, a data identifier of the source data of the target resource link and an event identifier corresponding to a sharing operation performed on the target resource link; The acquisition unit is further configured to acquire the source data of the target resource link according to the data identifier; a processing unit configured to perform data screening processing on the acquired source data according to the event identifier to obtain analytical reference data of the target resource link, wherein the analytical reference data records multiple user identifiers and sharing relationships between different users regarding the target resource link; The processing unit is further configured to analyze and process the analysis reference data to divide users corresponding to user identifiers recorded in the analysis reference data into different user sets to obtain a plurality of user sets; The acquisition unit is further configured to acquire feedback information generated by each user who has shared the target resource link after sharing the target resource link, the feedback information being used to determine, from the multiple user sets, a target user set when sharing other resource links to be shared, wherein the resource types indicated by the other resource links and the target resource link are the same; a method for determining the target user set comprises: determining, from the multiple user identifiers of the analysis reference data according to the sharing relationship, a sharing user identifier who has performed a sharing operation on the target resource link, the user indicated by the sharing user identifier being the user who has shared the target resource link; determining feedback information generated by the user indicated by each sharing user identifier after sharing the target resource link, and determining target feedback information from the feedback information of the user indicated by each sharing user identifier; determining a target user indicated by the user identifier corresponding to the target feedback information, and taking the user set to which the target user belongs as the target user set; The target feedback information includes one or more of the following: the maximum number of times the target resource link is shared, the maximum total number of users corresponding to sharing the target resource link with other users, the maximum total number of users accessing the shared target resource link, the maximum total amount of resource transfer corresponding to resource acquisition based on the shared target resource link, and the maximum sharing propagation coefficient of the target resource link.

10. A data processing device, characterized in that: The data processing device includes an input interface and an output interface, and further includes: a processor adapted to implement one or more instructions; and A computer storage medium storing one or more instructions, wherein the one or more instructions are suitable for being loaded by the processor and executing the data processing method according to any one of claims 1 to 8.

11. A computer storage medium, characterized in that The computer storage medium stores computer program instructions, which, when executed by a processor, are used to execute the data processing method according to any one of claims 1 to 8.

12. A computer program product, comprising computer instructions, wherein the computer instructions are stored in a computer-readable storage medium; a processor of a data processing device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, wherein when the computer instructions are executed by the processor, the computer instructions are used to execute the data processing method according to any one of claims 1 to 8.

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