Buried point data processing method and device, electronic equipment and storage medium

Abstract

The embodiment of the invention provides a burying point data processing method and device, electronic equipment and a storage medium, and belongs to the technical field of artificial intelligence. The method comprises the steps that object page operation is obtained, a point burying event is triggered according to the object page operation, object point burying data are obtained according to the point burying event, the object point burying data comprise point burying fields, a preset analysis rule table is inquired according to the point burying fields, and field analysis parameters of the point burying fields are obtained, querying a preset task configuration table according to the field analysis parameter to obtain a task script name, calling a field analysis script according to the task script name, executing the field analysis parameter through the field analysis script to obtain a target analysis parameter, and performing field analysis on the burying point field according to the target analysis parameter to obtain target analysis data, the difficulty of buried point data analysis can be reduced.

Description

Technical Field

[0001] The present application relates to the field of artificial intelligence technology, is applicable to the field of financial technology, and in particular to a method and device for processing buried data, an electronic device, and a storage medium. Background Art

[0002] Tracking is a data collection technology that can be used to record user operations in applications or websites, such as clicks, searches, or slides, so that tracking data can be obtained. For example, in a financial scenario, a tracking tool is used to define a tracking point on the transfer button of a financial management application. When a user clicks the transfer button in the financial management application, the tracking point can collect information such as the user's identity, click time, and target account to obtain tracking data. However, the data formats of different tracking data are different, which makes the analysis of tracking data more complicated and difficult. Summary of the invention

[0003] The main purpose of the embodiments of the present application is to propose a method and device for processing buried point data, an electronic device and a storage medium, aiming to reduce the difficulty of buried point data analysis.

[0004] To achieve the above-mentioned purpose, a first aspect of an embodiment of the present application proposes a method for processing buried point data, the method comprising:

[0005] Get object page operation;

[0006] Triggering a point-burying event according to the object page operation, and acquiring object point-burying data according to the point-burying event; the object point-burying data includes a point-burying field;

[0007] According to the embedding field, a preset parsing rule table is queried to obtain a field parsing parameter of the embedding field;

[0008] The preset task configuration table is queried according to the embedding point field to obtain the task script name;

[0009] Calling a field parsing script according to the task script name;

[0010] Execute the field parsing parameters through the field parsing script to obtain target parsing parameters;

[0011] The embedded point field is parsed according to the target parsing parameters to obtain target parsing data.

[0012] In some embodiments, performing field parsing on the tracking point field according to the target parsing parameter to obtain target parsing data includes:

[0013] Obtaining the field parsing order and the parsing model name according to the target parsing parameters;

[0014] Calling a field parsing model according to the parsing model name;

[0015] According to the field parsing order, the field parsing model is used to perform field parsing on the buried point field to obtain original field parsing data of the buried point field;

[0016] Data aggregation is performed on the original field analysis data of the buried point field to obtain the target analysis data.

[0017] In some embodiments, the step of aggregating the original field parsing data of the buried field to obtain the target parsing data includes:

[0018] Merge the original field analysis data of the embedded field to obtain reference analysis data;

[0019] Calculate the data index of the reference parsed data according to the preset index type configuration table to obtain object operation detailed data;

[0020] Acquire the data hierarchical relationship between the object operation detail data;

[0021] The object operation detail data is aggregated according to the data hierarchical relationship to obtain the target parsed data.

[0022] In some embodiments, the step of merging the original field parsed data of the buried point field to obtain reference parsed data includes:

[0023] Obtaining a temporary table name and a target table name according to the target parsing parameters;

[0024] Obtain a temporary data table according to the temporary table name;

[0025] Writing the original field parsed data of the buried point field into the temporary data table to obtain the first field parsed data;

[0026] Read the first field parsed data from the temporary data table to obtain the second field parsed data;

[0027] Obtain a target data table according to the target table name;

[0028] The second field parsed data is written into the target data table to obtain the reference parsed data.

[0029] In some embodiments, writing the original field parsed data of the buried point field into the temporary data table to obtain the first field parsed data includes:

[0030] Performing data segmentation on the original field parsing data of the embedding field to obtain original sub-data and data sequence identifiers of the original sub-data;

[0031] Encrypting the original sub-data to obtain encrypted sub-data;

[0032] Merging the encrypted sub-data according to the data sequence identifier to obtain reference encrypted data;

[0033] The reference encrypted data is written into the temporary data table to obtain the first field parsed data.

[0034] In some embodiments, encrypting the original sub-data to obtain encrypted sub-data includes:

[0035] Extracting data features from the original sub-data to obtain buried point data features;

[0036] Performing feature scoring on the buried point data feature to obtain a buried point feature score;

[0037] Predicting the encryption type of the original sub-data according to the embedding feature score and the embedding data feature to obtain the data encryption type;

[0038] The original sub-data is encrypted according to the data encryption type to obtain encrypted sub-data.

[0039] In some embodiments, the step of performing feature scoring on the buried point data feature to obtain the buried point feature score includes:

[0040] Position encoding is performed on the buried point data feature to obtain a position encoding feature;

[0041] Performing feature fusion on the buried point data feature and the position coding feature to obtain a reference buried point feature;

[0042] Perform attention calculation on the reference buried point feature to obtain the buried point feature score.

[0043] To achieve the above-mentioned purpose, a second aspect of an embodiment of the present application provides a buried point data processing device, the device comprising:

[0044] Operation acquisition module, used to obtain object page operations;

[0045] A data acquisition module, used for triggering a buried point event according to the object page operation, and acquiring object buried point data according to the buried point event; the object buried point data includes a buried point field;

[0046] A parsing query module, used to query a preset parsing rule table according to the embedding field to obtain a field parsing parameter of the embedding field;

[0047] A task query module is used to query the preset task configuration table according to the embedding field to obtain the task script name;

[0048] A calling module, used for calling a field parsing script according to the task script name;

[0049] An execution module, used for executing the field parsing parameters through the field parsing script to obtain target parsing parameters;

[0050] The parsing module is used to perform field parsing on the buried point field according to the target parsing parameters to obtain target parsing data.

[0051] To achieve the above objectives, a third aspect of an embodiment of the present application proposes an electronic device, which includes a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the method described in the first aspect is implemented.

[0052] To achieve the above-mentioned purpose, the fourth aspect of an embodiment of the present application proposes a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method described in the first aspect is implemented.

[0053] The buried point data processing method, buried point data processing device, electronic device and computer-readable storage medium proposed in the embodiment of the present application determine the user's operation on the front-end web page by obtaining the object page operation. In order to capture and record the user's operation behavior information on the front-end web page, a buried point is set on the front-end web page, and a buried point event is triggered according to the object page operation, and the object buried point data is obtained according to the buried point event. The object buried point data is usually original, unprocessed data and contains a large amount of redundant information. Therefore, it is necessary to parse the object buried point data to obtain key information useful for the business. The data formats of different buried point data are different, so that the buried point data have differences in field naming, field type and data structure. This difference increases the difficulty of buried point data parsing. In order to reduce the difficulty of buried point data parsing, a preset parsing rule table and a preset task configuration table are set. According to the buried point field of the object buried point data, the preset parsing rule table is queried, and the appropriate parsing rule is matched for the buried point field to obtain the field parsing parameter of the buried point field. In order to further reduce the difficulty of data parsing, the preset task configuration table is queried according to the buried point field to obtain the script for executing the parsing rule and obtain the task script name. Call the field parsing script according to the task script name, execute the field parsing parameters through the field parsing script, and obtain the target parsing parameters. Perform field parsing on the buried point field according to the target parsing parameters to perform fine processing on the buried point field, which can handle different types of buried point fields and obtain target parsing data, reducing the difficulty of buried point data parsing. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 It is a flow chart of the method for processing buried point data provided by an embodiment of the present application;

[0055] Figure 2 yes Figure 1 Flowchart of step S170 in FIG.

[0056] Figure 3 yes Figure 2 Flow chart of step S240 in FIG.

[0057] Figure 4 yes Figure 3 Flow chart of step S310 in FIG.

[0058] Figure 5 yes Figure 4 Flow chart of step S430 in FIG.

[0059] Figure 6 yes Figure 5 Flowchart of step S520 in FIG.

[0060] Figure 7 yes Figure 6 Flowchart of step S620 in FIG.

[0061] Figure 8 It is a structural schematic diagram of a buried point data processing device provided in an embodiment of the present application;

[0062] Fig. 9 It is a schematic diagram of the hardware structure of the electronic device provided in the embodiment of the present application. DETAILED DESCRIPTION

[0063] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0064] It should be noted that, although the functional modules are divided in the device schematic diagram and the logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart. The terms "first", "second", etc. in the specification, claims and the above drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.

[0065] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0066] Tracking is a data collection technology that can be used to record user operations in applications or websites, such as clicks, searches, or slides, so that tracking data can be obtained. For example, in a financial scenario, a tracking tool is used to define a tracking point on the transfer button of a financial management application. When a user clicks the transfer button in the financial management application, the tracking point can collect information such as the user's identity, click time, and target account to obtain tracking data. However, the data formats of different tracking data are different, which makes the analysis of tracking data more complicated and difficult.

[0067] Based on this, the embodiments of the present application provide a method for processing buried point data, a device for processing buried point data, an electronic device and a computer-readable storage medium, aiming to reduce the difficulty of analyzing buried point data.

[0068] The buried point data processing method, buried point data processing device, electronic device and computer-readable storage medium provided in the embodiments of the present application are specifically illustrated through the following embodiments. First, the buried point data processing method in the embodiments of the present application is described.

[0069] The method for processing buried data provided in the embodiment of the present application relates to the field of artificial intelligence technology. The method for processing buried data provided in the embodiment of the present application can be applied to a terminal, can be applied to a server side, or can be software running in a terminal or a server side. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, etc.; the server side can be configured as an independent physical server, or can be configured as a server cluster or a distributed system composed of multiple physical servers, or can be configured as a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms; the software can be an application that implements the method for processing buried data, etc., but is not limited to the above forms.

[0070] 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.

[0071] Figure 1 It is an optional flowchart of the buried point data processing method provided in an embodiment of the present application. The buried point data processing method may include but is not limited to steps S110 to S170.

[0072] Step S110, obtaining the object page operation;

[0073] Step S120, triggering a buried point event according to the object page operation, and obtaining object buried point data according to the buried point event; the object buried point data includes a buried point field;

[0074] Step S130, querying the preset parsing rule table according to the embedding field to obtain the field parsing parameters of the embedding field;

[0075] Step S140, query the preset task configuration table according to the embedding field to obtain the task script name;

[0076] Step S150, calling the field parsing script according to the task script name;

[0077] Step S160, executing the field parsing parameters through the field parsing script to obtain target parsing parameters;

[0078] Step S170, performing field parsing on the embedded point field according to the target parsing parameters to obtain target parsing data.

[0079] In steps S110 to S170 shown in the embodiment of the present application, by setting a preset parsing rule table and a preset task configuration table, the parsing rules of the buried point field are obtained according to the preset parsing rule table, and the task script for executing the parsing rules is obtained according to the preset task configuration table to cope with buried point data in different data formats, thereby reducing the difficulty of parsing the buried point data.

[0080] In step S110 of some embodiments, a document object model element and an operation event type are set, and an operation event detector is defined according to the document object model element and the operation event type, and the operation event type includes a click event, a keyboard input event, etc. An operation event detector is set on the front-end web page, and an object page operation is obtained through the operation event detector. The object page operation is an operation performed by a user on the front-end web page, and the object page operation includes clicking a button, submitting a form, or scrolling a page, etc. In a financial scenario, the object page operation involves the purchase of financial products, transfers, loans, etc.

[0081] In step S120 of some embodiments, the buried point event is usually bound to a specific operation and is automatically triggered when the user performs the specific operation. The buried point event is triggered according to the object page operation, and one or more buried point data records are generated according to the buried point event to obtain the object buried point data. The object buried point data includes a buried point field, which is a data item of the object buried point data. For example, when a user performs a transaction operation, a transaction event is triggered, and the specific values ​​of data items such as the transaction time, amount, transaction type, and transaction account are recorded to obtain the object buried point data.

[0082] In step S130 of some embodiments, the object embedding data is the original data collected from the interaction between the user and the website or application. This data is often unstructured and contains a large amount of information that is not important to the business processing target. By parsing the object embedding data, the original embedding data can be converted into structured and standardized useful information, thereby providing support for subsequent data analysis. For example, the user's stay time and click path on the page can be extracted from the original embedding data to optimize the page layout and functional design of the front-end page.

[0083] Existing websites or applications provide many business services. For example, insurance applications provide a variety of business functions such as customer management, insurance management, performance management, and insurance product information sharing. In order to accurately analyze user behavior in different business scenarios, it is necessary to set up tracking points for multiple business scenarios, which makes the data volume of tracking point data large. The parameter fields and data content formats of different tracking point events are different, which makes the data format of tracking point data complex. In addition, in order to cope with complex and changeable business scenarios, the business functions of websites or applications need to be quickly iterated and updated, which speeds up the tracking point update cycle. Because tracking point data has the characteristics of large data volume, complex data format, and fast tracking point update cycle, the difficulty of tracking point data parsing is relatively high. In order to reduce the difficulty of tracking point data parsing, the embodiment of the present application introduces a flexibly configured automated parsing mechanism to automatically process tracking point data, which greatly reduces the need for manual programming, improves the efficiency of tracking point data parsing, and makes the analysis of tracking point data more real-time and accurate.

[0084] The preset parsing rule table is a deliverable configuration page that defines the types of buried points that need to be parsed and the buried point parsing rules, and is used to indicate the mapping relationship between the buried point types and the buried point parsing rules. Get the field type and field data source of the buried point field. The field type is the field attribute of the buried point field, such as transaction time, transaction amount, etc. The field data source is used to indicate the source of the buried point field, such as application, website, etc. Query the preset parsing rule table according to the field type and field data source of the buried point field to obtain the field parsing parameters of the buried point field. The field parsing parameters are the buried point parsing rules of the buried point field, which are used to extract data, convert data formats, analyze data, and interpret data for the buried point field. The preset parsing rule table makes the parsing of buried point data more systematic and standardized, and improves the accuracy and efficiency of buried point data parsing.

[0085] In step S140 of some embodiments, the preset task configuration table is also a deliverable configuration page that defines the task script for executing the tracking point analysis rules. The preset task configuration table is queried according to the field type and field data source of the tracking point field to obtain the task script name and the task script location. The task script name is the script name of the task script, and the task script location is the storage location of the task script.

[0086] In step S150 of some embodiments, a field parsing script is obtained according to the task script name and the task script location, and the field parsing script is a task script used to execute the embedding point parsing rules.

[0087] In step S160 of some embodiments, the script status of the field parsing script is obtained, and the script status includes an online status or an offline status. If the script status is an online status, the field parsing script is run, and the field parsing parameters are executed by the field parsing script, and the field parsing parameters are converted from static parameters to running parameters to obtain target parsing parameters. Different field parsing scripts can be used to parse different buried point fields in parallel to improve the efficiency of buried point data parsing. If the script status is an offline status, a prompt message is returned. The prompt message is used to prompt that the script is in an offline state and cannot execute the parsing task of the buried point field.

[0088] See also Figure 2 In some embodiments, step S170 may include but is not limited to steps S210 to S240:

[0089] Step S210, obtaining the field parsing order and the parsing model name according to the target parsing parameters;

[0090] Step S220, calling the field parsing model according to the parsing model name;

[0091] Step S230, performing field parsing on the buried point field through the field parsing model according to the field parsing order to obtain the original field parsing data of the buried point field;

[0092] Step S240, aggregate the original field analysis data of the buried field to obtain target analysis data.

[0093] In step S210 of some embodiments, a field resolution order and a resolution model name are obtained from the target resolution parameters, where the field resolution order is used to indicate the resolution order of the buried point fields, and the resolution model name is the name of the model for performing field resolution on the buried point fields.

[0094] In step S220 of some embodiments, a field parsing model is obtained according to the parsing model name. The field parsing model is a model used to perform field parsing on the buried point field. The field parsing model can be a data extraction model, a data conversion model, etc. The data extraction model can be used to extract key fields and values ​​from the buried point field, such as timestamps, page paths, operation event types, etc. The data conversion model can be used to convert the buried point field into a data format suitable for analysis. For example, the timestamp is converted into a readable date and time format.

[0095] In step S230 of some embodiments, there is a correlation between the user's operation behaviors. For example, clicking a button will jump to another page. In order to correctly understand the user's operation context, the buried point fields are parsed through the field parsing model in accordance with the field parsing order to associate the user's operation behaviors. This allows each buried point field to be correctly parsed and the original field parsed data of the buried point field is obtained.

[0096] In step S240 of some embodiments, in order to ensure the parsing integrity of the object buried data, data aggregation is performed on the original field parsing data of each buried field to obtain target parsing data.

[0097] Through the above steps S210 to S240, appropriate parsing rules can be matched for different buried point fields to meet the business parsing requirements of different buried point fields, reducing the difficulty of buried point data parsing. In addition, according to the field parsing order, the field parsing model is called to parse the buried point fields, so that different buried point fields can be correctly parsed in a predetermined order, which can more easily associate the user's operation behavior events and understand the user's operation context.

[0098] See also Figure 3 In some embodiments, step S240 may include but is not limited to steps S310 to S340:

[0099] Step S310, merging the original field parsing data of the buried point field to obtain reference parsing data;

[0100] Step S320, calculating data indicators for the reference parsing data according to the preset indicator type configuration table to obtain object operation detailed data;

[0101] Step S330, obtaining the data hierarchical relationship between the object operation detail data;

[0102] Step S340, summarize the object operation detail data according to the data hierarchy relationship to obtain target parsed data.

[0103] In step S310 of some embodiments, the original field parsing data of each buried point field in the object buried point data is merged to obtain reference parsing data, so as to ensure the integrity of the data parsing, avoid missing the key operation behavior information of the user, and enhance the correlation between the original field parsing data to obtain a complete user operation behavior feature set.

[0104] In step S320 of some embodiments, the preset indicator type configuration table defines indicator parameters that need to be extracted from the buried point analysis data, such as the buried point type, the event number and event parameters of the buried point event, the field type of the buried point field, the event occurrence time of the buried point event, and the operation code, etc. For example, the operation code of the exposure event is show. Obtain the indicator parameters from the preset indicator type configuration table, perform statistical analysis on the reference analysis data according to the indicator parameters, such as mathematical combination operations or aggregation calculations, generate data with business significance, and obtain object operation detail data. Aggregate calculations can be counting, summing, or averaging, etc. The object operation detail data is data that reflects the details of the user's operation behavior, expressed as the indicator value of the indicator parameter. The object operation detail data can be the average dwell time of a user on a certain page, the click rate of a certain page, etc.

[0105] In step S330 of some embodiments, the object operation detail data has an operation level, and the data hierarchical relationship between the object operation detail data is obtained according to the operation level. The operation level refers to dividing the object operation detail data into different levels according to the content organization form. The data hierarchical relationship refers to the logical and structural subordination or dependency relationship between user operation behaviors. Taking the insurance application as an example, the operation level of the insurance product information post is the post level, the operation level of the insurance forum is the forum level, and the operation level of the insurance circle is the insurance circle level. The data hierarchical relationship formed according to the operation level is the insurance product information post, insurance forum and insurance circle level.

[0106] In step S340 of some embodiments, the object operation detail data is summarized level by level according to the data hierarchy relationship to obtain structured parsed data and obtain target parsed data. For example, the data hierarchy relationship is business circle level, insurance circle level, insurance forum, insurance product information post, and the object operation detail data can be summarized level by level according to the hierarchical order from insurance product information post to business circle level.

[0107] In the above steps S310 to S340, the indicator type configuration table can quickly obtain the indicator values ​​with business significance from the buried point analysis data, so as to accurately analyze the original buried point data in real time. By aggregating the data through the data hierarchy relationship, structured buried point analysis data can be obtained, so as to perform subsequent user behavior analysis, web page optimization and other tasks based on the buried point analysis data.

[0108] See also Figure 4 In some embodiments, step S310 may include but is not limited to steps S410 to S460:

[0109] Step S410, obtaining a temporary table name and a target table name according to the target resolution parameters;

[0110] Step S420, obtaining a temporary data table according to the temporary table name;

[0111] Step S430, writing the original field parsed data of the buried point field into the temporary data table to obtain the first field parsed data;

[0112] Step S440, reading the first field parsed data from the temporary data table to obtain the second field parsed data;

[0113] Step S450, obtaining a target data table according to the target table name;

[0114] Step S460, writing the second field parsed data into the target data table to obtain reference parsed data.

[0115] In step S410 of some embodiments, the target resolution parameters include a table name, and the storage location of the buried point resolution data can be controlled by the table name. The temporary table name, the temporary data storage format of the temporary table name, the target table name, and the target data storage format of the target table name are obtained from the target resolution parameters. The temporary table name is used to indicate the storage location of the original field resolution data of the buried point field. According to the temporary table name, a temporary data table can be obtained, and the temporary data storage format is used to indicate the data storage format of the original field resolution data in the temporary data table. The target table name is used to indicate the storage location of the resolution data of the object buried point data. According to the target table name, a target data table can be obtained, and the target data storage format is used to indicate the data storage format of the resolution data in the target data table. The data storage format can be text format, binary format, chart format, etc.

[0116] In step S420 of some embodiments, a predefined data table is obtained according to the temporary table name to obtain a temporary data table.

[0117] In step S430 of some embodiments, in order to simplify the analysis process of the buried point data and improve the efficiency and accuracy of the buried point data analysis, a temporary data table is used to store the intermediate calculation results, and the original field analysis data of the buried point field is stored in the temporary data table in a temporary data storage format to obtain the first field analysis data. The original field analysis data is the same as the first field analysis data.

[0118] In step S440 of some embodiments, in order to ensure the parsing integrity of the object embedding data, the first field parsing data is read from the temporary data table to obtain the second field parsing data. The first field parsing data and the second field parsing data are the same.

[0119] In step S450 of some embodiments, a predefined data table is acquired according to the target table name to obtain the target data table.

[0120] In step S460 of some embodiments, the second field parsing data of all buried point fields are stored in the target data table in the target data storage format to obtain reference parsing data. The reference parsing data is a collection of the second field parsing data of all buried point fields in the object buried point data.

[0121] In the above steps S410 to S460, the original field parsing data of the buried point fields are stored in a temporary data table, so that the original field parsing data of each buried point field are independent of each other, avoiding frequent write operations on the target data table, reducing data conflicts, and improving the security of buried point data parsing. At the same time, the original field parsing data of each buried point field can also be read and stored in parallel, which improves the efficiency of buried point data parsing while ensuring the accuracy of buried point data parsing.

[0122] See also Figure 5 In some embodiments, step S430 may include but is not limited to steps S510 to S540:

[0123] Step S510, data segmentation is performed on the original field parsing data of the buried point field to obtain original sub-data and data sequence identifiers of the original sub-data;

[0124] Step S520, encrypting the original sub-data to obtain encrypted sub-data;

[0125] Step S530, merging the encrypted sub-data according to the data sequence identifier to obtain reference encrypted data;

[0126] Step S540, writing the reference encrypted data into a temporary data table to obtain first field parsed data.

[0127] In step S510 of some embodiments, the original field parsed data of the buried field may contain sensitive information such as user identity, bank account, transaction records, etc. In order to improve data security, the original field parsed data is divided into data segments to increase the degree of data fragmentation and obtain original sub-data. Based on the distribution position of the original sub-data in the original field parsed data, an index is assigned to the original sub-data to obtain a data sequence identifier.

[0128] In step S520 of some embodiments, the original sub-data is encrypted, and the original sub-data is converted from a plain text form to a cipher text form that cannot be directly read to obtain encrypted sub-data, so as to ensure the security of the data during the storage process.

[0129] In step S530 of some embodiments, in order to ensure the integrity of the data, the encrypted sub-data are merged in the order of the data sequence identifiers to obtain reference encrypted data. For example, there are three encrypted sub-data, represented as x1, x2 and x3, respectively, and the data sequence identifiers of the three encrypted sub-data are 2, 1 and 3 respectively. According to the data sequence identifier, the three encrypted sub-data are merged to obtain reference encrypted data [x2; x1; x3].

[0130] In step S540 of some embodiments, the reference encrypted data is stored in a temporary data table to obtain first field parsed data. The first field parsed data is the same as the reference encrypted data.

[0131] Through the above steps S510 to S540, the security of the field parsing data during storage and transmission can be guaranteed.

[0132] See also Figure 6 In some embodiments, step S520 may include but is not limited to steps S610 to S640:

[0133] Step S610, extracting data features from the original sub-data to obtain buried point data features;

[0134] Step S620, scoring the features of the buried point data to obtain a buried point feature score;

[0135] Step S630, predicting the encryption type of the original sub-data according to the buried point feature score and the buried point data feature to obtain the data encryption type;

[0136] Step S640: encrypt the original sub-data according to the data encryption type to obtain encrypted sub-data.

[0137] In step S610 of some embodiments, in order to accurately understand the data semantic information implied in the original sub-data, data features are extracted from the original sub-data through a feature extraction network to obtain buried data features. The feature extraction network can be a pre-trained network such as a large language model or a recurrent neural network. The buried data features can be object operation intentions, object operation types, object operation paths, and data content features of buried parsed data. The object operation intention is the user's intention to perform an operation. For example, when a user clicks the Add to Cart button, his object operation intention is to purchase an insurance product. The object operation type can be click, browse, etc. The object operation path is a path formed by multiple consecutive object operation types. For example, when a user purchases an insurance product, the object operation path can be browsing insurance products, viewing product details, consulting customer service, etc.

[0138] If the feature extraction network is a large language model, a prompt instruction is obtained, and the prompt instruction and the original sub-data are input into the large prediction model for feature extraction to obtain the buried point data features.

[0139] If the feature extraction network is a recurrent neural network, the feature extraction network may include a first long short-term memory unit and a second long short-term memory unit. The original sub-data is vectorized to obtain a data embedding vector, and the data embedding vector is segmented to obtain a first data vector segment and a second data vector segment. In order to establish a long-term dependency relationship between the first data vector segment and the second data vector segment, the hidden state is initialized to obtain the original hidden state. The original hidden state and the first data vector segment are feature spliced ​​to obtain a first spliced ​​feature. The first spliced ​​feature is feature extracted by the first long short-term memory unit to obtain a reference hidden state. The reference hidden state and the second data vector segment are feature spliced ​​to obtain a second spliced ​​feature. The second spliced ​​feature is feature extracted by the second long short-term memory unit to obtain a buried point data feature.

[0140] In step S620 of some embodiments, the buried point data feature is scored to obtain a buried point feature score. The buried point feature score is used to indicate the importance of the original sub-data. The larger the buried point feature score, the more important the original sub-data.

[0141] In step S630 of some embodiments, if the buried point feature score is greater than or equal to the preset score threshold, it means that the original sub-data is more important. Different original sub-data have different confidentiality requirements. For example, financial business data such as transaction accounts and transaction amounts have higher requirements for security and confidentiality, and website operation data such as website traffic and website performance have lower requirements for security and confidentiality. In order to adapt to the confidentiality requirements of each buried point parsing data in different business scenarios, it is necessary to set different security levels for the buried point parsing data. In the embodiment of the present application, the buried point data features and buried point feature scores are input into the encryption type prediction model, and the encryption type of the original sub-data is predicted to obtain the data encryption type. The encryption type prediction model is obtained by training a large number of buried point data samples, and the buried point data samples include buried point data and encryption type labels. The encryption type prediction model can adopt transformer model, Bert model, random forest, etc. The data encryption type is used to indicate the category of the encryption algorithm, including symmetric encryption, asymmetric encryption, hash function encryption, hybrid encryption, etc. If the buried point feature score is less than the preset score threshold, it means that the original sub-data is not important, and there is no need to encrypt the original sub-data.

[0142] It should be noted that if the encryption type prediction model is a transformer model or a Bert model, the buried data features and the buried feature scores are input into the encryption type prediction model to predict the data encryption type of the original sub-data. If the encryption type prediction model is a random forest, and the random forest includes multiple decision trees, the buried data features and the buried feature scores are respectively input into each decision tree to obtain the encryption type probability that the original sub-data output by each decision tree belongs to the preset encryption type. For each decision tree, the encryption type probability and the weight of the decision tree are multiplied to obtain the initial probability that the original sub-data belongs to the preset encryption type. The initial probabilities of all decision trees are added to obtain the target probability that the original sub-data belongs to the preset encryption type. The preset encryption type with the largest target probability is selected as the data encryption type.

[0143] Specifically, if the random forest includes two decision trees, namely, the first decision tree and the second decision tree, the first decision tree has a first weight, and the second decision tree has a second weight. Both the first decision tree and the second decision tree can predict K categories, K is 2, and the two categories are the first preset encryption type and the second preset encryption type. The buried point data feature and the buried point feature score are spliced ​​to obtain the spliced ​​feature. The spliced ​​feature is input into the first decision tree for encryption prediction to obtain the probability of the first sub-encryption type of the original sub-data belonging to the first preset encryption type and the probability of the second encrypted sub-type of the original sub-data belonging to the second preset encryption type. The spliced ​​feature is input into the second decision tree for encryption prediction to obtain the probability of the third sub-encryption type of the original sub-data belonging to the first preset encryption type and the probability of the fourth encrypted sub-type of the original sub-data belonging to the second preset encryption type. The first weight is multiplied by the probability of the first sub-encryption type to obtain a first probability value. The second weight is multiplied by the probability of the third sub-encryption type to obtain a second probability value. The first probability value and the second probability value are added to obtain the probability of the first encryption type of the original sub-data belonging to the first preset encryption type. The first weight and the second encryption sub-type probability are multiplied to obtain a third probability value. The second weight is multiplied by the fourth encryption subtype probability to obtain a fourth probability value. The third probability value and the fourth probability value are added to obtain a second encryption type probability that the original sub-data belongs to the second preset encryption type. The first encryption type probability is compared with the second encryption type probability. If the first encryption type probability is greater than or equal to the second encryption type probability, the first preset encryption type is used as the data encryption type. If the first encryption type probability is less than the second encryption type probability, the second preset encryption type is used as the data encryption type.

[0144] In step S640 of some embodiments, different data encryption types correspond to different data encryption algorithms, and the data encryption algorithm is called according to the data encryption type to encrypt the original sub-data to obtain encrypted sub-data.

[0145] Through the above steps S610 to S640, when the original field parsing data is encrypted, different levels of security can be provided for each data segment of the original field parsing data, thereby ensuring the security of the buried point parsing data.

[0146] See also Figure 7 In some embodiments, step S620 may include but is not limited to steps S710 to S730:

[0147] Step S710, position coding the buried point data feature to obtain a position coding feature;

[0148] Step S720, performing feature fusion on the buried point data feature and the position coding feature to obtain a reference buried point feature;

[0149] Step S730, perform attention calculation on the reference buried point feature to obtain a buried point feature score.

[0150] In step S710 of some embodiments, relative position encoding is performed on the buried point data feature to obtain a position encoding feature, which is a relative position encoding vector.

[0151] In step S720 of some embodiments, the buried point data features and the position coding features are added to obtain reference buried point features.

[0152] In step S730 of some embodiments, the reference buried point feature is feature segmented to obtain reference sub-features. The reference sub-features are multiplied by the query weight matrix, the key weight matrix, and the value weight matrix respectively to obtain a query vector, a key vector, and a value vector. The similarity between the query vector and the key vector is calculated, and the similarity is converted into an attention weight through a softmax function. The attention weight is multiplied by the value vector to obtain a sub-feature score of the reference sub-feature. The sub-feature scores of the reference sub-features are scored and added to obtain a buried point feature score.

[0153] or,

[0154] Obtain the weight matrix of the position coding feature to obtain the first weight matrix, obtain the weight matrix of the reference buried point feature to obtain the second weight matrix. Multiply the first weight matrix and the position coding feature to obtain the first sub-score. Multiply the second weight matrix and the reference buried point feature to obtain the second sub-score. Multiply the reference buried point feature, the query weight matrix, the second weight matrix and the reference buried point feature to obtain the third sub-score. Multiply the reference buried point feature, the query weight matrix, the first weight matrix and the position coding feature to obtain the fourth sub-score. Add the first sub-score, the second sub-score, the third sub-score and the fourth sub-score to obtain the buried point feature score.

[0155] Through the above steps S710 to S730, a buried point feature score can be obtained to evaluate the importance of the original sub-data.

[0156] See also Figure 8 The embodiment of the present application further provides a buried point data processing device, which can implement the above-mentioned buried point data processing method, and the buried point data processing device includes:

[0157] The operation acquisition module 810 is used to acquire the object page operation;

[0158] The data acquisition module 820 is used to trigger a point-of-view event according to an object page operation, and acquire object point-of-view data according to the point-of-view event; the object point-of-view data includes a point-of-view field;

[0159] The parsing query module 830 is used to query the preset parsing rule table according to the buried point field to obtain the field parsing parameters of the buried point field;

[0160] The task query module 840 is used to query the preset task configuration table according to the embedding field to obtain the task script name;

[0161] A calling module 850 is used to call a field parsing script according to a task script name;

[0162] An execution module 860 is used to execute field parsing parameters through a field parsing script to obtain target parsing parameters;

[0163] The parsing module 870 is used to perform field parsing on the embedded point field according to the target parsing parameters to obtain target parsing data.

[0164] The specific implementation of the buried point data processing device is basically the same as the specific implementation of the buried point data processing method mentioned above, and will not be repeated here.

[0165] The embodiment of the present application also provides an electronic device, the electronic device includes a memory and a processor, the memory stores a computer program, and the processor implements the above-mentioned point data processing method when executing the computer program. The electronic device can be any intelligent terminal including a tablet computer, a car computer, etc.

[0166] See also Fig. 9 , Fig. 9 The hardware structure of an electronic device of another embodiment is illustrated, and the electronic device includes:

[0167] The processor 910 may be implemented by a general-purpose central processing unit (CPU), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application.

[0168] The memory 920 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 920 can store an operating system and other applications. When the technical solution provided in the embodiment of this specification is implemented by software or firmware, the relevant program code is stored in the memory 920, and the processor 910 calls and executes the buried point data processing method of the embodiment of this application;

[0169] Input / output interface 930, used to implement information input and output;

[0170] Communication interface 940, used to realize communication interaction between the device and other devices, which can be realized through wired mode (such as USB, network cable, etc.) or wireless mode (such as mobile network, WI FI, Bluetooth, etc.);

[0171] bus 950 , which transmits information between the various components of the device (e.g., processor 910 , memory 920 , input / output interface 930 , and communication interface 940 );

[0172] The processor 910 , the memory 920 , the input / output interface 930 , and the communication interface 940 are connected to each other in communication within the device via a bus 950 .

[0173] An embodiment of the present application also provides a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, the above-mentioned point data processing method is implemented.

[0174] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely disposed relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0175] The embodiments of the present application provide a method for processing buried point data, a device for processing buried point data, an electronic device, and a computer storage medium. By setting a preset parsing rule table and a preset task configuration table, the parsing rules of the buried point fields are obtained according to the preset parsing rule table, and the task script for executing the parsing rules is obtained according to the preset task configuration table to cope with buried point data in different data formats, thereby reducing the difficulty of parsing the buried point data.

[0176] The embodiments described in the embodiments of the present application are intended to more clearly illustrate the technical solutions of the embodiments of the present application and do not constitute a limitation on the technical solutions provided in the embodiments of the present application. Those skilled in the art will appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems.

[0177] Those skilled in the art will appreciate that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than shown in the figures, or a combination of certain steps, or different steps.

[0178] The device embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separated, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0179] Those skilled in the art will appreciate that all or some of the steps in the methods disclosed above, and the functional modules / units in the systems and devices may be implemented as software, firmware, hardware, or a suitable combination thereof.

[0180] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0181] It should be understood that in the present application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the objects associated before and after are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0182] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the above units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0183] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0184] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0185] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including multiple instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, referred to as ROM), random access memory (Random Access Memory, referred to as RAM), disk or optical disk and other media that can store programs.

[0186] The preferred embodiments of the present invention are described above with reference to the accompanying drawings, but the scope of the rights of the present invention is not limited thereto. Any modification, equivalent substitution and improvement made by a person skilled in the art without departing from the scope and essence of the present invention should be within the scope of the rights of the present invention.

Claims

1. A method for processing buried point data, characterized in that: The method comprises: Get object page operation; Triggering a point-burying event according to the object page operation, and acquiring object point-burying data according to the point-burying event; the object point-burying data includes a point-burying field; According to the embedding field, a preset parsing rule table is queried to obtain a field parsing parameter of the embedding field; The preset task configuration table is queried according to the embedding point field to obtain the task script name; Calling a field parsing script according to the task script name; Execute the field parsing parameters through the field parsing script to obtain target parsing parameters; The embedded point field is parsed according to the target parsing parameters to obtain target parsing data.

2. The method according to claim 1, characterized in that The performing field parsing on the embedding field according to the target parsing parameter to obtain target parsing data includes: Obtaining the field parsing order and the parsing model name according to the target parsing parameters; Calling a field parsing model according to the parsing model name; According to the field parsing order, the field parsing model is used to perform field parsing on the buried point field to obtain original field parsing data of the buried point field; Data aggregation is performed on the original field analysis data of the buried point field to obtain the target analysis data.

3. The method according to claim 2, characterized in that The step of aggregating the original field parsed data of the buried point field to obtain the target parsed data includes: Merge the original field analysis data of the embedded field to obtain reference analysis data; Calculate the data index of the reference parsed data according to the preset index type configuration table to obtain object operation detailed data; Acquire the data hierarchical relationship between the object operation detail data; The object operation detail data is aggregated according to the data hierarchical relationship to obtain the target parsed data.

4. The method according to claim 3, characterized in that: The data merging of the original field parsing data of the buried point field to obtain reference parsing data includes: Obtaining a temporary table name and a target table name according to the target parsing parameters; Obtain a temporary data table according to the temporary table name; Writing the original field parsed data of the buried point field into the temporary data table to obtain the first field parsed data; Read the first field parsed data from the temporary data table to obtain the second field parsed data; Obtain a target data table according to the target table name; The second field parsed data is written into the target data table to obtain the reference parsed data.

5. The method according to claim 4, characterized in that The step of writing the original field parsed data of the buried point field into the temporary data table to obtain the first field parsed data includes: Performing data segmentation on the original field parsing data of the embedding field to obtain original sub-data and data sequence identifiers of the original sub-data; Encrypting the original sub-data to obtain encrypted sub-data; Merging the encrypted sub-data according to the data sequence identifier to obtain reference encrypted data; The reference encrypted data is written into the temporary data table to obtain the first field parsed data.

6. The method according to claim 5, characterized in that The step of encrypting the original sub-data to obtain encrypted sub-data includes: Extracting data features from the original sub-data to obtain buried point data features; Performing feature scoring on the buried point data feature to obtain a buried point feature score; Predicting the encryption type of the original sub-data according to the embedding feature score and the embedding data feature to obtain the data encryption type; The original sub-data is encrypted according to the data encryption type to obtain encrypted sub-data.

7. The method according to claim 6, characterized in that The step of performing feature scoring on the buried point data feature to obtain a buried point feature score includes: Position encoding is performed on the buried point data feature to obtain a position encoding feature; Performing feature fusion on the buried point data feature and the position coding feature to obtain a reference buried point feature; Perform attention calculation on the reference buried point feature to obtain the buried point feature score.

8. A buried point data processing device, characterized in that: The device comprises: Operation acquisition module, used to obtain object page operations; A data acquisition module, used for triggering a buried point event according to the object page operation, and acquiring object buried point data according to the buried point event; the object buried point data includes a buried point field; A parsing query module, used to query a preset parsing rule table according to the embedding field to obtain a field parsing parameter of the embedding field; A task query module is used to query the preset task configuration table according to the embedding field to obtain the task script name; A calling module, used for calling a field parsing script according to the task script name; An execution module, used for executing the field parsing parameters through the field parsing script to obtain target parsing parameters; The parsing module is used to perform field parsing on the buried point field according to the target parsing parameters to obtain target parsing data.

9. An electronic device, characterized in that: The electronic device comprises a memory and a processor, the memory stores a computer program, and the processor implements the method according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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