Service burying point data processing method and device, equipment and medium

Abstract

The invention relates to the technical field of artificial intelligence and data processing, and provides a service burying point data processing method and device, equipment and a medium, and the method comprises the steps: obtaining corresponding first service burying point data and second service burying point data according to an extension field and a conventional field of a service burying point; analyzing a target structured field name corresponding to each target key field description in the semi-structured field description of the extended field according to a preset corresponding relationship between key fields and field roots; generating an extended field data table according to the target structured field name and the corresponding first business burying point data; generating a conventional field data table according to the conventional field and the corresponding second business burying point data; and in response to a received burying point data query request, outputting a corresponding target field data table according to the extended field data table and the conventional field data table. Through the technical scheme of the invention, the timeliness of complex service burying point analysis is improved, and the burying point analysis threshold is greatly reduced.

Description

[Technical field]

[0001] The present application relates to the field of artificial intelligence and data processing technology, and in particular to a method, device, equipment and medium for processing business tracking data. [Background technology]

[0002] Business tracking is an essential basis for operational decision-making after the terminal project is launched. Whether it is for C-end users or B-end business, business tracking analysis affects the development trend of the product and the company's business decisions. The analysis of tracking data can involve simple discrete analysis indicators, such as PV and UV, to help products understand the usage from modules to components in order to optimize user interaction paths and product marketing strategies; it also includes complex link analysis scenarios, such as funnel, retention, repurchase, interval and other analysis models, in order to optimize product structure, enhance user stickiness, and adjust business direction.

[0003] Due to the complexity of tracking analysis scenarios and the variety of methods, simple business tracking only contains user fields, positioning fields, and operation fields, which are not enough to meet the analysis requirements. Therefore, complex business tracking frequently and in large quantities. Complex business tracking uses a single extended field to carry a variety of contextual information, and the extended field is often stored in the database table as semi-structured characters, which leads to huge communication and development costs in the definition, storage, and analysis of the extended field, greatly affecting the efficiency and quality of subsequent tracking analysis. [Summary of the invention]

[0004] The embodiments of the present application provide a method, device, equipment and medium for processing business tracking data, aiming to solve technical problems such as low efficiency and poor quality of extended field analysis of business tracking existing in related technologies.

[0005] In a first aspect, an embodiment of the present application provides a method for processing business tracking data, including:

[0006] Acquire corresponding first business embedding point data and second business embedding point data according to an extended field and a regular field of the business embedding point, wherein the extended field corresponds to a semi-structured field description, and the regular field corresponds to a structured field description;

[0007] According to the correspondence between the preset key domain and the field root, the target structured field name corresponding to each target key domain description in the semi-structured field description of the extended field is parsed;

[0008] Generate an extended field data table according to the target structured field name and the corresponding first business tracking data;

[0009] Generate a regular field data table according to the regular field and the corresponding second business tracking data;

[0010] In response to the received buried data query request, the corresponding target field data table is output according to the extended field data table and the regular field data table.

[0011] In one embodiment, optionally, the method further comprises:

[0012] Receive input key domain and root word setting commands;

[0013] According to the key field and root setting command, the field root corresponding to each key field is set, wherein one key field may correspond to at least one field root.

[0014] In one embodiment, optionally, both the first business burial point data and the second business burial point data are structured data.

[0015] In one embodiment, optionally, the method further comprises:

[0016] Obtain a preset extended field summary table, wherein the extended field summary table includes a buried event identification field name and a preset number of parameter field names;

[0017] According to the order and labeling of the target key fields, a one-to-one mapping relationship between each target key field description and the parameter field name is established, and the mapping relationship is stored in a mapping table.

[0018] In one embodiment, optionally, generating an extended field data table according to the target structured field name and the corresponding first business tracking data includes:

[0019] According to the one-to-one mapping relationship between each target key field description and the parameter field name in the mapping table, find out the identification content corresponding to the embedding event identification name and the parameter field content corresponding to each parameter field name from the first business embedding data;

[0020] The identification content and each parameter field content are added to the extended field summary table to generate the extended field data table.

[0021] In one embodiment, optionally, a general field data table is generated according to the general field and the corresponding second business tracking data, including:

[0022] Determine the corresponding embedding event identification field name and the regular field name according to the structured field description of the regular field;

[0023] From the second business buried point data, find the buried point event identification field name and the buried point event identification field name content and the regular field name content corresponding to each regular field name, and generate a regular field data table.

[0024] In one embodiment, optionally, in response to a received query request for buried data, outputting a corresponding target field data table according to the extended field data table and the regular field data table includes:

[0025] Obtaining a buried point event identification field name and a pre-request field name from the buried point data query request;

[0026] According to the buried event identification field name and the pre-request field name, extract the target field name and the corresponding target field content from the extended field data table and the regular field data table to generate the target field data table;

[0027] Output the target field data table.

[0028] In a second aspect, an embodiment of the present application provides a service tracking data processing device, including:

[0029] An acquisition module, used to acquire corresponding first business buried point data and second business buried point data according to an extended field and a regular field of the business buried point, wherein the extended field corresponds to a semi-structured field description, and the regular field corresponds to a structured field description;

[0030] A parsing module, used for parsing the target structured field name corresponding to each target key field description in the semi-structured field description of the extended field according to the correspondence between the preset key field and the field root;

[0031] A first generating module, used to generate an extended field data table according to the target structured field name and the corresponding first business embedding data;

[0032] A second generating module, used to generate a regular field data table according to the regular field and the corresponding second business embedding data;

[0033] The output module is used to respond to the received buried data query request and output the corresponding target field data table according to the extended field data table and the regular field data table.

[0034] In a third aspect, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned business tracking data processing method when executing the computer program.

[0035] In a fourth aspect, a computer-readable storage medium is provided, which stores a computer program. When the computer program is executed by a processor, the steps of the above-mentioned business tracking data processing method are implemented.

[0036] In the scheme implemented by the above-mentioned business embedding data processing method, device, equipment and medium, the corresponding first business embedding data and second business embedding data can be obtained according to the extended field and regular field of the business embedding, wherein the extended field corresponds to the semi-structured field description, and the regular field corresponds to the structured field description; according to the correspondence between the preset key field and the field root, the target structured field name corresponding to each target key field description in the semi-structured field description of the extended field is parsed; according to the target structured field name and the corresponding first business embedding data, an extended field data table is generated; according to the regular field and the corresponding second business embedding data, a regular field data table is generated; in response to the received embedding data query request, the corresponding target field data table is output according to the extended field data table and the regular field data table. Through the above technical solution of the present invention, according to the correspondence between the preset key domain and the field root, the target structured field name corresponding to each target key domain description in the extended field semi-structured field description is parsed, and the association between the target key domain and the preset extended field table is established, and then the extended field data table is generated. Through such a structured processing method, the extended field and the regular field maintain a consistent experience, reducing the cost of secondary processing of semi-structured data in the later stage, improving the timeliness of complex business point analysis, and greatly reducing the threshold of point analysis. In this way, subsequent point analysis personnel can directly start analysis by themselves, avoiding communication costs, reducing maintenance costs and error probability.

Brief Description of the Drawings

[0037] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0038] Figure 1 A schematic flow chart of a method for processing business tracking data according to an embodiment of the present application is shown.

[0039] Figure 2 A schematic flow chart of a method for processing business tracking data according to another embodiment of the present application is shown.

[0040] Figure 3 A schematic flow chart of a business tracking data processing method according to another embodiment of the present application is shown.

[0041] Figure 4 A schematic flow chart of step S103 in a business tracking data processing method according to an embodiment of the present application is shown.

[0042] Figure 5A schematic flow chart of step S105 in a business tracking data processing method according to an embodiment of the present application is shown.

[0043] Figure 6 A block diagram of a business tracking data processing device according to an embodiment of the present application is shown.

[0044] Figure 7 A block diagram of a computer device according to an embodiment of the present application is shown. [Specific implementation method]

[0045] In order to better understand the technical solution of the present application, the embodiments of the present application are described in detail below with reference to the accompanying drawings.

[0046] It should be clear that the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.

[0047] The terms used in the embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to limit the present application. The singular forms "a", "said" and "the" used in the embodiments of the present application and the appended claims are also intended to include plural forms, unless the context clearly indicates other meanings.

[0048] In conjunction with the accompanying drawings, some embodiments of the present application are described in detail below. In the absence of conflict, the following embodiments and features in the embodiments can be combined with each other.

[0049] It should be noted that the embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Among them, artificial intelligence is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0050] AI basic technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing technology, operation / interaction systems, mechatronics, etc. AI software technologies mainly include computer vision technology, robotics technology, biometrics technology, speech processing technology, natural language processing technology, and machine learning / deep learning.

[0051] See also Figure 1 , Figure 1 A schematic flow chart of a method for processing business tracking data according to an embodiment of the present application is shown.

[0052] like Figure 1As shown, the process of the business tracking data processing method according to an embodiment of the present application includes:

[0053] Step S101, acquiring corresponding first business embedding point data and second business embedding point data according to an extended field and a regular field of the business embedding point, wherein the extended field corresponds to a semi-structured field description, and the regular field corresponds to a structured field description;

[0054] In one embodiment, optionally, both the first business burial point data and the second business burial point data are structured data.

[0055] Among them, structured data is mainly based on the characteristics of relational data structure, and is described in a list format according to the correspondence between fields and their contents, which facilitates the parsing of relational data structures. In contrast, extended fields are represented by semi-structured data, such as Map, JSON and other key-value pair-based data structure formats to represent them as a string, where the key field of each key-value pair corresponds to a field in the relational data structure, and its value field is the data content of the field.

[0056] In this step, each business tracking point has its own extension field definition, thus maintaining the flexibility of the extension field. The extension field can be described by a semi-structured field with a depth of 1, so that the field readability is better after the limited fields are flattened. Among them, each key field Key in the semi-structured field description is composed of pre-set roots, for example, productName is composed of two roots product and Name, and each key field Key needs to have an annotation, which reduces the communication cost of subsequent fields. In addition, the key field and value field in the semi-structured field description are both type-limited, which is consistent with the basic data types of the database, and types such as collections, arrays, and MAPs are not allowed. In this way, dynamic data types are prevented and the standardization of extension fields is guaranteed.

[0057] Step S102, parsing the target structured field name corresponding to each target key field description in the semi-structured field description of the extended field according to the correspondence between the preset key field and the field root;

[0058] like Figure 2 As shown, in one embodiment, optionally, the method further includes:

[0059] Step S201, receiving an input key domain and root word setting command;

[0060] Step S202: according to the key field and root setting command, set the field root corresponding to each key field, wherein one key field may correspond to at least one field root.

[0061] In this embodiment, the correspondence between the key field and the root word can be pre-set, so that each key field can find the root word corresponding to it during the subsequent embedding analysis, and the semi-structured field description can be converted into a structured field, reducing the communication cost of subsequent fields.

[0062] In this way, each key field Key in the semi-structured field description is composed of pre-set root words. For example, for the key field productName, according to the correspondence between the key field and the field root word, it is determined that it is composed of two root words, product and Name. Then, the corresponding target structured field name is the product name. Correspondingly, an annotation can be added to the key field productName, that is, it is marked, and the annotation is the product description.

[0063] like Figure 3 As shown, in one embodiment, optionally, the method further includes:

[0064] Step S301, obtaining a preset extended field table, wherein the extended field table includes a buried event identification field name and a preset number of parameter field names;

[0065] In this step, the extended fields are stored in a separate extended field table, which is the same wide table. The specific fields include the buried event identification field name and a preset number of parameter field names, where the preset number can be flexibly set according to business needs, such as 200. For example, the specific fields may include EventID, param1, param2..., param200.

[0066] Step S302: Establish a one-to-one mapping relationship between each target key field description and the parameter field name according to the order and label of the target key fields, and store the mapping relationship in a mapping table.

[0067] In this step, each target key field description is matched with the parameter field name one by one according to the order of the target key field and the corresponding target structured field name, and can be arranged according to the order and annotation of the target key field. That is, paramN corresponds to the key order in the field description with a depth of 1, solving the conflict between flexibility and consistency of the library table.

[0068] Among them, when adding a key to the key field, the newly added key can be bound to the first unbound paramX from the front to the back, and the mapping relationship is not allowed to be released. This not only ensures the traceability of historical tracking points, but also ensures the extensibility of extended fields.

[0069] Step S103, generating an extended field data table according to the target structured field name and the corresponding first business tracking data;

[0070] like Figure 4 As shown, in one embodiment, optionally, step S103 includes:

[0071] Step S401, according to the one-to-one mapping relationship between each target key field description and the parameter field name in the mapping table, find out the identification content corresponding to the embedding event identification name and the parameter field content corresponding to each parameter field name from the first business embedding data;

[0072] Step S402: Add the identification content and each parameter field content to the extended field summary table to generate the extended field data table.

[0073] In this embodiment, based on the one-to-one mapping relationship between the target key field description and the parameter field name, the identification content corresponding to the embedding event identification name and the parameter field content corresponding to each parameter field name can be found from the first business embedding data, and then added to the extended field total table to obtain the extended field data table and store it.

[0074] Before storing the extended field data table, the standardization of the extended field data table can also be automatically checked to ensure the accurate implementation of the field definition specifications.

[0075] Step S104, generating a regular field data table according to the regular field and the corresponding second business tracking data;

[0076] In one embodiment, optionally, step S104 includes:

[0077] Determine the corresponding embedding event identification field name and the regular field name according to the structured field description of the regular field;

[0078] From the second business buried point data, find the buried point event identification field name and the buried point event identification field name content and the regular field name content corresponding to each regular field name, and generate a regular field data table.

[0079] In this embodiment, for regular fields, the buried event identification field name and the regular field name can be directly determined according to the structured field description, and then data can be acquired from the second business buried data to generate a regular field data table. Similarly, the regular field data table can be stored for subsequent use by users when performing data queries.

[0080] Step S105, in response to the received buried data query request, output the corresponding target field data table according to the extended field data table and the regular field data table.

[0081] like Figure 5As shown, in one embodiment, optionally, step S105 includes:

[0082] Step S501, obtaining a buried point event identification field name and a pre-request field name from the buried point data query request;

[0083] Step S502, extracting the target field name and the corresponding target field content from the extended field data table and the regular field data table according to the buried event identification field name and the pre-request field name, so as to generate the target field data table;

[0084] Step S503: output the target field data table.

[0085] In this embodiment, when an input buried point data query request is received, the buried point event identification field name and the pre-request field name can be obtained from the buried point data query request. For example, the buried point event identification field name is user IDA, and the pre-request field names are time occurrence events, specific events, and user specific operations, respectively. In this way, the corresponding fields and contents can be extracted from the extended field data table and the regular field data table according to the buried point event identification field name and the pre-request field name, thereby obtaining the target field data table, that is, the field data table corresponding to the buried point data query request, and then outputting the target field data table.

[0086] In this way, according to the extended field mapping table, a regular-extended field association wide table is dynamically generated, which not only ensures the execution efficiency of the analysis statement, but also reduces the cost of embedded point analysis and handover. At the same time, through the key to ParamN conversion service, the details of the extended field library table are shielded from the user. From the user's perspective, the extended field is the regular field.

[0087] Through the above technical solution of the present invention, according to the correspondence between the preset key domain and the field root, the target structured field name corresponding to each target key domain description in the extended field semi-structured field description is parsed, and the association between the target key domain and the preset extended field table is established, and then the extended field data table is generated. Through such a structured processing method, the extended field and the regular field maintain a consistent experience, reducing the cost of secondary processing of semi-structured data in the later stage, improving the timeliness of complex business point analysis, and greatly reducing the threshold of point analysis. In this way, subsequent point analysis personnel can directly start analysis by themselves, avoiding communication costs, reducing maintenance costs and error probability.

[0088] The above technical solution of the present invention can be applied to the financial field, and specifically to applications such as Ping An Financial Manager, Ping An Pocket Bank and Ping An Bank. For example, in the Ping An Financial Manager application, the user's various click operations, such as purchased items, purchased insurance types, etc., can be obtained through the extended fields in the above technical solution, which is convenient for subsequent applications.

[0089] Of course, this case can also be applied to other scenarios that require business data acquisition, which are not listed here one by one.

[0090] Figure 6 A block diagram of a business tracking data processing device according to an embodiment of the present application is shown.

[0091] like Figure 6 As shown, in a second aspect, an embodiment of the present application provides a service tracking data processing device 60, including:

[0092] An acquisition module 61 is used to acquire corresponding first business embedding point data and second business embedding point data according to an extended field and a regular field of the business embedding point, wherein the extended field corresponds to a semi-structured field description, and the regular field corresponds to a structured field description;

[0093] A parsing module 62, configured to parse out a target structured field name corresponding to each target key field description in the semi-structured field description of the extended field according to a preset correspondence relationship between the key field and the field root;

[0094] A first generating module 63 is used to generate an extended field data table according to the target structured field name and the corresponding first business embedding data;

[0095] A second generating module 64 is used to generate a regular field data table according to the regular field and the corresponding second business embedding data;

[0096] The output module 65 is used to respond to the received buried data query request and output the corresponding target field data table according to the extended field data table and the regular field data table.

[0097] In one embodiment, optionally, the device further comprises:

[0098] A receiving module, used for receiving input key domain and root word setting commands;

[0099] The setting module is used to set the field root corresponding to each key field according to the key field and root setting command, wherein one key field can correspond to at least one field root.

[0100] In one embodiment, optionally, both the first business burial point data and the second business burial point data are structured data.

[0101] In one embodiment, optionally, the device further comprises:

[0102] A total table acquisition module is used to acquire a preset extended field total table, wherein the extended field total table includes a buried event identification field name and a preset number of parameter field names;

[0103] The storage module is used to establish a one-to-one mapping relationship between each target key field description and the parameter field name according to the order and label of the target key fields, and store the mapping relationship in a mapping table.

[0104] In one embodiment, optionally, the first generating module includes:

[0105] A search unit, configured to search for an identification content corresponding to the embedding event identification name and a parameter field content corresponding to each parameter field name from the first business embedding data according to a one-to-one mapping relationship between each target key field description and a parameter field name in the mapping table;

[0106] The first generating unit is used to add the identification content and each parameter field content to the extended field total table to generate the extended field data table.

[0107] In one embodiment, optionally, the second generating module includes:

[0108] A determination unit, used to determine a corresponding embedding event identification field name and a regular field name according to the structured field description of the regular field;

[0109] The second generating unit is used to search the buried event identification field name and the buried event identification field name content and the regular field name content corresponding to each regular field name from the second business buried event data, and generate a regular field data table.

[0110] In one embodiment, optionally, the output module includes:

[0111] A name acquisition unit, used to acquire a buried point event identification field name and a pre-request field name from the buried point data query request;

[0112] An extraction unit, used to extract a target field name and corresponding target field content from the extended field data table and the regular field data table according to the buried event identification field name and the pre-request field name, so as to generate the target field data table;

[0113] An output unit is used to output the target field data table.

[0114] In the scheme implemented by the above-mentioned business embedding data processing method, device, equipment and medium, the corresponding first business embedding data and second business embedding data can be obtained according to the extended field and regular field of the business embedding, wherein the extended field corresponds to the semi-structured field description, and the regular field corresponds to the structured field description; according to the correspondence between the preset key field and the field root, the target structured field name corresponding to each target key field description in the semi-structured field description of the extended field is parsed; according to the target structured field name and the corresponding first business embedding data, an extended field data table is generated; according to the regular field and the corresponding second business embedding data, a regular field data table is generated; in response to the received embedding data query request, the corresponding target field data table is output according to the extended field data table and the regular field data table. Through the above technical solution of the present invention, according to the correspondence between the preset key domain and the field root, the target structured field name corresponding to each target key domain description in the extended field semi-structured field description is parsed, and the association between the target key domain and the preset extended field table is established, and then the extended field data table is generated. Through such a structured processing method, the extended field and the regular field maintain a consistent experience, reducing the cost of secondary processing of semi-structured data in the later stage, improving the timeliness of complex business point analysis, and greatly reducing the threshold of point analysis. In this way, subsequent point analysis personnel can directly start analysis by themselves, avoiding communication costs, reducing maintenance costs and error probability.

[0115] In a third aspect, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned business tracking data processing method when executing the computer program.

[0116] In a fourth aspect, a computer-readable storage medium is provided, which stores a computer program. When the computer program is executed by a processor, the steps of the above-mentioned business tracking data processing method are implemented.

[0117] It should be noted that technical personnel in the relevant field can clearly understand that, for the convenience and conciseness of description, the specific working processes of the business embedding data processing device and each module described above can refer to the corresponding processes in the aforementioned business embedding data processing method embodiment, and will not be repeated here.

[0118] It should be noted that technical personnel in the relevant field can clearly understand that, for the convenience and conciseness of description, the specific working process of the model training device and each module described above can refer to the corresponding process in the aforementioned business embedding data processing method embodiment, and will not be repeated here.

[0119] The above-mentioned service embedding data processing device can be implemented in the form of a computer program. The computer program can be used in Figure 7 Runs on the computer device shown.

[0120] Figure 7 A block diagram of a computer device according to an embodiment of the present application is shown.

[0121] See also Figure 7 The computer device includes a processor, a memory and a network interface connected through a system bus, wherein the memory may include a storage medium and an internal memory.

[0122] The storage medium can store an operating system and a computer program. The computer program includes program instructions, and when the program instructions are executed, the processor can execute any one of the multi-source data business tracking data processing methods provided in the embodiments of the present application.

[0123] The processor is used to provide computing and control capabilities and support the operation of the entire computer equipment.

[0124] The internal memory provides an environment for the operation of the computer program in the storage medium. When the computer program is executed by the processor, the processor can execute any infectious disease transmission path analysis method or prediction neural network training method. The storage medium can be non-volatile or volatile.

[0125] The network interface is used for network communication, such as sending assigned tasks, etc. Those skilled in the art will understand that Figure 7 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0126] It should be understood that the processor may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among them, the general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0127] The computer device of the embodiment of the present application exists in various forms, including but not limited to:

[0128] (1) Mobile communication devices: These devices are characterized by their mobile communication functions and their main purpose is to provide voice and data communications. These terminals include: smart phones (such as iPhone), multimedia phones, functional phones, and low-end phones.

[0129] (2) Ultra-mobile personal computer devices: These devices fall into the category of personal computers, have computing and processing capabilities, and generally also have mobile Internet access features. These terminals include: PDA, MID and UMPC devices, such as iPad.

[0130] (3) Portable entertainment devices: These devices can display and play multimedia content. They include audio and video players (such as iPods), handheld game consoles, e-books, as well as smart toys and portable car navigation devices.

[0131] (4) Server: A device that provides computing services. The server consists of a processor, hard disk, memory, system bus, etc. The server is similar to a general computer architecture, but because it needs to provide highly reliable services, it has higher requirements in terms of processing power, stability, reliability, security, scalability, and manageability.

[0132] (5) Other electronic devices with data interaction functions.

[0133] In addition, an embodiment of the present application provides a computer-readable storage medium storing computer-executable instructions, wherein the computer-executable instructions are used to perform the following steps:

[0134] Acquire corresponding first business embedding point data and second business embedding point data according to an extended field and a regular field of the business embedding point, wherein the extended field corresponds to a semi-structured field description, and the regular field corresponds to a structured field description;

[0135] According to the correspondence between the preset key domain and the field root, the target structured field name corresponding to each target key domain description in the semi-structured field description of the extended field is parsed;

[0136] Generate an extended field data table according to the target structured field name and the corresponding first business tracking data;

[0137] Generate a regular field data table according to the regular field and the corresponding second business tracking data;

[0138] In response to the received buried data query request, the corresponding target field data table is output according to the extended field data table and the regular field data table.

[0139] In one embodiment, optionally, the method further comprises:

[0140] Receive input key domain and root word setting commands;

[0141] According to the key field and root setting command, the field root corresponding to each key field is set, wherein one key field may correspond to at least one field root.

[0142] In one embodiment, optionally, both the first business burial point data and the second business burial point data are structured data.

[0143] In one embodiment, optionally, the method further comprises:

[0144] Obtain a preset extended field summary table, wherein the extended field summary table includes a buried event identification field name and a preset number of parameter field names;

[0145] According to the order and labeling of the target key fields, a one-to-one mapping relationship between each target key field description and the parameter field name is established, and the mapping relationship is stored in a mapping table.

[0146] In one embodiment, optionally, generating an extended field data table according to the target structured field name and the corresponding first business tracking data includes:

[0147] According to the one-to-one mapping relationship between each target key field description and the parameter field name in the mapping table, find out the identification content corresponding to the embedding event identification name and the parameter field content corresponding to each parameter field name from the first business embedding data;

[0148] The identification content and each parameter field content are added to the extended field summary table to generate the extended field data table.

[0149] In one embodiment, optionally, a general field data table is generated according to the general field and the corresponding second business tracking data, including:

[0150] Determine the corresponding embedding event identification field name and the regular field name according to the structured field description of the regular field;

[0151] From the second business buried point data, find the buried point event identification field name and the buried point event identification field name content and the regular field name content corresponding to each regular field name, and generate a regular field data table.

[0152] In one embodiment, optionally, in response to a received query request for buried data, outputting a corresponding target field data table according to the extended field data table and the regular field data table includes:

[0153] Obtaining a buried point event identification field name and a pre-request field name from the buried point data query request;

[0154] According to the buried event identification field name and the pre-request field name, extract the target field name and the corresponding target field content from the extended field data table and the regular field data table to generate the target field data table;

[0155] Output the target field data table.

[0156] It should be noted that the above functions or steps that can be implemented by the computer-readable storage medium or electronic device can refer to the relevant description in the aforementioned method embodiment. To avoid repetition, they will not be described one by one here.

[0157] It should be understood that the term "and / or" used in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0158] It should be understood that, although the terms first, second, etc. may be used to describe the setting unit in the embodiments of the present application, these setting units should not be limited to these terms. These terms are only used to distinguish the setting units from each other. For example, without departing from the scope of the embodiments of the present application, the first setting unit may also be referred to as the second setting unit, and similarly, the second setting unit may also be referred to as the first setting unit.

[0159] The word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrases "if it is determined" or "if (stated condition or event) is detected" may be interpreted as "when it is determined" or "in response to determining" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)", depending on the context.

[0160] In the several embodiments provided in the present application, it should be understood that the disclosed systems, 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 units is only a logical function division. There may be other division methods in actual implementation. For example, 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.

[0161] 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 hardware plus software functional units.

[0162] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0163] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A method for processing business tracking data, characterized in that: The method comprises: Acquire corresponding first business embedding point data and second business embedding point data according to an extended field and a regular field of the business embedding point, wherein the extended field corresponds to a semi-structured field description, and the regular field corresponds to a structured field description; According to the correspondence between the preset key domain and the field root, the target structured field name corresponding to each target key domain description in the semi-structured field description of the extended field is parsed and annotated; Generate an extended field data table according to the target structured field name and the corresponding first business tracking data; Generate a regular field data table according to the regular field and the corresponding second business tracking data; In response to the received buried data query request, the corresponding target field data table is output according to the extended field data table and the regular field data table.

2. The method according to claim 1, characterized in that The method further comprises: Receive input key domain and root word setting commands; According to the key field and root setting command, the field root corresponding to each key field is set, wherein one key field may correspond to at least one field root.

3. The method according to claim 1, characterized in that The first business tracking data and the second business tracking data are both structured data.

4. The method according to claim 1, characterized in that: The method further comprises: Obtain a preset extended field summary table, wherein the extended field summary table includes a buried event identification field name and a preset number of parameter field names; According to the order and labeling of the target key fields, a one-to-one mapping relationship between each target key field description and the parameter field name is established, and the mapping relationship is stored in a mapping table.

5. The method according to claim 4, characterized in that Generate an extended field data table according to the target structured field name and the corresponding first business tracking data, including: According to the one-to-one mapping relationship between each target key field description and the parameter field name in the mapping table, find out the identification content corresponding to the embedding event identification name and the parameter field content corresponding to each parameter field name from the first business embedding data; The identification content and each parameter field content are added to the extended field summary table to generate the extended field data table.

6. The method according to claim 1, characterized in that Generate a regular field data table according to the regular field and the corresponding second business tracking data, including: Determine the corresponding embedding event identification field name and the regular field name according to the structured field description of the regular field; From the second business buried point data, find the buried point event identification field name and the buried point event identification field name content and the regular field name content corresponding to each regular field name, and generate a regular field data table.

7. The method according to claim 1, characterized in that In response to the received buried data query request, outputting a corresponding target field data table according to the extended field data table and the conventional field data table includes: Obtaining a buried point event identification field name and a pre-request field name from the buried point data query request; According to the buried event identification field name and the pre-request field name, extract the target field name and the corresponding target field content from the extended field data table and the regular field data table to generate the target field data table; Output the target field data table.

8. A business tracking data processing device, characterized in that: include: An acquisition module, used to acquire corresponding first business embedding point data and second business embedding point data according to an extended field and a regular field of the business embedding point, wherein the extended field corresponds to a semi-structured field description, and the regular field corresponds to a structured field description; A parsing module, used for parsing the target structured field name corresponding to each target key field description in the semi-structured field description of the extended field according to the correspondence between the preset key field and the field root; A first generating module, used to generate an extended field data table according to the target structured field name and the corresponding first business embedding data; A second generating module, used to generate a regular field data table according to the regular field and the corresponding second business embedding data; The output module is used to respond to the received buried data query request and output the corresponding target field data table according to the extended field data table and the regular field data table.

9. A computer device, characterized in that: include: at least one processor; and, a memory communicatively coupled to the at least one processor; The memory stores instructions executable by the at least one processor, wherein the instructions are configured to execute the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: Computer executable instructions are stored, and the computer executable instructions are used to execute the method according to any one of claims 1 to 7.

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