Method, device and equipment for processing buried point data, and storage medium

Abstract

The application provides a method and device for processing buried point data, equipment and a storage medium. The buried point data is obtained by a user-defined function, and buried point parameter information is extracted from the buried point data. The super position model identifier is matched in a mapping rule file by using the buried point parameter information, and the buried point position information is obtained according to the super position model identifier. The mapping rule file is obtained from a configuration file system and contains the mapping relationship between the super position model identifier and the buried point parameter information. In the embodiment of the present disclosure, the mapping rule is not stored in the user-defined function, but is stored in the configuration file system in the form of the mapping rule file. Therefore, on the side of the user-defined function, the processing task of the buried point data is decoupled from the mapping rule. The code of the user-defined function does not need to be modified at the same time when the mapping rule is updated, so that the buried point data can be processed quickly and normally, and the operation efficiency of the buried point position management system is improved.

Description

Technical Field

[0001] This invention relates to the field of computer technology, and in particular to a method, apparatus, device, and storage medium for processing embedded data. Background Technology

[0002] With the rapid development of computers and the internet, the installation and use of mobile applications (APPs) are becoming increasingly widespread, such as mobile phone apps, portable devices, and software and services installed on traditional computers. These clients typically need to interact with remote servers to provide users with functionalities and data. Therefore, there is a growing demand for statistical analysis of user behavior, campaign effectiveness, data access frequency, conversion rates, and other metrics. Currently, one of the main methods for collecting client-side data is to embed certain business, data, and interaction tracking points on the client side and transmit these data to the server for statistical analysis to evaluate the effectiveness of operational activities, traffic volume, and other metrics.

[0003] In related technologies, the number of event tracking points set for corresponding apps is increasing, including various click and impression tracking points. A single location may contain several tracking points with different content and trigger methods. To facilitate the management of such a large number of tracking points, the SuperPosition Model (SPM) has been proposed. The SPM uniquely identifies the location of a tracking point using a spmid and some supplementary information. The format of the spmid is P (page) xxx SP (subpage) xxx M (module) xxx ID xxx, containing each page and location of the product. While spmids facilitate tracking point management, the rapid iteration of apps means that the types, parameters, locations, and quantities of tracking points may change. This is due to the rapidly changing mapping rules between the corresponding spmids and tracking points.

[0004] Therefore, how to efficiently manage the data collected through tracking points is a topic that the industry needs to consider.

[0005] It should be noted that the information disclosed in the background section above is only used to enhance the understanding of the background of the present invention, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0006] In view of the problems in the prior art, the purpose of this invention is to provide a method, apparatus, device and storage medium for processing embedded data, which overcomes the difficulties of the prior art and can improve the operating efficiency of the embedded location management system.

[0007] This disclosure provides a method for processing embedded data, which includes:

[0008] Obtain event tracking data and extract event tracking parameter information from the event tracking data using user-defined functions;

[0009] The tracking point parameter information is used to match the super location model identifier in the mapping rule file, and the tracking point location information is obtained based on the super location model identifier.

[0010] The mapping rule file is obtained from the configuration file system and contains the mapping relationship between the super location model identifier and the embedding point parameter information.

[0011] Optionally, the data processing method for embedded points also includes:

[0012] Before using the tracking point parameter information to match the super location model identifier in the mapping rule file, the mapping rule file is pushed to the user-defined function through the configuration file system.

[0013] Optionally, the mapping rule file can be pushed to the user-defined function via a configuration file system, including:

[0014] When the configuration file system detects an update to the mapping rule file, it pushes the updated mapping rule file to the user-defined function.

[0015] Optionally, before pushing the updated mapping rule file to the user-defined function through the configuration file system, the data processing method for event tracking also includes:

[0016] Retrieve the updated mapping rules from the mapping rule data table;

[0017] Based on the data format supported by the configuration file system, the mapping rules are converted to obtain the converted mapping rule file;

[0018] Store the mapping rule files in the configuration file system.

[0019] Optionally, the updated mapping rules are retrieved from the mapping rule data table, specifically including the following steps:

[0020] Use SparkSession to create a session connection between the mapping rules data table and Spark SQL to retrieve the updated mapping rules stored in the mapping rules data table.

[0021] Optionally, the super location model identifier is matched in the mapping rule file using the embedding point parameter information, including:

[0022] Traverse the mapping rule file by line, and determine the current rule line if the tracking point identifier of the current rule line matches the tracking point identifier in the tracking point parameter information;

[0023] Extract additional information from the current rule row, match the additional information with the auxiliary information of the tracking point parameter information, and output the super location model identifier of the current row when at least one pair of data matches.

[0024] Optionally, the super location model identifier is matched in the mapping rule file using the embedding point parameter information, including:

[0025] If the additional information for the current rule row is empty, output the super location model identifier for the current rule row.

[0026] This disclosure also provides a data processing device for embedded data, which includes:

[0027] The extraction module obtains the tracking data through user-defined functions and extracts the tracking parameter information from the tracking data;

[0028] The mapping module uses the embedded point parameter information to match the super location model identifier in the mapping rule file, and obtains the embedded point location information based on the super location model identifier;

[0029] The mapping rule file is obtained from the configuration file system and contains the mapping relationship between the super location model identifier and the embedding point parameter information.

[0030] Embodiments of the present invention also provide a data processing device for embedded data, comprising:

[0031] processor;

[0032] Memory, which stores the processor's executable instructions;

[0033] The processor is configured to execute the steps of the above-described data processing method by executing executable instructions.

[0034] Embodiments of the present invention also provide a computer-readable storage medium for storing a program that, when executed, implements the steps of the above-described embedded data processing method.

[0035] The present invention discloses a method, apparatus, device, and storage medium for processing embedded data. This method acquires embedded data through user-defined functions and extracts embedded parameter information from the data. It then uses the embedded parameter information to match a super location model identifier in a mapping rule file and obtains embedded location information based on the super location model identifier. The mapping rule file is obtained from a configuration file system and contains the mapping relationship between the super location model identifier and the embedded parameter information. In this embodiment, the mapping rules are not stored in the user-defined functions but are stored in the configuration file system as a mapping rule file. Therefore, on the user-defined function side, the embedded data processing task is decoupled from the mapping rules. Updating the mapping rules does not require simultaneous code modification of the user-defined functions, thereby enabling rapid and normal processing of embedded data and improving the operational efficiency of the embedded location management system. Attached Figure Description

[0036] Other features, objects, and advantages of the invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings.

[0037] Figure 1 This is a flowchart of the data processing method for embedded points according to an embodiment of this disclosure.

[0038] Figure 2 A schematic diagram illustrating the principle of the specific implementation of the data processing method for embedded points according to an embodiment of this disclosure.

[0039] Figure 3 This is a schematic diagram of the structure of the embedded data processing device according to an embodiment of the present disclosure.

[0040] Figure 4 This is a structural schematic diagram of the embedded data processing device of the present invention.

[0041] Figure 5 This is a schematic diagram of the structure of a computer-readable storage medium according to an embodiment of the present invention. Detailed Implementation

[0042] The following specific examples illustrate the implementation methods of this application. Those skilled in the art can easily understand the other advantages and effects of this application from the content disclosed herein. This application can also be implemented or applied through other different specific embodiments, and various details in this application can be modified or changed according to different viewpoints and application systems without departing from the spirit of this application. It should be noted that, unless otherwise specified, the embodiments and features in the embodiments of this application can be combined with each other.

[0043] The embodiments of this application will now be described in detail with reference to the accompanying drawings, so that those skilled in the art can easily implement the application. This application may be embodied in many different forms and is not limited to the embodiments described herein.

[0044] Figure 1 This is a flowchart of the data processing method for embedded points according to an embodiment of this disclosure, as follows: Figure 1 As shown in the figure, this disclosure provides a method for processing embedded data, including the following steps:

[0045] Step 110: Obtain the tracking data and extract the tracking parameter information from the tracking data using user-defined functions;

[0046] Step 120: Use the embedding parameter information to match the super location model identifier in the mapping rule file, and obtain the embedding location information based on the super location model identifier;

[0047] The mapping rule file is obtained from the configuration file system and contains the mapping relationship between the super location model identifier and the embedding point parameter information.

[0048] In this embodiment of the disclosure, the mapping rules are not stored in the user-defined function, but are stored in the configuration file system in the form of a mapping rule file. Therefore, on the user-defined function side, the task of processing the tracking data is decoupled from the mapping rules. Updating the mapping rules does not require simultaneous code modification of the user-defined function, thereby enabling fast and normal processing of the tracking data and improving the operating efficiency of the tracking location management system.

[0049] In this embodiment of the disclosure, the event tracking data is data collected by event tracking points deployed in the application.

[0050] In this embodiment of the disclosure, a user-defined function (UDF) is used. A UDF operates on a single data row and produces a single data row as output. This user-defined function is created and deployed in advance and is specifically used to process the data collected at tracking points.

[0051] Optionally, the data processing method for embedded points also includes:

[0052] Before using the tracking point parameter information to match the super location model identifier in the mapping rule file, the mapping rule file is pushed to the user-defined function through the configuration file system.

[0053] In this embodiment, the configuration file system automatically pushes the mapping rule file to the user-defined function, eliminating the need for manual modification of the user-defined function and greatly improving the feasibility of this solution and the operating efficiency of the tracking point location management system.

[0054] In this embodiment of the disclosure, pushing the mapping rule file to the user-defined function through the configuration file system includes:

[0055] When the configuration file system detects an update to the mapping rule file, it pushes the updated mapping rule file to the user-defined function.

[0056] Using this embodiment, it can be ensured that the mapping rule file used by user-defined functions is up-to-date, and the resulting tracking point location information is also up-to-date, thereby improving the accuracy of the tracking point location information. Especially when the tracking point location changes or a tracking point is added or removed at a location, the mapping rule file is updated simultaneously. In this case, using this embodiment can ensure that the tracking point data is processed normally and accurately.

[0057] In this embodiment of the disclosure, the user-defined function requests the latest mapping rule file from the configuration file system.

[0058] In this embodiment of the disclosure, before pushing the updated mapping rule file to the user-defined function through the configuration file system, the data processing method for event tracking further includes:

[0059] Retrieve the updated mapping rules from the mapping rule data table;

[0060] Based on the data format supported by the configuration file system, the mapping rules are converted to obtain the converted mapping rule file;

[0061] Store the mapping rule files in the configuration file system.

[0062] In this embodiment, the updating and uploading of mapping rule files can be performed automatically. Uploading refers to automatically uploading the updated mapping rule files to the configuration file system, thereby saving a lot of manpower, avoiding possible errors, and improving the accuracy of subsequent data point location information.

[0063] In this embodiment of the disclosure, a scheduled task can be set, and when the scheduled task is triggered, the updated mapping rules can be obtained from the mapping rule data table.

[0064] In this embodiment of the disclosure, the mapping rule data table is implemented using a Hive table. Hive is a data warehouse tool based on Hadoop, used for data extraction, transformation, and loading. It is a mechanism for storing, querying, and analyzing large-scale data stored in Hadoop.

[0065] Optionally, in this embodiment of the disclosure, the method for processing embedded data further includes:

[0066] Before matching the super location model identifier in the mapping rule file using the embedded point parameter information, the mapping rule file is read through the interface provided by the configuration file system.

[0067] In this embodiment, a communication protocol between the user-defined function and the interface provided by the configuration file system is defined, thereby enabling the transmission of mapping rule files.

[0068] In this embodiment of the disclosure, obtaining updated mapping rules from the mapping rule data table specifically includes the following steps:

[0069] Use SparkSession to create a session connection between the mapping rules data table and Spark SQL to retrieve the updated mapping rules stored in the mapping rules data table.

[0070] SparkSession provides a unified entry point for users to learn about Spark's various functionalities. Spark SQL, a new module in Spark, integrates relational processing with Spark's functional programming API. It supports querying data using SQL or the Hive query language.

[0071] Among them, Apache Spark is a distributed data processing engine that is a lightweight upgrade compared to Hadoop. Based on elastic distributed datasets, it exchanges data in memory, which eliminates the disk input / output I / O steps compared to Hadoop, greatly shortening the time required for processing data.

[0072] In this embodiment, the tracking parameter information includes tracking identifiers, page identifiers, and other auxiliary information, such as the business scenario identifier of the page, like a day trip or vacation trip, etc., which are not limited here. Specifically, the tracking parameter information is used to match the super location model identifier in the mapping rule file, including:

[0073] The mapping rule file is traversed based on the embedded point parameter information until the corresponding super location model identifier is matched.

[0074] Specifically, the mapping rule file is traversed line by line. When the tracking point identifier of the current rule line in the mapping rule file matches the tracking point identifier in the current tracking point parameter information, i.e., they correspond, the current rule line is determined. Even if the page identifier pageid in the current rule line can also correspond or does not exist, the following matching steps are entered.

[0075] First, retrieve the corresponding additional information (combination info) from the current rule line. Combination info is a set of data in map format or other formats. If there is content in the current tracking parameter information that matches at least one pair of data in combination info, or if combination info is empty (i.e., in the current case, only pageid and tracking identifier are needed to uniquely identify an spmid), then output the spmid of the current rule line. If it cannot be found, continue traversing until a spmid that meets the conditions is found. If it is not found after traversing, return an empty string.

[0076] In this embodiment, the user-defined function obtains the tracking identifier, page identifier, and tracking auxiliary information from the tracking parameter information, and uses the above logic to uniquely determine a spmid.

[0077] The following is combined Figure 2 The technical solution of the data processing method for embedded points provided in the embodiments of this disclosure is described in the response code flow.

[0078] 1. Create a session connection between SparkSession and the Hive table, retrieve the required mapping rules for spmid from the Hive table using Spark SQL, split the data by row and store it in variables. The corresponding code flow is as follows:

[0079]

[0080]

[0081] Among them, spm_id is the same as spmid mentioned above.

[0082] 2. Since the data stored in Hive tables includes different data structures such as maps and arrays, if the configuration system has a different data structure than Hive, the mapping rules obtained from the Hive table can be parsed into String format or other formats. The code flow design is as follows:

[0083]

[0084]

[0085] 3. Upload the parsed mapping rules to the configuration file system.

[0086] 4. The user-defined function (udf) reads the mapping rule file in the configuration system and obtains the tracking point parameter information from the tracking point data. Based on the tracking point parameter information and the mapping rule file, a globally unique spmid is mapped. The code flow design for the udf is as follows:

[0087]

[0088]

[0089]

[0090]

[0091] Figure 3 This is a schematic diagram of the structure of the embedded data processing device of the present invention. Figure 3 As shown, the embedded data processing device 300 of the present invention includes:

[0092] Extraction module 310 obtains embedded data and extracts embedded parameter information from the embedded data through user-defined functions;

[0093] The mapping module 320 uses the embedded point parameter information to match the super location model identifier in the mapping rule file, and obtains the embedded point location information based on the super location model identifier;

[0094] The mapping rule file is obtained from the configuration file system and contains the mapping relationship between the super location model identifier and the embedding point parameter information.

[0095] In an optional embodiment, the extraction module 310 is further configured to:

[0096] Before using the tracking point parameter information to match the super location model identifier in the mapping rule file, the mapping rule file is pushed to the user-defined function through the configuration file system.

[0097] In an optional embodiment, the extraction module 310 is further configured to:

[0098] When the configuration file system detects an update to the mapping rule file, it pushes the updated mapping rule file to the user-defined function.

[0099] In an optional embodiment, the extraction module 310 is further configured to:

[0100] Before pushing the updated mapping rule file to the user-defined function through the configuration file system, retrieve the updated mapping rule from the mapping rule data table;

[0101] Based on the data format supported by the configuration file system, the mapping rules are converted to obtain the converted mapping rule file;

[0102] Store the mapping rule files in the configuration file system.

[0103] In an optional embodiment, the extraction module 310 is further configured to:

[0104] Use SparkSession to create a session connection between the mapping rules data table and Spark SQL to retrieve the updated mapping rules stored in the mapping rules data table.

[0105] In an optional embodiment, the mapping module 320 is specifically used for:

[0106] Traverse the mapping rule file by line, and determine the current rule line if the tracking point identifier of the current rule line matches the tracking point identifier in the tracking point parameter information;

[0107] Extract additional information from the current rule row, match the additional information with the auxiliary information of the tracking point parameter information, and output the super location model identifier of the current row when at least one pair of data matches.

[0108] In an optional embodiment, the mapping module 320 is specifically used for:

[0109] If the additional information for the current rule row is empty, output the super location model identifier for the current rule row.

[0110] The tracking data processing apparatus of this disclosure stores the mapping rules in the form of mapping rule files in the configuration file system. Therefore, on the user-defined function side, the tracking data processing task and the mapping rules are decoupled. Updating the mapping rules does not require simultaneous code modification of the user-defined function, thereby enabling fast and normal processing of tracking data and improving the operating efficiency of the tracking location management system.

[0111] This invention also provides a data processing device for embedded points, including a processor and a memory storing executable instructions for the processor. The processor is configured to execute steps of a data processing method for embedded points by executing the executable instructions.

[0112] As described above, the tracking data processing device of the present invention can store the mapping rules in the form of mapping rule files in the configuration file system. Therefore, on the user-defined function side, the tracking data processing task and the mapping rules are decoupled. Updating the mapping rules does not require simultaneous code modification of the user-defined function, thereby enabling fast and normal processing of tracking data and improving the operating efficiency of the tracking location management system.

[0113] Those skilled in the art will understand that various aspects of the present invention can be implemented as systems, methods, or program products. Therefore, various aspects of the present invention can be specifically implemented in the following forms: a completely hardware implementation, a completely software implementation (including firmware, microcode, etc.), or a combination of hardware and software aspects, collectively referred to herein as a "circuit," "module," or "platform."

[0114] Figure 4 This is a structural schematic diagram of the embedded data processing device of the present invention. See below for reference. Figure 4 To describe an electronic device 400 according to this embodiment of the present invention. Figure 4 The electronic device 400 shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of the present invention.

[0115] like Figure 4 As shown, the electronic device 400 is presented in the form of a general-purpose computing device. The components of the electronic device 400 may include, but are not limited to: at least one processing unit 410, at least one storage unit 420, a bus 430 connecting different platform components (including storage unit 420 and processing unit 410), a display unit 440, etc.

[0116] The storage unit stores program code, which can be executed by the processing unit 410 to perform the steps described in the section on the method for processing embedded data according to the various exemplary embodiments of the present invention. For example, the processing unit 410 can perform actions such as... Figure 1 The steps are shown in the figure.

[0117] Storage unit 420 may include a readable medium in the form of a volatile storage unit, such as random access memory (RAM) 421 and / or cache memory 422, and may further include a read-only memory (ROM) 423.

[0118] Storage unit 420 may also include a program / utility 424 having a set (at least one) of program modules 425, such program modules 425 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of these examples may include an implementation of a network environment.

[0119] Bus 430 can represent one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of the various bus structures.

[0120] Electronic device 400 can also communicate with one or more external devices 40 (e.g., keyboard, pointing device, Bluetooth device, etc.), and with one or more devices that enable a user to interact with electronic device 400, and / or with any device that enables electronic device 400 to communicate with one or more other computing devices (e.g., router, modem, etc.). This communication can be performed via input / output (I / O) interface 450. Furthermore, electronic device 400 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 460. Network adapter 460 can communicate with other modules of electronic device 400 via bus 430. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with electronic device 400, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage platforms.

[0121] This invention also provides a computer-readable storage medium for storing a program, which, when executed, implements the steps of a data tracking method. In some possible implementations, various aspects of this invention can also be implemented as a program product comprising program code, which, when run on a terminal device, causes the terminal device to perform the steps described in the above-described data tracking method section of this specification according to various exemplary embodiments of the invention.

[0122] As shown above, when the program of the computer-readable storage medium of this embodiment is executed, the mapping rules can be stored in the configuration file system in the form of mapping rule files. Therefore, on the user-defined function side, the task of processing the tracking data and the mapping rules are decoupled. Updating the mapping rules does not require simultaneous code modification of the user-defined function, thereby enabling fast and normal processing of tracking data and improving the operating efficiency of the tracking location management system.

[0123] Figure 5 This is a schematic diagram of the structure of the computer-readable storage medium of the present invention. (Reference) Figure 5 As shown, a program product 500 for implementing the above-described method according to an embodiment of the present invention is described. It may employ a portable compact disc read-only memory (CD-ROM) and include program code, and may run on a terminal device, such as a personal computer. However, the program product of the present invention is not limited thereto. In this document, the readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, apparatus, or device.

[0124] The program product may employ any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of readable storage media include: electrical connections having one or more wires, portable disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0125] Computer-readable storage media may include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable storage medium may also be any readable medium other than a readable storage medium that can transmit, propagate, or transfer a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the readable storage medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof.

[0126] Program code for performing the operations of this invention can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can execute entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0127] In summary, the tracking data processing method, apparatus, device, and storage medium of the present invention can store mapping rules in the form of mapping rule files in the configuration file system. Therefore, on the user-defined function side, the tracking data processing task and the mapping rules are decoupled. Updating the mapping rules does not require simultaneous code modification of the user-defined function, thereby enabling fast and normal processing of tracking data and improving the operating efficiency of the tracking location management system.

[0128] The above description, in conjunction with specific preferred embodiments, provides a further detailed explanation of the present invention. It should not be construed that the specific implementation of the present invention is limited to these descriptions. For those skilled in the art, various simple deductions or substitutions can be made without departing from the concept of the present invention, and all such modifications and substitutions should be considered within the scope of protection of the present invention.

Claims

1. A method for processing embedded data, characterized in that, include: Obtain event tracking data and extract event tracking parameter information from the event tracking data using user-defined functions; The embedding point parameter information is used to match the super location model identifier in the mapping rule file, and the embedding point location information is obtained based on the super location model identifier; The step of matching the super location model identifier in the mapping rule file using the tracking point parameter information includes: traversing the mapping rule file line by line, determining the current rule line if the tracking point identifier of the current rule line matches the tracking point identifier in the tracking point parameter information; extracting additional information from the current rule line, matching the additional information with the auxiliary information of the tracking point parameter information, and outputting the super location model identifier of the current line when at least one pair of data matches; the step of matching the super location model identifier in the mapping rule file using the tracking point parameter information includes: outputting the super location model identifier of the current rule line when the additional information of the current rule line is empty. The mapping rule file is obtained from the configuration file system and is pushed to the user-defined function by the configuration file system when it detects an update before matching the super location model identifier in the mapping rule file using the tracking point parameter information. The mapping rule file contains the mapping relationship between the super location model identifier and the tracking point parameter information.

2. The data processing method for embedded points according to claim 1, characterized in that, Before pushing the updated mapping rule file to the user-defined function through the configuration file system, the data processing method further includes: Retrieve the updated mapping rules from the mapping rule data table; Based on the data format supported by the configuration file system, the mapping rules are converted to obtain the converted mapping rule file; The mapping rule file is stored in the configuration file system.

3. The method for processing embedded data according to claim 2, characterized in that, The step of obtaining the updated mapping rules from the mapping rule data table specifically includes the following steps: Use SparkSession to create a session connection between the mapping rules data table and Spark SQL to retrieve the updated mapping rules stored in the mapping rules data table.

4. A device for processing embedded data, characterized in that, include: The extraction module obtains the tracking data through user-defined functions and extracts the tracking parameter information from the tracking data; The mapping module uses the embedded point parameter information to match the super location model identifier in the mapping rule file, and obtains the embedded point location information based on the super location model identifier. The step of matching the super location model identifier in the mapping rule file using the tracking point parameter information includes: traversing the mapping rule file line by line, determining the current rule line if the tracking point identifier of the current rule line matches the tracking point identifier in the tracking point parameter information; extracting additional information from the current rule line, matching the additional information with the auxiliary information of the tracking point parameter information, and outputting the super location model identifier of the current line when at least one pair of data matches; the step of matching the super location model identifier in the mapping rule file using the tracking point parameter information includes: outputting the super location model identifier of the current rule line when the additional information of the current rule line is empty. The mapping rule file is obtained from the configuration file system and is pushed to the user-defined function by the configuration file system when it detects an update before matching the super location model identifier in the mapping rule file using the tracking point parameter information. The mapping rule file contains the mapping relationship between the super location model identifier and the tracking point parameter information.

5. A device for processing embedded data, characterized in that, include: processor; A memory in which executable instructions of the processor are stored; The processor is configured to perform the steps of the data processing method according to any one of claims 1 to 3 by executing the executable instructions.

6. A computer-readable storage medium for storing a program, characterized in that, When the program is executed, it implements the steps of the data processing method for embedded data as described in any one of claims 1 to 3.

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