Buried point data collection method and device, equipment and medium

Abstract

The invention relates to the technical field of data collection, and discloses a burying point data collection method, device and equipment and a medium, and the method comprises the steps: obtaining a data collection task, analyzing the data collection task through a burying point system, and obtaining an analysis result; defining a point burying scene based on the analysis result, and configuring a point burying strategy according to the analysis result and the point burying scene; the point burying strategy is transmitted to an approval management module for approval, and when the point burying strategy passes approval, the point burying strategy is allocated according to a preset crowd label group; and collecting task data according to the point burying strategy, and storing the collected task data in a server. According to the method, the point burying flexibility is improved, the point burying period is shortened, and the point burying cost is reduced.

Description

Technical Field

[0001] The present invention relates to the fields of data collection technology and financial technology, and in particular to a method, device, equipment and medium for collecting buried data. Background Art

[0002] With the booming development of the digital economy, digital assets are gradually becoming an important strategic resource and a key component of the core competitiveness of enterprises. As one of the important intangible assets of enterprises, data assets include not only the data produced within the enterprise, but also the data collected from the outside, covering both structured and unstructured data. The efficient operation of the business and the improvement of innovation are increasingly dependent on the effective use of data assets.

[0003] However, in the rapidly iterating financial business environment, it has become a very common technical means to collect the required data, obtain data and use the data to solve the corresponding problems. For example, each e-commerce platform will collect data such as users' product browsing history, and build a product recommendation model based on the collected data to recommend products to users. Traditional tracking strategies often face a series of challenges. Developing business functions itself requires a lot of effort, and the handling of tracking matters adds to the workload. These traditional strategies usually involve complex, lengthy and cumbersome R&D and online processes. With the continuous growth of business needs, the maintenance workload of old tracking points is also increasing. The redundancy and dispersion of data definitions require code tracking points to keep up with version iterations, lack flexibility, and make it difficult to achieve dynamic configuration. In addition, developers from different teams customize the front-end and back-end tracking points of components, lacking unified specifications, which makes it difficult to integrate these data. Every time you need to view data, the data product team needs to separately propose requirements and develop and implement them, which is not only time-consuming but also costly. Summary of the invention

[0004] The present invention provides a method, device, computer equipment and medium for collecting tracking data, so as to solve the problems of complex tracking procedures, long tracking cycle and high tracking cost in traditional tracking strategies.

[0005] In a first aspect, a method for collecting buried data is provided, comprising:

[0006] Obtaining data collection tasks, analyzing the data collection tasks through a tracking system, and obtaining analysis results;

[0007] Defining a tracking scenario based on the analysis results, and configuring a tracking strategy according to the analysis results and the tracking scenario;

[0008] The embedding strategy is transmitted to the approval management module for approval. When the embedding strategy is approved, the embedding strategy is allocated according to the preset population tag group;

[0009] The task data is collected according to the tracking strategy, and the collected task data is stored in the server.

[0010] In order to solve the above technical problems, the embodiment of the present invention also provides a buried point data collection device, which adopts the following technical solution:

[0011] In a second aspect, a buried point data collection device is provided, comprising:

[0012] The data analysis module is used to obtain data collection tasks, analyze the data collection tasks through the tracking system, and obtain analysis results;

[0013] A tracking point strategy module, used to define tracking point scenarios based on the analysis results, and configure tracking point strategies according to the analysis results and tracking point scenarios;

[0014] A strategy approval and allocation module is used to transmit the tracking strategy to the approval management module for approval. When the tracking strategy is approved, the tracking strategy is allocated according to the preset population tag group;

[0015] The data collection and storage module is used to collect task data according to the tracking point strategy and store the collected task data in the server.

[0016] In a third aspect, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned method for collecting buried data when executing the computer program.

[0017] In a fourth aspect, a computer-readable storage medium is provided, which stores a computer program. When the computer program is executed by a processor, the steps of the above-mentioned burial point data collection method are implemented.

[0018] The scheme implemented by the above-mentioned tracking data collection method, device, computer equipment and storage medium involves a series of automated and optimized steps to improve the efficiency and accuracy of data collection. The specific process is as follows: First, the system obtains data collection tasks, which specify the type of data and target to be collected. Then, the tracking system conducts a detailed analysis of these data collection tasks to determine the best collection method and strategy. The analysis results will be used to define tracking scenarios, which describe the specific environment and conditions for data collection. According to the analysis results and the defined tracking scenarios, the system will configure the corresponding tracking strategies. These strategies detail under what circumstances data collection is triggered and how to collect data. In order to ensure the effectiveness and compliance of the strategy, the tracking strategy will be transmitted to the approval management module for approval. When the tracking strategy is approved, the system will assign these strategies according to the preset population tag group. This means that different user groups or user characteristics will correspond to different data collection strategies to ensure the relevance and accuracy of the data. Subsequently, the system starts to collect task data according to the approved tracking strategy. The data will be collected according to the parameters and conditions defined in the strategy. The collected task data will be stored on the server for further analysis and processing. The present invention realizes the automation and optimization management of data collection tasks, improves the efficiency and quality of data collection, and ensures the compliance and security of data collection. This method is suitable for scenarios that require large amounts of data collection and analysis, especially in the fields of digital marketing, user behavior analysis, and product optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative labor.

[0020] Figure 1 This is a schematic diagram of an application environment of a method for collecting data from buried points in one embodiment of the present invention;

[0021] Figure 2 It is a flow chart of a method for collecting buried data in one embodiment of the present invention;

[0022] Figure 3 It is a structural schematic diagram of a buried point data collection device in one embodiment of the present invention;

[0023] Figure 4 It is a structural schematic diagram of a computer device in one embodiment of the present invention. DETAILED DESCRIPTION

[0024] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by technicians in the technical field of the present invention; the terms used in the specification of the application herein are only for the purpose of describing specific embodiments and are not intended to limit the present invention; the terms "including" and "having" and any variations thereof in the specification and claims of the present invention and the above-mentioned drawings are intended to cover non-exclusive inclusions. The terms "first", "second", etc. in the specification and claims of the present invention or the above-mentioned drawings are used to distinguish different objects, not to describe a specific order.

[0025] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0026] The method for collecting buried data provided by the embodiment of the present invention can be applied in the following aspects: Figure 1 In application environments such as Figure 1 As shown, the system architecture 100 may include terminal devices 101, 102, 103, a network 104 and a server 105. The network 104 is used to provide a medium for communication links between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links or optical fiber cables, etc.

[0027] Users can use terminal devices 101, 102, 103 to interact with server 105 through network 104 to receive or send messages, etc. Various communication client applications can be installed on terminal devices 101, 102, 103, such as web browser applications, shopping applications, search applications, instant messaging tools, email clients, social platform software, etc.

[0028] Terminal devices 101, 102, 103 can be various electronic devices with display screens and supporting web browsing, including but not limited to smart phones, tablet computers, e-book readers, MP3 players (Moving Picture Experts Group Audio Layer III, Moving Picture Experts Group Audio Layer 3), MP4 (Moving Picture Experts Group Audio Layer IV, Moving Picture Experts Group Audio Layer 4) players, laptop computers and desktop computers, etc.

[0029] The server 105 may be a server that provides various services, such as a background server that provides support for web pages displayed on the terminal devices 101 , 102 , and 103 .

[0030] See also Figure 2 As shown, Figure 2 A flowchart of a method for collecting buried data provided by an embodiment of the present invention includes the following steps:

[0031] S10: Obtain a data collection task, and analyze the data collection task through a tracking point system to obtain an analysis result.

[0032] In this embodiment, before starting data collection, clarifying the goals and needs of data collection is a crucial first step. This usually requires in-depth communication with the business team to understand the problems they hope to solve or the business areas they hope to optimize through data collection. The data collection tasks obtained are analyzed in detail through the tracking point system. This analysis process may include reviewing existing data, studying user behavior, combing business processes, etc., to determine what data needs to be collected and how to collect it effectively.

[0033] After the analysis process is completed, a series of analysis results will be obtained, which may include insights into user behavior, evaluation of product performance, understanding of market trends, etc. These analysis results provide a solid basis for the subsequent tracking scenario definition and strategy configuration. In this way, the tracking system is not only a data collection tool, but also a complete data-driven decision support system.

[0034] In addition, the system also provides the function of operators viewing their online status, which is usually achieved through the so-called "lighting mechanism", which is a visual prompt system used to display the online status of operators in real time. This mechanism can be implemented through color coding, for example, green for online, red for offline, gray for busy, etc. Real-time synchronization ensures that the status displayed on all user interfaces is the latest, which is especially important for work environments that require rapid response and collaboration.

[0035] The permission module of the backend management platform is an important part of ensuring system security and data protection. By implementing role permission configuration and menu-level permission management, system administrators can assign different access rights based on user roles and responsibilities. The system supports multiple types of administrator roles, such as tenant administrators and platform administrators, to achieve isolation of platform role permissions and data permissions, which helps prevent data leakage and unauthorized access, especially in a multi-tenant environment, ensuring strict isolation of data from different tenants or customers.

[0036] Through these comprehensive measures, the boundless tracking system can ensure that the data collection is targeted, accurate, and closely related to business needs. Carefully defined tracking scenarios and strategies, as well as real-time online status synchronization and strict permission management, jointly improve the quality and efficiency of data collection, and provide a solid foundation for subsequent data analysis and business optimization.

[0037] Furthermore, when designing a tracking system, it is crucial to ensure that application performance is not affected. To this end, a series of measures need to be taken to achieve application lightweighting and ensure that the tracking strategy is both refined and minimized.

[0038] First, considering performance impact means that when designing tracking points, you must evaluate the potential impact of data collection and processing on application performance. This may include increased response time, increased system resource consumption, etc., all of which may have a negative impact on user experience.

[0039] Secondly, lightweight applications are achieved by optimizing the design and implementation of applications, reducing their occupation of system resources, and improving their operating efficiency. In tracking point systems, this may involve reducing unnecessary data collection, optimizing data transmission and storage processes, and improving the efficiency of data processing algorithms. Tracking point strategies involve scope refinement, that is, precise control and configuration of data collection points to collect only data that is truly valuable for business analysis and decision-making. This includes determining which user behaviors are critical, which data is necessary, and how to collect this data most effectively.

[0040] Finally, minimization means reducing the scope and frequency of data collection as much as possible to reduce the impact on application performance while ensuring the data collection goals. This may include reducing the collection of unnecessary data fields, optimizing the timing of data collection (such as synchronizing data when users are not active), and using efficient data compression and transmission technologies to reduce network load.

[0041] In summary, when designing a tracking system, you need to balance the need for data collection and the impact on application performance. By implementing lightweight applications, refined tracking strategies, and minimizing the scope and frequency of data collection, you can effectively collect and analyze user behavior data to support business decisions and product optimization without affecting the user experience. This approach helps improve the efficiency and quality of data collection while ensuring that application performance and user experience are not affected.

[0042] S20. Define a tracking scenario based on the analysis result, and configure a tracking strategy according to the analysis result and the tracking scenario.

[0043] In this embodiment, the analysis results are crucial for identifying key information, which may include how users interact with the product, the most popular features, and the problems users encounter when using the product. Using these analysis results, specific tracking scenarios can be defined, including determining which user behaviors or events are key and how these behaviors or events are specifically manifested in the application.

[0044] It is crucial to clearly define the monitoring objectives of the tracking scenarios, such as improving user engagement, optimizing user experience, increasing conversion rates, or monitoring system performance. Configuring the necessary parameters for each tracking scenario, such as trigger conditions, the level of detail of data collection, and the timing of data collection, are key steps to ensure the purposefulness and effectiveness of data collection.

[0045] Through this step, the boundless tracking system can ensure that data collection is both purposeful and effective, so that the collected data can provide valuable insights for the business and support data-driven decision-making. This process not only improves the quality and efficiency of data collection, but also ensures that the data can meet analysis needs and business goals, thereby providing enterprises with in-depth business insights and data-driven solutions that support decision-making.

[0046] In this embodiment, according to the analysis results and the defined tracking scenarios, setting specific rules for data collection is a key step to ensure data validity. This includes determining the data fields that need to be collected, which should provide sufficient information to support business needs and analysis goals. Typically, these fields may include user ID, behavior type, timestamp, page URL, user stay time, etc.

[0047] Set trigger conditions for each tracking point, which define under what circumstances data will be collected. Trigger conditions may be based on specific user behaviors, such as clicks, scrolls, form submissions, etc., or based on specific system events. In addition, determining the frequency and method of data collection, such as real-time, scheduled, and batch transmission or real-time streaming, is critical to ensuring the efficiency and quality of data collection.

[0048] When configuring tracking strategies, measures should be taken to ensure the accuracy and completeness of the collected data. This may include data validation, error handling mechanisms, data cleaning, and preprocessing steps. At the same time, when setting data collection rules, it is necessary to consider the impact of data collection on application performance and ensure that data collection does not negatively affect the user experience.

[0049] Through this step, the boundless tracking system can ensure the effectiveness of the data collection strategy, so that the collected data can support in-depth analysis and effective decision-making. At the same time, this also helps to improve the efficiency of data collection and reduce the cost of data processing.

[0050] After the tracking point definition is issued, the user side receives updates in quasi-real time and collects behavior data according to the latest issued strategy. This involves the update and execution process of the data collection strategy in the tracking point system, including the following key links:

[0051] 1. Tracking point definition and distribution: Determine which user behaviors or events need to be tracked and recorded, and set up corresponding data collection points in the application.

[0052] 2. Receive updates in near real time on the user side: Client applications can quickly receive tracking point definition updates from the server side to ensure data collection according to the latest tracking point strategy.

[0053] 3. Collect behavior data according to the latest policy issued: The client application collects user behavior data according to the latest policy issued by the server, ensuring the consistency and accuracy of data collection.

[0054] This dynamic update and data collection process is a key link in the tracking point system. By updating and issuing tracking point definitions in quasi-real time and client applications collecting data according to these latest definitions, the timeliness and accuracy of data collection can be ensured. This is crucial for data analysis and business decision-making, and also helps to quickly respond to market changes and user needs, allowing products to adapt to the ever-changing environment more flexibly.

[0055] S30, transmitting the embedding strategy to the approval management module for approval. When the embedding strategy is approved, the embedding strategy is allocated according to the preset population tag group;

[0056] S40: Collect task data according to the tracking strategy, and store the collected task data in a server.

[0057] In this embodiment, in the boundless tracking system, it is crucial to ensure the compliance, security and effectiveness of the data collection strategy. After the tracking strategy is defined and configured, it must be reviewed by the approval management module. This approval process ensures that all data collection activities comply with company policies, laws and regulations, and data protection standards. Once the tracking strategy is approved, it means that it meets all necessary compliance requirements and can be implemented.

[0058] Approved policies are assigned based on preset demographic groups, which are defined based on user behavior, preferences, geographic location, or other characteristics, ensuring that data collection policies are precisely targeted to specific user groups, thereby improving the relevance and efficiency of data collection.

[0059] On the user side (client), data collection begins according to the tracking point strategy issued. These strategies detail which user behaviors or events need to be tracked and how to track them. The collected data will be transmitted to the server through encryption and stored in the database to ensure the security of the data during transmission.

[0060] In addition, the attribution of personnel tags and the precise issuance of tracking strategies in the boundless tracking system are key steps to achieve data collection for specific user groups. Users are assigned to specific tags or groups based on their attributes, which help to classify and segment users. Crowd tag groups are system-predefined classifications that group users according to their characteristics or behaviors, allowing for flexible creation and modification of tag groups based on business needs.

[0061] The system accurately issues tracking strategies based on the user's group of population tags. This ensures that different user groups can have different data collection needs, and the system can adjust tracking strategies based on these needs to ensure targeted data collection. By accurately issuing tracking strategies, the system can achieve refined data collection for individual users and improve the relevance and value of data.

[0062] There is already a pre-defined and classified tag system in the system to classify users. These classifications help system administrators or data analysts better understand user groups and design and implement tracking strategies based on these classifications.

[0063] In this way, the Unbounded Tracking System can collect data more accurately for specific user groups, improving the pertinence and effectiveness of data analysis. This refined management method helps improve user experience, optimize product functions, and support more accurate market positioning and user services. The Unbounded Tracking System provides enterprises with a powerful tool that is data-driven and continuously optimizes and improves the quality of business decisions.

[0064] Furthermore, in order to reduce the impact of the subsequent basic software environment on user interaction, the real-time hot update of the Windows client is implemented to achieve no perception of the upgrade.

[0065] 1. Impact of basic software environment on user interaction: The basic software environment, including operating system, database, middleware, etc., has a direct impact on the user's interaction experience with the application. When updating the software environment, it is necessary to consider the possible interference of the update on the user experience and ensure that the update process does not interfere with the user's daily operations as much as possible.

[0066] 2. Real-time hot update: Real-time hot update technology allows running software to be updated without restarting the application. This technology is particularly suitable for emergency repairs or function additions, allowing users to get updates without restarting the application, thereby improving software availability and user convenience.

[0067] 3. Make the upgrade imperceptible: "Imperceptible" upgrade means that users will hardly be aware that the software is being updated while using the software. This upgrade method ensures that the update process minimizes interference with user operations by silently downloading update files in the background, automatically applying updates during user inactive periods, and using incremental update technology.

[0068] By carefully designing the update process, including silent background downloads, choosing the right time to update, and downloading only the necessary update parts, imperceptible upgrades can significantly improve the user experience. This strategy not only reduces the interruption and waiting time caused by updates, but also improves the stability of the software and user satisfaction, which is especially important for critical applications that need to run continuously, such as enterprise-level software and online service platforms. By implementing this update strategy, software developers can promptly fix software problems and add new features without affecting normal user use.

[0069] In one embodiment, defining a tracking scenario according to the analysis result includes:

[0070] Identify task information according to the analysis result, and perform tracking according to the task information;

[0071] Define a tracking point name for each tracking point and configure the system to which the tracking point belongs.

[0072] In this embodiment, when designing and implementing data collection tracking points, it is crucial to assign a clear and descriptive name to each tracking point. This name should intuitively reflect the purpose of the tracking point and the user behavior or event it monitors, so as to facilitate subsequent identification and management. For example, if the tracking point is used to track user purchase behavior, its name can intuitively indicate this, such as "User Purchase Behavior Tracking".

[0073] Determining which system or application module each tracking point belongs to is also a key step, which is usually achieved through the system global dictionary. This predefined configuration list contains all system components or modules, ensuring that the tracking point is associated with the correct system component, which helps with subsequent data management and analysis.

[0074] To ensure that tracking points can be accurately deployed on the pages that need to be monitored, you need to specify the URL address of the monitored web page or page. The URL address should be as specific and accurate as possible, and regular expressions can be used to match a series of similar URLs, which improves the flexibility and efficiency of tracking point configuration.

[0075] In addition, it is also important to associate tracking data with specific product operation lines. Product operation lines refer to specific areas or processes in product development, operation, or maintenance. Doing so helps organize and categorize tracking data, ensure that data collection is aligned with specific business goals and processes, and facilitate subsequent data analysis and decision support.

[0076] When designing a tracking strategy, you should select data fields that are critical to analyzing user behavior and business indicators for collection. This approach aims to reduce unnecessary data collection, thereby reducing system burden, increasing data processing speed, and protecting user privacy. Based on business goals and key performance indicators (KPIs), determine which data fields are most critical for measuring and analyzing user behavior. For example, if the business goal is to improve user retention, you may need to collect fields such as user login frequency and usage duration.

[0077] When implementing a tracking strategy, ensure that only pre-defined key fields are collected. This may involve optimizing the front-end code to ensure that only necessary data is sent to the server. Through these steps, the boundless tracking system can ensure that the data collection is targeted, precise in scope, and closely related to business needs. Carefully defining tracking scenarios can improve the quality and efficiency of data collection and provide a solid foundation for subsequent data analysis and business optimization.

[0078] In one embodiment, configuring the tracking strategy according to the analysis result and the tracking scenario includes:

[0079] Setting data collection rules according to the task information and the tracking scenario;

[0080] Determining the task data to be collected according to the analysis results;

[0081] Set trigger conditions for each tracking point.

[0082] In this embodiment, when implementing the tracking strategy, minimizing field management is a key step, which involves carefully selecting and configuring the tracking strategy to ensure that only data fields that are critical to business analysis and decision-making are collected. This approach helps reduce unnecessary data collection, thereby reducing system resource consumption, increasing data processing speed, and protecting user privacy.

[0083] First, determine which data fields are most critical for measuring and analyzing user behavior based on actual business goals and key performance indicators (KPIs). For example, if the business goal is to increase conversion rates, data fields related to user purchase behavior may need to be collected first.

[0084] Next, clearly specify the data fields that need to be collected in the tracking strategy. This may include user behavior data (such as clicks, swipes), user attribute data (such as age, gender), system performance data (such as page loading time, response speed), etc. When selecting key fields, it is necessary to balance the level of data detail and collection cost to avoid wasting resources or failing to meet analysis needs.

[0085] When implementing a tracking strategy, make sure to collect only those pre-defined key fields. This may involve optimizing the front-end code to ensure that only necessary data is sent to the server. In this way, the system can run more efficiently while reducing the impact on user devices and network resources.

[0086] In addition, according to the analysis results and the tracking scenarios, set specific rules for data collection. This includes determining the data fields, setting trigger conditions, and defining the frequency and method of data collection. Set trigger conditions for each tracking point, which define under what circumstances data will be collected. Trigger conditions may be based on specific user behaviors, such as clicks, scrolling, form submissions, etc., or based on specific system events.

[0087] Through this minimization strategy, the boundless tracking system can ensure the pertinence and effectiveness of data collection while protecting user privacy and data security. This approach helps improve the quality and efficiency of data analysis and provide more accurate and valuable insights for business decisions.

[0088] In one embodiment, the embedding strategy is transmitted to the approval management module for approval. When the embedding strategy is approved, the embedding strategy is allocated according to the preset population tag group, including:

[0089] Transmitting the tracking strategy to the approval management module for approval;

[0090] The approval management module reviews whether the tracking strategy is passed;

[0091] When the approval opinions of the approval chains of the approval management module are inconsistent or disagree, the tracking strategy fails to pass the review and is rejected;

[0092] When the approval opinions of each approval chain of the approval management module are in agreement, the tracking strategy is approved, and the tracking strategy is allocated to the corresponding user terminal according to the preset population tag group.

[0093] In this embodiment, after defining the tracking scenarios and configuring the corresponding data collection strategies, the next step is to send these strategies to the user end to start collecting data. The EOA report approval management process is an electronic approval process that ensures that all sent tracking strategies are properly reviewed and approved.

[0094] The approval chain clarifies the different roles and sequences involved in the approval process, and usually includes three main roles:

[0095] 1. The issuer’s immediate superior: responsible for reviewing whether the strategy meets business goals and needs.

[0096] 2. Business data owner: responsible for ensuring that the strategy complies with the standards and specifications for data management and use.

[0097] 3. Security Officer: Responsible for evaluating the security of the strategy and ensuring that there is no violation of privacy policy or security regulations.

[0098] If during the approval process, the approver at any stage has objections or rejects the strategy, then the tracking strategy cannot be issued. This mechanism ensures that all issued strategies have been strictly reviewed and agreed to by all relevant parties. If the tracking strategies that have been issued need to be modified, these modifications must also go through the EOA approval process again. This is done to ensure that all changes are properly reviewed and approved to prevent unauthorized changes.

[0099] The tracking strategy will only take effect after a complete approval process and with the consent of all relevant parties. This ensures the legitimacy and effectiveness of the strategy. Once the tracking strategy is approved and sent to the user end, the client will start collecting behavioral data according to these strategies. The collected data will be encrypted and transmitted to the server and stored in the database for further processing and analysis.

[0100] Analyze the stored data, extract valuable information and insights, and generate analysis reports to support business decisions and product optimization. Through such an approval process, the boundless tracking system can ensure that all data collection activities are carried out within a compliant and secure framework. This not only protects the user's data privacy, but also ensures that the company's data strategy complies with laws, regulations and internal policies. This strict approval process is a key step in ensuring the legality and effectiveness of data collection.

[0101] In one embodiment, collecting task data according to the tracking strategy and storing the collected task data on the server includes:

[0102] Performing desensitization processing on the task data to obtain desensitized data;

[0103] Encrypting the desensitized data to obtain encrypted data;

[0104] The encrypted data is transmitted to the database of the server for storage.

[0105] In this embodiment, a series of encryption and desensitization measures are taken to ensure the data security of the communication between the client and the backend. First, all data transmission from the client (such as the user's browser or mobile application) to the server uses encryption technologies such as SSL / TLS to ensure the security of data during transmission. The data is encrypted on the client and decrypted on the server, effectively preventing the data from being intercepted or tampered with during transmission.

[0106] Sensitive information displayed on the user interface, such as name, address, phone number, etc., is desensitized so that it cannot directly identify the individual to protect user privacy. This usually involves replacing some characters in the name with asterisks (*) or other placeholders. The server will desensitize all sensitive information before sending the data back to the client to ensure that no sensitive information is sent without desensitization.

[0107] On the server side, it is standard practice to desensitize the collected mission data. This may include using algorithms or scripts to replace sensitive information with non-sensitive representations. After desensitization, the data is encrypted before being sent to the client, adding an extra layer of security. Even if the data is intercepted during transmission, unauthorized users will not be able to interpret the data content.

[0108] Finally, the encrypted data is securely transmitted to the server and stored in the database. This ensures the security of the data on the server side and prevents unauthorized access and data leakage. Through these measures, the boundless tracking system not only ensures the security and privacy protection of data during collection, transmission, storage and analysis, but also improves the efficiency and quality of data processing.

[0109] In one embodiment, the collecting of task data according to the tracking strategy and storing the collected task data on the server further includes:

[0110] Perform full-field redundancy processing on the task data stored in the server;

[0111] Analyze the task data after all-field redundancy processing and extract key information from the task data;

[0112] Generate an analysis report based on the key information.

[0113] In this embodiment, the buried point statistics provide an important perspective for understanding user behavior and product performance by analyzing the data collected during the user interaction process. These data reveal how users interact with the product, which features are favored, and possible problems. Full field redundancy is a data storage strategy that allows each data record to contain all the information required for analysis without relying on other tables or records. The advantage of this strategy is that it simplifies the query process, because each data item can be used directly for query and analysis without performing multi-table association operations, thereby improving query efficiency, especially when processing large-scale data, it can reduce the load on the database and speed up response.

[0114] Querying without associating other tables means that when performing data query, there is no need to merge data from multiple tables through JOIN operations. Each data record already contains all the necessary information and can be directly used for statistics and analysis. This method can simplify query logic and increase the speed of data processing, especially when processing large amounts of data, which can significantly reduce the database load.

[0115] When displaying processed data on the user interface, fully redundant data can be presented directly without additional data processing or querying. After the system performs full-field redundant processing on the data stored in the server, it further analyzes the data to extract key information, such as user behavior patterns, product usage frequency, and performance bottlenecks. Based on this key information, the system generates analysis reports containing charts, trend analysis, and recommendations that support business decisions and product optimization. Through these steps, the boundless tracking system can efficiently collect, store, analyze, and display user behavior data, provide enterprises with in-depth business insights, and support data-driven decision-making solutions.

[0116] In one embodiment, the step of transmitting the encrypted task data to a database of a server for storage further includes:

[0117] Performing data verification on the task data, and when the task data passes the data verification, transmitting the task data to a database of a server for storage;

[0118] Use synchronization strategies to update stored task data in real time;

[0119] The storage environment of the server is detected in real time, and when an abnormality of the storage environment is detected, the task data is stored in a local device.

[0120] In this embodiment, the system takes data integrity as a core requirement at the beginning of its design, and adopts multiple mechanisms to ensure the security and integrity of data. During the data input stage, the system implements a strict verification process, and only data that conforms to the established format and rules will be accepted and stored, thereby effectively avoiding the entry of erroneous data. In addition, the system has a strong error detection and correction capability, which can promptly detect and correct possible deviations in data transmission or processing to ensure the accuracy of the data.

[0121] In order to cope with the risk of data loss or damage, the system regularly backs up data, so that even in the event of data loss or damage, the data can be quickly restored through the backup mechanism. At the database operation level, the system uses transaction management to ensure that all database operations meet atomicity, consistency, isolation and durability (ACID properties), thereby maintaining data integrity.

[0122] In order to improve data reliability, the system stores data copies in multiple locations. This redundant storage strategy effectively reduces the risk of single point failure. In terms of data synchronization, the system combines real-time synchronization and scheduled synchronization strategies to ensure both instant and regular data updates. Real-time synchronization ensures that once data is created or modified on the client, it can be immediately sent to the server, while scheduled synchronization automatically synchronizes data at predetermined time intervals to minimize the impact on client performance.

[0123] In the face of abnormal situations such as network fluctuations or plug-in crashes, the system can temporarily store the data locally and push the data to the cloud server after the network connection is restored or the software fault is repaired. This mechanism ensures that data will not be lost even in the event of communication interruptions. The system verifies the task data, and once the data passes the verification, it is transferred to the server's database for storage. At the same time, the system uses synchronization strategies to update the stored task data in real time to ensure the timeliness and accuracy of the data. The system also monitors the storage environment in real time, and once an abnormal situation is found, the task data is stored on the local device to ensure data security.

[0124] Through these comprehensive measures, the system ensures the security and integrity of data during collection, transmission, storage and analysis, while improving the efficiency and quality of data processing. These mechanisms not only improve the reliability of data, but also ensure that business decisions and product optimization can be made based on accurate and complete data, thus providing strong data support for enterprises.

[0125] It can be seen that the above scheme is suitable for scenarios that require a large amount of data collection and analysis, especially in financial fields such as digital marketing, user behavior analysis, and product optimization. First, the system obtains data collection tasks, which specify the type of data and goals to be collected. Then, these data collection tasks are analyzed in detail through the tracking system to determine the best collection methods and strategies. The analysis results will be used to define tracking scenarios, which describe the specific environment and conditions for data collection. According to the analysis results and the defined tracking scenarios, the system will configure the corresponding tracking strategies. These strategies detail under what circumstances data collection is triggered and how to collect data. In order to ensure the effectiveness and compliance of the strategy, the tracking strategy will be transmitted to the approval management module for approval. When the tracking strategy is approved, the system will assign these strategies according to the preset population tag group. This means that different user groups or user characteristics will correspond to different data collection strategies to ensure the relevance and accuracy of the data. Subsequently, the system starts collecting task data according to the approved tracking strategy. The data will be collected according to the parameters and conditions defined in the strategy. The collected task data will be stored on the server for further analysis and processing. The present invention realizes the automation and optimization management of data collection tasks, improves the efficiency and quality of data collection, and ensures the compliance and security of data collection.

[0126] It should be understood that the order of execution of the steps in the above embodiment does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0127] In one embodiment, a buried point data collection device is provided, and the buried point data collection device corresponds one-to-one to the buried point data collection method in the above embodiment. Figure 3 As shown, the tracking point data collection device includes a data analysis module 101, a tracking point strategy module 102, a strategy approval and allocation module 103 and a data collection and storage module 104. The detailed description of each functional module is as follows:

[0128] The data analysis module 101 is used to obtain a data collection task, analyze the data collection task through a tracking system, and obtain an analysis result;

[0129] A tracking point strategy module 102 is used to define a tracking point scenario based on the analysis result, and configure a tracking point strategy according to the analysis result and the tracking point scenario;

[0130] The strategy approval and allocation module 103 is used to transmit the tracking strategy to the approval management module for approval. When the tracking strategy is approved, the tracking strategy is allocated according to the preset population tag group;

[0131] The data collection and storage module 104 is used to collect task data according to the tracking strategy and store the collected task data in the server.

[0132] In one embodiment, the tracking strategy module 102 includes:

[0133] A tracking unit, used for identifying task information according to the analysis result, and tracking according to the task information;

[0134] The tracking point scene unit is used to define the tracking point name for each tracking point and configure the system to which the tracking point belongs.

[0135] In one embodiment, the tracking strategy module 102 includes:

[0136] A collection rule unit, used to set data collection rules according to the task information and the embedding scenario;

[0137] A task data unit, used to determine the task data to be collected according to the analysis result;

[0138] The trigger condition unit is used to set the trigger condition for each tracking point.

[0139] In one embodiment, the policy approval and allocation module 103 includes:

[0140] A tracking point strategy transmission unit, used to transmit the tracking point strategy to the approval management module for approval;

[0141] The tracking point strategy approval unit is used for the approval management module to review whether the tracking point strategy is passed;

[0142] An approval failure unit, used for failing to pass the review of the tracking strategy and rejecting the tracking strategy when the approval opinions of the approval chains of the approval management module are inconsistent or disagree.

[0143] The approval unit is used to approve the tracking strategy when the approval opinions of each approval chain of the approval management module are in agreement, and allocate the tracking strategy to the corresponding user terminal according to the preset population tag group.

[0144] In one embodiment, the data collection and storage module 104 includes:

[0145] A desensitized data unit, used for desensitizing the task data to obtain desensitized data;

[0146] An encryption data unit, used to encrypt the desensitized data to obtain encrypted data;

[0147] The data storage unit is used to transmit the encrypted data to a database of the server for storage.

[0148] In one embodiment, the data collection and storage module 104 further includes:

[0149] A redundancy processing unit, used for performing full-field redundancy processing on the task data stored in the server;

[0150] A data analysis unit is used to analyze the task data after full-field redundancy processing and extract key information from the task data;

[0151] An analysis report unit is used to generate an analysis report based on the key information.

[0152] In one embodiment, the data collection and storage module 104 further includes:

[0153] A data verification unit, used to perform data verification on the task data, and when the task data passes the data verification, transmit the task data to a database of a server for storage;

[0154] A data updating unit, used to update the stored task data in real time using a synchronization strategy;

[0155] The storage environment detection unit is used to detect the storage environment of the server in real time, and when the storage environment is detected to be abnormal, the task data is stored in a local device.

[0156] The present invention provides a data collection device for tracking data. First, the system obtains data collection tasks, which specify the data types and targets to be collected. Then, the tracking system analyzes these data collection tasks in detail to determine the best collection method and strategy. The analysis results will be used to define tracking scenarios, which describe the specific environment and conditions for data collection. According to the analysis results and the defined tracking scenarios, the system will configure corresponding tracking strategies. These strategies detail the circumstances under which data collection is triggered and how to collect data. In order to ensure the effectiveness and compliance of the strategy, the tracking strategy will be transmitted to the approval management module for approval. When the tracking strategy is approved, the system will assign these strategies according to the preset population tag group. Subsequently, the system starts to collect task data according to the approved tracking strategy. The data will be collected according to the parameters and conditions defined in the strategy. The collected task data will be stored on the server for further analysis and processing. The present invention realizes the automation and optimization management of data collection tasks, improves the efficiency and quality of data collection, and ensures the compliance and security of data collection.

[0157] For the specific limitations of the buried data collection device, please refer to the limitations of the intelligent question and answer method above, which will not be repeated here. Each module in the above buried data collection device can be implemented in whole or in part through software, hardware and a combination thereof. The above modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0158] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 4 As shown. The computer device includes a processor, a memory, a network interface and a database connected via a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile and / or volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external client through a network connection. When the computer program is executed by the processor, it realizes the functions or steps on the service side of a method for collecting buried data.

[0159] In one embodiment, a computer device is provided. The computer device may be a client, and its internal structure diagram may be as follows: Figure 4 As shown. The computer device includes a processor, a memory, a network interface, a display screen and an input device connected via a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external server via a network connection. When the computer program is executed by the processor, it implements the functions or steps of the client side of a method for collecting buried data

[0160] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the following steps when executing the computer program:

[0161] Obtaining data collection tasks, analyzing the data collection tasks through a tracking system, and obtaining analysis results;

[0162] Defining a tracking scenario based on the analysis results, and configuring a tracking strategy according to the analysis results and the tracking scenario;

[0163] The embedding strategy is transmitted to the approval management module for approval. When the embedding strategy is approved, the embedding strategy is allocated according to the preset population tag group;

[0164] The task data is collected according to the tracking strategy, and the collected task data is stored in the server.

[0165] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

[0166] Obtaining data collection tasks, analyzing the data collection tasks through a tracking system, and obtaining analysis results;

[0167] Defining a tracking scenario based on the analysis results, and configuring a tracking strategy according to the analysis results and the tracking scenario;

[0168] The embedding strategy is transmitted to the approval management module for approval. When the embedding strategy is approved, the embedding strategy is allocated according to the preset population tag group;

[0169] The task data is collected according to the tracking strategy, and the collected task data is stored in the server.

[0170] It should be noted that the above functions or steps that can be implemented by the computer-readable storage medium or computer device can refer to the relevant descriptions on the server side and the client side in the aforementioned method embodiment. To avoid repetition, they will not be described one by one here.

[0171] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed 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 can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an 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), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0172] 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 distributed and completed by 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.

[0173] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, a magnetic disk, or an optical disk), and includes a number of instructions for enabling a terminal device (which can be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to execute the methods described in each embodiment of the present invention.

[0174] It should be noted that if software tools or components other than those of the Company appear in the embodiments of the present application, they are only used for illustration and do not represent actual use. The above-described embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the above-mentioned embodiments, a person of ordinary skill in the art should understand that the technical solutions described in the above-mentioned embodiments can still be modified, or some of the technical features thereof can be replaced by equivalents; and these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A method for collecting buried data, characterized in that: include: Obtaining data collection tasks, analyzing the data collection tasks through a tracking system, and obtaining analysis results; Defining a tracking scenario based on the analysis results, and configuring a tracking strategy according to the analysis results and the tracking scenario; The embedding strategy is transmitted to the approval management module for approval. When the embedding strategy is approved, the embedding strategy is allocated according to the preset population tag group; The task data is collected according to the tracking strategy, and the collected task data is stored in the server.

2. The method for collecting buried data according to claim 1, characterized in that: Defining a tracking scenario according to the analysis result includes: Identify task information according to the analysis result, and perform tracking according to the task information; Define a tracking point name for each tracking point and configure the system to which the tracking point belongs.

3. The method for collecting buried data according to claim 2, characterized in that: The configuring the tracking strategy according to the analysis result and the tracking scenario includes: Setting data collection rules according to the task information and the tracking scenario; Determining the task data to be collected according to the analysis results; Set trigger conditions for each tracking point.

4. The method for collecting buried data according to claim 1, characterized in that: The step of transmitting the tracking strategy to the approval management module for approval, and when the tracking strategy is approved, allocating the tracking strategy according to the preset population tag group includes: Transmitting the tracking strategy to the approval management module for approval; When the approval opinions of the approval chains of the approval management module are inconsistent or disagree, the tracking strategy fails to pass the review and is rejected; When the approval opinions of each approval chain of the approval management module are in agreement, the tracking strategy is approved, and the tracking strategy is allocated to the corresponding user terminal according to the preset population tag group.

5. The method for collecting buried data according to claim 1, characterized in that: The collecting of task data according to the tracking strategy and storing the collected task data on the server include: Performing desensitization processing on the task data to obtain desensitized data; Encrypting the desensitized data to obtain encrypted data; The encrypted data is transmitted to the database of the server for storage.

6. The method for collecting buried data according to claim 1, characterized in that: After collecting the task data according to the tracking strategy and storing the collected task data in the server, the method further includes: Perform full-field redundancy processing on the task data stored in the server; Analyze the task data after all-field redundancy processing and extract key information from the task data; Generate an analysis report based on the key information.

7. The method for collecting buried data according to claim 1, characterized in that: The step of transmitting the encrypted task data to a database of a server for storage also includes: Performing data verification on the task data, and when the task data passes the data verification, transmitting the task data to a database of a server for storage; Use synchronization strategies to update stored task data in real time; The storage environment of the server is detected in real time, and when an abnormality of the storage environment is detected, the task data is stored in a local device.

8. A buried point data collection device, characterized in that: include: The data analysis module is used to obtain data collection tasks, analyze the data collection tasks through the tracking system, and obtain analysis results; A tracking point strategy module, used to define tracking point scenarios based on the analysis results, and configure tracking point strategies according to the analysis results and tracking point scenarios; A strategy approval and allocation module is used to transmit the tracking strategy to the approval management module for approval. When the tracking strategy is approved, the tracking strategy is allocated according to the preset population tag group; The data collection and storage module is used to collect task data according to the tracking point strategy and store the collected task data in the server.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the buried point data collection method as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the buried point data collection method as described in any one of claims 1 to 7 are implemented.

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