Exploration method and device for data index, equipment and medium

Abstract

One or more embodiments of the present disclosure provide a probing method and apparatus for a data index, a device and a medium, applied to the field of data analysis, the probing method for the data index comprising: according to a probing request for at least one data index triggered by a user, determining the probing request for the data index; determining exploration information of each data index in the at least one data index; for each data index in the at least one data index, executing the following steps to determine a first probing result of each data index: in response to a historical probing result, passing authentication, of the data index inquired according to the data index information, and determining the first probing result according to the historical probing result; and in response to the authenticated historical probing results of which the data indexes are not queried, determining a first probing result based on the probing information according to at least one probing mode. Therefore, the relatively accurate first probing result can be quickly determined, the probing efficiency is improved, and end-to-end data index probing is realized.

Description

Technical Field

[0001] One or more embodiments of this disclosure relate to a method for probing data metrics, an apparatus for probing data metrics, an electronic device, and a computer-readable storage medium. Background Technology

[0002] In the field of data analytics, it is necessary to investigate the data metrics used by a product to understand the data source and other metric metadata information. Metric metadata information is used to explain and describe the data metrics.

[0003] Currently, the main method for obtaining metadata information of data indicators is through manual exploration, which is inefficient. Summary of the Invention

[0004] This summary section is provided to briefly introduce the concepts, which will be described in detail in the detailed description section below. This summary section is not intended to identify key or essential features of the claimed technical solution, nor is it intended to limit the scope of the claimed technical solution.

[0005] At least one embodiment of this disclosure provides a method for probing data metrics, comprising: determining probing information for each of the at least one data metrics based on a user-triggered probing request for at least one data metric, wherein the probing information includes product information and data metric information, the product information being used to identify a product using the data metric, and the data metric information being used to identify the data metric; for each of the at least one data metric, performing the following steps to determine a first probing result for each data metric: in response to querying historical probing results of the data metric that have passed authentication based on the data metric information, determining the first probing result based on the historical probing results; in response to not querying historical probing results of the data metric that have passed authentication, determining the first probing result based on the probing information according to at least one probing method, wherein the first probing result includes at least one metric data source information and at least one metric definition of the data metric, and the at least one probing method includes obtaining the first probing result based on the probing information and the code repository corresponding to the product.

[0006] At least another embodiment of this disclosure provides a probe device for data metrics, comprising: a determining module configured to: determine probe information for each of the at least one data metrics based on a probe request triggered by a user, wherein the probe information includes product information and data metric information, the product information being used to identify a product using the data metric, and the data metric information being used to identify the data metric; and an execution module configured to: perform the following steps for each of the at least one data metric to determine a first probe result for each data metric: in response to querying historical probe results for the data metric that have passed authentication based on the data metric information, determine the first probe result based on the historical probe results; in response to not querying historical probe results for the data metric that have passed authentication, determine the first probe result based on the probe information according to at least one probe method, wherein the first probe result includes at least one metric data source information and at least one metric definition of the data metric, and the at least one probe method includes: obtaining the first probe result based on the probe information and the code repository corresponding to the product.

[0007] At least one further embodiment of this disclosure provides an electronic device, including: a processing device; and a storage device including one or more computer program instructions; wherein the one or more computer program instructions are executed by the processing device to perform the probing method for data indicators provided in at least one embodiment of this disclosure.

[0008] At least one further embodiment of this disclosure provides a computer-readable storage medium that non-transitory stores computer-readable instructions, wherein when the computer-readable instructions are executed by a processor, they implement the probing method for data metrics provided in at least one embodiment of this disclosure.

[0009] At least one embodiment of this disclosure provides a computer program product, including a computer program that, when executed by a processor, implements the data metric probing method provided in at least one embodiment of this disclosure. Attached Figure Description

[0010] The above and other features, advantages, and aspects of the embodiments of this disclosure will become more apparent from the accompanying drawings and the following detailed description. Throughout the drawings, the same or similar reference numerals denote the same or similar elements. It should be understood that the drawings are schematic, and the originals and elements are not necessarily drawn to scale.

[0011] Figure 1 The illustration schematically depicts an application scenario of a data indicator probing method provided in at least one embodiment of this disclosure;

[0012] Figure 2 The illustration shows a flowchart of a method for probing data indicators provided in at least one embodiment of the present disclosure;

[0013] Figure 3 The illustration shows a schematic diagram of a probe request provided by at least one embodiment of the present disclosure;

[0014] Figure 4 The illustration schematically shows a flowchart of an exploration request provided by at least one embodiment of the present disclosure;

[0015] Figure 5 This schematically illustrates a structural diagram of a data index probing device provided in at least one embodiment of the present disclosure; and

[0016] Figure 6 A schematic diagram of the structure of an electronic device suitable for implementing embodiments of the present disclosure is shown. Detailed Implementation

[0017] One or more embodiments of this disclosure will now be described in more detail with reference to the accompanying drawings. While some embodiments of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this disclosure. It should be understood that the accompanying drawings and embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of protection of this disclosure.

[0018] It should be understood that the steps described in the method embodiments of this disclosure may be performed in different orders and / or in parallel. Furthermore, the method embodiments may include additional steps and / or omit the steps shown. The scope of this disclosure is not limited in this respect.

[0019] The term "comprising" and its variations as used herein are open-ended inclusions, meaning "including but not limited to". The term "based on" means "at least partially based on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". Definitions of other terms will be given in the description below.

[0020] It should be noted that the concepts of "first" and "second" mentioned in this disclosure are used only to distinguish different devices, modules or units, and are not used to limit the order of functions performed by these devices, modules or units or their interdependencies.

[0021] It should be noted that the terms "a" and "a plurality of" used in this disclosure are illustrative rather than restrictive, and those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

[0022] The names of the messages or information exchanged between the various devices in the embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of these messages or information.

[0023] It is understood that the data involved in this technical solution (including but not limited to the data itself, the acquisition, use, storage or deletion of the data) shall comply with the requirements of relevant laws, regulations and related provisions.

[0024] It is understood that before using the technical solutions disclosed in the various embodiments of this disclosure, relevant users should be informed of the type, scope of use, and usage scenarios of the information involved in this disclosure through appropriate means in accordance with relevant laws and regulations, and authorization should be obtained from the relevant users. Among them, relevant users may include any type of rights holder, such as individuals, enterprises, and groups.

[0025] For example, in response to receiving an active request from a user, a prompt message is sent to the relevant user to clearly indicate that the operation requested by the user will require obtaining and using the user's information. This allows the relevant user to choose whether to provide information to the software or hardware such as the electronic device, application, server, or storage medium that performs the operation of any embodiment of the present disclosure based on the prompt message.

[0026] As an optional but non-restrictive implementation, in response to a user's active request, a prompt message can be sent to the user, such as a pop-up window, where the prompt message can be presented in text format. Furthermore, the pop-up window can also include a selection control allowing the user to choose "agree" or "disagree" to provide information to the electronic device.

[0027] It is understood that the above notification and user authorization process are merely illustrative and do not constitute a limitation on the implementation of this disclosure. Other methods that comply with relevant laws and regulations may also be applied to the implementation of this disclosure.

[0028] In data analytics scenarios, users of a product need to understand the metadata information of the product's data metrics. Metric metadata information can include one or more of the following: data source, metric definition, processing logic, and data lineage. For example, a user might want to know the data source for the transaction amount (data metric name) displayed on a shopping website (product).

[0029] Currently, users need to submit exploration requests for specific data metrics, and then manually explore the data source and definition of the metrics based on these requests. This manual exploration process can involve multiple people communicating, resulting in high communication costs, significant time consumption, and low efficiency. Furthermore, the accuracy of manually obtained exploration results is difficult to guarantee, and the results lack systematic storage and have low reusability. If further exploration of the data metrics is needed in the future, manual exploration must be performed again.

[0030] To at least partially solve the above-mentioned technical problems, at least one embodiment of this disclosure provides a method for probing data metrics. The method includes: determining probing information for each of the at least one data metrics based on a user-triggered probing request for at least one data metric, wherein the probing information includes product information and data metric information, the product information being used to identify products using the data metric, and the data metric information being used to identify the data metric; for each of the at least one data metric, performing the following steps to determine a first probing result for each data metric: in response to querying historical probing results of the data metric that have passed authentication based on the data metric information, determining a first probing result based on the historical probing results; in response to not querying historical probing results of the data metric that have passed authentication, determining a first probing result based on the probing information according to at least one probing method, wherein the first probing result includes at least one metric data source information and at least one metric definition of the data metric, and the at least one probing method includes obtaining the first probing result based on the probing information and the code repository corresponding to the product.

[0031] In a data indicator probing method provided in at least one embodiment of this disclosure, based on the data indicator probing information, the method first automatically searches for the existence of historical probing results for the data indicator that have passed authentication. If a historical probing result for the data indicator that has passed authentication is found, a first probing result is generated based on that result, enabling the reuse of accurate and valid historical probing results without re-probing the data indicator, thus improving probing efficiency. If no historical probing result for the data indicator that has passed authentication is found, an automatic probing is performed using at least one probing method to obtain the first probing result. Using an automatic probing method allows for the rapid determination of a relatively accurate first probing result, improving probing efficiency. This facilitates the automatic and intelligent probing of the first probing result based on a user-triggered probing request at the front end, and provides feedback to the user, achieving end-to-end data indicator probing.

[0032] Based on the data indicator probing method provided in at least one embodiment of this disclosure, at least one embodiment of this disclosure also provides a data indicator probing device, electronic device, computer-readable storage medium, and computer program product.

[0033] The present disclosure and some examples thereof will now be described in detail with reference to the accompanying drawings.

[0034] Figure 1 The illustration shows an application scenario of a data indicator exploration method provided by at least one embodiment of the present disclosure.

[0035] like Figure 1 As shown, the client can provide data metric exploration services. The client can be a standalone application (APP), embedded in the product's client, or a plugin installed in the product's client. For example, if the product is a webpage, the client can be a smart assistant displayed on the webpage.

[0036] Users can trigger a probe request for at least one data metric through the client. For example, a user can trigger a probe request for at least one data metric via a dialog on the client. The probe request includes a product screenshot and a request text. The product screenshot is an image containing product information and data metric information obtained by taking a screenshot of the product page. Users can annotate the product information and / or data metric information in the product screenshot. The request text indicates that the data metric should be probed. For example, the request text could be: "Probe the data metric circled in the image."

[0037] The client sends a probe request to the server for at least one data metric. Based on the user-triggered probe request for at least one data metric, the server determines the probe information for each of the at least one data metric. The probe information includes at least product information and data metric information. For example, if the product is a webpage, the product information might be a webpage link. Similarly, the data metric information might be the data metric name.

[0038] For each of the at least one data metric, the server performs the following steps 1-3 to determine the first probing result for each data metric.

[0039] Step 1: The server checks the probe information to see if there are any authenticated historical probe results for the data indicators. Authenticated historical probe results are accurate and valid probe results.

[0040] Step 2: If there are historical probe results that have passed authentication, the server determines the first probe result based on the historical probe results of the data indicators that have passed authentication.

[0041] Step 3: If no historical probe results are found that have passed authentication, the server determines the first probe result based on the probe information using at least one probe method. At least one probe method includes: obtaining the first probe result based on the probe information and the code repository corresponding to the product.

[0042] The initial probe results include at least one data source for each data metric and at least one metric definition. The server sends the initial probe results to the client. The client displays the initial probe results for each data metric for the user to view.

[0043] In this way, the system can automatically generate the first probe result based on the user-triggered probe request. Determining the first probe result by querying authenticated historical probe results, or by using at least one probe method, is highly efficient and fast, meeting users' needs for quickly understanding data metrics and improving user experience.

[0044] The following will combine Figures 2 to 4 A method for probing data indicators is described in detail according to at least one embodiment of the present disclosure.

[0045] Figure 2 The illustration shows a flowchart of a method for probing data indicators provided in at least one embodiment of the present disclosure.

[0046] like Figure 2 As shown, the data indicator probing method of this embodiment includes steps S201 to S202. In some embodiments, the executing entity of the data indicator probing method can be an electronic device with a server deployed, and one or more embodiments of this disclosure do not limit this. The data indicator probing method includes:

[0047] Step S201: Based on the exploration request triggered by the user for at least one data indicator, determine the exploration information for each of the at least one data indicator.

[0048] A probe request is a user-triggered request that instructs the user to probe at least one data metric. Probe requests can include multimodal content. For example, a probe request can include one or more of the following: text content, image content, audio content, and file content. This allows users to input multimodal content to trigger the generation of probe requests, facilitating users to probe data metrics in various ways.

[0049] Based on the exploration request, exploration information for each of at least one data metric can be determined. The exploration information for each data metric includes product information and data metric information.

[0050] Product information is used to identify products that use data metrics. For example, product information may include one or more identifying information such as product identifiers, product names, and product links.

[0051] Data metric information is used to identify data metrics. For example, data metric information includes one or more identifying elements such as the data metric name and data metric identifier. Data metric information can also include information about entities related to the data metric. For example, the entity information related to the data metric is a unique key. The unique key is used to identify the entity. Taking the data metric name "transaction amount" as an example, the entity related to the data metric could be a merchant, and the unique key would be the merchant identifier. Based on the information of entities related to the data metric, the data metric to be explored can be determined, clarifying the data metric to be explored and improving the accuracy of the initial exploration results.

[0052] In some embodiments, a probe request may include probe information. Where a probe request includes probe information, the probe information can be obtained from the probe request.

[0053] For example, if the probe request includes text content carrying probe information, the probe information can be extracted from the text content. As another example, if the probe request includes audio content carrying probe information, speech recognition can be performed on the audio content to obtain the probe information. Yet another example is if the probe request includes a table file containing probe information; the table file can be parsed to obtain the probe information.

[0054] In other embodiments, a probe request may be used to indicate how probe information is obtained.

[0055] For example, a probe request may include a product screenshot (image content) of a product page containing probe information and text content instructing the user to recognize the product screenshot (e.g., using an Optical Character Recognition (OCR) algorithm). Based on the text content, text recognition can be performed on the product screenshot to obtain the probe information. Additionally, the product screenshot may contain markers for data metrics and / or the product itself. These markers can be user-defined. For example, markers may be selection boxes or underlines used for highlighting. The text content can instruct the user to recognize the content marked in the product screenshot. Probe information obtained based on marker recognition is more accurate, avoids redundant information, and thus avoids interference with the probe of data metrics.

[0056] For example, a probe request may include a voice command (voice content) instructing the user to take a screenshot of the currently displayed product page and identify the probe information in the screenshot. Based on the voice command, a screenshot of the product page can be taken, and the probe information can be obtained by recognizing the screenshot.

[0057] As an example, step S201 includes: recognizing the content to be recognized according to the exploration request to obtain a recognition result for each of the at least one data indicator; for each of the at least one data indicator, performing the following steps to determine exploration information for each data indicator: displaying the recognition result; in response to obtaining confirmation information for the recognition result, using the recognition result as exploration information; in response to obtaining at least one editing operation for the recognition result, updating the recognition result according to the at least one editing operation, and determining exploration information based on the updated recognition result.

[0058] For example, the content to be identified is related to at least one data metric. The content to be identified can be determined based on the method of acquiring the probe information indicated in the probe request. For example, see... Figure 3 As shown, Figure 3 The illustration schematically depicts a probe request provided in at least one embodiment of this disclosure. The probe request includes image content (image.png, a screenshot of a product page) and text content titled "Identify the name of the indicator in the image".

[0059] The content to be recognized is a screenshot of the product entered by the user. The content to be recognized is then processed to obtain recognition results including multiple data indicators. For example... Figure 3 As shown, the identified data metrics include transaction amount, live stream duration, number of impressions per hour, and impression-view conversion rate. The identification result is a list of image metric names, including the metric name list. The metric name list includes the data metric name "Transaction Amount" for transactions and revenue, the data metric names "Live Stream Duration" and "Number of Impressions per Hour" for duration and efficiency, and the data metric name "Impression-View Conversion Rate" for conversion and funnel.

[0060] like Figure 3As shown, the recognition results are displayed on the interface so that users can confirm and / or adjust them. If the recognition result is correct, the user triggers a confirmation message for the result. In response to receiving the confirmation message, the recognition result is used as probe information. If there is a problem with the recognition result, such as incompleteness or errors, the user can edit the result at least once. For example, in some examples, when there is a problem with the recognition result, a first editing operation can be performed, i.e., the recognition result is updated based on the first editing operation to obtain an updated recognition result A, and the updated recognition result A is displayed for the user to confirm again. If the user confirms the updated recognition result A, probe information can be determined based on the updated recognition result A; or if the user believes that the updated recognition result A still has a problem, a second editing operation can be performed, i.e., the updated recognition result A is updated based on the second editing operation to obtain an updated recognition result B, and the updated recognition result B is displayed for the user to confirm, and so on, until a confirmation message is obtained, and probe information is determined based on the updated recognition result for which the confirmation message is applied.

[0061] Displaying recognition results, obtaining confirmation information, and enabling editing operations can all be achieved through interactive dialogue with the user. This allows for the acquisition of accurate investigation information through user interaction, thereby contributing to obtaining a highly accurate initial investigation result.

[0062] like Figure 2 As shown, step S202: For each of the at least one data indicator, steps S2021 and S2022 are performed to determine the first exploration result for each data indicator.

[0063] For example, the first investigation result includes at least one data source information for the data indicator and at least one indicator definition for the data indicator. The data source information indicates the origin of the data indicator; for example, it includes information about the raw data used to generate the indicator value. The indicator definition indicates the unified standard and rules for measuring the data indicator; for example, it includes statistical range, calculation logic, dimensional boundaries, time rules, etc., and is the basis for ensuring data comparability and reliability.

[0064] For example, the initial investigation results may also include information such as at least one indicator processing logic, at least one data lineage, and at least one call chain. For instance, indicator processing logic represents the complete calculation process and rules for transforming raw data into data indicators; it is the concrete implementation of indicator definitions at the technical level, essentially answering the question, "What needs to be done, how to do it, and according to what rules at each step from raw data to the final indicator?" For instance, data lineage describes the lineage relationships throughout the entire lifecycle of a data indicator, recording the processing of the data indicator from the data source to the final indicator value. For instance, the call chain refers to the complete call path and interaction logic of the data indicator from calculation and generation to terminal consumption; it describes how indicator data flows between different products, modules, and roles.

[0065] This disclosure does not impose specific limitations on the content included in the first exploration results.

[0066] When at least one data indicator includes multiple data indicators, the first exploration results of multiple data indicators can be determined in parallel to improve processing efficiency.

[0067] Step S2021: In response to the data indicator information, query the historical exploration results of the data indicator that have passed authentication, and determine the first exploration result based on the historical exploration results of the data indicator that have passed authentication. Based on the data indicator information, query whether there are any historical exploration results of the data indicator that have passed authentication. Historical exploration results that have passed authentication are relatively effective and accurate historical exploration results. Authentication can be done manually.

[0068] In one possible implementation, after confirming that the historical exploration results of a data metric have passed authentication, authentication information for the data metric is generated based on these authenticated historical exploration results. The authentication information includes the authenticated historical exploration results, exploration information (including product information and data metric information), and the authentication time. The historical exploration results include at least one data source for the data metric and at least one metric definition for the data metric.

[0069] Authentication information can be stored in an authentication information table. After obtaining the exploration information of a data metric, the authentication information of the data metric can be queried in the authentication information table. If the authentication information of the data metric is found, the historical exploration results that have passed authentication can be retrieved from the authentication information. As an example, the historical exploration results of a certified data metric may also include other information obtained during the exploration process, as well as other information that can be further analyzed based on the metric data source information and metric caliber. For example, the historical exploration results of a certified data metric may also include at least one metric processing logic, at least one data lineage, at least one call chain, etc.

[0070] The first exploration result is determined based on the historical exploration results of certified data indicators. For example, the historical exploration results of certified data indicators can be used as the first exploration result. Another example is using the historical exploration results of certified data indicators that include partial information about the indicator's data source and definition as the first exploration result. This allows for the reuse of effective historical exploration information, reducing the cost of determining the first exploration result and improving its efficiency.

[0071] In some possible embodiments, step S2021 includes: querying multiple authenticated candidate historical exploration results of the data indicators based on the data indicator information; determining the candidate historical exploration result whose authentication time is closest to the current time among the multiple authenticated candidate historical exploration results as the historical exploration result, and determining the first exploration result based on the historical exploration result.

[0072] For example, multiple verified candidate historical probe results for a data metric might be retrieved. When multiple verified candidate historical probe results are retrieved, the first probe result is determined based on the candidate historical probe result whose verification time is closest to the current time. The candidate historical probe result whose verification time is closest to the current time is the most effective candidate historical probe result, thus improving the effectiveness of the determined first probe result.

[0073] like Figure 2 As shown, step S2022: In response to the historical exploration results of the authentication that did not find any data indicators, a first exploration result is determined based on the exploration information according to at least one exploration method.

[0074] At least one exploration method includes obtaining a first exploration result based on exploration information and the code repository corresponding to the product. The code repository corresponding to the product includes the product's code. Based on the code repository, the process of analyzing product usage data metrics can be performed to obtain the first exploration result.

[0075] In one or more embodiments of this disclosure, at least one probing method may include one or more of code probing, log probing, and manual probing. Code probing refers to probing the code to be probed that is related to data metrics. Log probing refers to probing both the code to be probed and the logs to be probed that are related to data metrics. Manual probing refers to probing conducted manually. Code probing and log probing can be automated. Manual probing can be achieved by sending probing tasks to probing personnel, who then perform probing based on the code repository and probing information, and obtain initial probing results from the probing personnel.

[0076] Based on the relevant content of steps S201-S202 above, it can be seen that at least one embodiment of this disclosure provides a method for probing data indicators, which can automatically generate a first probing result, accurately, quickly and automatically find the indicator data source information and indicator metadata information such as indicator caliber of the data indicator, and realize end-to-end automatic processing.

[0077] In some embodiments, step S2022, based on the exploration information and according to at least one exploration method, determines the first exploration result, including steps A1 and A2:

[0078] Step A1: Based on the exploration information, determine the content to be explored in the code repository corresponding to the product that is related to the data indicators, and analyze the content to be explored to obtain the content analysis results.

[0079] Step A2: Determine the first exploration result based on the content analysis results.

[0080] In some embodiments, the step A1 of determining the content to be explored related to data metrics in the code repository corresponding to the product based on the exploration information may include: determining the code repository corresponding to the product based on the product information; and obtaining the content to be explored from the code repository based on one or more types of information included in the exploration information.

[0081] In step A1, the code repository corresponding to the product can be determined based on the product information in the exploration information. In one possible implementation, the product is a webpage, and the product information includes the webpage link (e.g., a Uniform Resource Locator (URL)). For example, firstly, an association table between products and code repositories can be established. This association table includes the correspondence between the product's webpage link and the corresponding code repository. Then, based on the webpage link, the code repository corresponding to the webpage link, i.e., the code repository corresponding to the product, is retrieved from the association table.

[0082] In step A1, the content to be explored is retrieved from the code repository based on one or more pieces of information included in the exploration information. The content to be explored is related to data metrics.

[0083] The method of obtaining the content to be explored, as well as the specific content included in the content to be explored, are related to the exploration method.

[0084] For code probing, the content to be probed is the code to be probed. Based on the data metric information included in the probing information, the code to be probed related to the data metric is determined from the code repository. For example, if the data metric information is the data metric name, the code to be probed is the code related to the data metric name field whose field value is the data metric name.

[0085] For log probing, the content to be probed includes the code to be probed and the logs to be probed. Based on the data metric information included in the probing information, the code to be probed that is related to the data metric is determined from the code repository. The method for determining the code to be probed in log probing can be the same as the method for determining the code to be probed in the code probing method described above.

[0086] Based on the exploration information, identify the logs related to the data metrics from the code repository. For example, based on the product information included in the exploration information, determine the Product Service Module (PSM) in the code repository; based on the data metric information included in the exploration information, determine the interfaces in the code repository that relate to the data metrics; based on the PSM and the data metric interfaces, query the log interfaces, and retrieve the logs related to the data metrics through the log interfaces. Query methods can include Retrieval-Augmented Generation (RAG) or Function Call, etc.

[0087] For example, content analysis results may include at least one data source information for a data metric and at least one data metric processing logic. The at least one data metric processing logic is used to determine at least one metric definition. For example, there is a one-to-one correspondence between the at least one data metric processing logic and the at least one data metric definition.

[0088] In some embodiments, for the code probing method, the analysis of the content to be probed in step A1 to obtain the content analysis result includes: calling the first language model to process the code to be probed and the code analysis prompt words to obtain the code analysis result.

[0089] The code analysis results include the primary data source information for the data metrics and the primary processing logic for the data metrics. At least one metric's data source information includes the primary data source information. At least one metric's processing logic includes the primary processing logic.

[0090] A large language model (LLM), also known as a large model, is used. The first large language model in this embodiment can also be a multimodal large model. The first large language model can be trained using the code to be explored and the code analysis results corresponding to the code to be explored.

[0091] The first major language model is invoked to process the code to be explored and the code analysis prompts, resulting in code analysis results, including steps B1-B3:

[0092] Step B1: Call the first major language model to process the code to be explored and the first sub-prompt word to obtain the table information of the first data table related to the data indicators and the first sub-processing logic.

[0093] For example, the first sub-hint is used to instruct the first language model to process the code to be explored in order to obtain table information of the first data table and the first sub-processing logic related to the data indicators. For example, the table information of the first data table includes the table name of the first data table. The first sub-processing logic is obtained from the code to be explored and refers to the processing logic that generates the indicator values ​​of the data indicators based on the first data table. The first processing logic includes the first sub-processing logic.

[0094] In some embodiments, the first language model is invoked based on the prompting engineering to process the code to be explored and the first sub-prompt word. The first sub-prompt word may be a prompt word optimized using the prompting engineering.

[0095] For example, the prompting engineering includes one or more of the following: Chain-of-Thought (CoT), Program-of-Thought (PoT), Self-Consistency (SC), Tree-of-Thought (ToT), and Reasoning and Acting / Program-Aided Language Models (ReAct / PAL). CoT is used for step-by-step reasoning, suitable for judging and interpreting complex logic. PoT can transform steps into executable programs or pseudocode, facilitating tool integration and verification. SC generates multiple independent CoT reasoning paths for the same problem, selecting the most consistent answer through majority voting or confidence aggregation, reducing random errors and improving robustness and stability. ToT generalizes the reasoning process into a tree structure, with each node being a "thinking unit," allowing branch exploration, evaluation, and backtracking to find the globally optimal path. ReAct / PAL is used for thinking and acting simultaneously, linking with static analyzers / executors, suitable for locating and verifying closed loops.

[0096] Step B2: Call the first major language model to process the table information of the first data table and the second sub-prompt words to obtain the base table information of the first authentication base table related to the data indicators.

[0097] For example, the second sub-prompt word is used to indicate that the first large language model obtains the base table information of the first authentication base table related to the data indicators based on the table information of the first data table.

[0098] For example, the first authentication base table is a data base table that has been certified as a data source and can serve as a data indicator. The first authentication base table is either the first data table or an upstream data table of the first data table. The upstream data table of the first data table is the data table used to generate the first data table and is located upstream in the lineage of the first data table. The upstream data table of the first data table can be determined based on the lineage of the first data table or the data production and processing task corresponding to the first data table.

[0099] The base table information for the first authentication base table can be the base table name or identifier. The first data source information includes the base table information for the first authentication base table.

[0100] In some embodiments, the first language model can determine whether the first data table is an authenticated base table. An authenticated base table can be recorded in the data source authentication table. An authenticated base table can serve as a data source.

[0101] If the first data table is a certified base table, then the first data table will be used as the first certification base table, and the table information of the first data table will be used as the base table information of the first certification base table.

[0102] If the first data table is not an authenticated base table, then based on the table information of the first data table, at least one upstream data table of the first data table is determined, and the first authentication base table is determined from at least one upstream data table of the first data table.

[0103] As an example, the data production and processing tasks corresponding to the first data table can be retrieved from the data production task processing table based on the table information of the first data table. The data production task processing table includes relevant information about the data production and processing tasks. The data production and processing tasks corresponding to the first data table are tasks involving processing the first data table. Based on the data production and processing tasks corresponding to the first data table, the first upstream data table of the first data table can be determined, for example, data table A. If data table A (the first upstream data table of the first data table) is a certified base table, then data table A is used as the first certified base table, and the table information of data table A is used as the base table information of the first certified base table. If data table A is not a certified base table, then based on the data production and processing tasks corresponding to data table A, the upstream data table B (the second upstream data table of the first data table) of data table A is determined. If data table B is a certified base table, then data table B is used as the first certified base table, and the table information of data table B is used as the base table information of the first certified base table. If data table B is not a certified base table, then the upstream data tables are further determined and it is determined whether they are certified base tables. This process continues until the first certification table is determined.

[0104] Step B3: Call the first major language model to process the base table information of the first authentication base table and the third sub-prompt word, determine the first data production and processing task related to the data indicators, and obtain the second sub-processing logic based on the first data production and processing task.

[0105] For example, the third sub-prompt word is used to instruct the first major language model to determine the first data production and processing task related to the data indicators based on the base table information of the first authentication base table, and to analyze the first data production and processing task to obtain the second sub-processing logic.

[0106] If the first certification base table is the first data table, the first data production and processing task related to the data indicators is the data production and processing task corresponding to the first data table.

[0107] If the first certification base table is an upstream data table of the first data table, the first data production and processing task related to the data indicators includes the data production and processing task corresponding to each data table involved in the process of obtaining the first certification base table from the first data table.

[0108] For example, the second sub-processing logic refers to the processing logic that generates the index values ​​of data indicators in the first data production and processing task. The first processing logic also includes the second sub-processing logic.

[0109] For example, code analysis hints may include the first sub-hint, the second sub-hint, and the third sub-hint mentioned above.

[0110] The above is an introduction to code probing methods. The following section introduces log probing methods.

[0111] In some embodiments, for the log probing method, the analysis of the content to be probed in step A1 to obtain the content analysis result includes: calling the first language model to process the code to be probed and the code analysis prompt words to obtain the code analysis result; calling the second language model to process the log to be probed and the log analysis prompt words to obtain the log analysis result.

[0112] For example, content analysis results include code analysis results and log analysis results.

[0113] The code analysis results include the primary data source information for the data metrics and the primary processing logic for the data metrics.

[0114] It should be noted that the processing procedures for data metrics recorded in the logs to be investigated may not be complete, while the processing procedures for data metrics involved in the code to be investigated are more complete. The log analysis results obtained based on the logs to be investigated can serve as a supplement to the code analysis results obtained from the code analysis of the code to be investigated.

[0115] The log analysis results may include secondary data source information and / or secondary processing logic for the data metrics, or may not include secondary data source information and secondary processing logic for the data metrics.

[0116] For log probing methods, at least one metric data source includes first data source information. If the log analysis results include second data source information, at least one metric data source also includes the second data source information. At least one metric processing logic includes first processing logic. If the log analysis results include second processing logic, at least one metric processing logic also includes the second processing logic.

[0117] The method of calling the first language model to process the code to be explored and the code analysis prompt words to obtain the code analysis results can be the same as the method of implementing steps B1-B3 above.

[0118] In one possible implementation, a second major language model is invoked to process the log to be explored and the log analysis prompts to obtain the log analysis results. The second major language model can be the same as the first major language model, or it can be a different major language model. The second major language model can be trained using the log to be explored and the corresponding log analysis results.

[0119] The second largest language model is called to process the log to be explored and the log analysis prompts to obtain the log analysis results, including steps C1-C4:

[0120] Step C1: Call the second language model to process the log to be explored and the fourth sub-prompt word to obtain the processing result.

[0121] For example, the fourth sub-cue word is used to instruct the second largest language model to analyze the log to be explored and obtain the processing results.

[0122] Step C2: In response to the processing result indication, obtain the table information of the second data table related to the data indicators, or, in response to the processing result indication, obtain the table information of the second data table related to the data indicators and the third sub-processing logic, call the second major language model to process the table information of the second data table and the fifth sub-prompt word, obtain the base table information of the second authentication base table related to the data indicators, call the second major language model to process the base table information of the second authentication base table and the sixth sub-prompt word, determine the second data production and processing task related to the data indicators, and obtain the fourth sub-processing logic based on the second data production and processing task.

[0123] For example, log analysis prompts include a fourth sub-prompt, a fifth sub-prompt, and a sixth sub-prompt.

[0124] For example, the fifth sub-hint is used to instruct the second language model to process the table information of the second data table to obtain the base table information of the second authentication base table related to the data indicators. The sixth sub-hint is used to instruct the second language model to process the base table information of the second authentication base table to determine the second data production and processing task related to the data indicators, and to obtain the fourth sub-processing logic by analyzing the second data production and processing task.

[0125] For example, the third sub-processing logic refers to the processing logic of the data indicators recorded in the log to be explored. The second processing logic includes the third sub-processing logic.

[0126] If the processing result indicates that table information of a second data table related to the data indicators can be obtained, a second authentication base table is determined based on the table information of the second data table. The second authentication base table is a data base table that has been authenticated and can serve as a data source. The second authentication base table is either the second data table or an upstream data table of the second data table. The implementation method for determining the base table information of the second authentication base table based on the table information of the second data table is similar to the implementation method for determining the base table information of the first authentication base table based on the table information of the first data table.

[0127] For example, the second data source information includes the base table information of the second authentication base table. For example, the base table information of the second authentication base table is the table name of the second authentication base table.

[0128] For example, the fourth sub-processing logic refers to the processing logic that generates the index values ​​of data indicators in the second data production and processing task. The second processing logic also includes the fourth sub-processing logic.

[0129] The implementation method of obtaining the fourth sub-processing logic based on the base table information of the second authentication base table is similar to the implementation method of obtaining the second sub-processing logic in step B3 above, and will not be repeated here.

[0130] Step C3: In response to the processing result indication, the third sub-processing logic is obtained to obtain the log analysis result excluding the second data source information.

[0131] Step C4: In response to the processing result indicating that no table information of the second data table and the third sub-processing logic were obtained, a log analysis result excluding the second processing logic and the second data source information is obtained.

[0132] In some embodiments, for the log probing method, step A2 above includes: in response to the log analysis result including the second processing logic and the second data source information, determining the first probing result based on the code analysis result and the log analysis result; in response to the log analysis result not including the second processing logic and / or the second data source information, determining the first probing result based on the code analysis result.

[0133] When the log analysis results include information from the second processing logic and the second data source, the information contained in the log analysis results is relatively complete and can be used together with the code analysis results to determine the first probing result. As an example, the better result can be selected from the log analysis results and the code analysis results as the first probing result. As another example, the log analysis results and the code analysis results can be used together as the first probing result.

[0134] If the log analysis results do not include the second processing logic and / or second data source information, meaning the information included in the log analysis results is incomplete, the first probing result is determined based on the code analysis results; that is, the code analysis results are directly used as the first probing result. This ensures the completeness of the first probing result. Furthermore, the log analysis results can be displayed as supplementary information to the first probing result for user reference.

[0135] In some embodiments, the probing method for data metrics provided in this disclosure further includes: if no historical probing results for the data metric that have passed authentication are found, after determining a first probing result based on the probing information according to at least one probing method, the first probing result can be authenticated. The authentication method can be manual authentication. For example, task information for an authentication task can be sent to an authenticator. The task information for the authentication task includes probing information and a first probing result. The authenticator can execute the authentication task and provide feedback on the authentication result of the first probing result.

[0136] If the authentication result of the first probe for the data metric is successful, then the authentication information for the data metric is generated based on the first probe result and the probe information, and the authentication information is stored. This enables the persistent storage of the successful first probe result, facilitating its subsequent reuse.

[0137] In some cases, the data source and definition of data metrics may change as business operations adjust. In some embodiments, the probing method for data metrics provided in this disclosure further includes: for each data metric: determining authentication information for the data metric; in response to meeting the update conditions for the authentication information, determining a second probing result for the data metric based on the probing information included in the authentication information of the data metric, according to at least one probing method; and in response to obtaining authentication pass information for the second probing result, updating the authentication information of the data metric according to the second probing result.

[0138] For example, if the historical exploration results of a data indicator that has passed authentication are found, the authentication information includes the historical exploration results and exploration information of the data indicator that has passed authentication; or, if the historical exploration results of a data indicator that has passed authentication are not found, the authentication information includes the first exploration result and exploration information of the data indicator that has passed authentication.

[0139] For example, update conditions include identifying code and / or log changes in the code repository that are related to data metrics. For instance, code and / or log changes related to data metrics might be related to changes in the data metric's data source. This allows for timely responses to updated code repositories to obtain second-stage probing results, enabling updates to authentication information and ensuring its validity.

[0140] In another possible implementation, the update condition can also include reaching a preset timed update cycle. By setting a timed update cycle, data metrics can be re-examined, reducing the problem of untimely updates to authentication information caused by missing data source data or changes in metric definitions.

[0141] In some embodiments, the data metric probing method provided in this disclosure further includes scoring and ranking the probing results generated by various probing methods. For example, at least one probing method includes code probing, log probing, and manual probing. At least one metric data source information includes a first metric data source information corresponding to the code probing method, a second metric data source information corresponding to the log probing method, and a third metric data source information corresponding to the manual probing method. At least one metric caliber includes a first metric caliber corresponding to the code probing method, a second metric caliber corresponding to the log probing method, and a third metric caliber corresponding to the manual probing method. The results obtained from the three probing methods can be scored and ranked.

[0142] As an example, the data indicator exploration method provided in this disclosure also includes: determining the scores of the first indicator data source information, the second indicator data source information, and the third indicator data source information based on the data source evaluation dimension.

[0143] Data source evaluation dimensions can be pre-defined dimensions used to evaluate the quality of the data source. For example, data source evaluation dimensions may include one or more of the following: integrity, stability, and traceability.

[0144] Scoring the information from the first, second, and third indicator data sources can be achieved by calling a third major language model. This third major language model can be trained using the training indicator data sources and their corresponding scores. The third major language model can be the first major language model, the second major language model, or a major language model that is different from both the first and second major language models.

[0145] As an example, the data indicator exploration method provided in this disclosure also includes: determining the score of the first indicator, the score of the second indicator, and the score of the third indicator based on the indicator caliber evaluation dimension.

[0146] The evaluation dimensions for indicators can be pre-selected dimensions used to evaluate the quality of the indicator definitions. For example, the evaluation dimensions for indicator definitions may include one or more of the following: content clarity, product adaptability, and maintainability.

[0147] Scoring the first, second, and third indicator metrics can be achieved by calling the fourth language model. The fourth language model can be trained using the training indicator metrics and their corresponding scores. The fourth language model can be the first, second, or third language model, or it can be a different language model from all three.

[0148] For example, a higher score for the indicator data source information indicates higher quality and reliability of the indicator data source information; a higher score for the indicator definition indicates higher quality and reliability of the indicator definition.

[0149] The scores obtained from the scoring can be used to select the best indicator data source information and the best indicator caliber, and recommend them to users. They can also be displayed to users together with the first investigation results as a basis for quality reference.

[0150] As an example, the flow of a method for probing data indicators provided in at least one embodiment of this disclosure is illustrated below.

[0151] See Figure 4 As shown, Figure 4 The illustration shows a schematic diagram of a probe request process provided by at least one embodiment of the present disclosure.

[0152] During the user input phase, users can trigger a search request by inputting product screenshots, text, voice, or uploading forms, among other methods. Intelligent recognition is performed based on the search request to determine the recognition result. If the user confirms the recognition result, the search result is determined based on the confirmed result, and the process enters the intelligent search phase. In the intelligent search phase, the system first checks for the existence of verified historical search results for data metrics. If verified historical search results are found, the first search result is determined based on these historical results. If no verified historical search results are found, one or more of the following methods—code search, log search, and manual search—are used to obtain the first search result. At least one metric data source and at least one metric definition obtained through different search methods within the first search result are then scored. Based on the scores, superior metric data sources and metric definitions are recommended to the user, or the user is provided with a score for reference. Then, the authentication phase begins, triggering the authentication task. If the authentication result for the first exploration result is obtained, the authentication information for the data indicators is generated based on the first exploration result and exploration information of the data indicators, and the authentication information is stored.

[0153] This disclosure provides at least one embodiment of a method for probing data metrics. Based on a user-inputted probing request, it can automatically identify probing information using image recognition technology and intelligently probe the data source information and metrics definitions of the backend data metrics using large-scale model inference technology. At least one probing method employs multi-dimensional feature matching, rather than simple keyword matching, improving the accuracy of the initial probing results. Automatic probing is achieved based on a large-scale model, possessing self-learning capabilities, and the probing effect is continuously optimized with use.

[0154] Technically, this approach analyzes code and / or logs without intruding on the code repository. Compared to manual probing, it improves the accuracy and efficiency of initial probing results, reducing the time required from hours to minutes or even seconds. Furthermore, it is compatible with manual authentication methods, enriching the ways to obtain initial probing results. Additionally, it can generate and store authentication information based on historical or initial probing results that have passed authentication, achieving systematic storage of probing results and improving their reusability. Finally, it can automatically re-probe and update authentication information when update conditions are met, ensuring the freshness of authentication information.

[0155] In terms of business applications, the initial exploration results can be applied to other data applications derived from data metrics, metric metadata, and processing logic. These include metric value exploration, data metric comparison, generating data metric consumption interfaces, recommending similar data metrics, and generating data metric cards. Furthermore, based on user-triggered exploration requests, it can reverse-engineer data metrics that users are highly interested in or that have a wide range of applications. This helps users identify differences in the metric definitions of similar data metrics across different products. When metric metadata changes are detected, it can explore data metrics in a way that reduces change costs and enables rapid, automated change detection, thus contributing to the consistency of data metrics and the clarity of metric definitions within the business domain.

[0156] Based on the data indicator probing method provided in at least one embodiment of this disclosure, at least one embodiment of this disclosure also provides a data indicator probing device. The following will be combined with... Figure 5 This device for detecting data indicators is described in detail.

[0157] Figure 5 The illustration shows a schematic diagram of a data index probing device provided in at least one embodiment of the present disclosure.

[0158] like Figure 5 As shown, the data indicator probing device 500 of this embodiment includes a determination module 501 and an execution module 502. For example, these units or modules can be implemented by hardware (e.g., circuit) modules or software modules, as is the case in the following embodiments, and will not be repeated here. For example, these units or modules can be implemented by a central processing unit (CPU), a general-purpose graphics processor (GPGPU), a graphics processing unit (GPU), a tensor processor (TPU), a field-programmable gate array (FPGA), or other forms of processing units with data indicator probing capabilities and / or instruction execution capabilities, along with corresponding computer instructions.

[0159] The determination module 501 is configured to: determine the exploration information of each of the at least one data indicator based on the exploration request triggered by the user for at least one data indicator, wherein the exploration information includes product information and data indicator information, the product information is used to identify the product using the data indicator, and the data indicator information is used to identify the data indicator.

[0160] Execution module 502 is configured to perform the following steps for each of the at least one data metric to determine a first probing result for each of the data metrics:

[0161] In response to querying the historical exploration results of the data indicator that have passed authentication based on the data indicator information, the first exploration result is determined based on the historical exploration results;

[0162] In response to the absence of a verified historical exploration result for the data indicator, the first exploration result is determined based on the exploration information according to at least one exploration method.

[0163] The first exploration result includes at least one indicator data source information of the data indicator and at least one indicator definition of the data indicator. The at least one exploration method includes obtaining the first exploration result based on the exploration information and the code repository corresponding to the product.

[0164] In at least one embodiment of this disclosure, the execution module 502 is further configured to:

[0165] Based on the exploration information, the content to be explored in the code repository corresponding to the product and related to the data indicator is determined, and the content to be explored is analyzed to obtain the content analysis result. The content analysis result includes at least one indicator data source information of the data indicator and at least one indicator processing logic of the data indicator. The at least one indicator processing logic is used to determine the at least one indicator caliber.

[0166] The first exploration result is determined based on the content analysis results.

[0167] In at least one embodiment of this disclosure, the execution module 502 is further configured to:

[0168] Based on the product information, determine the code repository corresponding to the product;

[0169] The content to be explored is obtained from the code repository based on one or more of the product information and the data indicator information.

[0170] In at least one embodiment of this disclosure, the at least one probing method includes a code probing method, and the execution module 502 is further configured to:

[0171] The first major language model is invoked to process the code to be explored and the code analysis prompts to obtain the code analysis results;

[0172] Wherein, the content to be explored is the code to be explored, the content analysis result is the code analysis result, the code analysis result includes the first data source information of the data indicator and the first processing logic of the data indicator, the at least one indicator data source information includes the first data source information, and the at least one indicator processing logic includes the first processing logic.

[0173] In at least one embodiment of this disclosure, the execution module 502 is further configured to:

[0174] The first large language model is invoked to process the code to be explored and the first sub-prompt word to obtain the table information of the first data table and the first sub-processing logic related to the data indicator. The code analysis prompt word includes the first sub-prompt word, and the first sub-processing logic refers to the processing logic that generates the indicator value of the data indicator based on the first data table. The first processing logic includes the first sub-processing logic.

[0175] The first large language model is called to process the table information of the first data table and the second sub-prompt word to obtain the base table information of the first authentication base table related to the data indicator. The code analysis prompt word also includes the second sub-prompt word. The first authentication base table is a data base table that has been certified and can be used as a data source. The first authentication base table is the first data table or the upstream data table of the first data table. The first data source information includes the base table information of the first authentication base table.

[0176] The first large language model is invoked to process the base table information of the first authentication base table and the third sub-prompt word, to determine the first data production and processing task related to the data indicator, and to obtain the second sub-processing logic based on the first data production and processing task. The code analysis prompt word also includes the third sub-prompt word. The second sub-processing logic refers to the processing logic that generates the indicator value of the data indicator in the first data production and processing task. The first processing logic also includes the second sub-processing logic.

[0177] In at least one embodiment of this disclosure, the at least one probing method includes a log probing method, and the execution module 502 is further configured to:

[0178] The first major language model is invoked to process the code to be explored and the code analysis prompts to obtain the code analysis results;

[0179] The second largest language model is called to process the logs to be explored and the log analysis prompts to obtain the log analysis results;

[0180] The content to be explored includes the code to be explored and the log to be explored. The content analysis results include the code analysis results and the log analysis results. The code analysis results include the first data source information and the first processing logic of the data indicator. The log analysis results include the second data source information and / or the second processing logic of the data indicator, or may not include the second data source information and the second processing logic of the data indicator.

[0181] The at least one indicator data source information includes the first data source information. If the log analysis result includes the second data source information, the at least one indicator data source information further includes the second data source information.

[0182] The at least one indicator processing logic includes the first processing logic, and if the log analysis result includes the second processing logic, the at least one indicator processing logic further includes the second processing logic.

[0183] In at least one embodiment of this disclosure, the execution module 502 is further configured to:

[0184] The second major language model is invoked to process the log to be explored and the fourth sub-prompt word to obtain the processing result, wherein the log analysis prompt word includes the fourth sub-prompt word:

[0185] In response to the processing result indication, table information of a second data table related to the data indicator is obtained; or, in response to the processing result indication, table information of a second data table related to the data indicator and a third sub-processing logic are obtained, wherein the third sub-processing logic refers to the processing logic of the data indicator recorded in the log to be explored, and the second processing logic includes the third sub-processing logic.

[0186] The second large language model is invoked to process the table information of the second data table and the fifth sub-prompt word to obtain the base table information of the second authentication base table related to the data indicator. The log analysis prompt word also includes the fifth sub-prompt word. The second authentication base table is a certified data base table that can serve as a data source. The second authentication base table is the second data table or an upstream data table of the second data table. The second data source information includes the base table information of the second authentication base table.

[0187] The second large language model is invoked to process the base table information of the second authentication base table and the sixth sub-prompt word, to determine the second data production and processing task related to the data indicator, and to obtain the fourth sub-processing logic based on the second data production and processing task. The log analysis prompt word further includes the sixth sub-prompt word, and the fourth sub-processing logic refers to the processing logic that generates the indicator value of the data indicator in the second data production and processing task. The second processing logic also includes the fourth sub-processing logic. In response to the processing result indication, the third sub-processing logic is obtained to obtain the log analysis result excluding the second data source information.

[0188] In response to the processing result indicating that the table information of the second data table and the third sub-processing logic were not obtained, the log analysis result excluding the second processing logic and the second data source information is obtained.

[0189] In at least one embodiment of this disclosure, the execution module 502 is further configured to:

[0190] In response to the log analysis results including the second processing logic and the second data source information, the first probing result is determined based on the code analysis results and the log analysis results;

[0191] In response to the log analysis results not including the second processing logic and / or the second data source information, the first probing result is determined based on the code analysis results.

[0192] In at least one embodiment of this disclosure, the execution module 502 is further configured to:

[0193] In response to the absence of a historical exploration result for the data indicator that has passed authentication, after determining the first exploration result based on the exploration information according to at least one exploration method, in response to obtaining the authentication pass result for the first exploration result, the authentication information for the data indicator is generated based on the first exploration result and the exploration information, and the authentication information is stored.

[0194] In at least one embodiment of this disclosure, the execution module 502 is further configured to:

[0195] For each of the aforementioned data metrics:

[0196] Determine the authentication information of the data indicator, wherein the authentication information includes the historical exploration results of the data indicator that have passed authentication and the exploration information, or includes the first exploration result that has passed authentication and the exploration information;

[0197] In response to meeting the update conditions for the authentication information, a second exploration result of the data metric is determined based on the exploration information included in the authentication information, according to the at least one exploration method, wherein the update conditions include determining code and / or log changes related to the data metric in the code repository;

[0198] In response to obtaining authentication pass information for the second probe result, the authentication information is updated based on the second probe result.

[0199] In at least one embodiment of this disclosure, the execution module 502 is further configured to:

[0200] Based on the data indicator information, multiple verified candidate historical exploration results for the data indicator are retrieved;

[0201] The candidate historical exploration result with the closest authentication time to the current time among the multiple authenticated candidate historical exploration results is determined as the historical exploration result, and the first exploration result is determined based on the historical exploration result.

[0202] In at least one embodiment of this disclosure, the exploration request includes exploration information, or the exploration request is used to indicate the method of obtaining the exploration information.

[0203] In at least one embodiment of this disclosure, the determining module 501 is further configured to:

[0204] According to the exploration request, the content to be identified is identified to obtain the identification result for each of the at least one data indicator, wherein the content to be identified is related to the at least one data indicator;

[0205] For each of the at least one data metric, the following steps are performed to determine the exploration information for each data metric:

[0206] Display the recognition results;

[0207] In response to obtaining confirmation information regarding the identification result, the identification result is used as the exploration information;

[0208] In response to obtaining at least one editing operation on the recognition result, the recognition result is updated according to the at least one editing operation, and the exploration information is determined based on the updated recognition result.

[0209] In at least one embodiment of this disclosure, the at least one probing method includes code probing, log probing, and manual probing.

[0210] The at least one indicator data source information includes a first indicator data source information corresponding to the code probing method, a second indicator data source information corresponding to the log probing method, and a third indicator data source information corresponding to the manual probing method.

[0211] The at least one indicator includes a first indicator corresponding to the code exploration method, a second indicator corresponding to the log exploration method, and a third indicator corresponding to the manual exploration method;

[0212] The execution module 502 is further configured to:

[0213] Based on the data source evaluation dimensions, the scores of the first indicator data source information, the second data source information, and the third indicator data source information are determined respectively. The data source evaluation dimensions include one or more of the following: integrity dimension, stability dimension, and traceability dimension.

[0214] Based on the evaluation dimensions of the indicator criteria, the scores of the first indicator criterion, the second indicator criterion, and the third indicator criterion are determined respectively. The evaluation dimensions of the indicator criterion include one or more of the following: content clarity dimension, product adaptability dimension, and maintainability dimension.

[0215] It should be noted that, for clarity and brevity, this disclosure does not provide all the constituent units of the data indicator detection device 500. To achieve the necessary functions of the data indicator detection device 500, those skilled in the art can provide and set other constituent units (not shown) according to specific needs, and one or more embodiments of this disclosure do not limit this.

[0216] At least one embodiment of this disclosure also provides an electronic device, including a processing device and a storage device, the storage device including one or more computer program modules; wherein the one or more computer program modules are stored in the storage device and configured to be executed by the processing device, the one or more computer program modules being used to implement the data index probing method provided in any embodiment of this disclosure.

[0217] For example, the processing device may be a processor, such as a central processing unit (CPU), digital signal processor (DSP), image processor (GPU), general-purpose graphics processor (GPGPU), or other form of processing unit with the ability to probe data metrics and / or execute instructions. It may be a general-purpose processor or a special-purpose processor and may control other components in the electronic device to perform the desired functions.

[0218] For example, the storage device may be a memory, which may include one or more computer program products. These computer program products may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may, for example, include random access memory (RAM) and / or cache memory. The non-volatile memory may, for example, include read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and a processing device may execute these program instructions to implement the functions (implemented by the processing device) in the embodiments of this disclosure and / or other desired functions. Various application programs and various data may also be stored in the computer-readable storage medium, which is not limited by the embodiments of this disclosure.

[0219] The following is for reference. Figure 6 The diagram illustrates a structural schematic of an electronic device (e.g., a terminal device or a server) 600 suitable for implementing embodiments of the present disclosure. The terminal device in the embodiments of the present disclosure may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 6 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments disclosed herein.

[0220] like Figure 6 As shown, electronic device 600 may include a processing device (e.g., a central processing unit, a graphics processor, etc.) 601, which can perform various appropriate actions and processes according to a program stored in read-only memory (ROM) 602 or a program loaded from storage device 608 into random access memory (RAM) 603. RAM 603 also stores various programs and data required for the operation of electronic device 600. Processing device 601, ROM 602, and RAM 603 are interconnected via bus 604. Input / output (I / O) interface 605 is also connected to bus 604.

[0221] Typically, the following devices can be connected to I / O interface 605: input devices 606 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 607 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 608 including, for example, magnetic tapes, hard disks, etc.; and communication devices 609. Communication device 609 allows electronic device 600 to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 6 An electronic device 600 with various devices is shown; however, it should be understood that it is not required to implement or possess all of the devices shown. More or fewer devices may be implemented or possessed alternatively.

[0222] In particular, according to one or more embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, one or more embodiments of this disclosure include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 609, or installed from a storage device 608, or installed from a ROM 602. When the computer program is executed by the processing device 601, it performs the functions defined in the methods of the embodiments of this disclosure.

[0223] It should be noted that the computer-readable medium described in this disclosure can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can 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 of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, 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 device, magnetic storage device, or any suitable combination thereof. In this disclosure, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this disclosure, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0224] In some implementations, clients and servers can communicate using any currently known or future-developed network protocol such as HTTP (Hypertext Transfer Protocol) and can interconnect with digital data communication (e.g., communication networks) of any form or medium. Examples of communication networks include local area networks (“LANs”), wide area networks (“WANs”), the Internet (e.g., the Internet of Things), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future-developed networks.

[0225] The aforementioned computer-readable medium may be included in the aforementioned electronic device; or it may exist independently and not assembled into the electronic device.

[0226] The aforementioned computer-readable medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device causes the following: based on a user-triggered probe request for at least one data metric, the electronic device determines probe information for each of the at least one data metric, wherein the probe information includes product information and data metric information, the product information being used to identify a product using the data metric, and the data metric information being used to identify the data metric; for each of the at least one data metric, the electronic device performs the following steps to determine a first probe result for each data metric: in response to querying historical probe results for the data metric that have passed authentication based on the data metric information, the electronic device determines the first probe result based on the historical probe results; in response to not querying historical probe results for the data metric that have passed authentication, the electronic device determines the first probe result based on the probe information according to at least one probe method, wherein the first probe result includes at least one metric data source information and at least one metric definition of the data metric, and the at least one probe method includes obtaining the first probe result based on the probe information and the code repository corresponding to the product.

[0227] Computer program code for performing the operations of this disclosure can be written in one or more programming languages ​​or a combination thereof, including but not limited to object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0228] One or more embodiments of this disclosure also provide a computer program product comprising one or more computer instructions. When the computer instructions are loaded and executed on a computing device, all or part of the processes or functions described in any embodiment of this disclosure are generated.

[0229] The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions may be transmitted from one website, computer, or data center to another website, computer, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means.

[0230] When the computer program product is executed by a computer, the computer performs any of the aforementioned methods for probing data indicators. The computer program product can be a software installation package; when any of the aforementioned methods for probing data indicators needs to be used, the computer program product can be downloaded and executed on the computer.

[0231] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0232] The units or modules described in the embodiments of this disclosure can be implemented in software or hardware. The names of the units or modules do not necessarily constitute a limitation on the unit or module itself.

[0233] The functions described above in this document can be performed at least in part by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), system-on-a-chip (SoCs), complex programmable logic devices (CPLDs), and so on.

[0234] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer 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 of the foregoing.

[0235] According to one or more embodiments of this disclosure, Example 1 provides a method for probing data metrics, including:

[0236] Based on a user-triggered exploration request for at least one data metric, exploration information for each of the at least one data metric is determined, wherein the exploration information includes product information and data metric information, the product information is used to identify the product using the data metric, and the data metric information is used to identify the data metric.

[0237] For each of the at least one data metric, the following steps are performed to determine a first probing result for each data metric:

[0238] In response to querying the historical exploration results of the data indicator that have passed authentication based on the data indicator information, the first exploration result is determined based on the historical exploration results;

[0239] In response to the absence of a verified historical exploration result for the data indicator, the first exploration result is determined based on the exploration information according to at least one exploration method.

[0240] The first exploration result includes at least one indicator data source information of the data indicator and at least one indicator definition of the data indicator. The at least one exploration method includes obtaining the first exploration result based on the exploration information and the code repository corresponding to the product.

[0241] According to one or more embodiments of this disclosure, Example 2 provides the method of determining the first exploration result based on the exploration information according to at least one exploration method, as in Example 1, including:

[0242] Based on the exploration information, the content to be explored in the code repository corresponding to the product and related to the data indicator is determined, and the content to be explored is analyzed to obtain the content analysis result. The content analysis result includes at least one indicator data source information of the data indicator and at least one indicator processing logic of the data indicator. The at least one indicator processing logic is used to determine the at least one indicator caliber.

[0243] The first exploration result is determined based on the content analysis results.

[0244] According to one or more embodiments of this disclosure, Example 3 provides the method in Example 2 for determining, based on the exploration information, the content to be explored in the code repository corresponding to the product that is related to the data metric, including:

[0245] Based on the product information, determine the code repository corresponding to the product;

[0246] The content to be explored is obtained from the code repository based on one or more of the product information and the data indicator information.

[0247] According to one or more embodiments of this disclosure, Example 4 provides at least one probing method from Example 2, including a code probing method, wherein the analysis of the content to be probed to obtain content analysis results includes:

[0248] The first major language model is invoked to process the code to be explored and the code analysis prompts to obtain the code analysis results;

[0249] Wherein, the content to be explored is the code to be explored, the content analysis result is the code analysis result, the code analysis result includes the first data source information of the data indicator and the first processing logic of the data indicator, the at least one indicator data source information includes the first data source information, and the at least one indicator processing logic includes the first processing logic.

[0250] According to one or more embodiments of this disclosure, Example 5 provides the method of calling the first language model in Example 4 to process the code to be explored and the code analysis prompts, and obtain code analysis results, including:

[0251] The first large language model is invoked to process the code to be explored and the first sub-prompt word to obtain the table information of the first data table and the first sub-processing logic related to the data indicator. The code analysis prompt word includes the first sub-prompt word, and the first sub-processing logic refers to the processing logic that generates the indicator value of the data indicator based on the first data table. The first processing logic includes the first sub-processing logic.

[0252] The first large language model is called to process the table information of the first data table and the second sub-prompt word to obtain the base table information of the first authentication base table related to the data indicator. The code analysis prompt word also includes the second sub-prompt word. The first authentication base table is a data base table that has been certified and can be used as a data source. The first authentication base table is the first data table or the upstream data table of the first data table. The first data source information includes the base table information of the first authentication base table.

[0253] The first large language model is invoked to process the base table information of the first authentication base table and the third sub-prompt word, to determine the first data production and processing task related to the data indicator, and to obtain the second sub-processing logic based on the first data production and processing task. The code analysis prompt word also includes the third sub-prompt word. The second sub-processing logic refers to the processing logic that generates the indicator value of the data indicator in the first data production and processing task. The first processing logic also includes the second sub-processing logic.

[0254] According to one or more embodiments of this disclosure, Example Six provides at least one probing method from Example Two, including a log probing method, wherein the analysis of the content to be probed to obtain content analysis results includes:

[0255] The first major language model is invoked to process the code to be explored and the code analysis prompts to obtain the code analysis results;

[0256] The second largest language model is called to process the logs to be explored and the log analysis prompts to obtain the log analysis results;

[0257] The content to be explored includes the code to be explored and the log to be explored. The content analysis results include the code analysis results and the log analysis results. The code analysis results include the first data source information and the first processing logic of the data indicator. The log analysis results include the second data source information and / or the second processing logic of the data indicator, or may not include the second data source information and the second processing logic of the data indicator.

[0258] The at least one indicator data source information includes the first data source information. If the log analysis result includes the second data source information, the at least one indicator data source information further includes the second data source information.

[0259] The at least one indicator processing logic includes the first processing logic, and if the log analysis result includes the second processing logic, the at least one indicator processing logic further includes the second processing logic.

[0260] According to one or more embodiments of this disclosure, Example 7 provides the method of calling the second largest language model in Example 6 to process the log to be explored and the log analysis prompt words, and obtain the log analysis results, including:

[0261] The second major language model is invoked to process the log to be explored and the fourth sub-prompt word to obtain the processing result, wherein the log analysis prompt word includes the fourth sub-prompt word:

[0262] In response to the processing result indication, table information of a second data table related to the data indicator is obtained; or, in response to the processing result indication, table information of a second data table related to the data indicator and a third sub-processing logic are obtained, wherein the third sub-processing logic refers to the processing logic of the data indicator recorded in the log to be explored, and the second processing logic includes the third sub-processing logic.

[0263] The second large language model is invoked to process the table information of the second data table and the fifth sub-prompt word to obtain the base table information of the second authentication base table related to the data indicator. The log analysis prompt word also includes the fifth sub-prompt word. The second authentication base table is a certified data base table that can serve as a data source. The second authentication base table is the second data table or an upstream data table of the second data table. The second data source information includes the base table information of the second authentication base table.

[0264] The second large language model is invoked to process the base table information of the second authentication base table and the sixth sub-prompt word, to determine the second data production and processing task related to the data indicator, and to obtain the fourth sub-processing logic based on the second data production and processing task. The log analysis prompt word further includes the sixth sub-prompt word, and the fourth sub-processing logic refers to the processing logic that generates the indicator value of the data indicator in the second data production and processing task. The second processing logic also includes the fourth sub-processing logic. In response to the processing result indication, the third sub-processing logic is obtained to obtain the log analysis result excluding the second data source information.

[0265] In response to the processing result indicating that the table information of the second data table and the third sub-processing logic were not obtained, the log analysis result excluding the second processing logic and the second data source information is obtained.

[0266] According to one or more embodiments of this disclosure, Example 8 provides the method in Example 6 for determining the first exploration result based on the content analysis results, including:

[0267] In response to the log analysis results including the second processing logic and the second data source information, the first probing result is determined based on the code analysis results and the log analysis results;

[0268] In response to the log analysis results not including the second processing logic and / or the second data source information, the first probing result is determined based on the code analysis results.

[0269] According to one or more embodiments of this disclosure, Example Nine provides the method of Example One, further comprising:

[0270] In response to the absence of a historical exploration result for the data indicator that has passed authentication, after determining the first exploration result based on the exploration information according to at least one exploration method, in response to obtaining the authentication pass result for the first exploration result, the authentication information for the data indicator is generated based on the first exploration result and the exploration information, and the authentication information is stored.

[0271] According to one or more embodiments of this disclosure, Example 10 provides the method of Example 1, further comprising:

[0272] For each of the aforementioned data metrics:

[0273] Determine the authentication information of the data indicator, wherein the authentication information includes the historical exploration results of the data indicator that have passed authentication and the exploration information, or includes the first exploration result that has passed authentication and the exploration information;

[0274] In response to meeting the update conditions for the authentication information, a second exploration result of the data metric is determined based on the exploration information included in the authentication information, according to the at least one exploration method, wherein the update conditions include determining code and / or log changes related to the data metric in the code repository;

[0275] In response to obtaining authentication pass information for the second probe result, the authentication information is updated based on the second probe result.

[0276] According to one or more embodiments of this disclosure, Example 11 provides the response of Example 1 to querying the historical probe results of the data indicator based on the data indicator information, and determining the first probe result based on the historical probe results, including:

[0277] Based on the data indicator information, multiple verified candidate historical exploration results for the data indicator are retrieved;

[0278] The candidate historical exploration result with the closest authentication time to the current time among the multiple authenticated candidate historical exploration results is determined as the historical exploration result, and the first exploration result is determined based on the historical exploration result.

[0279] According to one or more embodiments of this disclosure, Example Twelve provides that the probe request in Example One includes probe information, or that the probe request is used to indicate the method of obtaining the probe information.

[0280] According to one or more embodiments of this disclosure, Example Thirteen provides the method described in Example One for determining exploration information for each of the at least one data metric based on a user-triggered exploration request for at least one data metric, including:

[0281] According to the exploration request, the content to be identified is identified to obtain the identification result for each of the at least one data indicator, wherein the content to be identified is related to the at least one data indicator;

[0282] For each of the at least one data metric, the following steps are performed to determine the exploration information for each data metric:

[0283] Display the recognition results;

[0284] In response to obtaining confirmation information regarding the identification result, the identification result is used as the exploration information;

[0285] In response to obtaining at least one editing operation on the recognition result, the recognition result is updated according to the at least one editing operation, and the exploration information is determined based on the updated recognition result.

[0286] According to one or more embodiments of this disclosure, Example Fourteen provides at least one probing method from any of Examples One to Fourteen, including code probing, log probing, and manual probing.

[0287] The at least one indicator data source information includes a first indicator data source information corresponding to the code probing method, a second indicator data source information corresponding to the log probing method, and a third indicator data source information corresponding to the manual probing method.

[0288] The at least one indicator includes a first indicator corresponding to the code exploration method, a second indicator corresponding to the log exploration method, and a third indicator corresponding to the manual exploration method;

[0289] The method further includes:

[0290] Based on the data source evaluation dimensions, the scores of the first indicator data source information, the second data source information, and the third indicator data source information are determined respectively. The data source evaluation dimensions include one or more of the following: integrity dimension, stability dimension, and traceability dimension.

[0291] Based on the evaluation dimensions of the indicator criteria, the scores of the first indicator criterion, the second indicator criterion, and the third indicator criterion are determined respectively. The evaluation dimensions of the indicator criterion include one or more of the following: content clarity dimension, product adaptability dimension, and maintainability dimension.

[0292] According to one or more embodiments of this disclosure, Example Fifteen provides a probing apparatus for data metrics, comprising:

[0293] The determination module is configured to: determine the exploration information of each of the at least one data indicator based on the exploration request triggered by the user for at least one data indicator, wherein the exploration information includes product information and data indicator information, the product information is used to identify the product using the data indicator, and the data indicator information is used to identify the data indicator;

[0294] The execution module is configured to perform the following steps for each of the at least one data metric to determine a first probing result for each of the data metrics:

[0295] In response to querying the historical exploration results of the data indicator that have passed authentication based on the data indicator information, the first exploration result is determined based on the historical exploration results;

[0296] In response to the absence of a verified historical exploration result for the data indicator, the first exploration result is determined based on the exploration information according to at least one exploration method.

[0297] The first exploration result includes at least one indicator data source information of the data indicator and at least one indicator definition of the data indicator. The at least one exploration method includes obtaining the first exploration result based on the exploration information and the code repository corresponding to the product.

[0298] According to one or more embodiments of this disclosure, Example Sixteen provides an electronic device comprising:

[0299] Processing device; and

[0300] Storage device, including one or more computer program instructions;

[0301] The one or more computer program instructions are executed by the processing device to perform the method described in at least one embodiment of the present disclosure.

[0302] According to one or more embodiments of the present disclosure, Example Seventeen provides a computer-readable storage medium for non-transitory storage of computer-readable instructions, wherein the computer-readable instructions are executed by a processor using methods described in at least one embodiment of the present disclosure.

[0303] The above description is merely a preferred embodiment of this disclosure and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of this disclosure is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features disclosed in this disclosure that have similar functions.

[0304] Furthermore, while the operations are described in a specific order, this should not be construed as requiring these operations to be performed in the specific order shown or in a sequential order. In certain environments, multitasking and parallel processing may be advantageous. Similarly, while several specific implementation details are included in the above discussion, these should not be construed as limiting the scope of this disclosure. Certain features described in the context of individual embodiments may also be implemented in combination in a single embodiment. Conversely, various features described in the context of a single embodiment may also be implemented individually or in any suitable sub-combination in multiple embodiments.

[0305] Although the subject matter has been described using language specific to structural features and / or methodological logic, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or actions described above. Rather, the specific features and actions described above are merely illustrative examples of implementing the claims.

Claims

1. A method for probing data indicators, comprising: Based on a user-triggered exploration request for at least one data metric, exploration information for each of the at least one data metric is determined, wherein the exploration information includes product information and data metric information, the product information is used to identify the product using the data metric, and the data metric information is used to identify the data metric. For each of the at least one data metric, the following steps are performed to determine a first probing result for each data metric: In response to querying the historical exploration results of the data indicator that have passed authentication based on the data indicator information, the first exploration result is determined based on the historical exploration results; In response to the absence of a verified historical exploration result for the data indicator, the first exploration result is determined based on the exploration information according to at least one exploration method. The first exploration result includes at least one indicator data source information of the data indicator and at least one indicator definition of the data indicator. The at least one exploration method includes obtaining the first exploration result based on the exploration information and the code repository corresponding to the product.

2. The method according to claim 1, wherein, Determining the first exploration result based on the exploration information according to at least one exploration method includes: Based on the exploration information, the content to be explored in the code repository corresponding to the product and related to the data indicator is determined, and the content to be explored is analyzed to obtain the content analysis result. The content analysis result includes at least one indicator data source information of the data indicator and at least one indicator processing logic of the data indicator. The at least one indicator processing logic is used to determine the at least one indicator caliber. The first exploration result is determined based on the content analysis results.

3. The method according to claim 2, wherein, The step of determining the content to be explored in the code repository corresponding to the product and related to the data metric based on the exploration information includes: Based on the product information, determine the code repository corresponding to the product; The content to be explored is obtained from the code repository based on one or more of the product information and the data indicator information.

4. The method according to claim 2, wherein, The at least one exploration method includes a code exploration method, and the step of analyzing the content to be explored to obtain content analysis results includes: The first major language model is invoked to process the code to be explored and the code analysis prompts to obtain the code analysis results; Wherein, the content to be explored is the code to be explored, the content analysis result is the code analysis result, the code analysis result includes the first data source information of the data indicator and the first processing logic of the data indicator, the at least one indicator data source information includes the first data source information, and the at least one indicator processing logic includes the first processing logic.

5. The method according to claim 4, wherein, The process involves calling the first major language model to process the code to be explored and the code analysis hints to obtain code analysis results, including: The first large language model is invoked to process the code to be explored and the first sub-prompt word to obtain the table information of the first data table and the first sub-processing logic related to the data indicator. The code analysis prompt word includes the first sub-prompt word, and the first sub-processing logic refers to the processing logic that generates the indicator value of the data indicator based on the first data table. The first processing logic includes the first sub-processing logic. The first large language model is called to process the table information of the first data table and the second sub-prompt word to obtain the base table information of the first authentication base table related to the data indicator. The code analysis prompt word also includes the second sub-prompt word. The first authentication base table is a data base table that has been certified and can be used as a data source. The first authentication base table is the first data table or the upstream data table of the first data table. The first data source information includes the base table information of the first authentication base table. The first large language model is invoked to process the base table information of the first authentication base table and the third sub-prompt word, to determine the first data production and processing task related to the data indicator, and to obtain the second sub-processing logic based on the first data production and processing task. The code analysis prompt word also includes the third sub-prompt word. The second sub-processing logic refers to the processing logic that generates the indicator value of the data indicator in the first data production and processing task. The first processing logic also includes the second sub-processing logic.

6. The method according to claim 2, wherein, The at least one exploration method includes a log exploration method, and the analysis of the content to be explored to obtain the content analysis result includes: The first major language model is invoked to process the code to be explored and the code analysis prompts to obtain the code analysis results; The second largest language model is called to process the logs to be explored and the log analysis prompts to obtain the log analysis results; The content to be explored includes the code to be explored and the log to be explored. The content analysis results include the code analysis results and the log analysis results. The code analysis results include the first data source information and the first processing logic of the data indicator. The log analysis results include the second data source information and / or the second processing logic of the data indicator, or may not include the second data source information and the second processing logic of the data indicator. The at least one indicator data source information includes the first data source information. If the log analysis result includes the second data source information, the at least one indicator data source information further includes the second data source information. The at least one indicator processing logic includes the first processing logic, and if the log analysis result includes the second processing logic, the at least one indicator processing logic further includes the second processing logic.

7. The method according to claim 6, wherein, The process of calling the second largest language model to process the log to be explored and the log analysis prompts yields log analysis results, including: The second major language model is invoked to process the log to be explored and the fourth sub-prompt word to obtain the processing result, wherein the log analysis prompt word includes the fourth sub-prompt word: In response to the processing result indication, table information of a second data table related to the data indicator is obtained; or, in response to the processing result indication, table information of a second data table related to the data indicator and a third sub-processing logic are obtained, wherein the third sub-processing logic refers to the processing logic of the data indicator recorded in the log to be explored, and the second processing logic includes the third sub-processing logic. The second large language model is invoked to process the table information of the second data table and the fifth sub-prompt word to obtain the base table information of the second authentication base table related to the data indicator. The log analysis prompt word also includes the fifth sub-prompt word. The second authentication base table is a certified data base table that can serve as a data source. The second authentication base table is the second data table or an upstream data table of the second data table. The second data source information includes the base table information of the second authentication base table. The second large language model is called to process the base table information of the second authentication base table and the sixth sub-prompt word, to determine the second data production and processing task related to the data indicator, and to obtain the fourth sub-processing logic based on the second data production and processing task. The log analysis prompt word also includes the sixth sub-prompt word. The fourth sub-processing logic refers to the processing logic that generates the indicator value of the data indicator in the second data production and processing task. The second processing logic also includes the fourth sub-processing logic. In response to the processing result indication, the third sub-processing logic is obtained to obtain the log analysis result excluding the second data source information; In response to the processing result indicating that the table information of the second data table and the third sub-processing logic were not obtained, the log analysis result excluding the second processing logic and the second data source information is obtained.

8. The method according to claim 6, wherein, Determining the first exploration result based on the content analysis results includes: In response to the log analysis results including the second processing logic and the second data source information, the first probing result is determined based on the code analysis results and the log analysis results; In response to the log analysis results not including the second processing logic and / or the second data source information, the first probing result is determined based on the code analysis results.

9. The method according to claim 1, further comprising: In response to the absence of a historical exploration result for the data indicator that has passed authentication, after determining the first exploration result based on the exploration information according to at least one exploration method, in response to obtaining the authentication pass result for the first exploration result, the authentication information for the data indicator is generated based on the first exploration result and the exploration information, and the authentication information is stored.

10. The method according to claim 1, further comprising: For each of the aforementioned data metrics: Determine the authentication information of the data indicator, wherein the authentication information includes the historical exploration results of the data indicator that have passed authentication and the exploration information, or includes the first exploration result that has passed authentication and the exploration information; In response to meeting the update conditions for the authentication information, a second exploration result of the data metric is determined based on the exploration information included in the authentication information, according to the at least one exploration method, wherein the update conditions include determining code and / or log changes related to the data metric in the code repository; In response to obtaining authentication pass information for the second probe result, the authentication information is updated based on the second probe result.

11. The method according to claim 1, wherein, The response to querying the historical exploration results of the data indicator based on the data indicator information, and determining the first exploration result based on the historical exploration results, includes: Based on the data indicator information, multiple verified candidate historical exploration results for the data indicator are retrieved; The candidate historical exploration result with the closest authentication time to the current time among the multiple authenticated candidate historical exploration results is determined as the historical exploration result, and the first exploration result is determined based on the historical exploration result.

12. The method according to claim 1, wherein, The exploration request may include exploration information, or the exploration request may be used to indicate how the exploration information is obtained.

13. The method according to claim 1, wherein, The step of determining the exploration information for each of the at least one data indicator based on a user-triggered exploration request for at least one data indicator includes: According to the exploration request, the content to be identified is identified to obtain the identification result for each of the at least one data indicator, wherein the content to be identified is related to the at least one data indicator; For each of the at least one data metric, the following steps are performed to determine the exploration information for each data metric: Display the recognition results; In response to obtaining confirmation information regarding the identification result, the identification result is used as the exploration information; In response to obtaining at least one editing operation on the recognition result, the recognition result is updated according to the at least one editing operation, and the exploration information is determined based on the updated recognition result.

14. The method according to any one of claims 1-13, wherein, The at least one detection method includes code detection, log detection, and manual detection. The at least one indicator data source information includes a first indicator data source information corresponding to the code probing method, a second indicator data source information corresponding to the log probing method, and a third indicator data source information corresponding to the manual probing method. The at least one indicator includes a first indicator corresponding to the code exploration method, a second indicator corresponding to the log exploration method, and a third indicator corresponding to the manual exploration method; The method further includes: Based on the data source evaluation dimensions, the scores of the first indicator data source information, the second data source information, and the third indicator data source information are determined respectively. The data source evaluation dimensions include one or more of the following: integrity dimension, stability dimension, and traceability dimension. Based on the evaluation dimensions of the indicator criteria, the scores of the first indicator criterion, the second indicator criterion, and the third indicator criterion are determined respectively. The evaluation dimensions of the indicator criterion include one or more of the following: content clarity dimension, product adaptability dimension, and maintainability dimension.

15. A probe for data indicators, comprising: The determination module is configured to: determine the exploration information of each of the at least one data indicator based on the exploration request triggered by the user for at least one data indicator, wherein the exploration information includes product information and data indicator information, the product information is used to identify the product using the data indicator, and the data indicator information is used to identify the data indicator; The execution module is configured to perform the following steps for each of the at least one data metric to determine a first probing result for each of the data metrics: In response to querying the historical exploration results of the data indicator that have passed authentication based on the data indicator information, the first exploration result is determined based on the historical exploration results; In response to the absence of a verified historical exploration result for the data indicator, the first exploration result is determined based on the exploration information according to at least one exploration method. The first exploration result includes at least one indicator data source information of the data indicator and at least one indicator definition of the data indicator. The at least one exploration method includes obtaining the first exploration result based on the exploration information and the code repository corresponding to the product.

16. An electronic device comprising: Processing device; as well as Storage device, including one or more computer program instructions; The one or more computer program instructions are executed by the processing device to perform the method according to any one of claims 1 to 14.

17. A computer-readable storage medium for non-transitory storage of computer-readable instructions, wherein, The method of any one of claims 1 to 14 is implemented when the computer-readable instructions are executed by a processor.

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