Data analysis method, device and equipment based on data burying point, and medium

Abstract

The application discloses a data analysis method and device based on data burying, equipment and medium. Including: analyzing the verification burying point set to obtain M pieces of verification burying point data of M burying points associated with the business system; querying N pieces of running burying point data generated by the business system in the running process; comparing and analyzing the field values of the verification burying point set and the burying point event set of the business system, in the case that the running burying point data of a first burying point in the multiple burying points does not correspond to any verification burying point data, determining that the first burying point is a redundant burying point, and in the case that the verification burying point data of a second burying point in the M burying points does not correspond to any running burying point data, preliminarily determining that the second burying point is a missing burying point; for the same burying point, in the case that the field values under the same associated field in the running burying point data and the verification burying point data are inconsistent, determining that the burying point is an error burying point. According to the embodiment of the application, the troubleshooting efficiency of abnormal burying points can be improved.

Description

Technical Field

[0001] This application relates to the field of Internet technology, particularly to the field of Internet big data technology, and specifically to a data analysis method, apparatus, device, and medium based on data embedding. Background Technology

[0002] "Event tracking" is a computer science term referring to the process of embedding code in the client-side to listen for events during client operation. When an event is triggered, the code captures the event and uploads its context information to the server. The data collected through event tracking is called event tracking data. Large amounts of event tracking data can constitute big data, which can be used to analyze user behavior, optimize client applications, and more.

[0003] In related technologies, when a server receives a large amount of event tracking data, it is necessary to manually analyze and check each event tracking point to determine whether the event tracking point is abnormal. The checking efficiency is low and it is easy to miss some points, which leads to low verification efficiency of event tracking data. Summary of the Invention

[0004] In view of this, embodiments of this application provide a data analysis method, apparatus, device, medium and product based on data tracking points, which can efficiently verify the tracking point data of all tracking points in the business system and improve the efficiency of troubleshooting abnormal tracking points.

[0005] In a first aspect, embodiments of this application provide a data analysis method based on data tracking points. The method includes: upon obtaining a set of verification tracking points for a business system, parsing the set to obtain M verification tracking point data entries for M tracking points associated with the business system, wherein each verification tracking point data entry consists of field values ​​under multiple associated fields bound to the corresponding tracking point; querying N runtime tracking point data entries generated during the operation of the business system, and generating a tracking point event set for the business system based on the N runtime tracking point data entries, wherein the N runtime tracking point data entries correspond to multiple tracking points; comparing and analyzing the field values ​​of the verification tracking point set and the tracking point event set of the business system; determining that the first tracking point is a redundant tracking point if the runtime tracking point data of the first tracking point among the multiple tracking points does not correspond to any verification tracking point data entry; and preliminarily determining that the second tracking point is a missing tracking point if the verification tracking point data of the second tracking point among the M tracking points does not correspond to any runtime tracking point data entry; and determining that the tracking point is an erroneous tracking point if the field values ​​under the same associated field in the runtime tracking point data and the verification tracking point data are inconsistent.

[0006] In some possible implementations of the first aspect, before parsing the verification event set, the following steps are also included: based on the user's event configuration operation on the event management platform, setting up multiple associated event points for each business system, and configuring multiple associated fields and their values ​​bound to each event point. Among these, the multiple event points associated with different business systems are different, and the multiple event points associated with the same business system correspond to different event events. For each business system, the multiple associated fields and their values ​​of each event point are combined to obtain the verification event data for each event point. The M verification event data of the M event points corresponding to the business system are integrated to obtain the verification event set.

[0007] In some possible implementations of the first aspect, a field value comparison analysis is performed between the verification tracking point set and the tracking event set of the business system. This includes: from the M verification tracking point data and N runtime tracking point data corresponding to the business system, based on the field value under the target associated field among multiple associated fields, filtering the verification tracking point data and runtime tracking point data corresponding to the same tracking point; if the field value under the target associated field in the runtime tracking point data of the first tracking point does not appear in the M verification tracking point data, it is determined that the first tracking point does not correspond to any verification tracking point data; if the field value under the target associated field in the verification tracking point data of the second tracking point does not appear in the N runtime tracking point data, it is determined that the second tracking point does not correspond to any runtime tracking point data.

[0008] In some possible implementations of the first aspect, N runtime tracking data are generated by the business system during the first time period. After initially determining that the second tracking point is a missed tracking point, the method further includes: obtaining all historical runtime tracking data generated by the business system during a preset time period before the first time period to obtain a set of historical tracking events; querying historical runtime tracking data from the set of historical tracking events where the field values ​​of all associated fields in the verification tracking data corresponding to the second tracking point are consistent to obtain target historical runtime tracking data; and determining that the second tracking point is a missed tracking point if the number of target historical runtime tracking data is zero or greater than the first target tracking point count threshold.

[0009] In some possible implementations of the first aspect, after obtaining the target historical running event data, the method further includes: if the number of target historical running event data is greater than the second target event count threshold and not greater than the first target event count threshold, determining that the function corresponding to the second event is a medium-frequency function in the business system and that the second event is a potentially abnormal event; if the number of target historical running event data is greater than zero and less than the second target event count threshold, determining that the function corresponding to the second event is a low-frequency function in the business system and that the second event is a normal event.

[0010] In some possible implementations of the first aspect, the first target event tracking threshold is greater than the second target event tracking threshold. Both the first target event tracking threshold and the second target event tracking threshold are determined based on the user activity of the business system. Both the first target event tracking threshold and the second target event tracking threshold are positively correlated with the user activity of the business system.

[0011] In some possible implementations of the first aspect, the associated fields include at least system type, client type, client version, functional module, event type, event name, pageurl, and frompageurl. After determining that the tracking point is an erroneous tracking point, the method also includes: extracting the field values ​​under pageurl and frompageurl from the running tracking point data; locating the abnormal module and abnormal page in the business system based on the field values ​​under pageurl and frompageurl to obtain the analysis results; and periodically sending the analysis results to the target user.

[0012] Secondly, embodiments of this application provide a data analysis device based on data tracking points. The device includes: a parsing module, configured to parse the verification tracking point set of a business system to obtain M verification tracking point data entries for M tracking points associated with the business system, wherein each verification tracking point data entry consists of field values ​​under multiple associated fields bound to the corresponding tracking point; and a generation module, configured to query N runtime tracking point data entries generated by the business system during operation and generate a tracking point event set for the business system based on the N runtime tracking point data entries, wherein the N runtime tracking point data entries correspond to multiple... The tracking point and field value comparison analysis module is used to compare and analyze the field values ​​of the verification tracking point set and the tracking event set of the business system. If the running tracking point data of the first tracking point does not correspond to any verification tracking point data among multiple tracking points, the first tracking point is determined to be a redundant tracking point. If the verification tracking point data of the second tracking point does not correspond to any running tracking point data among M tracking points, the second tracking point is initially determined to be a missing tracking point. The determination module is used to determine the tracking point as an erroneous tracking point if the field value of the same associated field in the running tracking point data and the verification tracking point data is inconsistent.

[0013] Among the possible implementations of the second aspect, there are also: a tracking configuration module, used to set up multiple associated tracking points for each business system based on the user's tracking configuration operation on the tracking management platform before parsing the verification tracking point set, and to configure multiple associated fields and their values ​​bound to each tracking point, wherein the multiple tracking points associated with different business systems are different, and the multiple tracking points associated with the same business system correspond to different tracking point events; a combination module, used to combine multiple associated fields and their values ​​of each tracking point for each business system to obtain the verification tracking point data of each tracking point; and an integration module, used to integrate the M verification tracking point data of M tracking points corresponding to the business system to obtain the verification tracking point set.

[0014] In some possible implementations of the second aspect, the field value comparison and analysis module includes: a filtering submodule, used to filter the verification and operation data corresponding to the same data point from M verification data points and N operation data points corresponding to the business system, based on the field value under the target associated field among multiple associated fields; a determination submodule, used to determine that the first data point does not correspond to any verification data point if the field value under the target associated field in the operation data point of the first data point does not appear in the M verification data points; and a determination submodule, also used to determine that the second data point does not correspond to any operation data point if the field value under the target associated field in the verification data point of the second data point does not appear in the N operation data points.

[0015] In some possible implementations of the second aspect, where N running event data points are generated by the business system during the first time period, the following modules are also included: an acquisition module, used to acquire all historical running event data generated by the business system during a preset time period before the first time period after initially determining that the second event point is a missed event point, to obtain a set of historical event points; a query module, used to traverse the set of historical event points and query historical running event data from the set of historical event points where the field values ​​of all associated fields in the verification event data corresponding to the second event point are consistent, to obtain target historical running event data; and a determination module, used to determine that the second event point is a missed event point when the number of target historical running event data points is zero or greater than the first target event point count threshold.

[0016] In some possible implementations of the second aspect, the determining module is further configured to: after obtaining the target historical running event data, if the number of target historical running event data is greater than the second target event count threshold and not greater than the first target event count threshold, determine that the function corresponding to the second event is a medium-frequency function in the business system and that the second event is a potentially missed event; if the number of target historical running event data is greater than zero and less than the second target event count threshold, determine that the function corresponding to the second event is a low-frequency function in the business system and that the second event is a normal event.

[0017] In some possible implementations of the second aspect, the first target event tracking threshold is greater than the second target event tracking threshold. Both the first target event tracking threshold and the second target event tracking threshold are determined based on the user activity of the business system. Both the first target event tracking threshold and the second target event tracking threshold are positively correlated with the user activity of the business system.

[0018] In some possible implementations of the second aspect, the associated fields include at least system type, client type, client version, functional module, event type, event name, pageurl, and frompageurl, and also include: an extraction module, used to extract the field values ​​under pageurl and frompageurl from the running event data after determining that the event point is an erroneous event point; an anomaly location module, used to locate the abnormal module and abnormal page in the business system based on the field values ​​under pageurl and frompageurl, and obtain the analysis results; and an anomaly sending module, used to periodically send the analysis results to the target user.

[0019] Thirdly, embodiments of this application provide an electronic device, including: a processor and a memory storing computer program instructions; when the processor executes the computer program instructions, it implements the steps of the data analysis method based on data embedding points as described in the first aspect.

[0020] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer program instructions, which, when executed by a processor, implement the steps of the data analysis method based on data embedding points as described in the first aspect.

[0021] Fifthly, embodiments of this application provide a computer program product, which is stored in a non-volatile storage medium, and when executed by a processor, the computer program product implements the steps of the data analysis method based on data embedding points as described in the first aspect.

[0022] In a sixth aspect, embodiments of this application provide a chip that includes a processor and a communication interface, the communication interface being coupled to the processor, the processor being used to run programs or instructions to implement the steps of the data analysis method based on data embedding points as described in the first aspect.

[0023] This application provides a data analysis method, apparatus, device, medium, and product based on data tracking points. Upon obtaining a set of verification tracking points for a business system, the method parses the set to obtain M verification tracking point data entries for M tracking points associated with the business system. Each verification tracking point data entry consists of field values ​​for the corresponding tracking point under multiple associated fields. In other words, this application pre-sets associated tracking points for each business system and sets associated fields and their values ​​for each tracking point, generating one verification tracking point data entry for each tracking point, and integrating these to obtain the verification tracking point set for the business system. In the actual application of the business system, the method queries N runtime tracking point data entries generated during the system's operation and generates a set of tracking point events for the business system based on these N runtime tracking point data entries. The N runtime tracking point data entries correspond to multiple tracking points. Based on this, the field values ​​of the verification tracking point set and the tracking event set of the business system are compared and analyzed. If the running tracking point data of the first tracking point in the tracking event set does not correspond to any verification tracking point data, the first tracking point can be determined to be a redundant tracking point. If the verification tracking point data of the second tracking point in the M tracking points does not correspond to any running tracking point data, the second tracking point can be preliminarily determined to be a missing tracking point. Furthermore, for the same tracking point, if the field values ​​under the same associated field in the running tracking point data and the verification tracking point data are inconsistent, the tracking point can be determined to be an incorrect tracking point. Thus, after obtaining the runtime tracking data generated by the business system during operation, this application compares the field values ​​of the data with the verification tracking data to identify missing, erroneous, and redundant tracking points online. Compared to existing technologies that target a single tracking point and require manually selecting multiple fields to be compared from all fields of the tracking point during the investigation process, and manually checking the field values ​​of multiple fields to determine whether the tracking point is abnormal, this approach can accelerate the investigation of abnormal tracking points and improve the efficiency of investigating abnormal tracking points in various business systems. Attached Figure Description

[0024] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings of the embodiments of this application will be briefly described below.

[0025] Figure 1 This is a flowchart illustrating a data analysis method based on data tracking points provided in an embodiment of this application;

[0026] Figure 2 This is a flowchart illustrating a data analysis method based on data tracking points provided in another embodiment of this application;

[0027] Figure 3 This is a flowchart illustrating a data analysis method based on data tracking points provided in another embodiment of this application;

[0028] Figure 4 This is a flowchart illustrating a data analysis method based on data tracking points provided in another embodiment of this application;

[0029] Figure 5 This is a schematic diagram of the structure of a data analysis device based on data embedding points provided in an embodiment of this application;

[0030] Figure 6 This is a schematic diagram of the hardware structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0031] The principles and spirit of this application will be described below with reference to several exemplary embodiments. It should be understood that these embodiments are provided to make the principles and spirit of this application clearer and more thorough, enabling those skilled in the art to better understand and implement the principles and spirit of this application. The exemplary embodiments provided herein are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments described herein without inventive effort are within the scope of protection of this application.

[0032] In this document, terms such as first, second, and third are used only to distinguish one entity (or operation) from another, and are not intended to require or imply any order or relationship between these entities (or operations).

[0033] The data analysis method based on data tracking points provided in this application will be described in detail below with reference to the accompanying drawings, through specific embodiments and application scenarios.

[0034] Figure 1 This is a flowchart illustrating a data analysis method based on data tracking points provided in an embodiment of this application. The execution entity of this data analysis method based on data tracking points can be a server, which can be connected to various business systems. The server is used to analyze a large amount of runtime tracking point data reported by various business systems to identify abnormal tracking points corresponding to each business system based on big data. The abnormal tracking points can include redundant tracking points, missing tracking points, and erroneous tracking points.

[0035] For example, in the context of online recruitment, each business system may include the B-end and C-end corresponding to the online recruitment platform. The B-end may include the online talent recruitment system, online application recruitment system, training system, etc. used by recruiting users, while the C-end may include the job search system for fresh graduates, online application job search system, job recommendation system, etc. used by job seekers.

[0036] The following example uses a server as the execution subject of a data analysis method based on data tracking points to illustrate the data analysis method based on data tracking points in this application. It should be noted that the above-mentioned execution subject and application scenario do not constitute a limitation on this application.

[0037] like Figure 1 As shown, the data analysis method based on data tracking points provided in this application embodiment may include steps 110-140.

[0038] Step 110: After obtaining the set of verification points of the business system, parse the set of verification points to obtain M verification point data of M points associated with the business system.

[0039] Step 120: Query the N runtime tracking data generated by the business system during operation, and generate a set of tracking events for the business system based on the N runtime tracking data.

[0040] Step 130: Compare and analyze the field values ​​of the verification point set and the event set of the business system. If the running point data of the first point does not correspond to any verification point data among multiple points, the first point is determined to be a redundant point. If the verification point data of the second point does not correspond to any running point data among M points, the second point is initially determined to be a missing point.

[0041] Step 140: For the same tracking point, if the field value under the same associated field in the running tracking point data and the verification tracking point data is inconsistent, the tracking point is determined to be an incorrect tracking point.

[0042] The data analysis method based on data tracking provided in this application, upon obtaining a set of verification tracking points for a business system, parses the set to obtain M verification tracking point data entries for M tracking points associated with the business system. Each verification tracking point data entry consists of field values ​​for the corresponding tracking point under multiple associated fields. In other words, in this application, associated tracking points are pre-set for each business system, and associated fields and their values ​​are set for each tracking point. One verification tracking point data entry is generated for each tracking point, and these are integrated to obtain the verification tracking point set for the business system. In the actual application of the business system, N runtime tracking point data entries generated during the system's operation are queried, and a set of tracking point events for the business system is generated based on these N runtime tracking point data entries. The N runtime tracking point data entries correspond to multiple tracking points. Based on this, the field values ​​of the verification tracking point set and the tracking event set of the business system are compared and analyzed. If the running tracking point data of the first tracking point in the tracking event set does not correspond to any verification tracking point data, the first tracking point can be determined to be a redundant tracking point. If the verification tracking point data of the second tracking point in the M tracking points does not correspond to any running tracking point data, the second tracking point can be preliminarily determined to be a missing tracking point. Furthermore, for the same tracking point, if the field values ​​under the same associated field in the running tracking point data and the verification tracking point data are inconsistent, the tracking point can be determined to be an incorrect tracking point. Thus, after obtaining the runtime tracking data generated by the business system during operation, this application compares the field values ​​of the data with the verification tracking data to identify missing, erroneous, and redundant tracking points online. Compared to existing technologies that target a single tracking point and require manually selecting multiple fields to be compared from all fields of the tracking point during the investigation process, and manually checking the field values ​​of multiple fields to determine whether the tracking point is abnormal, this approach can accelerate the investigation of abnormal tracking points and improve the efficiency of investigating abnormal tracking points in various business systems.

[0043] The specific implementation of the above steps will be described in detail below with reference to specific embodiments.

[0044] In step 110, after obtaining the set of verification points of the business system, the set of verification points is parsed to obtain M verification point data of M points associated with the business system.

[0045] In step 110, M is a positive integer. Each business system has multiple associated tracking points. Each tracking point contains a system type field called "webid". The value of the "webid" field differs for different business systems, thus the verification tracking point data for different business systems is different. The number of tracking points associated with different business systems can be the same or different; this application does not impose a specific limitation on this. For each tracking point, multiple associated fields can be pre-bound and their corresponding values ​​set, thereby obtaining the verification tracking point data for that tracking point. The verification tracking point data is user-pre-configured tracking point data used to verify whether the tracking point is abnormal. The field values ​​under the multiple associated fields bound to each tracking point have the verification function of determining whether the tracking point is abnormal. Each piece of verification tracking point data consists of the field values ​​of the corresponding tracking point under the multiple associated fields.

[0046] For the same business system, different event tracking points refer to event tracking points for different functions. For example, for a C-end job-seeking system for recent graduates, event tracking points a1 and a2 can be associated. Event tracking point a1 is for SMS login, and event tracking point a2 is for password login. These two represent different function event tracking points. However, for different business systems, even if it is the same function event tracking point, it is still considered a different event tracking point because the business systems belong to them. For example, for the SMS login function event tracking point, the C-end job-seeking system for recent graduates sets the event tracking point a1 for this function event, while the B-end online application and recruitment system sets the event tracking point b1 for this function event.

[0047] In some embodiments of this application, in order to ensure the validity of the verification tracking data and effectively investigate abnormal tracking points, Figure 2 This is a flowchart illustrating a data analysis method based on data tracking points according to another embodiment of this application. Before parsing the verification tracking point set in step 110 above, the method may further include... Figure 2 Steps 210-230 are shown.

[0048] Step 210: Based on the user's event tracking configuration operation on the event tracking management platform, set up multiple associated event tracking points for each business system, and configure multiple associated fields and their values ​​for each event tracking point.

[0049] Step 220: For each business system, combine multiple associated fields and their values ​​for each tracking point to obtain the verification tracking point data for each tracking point;

[0050] Step 230: Integrate the M verification data points from the M data points corresponding to the business system to obtain a set of verification data points.

[0051] Specifically, different business systems are associated with different event tracking points, and the same business system is associated with different event tracking points. This event tracking point configuration operation can include a first configuration operation, a second configuration operation, and a third configuration operation. The first configuration operation is used to set up multiple event tracking points associated with each business system; the second configuration operation is used to select associated fields for each event tracking point from all event tracking point fields and bind them. The multiple associated fields bound to different event tracking points can be the same or different; the third configuration operation is used to input the field values ​​under the multiple associated fields bound to each event tracking point.

[0052] In one example, the associated fields for SMS login and password login event tracking are the same: system type (webid) + client type (apptype) + client version (version) + function module (frommould) + event type + event name. Specifically, for the SMS login event tracking, the field value under event type is "SMS login," and the field value under event name is "messagelogin." For the password login event tracking, the field value under event type is "passwordlogin," and the field value under event name is "loginresult."

[0053] In another example, the associated fields for the SMS verification code retrieval function tracking point can include system type (webid) + client type (apptype) + client version (version) + module name (modulename) + button name (buttonname) + event name. Specifically, the field values ​​under module name are "login", "binding", and "changepassword", the field values ​​under button name are "Obtainverificationcode" and "Resend", and the field value under event name is "LoginGetcodeResult".

[0054] In this embodiment, the desired functional data points can be set for each business system through data point configuration operations. This means that each business system is associated with a specific data point, and multiple associated fields are set for each data point. These associated fields are the verification fields used to verify abnormal data points, and the field values ​​under these associated fields are the field values ​​used to verify abnormal data points. Based on this, the verification data point data is obtained by combining the multiple associated fields and their field values ​​for each data point. This verification data point data can verify the anomalies of data points generated by the business system in the actual operating environment. During the anomaly data point investigation process, the validity of the verification data point data is guaranteed, enabling the verification data point data to have the function of investigating abnormal data points and achieving effective investigation of abnormal data points.

[0055] In some embodiments of this application, the first configuration operation may be an operation of selecting multiple associated functional event tracking points for each business system, and generating multiple tracking points for the business system for the multiple functional event tracking points in response to the first configuration operation; or, an operation of selecting business systems associated with the same functional event tracking point, and generating multiple tracking points for each business system for the same functional event tracking point in response to the first configuration operation.

[0056] Step 120 involves querying N runtime tracking data points generated during the operation of the business system, and generating a set of tracking events for the business system based on the N runtime tracking data points.

[0057] In step 120, the runtime tracking data refers to the tracking data generated by the business system during operation. Users need to manually perform operations on the client, such as login, browsing, and clicking. When the client detects user actions, it triggers functional events in the business system, i.e., triggers the tracking points set in the business system, thereby generating the corresponding runtime tracking data and recording it in the business system's log file. The client reports the log file to the server, and the server can query the received log file to obtain N runtime tracking data entries generated by the business system during operation, where N is a positive integer. Combining these N runtime tracking data entries yields the tracking event set for the business system.

[0058] For the same event tracking point, if a user triggers the event tracking point multiple times, the business system can generate a limited number of runtime event tracking data entries. Therefore, N runtime event tracking data entries can correspond to multiple event tracking points.

[0059] In step 130, the field values ​​of the verification point set and the event set of the business system are compared and analyzed. If the running point data of the first point does not correspond to any verification point data among multiple points, the first point is determined to be a redundant point. If the verification point data of the second point does not correspond to any running point data among M points, the second point is initially determined to be a missing point.

[0060] In step 130, the field values ​​of the verification tracking point set and the tracking event set of the business system are compared and analyzed. That is, all field values ​​contained in the M verification tracking point data and the N running tracking point data are compared and analyzed. If the running tracking point data and the verification tracking point data correspond to the same tracking point, it can be determined that there is a corresponding relationship between the running tracking point data and the verification tracking point data; otherwise, if the running tracking point data and the verification tracking point data do not correspond to the same tracking point, it can be determined that there is no corresponding relationship between the running tracking point data and the verification tracking point data.

[0061] Specifically, from N running event tracking data, running event tracking data that does not correspond to any verification event tracking data is selected, and the tracking point corresponding to this running event tracking data is the first tracking point; from M verification event tracking data, verification event tracking data that does not correspond to any running event tracking data is selected, and the tracking point corresponding to this verification event tracking data is the second tracking point.

[0062] For example, the verification tracking point set contains verification tracking point data r1, r2, and r3 corresponding to tracking points x1, x2, and x3, respectively. The tracking point event set contains runtime tracking point data r4, r5, and r6 corresponding to tracking points x1, x3, and x4, respectively. By comparing and analyzing the field values ​​of r1, r2, and r3 with r4, r5, and r6, it can be determined that r1 and r4 both correspond to tracking point x1, and r3 and r5 both correspond to tracking point x3. Therefore, r1 corresponds to r4, and r3 corresponds to r5. The runtime tracking point set does not contain runtime tracking point data corresponding to r2, so the tracking point x2 corresponding to r2 is a missing tracking point. The verification tracking point set does not contain verification tracking point data corresponding to r6, so the tracking point x4 corresponding to r6 is a redundant tracking point.

[0063] In some embodiments of this application, in order to accurately filter out redundant and missing embedding points, Figure 3 This is a flowchart illustrating a data analysis method based on data tracking points provided in another embodiment of this application. In step 130 above, comparing and analyzing the field values ​​of the verification tracking point set and the tracking event set of the business system may include... Figure 3 Steps 310-330 are shown.

[0064] Step 310: From the M verification data points and N runtime data points corresponding to the business system, filter the verification data points and runtime data points corresponding to the same data point based on the field values ​​of the target associated field among multiple associated fields.

[0065] Step 320: If the field value under the target associated field in the running data of the first tracking point does not appear in M ​​verification tracking data, it is determined that the first tracking point does not correspond to any verification tracking data.

[0066] Step 330: If the field value under the target associated field in the verification data of the second tracking point does not appear in N running tracking points, it is determined that the second tracking point does not correspond to any running tracking point.

[0067] Specifically, the target associated field can include event type and event name. If the field values ​​of the verification tracking data and the running tracking data are consistent under the target associated field, it is determined that they correspond to the same tracking point and there is a corresponding relationship between them. Conversely, if the field values ​​of the verification tracking data and the running tracking data are inconsistent under the target associated field, it is determined that they correspond to different tracking points and there is no corresponding relationship between them.

[0068] In this embodiment, based on the field value of the target associated field among multiple associated fields, M verification tracking data and N runtime tracking data can be matched and analyzed to filter out verification tracking data and runtime tracking data corresponding to the same tracking point, so as to conduct subsequent error tracking investigation. For verification tracking data and runtime tracking data that do not correspond to the same tracking point, it is further determined whether they are redundant tracking points or missing tracking points. If the field value of the target associated field in the runtime tracking data of the first tracking point does not appear in the M verification tracking data, it is determined that the first tracking point does not correspond to any verification tracking data, and the first tracking point is a redundant tracking point; if the field value of the target associated field in the verification tracking data of the second tracking point does not appear in the N runtime tracking data, it is determined that the second tracking point does not correspond to any runtime tracking data, and the second tracking point is a missing tracking point, thereby accurately filtering out redundant and missing tracking points in the business system.

[0069] In related technologies, if a certain function in a business system is not used by users on a given day, the server cannot obtain the running data corresponding to the function's tracking point. In this case, the function's tracking point will be judged as an abnormal tracking point and included in the missing tracking point set.

[0070] In some embodiments of this application, the N runtime tracking data points are generated by the business system during the first time period. This is to improve the accuracy of anomaly tracking point detection. Figure 4 This is a flowchart illustrating a data analysis method based on data tracking points according to another embodiment of this application. After initially determining the second tracking point as a missed tracking point in step 130 above, the method may further include... Figure 4 Steps 410-430 are shown.

[0071] Step 410: Obtain all historical event tracking data generated by the business system during the preset duration before the first time period to obtain a set of historical event tracking events.

[0072] Step 420: By traversing the historical event set, query the historical running event data from the historical event set where the values ​​of all associated fields in the verification event data corresponding to the second event are consistent, and obtain the target historical running event data.

[0073] Step 430: If the number of target historical running data points is zero, or greater than the first target data point count threshold, determine the second data point as a missed data point.

[0074] Specifically, the server can periodically acquire runtime tracking data generated by the business system during operation. The first time period can be the most recent period, and N runtime tracking data are generated by the business system within the most recent period. The preset duration can be an integer multiple of the period duration. For example, if the period duration is 1 day, the preset duration can be 3 days or 5 days, etc. This application does not make specific limitations on this. If the values ​​of all associated fields in the verified tracking data are consistent with those in the historical runtime tracking data, it means that they correspond to the same tracking point. Therefore, the filtered target historical runtime tracking data also corresponds to the second tracking point, and the number of target historical runtime tracking data is the record count of the second tracking point. If the number of target historical runtime tracking data is greater than the second target tracking point count threshold but not greater than the first target tracking point count threshold, the second tracking point is determined to be a normal tracking point.

[0075] For example, for any missed tracking points initially identified on the current day, a retrospective analysis is performed on the previous 3 or 7 days. If the number of records for that tracking point exceeds the first target tracking point count threshold of 10,000 within the previous 3 days, or the number of records for that tracking point exceeds 65,000 within the previous 7 days, then the tracking point can be considered lost and is considered a missed tracking point. If the number of tracking points for the previous 3 days or 7 days is 0, then the tracking point can be considered lost and is considered a missed tracking point.

[0076] In some embodiments of this application, after obtaining the target historical running data, the following steps may also be included:

[0077] If the number of historical data points for the target is greater than the second target data point count threshold but not greater than the first target data point count threshold, the function corresponding to the second data point is determined to be a medium-frequency function in the business system, and the second data point is a potentially missed data point.

[0078] If the number of historical data points for the target is greater than zero and less than the threshold for the number of times the second target data point is used, the function corresponding to the second data point is determined to be a low-frequency function in the business system, and the second data point is a normal data point.

[0079] In this embodiment, after initially determining that the second tracking point is a missed tracking point, it is considered that if the second tracking point is not used by the user on that day due to a certain function in the business system, there is a possibility that the second tracking point may be misjudged. Based on this, it is necessary to obtain historical running tracking point data over a period of time. By querying the historical running tracking point data, the number of records of the second tracking point within a period of time is determined, that is, the number of target historical running tracking point data. If the number of records is zero, it indicates that there are no tracking point records for the second tracking point within a period of time, and it is highly likely that it was lost before a certain period of time. Therefore, the second tracking point is determined to be a missed tracking point. If the number of records is greater than the first target tracking point count threshold, it indicates that the second tracking point was frequently triggered in the previous period of time, and it is a high-frequency function tracking point. However, there are no tracking point records in the first time period, and it is highly likely that it was lost in the first time period. Therefore, it can be finally determined to be a missed tracking point. In this way, we can further investigate based on the number of times the second tracking point was recorded in the previous period. We can accurately determine whether the second tracking point has been truly lost based on the number of previous tracking point records. When the number of records is less than the threshold of the second target tracking point, the second tracking point is determined to be a low-frequency functional tracking point. Even if it is not triggered in the first time period, it is normal. This avoids the situation where low-frequency functional tracking points are not triggered in time and are mistakenly judged as missing tracking points, thus improving the accuracy of abnormal tracking point investigation.

[0080] In some embodiments of this application, the first target event tracking threshold is greater than the second target event tracking threshold. Both the first target event tracking threshold and the second target event tracking threshold are determined based on the user activity of the business system. Both the first target event tracking threshold and the second target event tracking threshold are positively correlated with the user activity of the business system.

[0081] Specifically, user activity can be the average daily number of active users in the business system during the current period. The second preset value is less than the first preset value. The first target event tracking threshold can be the product of the first preset value and user activity. The second target event tracking threshold can be the product of the second preset value and user activity, or other parameters that are positively correlated with user activity. Alternatively, the first target event tracking threshold can be an integer multiple of the second target event tracking threshold. This application does not impose any specific limitations on these parameters.

[0082] In this application embodiment, a higher user activity level in the business system indicates a larger number of users using the system, and consequently, a greater number of times a single function is triggered. Based on this, this application sets a first target event tracking frequency threshold and a second target event tracking frequency threshold that match the user activity level. When subsequently comparing the historical number of second event tracking points against these thresholds to determine whether the second event tracking point is a missed tracking point, the accuracy of abnormal event tracking point determination can be improved.

[0083] In step 140, if the field values ​​under the same associated field in the running data and the verification data of the same tracking point are inconsistent, the tracking point is determined to be an incorrect tracking point.

[0084] In step 140, if the field values ​​under all associated fields in the running event tracking data and the verification event tracking data are consistent, then the event tracking is determined to be a normal event tracking; otherwise, it is an incorrect event tracking.

[0085] In some embodiments of this application, the associated fields include at least system type, client type, client version, functional module, event type, event name, pageurl, and frompageurl. After determining the event tracking point as an erroneous event tracking point in step 140 above, the following steps may also be included:

[0086] Extract the field values ​​under pageurl and frompageurl from the runtime tracking data;

[0087] Based on the field values ​​under pageurl and frompageurl, the abnormal modules and abnormal pages in the business system are located, and the analysis results are obtained.

[0088] The analysis results are periodically sent to the target users.

[0089] Specifically, the pageurl and frompageurl fields can be used to locate the source system, source module, and source page of the event tracking data. When the event tracking is an abnormal event tracking, the source module and source page can be identified as abnormal modules and abnormal pages.

[0090] In this embodiment, the actual set of event tracking points is compared with the integrated set of verification event tracking points to identify missing, incorrect, and redundant tracking points online. Automated analysis is then performed to identify the systems, modules, and corresponding pages with data tracking errors. The analysis results are sent to the test managers and development teams of the corresponding systems via email or other communication methods for early warning. Error results are promptly synchronized with relevant technical personnel to quickly correct tracking point errors and ensure the accuracy of business system data.

[0091] Corresponding to the method embodiments of this application, this application also provides a data analysis device based on data embedding points.

[0092] Figure 5 This is a schematic diagram of the structure of a data analysis device based on data tracking points provided in an embodiment of this application. Figure 5 As shown, the data analysis device 500 based on data tracking points may include: a parsing module 510, a generation module 520, a field value comparison and analysis module 530, and a determination module 540.

[0093] The parsing module 510, upon obtaining the set of verification tracking points from the business system, parses the set to obtain M verification tracking point data entries for M tracking points associated with the business system. Each verification tracking point data entry consists of field values ​​for the corresponding tracking point under multiple associated fields. The generation module 520 queries N runtime tracking point data entries generated during the operation of the business system and generates a set of tracking point events for the business system based on these N runtime tracking point data entries. The N runtime tracking point data entries correspond to multiple tracking points. The field value comparison and analysis module 530... This module is used to compare and analyze the field values ​​of the verification data set and the event set of the business system. If the running data of the first data point does not correspond to any verification data among multiple data points, the first data point is determined to be a redundant data point. If the verification data of the second data point does not correspond to any running data among M data points, the second data point is initially determined to be a missing data point. The determination module 540 is used to determine the data point as an erroneous data point if the field values ​​of the same associated field in the running data and verification data are inconsistent.

[0094] The data analysis device based on data tracking provided in this application, upon obtaining a set of verification tracking points for a business system, parses the set to obtain M verification tracking point data entries for M tracking points associated with the business system. Each verification tracking point data entry consists of field values ​​for the corresponding tracking point under multiple associated fields. In other words, in this application, associated tracking points are pre-set for each business system, and associated fields and their values ​​are set for each tracking point. One set of verification tracking point data is generated for each tracking point, and these are integrated to obtain the set of verification tracking points for the business system. In the actual application of the business system, N runtime tracking point data entries generated during the system's operation are queried, and a set of tracking point events for the business system is generated based on these N runtime tracking point data entries. The N runtime tracking point data entries correspond to multiple tracking points. Based on this, the field values ​​of the verification tracking point set and the tracking event set of the business system are compared and analyzed. If the running tracking point data of the first tracking point in the tracking event set does not correspond to any verification tracking point data, the first tracking point can be determined to be a redundant tracking point. If the verification tracking point data of the second tracking point in the M tracking points does not correspond to any running tracking point data, the second tracking point can be preliminarily determined to be a missing tracking point. Furthermore, for the same tracking point, if the field values ​​under the same associated field in the running tracking point data and the verification tracking point data are inconsistent, the tracking point can be determined to be an incorrect tracking point. Thus, after obtaining the runtime tracking data generated by the business system during operation, this application compares the field values ​​of the data with the verification tracking data to identify missing, erroneous, and redundant tracking points online. Compared to existing technologies that target a single tracking point and require manually selecting multiple fields to be compared from all fields of the tracking point during the investigation process, and manually checking the field values ​​of multiple fields to determine whether the tracking point is abnormal, this approach can accelerate the investigation of abnormal tracking points and improve the efficiency of investigating abnormal tracking points in various business systems.

[0095] In some embodiments of this application, the system further includes: a tracking configuration module, used to set multiple associated tracking points for each business system based on the user's tracking configuration operation on the tracking management platform before parsing the verification tracking point set, and to configure multiple associated fields and their values ​​bound to each tracking point, wherein the multiple tracking points associated with different business systems are different, and the multiple tracking points associated with the same business system correspond to different tracking events; a combination module, used to combine multiple associated fields and their values ​​of each tracking point for each business system to obtain the verification tracking point data of each tracking point; and an integration module, used to integrate the M verification tracking point data of the M tracking points corresponding to the business system to obtain the verification tracking point set.

[0096] In some embodiments of this application, the field value comparison and analysis module 530 includes: a filtering submodule, used to filter verification data and running data corresponding to the same data point from M verification data points and N running data points corresponding to the business system, based on the field value under the target associated field among multiple associated fields; a determining submodule, used to determine that the first data point does not correspond to any verification data point if the field value under the target associated field in the running data point of the first data point does not appear in the M verification data points; and a determining submodule, also used to determine that the second data point does not correspond to any running data point if the field value under the target associated field in the verification data point of the second data point does not appear in the N running data points.

[0097] In some embodiments of this application, where N running event data points are generated by the business system during a first time period, the system further includes: an acquisition module, used to acquire all historical running event data generated by the business system during a preset time period before the first time period after initially determining that the second event point is a missed event point, to obtain a historical event point set; a query module, used to traverse the historical event point set and query historical running event data from the historical event set where the field values ​​of all associated fields in the verification event data corresponding to the second event point are consistent, to obtain target historical running event data; and a determination module, used to determine that the second event point is a missed event point when the number of target historical running event data points is zero or greater than a first target event point count threshold.

[0098] In some embodiments of this application, the determining module 540 is further configured to: after obtaining the target historical running data, if the number of target historical running data is greater than the second target data point count threshold and not greater than the first target data point count threshold, determine that the function corresponding to the second data point is a medium-frequency function in the business system and that the second data point is a potentially missed data point; if the number of target historical running data points is greater than zero and less than the second target data point count threshold, determine that the function corresponding to the second data point is a low-frequency function in the business system and that the second data point is a normal data point.

[0099] In some embodiments of this application, the first target event tracking threshold is greater than the second target event tracking threshold. Both the first target event tracking threshold and the second target event tracking threshold are determined based on the user activity of the business system. Both the first target event tracking threshold and the second target event tracking threshold are positively correlated with the user activity of the business system.

[0100] In some embodiments of this application, the associated fields include at least system type, client type, client version, functional module, event type, event name, pageurl, and frompageurl, and further include: an extraction module, used to extract the field values ​​under pageurl and frompageurl from the running event data after determining that the event point is an erroneous event point; an anomaly location module, used to locate the abnormal module and abnormal page in the business system based on the field values ​​under pageurl and frompageurl, and obtain the analysis results; and an anomaly sending module, used to periodically send the analysis results to the target user.

[0101] The data analysis device based on data tracking points provided in this application embodiment can achieve... Figure 1-4 The various processes implemented by the service platform in the method embodiment can achieve the same technical effect, and will not be described again here to avoid repetition.

[0102] Figure 6 This is a schematic diagram of the hardware structure of an electronic device provided in an embodiment of this application.

[0103] like Figure 6 As shown, the electronic device 600 includes a memory 601, a processor 602, and a computer program stored in the memory 601 and executable on the processor 602.

[0104] In one example, the processor 602 described above may include a central processing unit (CPU), or an application-specific integrated circuit (ASIC), or one or more integrated circuits that may be configured to implement the embodiments of this application.

[0105] Memory 601 may include read-only memory (ROM), random access memory (RAM), disk storage media device, optical storage media device, flash memory device, electrical, optical, or other physical / tangible memory storage device. Therefore, typically, memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., memory devices) encoded with software including computer-executable instructions, and when the software is executed (e.g., by one or more processors), it is operable to perform the operations described with reference to the card opening method in the embodiments according to the first aspect of this application.

[0106] The processor 602 runs a computer program corresponding to the executable program code by reading the executable program code stored in the memory 601, in order to implement the card opening method in the embodiment of the first aspect above.

[0107] In some examples, electronic device 600 may also include communication interface 603 and bus 610. For example, Figure 6 As shown, the memory 601, processor 602, and communication interface 603 are connected through bus 610 and complete communication with each other.

[0108] The communication interface 603 is mainly used to enable communication between various modules, devices, units, and / or equipment in the embodiments of this application. Input devices and / or output devices can also be connected through the communication interface 603.

[0109] Bus 610 includes hardware, software, or both, that couples components of electronic device 600 together. For example, and not limitingly, bus 610 may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), Hyper Transport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an Infinite Bandwidth Interconnect, a Low Pin Count (LPC) bus, a memory bus, a Micro Channel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-E) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local Bus (VLB) bus, or other suitable buses, or combinations of two or more of these. Where appropriate, bus 610 may include one or more buses. Although specific buses are described and illustrated in the embodiments of this application, this application considers any suitable bus or interconnection.

[0110] The electronic device provided in this application embodiment is capable of achieving Figure 1-4 The various processes implemented by the electronic device in the method embodiment can achieve the same technical effect, and will not be described again here to avoid repetition.

[0111] In conjunction with the data analysis method based on data tracking points in the above embodiments, this application embodiment can provide a computer storage medium for implementation. The computer storage medium stores computer program instructions; when these computer program instructions are executed by a processor, they implement the steps of any of the data analysis methods based on data tracking points in the above embodiments.

[0112] Based on the data analysis method based on data tracking points in the above embodiments, this application embodiment can provide a computer program product to implement it. This (computer) program product is stored in a non-volatile storage medium, and when executed by at least one processor, it implements the steps of any of the data analysis methods based on data tracking points in the above embodiments.

[0113] This application embodiment also provides a chip, which includes a processor and a communication interface. The communication interface and the processor are coupled. The processor is used to run programs or instructions to implement the various processes of the above-described data analysis method embodiment based on data embedding points, and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0114] It should be understood that the chip mentioned in the embodiments of this application may also be referred to as a system-on-a-chip, system chip, chip system, or system-on-a-chip, etc.

[0115] It should be clarified that this application is not limited to the specific configurations and processes described above and shown in the figures. For the sake of brevity, detailed descriptions of known methods are omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of this application is not limited to the specific steps described and shown. Those skilled in the art can make various changes, modifications, and additions, or change the order of steps, after understanding the spirit of this application.

[0116] The functional blocks shown in the above-described block diagram can be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, they can be, for example, electronic circuits, application-specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, etc. When implemented in software, the elements of this application are programs or code segments used to perform the required tasks. Programs or code segments can be stored on a machine-readable medium or transmitted over a transmission medium or communication link via data signals carried on a carrier wave. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio frequency (RF) links, etc. Code segments can be downloaded via computer networks such as the Internet, intranets, etc.

[0117] It should also be noted that the exemplary embodiments mentioned in this application describe methods or systems based on a series of steps or apparatus. However, this application is not limited to the order of the above steps; that is, the steps can be performed in the order mentioned in the embodiments, or in a different order, or several steps can be performed simultaneously.

[0118] The aspects of this disclosure have been described above with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It should be understood that each block in the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that these instructions, executable via the processor of the computer or other programmable data processing apparatus, enable the implementation of the functions / actions specified in one or more blocks of the flowchart illustrations and / or block diagrams. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field-programmable logic circuit. It is also understood that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can also be implemented by special-purpose hardware performing the specified functions or actions, or can be implemented by a combination of special-purpose hardware and computer instructions.

[0119] The above description is merely a specific implementation of this application. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, modules, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. It should be understood that the protection scope of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the protection scope of this application.

Claims

1. A data analysis method based on data tracking points, characterized in that, include: Once the set of verification points for the business system is obtained, the set of verification points is parsed to obtain M verification point data for M points associated with the business system. Each verification point data consists of field values ​​of the corresponding point under multiple associated fields. Query N runtime tracking data points generated during the operation of the business system, and generate a set of tracking events for the business system based on the N runtime tracking data points, wherein the N runtime tracking data points correspond to multiple tracking points; The field values ​​of the verification point set and the event set of the business system are compared and analyzed. If the running point data of the first point does not correspond to any verification point data among the multiple points, the first point is determined to be a redundant point. If the verification point data of the second point does not correspond to any running point data among the M points, the second point is initially determined to be a missing point. For the same data point, if the field value under the same associated field in the running data point data and the verification data point data is inconsistent, the data point is determined to be an incorrect data point. Wherein, the N runtime tracking data are generated by the business system during the first time period, and after initially determining that the second tracking point is a missed tracking point, the process further includes: Obtain all historical running event data generated by the business system during a preset time period before the first time period to obtain a set of historical event data. By traversing the historical event set, query the historical running event data from the historical event set where the field values ​​of all associated fields in the verification event data corresponding to the second event are consistent, and obtain the target historical running event data. If the number of historical data points for the target is zero, or greater than the threshold for the number of times the first target data point is used, the second data point is determined to be a missed data point. After obtaining the target historical running data, the following is also included: If the number of target historical running data points is greater than the second target data point count threshold but not greater than the first target data point count threshold, the function corresponding to the second data point is determined to be a mid-frequency function in the business system, and the second data point is a potentially missed data point. If the number of target historical running data points is greater than zero and less than the second target data point count threshold, the function corresponding to the second data point is determined to be a low-frequency function in the business system, and the second data point is a normal data point.

2. The method according to claim 1, characterized in that, Before parsing the set of verification tracking points, the following steps are also included: Based on the user's event tracking configuration operations on the event tracking management platform, multiple event tracking points are set up for each business system, and multiple associated fields and their values ​​are configured for each event tracking point. The multiple event tracking points associated with different business systems are different, and the multiple event tracking points associated with the same business system correspond to different event tracking events. For each business system, multiple associated fields and their values ​​of each tracking point are combined to obtain the verification tracking point data for each tracking point; The set of verification data points is obtained by integrating the M verification data points from the M data points corresponding to the business system.

3. The method according to claim 1, characterized in that, The step of comparing and analyzing the field values ​​of the verification tracking point set and the tracking event set of the business system includes: From the M verification data points and N runtime data points corresponding to the business system, based on the field values ​​under the target associated field among the multiple associated fields, the verification data points and runtime data points corresponding to the same data point are filtered. If the field value under the target associated field in the running data of the first tracking point does not appear in the M verification tracking points, it is determined that the first tracking point does not correspond to any verification tracking point. If the field value under the target association field in the verification data of the second tracking point does not appear in the N running tracking points, it is determined that the second tracking point does not correspond to any running tracking point.

4. The method according to claim 1, characterized in that, The first target event tracking threshold is greater than the second target event tracking threshold. Both the first target event tracking threshold and the second target event tracking threshold are determined based on the user activity of the business system. Both the first target event tracking threshold and the second target event tracking threshold are positively correlated with the user activity of the business system.

5. The method according to claim 1, characterized in that, The associated fields include at least system type, client type, client version, functional module, event type, event name, pageurl, and frompageurl. After determining that the tracking point is an erroneous tracking point, it also includes: Extract the field values ​​under pageurl and frompageurl from the runtime tracking data; Based on the field values ​​under pageurl and frompageurl, the abnormal modules and abnormal pages in the business system are located, and the analysis results are obtained. The analysis results are periodically sent to the target users.

6. A data analysis device based on data tracking points, characterized in that, include: The parsing module is used to parse the set of verification points of the business system when the set of verification points is obtained, and to obtain M verification point data of M points associated with the business system. Each verification point data consists of the field values ​​of the corresponding point under multiple associated fields. The generation module is used to query N runtime tracking data generated by the business system during operation, and generate a set of tracking events for the business system based on the N runtime tracking data, wherein the N runtime tracking data correspond to multiple tracking points; The field value comparison and analysis module is used to compare and analyze the field values ​​of the verification point set and the event set of the business system. If the running point data of the first point does not correspond to any verification point data among the multiple points, the first point is determined to be a redundant point. If the verification point data of the second point does not correspond to any running point data among the M points, the second point is initially determined to be a missing point. The determination module is used to determine that a tracking point is an erroneous tracking point when the field value under the same associated field in the running tracking point data and the verification tracking point data is inconsistent for the same tracking point. The N data points generated by the business system during the first time period, and the device also includes: The acquisition module is used to acquire all historical running event data generated by the business system within a preset duration before the first time period after initially determining that the second event point is a missed event point, and obtain a set of historical event points. The query module is used to traverse the historical event set and query the historical running event data from the historical event set where the values ​​of all related fields in the verification event data corresponding to the second event are consistent, so as to obtain the target historical running event data. The determination module is used to determine the second tracking point as a missed tracking point when the number of historical tracking point data points of the target is zero or greater than the first target tracking point count threshold. The determining module is further configured to: after obtaining the target historical running data, if the number of target historical running data is greater than the second target data point count threshold and not greater than the first target data point count threshold, determine that the function corresponding to the second data point is a medium-frequency function in the business system and that the second data point is a potentially missed data point; if the number of target historical running data is greater than zero and less than the second target data point count threshold, determine that the function corresponding to the second data point is a low-frequency function in the business system and that the second data point is a normal data point.

7. An electronic device, characterized in that, The electronic device includes a processor and a memory storing computer program instructions; when the electronic device executes the computer program instructions, it implements the method as described in any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer program instructions that, when executed by a processor, implement the method as described in any one of claims 1-5.

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