User behavior point burying method and device, medium and equipment

Abstract

The invention relates to the technical field of data analysis, in particular to a user behavior burying point method and device, a medium and equipment, and the method comprises the steps: carrying out the data collection of different preset behaviors through a burying point collection SDK, obtaining the reporting state of a data reporting service module through a multi-round query and judgment mechanism based on a time dimension, the offline reporting mode is started according to different data reporting abnormal scenes, the online reporting mode is started according to different data reporting normal scenes, then targeted processing modes are adopted according to different cache state query results, and whether external conditions such as a network are normal or not, the data can be reported normally. According to the invention, the preset behavior data can be processed more flexibly and reasonably, smooth proceeding of the whole user behavior burying point data reporting process is ensured, it is ensured that the data can be efficiently and accurately transferred to subsequent links for analysis and application, and the integrity and continuity of user behavior data acquisition are improved.

Description

Technical Field

[0001] The present invention relates to the field of data analysis technology, and in particular to a user behavior tracking method, device, medium and equipment. Background Art

[0002] At a time when Building Information Modeling (BIM) technology is widely used, BIM applications play a core role in multiple aspects such as building design, construction management, and operation and maintenance. With the in-depth development of BIM applications and the continuous increase in project complexity, it has become increasingly critical to accurately collect and analyze user behavior data in BIM applications.

[0003] Traditional user behavior data collection methods have exposed many deficiencies in BIM application scenarios: On the one hand, BIM applications involve many complex model operations, file management, and project collaboration functions. Traditional tracking methods are difficult to comprehensively and efficiently capture and record these specific, highly professional related behaviors, which can easily lead to missing or inaccurate data. On the other hand, in the data reporting link, since BIM projects often involve multi-party collaboration and the network environment is complex and changeable, there may be network delays and interruptions, and traditional tracking solutions lack effective response mechanisms. Once an abnormality occurs in the network, data reporting may fail, resulting in the loss of a large amount of valuable user behavior data, which seriously affects the support for BIM application optimization and project management decisions based on user behavior analysis.

[0004] Therefore, how to improve the integrity and continuity of user behavior data collection has become an urgent problem to be solved. Summary of the invention

[0005] In view of the above technical problems, the technical solution adopted by the present invention is a user behavior tracking method, which includes the following steps:

[0006] S1, introduce the tracking data collection SDK into the front-end project of the target application and configure the preset information, where the preset information includes basic application information, basic user information, data reporting related configuration, event classification rules and event naming rules.

[0007] S2, collecting preset behavior data corresponding to the target user's preset behavior on the target application through the tracking collection SDK, where the preset behavior includes model operation behavior, file operation behavior and project operation behavior.

[0008] S3, querying the reporting status of the data reporting service module according to the current time and the preset time interval, and obtaining the reporting status query result, wherein the reporting status query result includes normal data reporting and abnormal data reporting.

[0009] S4: If the query result of the reporting status is that the data reporting is abnormal, the offline reporting mode is used, where the offline reporting mode means caching the preset behavior data into the preset database through the tracking point collection SDK.

[0010] S5. If the reporting status query result shows that the data reporting is normal, the online reporting mode is used, wherein the online reporting mode refers to reporting the preset behavior data cached in the preset database to the data reporting service module, and querying the cache status of the preset database through the embedding collection SDK, obtaining the cache status query result, and performing preset processing on the preset behavior data according to the cache status query result.

[0011] The present invention also provides a user behavior burying device, which includes:

[0012] The SDK configuration module is used to introduce the tracking data collection SDK into the front-end project of the target application and configure preset information, where the preset information includes basic application information, basic user information, data reporting related configuration, event classification rules and event naming rules.

[0013] The data collection module is used to collect preset behavior data corresponding to the target user's preset behavior on the target application through the tracking collection SDK, where the preset behavior includes model operation behavior, file operation behavior and project operation behavior.

[0014] The reporting status query module is used to query the reporting status of the data reporting service module according to the current time and the preset time interval, and obtain the reporting status query result, wherein the reporting status query result includes normal data reporting and abnormal data reporting.

[0015] The first data reporting module is used to use the offline reporting mode if the reporting status query result is that the data reporting is abnormal, wherein the offline reporting mode refers to caching the preset behavior data into the preset database through the embedding point collection SDK.

[0016] The second data reporting module is used to use the online reporting mode if the reporting status query result is that the data reporting is normal. The online reporting mode refers to reporting the preset behavior data cached in the preset database to the data reporting service module, and querying the cache status of the preset database through the embedding collection SDK, obtaining the cache status query result, and performing preset processing on the preset behavior data according to the cache status query result.

[0017] The present invention also provides a non-transitory computer-readable storage medium, in which at least one instruction or at least one program is stored, and the at least one instruction or at least one program is loaded and executed by a processor to implement the above-mentioned user behavior tracking method.

[0018] The present invention also provides an electronic device, comprising a processor and the above-mentioned non-transitory computer-readable storage medium.

[0019] The present invention has at least the following beneficial effects: comprehensive and detailed data collection of different preset behaviors is performed through the tracking point collection SDK, the reporting status of the data reporting service module is obtained based on the multi-round query and judgment mechanism of the time dimension, and the offline reporting mode is started according to different data reporting abnormal scenarios, so that no matter whether the reporting abnormality persists or experiences complex situations such as partial response and subsequent continuous no response, the security of the data can be guaranteed by caching the preset behavior data, and the online reporting mode is started according to different data reporting normal scenarios, ensuring that no matter whether the response of the data reporting service module is stable and normal at the beginning or returns to normal after experiencing certain fluctuations, the data reporting work can be completed efficiently and accurately, and then targeted processing methods are adopted according to different cache status query results, so that the preset behavior data can be processed more flexibly and reasonably, whether there is cache data that needs to be further improved and integrated, or there is no cache data directly reported, the smooth progress of the entire user behavior tracking point data reporting process can be guaranteed, and whether the external conditions such as the network are normal or not, it can ensure that the data can be efficiently and accurately transferred to the subsequent links for analysis and application, thereby improving the integrity and continuity of user behavior data collection. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0021] Figure 1 A flowchart of a user behavior tracking method provided in Embodiment 1 of the present invention;

[0022] Figure 2 A schematic diagram of the structure of a user behavior tracking device provided in Example 2 of the present invention. DETAILED DESCRIPTION

[0023] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0024] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It is understood that, where appropriate, the above-mentioned terms used to distinguish similar objects can be interchanged so that the present invention can also implement other embodiments other than the above-mentioned illustrated embodiments or described embodiments. In addition, the terms "including" and "having" and any variations are intended to cover non-exclusive inclusions. For example, a process, method, system, product or server that includes 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.

[0025] Embodiment 1

[0026] This embodiment provides a user behavior tracking method, which includes the following steps: Figure 1 As shown:

[0027] S1, introduce the tracking data collection SDK into the front-end project of the target application and configure the preset information, where the preset information includes basic application information, basic user information, data reporting related configuration, event classification rules and event naming rules.

[0028] Among them, SDK (Software Development Kit) is a tool used to realize user behavior data collection. It can automatically capture various behavior data generated when the target user interacts with the target application during the operation of the target application.

[0029] The basic application information includes the relevant attribute data of the target application itself, such as the application name, version number, business field, platform attributes of the application, etc. Among them, platform attributes include mobile applications, desktop applications, and web applications. Obtaining the basic application information will help you distinguish and compare user behaviors according to different application versions, application types, and other dimensions during subsequent data analysis.

[0030] The basic user information includes the basic information of the user who uses the target application, such as the user's unique identifier such as the user ID and account, the user's role, the user's organization or team information, etc. Among them, the user's role in the BIM project includes designer, engineer, project manager, etc. By obtaining the basic user information, we can deeply understand the differences in the behavior patterns of different user groups in the application, such as analyzing the differences in the operating habits of users from different teams when using the project collaboration function, so as to optimize the function in a targeted manner to meet the needs of different user groups.

[0031] Data reporting configuration determines how, when and where user behavior data will be reported, including the target server address, reporting time interval, and reporting data format requirements. Reasonable data reporting configuration can ensure the efficiency, accuracy and stability of data transmission, so that the collected user behavior data can smoothly reach the designated location such as the analysis platform.

[0032] The event classification rules specify how to classify various behavioral events generated by users in the application. For example, in the BIM application scenario, behaviors related to model operations are classified into one category, file operations are classified into one category, and project operations are classified into one category. It can also be further subdivided. For example, in the model operation category, different subcategories are subdivided according to the degree of impact of the operation on the model, the complexity of the operation, etc. Clear event classification rules help to quickly locate and sort out the occurrence of different types of behaviors during data analysis, and facilitate insights into the interaction logic between users and applications from macro to micro.

[0033] The event naming rules give each type of behavior event a standardized, easy-to-understand and unique name, which facilitates accurate reference and distinction of different behaviors in the entire data collection, reporting and subsequent analysis process. For example, the model translation operation can be named "Model_Translation", and the model zoom operation can be named "Model_Zoom", etc. The unified naming rules avoid data interpretation errors caused by name confusion, allowing different developers, data analysts, etc. to work based on consistent naming, improving the collaborative efficiency and accuracy of the entire user behavior data processing process.

[0034] S2, collecting preset behavior data corresponding to the target user's preset behavior on the target application through the tracking collection SDK, where the preset behavior includes model operation behavior, file operation behavior and project operation behavior.

[0035] Among them, when the target user performs various preset behaviors on the target application, the tracking collection SDK will collect the corresponding preset behavior data according to the pre-set mechanism, and record the key details of the interaction between the target user and the target application, so as to provide rich and reliable data support for the subsequent in-depth analysis of user behavior patterns and optimization of application functions.

[0036] In a specific implementation, the model operation behaviors include model translation, model scaling, model rotation, model dragging, component selection, property query, geometry modification, and component parameter adjustment.

[0037] File operations include opening and saving files.

[0038] Project operation behaviors include project creation and project switching.

[0039] Among them, when the target user performs a translation operation on the model, the tracking point collection SDK will record the time when the operation occurs, the direction of the translation, and the distance data of the translation. When the target user performs a zoom operation on the model, the tracking point collection SDK will record the time when the operation occurs, the initial state of the model during the zoom operation, and the zoom multiple. When the target user performs a rotation operation on the model, the tracking point collection SDK will record the time when the operation occurs, the coordinate axis around which the rotation occurs, and the rotation angle. When the target user performs a drag operation on the model, the tracking point collection SDK will record the time when the operation occurs, the coordinates of the starting position of the drag, and the coordinates of the ending position, so as to analyze the frequency and habits of the target user in panning, zooming, rotating, and dragging the model at different time periods, and understand the model areas that the user focuses on and the operation status when switching between different perspectives.

[0040] When the target user selects one or more components, the tracking collection SDK will record the time when the selection operation occurs, the unique identifier of the selected component, and the selection method, so as to analyze the target user's attention to different components and their habitual preferences in selecting component operations, thereby optimizing the usability and accuracy of the component selection function.

[0041] When the target user queries the constructed attributes, the tracking collection SDK will record the time of the query operation, the specific attribute name of the queried component and the specific value of the queried attribute, so as to analyze the target user's focus on viewing the model component attributes, so as to display important attributes more reasonably in the application interface and improve the efficiency of users in obtaining key information.

[0042] When the target user modifies the geometric shape of a component, the tracking point collection SDK will record the time of the modification operation, the specific modification type (such as stretching, compression, twisting, etc.) and the specific parameters of the modification, so as to facilitate the analysis of the target user's operating habits and needs during the model design optimization process, so as to help the development team determine which geometric shape modification operations are more commonly used, so as to improve the corresponding functional modules in a targeted manner.

[0043] When the target user adjusts the component parameters, the tracking collection SDK will record the time of the adjustment operation, the identifier of the adjusted component, the name of the adjusted parameter (such as material parameters, strength parameters, etc.) and the specific value after the adjustment, so as to facilitate the analysis of the target user's needs for component performance optimization in different scenarios, and help the application provide parameter adjustment functions that are more in line with actual usage needs.

[0044] When the target user performs a file opening operation, the tracking collection SDK will record the time of the opening operation, the name of the opened file, the file storage path and the file type, so as to optimize the file management and search functions and improve the convenience of users in obtaining files.

[0045] When the target user performs a file save operation, the tracking collection SDK will record the time of the save operation, the name of the saved file, whether the file has been modified before saving, and the saved target path, so as to improve the prompts and functions related to file saving and prevent problems such as data loss caused by user misoperation.

[0046] When the target user performs a project creation operation, the tracking collection SDK will record the time of project creation, the name of the created project, and the basic information of the project, which is convenient for analyzing the creation frequency of different types of projects, the situation of projects initiated by different teams, etc., and provide a reference basis for the optimization of project management and application functions in the project creation link.

[0047] When the target user performs a project switching operation, the tracking collection SDK will record the time of the switching operation and the project IDs before and after the switching, so as to optimize the project switching process and interface display and enhance the user experience in multi-project operation scenarios.

[0048] As mentioned above, by conducting comprehensive and detailed data collection on different preset behaviors, the tracking collection SDK can build a detailed and three-dimensional user behavior portrait for application developers and operation teams, making it easier for developers and operation teams to make scientific decisions based on actual data, continuously optimize the functions and services of the target application, and better meet user needs.

[0049] S3, querying the reporting status of the data reporting service module according to the current time and the preset time interval, and obtaining the reporting status query result, wherein the reporting status query result includes normal data reporting and abnormal data reporting.

[0050] In a specific implementation, S3 includes the following steps:

[0051] S31, acquiring the first historical time to the Nth historical time before the current time and the first reference time to the Nth reference time after the current time according to a preset time interval, wherein N is a preset number of time.

[0052] S32, respectively obtaining response status query results corresponding to the response status of the query data reporting service module from the first historical time to the Nth historical time, wherein the response status query results include response and non-response.

[0053] S33: If the N response status query results corresponding to the first historical time to the Nth historical time are all unresponsive, it is determined that the reporting status query result is a data reporting exception.

[0054] S34, if the response status query result corresponding to the first historical time is a response, it is determined that the reporting status query result is that the data reporting is normal.

[0055] S35, if the response status query result corresponding to the first historical time is no response, and the N-1 response status query results corresponding to the second historical time to the Nth historical time include a response, then based on the most recent historical time corresponding to the response, obtain the first number L of consecutive no-response historical times before the current time, and execute step S36.

[0056] S36, initialize j=1.

[0057] S37, obtaining a response status query result corresponding to the response status of the data reporting service module when querying the response status at the jth reference time.

[0058] S38: If the response status query result corresponding to the j-th reference time is a response, it is determined that the reporting status query result is that the data reporting is normal.

[0059] S39, if the response status query result corresponding to the j-th reference time is no response, update j=j+1, return to execute step S37, until j=NL, then determine that the reporting status query result is data reporting abnormality.

[0060] Among them, if the N response status query results corresponding to the first historical time to the Nth historical time are all unresponsive, it means that the data reporting service module is in an abnormal state for a continuous historical time period, and corresponding exception handling measures need to be taken, that is, enabling the offline reporting mode to cache the collected preset behavior data to avoid data loss.

[0061] If the response status query result corresponding to the first historical time is a response, the data can be processed according to the normal online reporting process.

[0062] If the first number L of consecutive unresponsiveness of the data reporting service module in the historical time period is less than N, the waiting time is respectively to the corresponding reference time, and the response status query results of the data reporting service module at each reference time are gradually analyzed. If the response status query result corresponding to the jth reference time is a response, it is determined that the reporting status query result corresponding to the jth reference time of the data reporting service module is normal for data reporting, and the data can be processed according to the normal online reporting process. If the response status query results corresponding to the 1st reference time to the NLth reference time are all unresponsive, combined with the first number L of consecutive unresponsiveness of the data reporting service module in the historical time period, it can be known that the data reporting service module has not responded in N consecutive time periods, then it can be judged that an abnormality has occurred in the data reporting service module, and corresponding abnormality handling measures need to be taken, that is, the offline reporting mode is enabled to cache the collected preset behavior data, thereby avoiding data loss.

[0063] In a specific implementation, S31 includes the following steps:

[0064] S311, acquiring a preset query schedule according to a preset start time and a preset time interval, wherein the preset query schedule includes a plurality of preset query times.

[0065] S312: In a time sequence from late to early, N preset query times before the current time in the preset query time table are sequentially determined as the first historical time to the Nth historical time.

[0066] S313, in a time sequence from early to late, N preset query times after the current time in the preset query time table are sequentially determined as the first reference time to the Nth reference time.

[0067] Among them, the preset start time, preset time interval and preset time quantity N can be set by the implementer according to actual conditions. For example, the preset start time can be set to the startup time of the target application, the preset time interval can be set to 20 minutes, and the preset time quantity N can be set to 3.

[0068] The above-mentioned multi-round query and judgment mechanism based on the time dimension can accurately grasp the reporting status of the data reporting service module, and then provide a reliable decision-making basis for the data reporting link of the entire user behavior tracking point, ensuring the stability and reliability of data reporting. Regardless of whether external conditions such as the network are normal, it can ensure that user behavior data is properly processed as much as possible, thereby improving the integrity and continuity of user behavior data collection.

[0069] S4: If the status query result is that the data reporting is abnormal, the offline reporting mode is used, where the offline reporting mode means caching the preset behavior data into the preset database through the tracking point collection SDK.

[0070] In a specific implementation, S4 includes the following steps:

[0071] S41, if the N response status query results corresponding to the first historical time to the Nth historical time are all unresponsive and the reporting status query result is determined to be a data reporting exception, the offline reporting mode is used at the current time.

[0072] S42: If the N-1 response status query results corresponding to the second historical time to the Nth historical time include a response, and the response status query results corresponding to the first reference time to the NLth reference time are all non-response and it is determined that the reporting status query result is a data reporting exception, then the offline reporting mode is used when the response status query result corresponding to the NLth reference time is obtained.

[0073] Among them, if the N response status query results corresponding to the first historical time to the Nth historical time are all unresponsive and the reporting status query result is determined to be a data reporting abnormality, it means that the reporting abnormality has lasted for a period of time and has been in an abnormal state from a historical perspective. Then, the offline reporting mode is immediately used at the current time to save the collected preset behavior data to avoid the loss of the preset behavior data, and wait for the subsequent network to return to normal and other appropriate conditions before reporting the data, thereby ensuring the integrity and traceability of the data.

[0074] If the N-1 response status query results corresponding to the second historical time to the Nth historical time include a response, considering that there has been a response in some historical time, the offline reporting mode is not switched directly, but the response status of the data reporting service module for analysis is further waited and obtained within the reference time period, until the response status query results corresponding to the first reference time to the NLth reference time are all no response, that is, the data reporting service module has not responded within N consecutive time periods, then it is determined that an abnormality has occurred in the data reporting service module, and the offline reporting mode is used when obtaining the judgment result, that is, when obtaining the response status query result corresponding to the NLth reference time, thereby improving the judgment reliability and accuracy of the reporting status of the data reporting service module.

[0075] As described above, the offline reporting mode is started according to different data reporting exception scenarios, so that no matter whether the reporting exception persists or experiences complex situations such as partial response and subsequent continuous no response, the data security can be guaranteed by caching the preset behavior data, and the complete data resources can be retained for the subsequent data re-reporting and application analysis.

[0076] S5. If the status query result shows that the data reporting is normal, the online reporting mode is used, wherein the online reporting mode refers to reporting the preset behavior data cached in the preset database to the data reporting service module, and querying the cache status of the preset database through the embedding collection SDK, obtaining the cache status query result, and performing preset processing on the preset behavior data according to the cache status query result.

[0077] Among them, when using the online reporting mode, the preset behavior data cached in the preset database may be empty, that is, there is no cached data in the preset database, then in the step of reporting the preset behavior data cached in the preset database to the data reporting service module, the data reported to the data service module based on the preset database is also correspondingly empty. In a specific implementation, S5 includes the following steps:

[0078] S51, if the response status query result corresponding to the first historical time is a response and the reporting status query result is determined to be normal data reporting, then the online reporting mode is used at the current time.

[0079] S52, if the N-1 response status query results corresponding to the second historical time to the Nth historical time include a response, and the response status query result corresponding to the jth reference time is a response and the reporting status query result is determined to be normal data reporting, then the online reporting mode is used when the response status query result corresponding to the jth reference time is obtained.

[0080] Among them, when the N-1 response status query results corresponding to the second historical time to the Nth historical time include a response, it indicates that the response of the data reporting service module fluctuates during the historical time and does not always maintain a stable response. Therefore, the reporting status query result is comprehensively determined in combination with the response status query result corresponding to the subsequent jth reference time, so that when the response status query result corresponding to the jth reference time is obtained as a response, that is, when it is judged that the data reporting service module has returned to normal, the online reporting mode is used.

[0081] As mentioned above, the online reporting mode is started according to different scenarios where data reporting is normal, ensuring that no matter whether the response of the data reporting service module is stable and normal from the beginning or returns to normal after a certain fluctuation, the data reporting work can be completed efficiently and accurately, ensuring smooth data flow and reliable data quality of the entire user behavior tracking system.

[0082] In a specific implementation, the cache status query result includes cached data and non-cached data, and S5 includes the following steps:

[0083] S53: If the cache status query result is that there is cached data, the preset behavior data is cached into the preset database through the tracking point collection SDK.

[0084] S54: If the cache status query result is that there is no cache data, the preset behavior data is reported to the data reporting service module through the tracking point collection SDK.

[0085] Among them, when the cache status query result is that there is cached data, it means that due to the short-term network fluctuations in the previous data reporting process, all cached data were not successfully reported in time, or new data was continuously generated and cached, which resulted in no time to process it, etc., resulting in the presence of previously accumulated preset behavior data in the preset database. This embodiment adds the newly collected preset behavior data to the preset database through the embedding collection SDK, so that the cached data can more comprehensively reflect the user's behavior trajectory, and give priority to reporting the cached data in the database to the data reporting service module, thereby improving the integrity and continuity of the data.

[0086] When the cache status query result is that there is no cached data, the preset behavior data is directly reported to the data reporting service module through the tracking point collection SDK, and the newly collected data is reported as soon as possible to avoid data retention, thereby ensuring the timeliness of data flow.

[0087] As described above, targeted processing methods are adopted according to different cache status query results, which can process preset behavior data more flexibly and reasonably. No matter there is cache data that needs to be further improved and integrated, or there is no cache data that is directly reported, it can ensure the smooth progress of the entire user behavior data reporting process, ensure that the data can be efficiently and accurately transferred to subsequent links for analysis and application, and improve the integrity and continuity of user behavior data collection.

[0088] As described above, the data of different preset behaviors is collected comprehensively and meticulously through the tracking collection SDK, and the reporting status of the data reporting service module is obtained based on the multi-round query and judgment mechanism of the time dimension. The offline reporting mode is started according to different data reporting abnormal scenarios, so that no matter whether the reporting abnormality persists or experiences complex situations such as partial response and subsequent continuous no response, the data security can be guaranteed by caching the preset behavior data, and the online reporting mode is started according to different data reporting normal scenarios to ensure that no matter whether the response of the data reporting service module is stable and normal at the beginning or returns to normal after a certain fluctuation, the data reporting work can be completed efficiently and accurately, and then targeted processing methods are taken according to different cache status query results, which can process the preset behavior data more flexibly and reasonably. Whether there is cache data that needs to be further improved and integrated, or there is no cache data that is directly reported, the entire user behavior tracking data reporting process can be guaranteed to be smooth, and whether the external conditions such as the network are normal or not, it can ensure that the data can be efficiently and accurately transferred to the subsequent links for analysis and application, thereby improving the integrity and continuity of user behavior data collection.

[0089] Embodiment 2

[0090] This embodiment 2 provides a user behavior tracking device, which includes: Figure 2 As shown:

[0091] The SDK configuration module 21 is used to introduce the tracking point collection SDK into the front-end project of the target application and configure the preset information, wherein the preset information includes basic application information, basic user information, data reporting related configuration, event classification rules and event naming rules.

[0092] The data collection module 22 is used to collect preset behavior data corresponding to the preset behavior performed by the target user on the target application through the tracking collection SDK, wherein the preset behavior includes model operation behavior, file operation behavior and project operation behavior.

[0093] The reporting status query module 23 is used to query the reporting status of the data reporting service module according to the current time and the preset time interval, and obtain the reporting status query result, wherein the reporting status query result includes normal data reporting and abnormal data reporting.

[0094] The first data reporting module 24 is used to use the offline reporting mode if the reporting status query result is that the data reporting is abnormal, wherein the offline reporting mode refers to caching the preset behavior data into the preset database through the embedding point collection SDK.

[0095] The second data reporting module 25 is used to use the online reporting mode if the reporting status query result is that the data reporting is normal, wherein the online reporting mode refers to reporting the preset behavior data cached in the preset database to the data reporting service module, and querying the cache status of the preset database through the embedded point collection SDK, obtaining the cache status query result, and performing preset processing on the preset behavior data according to the cache status query result.

[0096] In a specific implementation, the model operation behaviors include model translation, model scaling, model rotation, model dragging, component selection, property query, geometry modification, and component parameter adjustment.

[0097] File operations include opening and saving files.

[0098] Project operation behaviors include project creation and project switching.

[0099] In a specific implementation, the reporting status query module 23 includes:

[0100] The time acquisition submodule is used to acquire the first historical time before the current time to the Nth historical time, and the first reference time after the current time to the Nth reference time according to a preset time interval, where N is a preset number of times.

[0101] The first response status query submodule is used to obtain the response status query results corresponding to the response status of the query data reporting service module from the first historical time to the Nth historical time, wherein the response status query results include response and non-response.

[0102] The first reporting status determination submodule is used to determine that the reporting status query result is a data reporting exception if N response status query results corresponding to the first historical time to the Nth historical time are all unresponsive.

[0103] The second reporting status determination submodule is used to determine that the reporting status query result is that data reporting is normal if the response status query result corresponding to the first historical time is a response.

[0104] The first quantity acquisition submodule is used to obtain the first quantity L of consecutive unresponsive historical times before the current time based on the most recent historical time corresponding to the response, and execute the initialization submodule if the response status query result corresponding to the first historical time is unresponsive, and the N-1 response status query results corresponding to the second historical time to the Nth historical time include a response.

[0105] Initialization submodule, used to initialize j=1.

[0106] The second response status query submodule is used to obtain the response status query result corresponding to the response status of the data reporting service module when querying the response status at the jth reference time.

[0107] The third reporting status determination submodule is used to determine that the reporting status query result is that data reporting is normal if the response status query result corresponding to the j-th reference time is a response.

[0108] The fourth reporting status determination submodule is used to update j=j+1 if the response status query result corresponding to the jth reference time is no response, and return to execute the second response status query submodule until j=NL, then determine that the reporting status query result is a data reporting exception.

[0109] In a specific implementation, the time acquisition submodule includes:

[0110] The schedule acquisition unit is used to acquire a preset query schedule according to a preset start time and a preset time interval, wherein the preset query schedule includes a plurality of preset query times.

[0111] The historical time acquisition unit is used to determine N preset query times before the current time in the preset query time table as the first historical time to the Nth historical time in sequence according to the time order from late to early.

[0112] The reference time acquisition unit is used to determine N preset query times after the current time in the preset query time table as the first reference time to the Nth reference time in sequence from early to late.

[0113] In a specific implementation, the first data reporting module 24 includes:

[0114] The first data reporting submodule is used to use the offline reporting mode at the current time if the N response status query results corresponding to the first historical time to the Nth historical time are all unresponsive and the reporting status query result is determined to be a data reporting exception.

[0115] The second data reporting sub-module is used to use the offline reporting mode when obtaining the response status query result corresponding to the NLth reference time if the N-1 response status query results corresponding to the second historical time to the Nth historical time include a response, and the response status query results corresponding to the first reference time to the NLth reference time are all no response and the reporting status query result is determined to be a data reporting exception.

[0116] In a specific implementation, the second data reporting module 25 includes:

[0117] The third data reporting submodule is used to use the online reporting mode at the current time if the response status query result corresponding to the first historical time is a response and the reporting status query result is determined to be normal data reporting.

[0118] The fourth data reporting sub-module is used to use the online reporting mode when obtaining the response status query result corresponding to the jth reference time if the N-1 response status query results corresponding to the second historical time to the Nth historical time include a response, and the response status query result corresponding to the jth reference time is a response and the reporting status query result is determined to be normal for data reporting.

[0119] In a specific implementation, the second data reporting module 25 includes:

[0120] The first data processing submodule is used to cache the preset behavior data into the preset database through the embedding point collection SDK if the cache status query result is that there is cache data.

[0121] The second data processing submodule is used to report the preset behavior data to the data reporting service module through the tracking point collection SDK if the cache status query result is that there is no cache data.

[0122] It should be noted that the information interaction, execution process and other contents between the above modules are based on the same concept as the embodiment of the method of the present invention. Their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.

[0123] Embodiment 3

[0124] Embodiment 3 of the present invention provides a non-transitory computer-readable storage medium, wherein at least one instruction or at least one program is stored in the non-transitory computer-readable storage medium, and the at least one instruction or at least one program is loaded and executed by a processor to implement the steps:

[0125] S1, introduce the tracking data collection SDK into the front-end project of the target application and configure the preset information, where the preset information includes basic application information, basic user information, data reporting related configuration, event classification rules and event naming rules.

[0126] S2, collecting preset behavior data corresponding to the target user's preset behavior on the target application through the tracking collection SDK, where the preset behavior includes model operation behavior, file operation behavior and project operation behavior.

[0127] S3, querying the reporting status of the data reporting service module according to the current time and the preset time interval, and obtaining the reporting status query result, wherein the reporting status query result includes normal data reporting and abnormal data reporting.

[0128] S4: If the query result of the reporting status is that the data reporting is abnormal, the offline reporting mode is used, where the offline reporting mode means caching the preset behavior data into the preset database through the tracking point collection SDK.

[0129] S5. If the reporting status query result shows that the data reporting is normal, the online reporting mode is used, wherein the online reporting mode refers to reporting the preset behavior data cached in the preset database to the data reporting service module, and querying the cache status of the preset database through the embedding collection SDK, obtaining the cache status query result, and performing preset processing on the preset behavior data according to the cache status query result.

[0130] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronization link DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0131] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0132] Embodiment 4

[0133] A fourth embodiment of the present invention provides an electronic device, which includes a processor and the non-transitory computer-readable storage medium in the third embodiment of the present invention.

[0134] The above are only preferred embodiments of the present invention, and are not intended to limit the present invention in any form. Although the present invention has been disclosed as a preferred embodiment as above, it is not intended to limit the present invention. Any technician familiar with the profession can make some changes or modifications to equivalent embodiments of equivalent changes using the technical contents disclosed above without departing from the scope of the technical solution of the present invention. However, any simple modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solution of the present invention are still within the scope of the technical solution of the present invention.

Claims

1. A user behavior tracking method, characterized in that: The user behavior tracking method comprises the following steps: S1, introduce the tracking point collection SDK into the front-end project of the target application and configure the preset information, wherein the preset information includes basic application information, basic user information, data reporting related configuration, event classification rules and event naming rules; S2, collecting preset behavior data corresponding to the preset behavior performed by the target user on the target application through the tracking collection SDK, wherein the preset behavior includes model operation behavior, file operation behavior and project operation behavior; S3, querying the reporting status of the data reporting service module according to the current time and the preset time interval, and obtaining the reporting status query result, wherein the reporting status query result includes normal data reporting and abnormal data reporting; S4, if the reporting status query result is that the data reporting is abnormal, the offline reporting mode is used, wherein the offline reporting mode refers to caching the preset behavior data into a preset database through the tracking point collection SDK; S5. If the reporting status query result is that the data reporting is normal, the online reporting mode is used, wherein the online reporting mode refers to reporting the preset behavior data cached in the preset database to the data reporting service module, and querying the cache status of the preset database through the embedding point collection SDK, obtaining the cache status query result, and performing preset processing on the preset behavior data according to the cache status query result.

2. The user behavior tracking method according to claim 1, characterized in that: The model operation behaviors include model translation, model scaling, model rotation, model dragging, component selection, attribute query, geometric shape modification, and component parameter adjustment; The file operation behavior includes file opening and file saving; The project operation behaviors include project creation and project switching.

3. The user behavior tracking method according to claim 1, characterized in that: S3 includes the following steps: S31, acquiring the first historical time before the current time to the Nth historical time, and the first reference time after the current time to the Nth reference time according to the preset time interval, where N is the preset number of time; S32, respectively obtaining response status query results corresponding to the response status of the data reporting service module when querying the response status from the first historical time to the Nth historical time, wherein the response status query results include response and non-response; S33, if the N response status query results corresponding to the first historical time to the Nth historical time are all unresponsive, then determining that the reporting status query result is a data reporting exception; S34, if the response status query result corresponding to the first historical time is a response, then determining that the reporting status query result is that the data reporting is normal; S35, if the response status query result corresponding to the first historical time is no response, and the N-1 response status query results corresponding to the second historical time to the Nth historical time include a response, then based on the most recent historical time corresponding to the response, obtain a first number L of consecutive no-response historical times before the current time, and execute step S36; S36, initialize j=1; S37, obtaining a response status query result corresponding to the response status of the data reporting service module when querying the response status at the jth reference time; S38, if the response status query result corresponding to the j-th reference time is a response, then determining that the reporting status query result is that the data reporting is normal; S39, if the response status query result corresponding to the j-th reference time is no response, update j=j+1, return to execute step S37, until j=NL, then determine that the reporting status query result is data reporting abnormality.

4. The user behavior tracking method according to claim 3 is characterized in that: S31 includes the following steps: S311, acquiring a preset query schedule according to a preset start time and a preset time interval, wherein the preset query schedule includes a plurality of preset query times; S312, in a time order from late to early, sequentially determining N preset query times before the current time in the preset query time table as the first historical time to the Nth historical time; S313, in a time order from early to late, sequentially determining N preset query times after the current time in the preset query time table as the first reference time to the Nth reference time.

5. The user behavior tracking method according to claim 3, characterized in that: S4 includes the following steps: S41, if the N response status query results corresponding to the first historical time to the Nth historical time are all unresponsive and it is determined that the reporting status query result is a data reporting exception, then the offline reporting mode is used at the current time; S42: If the N-1 response status query results corresponding to the second historical time to the Nth historical time include a response, and the response status query results corresponding to the first reference time to the NLth reference time are all non-response and it is determined that the reporting status query result is a data reporting exception, then the offline reporting mode is used when the response status query result corresponding to the NLth reference time is obtained.

6. The user behavior tracking method according to claim 3, characterized in that: S5 includes the following steps: S51, if the response status query result corresponding to the first historical time is a response and the reporting status query result is determined to be normal data reporting, then the online reporting mode is used at the current time; S52, if the N-1 response status query results corresponding to the second historical time to the Nth historical time include a response, and the response status query result corresponding to the jth reference time is a response and the reporting status query result is determined to be normal data reporting, then the online reporting mode is used when the response status query result corresponding to the jth reference time is obtained.

7. The user behavior tracking method according to claim 1, characterized in that: The cache status query result includes cached data and non-cached data, and S5 includes the following steps: S53, if the cache status query result is that there is cache data, cache the preset behavior data into a preset database through the tracking point collection SDK; S54: If the cache status query result is that there is no cache data, the preset behavior data is reported to the data reporting service module through the tracking point collection SDK.

8. A user behavior tracking device, characterized in that: The user behavior tracking device includes: SDK configuration module, used to introduce the tracking point collection SDK into the front-end project of the target application and configure preset information, wherein the preset information includes basic application information, basic user information, data reporting related configuration, event classification rules and event naming rules; A data collection module, used to collect preset behavior data corresponding to the preset behavior performed by the target user on the target application through the tracking point collection SDK, wherein the preset behavior includes model operation behavior, file operation behavior and project operation behavior; A reporting status query module, used to query the reporting status of the data reporting service module according to the current time and the preset time interval, and obtain the reporting status query result, wherein the reporting status query result includes normal data reporting and abnormal data reporting; A first data reporting module, configured to use an offline reporting mode if the reporting status query result is that the data reporting is abnormal, wherein the offline reporting mode refers to caching the preset behavior data into a preset database through the embedding point collection SDK; The second data reporting module is used to use the online reporting mode if the reporting status query result is that the data reporting is normal, wherein the online reporting mode refers to reporting the preset behavior data cached in the preset database to the data reporting service module, and querying the cache status of the preset database through the embedding point collection SDK, obtaining the cache status query result, and performing preset processing on the preset behavior data according to the cache status query result.

9. A non-transitory computer-readable storage medium, wherein at least one instruction or at least one program is stored in the non-transitory computer-readable storage medium, characterized in that: The at least one instruction or the at least one program is loaded and executed by the processor to implement the user behavior tracking method as described in any one of claims 1-7.

10. An electronic device, characterized in that: The invention comprises a processor and the non-transitory computer-readable storage medium as claimed in claim 9.

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