Point burying data processing method and device, computer equipment and storage medium

Abstract

The invention provides a buried point data processing method and device, computer equipment and a storage medium. The method comprises the steps of obtaining a to-be-processed burying point verification task based on a preset data analysis verification service; analyzing the buried point verification task to obtain a to-be-verified target buried point event, and judging whether a first report ID matched with the target buried point event exists in a preset event verification result table or not; if yes, obtaining a second report ID corresponding to the target burying point event from a preset event attribute table; judging whether the first report ID is the same as the second report ID or not; if not, obtaining an attribute value corresponding to the attribute of the target burying point event from an event attribute table; performing matching processing on the attribute value and a preset target attribute value to obtain a corresponding attribute matching result; and generating a verification result corresponding to the target burying point event based on the attribute matching result. According to the method and the device, the processing efficiency and the accuracy of the verification processing of the burying point event are effectively improved.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a method, device, computer equipment and storage medium for processing buried point data. Background Art

[0002] In today's rapidly developing mobile Internet era, applications (APPs) have become an indispensable part of people's daily lives and work. In order to optimize user experience, improve product performance, and accurately promote marketing, APP developers usually embed a large number of embedded events in their applications. These embedded events can be triggered when users perform specific operations and report relevant data to the server, thus providing a basis for subsequent data analysis and decision-making.

[0003] A typical tracking event is often composed of multiple attributes, which comprehensively record the context information of the event, such as user behavior, timestamp, device information, etc. Each attribute is further refined into one or more enumeration values, which accurately describe the specific state or classification of the attribute. In this way, tracking events can provide a rich and multi-dimensional data set to help developers gain a deeper understanding of user behavior and product performance.

[0004] However, in the process of automated testing of tracking events, verifying the attribute values ​​of a large number of tracking events has become an urgent problem to be solved. Most of the existing methods for verifying the attributes of tracking events rely on manual operations, that is, testers need to manually check whether the attribute values ​​of tracking events meet expectations. This method is not only inefficient and difficult to cope with the processing needs of massive data, but also has a great risk in terms of accuracy. Due to the existence of human factors, omissions, misjudgments and other problems may occur during the test, resulting in inaccurate test results, which in turn affects subsequent product optimization and market decisions. Summary of the invention

[0005] The main purpose of the present invention is to provide a method, device, computer equipment and storage medium for processing buried point data, aiming to solve the technical problems that the existing attribute verification methods of buried point events mostly rely on manual operations, have low processing efficiency and low accuracy.

[0006] To achieve the above object, the present invention provides a method for processing buried point data, the method comprising:

[0007] Obtain pending point verification tasks based on the preset data analysis and verification service;

[0008] Parse the embedding verification task to obtain the target embedding event to be verified, and determine whether there is a first reporting ID matching the target embedding event in a preset event verification result table;

[0009] If yes, obtain a second reporting ID corresponding to the target tracking event from a preset event attribute table;

[0010] Determine whether the first reporting ID is the same as the second reporting ID;

[0011] If the first reporting ID is different from the second reporting ID, obtaining the attribute value corresponding to the attribute of the target tracking event from the event attribute table;

[0012] Matching the attribute value with a preset target attribute value to obtain a corresponding attribute matching result;

[0013] A verification result corresponding to the target buried point event is generated based on the attribute matching result.

[0014] Optionally, before obtaining the pending tracking point verification task based on the preset data analysis verification service, the method further includes:

[0015] Receive a buried point event reporting request based on a preset data collection and storage service; wherein the buried point event reporting request carries a buried point event;

[0016] Parsing the tracking event reporting request to obtain the tracking event;

[0017] Determine whether the tracking event is a specified event that needs to be verified;

[0018] If so, obtain all attributes to be verified contained in the tracking event, and filter out the unreported specified attributes from all the attributes to be verified based on the event attribute table;

[0019] Generate a specified reporting ID corresponding to the specified attribute of the tracking event;

[0020] The specified attributes of the buried event and the specified reporting ID are stored in the event attribute table.

[0021] Optionally, determining whether the tracking event is a specified event that needs to be verified includes:

[0022] Extract information from the buried event to obtain corresponding event information;

[0023] Performing data verification on the event information;

[0024] If the event information passes the data verification, the pre-configured event judgment rule is called;

[0025] Matching the event information with the event judgment rule to obtain a corresponding matching result;

[0026] If the matching result is that the event information successfully matches the event judgment rule, the buried event is determined to be a designated event that needs to be verified;

[0027] If the matching result is that the event information fails to match the event judgment rule, it is determined that the buried event is not a designated event that needs to be verified.

[0028] Optionally, the generating a verification result corresponding to the target buried event based on the attribute matching result includes:

[0029] Determining whether the attribute matching result is that the attribute value matches a preset target attribute value;

[0030] If so, generate a verification result corresponding to the target buried point event that passes the verification;

[0031] If not, a verification result indicating that the verification corresponding to the target buried point event failed is generated.

[0032] Optionally, after determining whether there is a first reporting ID matching the target burying point event in a preset event verification result table, the method further includes:

[0033] Obtain a first verification result from the event verification result table;

[0034] Preprocessing the first verification result to obtain a corresponding second verification result;

[0035] Call the preset anomaly detection model;

[0036] Anomaly detection is performed on the second verification result based on the anomaly detection model to obtain an anomaly detection result corresponding to the first verification result.

[0037] Optionally, before calling the preset anomaly detection model, the method further includes:

[0038] Obtain pre-collected historical verification result data;

[0039] Performing feature engineering processing on the historical verification result data to obtain corresponding sample data;

[0040] Dividing the sample data into a training set and a test set;

[0041] Call the preset initial model;

[0042] Based on the training set and the test set, the initial model is trained and optimized accordingly until a specified model that meets the preset construction requirements is obtained;

[0043] The designated model is used as the anomaly detection model, and the anomaly detection model is deployed.

[0044] Optionally, after performing anomaly detection on the second verification result based on the anomaly detection model to obtain an anomaly detection result corresponding to the first verification result, the method further includes:

[0045] Determine whether the abnormality detection result indicates that an abnormality exists;

[0046] If so, generating warning information corresponding to the first verification result based on the abnormality detection result;

[0047] Get the preset information notification method;

[0048] Based on the information notification method, the warning information is sent to relevant personnel.

[0049] In addition, to achieve the above-mentioned purpose, the present invention also provides a processing device for buried point data, the processing device for buried point data comprising:

[0050] The first acquisition module is used to acquire the pending point verification task based on the preset data analysis and verification service;

[0051] A first judgment module is used to parse the embedding point verification task to obtain a target embedding point event to be verified, and determine whether there is a first reporting ID matching the target embedding point event in a preset event verification result table;

[0052] A second acquisition module is used to obtain a second reporting ID corresponding to the target tracking event from a preset event attribute table;

[0053] A second judgment module, used to judge whether the first reporting ID is the same as the second reporting ID;

[0054] A third acquisition module, configured to acquire an attribute value corresponding to the attribute of the target tracking event from the event attribute table if the first reporting ID is different from the second reporting ID;

[0055] A matching module, used to match the attribute value with a preset target attribute value to obtain a corresponding attribute matching result;

[0056] The first generation module is used to generate a verification result corresponding to the target buried point event based on the attribute matching result.

[0057] In order to solve the above technical problems, the embodiment of the present application further provides a computer device, which adopts the following technical solution:

[0058] The computer device includes a memory and a processor, the memory stores a computer program, and the processor implements the steps of any one of the methods for processing buried point data proposed in the embodiments of the present application when executing the computer program.

[0059] In order to solve the above technical problems, the embodiment of the present application further provides a computer-readable storage medium, which adopts the following technical solution:

[0060] The computer-readable storage medium stores a computer program, and when the computer program is executed by the processor, the steps of the method for processing buried point data described in any one of the embodiments of the present application are implemented.

[0061] Compared with the prior art, the embodiments of the present application have the following beneficial effects:

[0062] The present invention provides a method, device, computer equipment and storage medium for processing buried point data, the method comprising: first, obtaining a buried point verification task to be processed based on a preset data analysis and verification service; then parsing the buried point verification task to obtain a target buried point event to be verified, and judging whether there is a first reporting ID matching the target buried point event in a preset event verification result table; if so, obtaining a second reporting ID corresponding to the target buried point event from a preset event attribute table; then judging whether the first reporting ID is the same as the second reporting ID; if the first reporting ID is not the same as the second reporting ID, obtaining the attribute value corresponding to the attribute of the target buried point event from the event attribute table; subsequently matching the attribute value with the preset target attribute value to obtain a corresponding attribute matching result; finally generating a verification result corresponding to the target buried point event based on the attribute matching result. The present invention obtains the pending point-of-sale verification task based on the use of a preset data analysis verification service, and parses the point-of-sale verification task to obtain the target point-of-sale event to be verified. When a first reporting ID matching the target point-of-sale event is detected in the preset event verification result table, a second reporting ID corresponding to the target point-of-sale event is obtained from the preset event attribute table, and when it is detected that the first reporting ID is different from the second reporting ID, the attribute value corresponding to the attribute of the target point-of-sale event is obtained from the event attribute table, and then the attribute value is matched with the preset target attribute value to obtain the corresponding attribute matching result, and finally a verification result corresponding to the target point-of-sale event is generated based on the attribute matching result. The present application can realize the efficient completion of real-time and accurate verification processing of point-of-sale events based on the use of data analysis verification services, thereby improving the processing efficiency and accuracy of the verification processing of point-of-sale events. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] In order to more clearly illustrate the scheme in the present application, a brief introduction is given below to the drawings required for use in the description of the embodiments of the present application. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0064] Figure 1 is an exemplary system architecture diagram to which the present application may be applied;

[0065] Figure 2 is a flow chart of a method for processing buried point data provided by an embodiment of the present invention;

[0066] Figure 3 It is a structural schematic diagram of an embodiment of a device for processing buried point data according to the present application;

[0067] Figure 4 This is a basic structural block diagram of the computer device in this embodiment. DETAILED DESCRIPTION

[0068] The method for processing buried point data provided by the embodiment of the present invention is applied to the processing device for buried point data. Unless otherwise defined, all technical and scientific terms used in this document have the same meaning as those generally understood by technicians in the technical field of this application; the terms used in the specification of the application herein are only for the purpose of describing specific embodiments and are not intended to limit this application; the terms "including" and "having" in the specification and claims of this application and the above-mentioned drawings and any variations thereof are intended to cover non-exclusive inclusions. The terms "first", "second", etc. in the specification and claims of this application or the above-mentioned drawings are used to distinguish different objects, not to describe a specific order.

[0069] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0070] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings.

[0071] like Figure 1As shown, the system architecture 100 may include terminal devices 101, 102, 103, a network 104 and a server 105. The network 104 is used to provide a medium for communication links between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links or optical fiber cables, etc.

[0072] Users can use terminal devices 101, 102, 103 to interact with server 105 through network 104 to receive or send messages, etc. Various communication client applications can be installed on terminal devices 101, 102, 103, such as web browser applications, shopping applications, search applications, instant messaging tools, email clients, social online platform software, etc.

[0073] Terminal devices 101, 102, 103 can be various electronic devices with display screens and supporting web browsing, including but not limited to smart phones, tablet computers, e-book readers, MP3 players (Moving Picture Experts Group Audio Layer III), MP4 (Moving Picture Experts Group Audio Layer IV) players, laptop computers, desktop computers, etc.

[0074] The server 105 may be a server that provides various services, such as a background server that provides support for web pages displayed on the terminal devices 101 , 102 , and 103 .

[0075] It should be noted that the method for processing the buried point data provided in the embodiment of the present application is generally executed by a server / terminal device, and accordingly, the processing device for the buried point data is generally arranged in the server / terminal device.

[0076] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is only for illustration. Any number of terminal devices, networks and servers may be provided according to implementation requirements.

[0077] In today's rapidly developing mobile Internet era, applications (APPs) have become an indispensable part of people's daily lives and work. In order to optimize user experience, improve product performance, and accurately promote marketing, APP developers usually embed a large number of embedded events in their applications. These embedded events can be triggered when users perform specific operations and report relevant data to the server, thus providing a basis for subsequent data analysis and decision-making.

[0078] A typical tracking event is often composed of multiple attributes, which comprehensively record the context information of the event, such as user behavior, timestamp, device information, etc. Each attribute is further refined into one or more enumeration values, which accurately describe the specific state or classification of the attribute. In this way, tracking events can provide a rich and multi-dimensional data set to help developers gain a deeper understanding of user behavior and product performance.

[0079] However, in the process of automated testing of tracking events, verifying the attribute values ​​of a large number of tracking events has become an urgent problem to be solved. Most of the existing methods for verifying the attributes of tracking events rely on manual operations, that is, testers need to manually check whether the attribute values ​​of tracking events meet expectations. This method is not only inefficient and difficult to cope with the processing needs of massive data, but also has a great risk in terms of accuracy. Due to the existence of human factors, omissions, misjudgments and other problems may occur during the test, resulting in inaccurate test results, which in turn affects subsequent product optimization and market decisions.

[0080] Continue to refer Figure 2 , shows a flow chart of an embodiment of a method for processing buried point data proposed in the present application. The embodiment of the present application can acquire and process relevant data based on artificial intelligence technology.

[0081] The method for processing buried point data provided by the embodiment of the present invention comprises the following steps:

[0082] S210, obtaining the pending point verification task based on the preset data analysis and verification service.

[0083] In this step, the execution subject of the present invention may specifically be a terminal device with an application installed, referred to as a terminal or device. The terminal device can effectively avoid the problems of service coupling and resource contention by splitting the collection and verification of the buried event data into two services (data collection storage service and data analysis and verification service) and deploying them independently, thereby significantly improving the collection and verification execution efficiency of the buried event data. Among them, the data analysis and verification service queries the task table at regular intervals to filter out the buried verification tasks that are in the started state and are to be processed. The above-mentioned task table is a pre-constructed data table that stores the buried verification tasks.

[0084] S220, parse the embedding point verification task to obtain the target embedding point event to be verified, and determine whether there is a first reporting ID matching the target embedding point event in a preset event verification result table.

[0085] In this step, the acquired buried point verification task can be parsed to obtain the target buried point event that needs to be verified. Among them, for the buried point events that have been verified, the verification result of the buried point event and the corresponding latest reporting ID will be stored in the above-mentioned event verification result table in advance according to the event ID of the buried point event. Specifically, the above-mentioned event verification result table can be queried by using the event ID of the above-mentioned target buried point event. If a reporting ID that matches the event ID of the target buried point event is queried in the above-mentioned event verification result table, it is determined that there is a first reporting ID that matches the target buried point event in the above-mentioned event verification result table. If the reporting ID that matches the event ID of the target buried point event is not queried in the above-mentioned event verification result table, it is determined that there is no first reporting ID that matches the target buried point event in the above-mentioned event verification result table. In addition, if the first reporting ID that matches the target buried point event does not exist in the above-mentioned event verification result table, it indicates that the target buried point event is a buried point event that is verified for the first time. In the subsequent process, it is also necessary to verify the target buried point event according to the verification steps of "obtaining the attribute value corresponding to the attribute of the target buried point event from the event attribute table; matching the attribute value with the preset target attribute value to obtain the corresponding attribute matching result; generating a verification result corresponding to the target buried point event based on the attribute matching result".

[0086] S230: If yes, obtain a second reporting ID corresponding to the target tracking event from a preset event attribute table.

[0087] In this step, the event attribute table is a pre-built data table that stores all attributes of each event, the attribute values ​​corresponding to each attribute, and the latest reported ID of the event. The event attribute table can be queried using the event ID of the target buried event to obtain the second reported ID corresponding to the target buried event. The event attribute table can also be called the event target attribute real-time attribute value table.

[0088] S240: Determine whether the first reporting ID is the same as the second reporting ID.

[0089] In this step, the first reporting ID and the second reporting ID can be compared to obtain the corresponding comparison result. The comparison result may include that the first reporting ID is the same as the second reporting ID, or the first reporting ID is different from the second reporting ID. If the first reporting ID is the same as the second reporting ID, it indicates that there is no new data reported for the current target burial event, and there is no need to perform subsequent attribute value verification and other processes, thereby reducing invalid calculations and directly entering the verification process of the next event, thereby improving the overall verification efficiency.

[0090] S250: If the first reporting ID is different from the second reporting ID, the attribute value corresponding to the attribute of the target buried event is obtained from the event attribute table.

[0091] In this step, if it is detected that the first reporting ID is different from the second reporting ID, it indicates that the current target buried event has new data reported and is not in the first verification state, and the first verification step needs to be repeated. All attributes and attribute values ​​of the target buried event can be obtained by querying the attribute value corresponding to the target buried event in the above event attribute table.

[0092] S260: Match the attribute value with a preset target attribute value to obtain a corresponding attribute matching result.

[0093] In this step, the corresponding attribute matching results are generated by traversing the attribute values ​​corresponding to the attributes of the target buried event and detecting whether the attribute values ​​corresponding to the attributes of the target buried event all match the set target attribute values. Among them, the preset target attribute value may be a single value or one of multiple values ​​(for example, an attribute may have multiple acceptable target values). In addition, the content of the above attribute matching results may include that the attribute value matches the preset target attribute value, or that the attribute value does not match the preset target attribute value.

[0094] S270, generating a verification result corresponding to the target buried point event based on the attribute matching result.

[0095] In this step, the specific implementation process of generating the verification result corresponding to the target buried point event based on the attribute matching result will be described in further detail in the subsequent specific embodiments of the present invention, and will not be elaborated on here. In addition, after obtaining the verification result of the target buried point event, the verification result of the target buried point event and the latest reported ID of the target buried point event are further stored in the above-mentioned event verification result table to complete the data update of the event verification result table.

[0096] The data analysis and verification service periodically pulls attribute values ​​from the event attribute table and compares them with the preset target attribute values. This pulling and comparison method also uses the incremental mode, that is, the subsequent comparison processing will be carried out only when the target embedded event has a new attribute value reported. Otherwise, it will directly jump to process the next embedded event data, which can effectively reduce invalid verification and improve processing efficiency.

[0097] In an embodiment of the present invention, first, a to-be-processed embedding point verification task is obtained based on a preset data analysis and verification service; then, the embedding point verification task is parsed to obtain a target embedding point event to be verified, and it is determined whether there is a first reporting ID matching the target embedding point event in a preset event verification result table; if so, a second reporting ID corresponding to the target embedding point event is obtained from a preset event attribute table; then, it is determined whether the first reporting ID is the same as the second reporting ID; if the first reporting ID is not the same as the second reporting ID, the attribute value corresponding to the attribute of the target embedding point event is obtained from the event attribute table; subsequently, the attribute value is matched with the preset target attribute value to obtain a corresponding attribute matching result; finally, a verification result corresponding to the target embedding point event is generated based on the attribute matching result. The present invention obtains the pending point-of-sale verification task based on the use of a preset data analysis verification service, and parses the point-of-sale verification task to obtain the target point-of-sale event to be verified. When a first reporting ID matching the target point-of-sale event is detected in the preset event verification result table, a second reporting ID corresponding to the target point-of-sale event is obtained from the preset event attribute table, and when it is detected that the first reporting ID is different from the second reporting ID, the attribute value corresponding to the attribute of the target point-of-sale event is obtained from the event attribute table, and then the attribute value is matched with the preset target attribute value to obtain the corresponding attribute matching result, and finally a verification result corresponding to the target point-of-sale event is generated based on the attribute matching result. The present application can realize the efficient completion of real-time and accurate verification processing of point-of-sale events based on the use of data analysis verification services, thereby improving the processing efficiency and accuracy of the verification processing of point-of-sale events.

[0098] Optionally, before obtaining the pending tracking point verification task based on the preset data analysis verification service, the method further includes:

[0099] The preset data collection and storage service receives a buried event reporting request, wherein the buried event reporting request carries a buried event.

[0100] In this step, an event monitoring module is integrated into the system. When a user performs a specific operation (such as clicking a button, page jump, product purchase, etc.), the event monitoring module will capture these behaviors and generate a tracking event. After that, the event monitoring module will encapsulate the captured tracking events in a specific format (such as JSON) and send them to the data collection storage service through a network request.

[0101] The burying point event reporting request is parsed to obtain the burying point event.

[0102] In this step, the above-mentioned burying point event reporting request can be parsed to extract the burying point event carried in the burying point event reporting request.

[0103] Determine whether the buried event is a specified event that needs to be verified.

[0104] In this step, the specific implementation process of the above-mentioned determination of whether the buried event is a specified event that needs to be verified will be further described in detail in the subsequent specific embodiments of the present invention, and will not be elaborated on here.

[0105] If so, all attributes to be verified contained in the buried event are obtained, and the unreported specified attributes are filtered out from all the attributes to be verified based on the event attribute table.

[0106] In this step, for the above-mentioned buried events, all the attributes to be verified (such as product ID, product category, product price, etc.) contained in the buried events can be further extracted through the data collection and storage service. Then, according to the above-mentioned event attribute table, check whether the attribute values ​​of these attributes to be verified have been reported. If they have been reported, skip the attribute value and continue to process the attribute value of the next attribute. If it has not been reported, concatenate the values ​​in the form of "event-attribute-attribute value 1|attribute value 2|attribute value 3", and filter out the attributes reported for the first time as the above-mentioned specified attributes.

[0107] Generate a specified reporting ID corresponding to the specified attribute of the tracking event.

[0108] In this step, if the above-mentioned buried event is a designated event that needs to be verified, the original data information of the buried event is further saved in the corresponding event real-time reporting data table. And the latest reporting ID corresponding to the designated attributes of the above-mentioned buried event will be generated to obtain the above-mentioned designated reporting ID. Among them, the generation method of the above-mentioned latest reporting ID can be set according to actual business needs.

[0109] The specified attributes of the buried event and the specified reporting ID are stored in the event attribute table.

[0110] In this step, an association relationship between the specified attributes of the above-mentioned burying point event and the specified reporting ID can be constructed, and then the specified attributes of the burying point event and the specified reporting ID can be stored in the event attribute table according to the association relationship.

[0111] In an embodiment of the present invention, a buried point event reporting request is received through a preset data acquisition and storage service; wherein the buried point event reporting request carries the buried point event; then the buried point event reporting request is parsed to obtain the buried point event; then, it is determined whether the buried point event is a designated event that needs to be verified; if so, all attributes to be verified contained in the buried point event are obtained, and the unreported designated attributes are filtered out from all the attributes to be verified based on the event attribute table; subsequently, a designated reporting ID corresponding to the designated attributes of the buried point event is generated; finally, the designated attributes of the buried point event and the designated reporting ID are stored in the event attribute table. The present invention receives a buried event reporting request based on a preset data acquisition and storage service, then parses the buried event reporting request to obtain the buried event, and when detecting that the buried event is a designated event that needs to be verified, intelligently obtains all attributes to be verified contained in the buried event, and filters out the unreported designated attributes from all attributes to be verified based on the event attribute table, and then generates a designated reporting ID corresponding to the designated attributes of the buried event, and subsequently stores the designated attributes of the buried event and the designated reporting ID in the event attribute table, thereby intelligently realizing real-time, fast and efficient collection and storage of buried event data based on the use of the data acquisition and storage service, thereby improving the collection efficiency and accuracy of buried event data.

[0112] Optionally, determining whether the tracking event is a specified event that needs to be verified includes:

[0113] Information is extracted from the buried event to obtain corresponding event information.

[0114] In this step, the data in JSON format is read from the above-mentioned buried event, and the JSON string is converted into an object or dictionary using a JSON parsing library (such as Jackson of Java, json module of Python, etc.). Then, basic information such as event type (event_type), event ID (event_id), trigger time (timestamp), user ID (user_id) and so on are extracted from the parsed object or dictionary, and used as the event information of the above-mentioned buried event. These basic information usually exist as attributes of an object or key-value pairs of a dictionary.

[0115] Perform data verification on the event information.

[0116] In this step, the data verification may include checking whether required fields are missing, whether the data type is correct, whether the time format is compliant, etc. By performing data verification on the event information, the integrity and correctness of the event information can be ensured.

[0117] If the event information passes the data verification, the pre-configured event judgment rule is called.

[0118] In this step, the event judgment rules are pre-configured judgment rules corresponding to the specified events that need to be verified according to actual business needs. Specifically, the event judgment rules may include event type matching, event ID matching, user ID whitelist or blacklist, etc. The event judgment rules can be hard-coded in the code or stored in a configuration file or database for dynamic adjustment.

[0119] The event information is matched with the event judgment rule to obtain a corresponding matching result.

[0120] In this step, the above event information can be used to match the above event judgment rules to obtain a corresponding matching result. Among them, for the matching of event types, it can be checked whether the event type field matches the event type specified in the event judgment rule. For other rules, such as the matching of event IDs or the filtering of user IDs, corresponding checks can also be performed. In addition, the content of the above matching result may include that the event information successfully matches the event judgment rule, or that the event information fails to match the event judgment rule.

[0121] If the matching result is that the event information successfully matches the event judgment rule, the buried event is determined to be a designated event that needs to be verified.

[0122] In this step, if the matching result is that the event information successfully matches the event judgment rule, the above-mentioned buried event is regarded as a designated event, and then the buried event is determined to be a designated event that needs to be verified.

[0123] If the matching result is that the event information fails to match the event judgment rule, it is determined that the buried event is not a designated event that needs to be verified.

[0124] In this step, if the matching result is that the event information fails to match the event judgment rule, the above-mentioned buried event will not be regarded as a designated event, and then it is determined that the buried event is not a designated event that needs to be verified.

[0125] In an embodiment of the present invention, the corresponding event information is obtained by extracting information from the buried event; then the event information is subjected to data verification; if the event information passes the data verification, the pre-configured event judgment rule is called; the event information is subsequently matched with the event judgment rule to obtain a corresponding matching result; if the matching result is that the event information successfully matches the event judgment rule, the buried event is determined to be a designated event that needs to be verified; and if the matching result is that the event information fails to match the event judgment rule, the buried event is determined not to be a designated event that needs to be verified. The present invention obtains the corresponding event information by extracting information from the buried event, then performs data verification on the event information, and after detecting that the event information passes the data verification, matches the event information with the preset event judgment rule to obtain the corresponding matching result, and then subsequently, according to the obtained matching result, it can be realized to efficiently and accurately determine whether the buried event is a designated event that needs to be verified, effectively improving the processing efficiency of the event discrimination processing of the buried event, and ensuring the accuracy of the event discrimination result of the buried event.

[0126] Optionally, the generating a verification result corresponding to the target buried event based on the attribute matching result includes:

[0127] It is determined whether the attribute matching result is that the attribute value matches a preset target attribute value.

[0128] In this step, the content of the attribute matching result may include that the attribute value matches the target attribute value, or that the attribute value does not match the target attribute value. Content recognition may be performed on the attribute matching result to determine whether the attribute matching result is that the attribute value matches the preset target attribute value.

[0129] If so, generate a verification result corresponding to the target buried point event.

[0130] In this step, if the attribute matching result is that the attribute value matches the preset target attribute value, a verification result corresponding to the target buried point event is generated.

[0131] If not, a verification result indicating that the verification has failed is generated corresponding to the target buried point event.

[0132] In this step, if the attribute matching result is that the attribute value does not match the preset target attribute value, a verification result indicating that the verification has failed is generated corresponding to the target buried point event.

[0133] In an embodiment of the present invention, it is determined whether the attribute matching result is that the attribute value matches the preset target attribute value; if so, a verification result corresponding to the target buried point event is generated, which passes the verification; if not, a verification result corresponding to the target buried point event is generated, which fails the verification. The present invention performs content recognition on the attribute matching result obtained by matching the attribute value with the preset target attribute value, and then based on the obtained content recognition result, it is possible to quickly and accurately generate a verification result corresponding to the target buried point event, thereby ensuring the accuracy of the verification result of the obtained target buried point event.

[0134] Optionally, after determining whether there is a first reporting ID matching the target burying point event in a preset event verification result table, the method further includes:

[0135] A first verification result is obtained from the event verification result table.

[0136] In this step, verification result data can be regularly collected from the event verification result table, including the event reporting ID, verification attributes, verification results (verification passed / verification failed), reporting time, etc.

[0137] The first verification result is preprocessed to obtain a corresponding second verification result.

[0138] In this step, the process of preprocessing the first verification result includes: cleaning the collected first verification results, removing duplicate, invalid or missing data records, and ensuring the accuracy and completeness of the data. Then, features related to anomaly detection are extracted from the cleaned data, such as the distribution of verification results, the trend of error rate changes, the distribution of reporting time, etc., and used as the second verification result to ensure that the format and features of the second verification result are consistent with those during model training.

[0139] Call the preset anomaly detection model.

[0140] In this step, the specific construction process of the above-mentioned anomaly detection model will be further described in detail in the subsequent specific embodiments of the present invention, and will not be elaborated in detail here.

[0141] Anomaly detection is performed on the second verification result based on the anomaly detection model to obtain an anomaly detection result corresponding to the first verification result.

[0142] In this step, the second verification result after preprocessing can be input into the anomaly detection model, and the second verification result can be classified by the anomaly detection model to determine whether it is abnormal data, and the corresponding anomaly detection result can be output. The content of the anomaly detection result can include the presence or absence of an anomaly.

[0143] In an embodiment of the present invention, a first verification result is obtained from the event verification result table; the first verification result is then preprocessed to obtain a corresponding second verification result; a preset anomaly detection model is then called; and an anomaly detection is subsequently performed on the second verification result based on the anomaly detection model to obtain an anomaly detection result corresponding to the first verification result. The present invention obtains a first verification result from the event verification result table, and preprocesses the first verification result to obtain a corresponding second verification result, and then performs an anomaly detection on the second verification result based on the use of an anomaly detection model to obtain an anomaly detection result corresponding to the first verification result, thereby effectively monitoring and analyzing abnormal data in the verification results, timely discovering and warning potential problems, and thus improving the stability and reliability of the system.

[0144] Optionally, before calling the preset anomaly detection model, the method further includes:

[0145] Obtain pre-collected historical verification result data.

[0146] In this step, the initial verification result data can be collected from the above-mentioned event verification result table, and the initial verification result data can be preprocessed, including data cleaning, missing value processing, outlier processing, etc., to ensure the data quality of the obtained historical verification result data.

[0147] Perform feature engineering processing on the historical verification result data to obtain corresponding sample data.

[0148] In this step, features related to anomaly detection can be extracted from the above historical verification result data according to business needs and data characteristics, and used as the above sample data. Among them, features can include statistics of verification results (such as mean, standard deviation, maximum value, minimum value, etc.), time-related features (such as timestamp, time interval, etc.), and attribute features of the event itself.

[0149] The sample data is divided into a training set and a test set.

[0150] In this step, the sample data may be divided according to a certain ratio (eg, 70% training set, 30% test set) to ensure that the training set and the test set are consistent in data distribution to avoid overfitting or underfitting problems.

[0151] Recall the preset initial model.

[0152] In this step, according to the characteristics of the data and actual business needs, a suitable model is selected as the above initial model, such as Isolation Forest, One-Class SVM, density-based clustering algorithm (such as DBSCAN), etc.

[0153] Based on the training set and the test set, the initial model is trained and optimized accordingly until a specified model that meets the preset construction requirements is obtained.

[0154] In this step, the selected initial model can be trained using the above training set to obtain a trained first model. During the training process, the parameters of the model (such as the number of trees in the isolation forest, the kernel function and parameters in the One-Class SVM, etc.) are adjusted to optimize the performance of the model. The trained first model is then evaluated using the test set to calculate the accuracy, recall rate, F1 score and other indicators of the first model. Then, based on the evaluation results, the first model is further adjusted and optimized until satisfactory performance is achieved and the corresponding second model is obtained, which is used as the above designated model.

[0155] The designated model is used as the anomaly detection model, and the anomaly detection model is deployed.

[0156] In this step, you can deploy the trained anomaly detection model to the data analysis and verification service to ensure that the model can receive and process new verification result data in real time.

[0157] In an embodiment of the present invention, by obtaining pre-collected historical verification result data; then performing feature engineering processing on the historical verification result data to obtain corresponding sample data; and dividing the sample data into a training set and a test set; then calling a preset initial model; subsequently performing corresponding training and optimization processing on the initial model based on the training set and the test set until a specified model that meets the preset construction requirements is obtained; finally, the specified model is used as the anomaly detection model, and the anomaly detection model is deployed. The present invention obtains pre-collected historical verification result data, and performs feature engineering processing on the historical verification result data to obtain corresponding sample data, and then divides the sample data into a training set and a test set, and then performs corresponding training and optimization processing on the initial model based on the use of the training set and the test set until a specified model that meets the preset construction requirements is obtained, and subsequently the specified model is used as the anomaly detection model, and the anomaly detection model is deployed, thereby achieving efficient and accurate completion of the model construction processing for the anomaly detection model, effectively improving the model construction efficiency of the anomaly detection model, and ensuring the model effect of the obtained anomaly detection model.

[0158] Optionally, after performing anomaly detection on the second verification result based on the anomaly detection model to obtain an anomaly detection result corresponding to the first verification result, the method further includes:

[0159] Determine whether the abnormality detection result indicates that an abnormality exists.

[0160] In this step, the content of the abnormality detection result may include the presence of an abnormality or the absence of an abnormality. Wherein, the abnormality detection result may be determined by performing content recognition on the abnormality detection result to determine whether the abnormality detection result indicates the presence of an abnormality.

[0161] If so, generate warning information corresponding to the first verification result based on the abnormality detection result.

[0162] In this step, the warning information may include detailed information corresponding to the first verification result, possible causes and suggested solutions.

[0163] Get the preset information notification method.

[0164] In this step, there is no specific limitation on the selection of the above information notification method, which can be set according to actual business needs, for example, email, text message, instant messaging tool, etc.

[0165] Based on the information notification method, the warning information is sent to relevant personnel.

[0166] In this step, the above-mentioned information notification method can be used to realize that the generated warning information corresponding to the first verification result is sent to the relevant personnel in a timely manner. Among them, after receiving the warning information, the relevant personnel may further conduct an in-depth analysis of the abnormal data (the buried point events corresponding to the verification results with abnormalities) to determine the cause of the abnormality and the scope of influence. In addition, according to the results of the abnormal analysis, corresponding processing measures can be taken, such as data correction, model adjustment, system optimization, etc. In addition, the processing results corresponding to the above-mentioned processing measures can also be fed back to the system, and the early warning mechanism can be optimized and improved based on the experience and lessons learned in the processing process. This helps to improve the accuracy and efficiency of the early warning and reduce false alarms and missed reports.

[0167] In an embodiment of the present invention, it is determined whether the abnormal detection result is abnormal; if so, warning information corresponding to the first verification result is generated based on the abnormal detection result; then a preset information notification method is obtained; and subsequently, the warning information is sent to relevant personnel based on the information notification method. When the present invention detects that the abnormal detection result of the first verification result is abnormal, it will automatically generate warning information corresponding to the first verification result based on the abnormal detection result, then obtain a preset information notification method, and send the warning information to relevant personnel based on the information notification method, thereby providing valuable data support for relevant personnel, helping them to quickly locate and solve potential problems corresponding to the abnormal verification results, thereby helping to improve the stability and reliability of the system.

[0168] In some optional implementations, the user information obtained is subject to the user's consent and complies with relevant laws and policies.

[0169] In addition, compared with the data verification in the single-service full mode, the multi-service incremental mode of the present invention (data storage in the full mode and data comparison in the incremental mode) can verify a large number of embedded point event data in a relatively short time, thereby ensuring the high efficiency of the verification processing of the embedded point event data, as well as the real-time and accuracy of the verification results of the embedded point event data.

[0170] Further references Figure 3 , as a response to the above Figure 2 In order to realize the method shown in the figure, the present application provides an embodiment of a processing device 300 for buried point data. Figure 2 Corresponding to the method embodiment shown, the device can be specifically applied to various electronic devices.

[0171] An embodiment of the present invention provides a device 300 for processing buried point data, and the device 300 for processing buried point data includes:

[0172] The first acquisition module 310 is used to acquire a pending point verification task based on a preset data analysis and verification service;

[0173] The first judgment module 320 is used to parse the embedding point verification task to obtain the target embedding point event to be verified, and determine whether there is a first reporting ID matching the target embedding point event in a preset event verification result table;

[0174] The second acquisition module 330 is used to obtain a second reporting ID corresponding to the target tracking event from a preset event attribute table;

[0175] A second determination module 340 is configured to determine whether the first reporting ID is the same as the second reporting ID;

[0176] A third acquisition module 350, configured to acquire an attribute value corresponding to the attribute of the target tracking event from the event attribute table if the first reporting ID is different from the second reporting ID;

[0177] A matching module 360 ​​is used to match the attribute value with a preset target attribute value to obtain a corresponding attribute matching result;

[0178] The first generation module 370 is used to generate a verification result corresponding to the target buried point event based on the attribute matching result.

[0179] Optionally, the device 300 for processing buried data further includes:

[0180] A receiving module, configured to receive a buried point event reporting request based on a preset data collection and storage service; wherein the buried point event reporting request carries a buried point event;

[0181] A parsing module, used for parsing the embedding event reporting request to obtain the embedding event;

[0182] A third judgment module is used to judge whether the buried event is a designated event that needs to be verified;

[0183] A screening module, for obtaining all attributes to be verified contained in the buried event, and screening out unreported specified attributes from all the attributes to be verified based on the event attribute table;

[0184] A second generating module is used to generate a specified reporting ID corresponding to a specified attribute of the tracking event;

[0185] A storage module is used to store the specified attributes of the buried event and the specified reporting ID in the event attribute table.

[0186] Optionally, the third judgment module includes:

[0187] The extraction submodule is used to extract information from the buried event to obtain corresponding event information;

[0188] A verification submodule, used to perform data verification on the event information;

[0189] A calling submodule, used for calling a pre-configured event judgment rule if the event information passes the data verification;

[0190] A matching submodule, used to match the event information with the event judgment rule to obtain a corresponding matching result;

[0191] A first determination submodule, configured to determine that the buried event is a designated event that needs to be verified if the matching result is that the event information successfully matches the event judgment rule;

[0192] The second determination submodule is used to determine that the buried event is not a designated event that needs to be verified if the matching result is that the event information fails to match the event judgment rule.

[0193] Optionally, the first generating module 370 includes:

[0194] A judgment submodule, used to judge whether the attribute matching result is that the attribute value matches a preset target attribute value;

[0195] A first generating submodule is used to generate a verification result corresponding to the target buried point event.

[0196] The second generation submodule is used to generate a verification result indicating that the verification corresponding to the target buried point event has failed.

[0197] Optionally, the device 300 for processing buried data further includes:

[0198] A fourth acquisition module, used to acquire the first verification result from the event verification result table;

[0199] A preprocessing module, used to preprocess the first verification result to obtain a corresponding second verification result;

[0200] A first calling module is used to call a preset anomaly detection model;

[0201] A detection module is used to perform anomaly detection on the second verification result based on the anomaly detection model to obtain an anomaly detection result corresponding to the first verification result.

[0202] Optionally, the device 300 for processing buried data further includes:

[0203] A fifth acquisition module, used to acquire pre-collected historical verification result data;

[0204] A first processing module is used to perform feature engineering processing on the historical verification result data to obtain corresponding sample data;

[0205] A partitioning module, used for partitioning the sample data into a training set and a test set;

[0206] The second calling module is used to call the preset initial model;

[0207] A second processing module is used to perform corresponding training and optimization processing on the initial model based on the training set and the test set until a specified model that meets the preset construction requirements is obtained;

[0208] A deployment module is used to use the specified model as the anomaly detection model and deploy the anomaly detection model.

[0209] Optionally, the device 300 for processing buried data further includes:

[0210] A fourth judgment module is used to judge whether the abnormality detection result is abnormal;

[0211] a third generating module, configured to generate warning information corresponding to the first verification result based on the abnormality detection result;

[0212] A sixth acquisition module, used to acquire a preset information notification method;

[0213] The sending module is used to send the warning information to relevant personnel based on the information notification method.

[0214] To solve the above technical problems, the present application also provides a computer device. Figure 4 , Figure 4 This is a basic structural block diagram of the computer device in this embodiment.

[0215] The computer device 4 includes a memory 41, a processor 42, and a network interface 43 that are interconnected through a system bus. It should be noted that the figure only shows a computer device 4 with components 41-43, but it should be understood that it is not required to implement all the components shown, and more or fewer components can be implemented instead. Among them, those skilled in the art can understand that the computer device here is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to microprocessors, application specific integrated circuits (Application Specific Integrated Circuit, ASIC), programmable gate arrays (Field-Programmable Gate Array, FPGA), digital processors (Digital Signal Processor, DSP), embedded devices, etc.

[0216] The computer device may be a computing device such as a desktop computer, a notebook, a PDA, a cloud server, etc. The computer device may interact with a user through a keyboard, a mouse, a remote controller, a touch pad, or a voice control device.

[0217] The memory 41 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, hard disk, multimedia card, card-type memory (for example, SD or DX memory, etc.), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, disk, optical disk, etc. In some embodiments, the memory 41 can be an internal storage unit of the computer device 4, such as a hard disk or memory of the computer device 4. In other embodiments, the memory 41 can also be an external storage device of the computer device 4, such as a plug-in hard disk equipped on the computer device 4, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (FlashCard), etc. Of course, the memory 41 can also include both the internal storage unit of the computer device 4 and its external storage device. In this embodiment, the memory 41 is generally used to store the operating system and various application software installed on the computer device 4, such as the program code of the method for processing buried point data, etc. In addition, the memory 41 can also be used to temporarily store various types of data that have been output or are to be output.

[0218] The processor 42 may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chip in some embodiments. The processor 42 is generally used to control the overall operation of the computer device 4. In this embodiment, the processor 42 is used to run the program code stored in the memory 41 or process data, such as running the program code of the method for processing the buried point data.

[0219] The network interface 43 may include a wireless network interface or a wired network interface. The network interface 43 is generally used to establish a communication connection between the computer device 4 and other electronic devices.

[0220] The present application also provides another embodiment, namely, providing a computer-readable storage medium, wherein the computer-readable storage medium stores the application crash processing program, and the application crash processing program can be executed by at least one processor to enable the at least one processor to perform the steps of the method for processing buried point data as described above.

[0221] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware online platform, and of course, by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in each embodiment of the present application.

[0222] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments, in which tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0223] Obviously, the embodiments described above are only some embodiments of the present application, rather than all embodiments. The preferred embodiments of the present application are given in the accompanying drawings, but they do not limit the patent scope of the present application. The present application can be implemented in many different forms. On the contrary, the purpose of providing these embodiments is to make the understanding of the disclosure of the present application more thorough and comprehensive. Although the present application is described in detail with reference to the aforementioned embodiments, for those skilled in the art, it is still possible to modify the technical solutions recorded in the aforementioned specific implementation methods, or to perform equivalent replacement of some of the technical features therein. Any equivalent structure made using the contents of the specification and drawings of this application, directly or indirectly used in other related technical fields, is similarly within the scope of patent protection of this application.

Claims

1. A method for processing buried point data, characterized in that: include: Obtain pending point verification tasks based on the preset data analysis and verification service; Parse the embedding verification task to obtain the target embedding event to be verified, and determine whether there is a first reporting ID matching the target embedding event in a preset event verification result table; If yes, obtain a second reporting ID corresponding to the target tracking event from a preset event attribute table; Determine whether the first reporting ID is the same as the second reporting ID; If the first reporting ID is different from the second reporting ID, obtaining the attribute value corresponding to the attribute of the target tracking event from the event attribute table; Matching the attribute value with a preset target attribute value to obtain a corresponding attribute matching result; A verification result corresponding to the target buried point event is generated based on the attribute matching result.

2. The method according to claim 1, characterized in that Before obtaining the pending tracking point verification task based on the preset data analysis verification service, the method further includes: Receive a buried point event reporting request based on a preset data collection and storage service; wherein the buried point event reporting request carries a buried point event; Parsing the tracking event reporting request to obtain the tracking event; Determine whether the tracking event is a specified event that needs to be verified; If so, obtain all attributes to be verified contained in the tracking event, and filter out the unreported specified attributes from all the attributes to be verified based on the event attribute table; Generate a specified reporting ID corresponding to the specified attribute of the tracking event; The specified attributes of the buried event and the specified reporting ID are stored in the event attribute table.

3. The method according to claim 2, characterized in that The determining whether the buried event is a designated event that needs to be verified includes: Extract information from the buried event to obtain corresponding event information; Performing data verification on the event information; If the event information passes the data verification, the pre-configured event judgment rule is called; Matching the event information with the event judgment rule to obtain a corresponding matching result; If the matching result is that the event information successfully matches the event judgment rule, the buried event is determined to be a designated event that needs to be verified; If the matching result is that the event information fails to match the event judgment rule, it is determined that the buried event is not a designated event that needs to be verified.

4. The method according to claim 1, characterized in that: The generating a verification result corresponding to the target buried event based on the attribute matching result includes: Determining whether the attribute matching result is that the attribute value matches a preset target attribute value; If so, generate a verification result corresponding to the target buried point event that passes the verification; If not, a verification result indicating that the verification has failed is generated corresponding to the target buried point event.

5. The method according to claim 1, characterized in that After determining whether there is a first reporting ID matching the target burying point event in the preset event verification result table, the method further includes: Obtain a first verification result from the event verification result table; Preprocessing the first verification result to obtain a corresponding second verification result; Call the preset anomaly detection model; Anomaly detection is performed on the second verification result based on the anomaly detection model to obtain an anomaly detection result corresponding to the first verification result.

6. The method according to claim 5, characterized in that Before calling the preset anomaly detection model, the method further includes: Obtain pre-collected historical verification result data; Performing feature engineering processing on the historical verification result data to obtain corresponding sample data; Dividing the sample data into a training set and a test set; Call the preset initial model; Based on the training set and the test set, the initial model is trained and optimized accordingly until a specified model that meets the preset construction requirements is obtained; The designated model is used as the anomaly detection model, and the anomaly detection model is deployed.

7. The method according to claim 5, characterized in that After performing anomaly detection on the second verification result based on the anomaly detection model to obtain an anomaly detection result corresponding to the first verification result, the method further includes: Determine whether the abnormality detection result indicates that an abnormality exists; If so, generating warning information corresponding to the first verification result based on the abnormality detection result; Get the preset information notification method; Based on the information notification method, the warning information is sent to relevant personnel.

8. A device for processing buried point data, characterized in that: include: The first acquisition module is used to acquire the pending point verification task based on the preset data analysis and verification service; A first judgment module is used to parse the embedding point verification task to obtain a target embedding point event to be verified, and determine whether there is a first reporting ID matching the target embedding point event in a preset event verification result table; A second acquisition module is used to obtain a second reporting ID corresponding to the target tracking event from a preset event attribute table; A second judgment module, used to judge whether the first reporting ID is the same as the second reporting ID; A third acquisition module, configured to acquire an attribute value corresponding to the attribute of the target tracking event from the event attribute table if the first reporting ID is different from the second reporting ID; A matching module, used to match the attribute value with a preset target attribute value to obtain a corresponding attribute matching result; The first generation module is used to generate a verification result corresponding to the target buried point event based on the attribute matching result.

9. A computer device, characterized in that: It includes a memory and a processor, the memory stores a computer program, and the processor implements the steps of the method for processing buried point data as described in any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method for processing buried point data as described in any one of claims 1 to 7 are implemented.

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