Data annotation-based tracking methods and tracking data processing methods

Abstract

This application provides a data annotation-based event tracking method and event tracking data processing method. In response to event tracking operations, the data annotation process is processed to obtain multiple target event tracking points; event tracking data collected at the target event tracking points is acquired; and the event tracking data is sent to a server, which instructs the server to determine a data analysis report based on the event tracking data. The technical solution provided by this application, by processing the data annotation process to obtain multiple target event tracking points and collecting event tracking data at these multiple target event tracking points, obtains event tracking data with high granularity and dimensionality, ensuring the stability of the data annotation service during event tracking data collection and resulting in more accurate data analysis reports determined based on the event tracking data.

Description

Technical Field

[0001] This application relates to the field of data tracking technology, and in particular to a data tracking method and a data tracking data processing method based on data annotation. Background Technology

[0002] With the continuous development of machine learning, data annotation products based on machine learning algorithms are becoming increasingly common. Analyzing the data generated during the annotation process and monitoring and maintaining the annotation process are crucial. Data annotation tools typically capture and record data through simple process tracking, primarily focusing on the data submission process. However, they neglect to capture user behavior and other data related to the annotation process, resulting in low-dimensionality and granularity of the captured data, which cannot meet the complex business needs.

[0003] Currently, the main approach is to utilize generalized third-party online services and embed code into the necessary code on the page to capture and record the data generated during the data annotation process into a third-party database, thereby improving the dimensionality and granularity of the data.

[0004] However, in data annotation scenarios, due to the different business rules for algorithm training, there are differences in data annotation methods. Using general third-party online services to capture and record data is not comprehensive enough and cannot achieve customized capture and recording of data for different data annotation methods. This results in poor dimensionality and granularity of the captured and recorded data, which may lead to poor accuracy and comprehensiveness of data analysis. Summary of the Invention

[0005] This application provides a data annotation-based method for tracking data and a method for processing tracking data, which can improve the dimensionality and granularity of the tracking data in the data annotation process, thereby improving the accuracy and comprehensiveness of the determined data analysis reports.

[0006] In a first aspect, embodiments of this application provide a data-annotated tracking method applied to a terminal device, the data-annotated tracking method comprising:

[0007] In response to the data tracking operation, the data annotation process is processed to obtain multiple target tracking points;

[0008] Acquire the embedded data collected at the target embedded point;

[0009] The data points are sent to the server, and the data points are used to instruct the server to determine a data analysis report based on the data points.

[0010] Optionally, the target tracking points include a first target tracking point and a second target tracking point. The first target tracking point includes tracking points where the time interval between two consecutive user operations is less than or equal to a preset time interval, and the second target tracking point includes tracking points where the time interval between two consecutive user operations is greater than the preset time interval.

[0011] The acquisition of the embedded data collected at the target embedded point includes:

[0012] Acquire the embedding data collected at the first target embedding point and the embedding data collected at the second target embedding point, respectively.

[0013] Optionally, the frequency of sending the embedded data collected at the first target embedded point is less than the frequency of sending the embedded data collected at the second target embedded point.

[0014] Optionally, sending the embedded data to the server includes:

[0015] After the preset time period is reached, all data collected at the first target embedding point within the preset time period will be sent to the server.

[0016] The data collected at the second target embedding point will be sent to the server in real time.

[0017] Secondly, this application provides another method for processing embedded data, applied to a server, the embedded data processing method including:

[0018] The terminal device receives multiple data points from the embedded points. These multiple data points are data collected by the terminal device at the target embedded point. The target embedded point is obtained by the terminal device after receiving the embedded point operation and performing embedded point processing on the data annotation process.

[0019] The multiple data points are stored in a data point database, which includes at least one preset component.

[0020] The data points in the data point database are processed by at least one preset component in the data point database to obtain a data analysis report corresponding to the preset component.

[0021] Optionally, storing the plurality of event tracking data in an event tracking database includes:

[0022] The target data is obtained by pre-analyzing the data from the multiple embedded points.

[0023] Based on the embedded content corresponding to the target data, multiple target data belonging to the same business are connected according to their corresponding collection times to generate at least one relationship link;

[0024] The at least one relationship link is stored in the data tracking database.

[0025] Optionally, the multiple embedded data points are pre-analyzed to obtain target data, including:

[0026] Based on the content to be analyzed, the content of the multiple tracking data points is extracted to obtain the target data;

[0027] or,

[0028] The data obtained after extracting content from the multiple data points is re-analyzed based on the content to be analyzed to obtain the target data.

[0029] Optionally, before storing the plurality of event tracking data in the event tracking database, the method includes:

[0030] Determine whether each of the multiple embedded data points sent by the terminal device is structured data;

[0031] If the data is unstructured, the data is converted into structured data and the converted data is stored in the data tracking database.

[0032] If the data points are structured data, then the data points are directly stored in the data point database.

[0033] Optionally, after obtaining the data analysis report corresponding to the preset component, the method includes:

[0034] Based on the data analysis report, determine whether any abnormal issues occurred during the data annotation process;

[0035] If an abnormal problem occurs during the data annotation process, the corresponding tracking data to be analyzed will be retrieved from the tracking data database.

[0036] The reasons for the anomalies were obtained by analyzing the data from the embedded points to be analyzed.

[0037] Optionally, the method further includes:

[0038] The operation tracking data corresponding to user operation behavior is extracted from the tracking data database, and the operation tracking data is subjected to behavior analysis to obtain a first analysis result. The first analysis result is used to characterize the user profile.

[0039] Thirdly, embodiments of this application provide a data annotation-based event tracking device, the data annotation-based event tracking device comprising:

[0040] The acquisition module is used to respond to the data tracking operation, perform data tracking processing on the data annotation process, and obtain multiple target tracking points;

[0041] The acquisition module is also used to acquire the embedded data collected at the target embedded point;

[0042] The sending module is used to send the embedded data to the server, and the embedded data is used to instruct the server to determine a data analysis report based on the embedded data.

[0043] Optionally, the target tracking points include a first target tracking point and a second target tracking point. The first target tracking point includes tracking points where the time interval between two consecutive user operations is less than or equal to a preset time interval, and the second target tracking point includes tracking points where the time interval between two consecutive user operations is greater than the preset time interval. The acquisition module is specifically used to acquire tracking point data collected at the first target tracking point and tracking point data collected at the second target tracking point, respectively.

[0044] Optionally, the frequency of sending the embedded data collected at the first target embedded point is less than the frequency of sending the embedded data collected at the second target embedded point.

[0045] Optionally, the sending module is specifically used to send all the data collected at the first target embedding point within the preset time period to the server after the preset time period has elapsed; and to send the data collected at the second target embedding point to the server in real time.

[0046] Fourthly, embodiments of this application provide another device for processing embedded data, the embedded data processing device comprising:

[0047] The receiving module is used to receive multiple data points sent by the terminal device. The multiple data points are data collected by the terminal device at the target data point. The target data point is obtained by the terminal device after receiving the data point operation and performing data point processing during the data annotation process.

[0048] A storage module is used to store the plurality of tracking data in a tracking database, wherein the tracking database includes at least one preset component;

[0049] The processing module is used to process the data points in the data point database through at least one preset component to obtain a data analysis report corresponding to the preset component.

[0050] Optionally, the storage module is specifically used to pre-analyze the multiple data points to obtain target data; according to the data point content corresponding to the target data, connect multiple target data belonging to the same business according to the corresponding collection time to generate at least one relationship link; and store the at least one relationship link in the data point database.

[0051] Optionally, the storage module is specifically used to extract content from the multiple tracking data points according to the content to be analyzed, to obtain target data; or, to re-analyze the data obtained after extracting content from the multiple tracking data points according to the content to be analyzed, to obtain target data.

[0052] Optionally, the storage module is further configured to determine whether each of the multiple tracking data points sent by the terminal device is structured data; when the tracking data point is unstructured data, the tracking data point is converted into structured data and the converted tracking data point is stored in the tracking database; when the tracking data point is structured data, the tracking data point is directly stored in the tracking database.

[0053] Optionally, the processing module is further configured to determine whether any abnormal problems have occurred in the data annotation process based on the data analysis report; when abnormal problems occur in the data annotation process, to obtain the tracking data to be analyzed corresponding to the abnormal problem from the tracking data database; and to analyze the tracking data to be analyzed to obtain the cause of the abnormality.

[0054] Optionally, the processing module is further configured to extract operation tracking data corresponding to user operation behavior from the tracking data database, perform behavior analysis on the operation tracking data, and obtain a first analysis result, wherein the first analysis result is used to characterize the user profile.

[0055] Fifthly, embodiments of this application also provide an electronic device, which includes: a processor and a memory communicatively connected to the processor;

[0056] The memory stores computer-executed instructions;

[0057] The processor executes computer execution instructions stored in the memory to implement the method described in any possible implementation of the first or second aspect above.

[0058] Sixthly, embodiments of this application also provide a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the method described in any possible implementation of the first or second aspect.

[0059] In a seventh aspect, embodiments of this application also provide a computer program product, including a computer program that, when executed by a processor, implements the method described in any possible implementation of the first or second aspect.

[0060] Therefore, this application provides a data annotation-based event tracking method and event tracking data processing method. By responding to event tracking operations, the data annotation process is processed to obtain multiple target event tracking points; event tracking data collected at the target event tracking points is acquired; and the event tracking data is sent to a server, whereby the event tracking data instructs the server to determine a data analysis report based on the event tracking data. The technical solution provided by this application obtains multiple target event tracking points by processing the data annotation process and collecting event tracking data at multiple target event tracking points. It does not rely on third-party services to collect the data generated during the data annotation process, resulting in higher granularity and dimensionality of the acquired event tracking data. This improves the comprehensiveness of the collected event tracking data and makes the data analysis report determined based on the event tracking data more accurate. Attached Figure Description

[0061] Figure 1 A schematic diagram illustrating an application scenario of a data annotation-based data tracking method provided in this application embodiment;

[0062] Figure 2 A flowchart illustrating a data annotation-based event tracking method provided in this application embodiment;

[0063] Figure 3 A flowchart illustrating another method for processing embedded data provided in this application embodiment;

[0064] Figure 4 A schematic diagram illustrating a framework for data collection and analysis based on embedded data points, provided in an embodiment of this application;

[0065] Figure 5 A schematic diagram of a data annotation-based data tracking device provided in this application embodiment.

[0066] Figure 6 This is a schematic diagram of the structure of a data processing device for embedded data provided in an embodiment of this application;

[0067] Figure 7 This is a schematic diagram of an electronic device structure provided in this application.

[0068] The accompanying drawings have illustrated specific embodiments of this disclosure, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concepts of this disclosure to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0069] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this disclosure. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this disclosure as detailed in the appended claims.

[0070] In the embodiments of this application, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone, where A and B can be singular or plural. In the textual description of this application, the character " / " generally indicates that the preceding and following related objects have an "or" relationship.

[0071] The technical solutions provided in this application can be applied to data processing scenarios. With the rapid development of autonomous driving and artificial intelligence, data annotation tools based on algorithm training are particularly important due to the need for training algorithm models for data such as images, point clouds, audio, and video. Data annotation tools are mainly defined according to the different business needs of different industries. Analyzing the data generated during the data annotation process provides certain standards for the management related to annotation tools, while also optimizing data annotation tools and solving problems that arise during the data annotation process. The dimensions and granularity that data analysis can analyze depend on the basic process "tracking points" recorded during the data annotation process. However, most data annotation tools capture and record data through simple process tracking. The captured and recorded data is mainly the process record generated when data is submitted, but it cannot obtain data corresponding to more granular annotation content such as user operation habits. The captured and recorded data can only meet the needs of rough data information statistics and summarization during the data flow of data annotation tools, that is, it can meet the data analysis needs of scenarios with strong independence and simple annotation settlement methods. However, for complex business and cross-team data collaboration projects, simple captured and recorded data cannot meet the actual business needs.

[0072] In existing technologies, when data analysis tools are tracked, it is necessary to rely on generalized third-party online services. This involves embedding code into the necessary code on the page to retrieve and record the data generated during the data annotation process into a third-party database.

[0073] However, in algorithm-based data annotation products, general-purpose operational statistics software struggles to meet actual needs, limiting the flexibility and granularity of data analysis dimensions. Furthermore, differing business rules for algorithm training lead to variations in the annotation methods of data annotation tools. Limited by the framework of third-party software, it's impossible to capture and record different data based on different tools; instead, users passively accept and use the capabilities provided by third-party products, resulting in poor comprehensiveness of the captured data and consequently, poor comprehensiveness and accuracy of data analysis.

[0074] To address the issue of poor comprehensiveness and accuracy in data analysis caused by relying on third-party online services to capture and record data during the data labeling process, multiple target data points can be identified through user-input data point operations. Based on these target data points, the data labeling process is processed to obtain multiple data points generated during the labeling process. This collected data can then be sent to a server to generate data analysis reports. This approach allows users to customize data labeling processes without being limited by third-party online services, resulting in more granular and dimensional data, thus improving the comprehensiveness and accuracy of data analysis.

[0075] Figure 1 This is a schematic diagram illustrating an application scenario of a data annotation-based event tracking method provided in an embodiment of this application. According to... Figure 1 As shown, the user can input the required data tracking points on the terminal device 101, i.e., input the data tracking operation, so that the terminal device 101 responds to the user's input data tracking operation and obtains multiple target data tracking points. The target data tracking points may include the data tracking points of the user's operation in the data tracking process and the data tracking points corresponding to the business. This application embodiment does not specifically limit the target data tracking points.

[0076] Furthermore, after obtaining multiple target tracking points, the terminal device 101 can acquire the tracking point data collected at each target tracking point during the data annotation process and send the collected tracking point data to the server 102. The server 102 can process the tracking point data received from the terminal device 11 to obtain a data analysis report. For example, the data analysis report can be obtained by statistically analyzing the tracking point data, such as operation statistics, duration statistics, user click statistics, etc. This application embodiment does not impose any limitations on the data analysis report.

[0077] Therefore, the technical solution provided in this application embodiment can obtain embedded data with high granularity and dimensionality, making the data analysis determined based on the embedded data more accurate.

[0078] The data annotation-based tracking method provided in this application will be described in detail below through specific embodiments. It is understood that the following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments.

[0079] Figure 2 This is a flowchart illustrating a data-annotation-based event tracking method provided in an embodiment of this application. This data-annotation-based event tracking method can be executed by software and / or hardware devices. For example, the hardware device can be a data-annotation-based event tracking device, which can be a terminal device or a processing chip within the terminal device. For example, please refer to [link to example]. Figure 2 As shown, this data annotation-based event tracking method may include:

[0080] S201. In response to the data tracking operation, perform data tracking processing on the data annotation process to obtain multiple target data tracking points.

[0081] For example, the data tracking operation can be data entered by the user on the display interface of the terminal device. For instance, it can be data to be monitored during the data annotation process specified according to business needs or the needs of the problem to be analyzed. This data can be in tabular, document, or other formats, and this embodiment does not impose any limitations on this. Furthermore, when performing data tracking processing on the data annotation process to obtain multiple target data tracking points, the terminal device can determine the location of the data tracking point in the data annotation process corresponding to the data tracking point information based on the data tracking information entered by the user, thereby obtaining multiple target data tracking points.

[0082] For example, users can categorize event tracking information into different types by analyzing the data annotation process to facilitate the determination of target event tracking points. For instance, event tracking information can include different types such as page load, behavioral operations, loading time, and event statistics. It is understandable that for different data annotation scenarios, event tracking can be implemented in key business processes based on business needs. Taking the lane line annotation tool as an example, the specific target event tracking points for the lane line annotation tool are shown in Table 1 below:

[0083] Table 1

[0084]

[0085]

[0086] As shown in Table 1, the technical solution provided in this application embodiment can collect various data generated during the data annotation process, including the number of times the user clicks the control and the click time. This application embodiment only uses the target embedding point of the lane line annotation tool shown in Table 1 as an example for illustration, but it does not mean that this application embodiment is limited to this.

[0087] S202. Obtain the data collected at the target embedding point.

[0088] For example, according to step S201 above, the target tracking points may include a first target tracking point and a second target tracking point. The first target tracking point includes tracking points where the time interval between two consecutive user operations is less than or equal to a preset time interval, and the second target tracking point includes tracking points where the time interval between two consecutive user operations is greater than the preset time interval. When acquiring tracking point data collected at the target tracking points, tracking point data collected at the first target tracking point and tracking point data collected at the second target tracking point can be acquired separately. The preset time interval is 1 second, or it can be 2 seconds, or 3 seconds, and can be set according to the actual situation. This application embodiment does not limit the preset time.

[0089] For example, the first target tracking point can be a frequent tracking point. Frequent tracking points can be applied to scenarios with frequent interaction with the visual editing page, that is, to operations that are frequently performed by the user. For example, considering that the user's operation or the page may change multiple times within 3 seconds, and the result of each change needs to be saved, frequent tracking points can be set to quick operations performed by the user between 1 and 3 seconds. Tracking points with an interval of more than 3 seconds between two operations can be set as infrequent tracking points, that is, the second target tracking point. The embodiments of this application are only illustrated by the above examples, but do not mean that the embodiments of this application are limited to this.

[0090] Understandably, event tracking can be implemented on pages where users perform a significant number of quick actions. For example, frequent event tracking can be implemented when various function buttons, page data status, and page requests change. For instance, when a user clicks a function button on the page or drags an element and releases the mouse, event tracking can record and store this information in real time.

[0091] For example, the tracking data collected through the first and second target tracking points may include key information such as user profile information, function operation information, page request information, page exception information, and page save information. This application embodiment only uses the above-mentioned tracking data as an example for illustration, but it does not mean that this application embodiment is limited to this. Key elements can be regarded as information elements that can help in the analysis of tracking issues. For example, key buttons and key operations on the page can be reflected in user movement, clicking, page jumping, refreshing, and other operations. Alternatively, information related to the operator, operation time, and process steps in the data annotation process can be included, such as what operation was performed in the first step and what operation was performed in the second step. This application embodiment does not specifically limit the key elements.

[0092] In this embodiment, by setting first and second target tracking points for users with frequent and infrequent operations respectively, the obtained tracking point data is more comprehensive, enabling more comprehensive monitoring of the data annotation process and further improving the accuracy of tracking point data analysis.

[0093] For example, the frequency of sending data collected at the first target data point is less than the frequency of sending data collected at the second target data point.

[0094] For example, as described above, within the same time period, the amount of data collected at the first target tracking point is larger than that collected at the second target tracking point. When the tracking data collected at the first target tracking point is sent to the server, the frequency of sending is less than that of sending the tracking data collected at the second target tracking point to the server, which can effectively reduce the transmission pressure on the data annotation tool.

[0095] In this embodiment of the application, by setting the transmission frequency of the embedded data collected at the first target embedded point to be less than the transmission frequency of the embedded data collected at the second target embedded point, the pressure on data transmission can be effectively reduced and the problem of data annotation process being blocked can be avoided.

[0096] S203. Send the data points to the server. The data points are used to instruct the server to determine the data analysis report based on the data points.

[0097] For example, when sending the data to the server, all data collected at the first target data point within the preset time period can be sent to the server after the preset time period has elapsed; data collected at the second target data point can be sent to the server in real time. The preset time period can be 2 hours, 5 hours, or 1 day, and can be set according to the actual situation. This application embodiment does not impose any limitation on the preset time period.

[0098] For example, data collected at the first target embedding point can be processed by storing it on a local terminal device. When the data storage time reaches a preset duration, the stored embedding data is sent to the server, meaning the data collected at the first target embedding point is periodically uploaded to the server. For example, the terminal device can be a device in the vehicle, such as a PC, or any device capable of deploying perception algorithms and driving sensors, such as a camera itself deployed in a chip or corresponding device module, an AI chip, an in-vehicle central computer or central domain controller, an integrated ECU, a driver's brain, a vehicle infotainment system, a DHU (Drilling Head Unit), an IHU (Infotainment Head Unit), or an IVI (In-Vehicle Infotainment system). This application embodiment does not limit the terminal device. Embedding data collected at the second target embedding point can be sent to the server in real-time. Specifically, when a user triggers a tracking operation, the backend collects the tracking information in real time and sends it back promptly through the API interface provided by the cloud server. The information sent back to the cloud is then stored in the cloud server in real time.

[0099] Understandably, by storing the data collected at the first target tracking point locally, performance resource issues can be effectively alleviated when the performance of the terminal device is limited, data transmission efficiency can be improved, and the impact on the data annotation process can be avoided.

[0100] In this embodiment, by sending all the tracking data collected at the first target tracking point within a preset time period to the server, the problem of interface lag caused by the high transmission pressure due to real-time transmission of tracking data collected at the first target tracking point can be avoided. Furthermore, by transmitting the tracking data collected at the second target tracking point in real-time, the efficiency of tracking data transmission is improved while ensuring the transmission process is completed.

[0101] Therefore, the data annotation-based event tracking method provided in this application embodiment, by responding to event tracking operations, performs event tracking processing on the data annotation process to obtain multiple target event tracking points; acquires event tracking data collected at the target event tracking points; and sends the event tracking data to the server, whereby the event tracking data instructs the server to determine a data analysis report based on the event tracking data. The technical solution provided in this application embodiment, by performing event tracking processing on the data annotation process to obtain multiple target event tracking points and collecting event tracking data at multiple target event tracking points, does not require reliance on third-party services to collect the data generated during the data annotation process. This improves the comprehensiveness of the collected event tracking data, making the data analysis report determined based on the event tracking data more accurate.

[0102] In another embodiment of this application, after the terminal device sends the embedded data to the server, the server can process the embedded data. For example, see [link to example]. Figure 3 As shown, Figure 3 This is a flowchart illustrating another method for processing event tracking data provided in an embodiment of this application. This method can be executed by software and / or hardware devices; for example, the hardware device can be an event tracking data processing device, which can be a server or a processing chip within a server. The method may include:

[0103] S301. Receive multiple data points sent by the terminal device. The multiple data points are data collected by the terminal device at the target data point. The target data point is obtained by the terminal device after receiving the data point operation and performing data point processing during the data annotation process.

[0104] As can be seen from the above embodiments, when the server receives multiple data points from the terminal device, the receiving frequency of the data points collected at the first target data point and the data points collected at the second target data point may be different. The specific receiving frequency is the same as the sending frequency of the terminal device. This application embodiment does not limit this in any way.

[0105] After receiving multiple data points from the terminal device, the following step S302 is executed:

[0106] S302. Store multiple tracking data in a tracking database, which includes at least one preset component.

[0107] For example, before storing multiple event tracking data in the event tracking database, it can be determined whether each event tracking data sent by the receiving terminal device is structured data. If the event tracking data is unstructured, it is converted into structured data and stored in the event tracking database. If the event tracking data is structured, it is directly stored in the event tracking database. It is understood that unstructured data refers to data that cannot form a fixed-structure file, such as images, point clouds, or messy text. Structured data refers to data with a standardized structure and format, such as a standardized file. For example, the data stored in the database may include event types, necessary timestamps, unique identifiers, and some extended information; this application embodiment does not impose any limitations on this.

[0108] In this embodiment, unstructured data is converted into structured data for storage, ensuring that all event tracking data stored in the database is structured. Furthermore, the structured data can be reused in subsequent event tracking data analysis, thus improving the efficiency of data analysis.

[0109] For example, when storing multiple tracking data points in a tracking database, the multiple tracking data points can be pre-analyzed to obtain target data; based on the tracking content corresponding to the target data, multiple target data points belonging to the same business can be connected according to their corresponding collection times to generate at least one relationship link; and at least one relationship link can be stored in the tracking database.

[0110] For example, when generating at least one relationship link, after extracting the event tracking data (i.e., after pre-analyzing and processing the event tracking data to obtain the target data), multiple target data belonging to the same business are logically assembled based on the event tracking content and the relationships between each event tracking information. During logical assembly, the data can be collected according to the corresponding collection time of the target data. For example, logical assembly could involve specifying the order in which the user performs operations during data annotation. This application embodiment does not impose any limitations on the specific logical assembly. Logical assembly is equivalent to classifying the event tracking data, grouping related and dependent event tracking data together, and associating multiple target data in chronological order to generate a relationship link.

[0111] In one possible implementation, statistical calculations can be performed on identical information in the generated relationship links to obtain statistically calculated values, such as counting the number of times a user performs operation A. It is understood that the statistically calculated values ​​can be stored in a database for secondary calculations, significantly reducing the time required to obtain the data analysis report. This allows for necessary data preprocessing to obtain the necessary calculation results before determining the data analysis report, such as data transmission link tracking information, data operation statistics, and data loading statistics. This application embodiment only uses the above as an example for calculation, and does not impose any limitations on this embodiment.

[0112] In this embodiment of the application, target data is obtained by pre-analyzing multiple tracking data points, a relationship link is generated based on the target data, and the relationship link is stored in the server. This allows the required tracking data or target data to be quickly determined from the relationship link when processing the tracking data in the database to obtain a data analysis report, thereby effectively improving the efficiency of data analysis.

[0113] For example, when pre-analyzing multiple tracking data points to obtain target data, there are two possible implementation methods. In one possible implementation method, content extraction can be performed on multiple tracking data points based on the content to be analyzed to obtain the target data. It is understood that during content extraction, targeted content extraction can be performed on multiple tracking points based on pre-determined content to be analyzed, such as duration or frequency, to obtain the target data. This application's embodiments do not impose any limitations on content extraction.

[0114] In another possible implementation, the data obtained after content extraction from multiple tracking points can be re-analyzed to obtain the target data. It is understood that the required data may not be obtained after content extraction; therefore, it is necessary to analyze the extracted data to determine the desired target data. This application does not limit the specific analysis process.

[0115] In this embodiment of the application, multiple tracking data points are analyzed and processed according to the content to be analyzed to obtain target data, thereby realizing the preprocessing of tracking data points and effectively improving the efficiency of subsequent data analysis.

[0116] S303. Process the data points in the data point database using at least one preset component in the data point database to obtain a data analysis report corresponding to the preset component.

[0117] For example, data analysis reports often require reassembly and development. Many reports use the same metrics, differing only in the dimensions of statistical analysis and the format of the data. Therefore, when obtaining data analysis reports corresponding to pre-defined components, analysis of each report can identify common functionalities. For instance, these functionalities may be independent and reusable. Designing these common functionalities as components allows for direct integration into various reports, reducing development costs.

[0118] For example, preset components may include operation statistics (statistics for each operable function), duration statistics (operation duration), error trend statistics (the range from when a problem occurs to when its impact is observed), user click statistics (total number of page operation clicks), user count statistics (total number of registered users), online user statistics (currently active online users), etc. This application embodiment only uses the above components as examples for illustration, but it does not mean that this application embodiment is limited to these. It is understood that statistical indicators of different analysis dimensions can be used to develop different data analysis reports. In other words, different statistical indicators constitute a common indicator component library for event tracking analysis, i.e., preset components.

[0119] Therefore, the data processing method for tracking points provided in this application embodiment receives multiple tracking point data sent by a terminal device. These multiple tracking point data are data collected by the terminal device at a target tracking point. The target tracking point is obtained by the terminal device after receiving the tracking point operation and performing tracking point processing on the data annotation process. The multiple tracking point data are stored in a tracking point database, which includes at least one preset component. The tracking point data in the database is processed by at least one preset component to obtain a data analysis report corresponding to the preset component. The technical solution provided in this application embodiment processes the tracking point data in the database using preset components to obtain a data analysis report. This allows for timely detection of problems arising during the data annotation process, or determination of the causes of problems based on the problems generated during the data annotation process, thereby effectively improving the accuracy of data analysis.

[0120] In another embodiment of this application, after obtaining the data analysis report corresponding to the preset component, it can be determined whether there is an abnormal problem in the data annotation process based on the data analysis report; if there is an abnormal problem in the data annotation process, the data to be analyzed corresponding to the abnormal problem is obtained from the data tracking database; the data to be analyzed is analyzed to obtain the cause of the abnormality.

[0121] For example, when determining whether there are any abnormal problems in the data annotation process based on the data analysis report, it is possible to determine whether there are problems such as excessively long page refresh time or page lag based on the data analysis report corresponding to the page response time. Alternatively, it is possible to determine whether there are any problems with the data extraction of control A based on the number of user clicks on control A. This application embodiment does not limit the specific abnormal problems.

[0122] In one possible implementation, the process involves retrieving the event tracking data corresponding to the anomaly from the event tracking database; analyzing the event tracking data to determine the cause of the anomaly; identifying the corresponding relationship chain in the event tracking database based on the content to be analyzed for the anomaly; locating the data related to the anomaly within the relationship chain; and determining the specific cause of the anomaly by analyzing the data related to the anomaly. This application embodiment only illustrates the above process of determining the cause of the anomaly, but does not imply that this application embodiment is limited to this.

[0123] In this embodiment of the application, it is possible to accurately determine whether there is an anomaly in the data annotation process based on the data analysis report, and to maintain the data according to the cause of the anomaly, which can effectively improve the user experience.

[0124] In another embodiment of this application, operation tracking data corresponding to user operation behaviors can be extracted from the tracking data database. Behavioral analysis is then performed on the operation tracking data to obtain a first analysis result, which is used to characterize the user profile. It is understood that behavioral analysis analyzes user habits by monitoring user operations on various functions. This application does not specifically limit the specific user operation behaviors described herein.

[0125] In one possible implementation, page tracking data corresponding to the displayed interface can be extracted from the tracking data database. This includes the execution status of services and content on the page, page dwell time, etc. Behavioral analysis can be performed on the page tracking data to obtain page analysis results. This allows users to monitor the page, promptly determine whether any abnormalities have occurred, and effectively improve the user experience.

[0126] In this embodiment, a user profile can be obtained based on the operation tracking data corresponding to user operation behavior in the tracking data database. This enables the monitoring of user behavior based on the data generated during the data annotation process, facilitating the subsequent maintenance of the data annotation tool based on user behavior.

[0127] To facilitate understanding of the data annotation-based event tracking method and event tracking data processing method provided in this application's embodiments, the following will use the collaborative operation of a terminal device and a server to analyze the data generated during the data annotation process as an example to describe the technical solution provided in this application's embodiments in detail. For specific details, please refer to... Figure 4 As shown, Figure 4 This is a schematic diagram of a framework for collecting and analyzing embedded data, provided in an embodiment of this application.

[0128] according to Figure 4 As shown, data monitoring services are achieved by implementing event tracking on the terminal device. These data monitoring services include monitoring user access, user source, and page rendering records, among others; however, this embodiment does not specifically limit these aspects. Monitoring data through event tracking allows for the collection of event tracking data at the designated points. This data can include behavioral analysis event tracking data and page analysis event tracking data. It is understood that event tracking includes frequent and infrequent tracking; for details, please refer to the above embodiments, which will not be repeated here.

[0129] Furthermore, the terminal device sends the collected behavioral analysis and page analysis data to the server, which then performs data analysis services on the received data. These data analysis services include data process tracking, key event association marking, and data statistics. This embodiment does not specifically limit the data analysis services provided. It is understood that data process tracking can be performed through the relationship chains generated in the above embodiments.

[0130] In summary, the technical solution provided in this application separates and schedules data tasks, separating data monitoring from computation. This reduces coupling between data analysis data, ensuring the stability of data annotation services during the data tracking process. It also ensures the processing and scheduling of frequent and infrequent data tracking operations, improving data transmission stability. Furthermore, it saves resources and improves performance and resource utilization during computational processing in the data analysis process.

[0131] Figure 5 A schematic diagram of a data annotation-based data tracking device 50 provided in this application embodiment is shown below. For an example, please refer to [link to example diagram]. Figure 5 As shown, the data annotation-based tracking device 50 may include:

[0132] The acquisition module 501 is used to respond to the data annotation operation, perform data annotation processing, and obtain multiple target data points.

[0133] The acquisition module 501 is also used to acquire the data collected at the target embedding point;

[0134] The sending module 502 is used to send the embedded data to the server. The embedded data is used to instruct the server to determine the data analysis report based on the embedded data.

[0135] Optionally, the target tracking points include a first target tracking point and a second target tracking point. The first target tracking point includes tracking points where the time interval between two consecutive user operations is less than or equal to a preset time interval, and the second target tracking point includes tracking points where the time interval between two consecutive user operations is greater than the preset time interval. The acquisition module 501 is specifically used to acquire tracking point data collected at the first target tracking point and tracking point data collected at the second target tracking point, respectively.

[0136] Optionally, the transmission frequency of the embedded data collected at the first target embedded point is less than the transmission frequency of the embedded data collected at the second target embedded point.

[0137] Optionally, the sending module 502 is specifically used to send all the data collected at the first target embedding point within the preset time period to the server after the preset time period has elapsed; and to send the data collected at the second target embedding point to the server in real time.

[0138] The data-annotation-based tracking device provided in this application can execute the technical solution of the data-annotation-based tracking method in any of the above embodiments. Its implementation principle and beneficial effects are similar to those of the data-annotation-based tracking method. Please refer to the implementation principle and beneficial effects of the data-annotation-based tracking method. It will not be repeated here.

[0139] Figure 6 This is a schematic diagram of the structure of a data processing device 60 for embedded data provided in an embodiment of this application. For example, please refer to [link to example diagram]. Figure 6 As shown, the embedded data processing device 60 may include:

[0140] The receiving module 601 is used to receive multiple data points sent by the terminal device. The multiple data points are data collected by the terminal device at the target data points. The target data points are obtained by the terminal device after receiving the data point operation and performing data point processing during the data annotation process.

[0141] Storage module 602 is used to store multiple tracking point data in a tracking point database, the tracking point database including at least one preset component;

[0142] The processing module 603 is used to process the embedded data in the embedded data database through at least one preset component to obtain the data analysis report corresponding to the preset component.

[0143] Optionally, the storage module 602 is specifically used to pre-analyze and process multiple embedded data points to obtain target data; according to the embedded content corresponding to the target data, connect multiple target data points belonging to the same business according to the corresponding collection time to generate at least one relationship link; and store at least one relationship link in the embedded data point database.

[0144] Optionally, the storage module 602 is specifically used to extract content from multiple tracking data points based on the content to be analyzed, to obtain target data; or, to re-analyze the data obtained after extracting content from multiple tracking data points based on the content to be analyzed, to obtain target data.

[0145] Optionally, the storage module 602 is also used to determine whether each of the multiple data points sent by the receiving terminal device is structured data; when the data point is unstructured, the data point is converted into structured data and the converted data point is stored in the data point database; when the data point is structured, the data point is stored directly in the data point database.

[0146] Optionally, the processing module 603 is also used to determine whether any abnormal problems have occurred in the data annotation process based on the data analysis report; when abnormal problems occur in the data annotation process, to obtain the data to be analyzed corresponding to the abnormal problem from the data tracking database; and to analyze the data to be analyzed to obtain the cause of the abnormality.

[0147] Optionally, the processing module 603 is also used to extract operation tracking data corresponding to user operation behavior from the tracking data database, perform behavior analysis on the operation tracking data, and obtain a first analysis result, which is used to characterize the user profile.

[0148] The tracking data processing device provided in this application embodiment can execute the technical solution of the tracking data processing method in any of the above embodiments. Its implementation principle and beneficial effects are similar to those of the tracking data processing method. Please refer to the implementation principle and beneficial effects of the tracking data processing method. It will not be repeated here.

[0149] Figure 7 This is a schematic diagram of an electronic device structure provided in this application. Figure 7 As shown, the electronic device 700 may include at least one processor 701 and a memory 702.

[0150] The memory 702 is used to store programs. Specifically, the program may include program code, which includes computer operation instructions.

[0151] The memory 702 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0152] The processor 701 executes computer execution instructions stored in the memory 702 to implement the data annotation-based event tracking method and event tracking data processing method described in the foregoing method embodiments. The processor 701 may be a Central Processing Unit (CPU), an Application Specific Integrated Circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application. Specifically, when implementing the data annotation-based event tracking method and event tracking data processing method described in the foregoing method embodiments, the electronic device may be, for example, a terminal, a server, or other electronic device with processing capabilities. When implementing the event tracking data processing method described in the foregoing method embodiments, the electronic device may be, for example, an electronic control unit in a vehicle.

[0153] Optionally, the electronic device 700 may also include a communication interface 703. In specific implementations, if the communication interface 703, memory 702, and processor 701 are implemented independently, they can be interconnected via a bus to complete communication. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc., but this does not imply that there is only one bus or one type of bus.

[0154] Optionally, in a specific implementation, if the communication interface 703, memory 702, and processor 701 are integrated on a single chip, then the communication interface 703, memory 702, and processor 701 can communicate through an internal interface.

[0155] This application also provides a computer-readable storage medium, which may include various media capable of storing program code, such as a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk. Specifically, the computer-readable storage medium stores program instructions, which are used in the methods described in the above embodiments.

[0156] This application also provides a program product including executable instructions stored in a readable storage medium. At least one processor of an electronic device can read the executable instructions from the readable storage medium, and the at least one processor executes the executable instructions to cause the electronic device to implement the data annotation-based embedding method and embedding data processing method provided in the various embodiments described above.

[0157] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

Claims

1. A data annotation-based event tracking method, characterized in that, Applied to terminal devices, including: In response to the tracking operation, the data annotation process is processed to obtain multiple target tracking points; the tracking operation refers to the tracking information entered by the user on the display interface of the terminal device, and the tracking information includes: page loading, behavior operation, loading time, and event statistics. Acquire the embedded data collected at the target embedded point; The embedded data is sent to the server, which then performs pre-analysis on the multiple embedded data points based on the content to be analyzed, obtaining target data. Based on the embedded content corresponding to the target data, multiple target data points belonging to the same business are connected according to their corresponding collection times. After generating at least one relationship link, the at least one relationship link is stored in the embedded data point database, thereby enabling the server to perform the following processing: The server extracts operation tracking data corresponding to user operation behaviors from the tracking data database; performs behavioral analysis on the operation tracking data to obtain a first analysis result, which is used to characterize the user profile; the behavioral analysis refers to analyzing the user's usage habits based on the user's operation behavior of various functions. The server extracts page tracking data corresponding to the displayed interface from the tracking data database; the page tracking data includes: the execution status of services and content on the page, and the page dwell time; behavioral analysis is performed on the page tracking data to obtain page analysis results; the page analysis results are used to indicate whether the page has any abnormalities; and, The server processes the relationship links in the data collection database using at least one preset component to obtain a data analysis report corresponding to the preset component; wherein, the content to be analyzed includes: duration and frequency; different preset components correspond to different statistical indicators.

2. The method according to claim 1, characterized in that, The target tracking points include a first target tracking point and a second target tracking point. The first target tracking point includes tracking points where the time interval between two consecutive user operations is less than or equal to a preset time interval, and the second target tracking point includes tracking points where the time interval between two consecutive user operations is greater than the preset time interval. The acquisition of the embedded data collected at the target embedded point includes: Acquire the embedding data collected at the first target embedding point and the embedding data collected at the second target embedding point, respectively.

3. The method according to claim 2, characterized in that, The frequency of sending the embedded data collected at the first target embedded point is less than the frequency of sending the embedded data collected at the second target embedded point.

4. The method according to claim 2 or 3, characterized in that, Sending the embedded data to the server includes: After the preset time period is reached, all data collected at the first target embedding point within the preset time period will be sent to the server. The data collected at the second target embedding point will be sent to the server in real time.

5. A method for processing embedded data, characterized in that, Applied to servers, including: The terminal device receives multiple data points from the terminal device. These multiple data points are data collected by the terminal device at target data points. The target data points are obtained by the terminal device after receiving the data point operation and performing data point processing on the data annotation process. The data point operation is the data point information entered by the user on the display interface of the terminal device. The data point information includes: page loading, behavior operation, loading time, and event statistics. The multiple data points are stored in a data point database, which includes at least one preset component. The data points in the data point database are processed by at least one preset component in the data point database to obtain a data analysis report corresponding to the preset component; different preset components correspond to statistical indicators of different dimensions. The step of storing the plurality of data points in a data point database includes: The multiple data points are pre-analyzed based on the content to be analyzed to obtain the target data; the content to be analyzed includes: duration and frequency. Based on the embedded content corresponding to the target data, multiple target data belonging to the same business are connected according to their corresponding collection times to generate at least one relationship link; Store the at least one relationship link in the data tracking database; It also includes: extracting operation tracking data corresponding to user operation behaviors from the tracking data database; Behavioral analysis is performed on the operation tracking data to obtain a first analysis result, which is used to characterize the user profile; the behavioral analysis refers to analyzing the user's usage habits based on the user's operational behavior for each function; and... Extract the page tracking data corresponding to the display interface from the tracking data database; the page tracking data includes: the execution status of services and content on the page, and the page dwell time; Behavioral analysis is performed on the page tracking data to obtain page analysis results; the page analysis results are used to indicate whether the page has any abnormalities.

6. The method according to claim 5, characterized in that, Based on the content to be analyzed, the multiple embedded data points are pre-analyzed to obtain target data, including: Based on the content to be analyzed, the content of the multiple tracking data points is extracted to obtain the target data; or, The data obtained after extracting content from the multiple data points is re-analyzed based on the content to be analyzed to obtain the target data.

7. The method according to claim 5, characterized in that, Before storing the plurality of event tracking data in the event tracking database, the method includes: Determine whether each of the multiple embedded data points sent by the terminal device is structured data; If the data is unstructured, the data is converted into structured data and the converted data is stored in the data tracking database. If the data points are structured data, then the data points are directly stored in the data point database.

8. The method according to claim 5, characterized in that, After obtaining the data analysis report corresponding to the preset component, the method includes: Based on the data analysis report, determine whether any abnormal issues occurred during the data annotation process; If an abnormal problem occurs during the data annotation process, the corresponding tracking data to be analyzed will be retrieved from the tracking data database. The reasons for the anomalies were obtained by analyzing the data from the embedded points to be analyzed.

9. A device for processing embedded data, characterized in that, include: The acquisition module is used to respond to the data tracking operation, perform data tracking processing on the data annotation process, and obtain multiple target tracking points; The event tracking operation refers to the event tracking information entered by the user on the display interface of the terminal device. The event tracking information includes: page loading, behavior operation, loading time, and event statistics. The acquisition module is also used to acquire the embedded data collected at the target embedded point; The sending module is used to send the embedded data to the server, so that the server can perform pre-analysis processing on the multiple embedded data according to the content to be analyzed, obtain target data, and connect multiple target data belonging to the same business according to the corresponding collection time based on the embedded content corresponding to the target data. After generating at least one relationship link, the at least one relationship link is stored in the embedded database, thereby enabling the server to perform the following processing: The server extracts operation tracking data corresponding to user operation behaviors from the tracking data database; performs behavioral analysis on the operation tracking data to obtain a first analysis result, which is used to characterize the user profile; the behavioral analysis refers to analyzing the user's usage habits based on the user's operation behavior of various functions. The server extracts page tracking data corresponding to the displayed interface from the tracking data database; the page tracking data includes: the execution status of services and content on the page, and the page dwell time; behavioral analysis is performed on the page tracking data to obtain page analysis results; the page analysis results are used to indicate whether the page has any abnormalities; and, The server processes the relationship links in the data collection database using at least one preset component to obtain a data analysis report corresponding to the preset component; wherein, the content to be analyzed includes: duration and frequency; different preset components correspond to different statistical indicators.

10. A device for processing embedded data, characterized in that, include: The receiving module is used to receive multiple data points sent by the terminal device. The multiple data points are data collected by the terminal device at the target data point. The target data point is obtained by the terminal device after receiving the data point operation and performing data point processing during the data annotation process. The event tracking operation refers to the event tracking information entered by the user on the display interface of the terminal device. The event tracking information includes: page loading, behavior operation, loading time, and event statistics. A storage module is used to store the plurality of tracking data in a tracking database, wherein the tracking database includes at least one preset component; The processing module is used to process the tracking data in the tracking data database through at least one preset component to obtain a data analysis report corresponding to the preset component; different preset components correspond to statistical indicators of different dimensions. The storage module is specifically used to pre-analyze the multiple data points based on the content to be analyzed, and obtain target data; the content to be analyzed includes: duration and number of times; based on the data points corresponding to the target data, multiple target data belonging to the same business are connected according to the corresponding collection time to generate at least one relationship link; the at least one relationship link is stored in the data point database; The processing module is further configured to extract operation tracking data corresponding to user operation behaviors from the tracking data database; perform behavioral analysis on the operation tracking data to obtain a first analysis result, which is used to characterize a user profile; the behavioral analysis refers to analyzing user usage habits based on user operation behaviors of various functions; and extract page tracking data corresponding to the display interface from the tracking data database; the page tracking data includes: the execution status of services and content on the page, and page dwell time; perform behavioral analysis on the page tracking data to obtain a page analysis result; the page analysis result is used to indicate whether the page has any abnormalities.

11. An electronic device, characterized in that, include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory to implement the method as described in any one of claims 1-8.

12. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1-8.

13. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the method described in any one of claims 1-8.

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