A data association method and device, electronic equipment and readable storage medium

Abstract

Embodiments of the present application provide a data association method and device, electronic equipment and a readable storage medium, the method comprising: obtaining authentication information of a plurality of application programs in a plurality of user terminals used by a user, the authentication information being used to verify the identity information of the user; determining a plurality of burying point information according to the authentication information, the burying point information being used to represent the relevant data of the user and the plurality of applications; performing data recognition on each burying point information in the plurality of burying point information and then performing feature aggregation to obtain a feature table; and performing association determination according to the feature table to obtain association data, the association data being used to reflect the interest content and purchasing power of the user. In the process of identity verification of the user, the burying point information is obtained, the feature table is generated through the burying point information, and the association data related to the user is obtained through the feature table, so that the analysis capability of the video software in data analysis is improved.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a data association method, apparatus, electronic device, and readable storage medium. Background Technology

[0002] Currently, in video software, risk control verification center systems are generally used for human-machine verification to prevent external hackers and users from engaging in profit-making activities. With the development of online video technology, video platforms require increasingly more data analysis. However, risk control verification center systems typically only verify whether a user is a human or a bot during login, or whether the login is secure. For data analysis, video software cannot integrate data analysis with the risk control verification center system, resulting in poor data analysis capabilities in video software. Summary of the Invention

[0003] The purpose of this invention is to provide a data association method, apparatus, electronic device, and readable storage medium to improve the data analysis capabilities of video software. The specific technical solution is as follows:

[0004] In a first aspect of this invention, a data association method is provided, comprising:

[0005] Obtain verification information from multiple applications across multiple user terminals used by the user, the verification information being used to verify the user's identity information;

[0006] Based on the verification information, multiple tracking points are determined, and the tracking points are used to characterize the data related to the user and the multiple applications.

[0007] After data identification of each of the multiple embedded point information, feature aggregation is performed to obtain a feature table;

[0008] The correlation is determined based on the feature table to obtain correlation data, which is used to reflect the user's interests and purchasing power.

[0009] Optionally, obtaining verification information from multiple applications across multiple user terminals used by the user includes:

[0010] Identify multiple user terminals used by the user and multiple applications among the multiple user terminals, including mobile terminals, TV terminals, and web terminals;

[0011] During the process of a user performing human verification through a target application in a target user terminal, the verification information of the target application in the target user terminal is obtained. The target user terminal is any one of the plurality of user terminals, and the target application is any one of the plurality of applications.

[0012] Optionally, after data identification of each of the multiple embedded information points, feature aggregation is performed to obtain a feature table, including:

[0013] Multiple analytical dimensions are obtained, including user dimension, series dimension, user terminal dimension, and age group dimension;

[0014] After data identification is performed on each of the multiple embedded information based on the multiple analysis dimensions, multiple identification data are obtained. The identification data is used to indicate the specific content and specific location of the embedded information.

[0015] At least two identification data that meet a preset similarity score from the multiple identification data are aggregated to obtain multiple data warehouse tool tables, and the multiple data warehouse tool tables correspond one-to-one with the multiple analysis dimensions.

[0016] Optionally, the step of determining the correlation based on the feature table to obtain the correlation data includes:

[0017] The feature table is correlated based on user viewing data to obtain the number of users exposed and the number of devices affected within the target time period;

[0018] The number of exposed users is associated with the TV series data, and the number of affected devices is associated with the TV series data to obtain TV series association data, which includes the name of the TV series, the duration of the TV series, and the number of times the TV series has been viewed.

[0019] Optionally, the step of determining the correlation based on the feature table to obtain the correlation data includes:

[0020] The feature table is correlated based on user viewing data to obtain the number of users exposed and the number of devices affected within the target time period;

[0021] The number of exposed users is associated with the order data, and the number of affected devices is associated with the order data to obtain order association data, which includes the number of orders and the order amount.

[0022] Optionally, after associating the number of exposed users with order data and associating the number of affected devices with order data to obtain order association data, the method further includes:

[0023] Push information is generated based on the order association data, and the push information includes order advertisements and order links;

[0024] The push information is combined with the verification information to generate target verification information, which is used to push the push information to the user during the user's human-machine verification process.

[0025] Optionally, the tracking information includes at least one of the following: the address of the user verification image, the episode ID corresponding to the verification image, the identity information, the business scenario information, and the device information.

[0026] In a second aspect of the invention, a data analysis apparatus is also provided, comprising:

[0027] The acquisition module is used to acquire verification information from multiple applications in multiple user terminals used by the user, and the verification information is used to verify the user's identity information;

[0028] The determination module is used to determine multiple tracking information based on the verification information, wherein the tracking information is used to characterize the relevant data between the user and the multiple applications;

[0029] The aggregation module is used to perform feature aggregation on each of the multiple embedded information points after data identification to obtain a feature table;

[0030] The association module is used to determine the association based on the feature table and obtain associated data, which is used to reflect the user's interests and purchasing power.

[0031] In another aspect of the present invention, a computer-readable storage medium is also provided, wherein instructions are stored therein, which, when executed on a computer, cause the computer to perform any of the data association methods described above.

[0032] In another aspect of the present invention, a computer program product containing instructions is also provided, which, when run on a computer, causes the computer to execute any of the data association methods described above.

[0033] This invention provides a data association method, apparatus, electronic device, and readable storage medium. The method includes: acquiring verification information from multiple applications across multiple user terminals used by a user, the verification information being used to verify the user's identity information; determining multiple tracking points based on the verification information, the tracking points being used to characterize data related to the user and the multiple applications; performing data identification and feature aggregation on each tracking point to obtain a feature table; and determining association based on the feature table to obtain associated data, the associated data being used to reflect the user's interests and purchasing power. This invention improves the data analysis capabilities of video software by acquiring tracking information during user authentication, generating a feature table from the tracking information, and obtaining user-related associations through the feature table. Attached Figure Description

[0034] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below.

[0035] Figure 1 This is a flowchart illustrating the data association method in an embodiment of the present invention;

[0036] Figure 2 This is a schematic diagram illustrating the implementation of the data association method in an embodiment of the present invention;

[0037] Figure 3 This is a schematic diagram of the data association device in an embodiment of the present invention;

[0038] Figure 4 This is a schematic diagram of the structure of an electronic device in an embodiment of the present invention. Detailed Implementation

[0039] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0040] Before discussing the exemplary embodiments in more detail, it should be noted that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although the flowcharts describe the steps as sequential processes, many of these steps can be performed in parallel, concurrently, or simultaneously. Furthermore, the order of the steps can be rearranged. A process can be terminated when its operation is complete, but it may also have additional steps not included in the figures. A process can correspond to a method, function, procedure, subroutine, subroutine, etc.

[0041] Furthermore, the terms "first," "second," etc., may be used herein to describe various directions, actions, steps, or elements, but these directions, actions, steps, or elements are not limited by these terms. These terms are only used to distinguish a first direction, action, step, or element from another direction, action, step, or element. For example, without departing from the scope of this application, a first speed difference may be referred to as a second speed difference, and similarly, a second speed difference may be referred to as a first speed difference. Both the first speed difference and the second speed difference are speed differences, but they are not the same speed difference. The terms "first," "second," etc., should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0042] This application provides a data association method, such as... Figure 1 As shown, the steps of this method include:

[0043] Step 101: Obtain verification information from multiple applications in multiple user terminals used by the user. The verification information is used to verify the user's identity information.

[0044] In this embodiment, the verification information includes the user's login account, password, login address, mobile phone number, and verification code entered on the application. The human verification function performs human verification when the user uses the application. Verification is typically completed through various methods such as sliding a slider, clicking a required image, clicking text sequentially, and selecting required numbers, thereby identifying the user's identity information. It should be noted that the identity information includes the user's ID, account data, etc. When the user passes verification, the server can identify whether the operation was performed by the user themselves, thus preventing account theft by hackers or other unauthorized means.

[0045] Step 102: Determine multiple tracking points based on the verification information. The tracking points are used to characterize the data related to the user and the multiple applications.

[0046] In this embodiment, refer to Figure 2 , Figure 2This is a schematic diagram illustrating the implementation process of the data association method in this embodiment of the invention. The tracking information refers to the data that needs to be acquired during the human-machine verification process. Specifically, the tracking information can acquire relevant data from multiple applications, including but not limited to the TV series watched by the user, the user's purchasing power for the TV series, and the user's viewing time. Specifically, this step can leverage the capability of integrating a tracking pingback SDK into the front end of the human-machine verification center. The pingback SDK is a pre-packaged programming program used to acquire the user's tracking information. During the video software development phase, the pingback SDK is pre-packaged at the locations where tracking is needed in the human-machine verification center to meet the tracking delivery needs of different terminals, such as mobile, TV, or web terminals. During the user verification phase, the pingback SDK reads the user's personal data to obtain multiple tracking information items and sends these items to the software backend for analysis. It should be noted that the tracking information includes at least one of the following: the address of the user verification image, the TV series ID corresponding to the verification image, the identity information, business scenario information, and device information.

[0047] Step 103: After data identification of each of the multiple embedded information points, feature aggregation is performed to obtain a feature table.

[0048] In this embodiment, the front-end SDK identifies the content in the monitored user tracking data to obtain multiple identified tracking information. It then aggregates the similarity of this identified tracking information to generate a feature table. There can be multiple feature tables, each reflecting data characteristics across different dimensions. During analysis, correlation analysis can be performed across these different dimensions. Each feature table corresponds to one analysis dimension. It should be noted that the feature table can also be in a database or other format capable of storing data; this embodiment does not impose specific limitations.

[0049] Step 104: Determine the correlation based on the feature table to obtain the correlation data, which is used to reflect the user's interests and purchasing power.

[0050] In this embodiment, the correlation determination can be attribution analysis. Attribution analysis refers to the conscious or unconscious explanations people make of various social behaviors occurring in their surroundings in order to effectively control and adapt to their environment during daily social interactions. Specifically, in this embodiment, attribution data is obtained by performing attribution analysis on the feature table acquired in step 103. This attribution data can reflect users' emotional interests and purchasing power in multiple aspects. Therefore, information can be pushed to users during the human verification process, such as relevant purchase content or links. For example, an advertisement for a TV series and a VIP link can be inserted into the human verification image. If a user is interested in the TV series, they can directly click the VIP link.

[0051] This invention provides a data association method, comprising: acquiring verification information from multiple applications across multiple user terminals used by a user, the verification information being used to verify the user's identity information; determining multiple tracking points based on the verification information, the tracking points being used to characterize data related to the user and the multiple applications; performing data identification and feature aggregation on each tracking point to obtain a feature table; and determining association based on the feature table to obtain associated data, the associated data being used to reflect the user's interests and purchasing power. This invention improves the data analysis capabilities of video software by acquiring tracking information during user authentication, generating a feature table from the tracking information, and obtaining user-related associated data through the feature table.

[0052] Optionally, step 101, obtaining verification information from multiple applications among multiple user terminals used by the user, includes:

[0053] Identify multiple user terminals used by the user and multiple applications among the multiple user terminals, including mobile terminals, TV terminals, and web terminals;

[0054] During the process of a user performing human verification through a target application in a target user terminal, the verification information of the target application in the target user terminal is obtained. The target user terminal is any one of the plurality of user terminals, and the target application is any one of the plurality of applications.

[0055] In this embodiment, the user terminal can be a mobile terminal, a TV terminal, or a web terminal, etc. The mobile terminal can be further divided into a phone terminal, a tablet terminal, a PC terminal, etc. The various applications can be animation programs, movie programs, live streaming programs, etc., and are not specifically limited in this embodiment.

[0056] During the process of human verification by a user using the target application on the target user's device, the backend server will obtain verification information during the verification process, so as to directly obtain the data points of the target application on the target user's device. When the user uses different user devices or different applications, the backend server can obtain different verification information respectively.

[0057] Optionally, step 103, which involves performing data identification on each of the multiple embedded information points and then aggregating features to obtain a feature table, includes:

[0058] Multiple analytical dimensions are obtained, including user dimension, series dimension, user terminal dimension, and age group dimension;

[0059] After data identification is performed on each of the multiple embedded information based on the multiple analysis dimensions, multiple identification data are obtained. The identification data is used to indicate the specific content and specific location of the embedded information.

[0060] At least two identification data that meet a preset similarity score from the multiple identification data are aggregated to obtain multiple data warehouse tool tables, and the multiple data warehouse tool tables correspond one-to-one with the multiple analysis dimensions.

[0061] In this embodiment, the analysis process can be performed through multiple dimensions to obtain data warehouse tool tables (HIVE tables) with different dimensions. The user dimension indicates that different user IDs, such as students, adults, and the elderly, reflect different content interests. The drama series dimension indicates varying levels of user preference for different dramas; for example, popular dramas attract more viewers, while documentaries attract fewer. The user terminal dimension indicates varying numbers of users using different devices; for example, most people prefer watching videos on their phones, while fewer watch on computers. The age group dimension indicates different viewing data for users of different ages; for example, younger users watch more dramas because they enjoy television dramas, while older users watch fewer. Generating data warehouse tool tables through multiple dimensions allows for better reflection of differences between dimensions during subsequent attribution analysis, improving the accuracy of the analysis.

[0062] In one feasible implementation, step 104, determining the correlation based on the feature table to obtain the correlation data, includes:

[0063] The feature table is correlated based on user viewing data to obtain the number of users exposed and the number of devices affected within the target time period;

[0064] The number of exposed users is associated with the TV series data, and the number of affected devices is associated with the TV series data to obtain TV series association data, which includes the name of the TV series, the duration of the TV series, and the number of times the TV series has been viewed.

[0065] In this embodiment, the correlation between the acquired multi-dimensional feature tables and user viewing data is determined to obtain the number of users exposed and the number of devices affected within a target time period. Correlation determination involves analyzing multiple data points, associating them, and providing the same explanation or definition. For example, correlation determination can be a type of correlation analysis such as attribution analysis. User viewing data can be obtained from the recordings in the video software's backend. The target time period can be evening or noon, etc. The number of exposed users refers to the data of users watching the content, and the number of affected devices refers to the number of devices used by the users. By associating the number of exposed users and the number of affected devices with all the drama series data and the aforementioned user viewing data, accurate drama series correlation data can be obtained. This drama series correlation data can intuitively display various aspects of each drama series, such as the number of users watching, the viewing time, and the viewing devices. In this embodiment, no specific limitations are imposed.

[0066] In another feasible implementation, step 104, determining the correlation based on the feature table to obtain the correlation data, includes:

[0067] The feature table is correlated based on user viewing data to obtain the number of users exposed and the number of devices affected within the target time period;

[0068] The number of exposed users is associated with the order data, and the number of affected devices is associated with the order data to obtain order association data, which includes the number of orders and the order amount.

[0069] In this embodiment, the correlation between the acquired multi-dimensional feature tables and user viewing data is determined to obtain the number of users exposed and the number of devices affected within a target time period. User viewing data can be obtained from the recording data in the video software's backend. The target time period can be evening or noon, etc. The number of exposed users refers to the data of users watching the video, and the number of affected devices refers to the number of devices used by the users. By correlating the number of exposed users and the number of affected devices with order data and the aforementioned user viewing data, order-related data can be obtained. This order-related data can intuitively show whether users made purchases for each series, the quantity purchased, and the amount spent, thus reflecting the series with the highest purchasing power. It should be noted that the order-related data differs for different series. For example, the number of purchases for the first and second episodes of a certain TV series may differ. This could be because some users may not want to continue watching after watching the first episode.

[0070] Optionally, after associating the number of exposed users with order data and associating the number of affected devices with order data to obtain order association data, the method further includes:

[0071] Push information is generated based on the order association data, and the push information includes order advertisements and order links;

[0072] The push information is combined with the verification information to generate target verification information, which is used to push the push information to the user during the user's human-machine verification process.

[0073] In this embodiment, after acquiring order association data, the video software can also generate push notifications based on the order association data and combine the push notifications with verification information to generate target verification information. Specifically, the more orders or the higher the order amount for a particular drama series, the more push notifications related to that drama series will be generated, thus letting users know that the drama series is popular. Furthermore, the combination of push notifications and verification information can involve replacing the verification image with an image of the drama series to be recommended to the user, or replacing the verification text with text about the drama series to be recommended to the user, etc. During the user's verification process, the target verification information is displayed. For example, for different users, advertisements and VIP links for drama series matching the user are inserted into the human-machine verification image. The matching drama series advertisements and VIP links are generated individually based on each user's viewing data, and the target verification information recommended to different users is different. When a user is interested in a drama series, they can directly click on the VIP link. It should be noted that the push notification method in this embodiment is not limited to order advertisements and order links; other push notification formats are also possible.

[0074] This invention provides a data association method, which includes: acquiring verification information from multiple applications across multiple user terminals used by a user, the verification information being used to verify the user's identity information; determining multiple tracking points based on the verification information, the tracking points being used to characterize the user's related data with the multiple applications; performing data identification and feature aggregation on each tracking point to obtain a feature table; and determining the association based on the feature table to obtain associated data, the associated data being used to reflect the user's interests and purchasing power. This invention improves the data analysis capabilities of video software by acquiring tracking information during user authentication, generating a feature table from the tracking information, and obtaining user-related associations through the feature table.

[0075] This invention also provides a data analysis device 300, such as... Figure 3 As shown, the data analysis device 300 includes:

[0076] The acquisition module 310 is used to acquire verification information of multiple applications among multiple user terminals used by the user, and the verification information is used to verify the user's identity information;

[0077] The determination module 320 is used to determine multiple tracking information based on the verification information, wherein the tracking information is used to characterize the relevant data between the user and the multiple applications;

[0078] The aggregation module 330 is used to perform feature aggregation on each of the multiple embedded information points after data identification to obtain a feature table;

[0079] Analysis module 340 is used to determine the correlation based on the feature table and obtain the correlation data, which is used to reflect the user's interests and purchasing power.

[0080] Optionally, the acquisition module 310 also includes:

[0081] The determination submodule is used to determine multiple user terminals used by the user and multiple applications among the multiple user terminals, including mobile terminals, TV terminals and WEB terminals;

[0082] The acquisition submodule is used to acquire the verification information of the target application in the target user terminal during the human-computer verification process of the user through the target application in the target user terminal. The target user terminal is any one of the multiple user terminals, and the target application is any one of the multiple applications.

[0083] Optionally, the aggregation module 330 also includes:

[0084] The dimension acquisition submodule is used to acquire multiple analysis dimensions, including user dimension, series dimension, user terminal dimension, and age group dimension;

[0085] The dimension analysis submodule is used to perform data identification on each of the multiple tracking information based on the multiple analysis dimensions to obtain multiple identification data. The identification data is used to indicate the specific content and specific location of the tracking information.

[0086] The dimension aggregation submodule aggregates at least two identification data that meet a preset similarity score from the multiple identification data to obtain multiple data warehouse tool tables, which correspond one-to-one with the multiple analysis dimensions.

[0087] Optionally, the analysis module 340 also includes:

[0088] The first analysis submodule is used to determine the correlation between the feature table and user viewing data to obtain the number of users exposed and the number of devices affected within the target time period;

[0089] The second association submodule is used to associate the number of exposed users with the TV series data and the number of affected devices with the TV series data to obtain TV series association data, which includes the name of the TV series, the duration of the TV series, and the number of times the TV series has been viewed.

[0090] Optionally, the analysis module 340 also includes:

[0091] The second analysis submodule is used to determine the correlation between the feature table and the user viewing data to obtain the number of users exposed and the number of devices affected within the target time period;

[0092] The second association submodule is used to associate the number of exposed users with order data, and to associate the number of affected devices with order data to obtain order association data, wherein the order data includes the number of orders and the order amount.

[0093] Optional, also includes:

[0094] The information generation submodule is used to generate push information based on the order association data, the push information including order advertisements and order links;

[0095] The information push submodule is used to combine the push information with the verification information to generate target verification information, which is used to push the push information to the user during the user's human-machine verification process.

[0096] Optionally, the tracking information includes at least one of the following: the address of the user verification image, the episode ID corresponding to the verification image, the identity information, the business scenario information, and the device information.

[0097] This invention improves the data analysis capabilities of video software by acquiring tracking information during user authentication, generating a feature table from the tracking information, and obtaining user-related associations through the feature table.

[0098] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention, such as... Figure 4 As shown, the electronic device 400 includes a memory 410 and a processor 420. The number of processors 420 in the electronic device 400 can be one or more. Figure 4 Taking a processor 420 as an example; the memory 410 and processor 420 in the server can be connected via a bus or other means. Figure 4 Taking the example of a connection between China and Israel via a bus.

[0099] The memory 410, as a computer-readable storage medium, can be used to store software programs, computer-executable programs, and modules, such as the program instructions / modules corresponding to the title generation method in this embodiment of the invention. The processor 420 executes various functional applications and data processing of the server / terminal / server by running the software programs, instructions, and modules stored in the memory 410, thereby realizing the above-mentioned data association method.

[0100] The processor 420 is used to run the computer program stored in the memory 410, and performs the following steps:

[0101] Obtain verification information from multiple applications across multiple user terminals used by the user, the verification information being used to verify the user's identity information;

[0102] Based on the verification information, multiple tracking points are determined, and the tracking points are used to characterize the data related to the user and the multiple applications.

[0103] After data identification of each of the multiple embedded point information, feature aggregation is performed to obtain a feature table;

[0104] The correlation is determined based on the feature table to obtain correlation data, which is used to reflect the user's interests and purchasing power.

[0105] Optionally, obtaining verification information from multiple applications across multiple user terminals used by the user includes:

[0106] Identify multiple user terminals used by the user and multiple applications among the multiple user terminals, including mobile terminals, TV terminals, and web terminals;

[0107] During the process of a user performing human verification through a target application in a target user terminal, the verification information of the target application in the target user terminal is obtained. The target user terminal is any one of the plurality of user terminals, and the target application is any one of the plurality of applications.

[0108] Optionally, after data identification of each of the multiple embedded information points, feature aggregation is performed to obtain a feature table, including:

[0109] Multiple analytical dimensions are obtained, including user dimension, series dimension, user terminal dimension, and age group dimension;

[0110] After data identification is performed on each of the multiple embedded information based on the multiple analysis dimensions, multiple identification data are obtained. The identification data is used to indicate the specific content and specific location of the embedded information.

[0111] At least two identification data that meet a preset similarity score from the multiple identification data are aggregated to obtain multiple data warehouse tool tables, and the multiple data warehouse tool tables correspond one-to-one with the multiple analysis dimensions.

[0112] Optionally, the step of determining the correlation based on the feature table to obtain the correlation data includes:

[0113] The feature table is correlated based on user viewing data to obtain the number of users exposed and the number of devices affected within the target time period;

[0114] The number of exposed users is associated with the TV series data, and the number of affected devices is associated with the TV series data to obtain TV series association data, which includes the name of the TV series, the duration of the TV series, and the number of times the TV series has been viewed.

[0115] Optionally, the step of determining the correlation based on the feature table to obtain the correlation data includes:

[0116] The feature table is correlated based on user viewing data to obtain the number of users exposed and the number of devices affected within the target time period;

[0117] The number of exposed users is associated with the order data, and the number of affected devices is associated with the order data to obtain order association data, which includes the number of orders and the order amount.

[0118] Optionally, after associating the number of exposed users with order data and associating the number of affected devices with order data to obtain order association data, the method further includes:

[0119] Push information is generated based on the order association data, and the push information includes order advertisements and order links;

[0120] The push information is combined with the verification information to generate target verification information, which is used to push the push information to the user during the user's human-machine verification process.

[0121] Optionally, the tracking information includes at least one of the following: the address of the user verification image, the episode ID corresponding to the verification image, the identity information, the business scenario information, and the device information.

[0122] In one embodiment, the computer program of the electronic device provided by the present invention is not limited to the above-described method operation, but can also perform related operations in the data association method provided by any embodiment of the present invention.

[0123] The memory 410 may primarily include a program storage area and a data storage area. The program storage area may store the operating system and at least one application program required for a given function; the data storage area may store data created based on terminal usage. Furthermore, the memory 410 may include high-speed random access memory and non-volatile memory, such as at least one disk storage device, flash memory, or other non-volatile solid-state storage device. In some instances, the memory 410 may further include memory remotely located relative to the processor 420, which can be connected to a server / terminal / server via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0124] This invention improves the data analysis capabilities of video software by acquiring tracking information during user authentication, generating a feature table from the tracking information, and obtaining user-related associations through the feature table.

[0125] This invention also provides a storage medium containing computer-executable instructions, which, when executed by a computer processor, are used to perform a data association method, the method comprising:

[0126] Obtain verification information from multiple applications across multiple user terminals used by the user, the verification information being used to verify the user's identity information;

[0127] Based on the verification information, multiple tracking points are determined, and the tracking points are used to characterize the data related to the user and the multiple applications.

[0128] After data identification of each of the multiple embedded point information, feature aggregation is performed to obtain a feature table;

[0129] The correlation is determined based on the feature table to obtain correlation data, which is used to reflect the user's interests and purchasing power.

[0130] Optionally, obtaining verification information from multiple applications across multiple user terminals used by the user includes:

[0131] Identify multiple user terminals used by the user and multiple applications among the multiple user terminals, including mobile terminals, TV terminals, and web terminals;

[0132] During the process of a user performing human verification through a target application in a target user terminal, the verification information of the target application in the target user terminal is obtained. The target user terminal is any one of the plurality of user terminals, and the target application is any one of the plurality of applications.

[0133] Optionally, after data identification of each of the multiple embedded information points, feature aggregation is performed to obtain a feature table, including:

[0134] Multiple analytical dimensions are obtained, including user dimension, series dimension, user terminal dimension, and age group dimension;

[0135] After data identification is performed on each of the multiple embedded information based on the multiple analysis dimensions, multiple identification data are obtained. The identification data is used to indicate the specific content and specific location of the embedded information.

[0136] At least two identification data that meet a preset similarity score from the multiple identification data are aggregated to obtain multiple data warehouse tool tables, and the multiple data warehouse tool tables correspond one-to-one with the multiple analysis dimensions.

[0137] Optionally, the step of determining the correlation based on the feature table to obtain the correlation data includes:

[0138] The feature table is correlated based on user viewing data to obtain the number of users exposed and the number of devices affected within the target time period;

[0139] The number of exposed users is associated with the TV series data, and the number of affected devices is associated with the TV series data to obtain TV series association data, which includes the name of the TV series, the duration of the TV series, and the number of times the TV series has been viewed.

[0140] Optionally, the step of determining the correlation based on the feature table to obtain the correlation data includes:

[0141] The feature table is correlated based on user viewing data to obtain the number of users exposed and the number of devices affected within the target time period;

[0142] The number of exposed users is associated with the order data, and the number of affected devices is associated with the order data to obtain order association data, which includes the number of orders and the order amount.

[0143] Optionally, after associating the number of exposed users with order data and associating the number of affected devices with order data to obtain order association data, the method further includes:

[0144] Push information is generated based on the order association data, and the push information includes order advertisements and order links;

[0145] The push information is combined with the verification information to generate target verification information, which is used to push the push information to the user during the user's human-machine verification process.

[0146] Optionally, the tracking information includes at least one of the following: the address of the user verification image, the episode ID corresponding to the verification image, the identity information, the business scenario information, and the device information.

[0147] Of course, the computer-executable instructions provided in the embodiments of the present invention are not limited to the method operations described above, but can also perform related operations in a data association method provided in any embodiment of the present invention.

[0148] This invention improves the data analysis capabilities of video software by acquiring tracking information during user authentication, generating a feature table from the tracking information, and obtaining user-related associations through the feature table.

[0149] The computer-readable storage medium of this invention can be any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. For example, a computer-readable storage medium can be—but is not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0150] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, capable of sending, propagating, or transmitting programs for use by or in connection with an instruction execution system, apparatus, or device.

[0151] The program code contained on the storage medium can be transmitted using any suitable medium, including—but not limited to—wireless, wired, optical fiber, RF, etc., or any suitable combination thereof.

[0152] Computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as "C" or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or terminal. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0153] Note that the above description is merely a preferred embodiment of the present invention and the technical principles employed. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and various obvious changes, readjustments, and substitutions can be made without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in detail through the above embodiments, the present invention is not limited to the above embodiments, and may include many other equivalent embodiments without departing from the concept of the present invention, the scope of which is determined by the scope of the appended claims.

Claims

1. A data association method, characterized by, The method comprises the following steps: obtaining authentication information of a plurality of application programs in a plurality of user terminals used by a user, the authentication information being used to verify identity information of the user, the obtaining of the authentication information of the plurality of application programs in the plurality of user terminals used by the user comprising: determining a plurality of user terminals used by the user and a plurality of application programs in the plurality of user terminals, the plurality of user terminals comprising a mobile terminal, a television terminal and a WEB terminal; obtaining authentication information of a target application program in a target user in a process in which the target user performs human-computer verification through a target application program in a target user terminal, the target user terminal being any one of the plurality of user terminals, and the target application program being any one of the plurality of application programs; determining a plurality of buried point information according to the authentication information, the buried point information being used to represent relevant data of the user and the plurality of application programs; performing data recognition on each of the plurality of buried point information and then performing feature aggregation to obtain a feature table; determining correlation according to the feature table to obtain correlation data, the correlation data being used to reflect interest content and purchasing power of the user, the determining of the correlation according to the feature table to obtain the correlation data comprising: determining correlation of the feature table according to user viewing data to obtain a number of users exposed in a target time period and a number of devices affected; associating the number of users exposed with order data, and associating the number of devices affected with the order data to obtain order correlation data, the order data comprising an order quantity and an order amount; after the associating of the number of users exposed with the order data and the associating of the number of devices affected with the order data to obtain the order correlation data, the method further comprises: generating push information according to the order correlation data, the push information comprising order advertisements and order links; combining the push information with the authentication information to generate target authentication information, the target authentication information being used to push the push information to the user in the process in which the user performs human-computer verification; the buried point information comprises at least one of the following: an address of a user authentication picture, an episode ID corresponding to the authentication picture, the identity information, business scenario information and device information.

2. The method of claim 1, wherein, The performing of data recognition on each of the plurality of buried point information and then the performing of feature aggregation to obtain the feature table comprises: obtaining a plurality of analysis dimensions, the plurality of analysis dimensions comprising a user dimension, an episode dimension, a user terminal dimension and an age range dimension; performing data recognition on each of the plurality of buried point information based on the plurality of analysis dimensions to obtain a plurality of recognition data, the recognition data being used to indicate specific content and a specific position of the buried point information; aggregating at least two recognition data in the plurality of recognition data that satisfy a preset similarity to obtain a plurality of data warehouse tool tables, the plurality of data warehouse tool tables corresponding to the plurality of analysis dimensions one by one.

3. The method of claim 1, wherein, The determining of the correlation according to the feature table to obtain the correlation data comprises: determining correlation of the feature table according to user viewing data to obtain a number of users exposed in a target time period and a number of devices affected; The number of exposed users is associated with the TV series data, and the number of affected devices is associated with the TV series data to obtain TV series association data, which includes the name of the TV series, the duration of the TV series, and the number of times the TV series has been viewed.

4. A data association apparatus characterized by comprising: include: The acquisition module is used to acquire verification information from multiple applications among multiple user terminals used by the user. The verification information is used to verify the user's identity information. The acquisition module further includes: a determination submodule, used to determine the multiple user terminals used by the user and the multiple applications among the multiple user terminals, the multiple user terminals including mobile terminals, TV terminals and WEB terminals; and an acquisition submodule, used to acquire the verification information of the target application in the target user terminal during the process of the user performing human-computer verification through the target application in the target user terminal, the target user terminal being any one of the multiple user terminals and the target application being any one of the multiple applications. The determination module is used to determine multiple tracking information based on the verification information, wherein the tracking information is used to characterize the relevant data between the user and the multiple applications; The aggregation module is used to perform feature aggregation on each of the multiple embedded information points after data identification to obtain a feature table; The association module is used to determine the association based on the feature table to obtain association data, which reflects the user's interests and purchasing power. The association module further includes: a second analysis submodule, used to determine the association based on user viewing data to obtain the number of users exposed and the number of devices affected within a target time period; a second association submodule, used to associate the number of exposed users with order data and the number of affected devices with the order data to obtain order association data, which includes the number of orders and the order amount; an information generation submodule, used to generate push information based on the order association data, which includes order advertisements and order links; and an information push submodule, used to combine the push information with the verification information to generate target verification information, which is used to push the push information to the user during the user's human-machine verification process. The tracking information includes at least one of the following: the address of the user's verification image, the drama ID corresponding to the verification image, the identity information, the business scenario information, and the device information.

5. An electronic device, comprising: It includes a processor, a memory, and a program or instructions stored in the memory and executable on the processor, wherein the program or instructions, when executed by the processor, implement the steps of the data association method as described in any one of claims 1 to 3.

6. A computer-readable storage medium, characterized in that, The readable storage medium stores a program or instructions that, when executed by a processor, implement the steps of the data association method as described in any one of claims 1 to 3.

Images