Point burying data processing method and device, equipment and storage medium

Abstract

The invention discloses a point burying data processing method and device, equipment and a storage medium, and relates to the technical field of computers.The point burying data is obtained, and a target object corresponding to the point burying data is determined according to an object variable in the point burying data; the target object is an object in an application corresponding to the operation of generating the buried point data, and based on the multiple pieces of buried point data corresponding to the target object, obtaining application analysis data and user analysis data corresponding to the target object, the application analysis data corresponding to the target object is used for representing the activeness of the target object, and the user analysis data corresponding to the target object is used for representing the activeness of a user using the target object; according to the method, statistical analysis is carried out by using unified burying point data, the problems of difficult data reporting and high data use cost are solved, the user behaviors are analyzed by adopting two dimensions of the application side and the user side, and the real-time performance and accuracy of user behavior analysis are improved.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and more specifically, to a method, device, equipment and storage medium for processing buried point data. Background Art

[0002] There is an urgent need for user behavior analysis in the era of big data. With the rapid development of the Internet, the behavioral data generated by users when using various applications or websites has exploded. This data is crucial for understanding user needs, optimizing product functions, and improving user experience.

[0003] In the prior art, the acquisition of user behavior data relies on the log data actively reported by various application systems (such as desktop, web, and mobile). These log data capture the user's clicks, browsing, and searching behaviors on the application interface; user behavior data analysis is through the analysis of log data.

[0004] However, in the prior art, the application system reports and analyzes log data, but there are problems such as low efficiency in reporting user behavior data and omissions or misreporting in the user behavior data reporting process, resulting in poor real-time performance and insufficient accuracy in user behavior analysis. Summary of the invention

[0005] In order to solve the above-mentioned problems in the prior art, namely, low efficiency in reporting user behavior data and problems of underreporting or misreporting in the user behavior data reporting process, the present application provides a method, device, equipment and storage medium for processing buried data.

[0006] In a first aspect, the present application provides a method for processing buried point data, comprising:

[0007] Acquire the buried point data, and determine the target object corresponding to the buried point data according to the object variable in the buried point data, wherein the target object is an object in the application targeted by the operation generating the buried point data;

[0008] Based on the multiple embedded data corresponding to the target object, application analysis data and user analysis data corresponding to the target object are obtained, wherein the application analysis data corresponding to the target object is used to characterize the activity of the target object, and the user analysis data corresponding to the target object is used to characterize the activity of the user in using the target object.

[0009] Optionally, the target object is any one of a menu, a mini-program, a web page, a widget, and a native capability.

[0010] Optionally, the application analysis data corresponding to the target object includes one or more of the inventory information, authorization information and usage records of the target object, wherein the inventory information includes the number of target objects in multiple dimensions, the authorization information includes the authorization quantity of the target objects in multiple dimensions, and the usage records include the number of visits to the target object in multiple dimensions.

[0011] Optionally, the user analysis data corresponding to the target object includes one or more of the user's inventory information, authorization information and access information, wherein the user's inventory information includes the number of users in multiple dimensions, the authorization information includes the number of target objects authorized for users in multiple dimensions, and the access information includes the number of visits to the target object by users in multiple dimensions.

[0012] Optionally, also include:

[0013] According to the functions to which the target object belongs, application analysis data and user analysis data corresponding to different functions are determined, wherein the application analysis data corresponding to the functions are used to characterize the activity of the functions, and the user analysis data corresponding to the functions are used to characterize the activity of users in using the functions.

[0014] Optionally, also include:

[0015] Determine one or more functions with the highest activity in the application according to the application analysis data corresponding to the different functions;

[0016] According to the user analysis data corresponding to the function, a group of users with the highest activity corresponding to the function is determined.

[0017] Optionally, also include:

[0018] In response to a request for creating a burying point event, determining whether the burying point event in the request for creating the burying point event meets a preset burying point specification;

[0019] If the tracking event meets the preset tracking specification, determine to create the tracking event;

[0020] Add the tracking program corresponding to the tracking event to the program of the application.

[0021] In a second aspect, the present application provides a device for processing buried point data, including:

[0022] Acquisition module, used to obtain buried point data;

[0023] A determination module, used to determine a target object corresponding to the buried point data according to an object variable in the buried point data, wherein the target object is an object in an application targeted by an operation that generates the buried point data;

[0024] The acquisition module is also used to acquire application analysis data and user analysis data corresponding to the target object based on the multiple embedded data corresponding to the target object, wherein the application analysis data corresponding to the target object is used to characterize the activity of the target object, and the user analysis data corresponding to the target object is used to characterize the activity of the user in using the target object.

[0025] Optionally, the determination module is also used to determine application analysis data and user analysis data corresponding to different functions according to the functions to which the target object belongs, wherein the application analysis data corresponding to the functions are used to characterize the activity of the functions, and the user analysis data corresponding to the functions are used to characterize the activity of users in using the functions.

[0026] Optionally, the determination module is further used to determine one or more functions with the highest activity in the application according to the application analysis data corresponding to the different functions;

[0027] The determination module is further used to determine a group of users with the highest activity corresponding to the function based on user analysis data corresponding to the function.

[0028] Optionally, the device for processing buried point data further includes: a processing module;

[0029] The determination module is further configured to respond to a burying point event creation request and determine whether the burying point event in the burying point event creation request meets a preset burying point specification;

[0030] The determining module is further configured to determine to create the burying point event if the burying point event meets a preset burying point specification;

[0031] The processing module is used to add the tracking program corresponding to the tracking event to the program of the application.

[0032] In a third aspect, the present application provides a device for processing buried point data, including:

[0033] Memory;

[0034] processor;

[0035] Wherein, the memory stores computer-executable instructions;

[0036] The processor executes the computer-executable instructions stored in the memory to implement the method for processing buried point data as described in the first aspect and various possible implementation methods of the first aspect.

[0037] In a fourth aspect, the present application provides a computer storage medium having computer execution instructions stored thereon, wherein the computer execution instructions are executed by a processor to implement the method for processing burial point data as described in the first aspect and various possible implementations of the first aspect.

[0038] The present application provides a method for processing buried point data, which obtains buried point data and determines the target object corresponding to the buried point data according to the object variables in the buried point data, wherein the target object is an object in the application targeted by the operation that generates the buried point data, and based on multiple buried point data corresponding to the target object, obtains application analysis data and user analysis data corresponding to the target object, wherein the application analysis data corresponding to the target object is used to characterize the activity of the target object, and the user analysis data corresponding to the target object is used to characterize the activity of the user using the target object; the method uses unified buried point data for statistical analysis to solve the problems of difficult data reporting and high data usage cost, and adopts two dimensions of application end and user end to analyze user behavior, thereby improving the real-time and accuracy of user behavior analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0040] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0041] Figure 1 The process of the method for processing the buried data provided for this application Figure 1 ;

[0042] Figure 2 The process of the method for processing the buried data provided for this application Figure 2 ;

[0043] Figure 3 It is a structural schematic diagram of a device for processing buried point data provided by the present application;

[0044] Figure 4 It is a structural diagram of the processing equipment of the buried point data provided in this application. DETAILED DESCRIPTION

[0045] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0046] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings 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 present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations 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.

[0047] In the technical solution of this application, the collection, storage, use, processing, transmission, provision and disclosure of information such as financial data or user data involved shall comply with the provisions of relevant laws and regulations and shall not violate public order and good morals.

[0048] With the rapid expansion of the Internet, the amount of user behavior data on various applications and websites has increased dramatically. These data contain deep insights into user needs, product function optimization directions, and room for improvement in user experience. Therefore, analyzing user behavior has become an indispensable requirement. In order to meet this challenge, point-of-care data processing technology has emerged.

[0049] In the prior art, the acquisition of user behavior data mainly relies on the log data reported by application systems distributed across various terminals, including desktop terminals, web terminals, and mobile terminals. These log data record every interactive behavior of users on the application interface, such as clicking, browsing pages, and performing searches. Subsequently, through the analysis of the log data, the user's behavior patterns, interest preferences, and potential needs can be obtained, providing strong data support for product optimization, personalized recommendations, and market strategy formulation.

[0050] However, in the prior art, the application system reports and analyzes log data, but there are problems such as low efficiency in reporting user behavior data and omissions or misreporting in the user behavior data reporting process, resulting in poor real-time performance and insufficient accuracy in user behavior analysis.

[0051] Based on the above problems, the method for processing buried data provided in this embodiment tracks and records various behavioral data (i.e. buried data) of users during use by implanting data collection code or tools (i.e. "buried data") in preset locations of applications or websites, and then locates the target object that generates the data based on the object variables of the buried data. Subsequently, based on multiple buried data of the target object, application analysis data and user analysis data are extracted from the two dimensions of the application end and the user end respectively. The former determines the activity of the target object in the application, and the latter measures the user's active use of the object; using unified buried data for statistical analysis solves the problems of difficult data reporting and high data usage costs. The two-dimensional analysis method significantly improves the real-time and accuracy of user behavior analysis.

[0052] Figure 1 The process of the method for processing the buried data provided for this application Figure 1 For example, the execution subject of this application may be a user management system. Figure 1 As shown, the method for processing buried point data provided in this embodiment includes:

[0053] S101: Acquire buried point data, and determine a target object corresponding to the buried point data according to an object variable in the buried point data, wherein the target object is an object in an application targeted by an operation that generates the buried point data.

[0054] Among them, tracking data is user behavior data collected through tracking points pre-set in the application.

[0055] Object variables refer to variables used to uniquely identify specific objects that users interact with within an application during the user behavior data collection process.

[0056] Acquiring tracking data means capturing and recording various interactive behavior data of users in the application in real time through monitoring points (i.e. tracking points) pre-set in the application. These data contain rich information, such as user clicks, slides, stays and other actions, as well as timestamps and event types when these actions occur. What is particularly critical is that tracking data also includes object variables. These variables, as unique identifiers, can clearly point to the specific objects in the application targeted by user operations, such as a product or a button. Therefore, acquiring tracking data and determining the target object based on it is an important prerequisite for understanding user behavior, analyzing application performance and optimizing user experience.

[0057] First, obtaining tracking data helps to accurately restore the interaction scenarios between users and applications, and understand the users' true intentions and behavior paths. By knowing which specific object the user has operated, we can more accurately determine the attractiveness and efficiency of each object or function in the application. Secondly, this is crucial for optimizing product design and improving user experience. By analyzing the frequency, duration and feedback of users' operations on different objects, we can discover potential problems and room for improvement, and then optimize the product in a targeted manner. Finally, this is also to understand users' preferences and interests in different objects, which helps to provide users with content and services that better meet their needs.

[0058] As you can understand, as users interact with the application, the user management system will obtain user behavior data. The data includes information such as event type, timestamp, object variables (such as the identifier of the target object), etc. Then, the data can accurately determine the target object corresponding to each operation through the parsing and mapping of object variables.

[0059] S102: Based on the multiple tracking data corresponding to the target object, obtain application analysis data and user analysis data corresponding to the target object, wherein the application analysis data corresponding to the target object is used to characterize the activity of the target object, and the user analysis data corresponding to the target object is used to characterize the activity of the user using the target object.

[0060] Among them, application analysis data can determine the overall activity of the target object, that is, its popularity in the market and user engagement, which is of great guiding significance for product iteration, market strategy adjustment and resource allocation decision-making. At the same time, user analysis data reveals the usage habits, preferences and existing problems of different user groups, and can accurately build user portraits, personalize recommendations and optimize user experience, thereby improving user satisfaction and loyalty.

[0061] It is understandable that to obtain application analysis data and user analysis data based on tracking data, first of all, it is necessary to deploy tracking strategies in the target objects to ensure that user behavior and application status information can be fully and accurately captured. Next, use data analysis techniques (such as statistical analysis, machine learning, etc.) to clean, organize, aggregate and mine the acquired tracking data to extract valuable information and insights. Finally, based on the analysis results of the tracking data, generate the corresponding application analysis data and user analysis data.

[0062] That is to say, in order to obtain application analysis data and user analysis data comprehensively and deeply based on the tracking data, first of all, the tracking strategy should be deployed in the key paths and target objects of the application. Ensure that the tracking strategy can capture every interaction between the user and the application without omission, as well as the performance information of the application in different states, so as to build a detailed and accurate tracking data. Subsequently, data analysis technology, including but not limited to statistical analysis, machine learning and other algorithms, is used to deeply process the massive tracking data. This process covers data cleaning to remove impurities, data organization to standardize the format, data aggregation to extract trends, and data mining to discover hidden patterns and associations. Through these technical means, information such as user behavior, application performance and market trends can be extracted. Finally, based on these analysis results, detailed application analysis data is generated to reveal the operation status, performance bottlenecks and optimization directions of the application; at the same time, rich user analysis data can also be constructed to depict the user's portrait, behavior preferences and potential needs, providing strong data support for product iteration, personalized recommendation and precision marketing.

[0063] The method for processing buried point data provided in the present embodiment obtains buried point data, determines the target object corresponding to the buried point data according to the object variables in the buried point data, the target object being the object in the application targeted by the operation generating the buried point data, and obtains application analysis data and user analysis data corresponding to the target object based on the multiple buried point data corresponding to the target object, wherein the application analysis data corresponding to the target object is used to characterize the activity of the target object, and the user analysis data corresponding to the target object is used to characterize the activity of the user using the target object; the method uses unified buried point data for statistical analysis, solves the problems of difficult data reporting and high data usage cost, and adopts two dimensions of application end and user end to analyze user behavior, thereby improving the real-time and accuracy of user behavior analysis.

[0064] Figure 2 The process of the method for processing the buried data provided for this application Figure 2 .like Figure 2 As shown, in this embodiment Figure 1 Based on the embodiment, the processing method of the buried point data is described in detail. The automated data collection method shown in this embodiment includes:

[0065] S201: In response to a burying point event creation request, determine whether a burying point event in the burying point event creation request meets a preset burying point specification.

[0066] Data embedding processing is designed to effectively collect, organize, analyze and utilize user behavior data. Data embedding processing is to embed data collection codes or tools (i.e. “embedding”) in the preset locations of applications or websites to track and record various user behavior data during use, such as clicks, browsing and searches. These behavior data are then used to analyze user behavior patterns, preferences and needs, thereby supporting product optimization, improving user experience and conversion rates.

[0067] When receiving a tracking event creation request, you first need to confirm whether the tracking event defined in the request meets the preset tracking specification. The preset tracking specification usually specifies the event name, attribute settings, and trigger logic of the tracking event to ensure the consistency, accuracy, and maintainability of the tracking data.

[0068] It can be understood that, first of all, the preset burying point specification includes event name, attribute settings and trigger logic. Secondly, when a request to create a burying point event is received, the burying point event in the request is compared with the preset burying point specification one by one. Determine whether the event name, attribute settings and trigger logic meet the specification requirements. If the burying point event does not meet the preset burying point specification, the burying point event will be returned to the request; if the burying point event meets the preset burying point specification, it is determined to create the burying point event.

[0069] S202: If the burying point event meets the preset burying point specification, determine to create the burying point event.

[0070] Among them, tracking events are used to record users' specific behaviors or events.

[0071] As you can understand, first, check the event name, attribute settings, and trigger logic against the preset tracking specifications for the tracking event creation request. Secondly, after confirming that the tracking event fully complies with the preset tracking specifications, configure and deploy the tracking event. This includes setting the trigger logic of the tracking event, defining event attributes and their value ranges, and specifying event data. Finally, complete the creation of the tracking event to ensure that the tracking event can be correctly triggered and the tracking data can be obtained.

[0072] When a tracking event is triggered, the user management system will check whether the tracking event meets the preset tracking specifications. This process includes verifying whether the event name meets the naming rules, whether the data carried by the event meets the data structure requirements, whether the event triggering conditions are met, etc. Only when the tracking event fully meets the preset specifications will the user management system create a tracking event for subsequent data recording and processing.

[0073] The decision to create a tracking event is based on the matching result between the tracking event and the preset specifications. If the tracking event meets the specifications, it will be regarded as a valid and reliable data source, and the user management system will create and record the tracking event. This decision-making process ensures that only high-quality, standard tracking data is included in the analysis scope, thereby improving the accuracy and reliability of tracking data analysis. At the same time, it also avoids analysis errors and misleading conclusions caused by non-standard tracking data.

[0074] S203: Add the tracking program corresponding to the tracking event to the program of the application.

[0075] Integrate the tracking program corresponding to the developed tracking event into the application program to ensure that the tracking program can run correctly and work with the application program.

[0076] Effectively adding tracking programs to the application program is crucial to the accuracy and comprehensiveness of tracking data collection and analysis. An effective tracking program can ensure that user behavior data is fully captured without affecting the user experience. This data not only helps to understand user habits and needs, but also provides strong support for product optimization, function iteration and market promotion. At the same time, a reasonable tracking strategy can also improve the efficiency and accuracy of data analysis, discover problems faster, solve problems and promote continuous product improvement.

[0077] S204: Acquire the buried point data, and determine the target object corresponding to the buried point data according to the object variables in the buried point data.

[0078] Optionally, the target object is any one of a menu, a mini-program, a web page, a widget, and a native capability.

[0079] First, acquire the buried data. Then, clean, integrate, and convert the buried data to determine the object variables in the buried data. Finally, extract and parse the object variables to determine the menu, applet, web page, widget, or native capability that the buried data corresponds to.

[0080] According to the object variables in the embedded data, the corresponding target objects (such as menus, applets, web pages, widgets, native capabilities, etc.) are determined. This process extracts key object variable information from the massive data generated by the interaction between users and applications, and then identifies the specific functions or content that users are using or operating. This method can understand user behavior patterns, optimize product layout, and improve user experience.

[0081] In modern software development and operations, understanding user behavior is the key to optimizing products and improving service quality. By acquiring and analyzing object variables in embedded data, we can accurately understand how users interact with various parts of the product, including their most frequently visited menus, favorite applets, and most frequently browsed web pages. This information is crucial for guiding product iterations, optimizing interface design, and improving user experience.

[0082] S205: Based on the multiple pieces of buried data corresponding to the target object, obtain application analysis data and user analysis data corresponding to the target object.

[0083] Optionally, the application analysis data corresponding to the target object includes one or more of the inventory information, authorization information and usage records of the target object, wherein the inventory information includes the number of target objects in multiple dimensions, the authorization information includes the authorization quantity of the target objects in multiple dimensions, and the usage records include the number of visits to the target object in multiple dimensions.

[0084] Among them, the functions of the target object include inventory information, authorization information and usage records. The inventory information includes the number of menus / applets / web pages / widgets / native capabilities, the number of applications, the number of functions, the number of modules, the number of sub-products and the number of products in the space, field and time dimensions. The authorization information includes the application menu / application applets / application web pages / application widgets / application native capabilities, functions / and above management levels, and the number of authorized people, the number of authorized roles, the number of authorized groups, the number of authorized organizations and the number of authorized fields in the time dimension. The usage records include the application management level and the function management level. The application management level is from the application menu / application applets / application web pages / application widgets / application native capabilities and the number of visits and the number of visitors in the time dimension. The function management level is from the number of active applications, function clicks and function visitors in the function and time dimensions.

[0085] Optionally, the user analysis data corresponding to the target object includes one or more of the user's inventory information, authorization information and access information, wherein the user's inventory information includes the number of users in multiple dimensions, the authorization information includes the number of target objects authorized for users in multiple dimensions, and the access information includes the number of visits to the target object by users in multiple dimensions.

[0086] Among them, the user analysis data corresponding to the target object includes the user's stock information, authorization information and access information. The user's stock information includes the number of people in employee management (users, positions, roles, organizations, fields and platforms) and time dimensions. The authorized information includes the number of authorized applications in the user and time dimensions. The access information includes the number of application visits, login times and the number of visited applications in the employee management and time dimensions.

[0087] Based on the multiple embedded data corresponding to the target object, the application analysis data and user analysis data corresponding to these functions are determined according to the different functions of the target object. The target object can be a menu item, mini-program, web page, widget or native capability. Application analysis data mainly focuses on the status and usage of the target object itself, including inventory information (such as the number of target objects), authorization information (such as authorization quantity) and usage records (such as visit volume). User analysis data focuses on the interaction between users and target objects, including the user's inventory, the number of authorized target objects and the user's visit volume. Application analysis data and user analysis data are presented in a multi-dimensional manner to provide a more comprehensive analysis perspective.

[0088] This step not only helps us understand the performance and user behavior of the product at different functional levels. By applying analytical data, we can understand the usage, authorization status and access popularity of each functional module, so as to identify which functions are popular and which ones need to be optimized or improved. At the same time, user analysis data reveals the characteristics, preferences and interaction patterns of user groups with target objects, which is crucial for improving user experience and optimizing product function layout.

[0089] S206: Determine application analysis data and user analysis data corresponding to different functions according to the functions to which the target object belongs.

[0090] Among them, the application analysis data corresponding to the function is used to characterize the activity of the function, and the user analysis data corresponding to the function is used to characterize the activity of users using the function.

[0091] According to the functions of the target object, determining the application analysis data and user analysis data corresponding to different functions is an analysis of the buried data. According to the characteristics of each function, define and collect the relevant application analysis data and user analysis data.

[0092] Different functions often serve different user needs and have different business goals and performance indicators. Therefore, targeted data analysis of them can more accurately reveal the actual performance and user feedback of each function, providing strong support for product optimization and decision-making. At the same time, this segmented data analysis also helps to discover the correlation and impact between cross-functional modules, thereby achieving more comprehensive product improvements.

[0093] S207: Determine one or more functions with the highest activity in the application according to the application analysis data corresponding to the different functions.

[0094] For different functions within an application, application usage data is collected and analyzed. Application usage data includes multiple indicators such as number of visits, dwell time, interaction frequency, conversion rate, etc. Then, one or more functions with the highest activity in the application are determined. The activity of an application is often a reflection of the user's interest, frequency of use, or value to the user.

[0095] Functions with high activity levels are often the concentrated embodiment of user needs. Optimizing these functions can further improve user experience and satisfaction. At the same time, by comparing functions with low activity levels, potential problems and room for improvement can be discovered, providing direction for comprehensive product optimization.

[0096] S208: Determine a group of users with the highest activity corresponding to the function according to the user analysis data corresponding to the function.

[0097] First, after collecting the user analysis data corresponding to the function, including multi-dimensional user behavior data such as the number of visits to the application, dwell time, interaction frequency, conversion rate, etc., we then identify one or more users with the highest user activity in the function. User activity in an application is often a reflection of the user's interest, frequency of use, or.

[0098] Determining the most active group of users corresponding to a function based on the user analysis data corresponding to the function is a method of identifying and locating the most active user group for a specific function through data analysis. This involves analyzing various data generated by users when using the function (such as access frequency, usage time, interactive behavior, etc.) to find those users who are highly active and engaged in the function.

[0099] The method for processing buried point data provided in the present embodiment determines whether the buried point event in the buried point event creation request meets the preset buried point specification by responding to the buried point event creation request. If the buried point event meets the preset buried point specification, it is determined to create the buried point event, add the buried point program corresponding to the buried point event to the program of the application, obtain the buried point data, determine the target object corresponding to the buried point data according to the object variables in the buried point data, and based on the multiple buried point data corresponding to the target object and according to the function to which the target object belongs, determine the application analysis data and user analysis data corresponding to different functions, determine one or more functions with the highest activity in the application according to the application analysis data corresponding to the different functions, and determine a group of users with the highest activity corresponding to the function according to the user analysis data corresponding to the function. The method uses unified buried point data for statistical analysis to solve the problems of difficult data reporting and high data usage cost, and adopts two dimensions of application end and user end to analyze user behavior, thereby improving the real-time and accuracy of user behavior analysis.

[0100] Figure 3 Schematic diagram of the structure of the device for processing buried point data provided by this application. Figure 3 As shown, the present application provides a device for processing buried point data, and the device 300 for processing buried point data includes:

[0101] The acquisition module 301 is used to acquire the buried point data;

[0102] A determination module 302 is used to determine a target object corresponding to the buried point data according to an object variable in the buried point data, wherein the target object is an object in an application targeted by an operation that generates the buried point data;

[0103] The acquisition module 301 is also used to acquire application analysis data and user analysis data corresponding to the target object based on the multiple embedded data corresponding to the target object, wherein the application analysis data corresponding to the target object is used to characterize the activity of the target object, and the user analysis data corresponding to the target object is used to characterize the activity of the user in using the target object.

[0104] Optionally, the determination module 302 is also used to determine application analysis data and user analysis data corresponding to different functions according to the functions to which the target object belongs, wherein the application analysis data corresponding to the functions is used to characterize the activity of the functions, and the user analysis data corresponding to the functions is used to characterize the activity of users in using the functions.

[0105] Optionally, the determination module 302 is further configured to determine one or more functions with the highest activity in the application according to the application analysis data corresponding to the different functions;

[0106] The determination module 302 is further configured to determine a group of users with the highest activity corresponding to the function based on user analysis data corresponding to the function.

[0107] Optionally, the device for processing buried point data further includes: a processing module 303;

[0108] The determination module 302 is further configured to respond to a burying point event creation request and determine whether the burying point event in the burying point event creation request meets a preset burying point specification;

[0109] The determining module 302 is further configured to determine to create the burying point event if the burying point event meets a preset burying point specification;

[0110] The processing module 303 is used to add the tracking program corresponding to the tracking event to the program of the application.

[0111] Figure 4 This is a schematic diagram of the structure of the processing device for the buried point data provided by this application. Figure 4 As shown, the present application provides a device for processing buried point data, and the device 400 for processing buried point data includes: a receiver 401, a transmitter 402, a processor 403 and a memory 404.

[0112] Receiver 401, used for receiving instructions and data;

[0113] A transmitter 402, used for sending instructions and data;

[0114] Memory 404, for storing computer-executable instructions;

[0115] The processor 403 is used to execute the computer-executable instructions stored in the memory 404 to implement the various steps performed by the method for processing buried point data in the above embodiment. For details, please refer to the relevant description in the above embodiment of the method for processing buried point data.

[0116] Optionally, the memory 404 may be independent or integrated with the processor 403 .

[0117] When the memory 404 is independently provided, the electronic device further includes a bus for connecting the memory 404 and the processor 403 .

[0118] The present application also provides a computer storage medium, in which computer-readable storage medium is stored computer execution instructions. When a processor executes the computer execution instructions, a method for processing buried point data as executed by the above-mentioned buried point data processing device is implemented.

[0119] The above is only a preferred implementation of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A method for processing buried point data, characterized in that: include: Acquire the buried point data, and determine the target object corresponding to the buried point data according to the object variable in the buried point data, wherein the target object is an object in the application targeted by the operation generating the buried point data; Based on the multiple embedded data corresponding to the target object, application analysis data and user analysis data corresponding to the target object are obtained, wherein the application analysis data corresponding to the target object is used to characterize the activity of the target object, and the user analysis data corresponding to the target object is used to characterize the activity of the user in using the target object.

2. The method according to claim 1, characterized in that The target object is any one of a menu, a mini-program, a web page, a widget, and a native capability.

3. The method according to claim 1, characterized in that The application analysis data corresponding to the target object includes one or more of the inventory information, authorization information and usage records of the target object, wherein the inventory information includes the number of the target objects in multiple dimensions, the authorization information includes the authorization quantity of the target objects in multiple dimensions, and the usage records include the access quantity of the target objects in multiple dimensions.

4. The method according to claim 1, characterized in that: The user analysis data corresponding to the target object includes one or more of the user's inventory information, authorization information and access information, wherein the user's inventory information includes the number of users in multiple dimensions, the authorization information includes the number of target objects authorized by users in multiple dimensions, and the access information includes the number of visits to the target object by users in multiple dimensions.

5. The method according to any one of claims 1 to 4, characterized in that: Also includes: According to the functions to which the target object belongs, application analysis data and user analysis data corresponding to different functions are determined, wherein the application analysis data corresponding to the functions are used to characterize the activity of the functions, and the user analysis data corresponding to the functions are used to characterize the activity of users in using the functions.

6. The method according to claim 5, characterized in that Also includes: Determine one or more functions with the highest activity in the application according to the application analysis data corresponding to the different functions; According to the user analysis data corresponding to the function, a group of users with the highest activity corresponding to the function is determined.

7. The method according to any one of claims 1 to 4, characterized in that: Also includes: In response to a request for creating a burying point event, determining whether the burying point event in the request for creating the burying point event meets a preset burying point specification; If the tracking event meets the preset tracking specification, determine to create the tracking event; Add the tracking program corresponding to the tracking event to the program of the application.

8. A device for processing buried point data, characterized in that: include: Acquisition module, used to obtain buried point data; A determination module, used to determine a target object corresponding to the buried point data according to an object variable in the buried point data, wherein the target object is an object in an application targeted by an operation that generates the buried point data; The acquisition module is also used to acquire application analysis data and user analysis data corresponding to the target object based on the multiple embedded data corresponding to the target object, wherein the application analysis data corresponding to the target object is used to characterize the activity of the target object, and the user analysis data corresponding to the target object is used to characterize the activity of the user in using the target object.

9. A device for processing buried point data, characterized in that: include: Memory; processor; Wherein, the memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the method for processing buried point data as described in any one of claims 1-7.

10. A computer storage medium, characterized in that: The computer storage medium stores computer execution instructions, which, when executed by a processor, are used to implement the method for processing buried point data as described in any one of claims 1-7.

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