Data processing method and device, electronic equipment and storage medium

Abstract

The invention discloses a data processing method and device, electronic equipment and a storage medium, and the method comprises the steps: collecting metadata of a target product, obtaining analysis dimension information and analysis index information in an analysis model, and enabling the analysis index information to comprise object event information and object user feature information; screening target event metadata from the event metadata in combination with the user metadata according to the target event information and the target user feature information; and performing data analysis under the analysis dimension information based on the target event metadata to generate a product analysis result of the target product. According to the technical scheme provided by the embodiment of the invention, a worker does not need to check multiple items of data of the software product through different platforms and then analyze the related data to obtain the analysis result, so that the time cost for the worker to analyze the related data of the software product to obtain the analysis result is reduced, and the working efficiency is improved.

Description

Technical Field

[0001] The present disclosure belongs to the field of information processing technology, and in particular, relates to a data processing method, device, electronic device and storage medium. Background Art

[0002] With the development of science and technology, various software products are used in major electronic devices. In order to better understand the customers, product information, market and other aspects of software products, relevant technologies will analyze various data of software products to obtain analysis results and provide them to staff for review. However, in the process of analyzing various data of software products and obtaining analysis results, staff are required to view multiple data of software products through different platforms, and then analyze the relevant data to obtain analysis results, which increases the time cost of staff analyzing the relevant data of software products to obtain analysis results, and the work efficiency is low. Summary of the invention

[0003] The disclosed embodiments provide a solution to solve the problem in related technologies that workers need to view multiple data of software products through different platforms and then analyze the relevant data to obtain analysis results, thereby increasing the time cost for workers to analyze the relevant data of software products to obtain analysis results and resulting in low work efficiency.

[0004] In a first aspect, the present disclosure provides a data processing method, the method comprising:

[0005] Collecting metadata of the target product, wherein the metadata includes event metadata and user metadata;

[0006] Acquire analysis dimension information and analysis indicator information in the analysis model, wherein the analysis indicator information includes target event information and target user feature information;

[0007] In combination with the user metadata, according to the target event information and the target user characteristic information, target event metadata is filtered from the event metadata;

[0008] Data analysis is performed based on the target event metadata under the analysis dimension information to generate a product analysis result of the target product.

[0009] In a second aspect, the present disclosure provides a data processing device, the device comprising:

[0010] A collection unit, used to collect metadata of a target product, wherein the metadata includes event metadata and user metadata;

[0011] An acquisition unit, used to acquire analysis dimension information and analysis indicator information in the analysis model, wherein the analysis indicator information includes target event information and target user feature information;

[0012] a screening unit, configured to screen target event metadata from the event metadata in combination with the user metadata according to the target event information and the target user feature information;

[0013] A generating unit is used to perform data analysis under the analysis dimension information based on the target event metadata to generate a product analysis result of the target product.

[0014] In a third aspect, the present disclosure provides an electronic device, including:

[0015] Processor; and

[0016] A memory, configured to store executable instructions of the processor;

[0017] The processor is configured to execute any method in the first aspect or any possible implementation of the first aspect by executing the executable instructions.

[0018] In a fourth aspect, an embodiment of the present disclosure provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the computer program implements any method in the first aspect or any possible implementation of the first aspect.

[0019] The technical solution provided by the present disclosure collects metadata of the target product, the metadata includes event metadata and user metadata; obtains analysis dimension information and analysis indicator information in the analysis model, the analysis indicator information includes target event information and target user feature information; combines the user metadata, and filters the target event metadata from the event metadata according to the target event information and the target user feature information; performs data analysis based on the target event metadata under the analysis dimension information to generate a product analysis result of the target product. The technical solution provided by each embodiment of the present disclosure does not require the staff to view multiple pieces of software product data through different platforms, but instead obtains the metadata of the target product, determines the target event metadata from the metadata, and then generates the product analysis result of the target product based on the target event metadata, thereby reducing the time cost of the staff analyzing the relevant data of the software product to obtain the analysis result and improving work efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure or related technologies, the following briefly introduces the drawings required for use in the embodiments or related technical descriptions. Obviously, the drawings described below are some embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. In the drawings:

[0021] Figure 1 A flowchart of a data processing method provided by an embodiment of the present disclosure;

[0022] Figure 2 A schematic diagram of metadata management provided by an embodiment of the present disclosure;

[0023] Figure 3 A schematic diagram of an event analysis provided by an embodiment of the present disclosure;

[0024] Figure 4 A schematic diagram of a funnel analysis provided by an embodiment of the present disclosure;

[0025] Figure 5 A schematic diagram of a distribution analysis provided by an embodiment of the present disclosure;

[0026] Figure 6 A schematic diagram of the structure of a data processing device provided by an embodiment of the present disclosure;

[0027] Figure 7 A schematic diagram of the structure of an electronic device provided by an embodiment of the present disclosure. DETAILED DESCRIPTION

[0028] Embodiments of the present disclosure are described in detail below, and examples of the embodiments are shown in the accompanying drawings. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present disclosure, but should not be understood as limiting the present disclosure.

[0029] The terms "first" and "second" and the like in the specification, claims, and drawings of the disclosed embodiments are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the disclosed embodiments described herein can, for example, be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products, or devices.

[0030] The data processing method provided in the embodiments of the present disclosure may be run on a terminal device or a server. The terminal device may be a local terminal device. The server may 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, CDN, and big data and artificial intelligence platforms.

[0031] With the development of science and technology, various software products are used in major electronic devices. In order to better understand the customers, product information, market and other aspects of software products, relevant technologies will analyze various data of software products to obtain analysis results and provide them to staff for review. However, in the process of analyzing various data of software products and obtaining analysis results, staff are required to view multiple data of software products through different platforms, and then analyze the relevant data to obtain analysis results, which increases the time cost of staff analyzing the relevant data of software products to obtain analysis results, and the work efficiency is low.

[0032] Taking the data processing method applied to a server and the target product being an application (Application, APP for short) as an example, when a user wants to analyze an APP, it is necessary to obtain the metadata of the APP and determine the analysis model that the user wants to analyze. After determining the analysis dimension information and analysis indicator information in the analysis model, the target event metadata is determined from the event metadata included in the metadata in combination with the metadata according to the target event information and the target user feature information. Finally, data analysis is performed based on the target event metadata under the dimension information to generate the analysis results of the APP. This process does not require staff to view multiple APP data through different platforms, which reduces the time cost of users analyzing the relevant data of software products to obtain analysis results and improves work efficiency.

[0033] The following specific embodiments are used to describe in detail the technical solution of the present invention and how the technical solution of the present invention solves the above-mentioned technical problems. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present invention will be described below in conjunction with the accompanying drawings.

[0034] Figure 1 A flowchart of a data processing method provided by an exemplary embodiment of the present disclosure is provided. The method can be applied to various devices with data processing functions. Taking the method applied to a server as an example, the solution at least includes the following steps S101-S104:

[0035] S101, collecting metadata of target products.

[0036] In some embodiments, the target product refers to a product or product function to be subjected to data analysis, and specifically may be an application program, or an application function in an application program.

[0037] In some embodiments, the metadata includes event metadata and user metadata.

[0038] In some embodiments, the event metadata includes event attribute information corresponding to the meta-event.

[0039] In some embodiments, the user metadata includes user feature information corresponding to the meta-user.

[0040] Among them, a meta-event refers to a single operation performed by a user or a single action that occurs within a specific time. Taking the target product as an application as an example, a meta-event can refer to an operation or action triggered by a user based on the application, for example, it can be a login operation to log in to the application, a registration operation to register a corresponding account in the application, or an action of a user participating in a certain activity, etc. It can be understood that when user A triggers an operation or action corresponding to a meta-event, an event metadata of the meta-event can be collected and recorded accordingly, and when user B triggers an operation or action corresponding to the meta-event, an event metadata of the meta-event can be collected and recorded accordingly.

[0041] Event attribute information refers to the attribute information used to describe the occurrence of an event, such as device information when the event occurs, business parameter information of the business associated with the event, event parameter information corresponding to the event, etc.; taking the target product as an application as an example, device information includes but is not limited to app version, terminal device number, IP address, terminal device model, etc., business parameter information includes but is not limited to source business name, business operation platform, etc., event parameter information includes but is not limited to event name, event reporting time, event triggering timing, event online time, event reporting volume, event triggering user, etc., among which event reporting time refers to the time when an event is collected, and event triggering timing refers to the time when the corresponding event is triggered (to collect an event) The event online time refers to the time when the corresponding event collection starts, and the event reporting volume refers to the number of times the corresponding event occurs within a certain time period. For example, taking the event as the action of the user participating in a product activity as an example, the event online time can be the online time of the product activity, and the event triggering timing can be the user clicking the "Participate in the Activity" page control in the corresponding activity page, that is, when the user clicks "Participate in the Activity" in the corresponding activity page, the event is reported once. At this time, the server can collect an event data corresponding to the event. The event reporting time refers to the time when an event metadata corresponding to the event is collected, and the event reporting number refers to the amount of event data corresponding to the event collected within a certain time period (such as 30 days).

[0042] Among them, meta-user refers to the user account that uses the target product; the user characteristic information refers to the characteristic values ​​corresponding to the user account registered by the meta-user under different user characteristic information dimensions. For example, user characteristic information includes but is not limited to the user's nationality, gender, date of birth, registration time, etc.

[0043] It is understandable that event metadata may refer to meta-events and their event attribute information that have not been analyzed and processed, and user metadata refers to meta-users and their user feature information that have not been analyzed and processed and classified.

[0044] In some embodiments, in actual operation, the collected metadata of the target product can be displayed to the user through a visual interface, and the metadata of the target product can also be managed through the visual interface; for example, the metadata management page can be displayed through the display device corresponding to the server, so as to display the event metadata to the user through the metadata management page.

[0045] Combination Figure 2As shown, when the user clicks the "meta event" button in the visualization interface corresponding to the "meta data management" module provided by the display device, the visualization interface can jump to display the corresponding meta event and its corresponding event attribute information. For example, the event name corresponding to the meta event is 11th gift grabbing-participating in answering questions, and the attribute information corresponding to the meta event may include security level, triggering time (i.e. event triggering time), event reporting source, reporting volume in the past 30 days (i.e. corresponding event reporting times), operation platform (i.e. corresponding business operation platform) and online time (i.e. corresponding event online time), etc.

[0046] S102, obtaining analysis dimension information and analysis indicator information in the analysis model.

[0047] In some embodiments, the analysis indicator information includes target event information and / or target user feature information.

[0048] In some embodiments, the analysis model includes at least one of the following: an event analysis model, a funnel analysis model, a distribution analysis model, and a retention analysis model.

[0049] In some embodiments, the analysis dimension information refers to the analysis angle specified in the analysis model, which can be specifically from different angles such as the time dimension, the event parameter dimension, the business parameter dimension associated with the event, and the user feature dimension, to analyze the event metadata or user metadata input into the analysis model. The analysis indicator information refers to the analysis target specified in the analysis model, wherein the target event information may include target event object information for specifying the event object to be analyzed and / or event analysis result information for specifying the event analysis result to be obtained, and the target user feature information may include target user object information for specifying the user object to be analyzed and / or user analysis result information for specifying the user analysis result to be obtained. It can be understood that the combination of different target event object information and different event analysis result information can obtain target event information for indicating different analysis indicators; similarly, different target user object information and different event analysis result information. Target user feature information for indicating different analysis indicators can be obtained.

[0050] For example, Figure 3 As shown, in the event analysis model, the analysis indicator information includes the target event information, specifically including the target event object to be analyzed, such as the "start APP" event, and the event analysis results to be analyzed, such as the "total number of times". The analysis dimension information is the time dimension, such as the day dimension in the time period from 2023-11-01 to 2023-11-31. That is to say, Figure 3In the event analysis model shown, the total number of "start APP" events per day is analyzed during the period from 2023-11-01 to 2023-11-31.

[0051] For another example, in the event analysis model, the analysis indicator information includes target event information and target user characteristic information, wherein the target event information specifically includes target event object information, such as the "start APP" event, and event analysis result information, such as the "total number of times"; the target user characteristic information specifically includes target user object information, such as the user characteristic information is "Country A" and "male"; the analysis dimension information is the time dimension, such as the weekly dimension in the time period from 2023-11-01 to 2023-11-31; that is to say, in the event analysis model, the total number of "start APP" events per week for "male" users in "Country A" is analyzed in the time period from 2023-11-01 to 2023-11-31.

[0052] Specifically, the analysis dimension information and analysis indicator information of the analysis model can be obtained through the visualization interface; for example, when the user clicks the button corresponding to the "event analysis" model in the visualization interface corresponding to the "data analysis" module provided by the display device, the visualization interface can jump to display the corresponding interface of the event analysis model, and then adjust the analysis dimension information and / or analysis indicator information in the event analysis model through the visualization interface. Figure 3 As shown, the user can select the analysis dimension information as the time dimension, and adjust the time dimension to the time period from 2023-11-01 to 2023-11-31. The user can also specify the target event object to be analyzed, select the "Start APP" event, and specify the event analysis results to be analyzed, such as the "total number of times". The user can also add other dimensions according to their own needs through the "More Analysis Dimensions" button, and add parameters such as "Country A" and "Male" through the "Customize Event Indicators" button.

[0053] S103 , combining the user metadata, filtering target event metadata from the event metadata according to the target event information and the target user feature information.

[0054] In some embodiments, the target event metadata refers to the meta-event and its event attribute information corresponding to the analysis indicator information. Specifically, the target event metadata can be screened from the event metadata according to the target event object information, event analysis result information indicated by the target event information in the analysis indicator information, and / or the target user object information and user analysis result information indicated by the target user feature information.

[0055] Taking the target product as an application as an example, the target event metadata may refer to all data generated by a specific user based on a specific operation or action triggered by the specific user on the application.

[0056] In some embodiments, combining the user metadata, filtering the target event metadata from the event metadata according to the target event information and the target user feature information, comprises steps S11-S13:

[0057] S11, screening candidate event metadata from the event metadata according to the target event information.

[0058] In some embodiments, the candidate event metadata is the event metadata corresponding to the target event information. For example, if the target event information includes "launching APP" for specifying the target event object to be analyzed, the candidate event metadata screened from the event metadata is all event metadata related to the launch APP in the event metadata.

[0059] S12: Acquire target user metadata corresponding to the candidate event metadata according to event attribute information of the candidate event metadata.

[0060] Specifically, the event triggering user in the event attribute information corresponding to the candidate event metadata may be obtained, and based on the event triggering user, the target user metadata corresponding to each candidate event metadata may be obtained in the user metadata.

[0061] S13: based on the target user characteristic information and the target user metadata corresponding to the candidate event metadata, filter the target event metadata from the candidate event metadata.

[0062] Specifically, the target user object information used to specify the object to be analyzed is determined based on the target user characteristic information, and the final user metadata matching the target user object information is determined from the target user metadata corresponding to the candidate event metadata, and the target event metadata corresponding to the final user metadata is filtered out from the selected candidate event metadata based on the final user metadata.

[0063] Specifically, in combination with the above embodiments, taking the target user characteristic information specifically including the target user object information "Country A" and "male" as an example, after obtaining the target user metadata corresponding to the candidate event metadata related to launching the APP, the final user metadata of Country A and male can be determined from all the target user metadata corresponding to the candidate event metadata related to launching the APP based on the target user object information such as male and Country A, wherein the candidate event metadata corresponding to the final user metadata is the target event metadata.

[0064] S104: Perform data analysis based on the target event metadata under the analysis dimension information to generate a product analysis result of the target product.

[0065] Specifically, after obtaining the target event metadata, the target event metadata can be analyzed based on the analysis dimension information to obtain the product analysis results of the target product; for example, after determining the target event metadata corresponding to the target event information, the target event metadata can be analyzed accordingly based on the time dimension indicated by the analysis dimension information, according to the time dimension and the event analysis result information indicated by the target event information; as in the above example, the target event metadata corresponding to the target event information is the event of male users in country A starting an APP. Based on the time dimension indicated by the analysis dimension information, the event analysis result information indicated by the analysis indicator information corresponding to the target event metadata can be analyzed according to the time dimension to obtain the total number of times male users in country A start an APP event at different time points or in different time periods.

[0066] In some embodiments, when the analysis model is the event analysis model, obtaining the analysis dimension information and analysis indicator information in the analysis model includes: obtaining first analysis dimension information and first analysis indicator information of the event analysis model.

[0067] In this embodiment, the event analysis model is mainly used to analyze the event indicator information of a certain event of a user within a preset time period. The event indicator information may be the number of users who trigger a certain event in the target product, the total number of times or average number of times all users trigger a certain time in the target product, the total number of times or average number of times a specific user triggers a certain time in the target product, etc.

[0068] In some embodiments, in the event analysis model, the first analysis indicator information includes the first target event information and the first target user feature information, wherein the first target event information specifically includes the target event object to be analyzed, such as the "start APP" event, and the event analysis results to be analyzed, such as the "total number of times", and the first target user feature information specifically includes the target user object to be analyzed, such as the user feature information is "high-level user". Among them, the first analysis dimension information can be a time dimension, such as the weekly dimension in the time period from 2023-11-01 to 2023-11-31; that is, in the event analysis model, specifically the total number of "start APP" events of "high-level users" per week in the time period from 2023-11-01 to 2023-11-31 is analyzed.

[0069] In some embodiments, in combination with the user metadata, target event metadata is filtered from the event metadata according to the target event information and the target user characteristic information, including: in combination with the user metadata, first target event metadata is filtered from the event metadata according to the first target event information and the first target user characteristic information.

[0070] The first target event metadata at least includes: all event metadata of the target event object information specified by the first target event information triggered by the target user object specified by the first target user characteristic information.

[0071] In this embodiment, taking the target product as APP, the first target event information as launching APP, and the first target user characteristic information as high-asset user as an example, combined with the user metadata, the event metadata of the target event object of high-asset user launching APP is obtained from the event metadata, that is, the first target event metadata.

[0072] In some embodiments, performing data analysis under the analysis dimension information based on the target event metadata to generate a product analysis result of the target product includes: performing data analysis under the first analysis dimension information based on the first target event metadata to generate a first product analysis result of the target product.

[0073] The first product analysis result at least includes a line chart corresponding to the event analysis result specified by the first target event information and the first target event metadata under the first analysis dimension information.

[0074] For example, the first product analysis result may include a line graph of the number of users who trigger meta-events corresponding to the first target event metadata in the target product at different time points, or may include a line graph of the total number of times or average number of times all users trigger meta-events corresponding to the first target event metadata in the target product at different time periods. Specifically, different first analysis dimension information may result in different first product analysis results.

[0075] Specifically, combined Figure 3 Come and see, Figure 3 The event analysis in the data analysis corresponds to the event analysis model. Users can determine the model they need by clicking various options in the data analysis in the autonomous analysis platform according to their own needs, that is, operate through the visual interface. When the user selects event analysis, the visual interface on the right will display the analysis dimension information and analysis indicator information corresponding to the event analysis model.

[0076] Specifically, Figure 3As shown, when the first analysis dimension information is a time dimension, it is processed according to a preset time period, for example, from 2023-11-01 to 2023-11-31. The first analysis indicator information can be the total number of times the APP is started. Other first analysis indicator information can also be added through the custom event indicator button according to user needs, or other analysis dimensions can be added through more analysis dimensions.

[0077] Further, continue as Figure 3 As shown, after the user clicks the query button through the visualization interface, the product analysis results will be displayed on the visualization interface. If the user selects the total number of times, a line chart of the total number of times the APP is launched from 2023-11-01 to 2023-11-31 will be displayed on the visualization interface. If the user changes the total number of times the APP is launched to the average number of times the APP is used, a line chart of the average number of times the APP is used from 2023-11-01 to 2023-11-31 will be displayed on the visualization interface.

[0078] In other embodiments, the event indicator information may be the number of users using the target product, the total number of users using the target product, the average number of times the target product is used, etc., or may be a string, a string array, etc. Specifically, in the calculation process, the mean, mode, median, lower quartile, upper quartile, mean, and maximum value, etc. may be used.

[0079] The lower quartile is the value at the 25% position after a set of data is arranged from small to large.

[0080] The upper quartile is the value at the 75% position after a set of data is arranged from small to large.

[0081] In some embodiments, when the analysis model is the funnel analysis model, obtaining the analysis dimension information and analysis indicator information in the analysis model includes: obtaining second analysis dimension information and second analysis indicator information of the funnel analysis model.

[0082] In this embodiment, the funnel analysis model is mainly used to analyze the event conversion of users within a preset time period, for example, the payment conversion rate of users.

[0083] In some embodiments, the second analysis indicator information includes the second analysis indicator information corresponding to each layer of funnel conditions in the funnel analysis model. It is understandable that the second analysis indicator information corresponding to each layer of funnel conditions may include the second target event information and / or the second target user feature information.

[0084] For example, the second analysis indicator information includes the second analysis indicator information corresponding to three layers of funnel conditions. The second analysis indicator information corresponding to each layer of funnel conditions includes the second target event information and the second target user feature information. The second target event information corresponding to each layer of funnel conditions includes the event objects "registration" event, "browse individual stock page" event, "individual stock quotation payment" event and the event result information to be analyzed "total number of users". The second target user feature information corresponding to each layer of funnel conditions is "user account registration time" within 7 days of the time dimension indicated by the second analysis dimension information.

[0085] In some embodiments, in combination with the user metadata, target event metadata is filtered from the event metadata according to the target event information and the target user characteristic information, including: in combination with the user metadata, in accordance with the second target event information corresponding to each layer funnel condition and the second target user characteristic information, second target event metadata corresponding to each layer funnel condition is filtered from the event metadata.

[0086] Specifically, for any layer of funnel conditions, the corresponding candidate event metadata can be filtered from the event metadata according to the second target event information in the funnel conditions of this layer, and then the target user metadata corresponding to the candidate event metadata can be obtained according to the event attribute information of the candidate event metadata, and based on the user feature information of the second target and the target user metadata corresponding to the candidate event metadata, the second target event metadata corresponding to the funnel conditions of this layer can be filtered from the candidate event metadata.

[0087] Continuing with the above example, the second analysis indicator information corresponding to the first-layer funnel condition includes the second-target event information and the second-target user characteristic information. The second-target event information corresponding to the funnel condition includes the event object "registration" event and the event result information "total number of users" to be analyzed. The corresponding second-target user characteristic information is "user account registration time" in the time dimension "within 7 days" indicated by the second analysis dimension information. The second analysis indicator information corresponding to the second-layer funnel condition includes the second-target event information and the second-target user characteristic information. The second-target event information corresponding to the funnel condition includes the event object "browse individual stock page" event and the event result information "total number of users" to be analyzed. The corresponding second-target user characteristic information is "user account registration time" in the time dimension "within 7 days" indicated by the second analysis dimension information. The second analysis indicator information corresponding to the third-layer funnel condition also includes the second-target event information and the second-target user characteristic information. The second-target event information corresponding to the funnel condition includes the event object "individual stock market payment" event and the event result information "total number of users" to be analyzed. The corresponding second-target user characteristic information is "user account registration time" in the time dimension "within 7 days" indicated by the second analysis dimension information. After determining the second analysis indicator information corresponding to each layer of the funnel conditions, for the second analysis indicator information corresponding to the first layer of the funnel conditions, in combination with the user metadata, the event metadata of the registration event corresponding to the time dimension "within 7 days" of the second analysis dimension information is obtained from the event metadata, as the second target event metadata corresponding to the first layer of the funnel conditions; for the second analysis indicator information corresponding to the second layer of the funnel conditions, in combination with the user metadata, the event metadata of the "browse individual stock page" event corresponding to the time dimension "within 7 days" of the second analysis dimension information is obtained from the event metadata, as the second target event metadata corresponding to the second layer of the funnel conditions; for the second analysis indicator information corresponding to the third layer of the funnel conditions, in combination with the user metadata, the event metadata of the "pay for individual stock quotes" event corresponding to the time dimension "within 7 days" of the second analysis dimension information is obtained from the event metadata, as the second target event metadata corresponding to the third layer of the funnel conditions.

[0088] In some embodiments, performing data analysis under the analysis dimension information based on the target event metadata to generate a product analysis result of the target product includes: performing data analysis under the second analysis dimension information based on the second target event metadata to generate a second product analysis result of the target product.

[0089] Among them, the second product analysis results at least include a user conversion line chart.

[0090] In other embodiments, the second product analysis result also includes a line graph corresponding to the average number of times that paying users use the target product.

[0091] Specifically, combined Figure 4 Come and see, Figure 4 The funnel analysis in the data analysis corresponds to the funnel analysis model. Users can determine the model they need by clicking various options in the data analysis in the self-service analysis platform according to their own needs, that is, operate through the visual interface. When the user selects funnel analysis, the visual interface on the right will display the analysis dimension information and analysis indicator information corresponding to the funnel analysis model.

[0092] Specifically, combined Figure 4 As shown in the above example, when the second analysis dimension information is a time dimension, it is processed according to a preset time period, for example, 2023-11-20 to 2023-11-26. The funnel analysis model can be the conversion rate of paying users among newly registered users (such as "user account registration time" within 7 days of the time dimension indicated by the second analysis dimension information). Other analysis dimensions can also be added according to user needs through more analysis dimensions.

[0093] Further, continue as Figure 4 As shown, after the user clicks the query button through the visualization interface, the product analysis results will be displayed on the visualization interface. If the user selects the paid user conversion rate, the visualization interface will display a line chart of the conversion rate of newly registered users (such as "user account registration time" within "7 days" of the date indicated in the time period) entering the individual stock page and paying for individual stock quotes on the individual stock page during the time period from 2023-11-20 to 2023-11-26. If the paid user conversion rate is modified to the conversion rate of high-asset users, a line chart of the conversion rate of newly registered users to high-asset users during the time period from 2023-11-20 to 2023-11-26 will be displayed on the visualization interface.

[0094] In some embodiments, when the analysis model is the distribution analysis model, obtaining the analysis dimension information and analysis indicator information in the analysis model includes: obtaining third analysis dimension information and third analysis indicator information of the distribution analysis model.

[0095] In some embodiments, the third analysis indicator information includes third target event information and third target user feature information. Specifically, for example, the third target event information may be the use of an APP, and the third target user feature information may be a high-asset user.

[0096] In this embodiment, the distribution analysis model is mainly used to calculate the relevant number of people or relevant times corresponding to the target product within a preset time period. The relevant number of people can be the number of users, the total number of users, and the average number of times using the target product. The relevant times can be the number of users, the total number of users, and the average number of times using a certain function of the target product.

[0097] In some embodiments, in the distribution analysis model, the third analysis indicator information includes the event information of the third label and the user characteristic information of the third label, wherein the event information of the third label specifically includes information for specifying the target event object to be analyzed, such as the "start APP" event, and information for specifying the event analysis results to be analyzed, such as the "average number of times", and the user characteristic information of the third label specifically includes information for specifying the target user object to be analyzed, such as the user characteristic information is "high-income users". Among them, the third analysis dimension information can be a time dimension, such as the weekly dimension in the time period from 2023-11-01 to 2023-11-31; that is, in the distribution analysis model, specifically, the average number of times per capita of the event "start APP" of "high-income users" per week is analyzed in the time period from 2023-11-01 to 2023-11-31.

[0098] In some embodiments, combining the user metadata and filtering target event metadata from the event metadata according to the target event information and the target user characteristic information includes: combining the user metadata and filtering third target event metadata from the event metadata according to the third target event information and the third target user characteristic information.

[0099] The third target event metadata includes at least: the number of users who use the target product and / or the total number of times used by the users and / or the average number of times used by the users.

[0100] In this embodiment, taking the target product as APP, the third target event information as using APP, and the third target user characteristic information as high-asset users as an example, combined with user metadata, the average number of times high-asset users use APP is obtained from the event metadata, that is, the third target event metadata.

[0101] In some embodiments, performing data analysis under the analysis dimension information based on the target event metadata to generate a product analysis result of the target product includes: performing data analysis under the third analysis dimension information based on the third target event metadata to generate a third product analysis result of the target product.

[0102] In some embodiments, the third product analysis result includes at least a line graph of the number of people distributed.

[0103] In other embodiments, the third product analysis results also include a cylinder chart of the number of people distribution, a table chart of the number of people, etc.

[0104] Specifically, combined Figure 5 Come and see, Figure 5 The distribution analysis in the data analysis corresponds to the distribution analysis model. Users can determine the model they need by clicking various options in the data analysis in the autonomous analysis platform according to their own needs, that is, operate through the visual interface. When the user selects distribution analysis, the visual interface on the right will display the analysis dimensions and analysis indicator information corresponding to the distribution analysis model.

[0105] Specifically, Figure 5 As shown, the third analysis dimension information is the time dimension, that is, it is processed according to a preset time period, for example, from 2023-11-01 to 2023-11-31. The third analysis indicator information can be the total number of clicks into the main interface of the APP. Other third analysis indicator information can be added according to user needs through the Add Indicator button, or other analysis dimensions can be added through More Analysis Dimensions.

[0106] Further, continue as Figure 5 As shown, after the user clicks the query button through the visualization interface, the product analysis results will be displayed on the visualization interface. If the user chooses the total number of times the main interface is clicked, a grid chart of the total number of people who clicked into the main interface in different time intervals from 2023-11-01 to 2023-11-31 will be displayed on the visualization interface.

[0107] In some embodiments, when the analysis model is the retention analysis model, obtaining analysis dimension information and analysis indicator information in the analysis model includes: obtaining fourth analysis dimension information and fourth analysis indicator information of the retention analysis model.

[0108] In some embodiments, the fourth analysis indicator information includes fourth target event information and fourth target user feature information. Specifically, for example, the fourth target event information may be subsequent continued use of the APP, and the fourth target user feature information may be a high-asset user.

[0109] In this embodiment, the retention analysis model is mainly used to determine how many users who have performed the target behavior within a preset time range have performed the subsequent target behavior on the nth day / week / month. Specifically, if 100 people open the app on the first day, how many of these 100 people still open the app on the second day.

[0110] In some embodiments, combining the user metadata and filtering target event metadata from the event metadata according to the target event information and the target user characteristic information includes: combining the user metadata and filtering fourth target event metadata from the event metadata according to the fourth target event information and the fourth target user characteristic information.

[0111] The fourth target event metadata includes at least: the number of users who continue to use the target product, the total number of times of users, and the average number of times per person.

[0112] In this embodiment, taking the target product as APP, the fourth target event information as continued use of APP, and the fourth target user characteristic information as high-asset users as an example, combined with user metadata, relevant data on the continued use of APP by high-asset users is obtained from the event metadata, namely, the fourth target event metadata, which includes the number of high-asset users who continue to use APP, the total number of users, and the average number of times per person.

[0113] In some embodiments, performing data analysis based on the target event metadata under the analysis dimension information to generate a product analysis result of the target product includes: performing data analysis based on the fourth target event metadata under the fourth analysis dimension information to generate a fourth product analysis result of the target product.

[0114] In some embodiments, the fourth product analysis result includes at least a line graph of the distribution of the number of people using the target product at different times.

[0115] In other embodiments, the fourth product analysis result also includes a cylinder chart of the number of people distribution, a table chart of the number of people, etc.

[0116] In some embodiments, the metadata further includes derived data, wherein the derived data includes virtual event data.

[0117] In some embodiments, the method also includes: obtaining first event information and second event information of a virtual event; filtering first event metadata and second event metadata from the event metadata based on the first event information and the second event information; and constructing virtual event data of the virtual event based on the first event metadata and the second event metadata.

[0118] In some embodiments, the virtual event data refers to new event metadata formed by combining existing event metadata.

[0119] Among them, the virtual event data can be data information used to describe the new event attribute information of the meta-event, the first event information is used to indicate an event attribute information required to generate the virtual event data, and the second event information is used to indicate another event attribute information required to generate the virtual event data; after obtaining the first event information and the second event information, the first attribute value corresponding to the first event information and the second attribute value corresponding to the second event information can be matched from the event metadata of the meta-event, and then the first attribute value and the second attribute value are combined to construct the attribute value of the meta-event on the new event attribute information to obtain the virtual event data.

[0120] For example, the event "entering page A" includes the start time of event attribute information and the end time of time attribute information. The virtual event data is the duration data of the event "entering page A", which corresponds to the first event information as the start time and the second event information as the end time. After obtaining the first event information and the second event information, the first attribute value corresponding to the start time and the second attribute value corresponding to the end time can be matched from the event metadata of the event "entering page A", and then the attribute value of the event "entering page A" on the new event attribute information (that is, duration data) can be determined according to the first attribute value and the second attribute value to obtain the virtual event data.

[0121] Among them, the virtual event data can also be virtual event data used to describe a new event, the first event information is used to indicate an event for constructing a virtual event, and the second event information is used to construct another event for constructing a virtual event; after obtaining the first event information and the second event information, the first meta event corresponding to the first event information and the second meta event corresponding to the second event information can be matched from all meta events, and then the virtual event data corresponding to the virtual event can be constructed according to the event metadata of the first meta event and the event metadata of the second meta event.

[0122] For example, the virtual event data refers to the event of participating in activity B through page A. The first event information refers to entering page A, and the second event information refers to participating in activity B. Then, the event metadata corresponding to the first meta-event of "entering page A" can be matched from all meta-events, and the event metadata corresponding to the second meta-event of "participating in activity B" can be matched from all meta-events. Finally, based on the event attribute information in the event metadata, the associated event metadata is determined from the event metadata corresponding to the first meta-event and the event metadata corresponding to the second meta-event to construct the virtual event data of the virtual event, that is, the relevant event data of the event of the user participating in activity B through page A.

[0123] Specifically, according to each event attribute information in the event metadata, the associated event metadata is determined from the event metadata corresponding to the first meta event and the event metadata corresponding to the second meta event, which may specifically include: constructing a candidate event pair based on the first meta event and the second meta event through the event parameter information constraint; for any candidate event pair, obtaining a first degree of association between the event metadata corresponding to the first meta event and the event metadata corresponding to the second event in the candidate event pair under the device information constraint, and at the same time, obtaining a second degree of association between the event metadata corresponding to the first meta event and the event metadata corresponding to the second event in the candidate event pair under the business parameter constraint; determining a target event pair in the candidate event pair based on the first degree of association and the second degree of association; and determining associated event metadata from the event metadata corresponding to the first meta event and the second meta event in the target event pair.

[0124] Among them, the event parameter information constraint refers to the condition information used to limit the candidate event pair composed of the first meta event and the second meta event; specifically, the event parameter information constraint can be used to control the timing of the event triggering timing corresponding to the first meta event and the second meta event in the candidate event pair; for example, continuing to take the example of the virtual event referring to the event of participating in activity B through page A, the event parameter information constraint is specifically that the event triggering timing corresponding to the first meta event is earlier than the event triggering timing corresponding to the second meta event, so as to ensure the sequence of the first meta event and the second meta event, so as to determine the accuracy of the subsequently constructed virtual event.

[0125] Through the constraint of event parameter information, the first meta-event and the second meta-event can be accurately associated in the event parameter information, which is the event attribute information, so as to improve the accuracy of the first meta-event and the second meta-event in the candidate event pair in representing the virtual event in the actual scenario.

[0126] Among them, the device information constraint refers to the condition information used to restrict the event attribute information of the first meta-event and the second meta-event in the candidate event pair on the device information; the business parameter constraint refers to the condition information used to restrict the event attribute information of the first meta-event and the second meta-event in the candidate event pair on the device information. Specifically, taking the device information constraint as an example, the first correlation between the event metadata corresponding to the first meta-event and the event metadata corresponding to the second event in the candidate event pair on the device information constraint is obtained. Specifically, the first device information in the event metadata corresponding to the first meta-event in the candidate event pair and the second device information in the event metadata corresponding to the second event in the candidate event pair can be obtained, and the first correlation on the device information constraint is obtained according to the distance value between the first device information and the second device information. Similarly, taking the business parameter constraint condition as an example, the second correlation degree between the event metadata corresponding to the first meta-event in the candidate event pair and the event metadata corresponding to the second event in the candidate event pair on the business parameter constraint condition is obtained. Specifically, the first business parameter information in the event metadata corresponding to the first meta-event in the candidate event pair and the second business parameter information in the event metadata corresponding to the second event in the candidate event pair can be obtained. According to the distance value between the first business parameter information and the second business parameter information, the second correlation degree on the device information constraint condition is determined.

[0127] After obtaining the first correlation degree and the second correlation degree corresponding to the candidate event pair, the target event pair can be determined from the candidate event pairs when both the first correlation degree and the second correlation degree satisfy the preset correlation degree threshold. Furthermore, for any first meta-event, the candidate event pair with the largest sum of the first correlation degree and the second correlation degree can be screened from all the candidate event pairs associated with the first meta-event and determined as the target event pair.

[0128] In the above embodiment, not only are the first meta-event and the second meta-event grouped by event parameter information constraints to construct candidate event pairs, but the correlation between the first meta-event and the second meta-event in the constructed candidate event pairs is also calculated by device information and business parameter information, so that multi-dimensional indicators are used to describe the relationship between the first meta-event and the second meta-event, so that the target event pair subsequently determined from the candidate event pair will be closer to the actual operation process corresponding to the virtual event, and can more accurately describe the virtual event, thereby improving the accuracy and efficiency of the product data analysis results.

[0129] By combining event metadata, we can obtain new event attribute information for describing meta-events, or virtual event data for describing new events, which can adapt to product data analysis needs with more different analysis indicators or analysis dimensions, reducing the time cost for analysts to analyze relevant data of software products to obtain analysis results, and improving work efficiency.

[0130] The technical solution provided by the present disclosure collects metadata of the target product, the metadata includes event metadata and user metadata; obtains analysis dimension information and analysis indicator information in the analysis model, the analysis indicator information includes target event information and target user feature information; combines the user metadata, and filters the target event metadata from the event metadata according to the target event information and the target user feature information; performs data analysis based on the target event metadata under the analysis dimension information to generate a product analysis result of the target product. The technical solution provided by each embodiment of the present disclosure does not require the staff to view multiple pieces of software product data through different platforms, but instead obtains the metadata of the target product, determines the target event metadata from the metadata, and then generates the product analysis result of the target product based on the target event metadata, thereby reducing the time cost of the staff analyzing the relevant data of the software product to obtain the analysis result and improving work efficiency.

[0131] Figure 6 A schematic diagram of the structure of a data processing device provided by an exemplary embodiment of the present disclosure;

[0132] The device includes: a collection unit 201, an acquisition unit 202, a screening unit 203 and a generation unit 204;

[0133] A collection unit 201 is used to collect metadata of a target product, wherein the metadata includes event metadata and user metadata;

[0134] An acquisition unit 202 is used to acquire analysis dimension information and analysis indicator information in the analysis model, wherein the analysis indicator information includes target event information and target user feature information;

[0135] A screening unit 203 is used to screen target event metadata from the event metadata according to the target event information and the target user feature information in combination with the user metadata;

[0136] The generating unit 204 is used to perform data analysis based on the target event metadata under the analysis dimension information to generate a product analysis result of the target product.

[0137] In some embodiments, the event metadata includes event attribute information corresponding to the meta-event, and the user metadata includes user feature information corresponding to the meta-user;

[0138] The device is used to combine the user metadata, filter the target event metadata from the event metadata according to the target event information and the target user feature information, including:

[0139] Filtering candidate event metadata from the event metadata according to the target event information;

[0140] According to the event attribute information of the candidate event metadata, obtaining the target user metadata corresponding to the candidate event metadata;

[0141] Based on the target user feature information and the target user metadata corresponding to the candidate event metadata, the target event metadata is filtered out from the candidate event metadata.

[0142] In some embodiments, the metadata further includes derived data, the derived data including virtual event data;

[0143] The device is used for:

[0144] Obtaining first event information and second event information of a virtual event;

[0145] Filtering first event metadata and second event metadata from the event metadata according to the first event information and the second event information;

[0146] The virtual event data of the virtual event is constructed according to the first event metadata and the second event metadata.

[0147] In some embodiments, the analysis model includes at least one of the following: an event analysis model, a funnel analysis model, a distribution analysis model, and a retention analysis model.

[0148] In some embodiments, when the analysis model is the event analysis model, the device is used to obtain the analysis dimension information and the analysis indicator information in the analysis model, specifically for:

[0149] Acquire first analysis dimension information and first analysis indicator information of the event analysis model, where the first analysis indicator information includes first target event information and first target user feature information;

[0150] The device is used to combine the user metadata, filter the target event metadata from the event metadata according to the target event information and the target user feature information, and is specifically used to:

[0151] In combination with the user metadata, first target event metadata is filtered from the event metadata according to the event information of the first target and the user characteristic information of the first target, wherein the first target event metadata at least includes: all event metadata of the event object specified by the event information of the first target triggered by the user object specified by the user characteristic information of the first target;

[0152] The device is used to perform data analysis based on the target event metadata under the analysis dimension information to generate a product analysis result of the target product, specifically for:

[0153] Based on the first target event metadata, data analysis is performed under the first analysis dimension information to generate a first product analysis result of the target product, wherein the first product analysis result at least includes a line chart corresponding to the event analysis result specified by the first target event metadata in the first analysis dimension information.

[0154] In some embodiments, when the analysis model is the funnel analysis model, the device is used to obtain the analysis dimension information and the analysis indicator information in the analysis model, specifically for:

[0155] Acquire second analysis dimension information and second analysis indicator information of the funnel analysis model, where the second analysis indicator information includes second target event information and / or second target user feature information corresponding to each layer of funnel conditions in the funnel analysis model;

[0156] The device is used to combine the user metadata, filter the target event metadata from the event metadata according to the target event information and the target user feature information, and is specifically used to:

[0157] In combination with the user metadata, according to the second target event information corresponding to each of the layer funnel conditions and the second target user feature information, second target event metadata is filtered from the event metadata;

[0158] The device is used to perform data analysis based on the target event metadata under the analysis dimension information to generate a product analysis result of the target product, specifically for:

[0159] Based on the second target event metadata, data analysis is performed under the second analysis dimension information to generate a second product analysis result of the target product, wherein the second product analysis result at least includes a user conversion line chart.

[0160] In some embodiments, when the analysis model is the distribution analysis model, the device is used to obtain the analysis dimension information and the analysis indicator information in the analysis model, specifically for:

[0161] Acquire third analysis dimension information and third analysis indicator information of the distribution analysis model, wherein the third analysis indicator information includes event information of a third target and user characteristic information of a third target;

[0162] The device is used to combine the user metadata, filter the target event metadata from the event metadata according to the target event information and the target user feature information, and is specifically used to:

[0163] In combination with the user metadata, according to the event information of the third target and the user characteristic information of the third target, third target event metadata is filtered from the event metadata, wherein the third target event metadata at least includes: the number of users who use the target product, the total number of times of users, and the average number of times per person;

[0164] The device is used to perform data analysis based on the target event metadata under the analysis dimension information to generate a product analysis result of the target product, specifically for:

[0165] Based on the third target event metadata, data analysis is performed under the third analysis dimension information to generate a third product analysis result of the target product, and the third product analysis result at least includes a line graph of the number of people distribution.

[0166] The technical solution provided by the present disclosure collects metadata of the target product, the metadata includes event metadata and user metadata; obtains analysis dimension information and analysis indicator information in the analysis model, the analysis indicator information includes target event information and target user feature information; combines the user metadata, and filters the target event metadata from the event metadata according to the target event information and the target user feature information; performs data analysis based on the target event metadata under the analysis dimension information to generate a product analysis result of the target product. The technical solution provided by each embodiment of the present disclosure does not require the staff to view multiple pieces of software product data through different platforms, but instead obtains the metadata of the target product, determines the target event metadata from the metadata, and then generates the product analysis result of the target product based on the target event metadata, thereby reducing the time cost of the staff analyzing the relevant data of the software product to obtain the analysis result and improving work efficiency.

[0167] It should be understood that the device embodiment and the method embodiment may correspond to each other, and similar descriptions may refer to the method embodiment. To avoid repetition, no further description is given here. Specifically, the device may perform the above method embodiment, and the above and other operations and / or functions of each module in the device are the corresponding processes in each method in the above method embodiment, respectively, and no further description is given here for the sake of brevity.

[0168] The above describes the device of the embodiment of the present disclosure from the perspective of the functional module in conjunction with the accompanying drawings. It should be understood that the functional module can be implemented in hardware form, can be implemented by instructions in software form, and can also be implemented by a combination of hardware and software modules. Specifically, the steps of the method embodiment in the embodiment of the present disclosure can be completed by the hardware integrated logic circuit and / or software form instructions in the processor, and the steps of the method disclosed in conjunction with the embodiment of the present disclosure can be directly embodied as a hardware decoding processor to execute, or a combination of hardware and software modules in the decoding processor to execute. Optionally, the software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory, and the processor reads the information in the memory and completes the steps in the above method embodiment in conjunction with its hardware.

[0169] Figure 7 is a schematic block diagram of an electronic device provided by an embodiment of the present disclosure, and the electronic device may include:

[0170] The memory 301 and the processor 302, the memory 301 is used to store the computer program and transmit the program code to the processor 302. In other words, the processor 302 can call and run the computer program from the memory 301 to implement the method in the embodiment of the present disclosure.

[0171] For example, the processor 302 may be configured to execute the above method embodiments according to instructions in the computer program.

[0172] In some embodiments of the present disclosure, the processor 302 may include but is not limited to:

[0173] General-purpose processor, digital signal processor (DSP), application-specific integrated circuit (ASIC), field programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware components, etc.

[0174] In some embodiments of the present disclosure, the memory 301 includes but is not limited to:

[0175] Volatile memory and / or non-volatile memory. Among them, the non-volatile memory can be read-only memory (ROM), programmable ROM (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM) or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of RAM are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link DRAM (SLDRAM), and direct RAM bus random access memory (DR RAM).

[0176] In some embodiments of the present disclosure, the computer program may be divided into one or more modules, which are stored in the memory 301 and executed by the processor 302 to complete the method provided by the present disclosure. The one or more modules may be a series of computer program instruction segments capable of completing specific functions, and the instruction segments are used to describe the execution process of the computer program in the electronic device.

[0177] like Figure 7 As shown, the electronic device may also include:

[0178] The transceiver 303 may be connected to the processor 302 or the memory 301 .

[0179] The processor 302 may control the transceiver 303 to communicate with other devices, specifically, to send information or data to other devices, or to receive information or data sent by other devices. The transceiver 303 may include a transmitter and a receiver. The transceiver 303 may further include an antenna, and the number of antennas may be one or more.

[0180] It should be understood that the various components in the electronic device are connected via a bus system, wherein the bus system includes not only a data bus but also a power bus, a control bus and a status signal bus.

[0181] The present disclosure also provides a computer storage medium on which a computer program is stored, and when the computer program is executed by a computer, the computer can perform the method of the above method embodiment. In other words, the present disclosure embodiment also provides a computer program product containing instructions, and when the instructions are executed by a computer, the computer can perform the method of the above method embodiment.

[0182] When software is used for implementation, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function according to the embodiment of the present disclosure is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from a website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (digital subscriber line, DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrated. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a digital video disc (digital video disc, DVD)), or a semiconductor medium (e.g., a solid state disk (solid state disk, SSD)), etc.

[0183] Those of ordinary skill in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this disclosure.

[0184] In the several embodiments provided in the present disclosure, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the module is only a logical function division. There may be other division methods in actual implementation, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or modules, which can be electrical, mechanical or other forms.

[0185] The modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical modules, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. For example, each functional module in each embodiment of the present disclosure may be integrated into a processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0186] The above are only specific embodiments of the present disclosure, but the protection scope of the present disclosure is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present disclosure, which should be included in the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure should be based on the protection scope of the claims.

Claims

1. A data processing method, characterized in that: The method comprises: Collecting metadata of the target product, wherein the metadata includes event metadata and user metadata; Acquire analysis dimension information and analysis indicator information in the analysis model, wherein the analysis indicator information includes target event information and target user feature information; In combination with the user metadata, according to the target event information and the target user characteristic information, target event metadata is filtered from the event metadata; Data analysis is performed based on the target event metadata under the analysis dimension information to generate a product analysis result of the target product.

2. The method according to claim 1, characterized in that: The event metadata includes event attribute information corresponding to the meta-event, and the user metadata includes user feature information corresponding to the meta-user; The combining the user metadata and filtering the target event metadata from the event metadata according to the target event information and the target user feature information includes: Filtering candidate event metadata from the event metadata according to the target event information; According to the event attribute information of the candidate event metadata, obtaining the target user metadata corresponding to the candidate event metadata; Based on the target user feature information and the target user metadata corresponding to the candidate event metadata, the target event metadata is filtered out from the candidate event metadata.

3. The method according to claim 1, characterized in that The metadata also includes derived data, and the derived data includes virtual event data; The method further comprises: Obtaining first event information and second event information of a virtual event; Filtering first event metadata and second event metadata from the event metadata according to the first event information and the second event information; The virtual event data of the virtual event is constructed according to the first event metadata and the second event metadata.

4. The method according to claim 1, characterized in that: The analysis model includes at least one of the following: an event analysis model, a funnel analysis model, a distribution analysis model, and a retention analysis model.

5. The method according to claim 4, characterized in that When the analysis model is the event analysis model, the obtaining of analysis dimension information and analysis indicator information in the analysis model includes: Acquire first analysis dimension information and first analysis indicator information of the event analysis model, where the first analysis indicator information includes first target event information and first target user feature information; The combining the user metadata and filtering the target event metadata from the event metadata according to the target event information and the target user feature information includes: In combination with the user metadata, first target event metadata is filtered from the event metadata according to the event information of the first target and the user characteristic information of the first target, wherein the first target event metadata at least includes: all event metadata of the event object specified by the event information of the first target triggered by the user object specified by the user characteristic information of the first target; The performing data analysis based on the target event metadata under the analysis dimension information to generate a product analysis result of the target product includes: Based on the first target event metadata, data analysis is performed under the first analysis dimension information to generate a first product analysis result of the target product, wherein the first product analysis result at least includes a line chart corresponding to the event analysis result specified by the first target event metadata in the first analysis dimension information.

6. The method according to claim 4, characterized in that When the analysis model is the funnel analysis model, the obtaining of analysis dimension information and analysis indicator information in the analysis model includes: Acquire second analysis dimension information and second analysis indicator information of the funnel analysis model, where the second analysis indicator information includes second target event information and / or second target user feature information corresponding to each layer of funnel conditions in the funnel analysis model; The combining the user metadata and filtering the target event metadata from the event metadata according to the target event information and the target user feature information includes: In combination with the user metadata, according to the second target event information corresponding to each of the layer funnel conditions and the second target user feature information, second target event metadata is filtered from the event metadata; The performing data analysis based on the target event metadata under the analysis dimension information to generate a product analysis result of the target product includes: Based on the second target event metadata, data analysis is performed under the second analysis dimension information to generate a second product analysis result of the target product, wherein the second product analysis result at least includes a user conversion line chart.

7. The method according to claim 4, characterized in that When the analysis model is the distribution analysis model, the obtaining of analysis dimension information and analysis indicator information in the analysis model includes: Acquire third analysis dimension information and third analysis indicator information of the distribution analysis model, wherein the third analysis indicator information includes event information of a third target and user characteristic information of a third target; The combining the user metadata and filtering the target event metadata from the event metadata according to the target event information and the target user feature information includes: In combination with the user metadata, according to the event information of the third target and the user characteristic information of the third target, third target event metadata is filtered from the event metadata, wherein the third target event metadata at least includes: the number of users who use the target product, the total number of times of users, and the average number of times per person; The performing data analysis based on the target event metadata under the analysis dimension information to generate a product analysis result of the target product includes: Based on the third target event metadata, data analysis is performed under the third analysis dimension information to generate a third product analysis result of the target product, and the third product analysis result at least includes a line graph of the number of people distribution.

8. A data processing device, characterized in that: The device comprises: A collection unit, used to collect metadata of a target product, wherein the metadata includes event metadata and user metadata; An acquisition unit, used to acquire analysis dimension information and analysis indicator information in the analysis model, wherein the analysis indicator information includes target event information and target user feature information; a screening unit, configured to screen target event metadata from the event metadata in combination with the user metadata according to the target event information and the target user feature information; A generating unit is used to perform data analysis under the analysis dimension information based on the target event metadata to generate a product analysis result of the target product.

9. An electronic device, characterized in that: include: processor; as well as A memory, configured to store executable instructions of the processor; The processor is configured to perform the method of any one of claims 1 to 7 by executing the executable instructions.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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