Method, device, storage medium and computer device for generating buried point definition information

Abstract

The method, apparatus, storage medium, and computer equipment for generating event tracking information provided in this application, upon receiving an information generation instruction, acquire test logs and associated event tracking business documents, avoiding the tedious process of manual multi-source queries. Then, the test logs are parsed to extract basic event tracking information and parameter fields. Using a pre-defined event tracking analysis model, the extracted basic event tracking information and parameter fields are searched and matched within the associated event tracking business documents to obtain supplementary event tracking information, effectively solving the problems of information inconsistency and omission. Finally, by structurally integrating the basic event tracking information, parameter fields, and supplementary event tracking information, complete and standardized event tracking definition data is formed and stored in a unified management system, establishing a single, reliable data source, thereby improving the efficiency of subsequent data analysis and the reliability of conclusions.

Description

Technical Field

[0001] This application relates to the field of software testing technology, and in particular to a method, apparatus, storage medium, and computer equipment for generating embedded point definition information. Background Technology

[0002] As data-driven decision-making becomes increasingly important in business analytics, event tracking data has become the foundation for supporting product iteration and business analysis. However, the current definition of event tracking faces the problem of inconsistent standards in actual management. Its specific content includes event tracking names, introductory documents, trigger scenarios, etc., and this information may be scattered across different platforms. For example, business scenario descriptions and field enumeration values ​​may be recorded in unstructured form in related documents or group chat records.

[0003] This decentralized and non-standardized management approach makes it difficult to accurately and efficiently extract and utilize the data tracking information. It often requires manual querying and verification across multiple sources, which is not only inefficient but also prone to misunderstandings due to inconsistencies or omissions, severely impacting the efficiency and accuracy of subsequent analyses. Summary of the Invention

[0004] The purpose of this application is to address at least one of the aforementioned technical deficiencies, particularly the difficulty in accurately and efficiently extracting and utilizing tracking point definition information due to the fragmented and non-standardized management methods in existing technologies. This often requires manual querying and verification across multiple sources, which is not only inefficient but also prone to misunderstandings due to inconsistencies or omissions, severely impacting the efficiency and accuracy of subsequent analyses.

[0005] Firstly, this application provides a method for generating tracking point definition information, the method comprising: When an information generation instruction is received, the test logs and associated event tracking documents are retrieved. The test logs are parsed to extract basic information about the tracking points and corresponding parameter fields. The basic tracking information, the parameter field information, and the tracking business document are input into a preset tracking analysis model, so that the tracking analysis model can perform information matching in the tracking business document based on the basic tracking information and the parameter field information to obtain supplementary tracking information. The basic information of the tracking points, the parameter field information, and the supplementary information of the tracking points are structured and integrated to form standardized tracking point definition data, which is then stored in the tracking point management system.

[0006] In one embodiment, obtaining the test logs and associated event tracking documents includes: The information generation instructions are parsed to extract the application identifier and test period range to be analyzed; The target application is determined based on the application identifier to be analyzed, and the logs of the target application within the test period are retrieved from the preset log system and the retrieved logs are determined as test logs. The associated event tracking documents are determined based on the target application.

[0007] In one embodiment, parsing the test logs to extract basic tracking information and corresponding parameter fields includes: The test logs are structured and parsed to identify multiple sets of event tracking identifiers, event tracking types, and platforms contained in the test logs, forming multiple event tracking information entries, and generating basic event tracking information based on the multiple event tracking information entries; Traverse the event trigger records in the test log, extract the parameter fields corresponding to each event trigger record, and associate each parameter field with each event information in the event basic information; Generate corresponding parameter field information based on the associated parameter fields.

[0008] In one embodiment, the step of matching the basic tracking information and the parameter field information in the tracking business document to obtain supplementary tracking information includes: Based on the basic information of the data points and the parameter field information, a multi-dimensional retrieval vector is constructed; Using the aforementioned tracking point analysis model, semantic retrieval and matching are performed on the tracking point business documents based on the multi-dimensional retrieval vectors to obtain the matched relevant document fragments; Supplementary information for each embedded point corresponding to the multi-dimensional retrieval vector is parsed from each relevant document fragment; The supplementary information obtained from the parsing is deduplicated and semantically integrated to obtain the additional information of the tracking points.

[0009] In one embodiment, the step of utilizing the event tracking analysis model to perform semantic retrieval and matching on the event tracking business document based on the multi-dimensional retrieval vector to obtain the matched relevant document fragments includes: The embedded point analysis model is used to call a preset search and parsing tool; Based on the search and parsing tool, relevant document fragments corresponding to the multi-dimensional search vector are retrieved and matched in the data tracking business documents.

[0010] In one embodiment, the step of structurally integrating the basic tracking information, the parameter field information, and the supplementary tracking information to form standardized tracking definition data includes: Obtain the preset event tracking definition template; According to the tracking point identifier, the basic tracking point information, the parameter field information, and the supplementary tracking point information are divided into multiple sets of tracking point definition information; Fill the corresponding positions in the tracking point definition template with the tracking point definition information of each group in sequence to form standardized tracking point definition data.

[0011] In one embodiment, the method further includes: Determine the scope of data analysis, and obtain target data points from the data point management system according to the scope of data analysis; A visualization chart is generated based on the target data points.

[0012] Secondly, this application provides a device for generating embedding point definition information, the device comprising: The instruction receiving module is used to obtain test logs and associated event tracking documents when an instruction to generate information is received. The information extraction module is used to parse the test logs and extract basic information about the tracking points and corresponding parameter fields. The information supplementation module is used to input the basic tracking information, the parameter field information, and the tracking business document into a preset tracking analysis model, so that the tracking analysis model can perform information matching in the tracking business document based on the basic tracking information and the parameter field information to obtain supplementary tracking information. The data generation module is used to structurally integrate the basic information of the tracking points, the parameter field information, and the supplementary information of the tracking points to form standardized tracking point definition data, and store it in the tracking point management system.

[0013] Thirdly, this application provides a storage medium storing computer-readable instructions, which, when executed by one or more processors, cause the one or more processors to perform the steps of the method for generating embedded point definition information as described in any of the above embodiments.

[0014] Fourthly, this application provides a computer device, including: one or more processors, and a memory; The memory stores computer-readable instructions, and when the one or more processors execute the computer-readable instructions, they perform the steps of the method for generating embedded point definition information as described in any of the above embodiments.

[0015] As can be seen from the above technical solutions, the embodiments of this application have the following advantages: The method, apparatus, storage medium, and computer equipment for generating event tracking information provided in this application automatically acquire test logs and associated event tracking business documents upon receiving an information generation instruction, avoiding the tedious process of manual multi-source queries. Subsequently, the test logs are parsed to extract basic event tracking information and parameter field information, achieving automation and standardization of information extraction. Furthermore, through a pre-defined event tracking analysis model, the extracted basic event tracking information and its parameter field information are searched and matched within the associated event tracking business documents to obtain supplementary event tracking information formed by key descriptive information such as business meaning and triggering scenarios, effectively solving the problems of information inconsistency and omission. Finally, by structurally integrating the basic event tracking information, parameter field information, and supplementary event tracking information, complete and standardized event tracking definition data is formed and stored in a unified management system, establishing a single reliable data source and ensuring the consistency and accuracy of event tracking definitions in terms of name, hierarchy, and business semantics. This improves the efficiency of subsequent data analysis and the reliability of conclusions. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 A flowchart illustrating a method for generating embedding point definition information provided in an embodiment of this application; Figure 2 A schematic diagram of a device for generating embedded point definition information provided in an embodiment of this application; Figure 3 This is an internal structural diagram of a computer device provided in an embodiment of this application. Detailed Implementation

[0018] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0019] In one embodiment, this application provides a method for generating tracking point definition information. The following embodiments illustrate the application of this method to a server. It is understood that the tracking point definition information generation method can be performed by a single server or by a server cluster consisting of multiple servers, and this application does not impose any specific limitations on this.

[0020] like Figure 1 As shown, this application provides a method for generating tracking point definition information, the method comprising: S101: When an information generation instruction is received, obtain the test logs and associated event tracking documents.

[0021] Test logs are records generated during software testing, documenting information such as the test environment, test case execution, user actions, and system responses. Tracking point documentation describes the tracking point design, business logic, data structure, and related field information. It typically includes the tracking point's name, business significance, and triggering scenarios.

[0022] In this step, a scheduled task can be set up to periodically send information generation instructions to the server, or the information generation instructions can be manually sent through the client. The information generation instructions include the application identifier to be analyzed and the test period range. Based on the information in the information generation instructions, the test logs and the data tracking documents involved in the test logs can be determined.

[0023] Specifically, when retrieving test logs and associated event tracking documents, the corresponding interface can be determined based on the information generated in the information generation instruction, and that interface can be called to obtain the data. Alternatively, information such as identifiers or paths that can uniquely locate the test logs and event tracking documents can be determined based on the information generated in the information generation instruction, and then the data can be obtained based on that information. This application does not impose specific limitations in this regard.

[0024] For example, on an e-commerce platform, test logs may record users' click behavior on the page, dwell time, and every step of the purchase process; while the data tracking business documentation details the collection rules and business implications of this behavioral data.

[0025] S102: Parse the test logs and extract basic information about the tracking points and the corresponding parameter fields.

[0026] Among them, the basic information of the tracking point refers to the key information used to identify the core attributes of the tracking point, such as the tracking point name, tracking point identifier, tracking point type, and platform. The parameter field information refers to the specific data-related attributes carried when the tracking point is triggered, including field name, level, type, and enumeration value.

[0027] In this step, test logs can be parsed by writing a parsing script or using specialized data processing tools. During this process, when using a parsing script to parse the logs, the script identifies and extracts relevant data about the event tracking points from the test logs according to preset rules and patterns. For example, the script can use regular expressions to match specific patterns in the logs to extract basic information such as the event tracking point name and trigger time, while also parsing the parameter fields carried by each event tracking point.

[0028] For example, in the test logs of an e-commerce app, log entries marked with "Event Tracking Triggered" can be filtered out first. From the "Event Tracking Name" field marked in the entries, names such as "Added to Cart" and "Order Submission Successful" can be extracted. From the "Parameter List" field, fields such as "Product ID," "User ID," and "Payment Method" can be extracted. By identifying the field hierarchy, "Product ID" is determined to be an L2 data item, and its data type is determined to be a string based on the data format. Statistical analysis of the "Payment Method" values ​​reveals that it only includes three categories: "WeChat Pay," "Alipay," and "Bank Card Payment," and the corresponding enumeration values ​​are then extracted. Finally, based on the obtained data, the basic event tracking information and corresponding parameter field information are determined.

[0029] S103: Input the basic information of the tracking points, the parameter field information, and the tracking point business document into the preset tracking point analysis model so that the tracking point analysis model can perform information matching in the tracking point business document based on the basic information of the tracking points and the parameter field information to obtain supplementary tracking point information.

[0030] The event tracking analysis model refers to a large language model with semantic understanding and reasoning capabilities. It is used to automatically identify, extract, and organize semantic descriptions corresponding to the basic event tracking information and parameter fields from event tracking business documents. Supplementary event tracking information refers to additional information matched from event tracking business documents through the event tracking analysis model.

[0031] In this step, the extracted basic tracking information and parameter field information are first used as input data, while the tracking business document is used as a reference data source, and both are input into the tracking analysis model. The tracking analysis model uses its built-in algorithms and rules to analyze the input basic tracking information and parameter field information, and searches for relevant supplementary information in the tracking business document. For example, the tracking analysis model can use techniques such as keyword matching and semantic analysis to find business descriptions, field definitions, and other content related to the input information, and extract them as supplementary information.

[0032] For example, suppose an e-commerce platform needs to analyze the event tracking data of user purchase behavior. At this point, basic event tracking information (such as the event tracking name "Product Purchase" and the trigger condition "User clicks the purchase button") and parameter field information (such as product ID, purchase quantity, and purchase time) have been extracted. Meanwhile, the event tracking business documentation details the business significance of each event tracking point and the specific meaning of each field. By inputting this information into the event tracking analysis model, the model can match supplementary information related to the "Product Purchase" event tracking point from the event tracking business documentation, such as "The business scenario of the purchase behavior is the user completing the payment process" and "The business meaning of the product ID field is to identify a specific product," thereby generating supplementary event tracking information.

[0033] S104: Structure and integrate the basic information, parameter field information and supplementary information of the tracking points to form standardized tracking point definition data, and store it in the tracking point management system.

[0034] Standardized event tracking definition data refers to event tracking information that has been structured and integrated. This information has a unified format and specifications, including basic event tracking information, parameter field information, and supplementary information. The event tracking management system is a software platform used for centralized management and maintenance of event tracking definitions, storing standardized event tracking definition data.

[0035] In this step, a pre-defined structured integration rule is obtained. This rule clarifies the classification dimensions, field formats, and presentation order of various types of information. Then, the extracted basic tracking information (such as tracking name, type, and platform), parameter field information (such as field name, level, data type, and enumeration value), and supplementary tracking information (such as business significance, triggering scenarios, and usage restrictions) are filled into the corresponding positions according to this rule, completing the systematic integration of information and forming standardized tracking definition data with a unified format and clear logic. Subsequently, the integrated standardized data is uploaded to the pre-defined tracking management system through interface integration or automated script transmission to complete the centralized storage of the data.

[0036] Specifically, by structurally integrating basic tracking information, parameter fields, and supplementary tracking information, the differences in format between different tracking information are eliminated, ensuring the consistency and universality of tracking definitions. Furthermore, it solves the problems of scattered and inconsistent tracking information, making the tracking definition data standardized, complete, and directly usable, eliminating the need for users to query and verify across different sources, and significantly improving the efficiency of using tracking information.

[0037] In the above embodiments, upon receiving the information generation instruction, test logs and associated event tracking documents are automatically retrieved, avoiding the tedious process of manual multi-source queries. Then, the test logs are parsed to extract basic event tracking information and parameter fields, achieving automation and standardization of information extraction. Furthermore, through a pre-defined event tracking analysis model, the extracted basic event tracking information and its parameter fields are searched and matched within the associated event tracking documents to obtain supplementary event tracking information formed by key descriptive information such as business meaning and triggering scenarios, effectively solving the problems of information inconsistency and omission. Finally, by structurally integrating the basic event tracking information, parameter fields, and supplementary event tracking information, complete and standardized event tracking definition data is formed and stored in a unified management system, establishing a single, reliable data source and ensuring the consistency and accuracy of event tracking definitions in terms of name, hierarchy, and business semantics. This improves the efficiency of subsequent data analysis and the reliability of conclusions.

[0038] In one embodiment, the test logs and associated event tracking documents are obtained, including: S1: Parse information generation instructions to extract the application identifier and test period range to be analyzed.

[0039] S2: Determine the target application based on the application identifier to be analyzed, and retrieve the logs of the target application within the test period from the preset log system, and then determine the retrieved logs as test logs.

[0040] S3: Determine the associated event tracking documents based on the target application.

[0041] The application identifier to be analyzed refers to a string or code used to uniquely identify the application to be processed. The test period range is used to limit the generation time of the test logs to be analyzed. The preset log system refers to the storage and retrieval platform used for unified management of test logs.

[0042] In this embodiment, the received information generation instructions are structured and parsed to identify fields related to application location and time range, thereby extracting the application identifier and test period range to be analyzed. Next, based on the extracted application identifier, the corresponding target application is matched against a preset application list. Then, using the application identifier as a search condition, combined with the time interval corresponding to the test period range, all logs generated by the target application within that time interval are filtered and retrieved from a preset log system, and these logs are identified as test logs. Finally, based on the target application's attributes (such as application name, business module, version number, etc.), the corresponding event tracking business documents are retrieved and associated with a preset document management platform.

[0043] Specifically, by generating information commands, test logs and associated event tracking documents are obtained, enabling precise location and filtering from commands to test logs and associated documents. This avoids the inefficiency caused by blindly retrieving data and documents, and improves the efficiency and accuracy of obtaining test logs and event tracking documents.

[0044] In one embodiment, the test logs are parsed to extract basic information about the tracking points and corresponding parameter fields, including: S1: Perform structured parsing on the test logs, identify multiple sets of event tracking identifiers, event tracking types, and platforms contained in the test logs, form multiple event tracking information entries, and generate basic event tracking information based on these multiple entry entries.

[0045] S2: Traverse the event trigger records in the test log, extract the parameter fields corresponding to each event trigger record, and associate each parameter field with the event information in the event basic information.

[0046] S3: Generate corresponding parameter field information based on the associated parameter fields.

[0047] In this embodiment, according to preset field identification rules (such as event tracking keywords, type encoding format, platform identifier, etc.), multiple sets of corresponding event tracking identifiers, event tracking types, and their respective platforms are filtered and extracted from the test logs. Each set of related information is integrated into an independent event tracking record. Then, all event tracking information is summarized to obtain the basic event tracking information. Next, all event tracking trigger records in the test logs are traversed one by one, and the parameter fields (such as field names, data values, etc.) carried in each record are extracted. Based on the event tracking identifier, the association relationship between the parameter fields and the corresponding event tracking information in the basic event tracking information is established to ensure that each parameter field can be accurately assigned to the corresponding event tracking. Finally, the associated parameter fields are summarized to generate parameter field information corresponding to each event tracking in the basic event tracking information. This provides accurate and reliable core data support for subsequent matching and supplementing information with event tracking business documents and forming standardized event tracking definition data.

[0048] In one embodiment, information matching is performed in the event tracking business document based on the basic event tracking information and parameter field information to obtain supplementary event tracking information, including: S1: Construct a multi-dimensional retrieval vector based on the basic information of the tracking points and the parameter field information.

[0049] S2: Using the tracking point analysis model, semantic retrieval and matching are performed on the tracking point business documents based on multi-dimensional retrieval vectors to obtain the matched relevant document fragments.

[0050] S3: Extract supplementary information for each tracking point corresponding to the multi-dimensional retrieval vector from various relevant document fragments.

[0051] S4: Perform deduplication and semantic integration on the supplementary information obtained from the parsing to obtain the additional information of the tracking points.

[0052] The multi-dimensional retrieval vector refers to a vector representation used for retrieval, constructed based on basic tracking information and parameter field information. Related document fragments refer to the text content in the tracking business documents that matches the multi-dimensional retrieval vector. Supplementary information refers to the detailed information of each tracking point corresponding to the multi-dimensional retrieval vector, parsed from the related document fragments.

[0053] In this embodiment, the basic information and parameter fields of the tracking points can be converted into vector form, where each dimension of the multi-dimensional retrieval vector can represent a feature or attribute. Then, the tracking point analysis model is invoked, and semantic retrieval and content matching are performed on the tracking point business documents in conjunction with the multi-dimensional retrieval vector. This allows for the search of semantically similar text fragments within the tracking point business documents based on the retrieval vector. For example, cosine similarity or other semantic similarity metrics can be used to find the document fragment that best matches the multi-dimensional retrieval vector, i.e., the relevant document fragment.

[0054] Subsequently, specific supplementary information can be extracted from the matched relevant document fragments using text parsing techniques. For example, regular expressions or NLP techniques can be used to parse the relevant document fragments and extract information such as the business scenario description of the tracking point and the detailed definition of the fields. Finally, the parsed supplementary information is cleaned to remove duplicate information, and similar supplementary information is semantically integrated. Finally, the processed supplementary information is summarized to obtain the tracking point supplementary information.

[0055] Furthermore, after deduplication and semantic integration of the supplementary information, the processed supplementary information can be polished, and the polished supplementary information can be determined as the final supplementary information.

[0056] It's understandable that by constructing multi-dimensional retrieval vectors, complex event tracking information is transformed into a searchable form, ensuring the efficiency and accuracy of the retrieval process. Then, the event tracking analysis model is called for semantic retrieval and matching, which can find relevant document fragments matching the retrieval vectors, thereby extracting supplementary information related to the event tracking data. Finally, the parsed supplementary information is deduplicated and semantically integrated, removing duplicate information and integrating similar information to form complete and accurate supplementary event tracking information.

[0057] In one embodiment, a tracking point analysis model is used to perform semantic retrieval and matching on the tracking point business document based on multi-dimensional retrieval vectors, resulting in matched relevant document fragments, including: The tracking point analysis model is used to call the preset search and parsing tools.

[0058] Based on the search parsing tool, relevant document fragments corresponding to multi-dimensional search vectors are retrieved and matched in the data tracking business documents.

[0059] Among them, search parsing tools refer to document tools that are adapted to the business scenarios of event tracking analysis and have the ability to perform multi-dimensional vector retrieval and document semantic matching.

[0060] In this embodiment, the tracking analysis model can call a search parsing tool, such as a Wiki document tool, to retrieve and match relevant document fragments corresponding to the multi-dimensional retrieval vector in the tracking business documents. The matched relevant document fragments are then returned to the tracking analysis model so that the tracking analysis model can perform semantic understanding and information organization based on these relevant document fragments.

[0061] Furthermore, the search and parsing tools can be invoked within the event tracking analysis model via real-time API toolcalls or based on the MCP protocol. This application does not impose any specific limitations on this.

[0062] In one embodiment, the basic information of the tracking points, parameter field information, and supplementary information of the tracking points are structurally integrated to form standardized tracking point definition data, including: S1: Get the preset tracking point definition template.

[0063] S2: Based on the tracking point identifier, the basic tracking point information, parameter field information, and supplementary tracking point information are divided into multiple groups of tracking point definition information.

[0064] S3: Fill in the corresponding positions in the tracking point definition template with the tracking point definition information of each group in sequence to form standardized tracking point definition data.

[0065] Among them, the tracking point definition template is used to standardize and unify the storage and expression of tracking point definitions.

[0066] In this embodiment, a tracking point definition template can be extracted from a preset template database. Then, according to the tracking point identifier, multiple sets of tracking point definition information are divided from the basic tracking point information, parameter field information, and supplementary tracking point information. This data is converted into tracking point definition information corresponding to each tracking point. Then, each set of tracking point definition information is sequentially filled into the corresponding position in the tracking point definition template, thereby determining the filled tracking point definition template as standardized tracking point definition data. This method not only makes tracking point-related data easy to store and query, but also provides high-quality data support for subsequent data analysis and business decision-making.

[0067] For example, suppose an e-commerce platform needs to manage multiple event tracking points, such as "user login" and "product purchase". The system first obtains a pre-defined event tracking point definition template, which may contain fields such as "event name", "trigger condition", "parameter field", and "supplementary information". Then, the system organizes the basic information, parameter field information, and supplementary information for "user login" and "product purchase" into two sets of data according to the event tracking point identifier. Next, the system fills these data into the corresponding positions in the template, generating standardized event tracking point definition data. For example, for the "user login" event tracking point, the generated standardized data might look like this: { "Event Tracker Name": "User Login", Trigger condition: "User enters username and password", "Parameter fields": [ {"Field Name": "Username", "Data Type": "String"}, {"Field Name": "Login Time", "Data Type": "Timestamp"} ], Additional Information: "Users log in via the main page" } In one embodiment, the method for generating tracking point definition information further includes: S1: Determine the scope of data analysis and obtain the target data points from the data point management system according to the scope of data analysis.

[0068] S2: Generate a visual chart based on the target data points.

[0069] In this embodiment, upon receiving a visualization instruction, the server can first determine the data analysis scope based on the instruction. This scope can be data tracking within a specific time period, data tracking belonging to a specific platform, or data tracking of a specific type; this application does not impose specific limitations on this. After obtaining the target data tracking based on the data analysis scope, visualization tools or libraries (such as Tableau, Power BI, Matplotlib, etc.) can be called to convert the target data tracking into a visual chart. For example, if the target data tracking is the number of user logins, the system can generate a time-series line chart to show the daily trend of login frequency changes.

[0070] Specifically, by generating visual charts from the target data points, the data is further transformed into an intuitive form, making complex data information easier to understand and analyze, and enhancing the readability and usability of the data.

[0071] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0072] The following describes the device for generating tracking point definition information provided in the embodiments of this application. The tracking point definition information generation device described below and the tracking point definition information generation method described above can be referred to in correspondence.

[0073] like Figure 2 As shown, this application provides a device 200 for generating embedded point definition information, the device comprising: The instruction receiving module 201 is used to obtain test logs and associated data tracking documents when it receives an information generation instruction. The information extraction module 202 is used to parse the test logs and extract basic information of the tracking points and corresponding parameter fields. The information supplementation module 203 is used to input the basic information of the tracking point, the parameter field information, and the tracking point business document into the preset tracking point analysis model, so that the tracking point analysis model can perform information matching in the tracking point business document based on the basic information of the tracking point and the parameter field information to obtain supplementary tracking point information. The data generation module 204 is used to structurally integrate the basic information, parameter field information and supplementary information of the tracking points to form standardized tracking point definition data and store it in the tracking point management system.

[0074] In the above embodiments, upon receiving the information generation instruction, test logs and associated event tracking documents are automatically retrieved, avoiding the tedious process of manual multi-source queries. Then, the test logs are parsed to extract basic event tracking information and parameter fields, achieving automation and standardization of information extraction. Furthermore, through a pre-defined event tracking analysis model, the extracted basic event tracking information and its parameter fields are searched and matched within the associated event tracking documents to obtain supplementary event tracking information formed by key descriptive information such as business meaning and triggering scenarios, effectively solving the problems of information inconsistency and omission. Finally, by structurally integrating the basic event tracking information, parameter fields, and supplementary event tracking information, complete and standardized event tracking definition data is formed and stored in a unified management system, establishing a single, reliable data source and ensuring the consistency and accuracy of event tracking definitions in terms of name, hierarchy, and business semantics. This improves the efficiency of subsequent data analysis and the reliability of conclusions.

[0075] In one embodiment, the instruction receiving module includes: The instruction parsing submodule is used to parse information generation instructions to extract the application identifier and test cycle range to be analyzed; The log retrieval submodule is used to determine the target application based on the application identifier to be analyzed, and after retrieving the logs of the target application within the test period from the preset log system, the retrieved logs are determined as test logs. The document determination submodule is used to determine the associated event tracking documents based on the target application.

[0076] In one embodiment, the information extraction module includes: The log parsing submodule is used to perform structured parsing of test logs, identify multiple sets of event tracking identifiers, event tracking types and the platform to which they belong in the test logs, form multiple event tracking information, and generate basic event tracking information based on the multiple event tracking information; The information association submodule is used to traverse the event trigger records in the test log, extract the parameter fields corresponding to each event trigger record, and associate each parameter field with the event information in the event basic information. The information generation submodule is used to generate corresponding parameter field information based on the associated parameter fields.

[0077] In one embodiment, the information supplementation module includes: The vector construction submodule is used to construct multi-dimensional retrieval vectors based on the basic information of the tracking points and parameter fields. The semantic retrieval submodule is used to perform semantic retrieval and matching in the tracked business documents based on the multi-dimensional retrieval vector using the tracking analysis model, and to obtain the matched relevant document fragments. The information parsing submodule is used to parse supplementary information of each tracking point corresponding to the multi-dimensional retrieval vector from various relevant document fragments; The semantic integration submodule is used to deduplicate and semantically integrate the parsed supplementary information to obtain the additional information of the tracking points.

[0078] In one embodiment, the semantic retrieval submodule includes: The tool invocation unit is used to invoke preset search and parsing tools using the data tracking analysis model; The document search unit is used to retrieve and match relevant document fragments corresponding to multi-dimensional search vectors in the data tracking business documents based on search parsing tools.

[0079] In one embodiment, the data generation module includes: The template retrieval submodule is used to retrieve preset tracking point definition templates; The information segmentation submodule is used to divide the basic information of the tracking point, parameter field information, and supplementary information of the tracking point into multiple groups of tracking point definition information according to the tracking point identifier; The data generation submodule is used to sequentially fill the corresponding positions in the tracking point definition template with the tracking point definition information of each group to form standardized tracking point definition data.

[0080] In one embodiment, the device for generating tracking point definition information further includes: The data acquisition module is used to determine the scope of data analysis and to acquire target data points from the data point management system according to the scope of data analysis. The visualization module is used to generate visual charts based on the target data points.

[0081] The division of modules in the above-described tracking point definition information generation device is merely illustrative. In other embodiments, the tracking point definition information generation device can be divided into different modules as needed to complete all or part of the functions of the above-described tracking point definition information generation device. Each module in the above-described tracking point definition information generation device can be implemented entirely or partially through software, hardware, or a combination thereof. Each module can be embedded in or independent of the processor in a computer device in hardware form, or it can be stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0082] In one embodiment, this application also provides a storage medium storing computer-readable instructions that, when executed by one or more processors, cause the one or more processors to perform the steps of the method for generating embedded point definition information as described in any of the above embodiments.

[0083] In one embodiment, this application also provides a computer device storing computer-readable instructions that, when executed by one or more processors, cause the one or more processors to perform the steps of the method for generating embedded point definition information as described in any of the above embodiments.

[0084] Indicatively, such as Figure 3 As shown, Figure 3 This is a schematic diagram of the internal structure of a computer device 300 provided in an embodiment of this application. The computer device 300 can be provided as a server. (Refer to...) Figure 3 The computer device 300 includes a processing component 302, which further includes one or more processors, and memory resources represented by memory 301 for storing instructions, such as application programs, that can be executed by the processing component 302. The application programs stored in memory 301 may include one or more modules, each corresponding to a set of instructions. Furthermore, the processing component 302 is configured to execute instructions to perform the data point definition information generation method of any of the above embodiments.

[0085] The computer device 300 may also include a power supply component 303 configured to perform power management of the computer device 300, a wired or wireless network interface 304 configured to connect the computer device 300 to a network, and an input / output (I / O) interface 305. The computer device 300 may operate on an operating system stored in memory 301, such as Windows Server™, Mac OS X™, Unix™, Linux™, Free BSD™, or similar.

[0086] Those skilled in the art will understand that Figure 3 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0087] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising a…" does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element. In this document, the singular forms "a," "an," and "the" may also include the plural forms unless the context clearly indicates otherwise. It should also be understood that the terms “comprising / including” or “having” specify the presence of the stated features, wholes, steps, operations, components, parts or combinations thereof, but do not exclude the possibility of the presence or addition of one or more other features, wholes, steps, operations, components, parts or combinations thereof. Meanwhile, the term “and / or” as used in this specification includes any and all combinations of the associated listed items.

[0088] The various embodiments in this specification are described in a progressive manner. Each embodiment focuses on the differences from other embodiments. The various embodiments can be combined as needed, and the same or similar parts can be referred to each other.

[0089] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for generating embedding point definition information, characterized in that, The method includes: When an information generation instruction is received, the test logs and associated event tracking documents are retrieved. The test logs are parsed to extract basic information about the tracking points and corresponding parameter fields. The basic tracking information, the parameter field information, and the tracking business document are input into a preset tracking analysis model, so that the tracking analysis model can perform information matching in the tracking business document based on the basic tracking information and the parameter field information to obtain supplementary tracking information. The basic information of the tracking points, the parameter field information, and the supplementary information of the tracking points are structured and integrated to form standardized tracking point definition data, which is then stored in the tracking point management system.

2. The method for generating embedded point definition information according to claim 1, characterized in that, The acquisition of test logs and associated event tracking documents includes: The information generation instructions are parsed to extract the application identifier and test period range to be analyzed; The target application is determined based on the application identifier to be analyzed, and the logs of the target application within the test period are retrieved from the preset log system and the retrieved logs are determined as test logs. The associated event tracking documents are determined based on the target application.

3. The method for generating embedded point definition information according to claim 1, characterized in that, The step of parsing the test logs to extract basic information about the tracking points and corresponding parameter fields includes: The test logs are structured and parsed to identify multiple sets of event tracking identifiers, event tracking types, and platforms contained in the test logs, forming multiple event tracking information entries, and generating basic event tracking information based on the multiple event tracking information entries; Traverse the event trigger records in the test log, extract the parameter fields corresponding to each event trigger record, and associate each parameter field with each event information in the event basic information; Generate corresponding parameter field information based on the associated parameter fields.

4. The method for generating embedded point definition information according to claim 1, characterized in that, The process involves matching the basic tracking information and the parameter field information within the tracking business document to obtain supplementary tracking information, including: Based on the basic information of the data points and the parameter field information, a multi-dimensional retrieval vector is constructed; Using the aforementioned tracking point analysis model, semantic retrieval and matching are performed on the tracking point business documents based on the multi-dimensional retrieval vectors to obtain the matched relevant document fragments; Supplementary information for each embedded point corresponding to the multi-dimensional retrieval vector is parsed from each relevant document fragment; The supplementary information obtained from the parsing is deduplicated and semantically integrated to obtain the additional information of the tracking points.

5. The method for generating embedded point definition information according to claim 4, characterized in that, The step involves using the aforementioned tracking analysis model to perform semantic retrieval and matching on the tracking business documents based on the multi-dimensional retrieval vector, thereby obtaining the matched relevant document fragments, including: The embedded point analysis model is used to call a preset search and parsing tool; Based on the search and parsing tool, relevant document fragments corresponding to the multi-dimensional search vector are retrieved and matched in the data tracking business documents.

6. The method for generating embedded point definition information according to claim 1, characterized in that, The process of structurally integrating the basic information of the tracking points, the parameter field information, and the supplementary information of the tracking points to form standardized tracking point definition data includes: Obtain the preset event tracking definition template; According to the tracking point identifier, the basic tracking point information, the parameter field information, and the supplementary tracking point information are divided into multiple sets of tracking point definition information; Fill the corresponding positions in the tracking point definition template with the tracking point definition information of each group in sequence to form standardized tracking point definition data.

7. The method for generating embedded point definition information according to any one of claims 1 to 6, characterized in that, The method further includes: Determine the scope of data analysis, and obtain target data points from the data point management system according to the scope of data analysis; A visualization chart is generated based on the target data points.

8. A device for generating embedded point definition information, characterized in that, The device includes: The instruction receiving module is used to obtain test logs and associated event tracking documents when an instruction to generate information is received. The information extraction module is used to parse the test logs and extract basic information about the tracking points and corresponding parameter fields. The information supplementation module is used to input the basic tracking information, the parameter field information, and the tracking business document into a preset tracking analysis model, so that the tracking analysis model can perform information matching in the tracking business document based on the basic tracking information and the parameter field information to obtain supplementary tracking information. The data generation module is used to structurally integrate the basic information of the tracking points, the parameter field information, and the supplementary information of the tracking points to form standardized tracking point definition data, and store it in the tracking point management system.

9. A storage medium, characterized in that: The storage medium stores computer-readable instructions, which, when executed by one or more processors, cause the one or more processors to perform the steps of the embedding point definition information generation method as described in any one of claims 1 to 7.

10. A computer device, characterized in that, include: One or more processors, and memory; The memory stores computer-readable instructions, which, when executed by the one or more processors, perform the steps of the method for generating embedded point definition information as described in any one of claims 1 to 7.

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