Data point burying method and device, electronic equipment and storage medium

Abstract

The invention relates to the technical field of data acquisition and analysis, and discloses a data burying point method and device, electronic equipment and a storage medium, the method comprises the following steps: selecting a preset trigger mode based on a business scene, and constructing a multi-mode burying point entrance based on the preset trigger mode; generating a rule instance adaptive to the service scene based on the multi-modal burying point entry and a preset rule type; and matching a corresponding point burying method from a preset rule-method mapping table based on the rule instance and the business scene acquisition demand, and generating a point burying strategy based on the point burying method. According to the method, through a core process of constructing a multi-mode entrance in a configuration manner, generating a rule instance capable of adapting to a service, and dynamically matching and generating a buried point strategy, a traditional hard coding and curing buried point logic is converted into a highly flexible and configurable strategy driving mode.

Description

Technical Field

[0001] This invention relates to the field of data acquisition and analysis technology, specifically to data embedding methods, devices, electronic equipment, and storage media. Background Technology

[0002] As internet businesses become increasingly digitalized, user behavior data has become a core basis for corporate decision-making, but existing technologies have significant shortcomings: 1. Defects of traditional event tracking: It requires hard-coding embedded logic, and adding new event tracking points (such as "product favorites" in e-commerce) requires modifying the code and relaunching, resulting in low adaptation efficiency; extending rules (such as "only count new user behavior") requires refactoring the module, which is prone to introducing risks; there is no standardized validation, which often results in invalid data such as "abnormal timestamp" and "missing user ID", resulting in high maintenance costs.

[0003] 2. Existing event tracking technologies are inadequate: While some technologies support reading event tracking configurations, they lack the ability to trigger multimodal events and combine rules; some data processing technologies focus on retention analysis and visualization, but have not solved the problem of dynamic adaptation of event tracking rules.

[0004] Therefore, there is an urgent need for a data tracking method that can solve the problems of insufficient flexibility in traditional tracking configuration, poor scalability, difficulty in guaranteeing data quality, high maintenance costs, and the single trigger, non-combinable rules, and disconnect between tracking and analysis in existing technologies. Summary of the Invention

[0005] This invention provides a data tracking method, apparatus, electronic device, and storage medium to solve the problems of insufficient flexibility in traditional tracking configuration, poor scalability, difficulty in guaranteeing data quality, high maintenance costs, and the single trigger, non-combinable rules, and disconnect between tracking and analysis in existing technologies.

[0006] In a first aspect, the present invention provides a data embedding method, the method comprising: Select a preset triggering method based on the business scenario, and build a multimodal data entry point based on the preset triggering method; Based on multimodal tracking entry points and preset rule types, generate rule instances that adapt to business scenarios; Based on rule instances and business scenario collection requirements, the corresponding tracking methods are matched from the preset rule-method mapping table, and tracking strategies are generated based on the tracking methods.

[0007] This invention provides a data tracking method that transforms traditional hard-coded, fixed tracking logic into a highly flexible, configurable, and strategy-driven model through a core process: configuring a multimodal entry point → generating rule instances adaptable to business needs → dynamically matching and generating tracking strategies. This method directly solves the problem of triggering a single element through a multimodal entry point; by generating rule instances and dynamically matching tracking methods, it achieves flexible rule combination and on-demand strategy generation, overcoming the shortcomings of insufficient configuration flexibility, poor scalability, and non-combinability of rules; ultimately, this end-to-end strategy-based configuration management system lays a solid foundation for achieving close integration of tracking and analysis and reducing maintenance costs.

[0008] In one optional implementation, a preset triggering method is selected based on the business scenario, and a multimodal data entry point is constructed based on the preset triggering method, including: Receive business scenario selection instructions and trigger method selection instructions; the business scenario selection instructions are used to determine the target scenario from the predefined business scenario library, and the trigger method selection instructions are used to select at least one method from a variety of preset trigger methods; Based on the business scenario selection instruction, the preset scenario-trigger method mapping relationship is invoked to obtain a recommended combination of trigger methods that is compatible with the target scenario; Based on the selected trigger method or the recommended combination of trigger methods, generate corresponding multimodal tracking entry points; the multimodal tracking entry points adopt at least two of the following trigger methods: page load trigger method, user interaction trigger method, timed trigger method, condition met trigger method, API call trigger method, and SDK integration trigger method.

[0009] This invention provides a data tracking method that achieves intelligent recommendation by introducing a preset scenario-triggering method mapping relationship and combining it with user self-selection to construct a multimodal tracking entry point that integrates at least two triggering methods. This significantly improves the flexibility and automation of tracking configuration, and can intelligently adapt to the complex collection needs of different business scenarios. It effectively overcomes the problems of single triggering method and low configuration efficiency in traditional tracking, laying a solid foundation for subsequent accurate and efficient data collection.

[0010] In one optional implementation, based on the multimodal data entry point and preset rule types, rule instances adapted to the business scenario are generated, including: Select the target rule type and the rule parameters corresponding to the target rule type from the preset rule types; Based on the target rule type and its corresponding rule parameters, query the preset rule type-class mapping table to obtain the rule implementation class corresponding to the target rule type; The rule implementation class is instantiated using reflection, and rule parameters are injected into the rule implementation class to generate the initial rule object; Verify the validity of the rule parameters in the initial rule object, and generate rule instances adapted to the business scenario based on the verified initial rule object.

[0011] This invention provides a data tracking method that completely decouples rule configuration from hard-coding through a rule type-class mapping table and a reflection mechanism, enabling dynamic and automated generation of rule instances. This not only significantly improves the efficiency and flexibility of rule configuration, making business scenario adaptation more accurate and convenient, but also ensures the reliability of rules and the accuracy of data collection from the source through strict parameter validity verification, effectively solving the core pain points of poor scalability and high maintenance costs of traditional tracking methods.

[0012] In one optional implementation, based on rule instances and business scenario collection requirements, the corresponding tracking method is matched from a preset rule-method mapping table, and a tracking strategy is generated based on the tracking method, including: Based on rule instances and business scenario collection requirements, candidate tracking methods are matched from the preset rule-method mapping table; the candidate tracking methods include at least one of immediate tracking, batch tracking, delayed tracking, conditional tracking, heartbeat tracking, page dwell tracking, event-driven tracking, and custom tracking. Based on the data collection requirements of the business scenario, the final target data collection method is determined from the candidate data collection methods. Instantiate the target tracking method and associate it with the corresponding rule instance to generate a tracking strategy.

[0013] This invention provides a data tracking method that achieves intelligent matching between rules and tracking methods through a preset rule-method mapping table. It also selects the optimal target tracking method based on specific business needs, and finally generates a complete tracking strategy by associating instantiation with rule instances. This achieves efficient decoupling between data collection logic and business rules, significantly improves the automation and accuracy of tracking strategy generation, and effectively solves the problems of single data collection methods, rigid strategies, and disconnect from business scenarios in traditional tracking. It provides core support for flexible and accurate data collection.

[0014] In one optional implementation, based on rule instances and business scenario collection requirements, the corresponding tracking method is matched from a preset rule-method mapping table, and the method further includes: It accepts the new tracking method type, the new tracking method implementation class, and the basic tracking method class; the basic tracking method class includes immediate tracking, batch tracking, delayed tracking, conditional tracking, heartbeat tracking, page dwell tracking, event-driven tracking, and custom tracking; Verify whether the implementation class of the new tracking method inherits from the base tracking method class; If the new method type is inherited from the base tracking method class and does not exist in the preset rule-method mapping table, the new mapping relationship will be added to the preset rule-method mapping table.

[0015] This invention provides a data tracking method that, through a clearly defined inheritance specification and registration process, allows developers to seamlessly integrate custom tracking methods without modifying the core system logic. This endows the system with strong scalability and flexibility, enabling rapid response to future changing business data collection needs. Simultaneously, strict inheritance relationship verification ensures that newly added methods maintain consistency with the system architecture in behavior, fundamentally solving the technical bottleneck of traditional tracking solutions' poor scalability in adapting to new scenarios.

[0016] In one optional implementation, the data tracking method further includes: Execute the event tracking strategy to obtain valid event tracking data; Perform multi-dimensional analysis and visualization processing on effective data points to generate business analysis reports and visualization charts.

[0017] This invention provides a data tracking method that, through a clearly defined inheritance specification and registration process, allows developers to seamlessly integrate their custom tracking methods into the system. This greatly enhances the system's scalability and flexibility, enabling it to quickly respond to future changing business data collection needs. At the same time, strict inheritance relationship verification ensures the consistency of the new method's behavior, maintaining the stability of the system architecture. This fundamentally solves the technical bottlenecks of traditional tracking solutions, such as poor scalability and difficulty in adapting to new scenarios.

[0018] In one optional implementation, a tracking strategy is executed to obtain valid tracking data, including: When the triggering condition of a rule instance is met, the associated tracking method in the tracking strategy is executed to collect raw tracking data. The raw data points are encapsulated and standardized to generate data to be verified. A seven-dimensional data verification mechanism is used to verify the quality of the data to be verified, thereby obtaining valid embedded data.

[0019] This invention provides a data tracking method that automatically triggers data collection based on response rules and performs standardized encapsulation and seven-dimensional verification on the raw data. This constructs a closed-loop process from data collection to quality inspection, effectively solving the pain points of chaotic data formats and inconsistent quality in traditional data tracking. It ensures the standardization and credibility of data from the source, providing a high-quality data foundation for subsequent data analysis and business decisions, while significantly reducing maintenance and cleaning costs caused by data errors.

[0020] Secondly, the present invention provides a data embedding device, the device comprising: The multimodal event tracking entry construction module is used to select a preset triggering method based on the business scenario and construct a multimodal event tracking entry based on the preset triggering method; The rule instance generation module is used to generate rule instances that adapt to business scenarios based on multimodal tracking entry points and preset rule types. The event tracking strategy generation module is used to match the corresponding event tracking method from the preset rule-method mapping table based on the rule instance and business scenario collection requirements, and generate the event tracking strategy based on the event tracking method.

[0021] Thirdly, the present invention provides an electronic device, comprising: a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to perform the data embedding method described in the first aspect or any corresponding embodiment thereof.

[0022] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to execute the data embedding method described in the first aspect or any corresponding embodiment thereof.

[0023] Fifthly, the present invention provides a computer program product, including computer instructions, which are used to cause a computer to execute the data embedding method described in the first aspect or any corresponding embodiment thereof. Attached Figure Description

[0024] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0025] Figure 1 This is a schematic diagram of an application scenario according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the first type of data embedding method according to an embodiment of the present invention; Figure 3 This is a schematic diagram of a second process for a data embedding method according to an embodiment of the present invention; Figure 4 This is a schematic diagram of the third process of the data embedding method according to an embodiment of the present invention; Figure 5 This is a schematic diagram of the fourth process of the data embedding method according to an embodiment of the present invention; Figure 6 This is a schematic diagram of the architecture of the multimodal embedded point entry according to an embodiment of the present invention; Figure 7 This is a schematic diagram illustrating the implementation process of the preset embedding point method according to an embodiment of the present invention; Figure 8 This is a schematic diagram illustrating the implementation process of the seven-dimensional data verification mechanism according to an embodiment of the present invention; Figure 9 This is a structural block diagram of a data embedding device according to an embodiment of the present invention; Figure 10 This is a schematic diagram of the hardware structure of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0026] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0027] It is understood that before using the technical solutions disclosed in the various embodiments of the present invention, users should be informed of the types, scope of use, and usage scenarios of the personal information involved in the present invention and their authorization should be obtained in accordance with relevant laws and regulations through appropriate means.

[0028] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0029] As an optional application scenario of this invention, such as Figure 1 As shown, application 101 is installed in terminal device 110, and user 130 can interact with application 101 through terminal device 110 and / or access device of terminal device 110.

[0030] For example, application 101 can be any application that provides question-and-answer related services. For instance, application 101 could be a question-and-answer interactive application, such as a text-to-text application, an image-to-text application, etc. Figure 1In the application scenario shown, if application 101 is active, the terminal device 110 can display the interface 102 of application 101. The interface 102 may include various pages that application 101 can provide, such as interactive pages, settings pages, query pages, etc.

[0031] In some embodiments, terminal device 110 is communicatively connected to server 120 to provide services to application 101. Terminal device 110 may be a mobile terminal, fixed terminal, or portable terminal, etc., including but not limited to mobile phones, desktop computers, laptop computers, multimedia tablets, e-book devices, gaming devices, or any combination thereof, including accessories and peripherals of these devices or any combination thereof. In some embodiments, terminal device 110 may also support any type of interface, and server 120 may be various types of computing systems or servers capable of providing computing power, including but not limited to mainframes, edge computing nodes, computing devices in cloud environments, etc.

[0032] It should be noted that, Figure 1 This is merely an example of an application scenario and does not limit the scope of protection of this invention.

[0033] The embodiments of the present invention will now be described with reference to the accompanying drawings. It should be understood that the pages shown in the drawings are merely examples, and various page designs are possible in practice. The various graphic elements on the page may have different arrangements and different visual representations; one or more elements may be omitted or replaced, and one or more other elements may also be present, without any limitation in the embodiments of the present invention. Furthermore, the embodiments described below primarily pertain to terminal device 110. It should be understood that the actions described relative to terminal device 110 can be performed by application 101 on terminal device 110, or can be performed by application 101 in conjunction with its server (e.g., server 120).

[0034] Traditional data tracking methods suffer from insufficient configuration flexibility, poor scalability, difficulty in guaranteeing data quality, high maintenance costs, and problems such as single triggering, non-combinable rules, and disconnect between tracking and analysis.

[0035] This invention provides a data tracking method that achieves intelligent data tracking by configuring multimodal entry points, generating rule instances that can adapt to business needs, and dynamically matching and generating tracking strategies through the core process of configurable multimodal entry points, automatic method matching, data monitoring, and result visualization.

[0036] According to an embodiment of the present invention, a data embedding method embodiment is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0037] This embodiment provides a data tracking method that can be used in the aforementioned electronic or terminal devices. The electronic or terminal device is equipped with a tracking tool, which is a software system or service for automatically collecting user behavior data and related contextual information. Its core value lies in transforming the data collection logic, which originally required developers to manually write code, into a standardized process that can be configured, managed, and executed through a visual interface. Figure 2 This is a flowchart of a data embedding method according to an embodiment of the present invention, such as... Figure 2 As shown, the process includes the following steps: Step S201: Select a preset triggering method based on the business scenario, and construct a multimodal data entry point based on the preset triggering method.

[0038] Specifically, such as Figure 5 As shown, before this step, users can access the configuration system through the client. At this time, users can set up multimodal data collection points according to the company's actual business scenarios. They can also preset some related rule combinations to form multimodal data for some common scenarios.

[0039] A multimodal data collection entry point refers to a unified and configurable trigger gateway that integrates multiple different data collection initiation methods. It allows for the flexible selection, combination, and activation of at least two trigger methods based on specific business scenarios to initiate the subsequent data collection process.

[0040] The specific steps are as follows: After the user sets up a multimodal application that fits the business scenario, the interaction triggering method used for each modality needs to be configured to build a multimodal event tracking entry point. Taking the video streaming event tracking collection modality as an example, the event tracking entry point can use a combination of page load triggering, user interaction triggering, and timed triggering. This process can support multiple triggering methods.

[0041] Step S202: Based on the multimodal data entry point and the preset rule type, generate rule instances that are adapted to the business scenario.

[0042] Specifically, based on the set multimodal event tracking entry points for the business, rules and event tracking methods that fit the scenario can be selected from eight event tracking types and eight event tracking methods for combination. Taking video streaming event tracking as an example, the rule configuration can select a combination of time interval, dwell time and user behavior rules. The method can be a combination of heartbeat event tracking, user dwell event tracking and event-driven event tracking. Finally, the combination is instantiated to obtain a rule instance adapted to the business scenario.

[0043] Step S203: Based on the rule instance and business scenario collection requirements, match the corresponding tracking method from the preset rule-method mapping table, and generate a tracking strategy based on the tracking method.

[0044] Specifically, the manager is represented in the code by the ConfigManager class, which mainly manages the combination of rules and tracking methods, receives and outputs the corresponding tracking strategies. It is a centralized processing link that can process rules and methods and output data to obtain the corresponding tracking strategies.

[0045] The data tracking method provided in this embodiment transforms the traditional hard-coded, fixed tracking logic into a highly flexible, configurable, and strategy-driven model through a core process of configuring a multimodal entry point → generating rule instances adaptable to business needs → dynamically matching and generating tracking strategies. This method directly solves the problem of triggering a single element through a multimodal entry point; by generating rule instances and dynamically matching tracking methods, it achieves flexible rule combination and on-demand strategy generation, thus overcoming the shortcomings of insufficient configuration flexibility, poor scalability, and non-combinability of rules; ultimately, this end-to-end strategy-based configuration management system lays a solid foundation for achieving close integration between tracking and analysis and reducing maintenance costs.

[0046] This embodiment provides a data tracking method, which can be used in the aforementioned electronic or terminal devices, wherein the electronic or terminal devices are equipped with tracking tools. Figure 3 This is a flowchart of a data embedding method according to an embodiment of the present invention, such as... Figure 3 As shown, the process includes the following steps: Step S301: Select a preset triggering method based on the business scenario, and construct a multimodal data entry point based on the preset triggering method.

[0047] Specifically, the architecture diagram of the multimodal embedding entry point is as follows: Figure 6 As shown, step S301 above includes: Step S3011: Receive a business scenario selection instruction and a triggering method selection instruction; the business scenario selection instruction is used to determine the target scenario from the predefined business scenario library, and the triggering method selection instruction is used to select at least one method from a variety of preset triggering methods.

[0048] Step S3012: Based on the business scenario selection instruction, the preset scenario-triggering method mapping relationship is invoked to obtain a recommended combination of triggering methods that is compatible with the target scenario.

[0049] Step S3013: Based on the selected trigger method or the recommended combination of trigger methods, generate the corresponding multimodal tracking entry point; the multimodal tracking entry point adopts at least two of the following trigger methods: page loading trigger method, user interaction trigger method, timed trigger method, condition met trigger method, API call trigger method, and SDK integration trigger method.

[0050] The following is a unified description of steps S3011 to S3013: Specifically, the preset triggering methods include: Page load trigger: The event tracking will automatically start when the application page finishes loading; User interaction trigger: The tracking point is triggered when the user performs a specific interactive operation; Timed trigger: Automatically triggers the data collection points according to a preset time period; Triggering when conditions are met: The tracking point is triggered when preset conditions are met; API (Application Programming Interface) call trigger: Actively calling the tracking interface through an external API interface; SDK (Software Development Kit) Integration Trigger: Basic tracking points are automatically triggered after the SDK initialization is complete.

[0051] Configure the multimodal trigger entry point and operate through the entry configuration module of the event tracking tool.

[0052] Input Data: Select the business scenario instruction, such as watching short videos, customer order entry, etc.; select the trigger method instruction, which can be selected from page load / user interaction / timed / condition met / API call / SDK integration, etc. This input data can be a historical template or a newly added input scenario. For example, if you need to implement event tracking based on the video watching business, then in the input data module, you first need to set the name of the event tracking model: "Video Watching Event Tracking Model", then select the trigger method instruction, which is one of the six trigger methods mentioned above. After selecting, select the rule, referring to the process sequence diagram, or select an existing model to set it directly.

[0053] Processing steps: The entry configuration module matches a combination of triggering methods based on the requirements of the selected business scenario. Taking short video browsing as an example, it matches triggering methods such as page loading, user interaction, and timed events. No code modification is required; different businesses can be directly adapted through configuration.

[0054] Output: Trigger entry configuration scheme for the corresponding business scenario.

[0055] Step S302: Based on the multimodal data entry point and the preset rule type, generate rule instances that are adapted to the business scenario.

[0056] Specifically, this step generates rule instances through the rule factory operation of the event tracking tool, supporting custom rule type extensions. Step S302 includes: Step S3021: Select the target rule type and the rule parameters corresponding to the target rule type from the preset rule types.

[0057] Specifically, this step is the input data step, where the input data includes: trigger entry configuration scheme, preset rule type, and rule parameters.

[0058] The preset rule types include: Time interval rule: Trigger repeatedly at set time intervals; Time range rule: Triggered within a specified time range; The specified time range refers to the time period within which data needs to be collected, such as a daily time point or time interval, such as 24:00 every day, or only a certain time period needs to be collected, such as the 5th to the 20th of each month, or each year. This specified time range is mainly determined by the tracking time required in the real scenario.

[0059] Counting threshold rule: Triggered when the count of a specific event reaches a set threshold; this threshold rule means that under the current triggering method, after the triggering method is set, the related derivative events will only be reported when they reach a set number of times. For example, if you choose API call triggering, the rule is to select a specific event that reaches a set threshold. At this time, the specific event can be the number of API calls or the number of responses returned by the API interface. The threshold is set to 5. When the interface reaches 5 calls, the data will be reported.

[0060] Scroll depth rule: Triggered when the page scroll depth reaches a set percentage; Dwell time rule: Triggered when a user stays on the target page for a set duration; this duration is determined in the same way as the specified time range above, that is, it is determined according to the specific scenario. Dwell time is generally used in some video streaming scenarios that require data tracking, or in some shopping businesses. For example, if a user stays on a video for 10 minutes, I will report it once, or if a user's mouse hovers over a product for 3 minutes, I will record and report it once. Generally, this time range is capped at hours for data collection.

[0061] Element visibility rules: triggered when a specified page element enters the user's visible area; User behavior rules: triggered based on specific user behavior patterns; Custom rules: Define trigger conditions based on business needs.

[0062] Rule parameters, such as "timed for 5 seconds" or "stays for 15 seconds" for short video browsing scenarios.

[0063] Step S3022: Based on the target rule type and the corresponding rule parameters, query the preset rule type-class mapping table to obtain the rule implementation class corresponding to the target rule type.

[0064] Specifically, this step is the "addressing" phase of rule instance generation. At its core is a pre-defined "rule type-class mapping table," which maintains the association between "rule types" (such as "stay duration rule") and specific backend "rule implementation classes" (such as ResidenceTimeRule.class). The system receives the "target rule type" as input and, by querying this mapping table, can quickly locate the specific Java class that implements the rule logic, much like looking up a dictionary.

[0065] Step S3023: Instantiate the rule implementation class using reflection and inject the rule parameters into the rule implementation class to generate an initial rule object. Specifically, this step is the "creation" phase of rule instance generation. After obtaining the "rule implementation class," the system uses the reflection mechanism of the programming language to dynamically create an object instance of that class. The reflection mechanism allows the program to inspect class information and construct objects at runtime without hard-coding at compile time. After the object is created, the system injects the "rule parameters" set in the configuration phase (such as a dwell time of "15 seconds") into the corresponding attributes of the object, thereby generating an "initial rule object" containing specific business logic and parameters.

[0066] Step S3024: Verify the validity of the rule parameters in the initial rule object, and generate a rule instance adapted to the business scenario based on the verified initial rule object.

[0067] Specifically, this step is the "quality inspection" and "delivery" phase of rule instance generation. After the initial rule object is generated, the system calls its built-in validation logic (such as the validateParams() method) to check the validity of the injected parameters (e.g., verifying whether the "time interval" is greater than 0, whether the "duration" is a valid number, etc.). Only rule objects that pass all validations are ultimately recognized as a valid, stable, and usable "rule instance" and output to subsequent processes (such as method factories). Those that fail validation will be marked as errors and logged.

[0068] In summary, steps S3022 to S3024 are the processing steps of the rule factory. The createRule method of the rule factory is called to obtain the corresponding rule implementation class from the preset rule type-class mapping table, inject the rule parameters and generate an instance. If a new business-specific rule needs to be added, the registerRule method is called to verify that the new rule class inherits from the base rule class and then registers it to the preset rule type-class mapping table.

[0069] Furthermore, the specific implementations of the `createRule` and `registerRule` methods in the rule factory are as follows: Input data (createRule): rule type, rule parameters, scenario ID; Processing steps (createRule): ① Query the rule type-class mapping table to obtain the corresponding rule class; ② Instantiate the rule class through reflection, inject parameters, and call the validateParams() method to verify its validity (e.g., time interval > 0); ③ Associate the scene ID to generate an available rule instance; Output (createRule): Rule instance, parameter validity verification results; If it is a new rule type, the following steps need to be performed: Input data (registerRule): New rule type, new rule class, basic rule class (i.e., the preset rule type recorded above); Processing steps (registerRule): ① Verify whether the new rule class inherits from the base rule class; ② Determine if the new rule type does not exist in the rule type-class mapping table; ③ Add the mapping relationship between the new rule type and the class to the rule type-class mapping table; Output (registerRule): Rule type - class mapping table update success receipt / failure reason log (e.g., not inheriting the base rule class).

[0070] Step S303: Based on the rule instance and business scenario data collection requirements, match the corresponding tracking method from the preset rule-method mapping table, and generate a tracking strategy based on the tracking method. For details, please refer to [link to relevant documentation]. Figure 2 Step S203 of the illustrated embodiment will not be described again here.

[0071] The data tracking method provided in this embodiment completely decouples rule configuration from hard coding through a rule type-class mapping table and a reflection mechanism, enabling dynamic and automated generation of rule instances. This not only significantly improves the efficiency and flexibility of rule configuration, making business scenario adaptation more accurate and convenient, but also ensures the reliability of rules and the accuracy of data collection from the source through strict parameter validity verification, effectively solving the core pain points of poor scalability and high maintenance costs of traditional tracking methods.

[0072] This embodiment provides a data tracking method, which can be used in the aforementioned electronic or terminal devices, wherein the electronic or terminal devices are equipped with tracking tools. Figure 4 This is a flowchart of a data embedding method according to an embodiment of the present invention, such as... Figure 4 As shown, the process includes the following steps: Step S401: Select a preset triggering method based on the business scenario, and construct a multimodal data entry point based on the preset triggering method. For details, please refer to [link to relevant documentation]. Figure 3 Step S301 of the illustrated embodiment will not be described again here.

[0073] Step S402: Based on the multimodal data entry point and preset rule types, generate rule instances adapted to the business scenario. For details, please refer to [link to relevant documentation]. Figure 3 Step S302 of the illustrated embodiment will not be described again here.

[0074] Step S403: Based on the rule instance and business scenario collection requirements, match the corresponding tracking method from the preset rule-method mapping table, and generate a tracking strategy based on the tracking method.

[0075] Specifically, this step is performed using the method factory of the event tracking tool, which supports custom event tracking method extensions. The implementation flow of the preset event tracking methods is as follows: Figure 7 As shown, step S403 above includes: Step S4031: Based on the rule instance and business scenario collection requirements, candidate tracking methods are matched from the preset rule-method mapping table; the candidate tracking methods include at least one of immediate tracking, batch tracking, delayed tracking, conditional tracking, heartbeat tracking, page dwell tracking, event-driven tracking, and custom tracking.

[0076] Specifically, this step is the initial screening stage of strategy generation. Its core is a "rule-method mapping table," which pre-stores the logical associations between various rules and possible tracking methods (for example, "time interval rule" is usually associated with "heartbeat tracking" or "batch tracking"). Received rule instances and business scenario collection requirements as input, the system first automatically retrieves all technically feasible tracking methods from this mapping table based on the type of rule instance, forming a candidate method set.

[0077] Custom tracking method extensions include: It receives the new tracking method type, the new tracking method implementation class, and the basic tracking method class. The basic tracking method class includes immediate tracking, batch tracking, delayed tracking, conditional tracking, heartbeat tracking, page dwell tracking, event-driven tracking, and custom tracking. It verifies whether the new tracking method implementation class inherits from the basic tracking method class. If it inherits from the basic tracking method class and the new tracking method type does not exist in the preset rule-method mapping table, it adds the new mapping relationship to the preset rule-method mapping table.

[0078] For example, an enterprise application needs to implement a new feature: intelligently adjusting the clarity and frequency of data reporting based on the user's network status (such as switching from Wi-Fi to mobile data) while the user is watching a video. The existing eight data tracking methods cannot directly meet this data collection requirement of dynamic adjustment based on real-time network conditions. Therefore, the development team needs to create a completely new "adaptive network data tracking method."

[0079] The specific execution process is as follows: Enter registration information: Developers submit an application to the registration module, providing three key types of information: First, define the type name of the new method as "Adaptive Network Tracking"; second, provide the program class file that has been written and implements the method; and finally, specify the "basic tracking method class" as the standard template.

[0080] Inheritance verification: Upon receiving a registration application, the core verification is first performed: This checks whether the "Adaptive Network Tracking" program class provided by the developer strictly adheres to the specifications defined in the "Basic Tracking Method Class." This process aims to confirm that the new method possesses the basic capabilities that all tracking methods must have (such as executing data transmission and verifying parameters), ensuring seamless integration into the existing management and execution framework.

[0081] Mapping table update: After passing the above compliance verification, the system will further check for duplicates in its core configuration library, the "Rule-Method Mapping Table". If it is confirmed that the new type "Adaptive Network Tracking" has not been used, the system will officially record this new correspondence into the library. For example, it will be associated with the existing "Condition Satisfaction Rule", which means that any data collection task triggered by conditions can choose to use this new intelligent method.

[0082] Practical application example: After successful registration, when a user switches networks during online video playback: The pre-defined "condition-satisfied rule instance" will immediately detect this change and be activated.

[0083] Automatically query the updated "rule-method mapping table" and match the newly registered "adaptive network tracking method" for the rule.

[0084] An instance of the method is then created and its internal logic is executed: the frequency of video resolution data acquisition is automatically reduced, and the size of the data packet reported at one time is reduced, thereby ensuring the success rate of data reporting and user experience in weak network environments.

[0085] Step S4032: Based on the business scenario data collection requirements, determine the final target data collection method from the candidate data collection methods.

[0086] Specifically, this step is the decision optimization stage of strategy generation. After obtaining the set of candidate methods, the system needs to perform a secondary screening based on the specific business scenario's data collection requirements to determine the optimal solution. Here, "data collection requirements" refers to business requirements regarding data real-time performance, network load, and data integrity (for example, if the requirement is "real-time monitoring of user online status," then "heartbeat tracking" is preferred; if the requirement is "high-frequency but tolerable latency click statistics," then "batch tracking" is preferred). Based on these requirements, the most suitable target tracking method is intelligently selected from the candidate methods or manually specified by the configuration personnel.

[0087] Step S4033: Instantiate the target tracking method and associate it with the corresponding rule instance to generate a tracking strategy.

[0088] Specifically, this step is the final assembly and output stage of strategy generation. Once the target tracking method is determined, it is instantiated through the "method factory" to create an executable method object. Subsequently, the system associates and binds this method object with the input rule instance to form a complete "tracking strategy" object. This strategy object encapsulates the complete logic of "under what conditions (rule instance)" to trigger "what data collection and sending actions (method instance)".

[0089] In summary, steps S4031 to S4033 are described uniformly as follows: Input data: Rule instances from step S402 above, business data collection requirements (such as "real-time recording of viewing status" and "batch sending of high-frequency data" when watching short videos); Processing steps: ① Call the createMethod method of the method factory to match the collection method (such as time interval rule → heartbeat collection, dwell time rule → page dwell collection) from the preset rule-method mapping table, inject parameters to generate an instance; ② If a new collection method needs to be added, call the registerMethod method to verify that the new method class inherits from the basic tracking method class, and then register it to the preset rule-method mapping table. Output results: at least one collection instance that adapts to the rule, i.e., the tracking strategy (such as the heartbeat collection instance for watching short videos, batch collection instance), and a new method registration success receipt (if any).

[0090] For example, taking the batch event tracking method as an example, the specific implementation of the batch event tracking method is as follows: Input data: rule instance trigger signal, raw data points, batch parameters (batch size M, refresh interval T), maximum number of retries K; Processing steps: ① Store the raw data to be sent in the data queue; ② Batch timer triggers data transmission at refresh interval T. If the amount of data in the queue is ≥ M, transmission is triggered immediately; ③ If transmission fails, the retry mechanism determines that the number of retries is < K, and re-enqueues the data until the number of retries reaches the target or transmission is successful. Output results: Batch send request, remaining data in the queue, and final receipt for success / failure.

[0091] Step S404: Execute the event tracking strategy to obtain valid event tracking data; perform multi-dimensional analysis and visualization processing on the valid event tracking data to generate business analysis reports and visualization charts.

[0092] Specifically, step S404 includes: Step S4041: In response to the triggering condition of the rule instance being met, the associated tracking method in the tracking strategy is executed to collect raw tracking data.

[0093] Specifically, this step is the "triggering and execution" phase of the event tracking strategy. The system continuously monitors the event flow generated by the multimodal entry point. When an event causes a logical condition defined in a "rule instance" to become true (e.g., the dwell time reaches a preset 15 seconds, or the page scroll depth exceeds 50%), the rule instance is activated. The system then locates the "event tracking method instance" bound to this rule instance (such as "page dwell tracking" or "batch tracking") and executes its core data collection logic. This process collects the initial, unprocessed "raw event tracking data," such as element identifiers, time points, and behavior types.

[0094] Step S4042: The original embedded data is encapsulated and standardized to generate data to be verified.

[0095] Specifically, this step is the "cleaning and formatting" stage of data processing. The collected raw data is often fragmented and heterogeneous (for example, data from different browsers or operating systems may have different formats). The system will call a data processor to encapsulate and standardize this raw data, mainly including: completing missing fields (such as session ID, data source), unifying data format (such as converting all timestamps to uniform millisecond-level timestamps), standardizing field naming (such as unifying "uid" to "userId"), and assembling it into a data object with a uniform structure.

[0096] Furthermore, steps S4041 and S4042 call the BurialData class for processing through the "configData Central Processor" of the data tracking tool; Input data: Raw data collected (such as viewing time and number of swipes when watching short videos), basic device information; Processing steps: The configData central processor receives the collected data, calls the BurialData class to perform data initialization and device information supplementation, and converts it into a standard format; Output: The data to be verified, encapsulated in the BurialData class.

[0097] It should be noted that the specific modules and implementations of the BurialData class are as follows: Includes the following modules: data initialization module, seven-dimensional verification module, device information acquisition module, and JSON conversion module; among which: The specific implementation of the data initialization module includes: Input data (data initialization module): raw data points, global default configuration; Processing steps (data initialization module): fill in missing fields in the original data (such as "data source default = Web"), and unify field formats (such as converting timestamps to integers). Output (Data Initialization Module): Data after initialization; The specific implementation of the device information acquisition module includes: Input data (Device information acquisition module): Device type (Web / Mobile device); Processing steps (device information acquisition module): Call the browser / system API to obtain information such as "userAgent", "platform", and "device model", and encapsulate it into a device information object; Output (Device Information Acquisition Module): Device Information Object; The JSON conversion module is implemented as follows: Input data (JSON conversion module): Valid tracking data that has passed verification; Processing steps (JSON conversion module): Convert valid data into a valid JSON string, handle special character escaping, and optionally use Gzip compression; Output (JSON conversion module): Valid JSON data in JSON format, and compressed JSON data.

[0098] Step S4043: Use a seven-dimensional data verification mechanism to perform quality verification on the data to be verified, and obtain valid embedded data.

[0099] Specifically, the implementation process of the seven-dimensional data verification mechanism is as follows: Figure 8 As shown, this step is the "audit and filtering" phase of data quality. A rigorous verification process consisting of seven dimensions is initiated on the standardized "data to be verified." The seven dimensions include: Timestamp verification: Verify that the timestamp is a valid number, greater than 0, and does not exceed the current system time; User ID validation: Verify that the user ID is not empty and has a valid format; Page path validation: Verify that the page path is not empty and has the correct format; Event type validation: Verifies that the event type belongs to the predefined range; Device information verification: Verify that the device information contains the necessary fields and is not empty; Custom data validation: Verify that custom data is in valid JSON format and contains no sensitive fields; Data source verification: Verify that the data source belongs to a predefined trusted source.

[0100] This step is performed using the "Seven-Dimensional Verification Module" of the BurialData class.

[0101] Input data: Data to be validated; Processing steps: Perform validation in sequence for timestamp / user ID / page path / event type / device information / custom data / data source, and filter invalid data; Output: Valid tracking data that passed verification, and invalid data logs with reasons for invalidity. In other words, only data that passes verification across all dimensions proceeds to the next stage; data that fails verification is marked as invalid and logged.

[0102] Data import involves storing verified event tracking data into the database, supporting multiple storage methods: Relational databases (such as MySQL and PostgreSQL) store highly structured data that is frequently queried. NoSQL databases (such as MongoDB and Redis): store unstructured / semi-structured data and data that is written frequently; Time-series databases (such as InfluxDB and Prometheus): store data with strong time-series characteristics; Distributed storage systems (such as HDFS and MinIO): store massive amounts of historical data.

[0103] Standardize the data before storing it to ensure data consistency.

[0104] The above data was entered into the database through the "Storage and Analysis Module" of the data tracking tool; Input data: valid event tracking data, business analysis requirements (such as "real-time viewership" and "completion rate in the past 7 days" for short videos); Processing steps: Match storage method according to data type (structured data → relational database, time series data → time series database, massive data → distributed storage), calculate business indicators and generate visualization results; Output results: Data storage success receipt, real-time dashboard, business reports (such as completion rate trend chart).

[0105] During output, the stored valid tracking data is analyzed and visualized in multiple dimensions: Real-time data analysis: Calculates real-time metrics based on a stream processing framework and displays them on a dashboard; Historical data statistics: Perform offline statistics on historical data; User behavior analysis: Constructing user behavior paths and segmenting users; Business metrics calculation: Calculate core business metrics such as click-through rate and conversion rate; Visual report generation: Generate standardized reports using charts, supporting export and sharing.

[0106] The data tracking method provided in this embodiment, through a clearly defined inheritance specification and registration process, allows developers to seamlessly integrate custom tracking methods without modifying the core system logic. This endows the system with strong scalability and flexibility, enabling rapid response to future changing business data collection needs. Simultaneously, strict inheritance relationship verification ensures that newly added methods maintain behavioral consistency with the system architecture, fundamentally solving the technical bottleneck of traditional tracking solutions' poor scalability and inability to adapt to new scenarios. The clearly defined inheritance specification and registration process allow developers to seamlessly integrate their custom tracking methods into the system, greatly enhancing the system's scalability and flexibility, enabling rapid response to future changing business data collection needs. Furthermore, strict inheritance relationship verification ensures the consistency of new methods' behavior, maintaining the stability of the system architecture and fundamentally solving the technical bottleneck of traditional tracking solutions' poor scalability and inability to adapt to new scenarios.

[0107] As one or more specific application embodiments of the present invention, the data embedding method provided by the present invention will be further described in detail as follows: Taking data entry system event tracking as an example, if a company has data entry-related business, such as insurance business involving the entry of user information and insurance plan information, then data tracking design is needed. This means adding a new event tracking business to the event tracking system: insurance data entry event tracking. After setting the event tracking name, the event tracking model is set up, named "Data Entry Event Tracking Model." Then, the trigger method is selected, choosing from six options: page load trigger, user interaction trigger, and conditional trigger. After selecting the trigger method combination, the event tracking rules are chosen. Based on the selected trigger method, a combination of counting threshold and user behavior rules is selected from eight event tracking rule types. After configuring the settings, the system will prompt for the trigger method. The rules consist of conditions, and then you need to set the scenarios derived from those rules. For example, in order entry, page load + count threshold + each time constitutes a complete rule, meaning that event tracking and reporting are required every time the page is accessed. Alternatively, you can set a conditional trigger + user behavior + event name. When the user inputs or operates on an option, if the event name bound to the option reaches a certain threshold (e.g., if the premium calculation event reaches 10,000), event tracking and reporting are performed. These two methods combine triggering methods and event tracking rules. Finally, a specific instance is generated through the manager. When a user integrates this event tracking model into their project, event tracking and reporting are automatically performed according to these rules. The reported data is then validated, and both successful and failed validations generate logs.

[0108] The data tracking method provided in this embodiment has the following significant advantages compared to traditional tracking methods: 1. Significantly improved configuration flexibility: Supports dynamic adjustment of tracking logic without modifying code, significantly improving configuration efficiency; 2. Significantly enhanced scalability: The adoption of the "rule factory pattern" and "strategy pattern" design greatly reduces the cost of system expansion; 3. Data quality is reliably guaranteed: The seven-dimensional data verification mechanism can effectively identify and filter invalid data, significantly reducing the proportion of invalid data; 4. Achieve intelligent triggering: Through multimodal entry and rule matching, automatic and precise triggering of data points is achieved, improving the accuracy and timeliness of data collection; 5. Significantly reduced maintenance costs: By managing the tracking strategy through a unified configuration manager, the workload of maintenance is significantly reduced.

[0109] This embodiment also provides a data tracking device for implementing the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0110] This embodiment provides a data embedding device, such as... Figure 9 As shown, it includes: The multimodal event tracking entry construction module 901 is used to select a preset triggering method based on the business scenario and construct a multimodal event tracking entry based on the preset triggering method.

[0111] The rule instance generation module 902 is used to generate rule instances that adapt to business scenarios based on multimodal tracking entry points and preset rule types.

[0112] The event tracking strategy generation module 903 is used to match the corresponding event tracking method from the preset rule-method mapping table based on the rule instance and business scenario collection requirements, and generate an event tracking strategy based on the event tracking method.

[0113] In some optional implementations, the multimodal embedding point construction module 901 includes: The instruction receiving unit is used to receive business scenario selection instructions and trigger method selection instructions; the business scenario selection instructions are used to determine the target scenario from a predefined business scenario library, and the trigger method selection instructions are used to select at least one method from a variety of preset trigger methods.

[0114] The recommended trigger method generation unit is used to select instructions based on business scenarios, call the preset scenario-trigger method mapping relationship, and obtain a combination of recommended trigger methods that are adapted to the target scenario.

[0115] The multimodal event tracking entry generation unit is used to generate corresponding multimodal event tracking entries based on the selected trigger method or the recommended combination of trigger methods. The multimodal event tracking entry adopts at least two of the following trigger methods: page load trigger method, user interaction trigger method, timed trigger method, condition met trigger method, API call trigger method, and SDK integration trigger method.

[0116] In some optional implementations, the rule instance generation module 902 includes: The rule type and rule parameter selection unit is used to select the target rule type and the rule parameters corresponding to the target rule type from the preset rule types.

[0117] The rule implementation class query unit is used to query the preset rule type-class mapping table based on the target rule type and the corresponding rule parameters, and obtain the rule implementation class corresponding to the target rule type.

[0118] The initial rule object generation unit is used to instantiate the rule implementation class through reflection and inject rule parameters into the rule implementation class to generate the initial rule object.

[0119] The rule instance generation unit is used to verify the validity of the rule parameters in the initial rule object and generate rule instances adapted to the business scenario based on the verified initial rule object.

[0120] In some optional implementations, the event tracking strategy generation module 903 includes: The candidate event tracking method matching unit is used to match candidate event tracking methods from a preset rule-method mapping table based on rule instances and business scenario collection requirements. The candidate event tracking methods include at least one of the following: immediate event tracking, batch event tracking, delayed event tracking, conditional event tracking, heartbeat event tracking, page dwell event tracking, event-driven event tracking, and custom event tracking.

[0121] The target event tracking method determination unit is used to determine the final target event tracking method from candidate event tracking methods based on the business scenario collection requirements.

[0122] The event tracking strategy generation unit is used to instantiate the target event tracking method, associate it with the corresponding rule instance, and combine them to generate the event tracking strategy.

[0123] In some optional implementations, the event tracking strategy generation module 903 further includes: The custom method extension unit is used to receive the new tracking method type, the new tracking method implementation class, and the basic tracking method class. The basic tracking method class includes immediate tracking, batch tracking, delayed tracking, conditional tracking, heartbeat tracking, page dwell tracking, event-driven tracking, and custom tracking. It verifies whether the new tracking method implementation class inherits from the basic tracking method class. If it inherits from the basic tracking method class and the new tracking method type does not exist in the preset rule-method mapping table, it adds the new mapping relationship to the preset rule-method mapping table.

[0124] In some optional implementations, the data tracking device further includes: The effective tracking data generation and visualization module is used to execute tracking strategies and obtain effective tracking data; it performs multi-dimensional analysis and visualization processing on the effective tracking data to generate business analysis reports and visualization charts.

[0125] In some optional implementations, the effective data tracking and visualization module includes: The event tracking strategy execution unit is used to execute the event tracking methods associated with the event tracking strategy in response to the fulfillment of the triggering conditions of the rule instance, so as to collect raw event tracking data.

[0126] The encapsulation unit is used to encapsulate and standardize the original embedded data to generate data to be verified.

[0127] The seven-dimensional verification unit is used to perform quality verification on the data to be verified using a seven-dimensional data verification mechanism to obtain valid embedded data.

[0128] The data tracking device provided in this embodiment of the invention can execute the data tracking method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method. Further functional descriptions of the various modules and units are the same as those in the corresponding embodiments described above, and will not be repeated here.

[0129] Figure 10 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention.

[0130] The following is a detailed reference. Figure 10 This diagram illustrates a suitable structural schematic for implementing an electronic device according to embodiments of the present invention. The electronic device may include a processor (e.g., a central processing unit, graphics processor, etc.) 1001, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from memory 1008 into random access memory (RAM) 1003. The RAM 1003 also stores various programs and data required for the operation of the electronic device. The processor 1001, ROM 1002, and RAM 1003 are interconnected via a bus 1004. An input / output (I / O) interface 1005 is also connected to the bus 1004.

[0131] Typically, the following devices can be connected to the I / O interface 1005: input devices 1006 including, for example, a touchscreen, touchpad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; output devices 1007 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; memory devices 1008 including, for example, magnetic tape, hard disk, etc.; and communication devices 1009. Communication device 1009 allows electronic devices to exchange data via wireless or wired communication with other devices. Although Figure 10 Electronic devices with various devices are shown, but it should be understood that it is not required to implement or have all of the devices shown, and more or fewer devices may be implemented or have instead.

[0132] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 1009, or installed from a memory 1008, or installed from a ROM 1002. When the computer program is executed by the processor 1001, it performs the functions defined in the data embedding method of the embodiments of the present invention.

[0133] Figure 10The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention.

[0134] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as recordable on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and subsequently stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code. When the software or computer code is accessed and executed by the computer, processor, or hardware, the data embedding method shown in the above embodiments is implemented.

[0135] A portion of this invention can be applied as a computer program product, such as computer program instructions, which, when executed by a computer, can invoke or provide the methods and / or technical solutions according to the invention through the operation of the computer. Those skilled in the art will understand that the forms in which computer program instructions exist in a computer-readable medium include, but are not limited to, source files, executable files, installation package files, etc. Correspondingly, the ways in which computer program instructions are executed by a computer include, but are not limited to: the computer directly executing the instructions, or the computer compiling the instructions and then executing the corresponding compiled program, or the computer reading and executing the instructions, or the computer reading and installing the instructions and then executing the corresponding installed program. Here, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to a computer.

[0136] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A data tracking method, characterized in that, The method includes: Select a preset triggering method based on the business scenario, and build a multimodal data entry point based on the preset triggering method; Based on the multimodal data entry points and preset rule types, rule instances adapted to business scenarios are generated. Based on the rule instance and business scenario collection requirements, the corresponding tracking method is matched from the preset rule-method mapping table, and a tracking strategy is generated based on the tracking method.

2. The data embedding method according to claim 1, characterized in that, The step of selecting a preset triggering method based on the business scenario and constructing a multimodal data entry point based on the preset triggering method includes: Receive a business scenario selection instruction and a trigger method selection instruction; the business scenario selection instruction is used to determine the target scenario from a predefined business scenario library, and the trigger method selection instruction is used to select at least one method from a variety of preset trigger methods; Based on the business scenario selection instruction, the preset scenario-triggering method mapping relationship is invoked to obtain a recommended combination of triggering methods that is compatible with the target scenario; Based on the selected trigger method or the recommended combination of trigger methods, a corresponding multimodal tracking entry point is generated; the multimodal tracking entry point adopts at least two of the following trigger methods: page loading trigger method, user interaction trigger method, timed trigger method, condition met trigger method, API call trigger method, and SDK integration trigger method.

3. The data embedding method according to claim 1, characterized in that, Based on the multimodal tracking entry point and preset rule types, rule instances adapted to business scenarios are generated, including: Select the target rule type and the rule parameters corresponding to the target rule type from the preset rule types; Based on the target rule type and the corresponding rule parameters, query the preset rule type-class mapping table to obtain the rule implementation class corresponding to the target rule type; The rule implementation class is instantiated using reflection, and the rule parameters are injected into the rule implementation class to generate an initial rule object; Verify the validity of the rule parameters in the initial rule object, and generate rule instances adapted to the business scenario based on the verified initial rule object.

4. The data embedding method according to claim 1, characterized in that, Based on the rule instance and business scenario data collection requirements, the corresponding tracking method is matched from the preset rule-method mapping table, and a tracking strategy is generated based on the tracking method, including: Based on the rule instance and business scenario collection requirements, candidate tracking methods are matched from the preset rule-method mapping table; the candidate tracking methods include at least one of the following: immediate tracking method, batch tracking method, delayed tracking method, conditional tracking method, heartbeat tracking method, page dwell tracking method, event-driven tracking method, and custom tracking method. Based on the data collection requirements of the aforementioned business scenario, the final target data collection method is determined from the candidate data collection methods. Instantiate the target tracking method and associate it with the corresponding rule instance to generate a tracking strategy.

5. The data embedding method according to claim 4, characterized in that, Based on rule instances and business scenario data collection requirements, the system matches the corresponding tracking methods from the preset rule-method mapping table, and also includes: The system receives a new tracking method type, a new tracking method implementation class, and a basic tracking method class. The basic tracking method class includes immediate tracking methods, batch tracking methods, delayed tracking methods, conditional tracking methods, heartbeat tracking methods, page dwell tracking methods, event-driven tracking methods, and custom tracking methods. Verify whether the implementation class of the new tracking method inherits from the basic tracking method class; If the new tracking method type is inherited from the basic tracking method class and does not exist in the preset rule-method mapping table, the new mapping relationship will be added to the preset rule-method mapping table.

6. The data embedding method according to claim 1, characterized in that, The method further includes: Execute the aforementioned tracking strategy to obtain valid tracking data; The effective data points are analyzed and visualized in multiple dimensions to generate business analysis reports and visualization charts.

7. The data embedding method according to claim 6, characterized in that, The execution of the tracking strategy to obtain valid tracking data includes: In response to the fulfillment of the triggering condition of the rule instance, the associated tracking method in the tracking strategy is executed to collect raw tracking data; The original embedded data is encapsulated and standardized to generate data to be verified. A seven-dimensional data verification mechanism is used to perform quality verification on the data to be verified, and valid embedded data is obtained.

8. A data embedding device, characterized in that, The device includes: The multimodal event tracking entry construction module is used to select a preset triggering method based on the business scenario and construct a multimodal event tracking entry based on the preset triggering method; The rule instance generation module is used to generate rule instances that adapt to business scenarios based on the multimodal tracking entry and preset rule types. The event tracking strategy generation module is used to match the corresponding event tracking method from the preset rule-method mapping table based on the rule instance and business scenario collection requirements, and generate an event tracking strategy based on the event tracking method.

9. An electronic device, characterized in that, include: A memory and a processor are communicatively connected, the memory stores computer instructions, and the processor executes the computer instructions to perform the data embedding method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to perform the data embedding method according to any one of claims 1 to 7.

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