Business data processing method and system, computer equipment and storage medium

Abstract

The invention relates to a business data processing method and system, computer equipment and a storage medium, and the method comprises the following steps: a data obtaining step: obtaining user behavior data of a data source through a data engine and pre-configuring a filtering rule which comprises an event theme and a data field; a scene event configuration step: creating an atomic event based on a business scene in an event system, generating an index and mapping in Elasticsearch based on the atomic event, consuming and analyzing the data field of the corresponding event theme in the data acquisition step by the event system, storing the data of the data field into the corresponding mapping, and generating an ES table structure; and a time axis display step: obtaining a query request of an access system, determining an atomic event by an event system based on the access system and the time axis type, querying data in the ES table structure based on an event theme of the atomic event, and displaying the data in the access system in a time axis page form. According to the invention, the purposes of improving data management efficiency and supporting business decisions are achieved.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular to a business data processing method, system, computer device, and computer-readable storage medium. Background Art

[0002] In modern credit business, users usually complete a series of operations through electronic platforms, including real-name authentication, credit application, credit limit adjustment, simultaneous credit, application for expenditure, repayment deduction, repayment reminder and other process nodes. User behavior in these nodes is usually recorded in the platform in the form of embedded data. However, since the operation records of different process nodes are scattered in various parts of the platform, these data lack a unified storage and management method, resulting in difficulty in timely and accurate acquisition of required information when business departments need to query relevant data.

[0003] The data storage methods in the existing technology have the following major problems: First, user behavior data is scattered in various process nodes and operation modules, lacking a unified storage and management mechanism. Second, when it is necessary to query and analyze these data, there is a lack of a centralized entry or tool, which makes the data extraction and analysis process cumbersome and time-consuming. Finally, with the development of credit business, the amount of data has increased rapidly, and the traditional decentralized storage method is difficult to support the efficient processing and utilization of large-scale data.

[0004] In order to solve the above problems, it is necessary to establish a unified data warehouse to integrate and centrally store these user behavior data. In this way, a unified query portal can be provided to facilitate business departments to quickly obtain and analyze the required user behavior data, thereby improving the efficiency of data management and supporting more accurate business decisions. Summary of the invention

[0005] The embodiments of the present application provide a business data processing method, system, computer device and computer-readable storage medium to achieve the purpose of improving data management efficiency and supporting business decision-making.

[0006] In a first aspect, an embodiment of the present application provides a method for processing business data, including:

[0007] Data acquisition step, obtaining user behavior data from a data source through a data engine and pre-configuring filtering rules, wherein the filtering rules include event topics and data fields; based on the filtering rules, data can be consumed from various data sources for subsequent real-time processing and analysis, wherein the data source is connected to a credit business platform, etc., wherein the data includes: login, registration, real-name information, credit application, expenditure application, outbound call tasks, SMS notifications, coupon issuance, red envelope issuance and other messages;

[0008] Scenario event configuration step: create atomic events based on business scenarios in the event system, generate indexes and mappings in Elasticsearch based on the atomic events, create a table structure in Elasticsearch to store and query atomic event data, retrieve and analyze atomic events through the query interface provided by Elasticsearch, and the event system consumes and parses the data fields of the corresponding event topics in the data acquisition step, stores the data of the data fields in the corresponding mappings, and generates an ES table structure;

[0009] In the timeline display step, a query request from an access system is obtained, the event system determines the atomic event based on the access system and the timeline type, queries the data in the ES table structure based on the event subject of the atomic event and displays it in the access system in the form of a timeline page.

[0010] In some embodiments, the data acquisition step further includes: the data source includes; Kafka and / or Archer, the upstream system sends the user behavior data in JSON format messages to the message queue of the data source, the data engine pulls the message from the message queue and parses the message content, and obtains information such as the message name, description, communication channel channel and message container queue.

[0011] In some embodiments, the scene event configuration step further includes:

[0012] The scene configuration step is to create a scene and configure the scene information, which includes: scene number, scene name, scene description, scene status, scene trigger type and scene access connection, etc., based on the log query and export operations that can be provided by the scene. The scenes configured in this embodiment include but are not limited to: MA SMS, marketing outbound call results, quota application accounting, expenditure information accounting, resource position data, coupons and red envelopes, coupon and red envelope messages, interest-free coupons, etc. The scene status includes enabled or disabled.

[0013] An event configuration step, creating an atomic event based on the scene association, wherein the atomic event information includes: event code, event name, event description, event subject, event definition, data source access configuration and corresponding user identifier;

[0014] Table structure configuration steps: synchronize atomic events to an event system through scheduled tasks, save atomic events and their corresponding behavior groups in the database, and create Elasticsearch to generate indexes and mappings based on atomic event information.

[0015] In some embodiments, the timeline display step further includes:

[0016] Create a timeline display page. Each timeline to be displayed is configured with an access system, a behavior primary key, an atomic event and a time interval. The detailed fields displayed under the atomic event are configured. The time interval can be selected in months.

[0017] Furthermore, the present application can provide a query list based on time intervals and access system paging, can concurrently query events of multiple months in multiple indexes based on the ES table structure, and concurrently assemble parameters for query according to index type.

[0018] Based on the above steps, the embodiment of the present application utilizes the data engine and the event system to work together to process messages received from the data source, generate atomic events according to the configuration information, and implement query and analysis based on Elasticsearch, thereby realizing a flexible and scalable event processing pipeline. It can efficiently process and analyze data from different data sources and provide users with external behavior queries on the credit business platform, so that business parties can carry out business activities based on the data.

[0019] In some of the embodiments, the data engine provides a configuration interaction interface, allowing users to configure data sources and event topics by simply dragging and filling out forms. The data engine provides flexible configuration options, allowing users to specify event topics of data sources, data field information, etc. through page configuration.

[0020] Based on this, users can switch data sources and topics through simple page configuration without modifying the code, which improves configuration flexibility. This configuration method also makes the management and switching of data sources very simple and fast.

[0021] In some embodiments, the data engine supports the use of JSONPath expressions to extract fields in JSON messages. A user can specify a JSONPath expression on a configuration interaction interface to extract the desired data fields.

[0022] Based on this, this embodiment is easy to use, and users can extract required data through simple JSONPath expressions without having to have an in-depth understanding of JSON structure and programming.

[0023] In a second aspect, an embodiment of the present application provides a business data processing system, including:

[0024] The data acquisition module is configured to acquire user behavior data from a data source through a data engine and pre-configure filtering rules, wherein the filtering rules include event topics and data fields; based on the filtering rules, data can be consumed from various data sources for subsequent real-time processing and analysis, wherein the data source is connected to a credit business platform, etc., wherein the data includes: login, registration, real-name information, credit application, expenditure application, outbound call tasks, SMS notifications, coupon issuance, red envelope issuance and other messages;

[0025] A scenario event configuration module is configured to create atomic events based on business scenarios in the event system, generate indexes and mappings in Elasticsearch based on the atomic events, create a table structure in Elasticsearch to store and query atomic event data, retrieve and analyze atomic events through the query interface provided by Elasticsearch, and the event system consumes and parses the data fields of the corresponding event topics in the data acquisition step, stores the data of the data fields in the corresponding mappings, and generates an ES table structure;

[0026] The timeline display module is configured to obtain a query request from an access system. The event system determines the atomic event based on the access system and the timeline type, queries the data in the ES table structure based on the event subject of the atomic event, and displays it in the access system in the form of a timeline page.

[0027] In some embodiments, the scene event configuration module further includes:

[0028] The scenario configuration module is configured to create scenarios and configure scenario information, wherein the scenario information includes: scenario number, scenario name, scenario description, scenario status, scenario trigger type, and scenario access connection, etc., and log query and export operations can be provided based on the scenario. The scenarios configured in this embodiment include but are not limited to: MA text messages, marketing outbound call results, quota application accounting, expenditure information accounting, resource position data, coupons and red envelopes, coupon and red envelope messages, interest-free coupons, etc. The scenario status includes enabled or disabled.

[0029] An event configuration module, configured to create an atomic event based on the scene association, the atomic event information including: event code, event name, event description, event subject, event definition, data source access configuration and corresponding user identifier;

[0030] The table structure configuration module is configured to synchronize atomic events to an event system through a scheduled task, save atomic events and their corresponding behavior groups in a database, and create an Elasticsearch index and mapping based on the atomic event information.

[0031] In a third aspect, an embodiment of the present application provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the business data processing method as described in the first aspect above is implemented.

[0032] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the business data processing method as described in the first aspect above.

[0033] Compared with the related art, the business data processing method, system, computer device and computer-readable storage medium provided in the embodiments of the present application consume various business messages, such as login, registration, real name, credit limit application, credit limit change, expenditure application, outbound call, text message, coupon issuance and red envelope issuance, etc., to build a timeline query data warehouse, provide user behavior data query services, and realize a flexible and scalable event processing pipeline, which can efficiently process and analyze data from different data sources and provide users with external behavior queries on the credit business platform.

[0034] Details of one or more embodiments of the present application are set forth in the following drawings and description to make other features, objects, and advantages of the present application more readily apparent. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0036] Figure 1 is a flowchart of a business data processing method according to an embodiment of the present application;

[0037] Figure 2 is a step-by-step flow chart of a method for processing business data according to an embodiment of the present application;

[0038] Figure 3 It is a structural block diagram of a business data processing system according to an embodiment of the present application.

[0039] In the figure:

[0040] 1. Data acquisition module; 2. Scene event configuration module; 3. Timeline display module;

[0041] 201. Scenario configuration module; 202. Event configuration module; 203. Table structure configuration module. DETAILED DESCRIPTION

[0042] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application is described and illustrated 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 intended to limit the present application. Based on the embodiments provided in the present application, all other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of the present application.

[0043] Obviously, the drawings described below are only some examples or embodiments of the present application. For ordinary technicians in this field, the present application can also be applied to other similar scenarios based on these drawings without creative work. In addition, it can also be understood that although the efforts made in this development process may be complicated and lengthy, for ordinary technicians in this field related to the content disclosed in this application, some changes in design, manufacturing or production based on the technical content disclosed in this application are just conventional technical means, and should not be understood as insufficient content disclosed in this application.

[0044] Reference to "embodiments" in this application means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those of ordinary skill in the art that the embodiments described in this application may be combined with other embodiments without conflict.

[0045] Unless otherwise defined, the technical terms or scientific terms involved in this application should be understood by people with ordinary skills in the technical field to which this application belongs. The words "one", "a", "a", "the" and the like involved in this application do not indicate a quantitative limitation, and may represent the singular or plural. The terms "include", "comprise", "have" and any of their variations involved in this application are intended to cover non-exclusive inclusions; for example, a process, method, system, product or device that includes a series of steps or modules (units) is not limited to the listed steps or units, but may also include steps or units that are not listed, or may also include other steps or units inherent to these processes, methods, products or devices. The words "connect", "connected", "coupled" and the like involved in this application are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The "multiple" involved in this application refers to two or more. "And / or" describes the association relationship of associated objects, indicating that there may 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. The character " / " generally indicates that the objects before and after are in an "or" relationship. The terms "first", "second", "third", etc. involved in this application are only used to distinguish similar objects and do not represent a specific ordering of the objects.

[0046] This embodiment provides a service data processing method. Figure 1 to Figure 2 is a flowchart of a method for processing business data according to an embodiment of the present application, such as Figure 1 to Figure 2 As shown, the process includes the following steps:

[0047] Data acquisition step S1, obtains user behavior data from the data source through the data engine and pre-configures filtering rules, the filtering rules include event topics and data fields; based on the filtering rules, data can be consumed from various data sources for subsequent real-time processing and analysis, the data source is connected to the credit business platform, etc., where the data includes: login, registration, real-name information, credit application, expenditure application, outbound call tasks, SMS notifications, coupon issuance, red envelope issuance and other messages;

[0048] Scenario event configuration step S2, creating atomic events based on business scenarios in the event system, generating indexes and mappings in Elasticsearch based on atomic events, so as to create a table structure in Elasticsearch for storing and querying atomic event data, retrieving and analyzing atomic events through the query interface provided by Elasticsearch, and the event system consumes and parses the data fields of the corresponding event topics in the data acquisition step, and stores the data of the data fields in the corresponding mappings to generate the ES table structure. The index is the basic unit for storing and retrieving data in Elasticsearch, and the mapping is used to define the structure and type of data in the index. Atomic events are pre-configured by administrators or users based on business scenarios. The types, filtering rules, and processing logic of atomic events are configured for the corresponding business scenarios. Frequently queried atomic events are stored in the cache so that the required data can be directly obtained from the cache later without querying or calculating again.

[0049] In the timeline display step S3, a query request from an access system is obtained, the event system determines the atomic event based on the access system and the timeline type, queries the data in the ES table structure based on the event subject of the atomic event, and displays it in the access system in the form of a timeline page.

[0050] Based on the above steps, this embodiment obtains user behavior through the data engine and performs real-time processing and analysis. Real-time processing ensures the immediacy of data processing. By creating atomic events to extract user behavior on the business side, comprehensive business monitoring and analysis can be provided. Based on the display of data in the form of a timeline page, intuitive and easy-to-understand data visualization effects are provided to enhance user experience, and data query services with a unified query interface are implemented in combination with atomic events.

[0051] In the above embodiment, in the data acquisition step S1, the data source includes; Kafka and / or Archer, the upstream system sends the user behavior data in JSON format to the message queue of the data source, and the data engine pulls the message from the message queue and parses the message content to obtain information such as the message name, description, communication channel channel and message container queue.

[0052] In some embodiments, the scene event configuration step S2 further includes:

[0053] Scenario configuration step S201, create a scenario and configure scenario information, the scenario information includes: scenario number, scenario name, scenario description, scenario status, scenario trigger type and scenario access connection, etc., based on the log query and export operations that can be provided by the scenario, the scenarios configured in this embodiment include but are not limited to: MA SMS, marketing outbound call results, quota application accounting, expenditure information accounting, resource position data, coupons and red envelopes, coupon and red envelope messages, interest-free coupons, etc., and the scene status includes enabled or disabled.

[0054] Taking SMS service as an example, configure the scenario information of MA SMS scenario, where the scenario trigger type includes message queue, configure SMS service interface, set message queue parameters to access MA SMS, monitor and extract corresponding data based on preset filtering rules, such as setting the filtering condition expression of MA SMS based on AVIATOR expression.

[0055] The event configuration step S202 creates an atomic event based on scene association. The atomic event information includes: event code, event name, event description, event subject, event definition, data source access configuration and corresponding user identifier; optionally, the atomic event includes but is not limited to: registration events, real-name events, credit limit application events, expenditure application events, outbound call events, MA SMS events, etc.

[0056] Table structure configuration step S203, synchronizes atomic events to an event system through a scheduled task, saves atomic events and their corresponding behavior groups into a database, such as marketing outbound call result user events, marketing outbound call result events belonging to marketing outbound call behaviors, and creates an Elasticsearch index and mapping based on the atomic event information.

[0057] Taking application expenditure accounting as an example, the associated attribute information obtained based on the data source kd_es and the data table ae_apply_use includes: application serial number, event name, approval amount, approval result, operation time, operation type, etc.

[0058] In the above embodiment, event details can be extracted based on atomic events and whether to send event details can be selected. The variables of event details can be flexibly selected, and the exportable event details at least include: variable name, rule description, and variable type. Taking the MA SMS customer number event as an example, its data source access is the MA SMS message queue, and the event subject is configured as cust_no. The selectable event details include customer number, user number, sending status, three-level list categories, seat group, work number and other related information. Corresponding rule descriptions or variable types are configured for these information. Optionally, the variable type is mainly string type.

[0059] In another embodiment, based on atomic events, multiple atomic events can be associated to construct a composite event. Taking marketing behavior as an example, it can be composed of atomic events such as marketing outbound call events, AI outbound call events, manual outbound call events, AI outbound call record events, and SMS message push events.

[0060] Combined events can more comprehensively describe complex behaviors, such as multiple links in marketing activities, and improve event expression capabilities. By combining different atomic events, user behavior patterns can be analyzed more deeply and the depth of data analysis can be enhanced. Combined events can help reduce the complexity of data processing and improve event processing efficiency. Analysis based on combined events can provide more comprehensive data support for business decisions.

[0061] In some embodiments, the timeline display step S3 further includes:

[0062] Create a timeline display page. Each timeline to be displayed is configured with an access system, a primary key for behavior, an atomic event, and a time interval. The detailed fields displayed under the atomic event can also be configured with dynamic text. The time interval can be selected in months. The timeline page of this application can provide a query list in pages according to the customer ID and time range.

[0063] Furthermore, the present application can provide a query list based on time intervals and access system paging, can concurrently query events of multiple months in multiple indexes based on the ES table structure, and concurrently assemble parameters for query according to index type, thereby improving query efficiency and management convenience.

[0064] The time axis configured in this application includes: marketing time axis, customer service time axis, reminder time axis, quota application and expenditure time axis, etc., which are convenient for displaying the customer's historical behavior from different dimensions and providing a comprehensive perspective. The marketing time axis can be further divided into marketing time axis MDS, marketing time axis customers, marketing time axis users, etc. The customer service time axis further includes customer service message push time axis and customer service marketing outbound call time axis, so as to display the time axis from different dimensions. Taking the marketing time axis as an example, its access system is the marketing system, with the customer number as the primary key of behavior, and its associated atomic events include quota application events, expenditure application events, etc.; taking the customer service message push time axis as an example, its access system is the customer platform platform, with the mobile phone number as the primary key, and its associated atomic events include: SMS message events, new message platform, etc. Based on this, it is possible to query the customer's historical behavior trajectory based on the customer number, user number, mobile phone number, etc. as the primary key of behavior, and perform query display.

[0065] Based on the above steps, the embodiment of the present application utilizes the data engine and the event system to work together to process messages received from the data source, generate atomic events according to the configuration information, and implement query and analysis based on Elasticsearch, thereby realizing a flexible and scalable event processing pipeline. It can efficiently process and analyze data from different data sources and provide users with external behavior queries on the credit business platform, so that business parties can carry out business activities based on the data.

[0066] In some of the embodiments, the data engine provides a configuration interaction interface that allows users to configure data sources and event topics by simply dragging and filling out forms. The data engine provides flexible configuration options, allowing users to specify the event topic of the data source, data field information, etc. through page configuration.

[0067] Based on this, users can switch data sources and topics through simple page configuration without modifying the code, which improves configuration flexibility. This configuration method also makes the management and switching of data sources very simple and fast.

[0068] In some of the embodiments, since the data in Kafka and Archer are usually stored in JSON format, the data engine supports the use of JSONPath expressions to extract fields in JSON messages. Users can specify JSONPath expressions on the configuration interaction interface to extract the required data fields.

[0069] Based on this, this embodiment is easy to use, and users can extract required data through simple JSONPath expressions without having to have an in-depth understanding of JSON structure and programming.

[0070] The data engine of this embodiment is particularly suitable for scenarios that require real-time processing and analysis of large amounts of data streams, such as the Internet of Things (IoT), financial transactions, online advertising, etc. Through flexible configuration and powerful data processing capabilities, the data engine can help users quickly build an efficient data processing and analysis system.

[0071] It should be noted that the steps shown in the above process or the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0072] This embodiment also provides a business data processing system, which is used to implement the above embodiments and preferred implementation modes, and the descriptions that have been made will not be repeated. As used below, the terms "module", "unit", "subunit" and the like can implement a combination of software and / or hardware of a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0073] Figure 3 is a structural block diagram of a business data processing system according to an embodiment of the present application, such as Figure 3 As shown, the system includes: a data acquisition module 1, a scene event configuration module 2 and a time axis display module 3.

[0074] The data acquisition module 1 is configured to acquire user behavior data from a data source through a data engine and pre-configure filtering rules, wherein the filtering rules include event topics and data fields; based on the filtering rules, data can be consumed from various data sources for subsequent real-time processing and analysis, wherein the data source is connected to a credit business platform, etc., wherein the data includes: login, registration, real-name information, credit application, expenditure application, outbound call tasks, SMS notifications, coupon issuance, red envelope issuance and other messages;

[0075] Scenario event configuration module 2 is configured to create atomic events based on business scenarios in the event system, generate indexes and mappings in Elasticsearch based on atomic events, so as to create a table structure for storing and querying atomic event data in Elasticsearch, retrieve and analyze atomic events through the query interface provided by Elasticsearch, and the event system consumes and parses the data fields of the corresponding event topics in the data acquisition step, stores the data of the data fields in the corresponding mappings, and generates an ES table structure. The index is the basic unit for storing and retrieving data in Elasticsearch, and the mapping is used to define the structure and type of data in the index. Atomic events are pre-configured by administrators or users based on business scenarios. The types, filtering rules, and processing logic of atomic events are configured for the corresponding business scenarios. Frequently queried atomic events are stored in the cache so that the required data can be directly obtained from the cache later without querying or calculating again.

[0076] The timeline display module 3 is configured to obtain a query request from an access system. The event system determines the atomic event based on the access system and the timeline type, queries the data in the ES table structure based on the event subject of the atomic event, and displays it in the access system in the form of a timeline page.

[0077] In the above embodiment, the scene event configuration module 2 further includes: a scene configuration module 201, an event configuration module 202 and a table structure configuration module 203, and the specific functions are as follows.

[0078] The scene configuration module 201 is configured to create scenes and configure scene information. The scene information includes: scene number, scene name, scene description, scene status, scene trigger type and scene access connection, etc., based on the log query and export operations that can be provided by the scene. The scenes configured in this embodiment include but are not limited to: MA text messages, marketing outbound call results, quota application accounting, expenditure information accounting, resource position data, coupons and red envelopes, coupon red envelope messages, interest-free coupons, etc. The scene status includes enabled or disabled.

[0079] Taking SMS service as an example, configure the scenario information of MA SMS scenario, where the scenario trigger type includes message queue, configure SMS service interface, set message queue parameters to access MA SMS, monitor and extract corresponding data based on preset filtering rules, such as setting the filtering condition expression of MA SMS based on AVIATOR expression.

[0080] The event configuration module 202 is configured to create atomic events based on scenario associations. The atomic event information includes: event code, event name, event description, event subject, event definition, data source access configuration and corresponding user identifier; optionally, atomic events include but are not limited to: registration events, real-name events, credit limit application events, expenditure application events, outbound call events, MA SMS events, etc.

[0081] The table structure configuration module 203 is configured to synchronize atomic events to an event system through a scheduled task, save atomic events and their corresponding behavior groups into a database, such as marketing outbound call result user events and marketing outbound call result events belonging to marketing outbound call behaviors, and create an Elasticsearch index and mapping based on the atomic event information.

[0082] Taking application expenditure accounting as an example, the associated attribute information obtained based on the data source kd_es and the data table ae_apply_use includes: application serial number, event name, approval amount, approval result, operation time, operation type, etc.

[0083] In the above embodiment, event details can be extracted based on atomic events and whether to send event details can be selected. The variables of event details can be flexibly selected, and the exportable event details at least include: variable name, rule description, and variable type. Taking the MA SMS customer number event as an example, its data source access is the MA SMS message queue, and the event subject is configured as cust_no. The selectable event details include customer number, user number, sending status, three-level list categories, seat group, work number and other related information. Corresponding rule descriptions or variable types are configured for these information. Optionally, the variable type is mainly string type.

[0084] In another embodiment, based on atomic events, multiple atomic events can be associated to construct a composite event. Taking marketing behavior as an example, it can be composed of atomic events such as marketing outbound call events, AI outbound call events, manual outbound call events, AI outbound call record events, and SMS message push events.

[0085] Based on the above structure, the embodiment of the present application utilizes the data engine and the event system to work together to process messages received from the data source, generate atomic events according to the configuration information, and implement queries and analysis based on Elasticsearch, thereby realizing a flexible and scalable event processing pipeline. It can efficiently process and analyze data from different data sources and provide users with external behavior queries on the credit business platform, so that business parties can carry out business activities based on the data.

[0086] It should be noted that the above modules can be functional modules or program modules, and can be implemented by software or hardware. For modules implemented by hardware, the above modules can be located in the same processor; or the above modules can be located in different processors in any combination.

[0087] In addition, combined Figure 1 The business data processing method described in the embodiment of the present application can be implemented by a computer device. The computer device may include a processor and a memory storing computer program instructions.

[0088] Specifically, the processor may include a central processing unit (CPU), or an application-specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present application.

[0089] Among them, the memory may include a large capacity memory for data or instructions. By way of example and not limitation, the memory may include a hard disk drive (HDD), a floppy disk drive, a solid state drive (SSD), a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a universal serial bus (USB) drive, or a combination of two or more of these. Where appropriate, the memory may include a removable or non-removable (or fixed) medium. Where appropriate, the memory may be inside or outside the data processing device. In a specific embodiment, the memory is a non-volatile memory. In a specific embodiment, the memory includes a read-only memory (ROM) and a random access memory (RAM). Where appropriate, the ROM may be a mask-programmed ROM, a programmable ROM (Programmable Read-Only Memory, PROM for short), an erasable PROM (Erasable ProgrammableRead-Only Memory, EPROM for short), an electrically erasable PROM (Electrically Erasable ProgrammableRead-Only Memory, EEPROM for short), an electrically alterable ROM (Electrically Alterable Read-Only Memory, EAROM for short) or a flash memory (FLASH) or a combination of two or more of these. Under appropriate circumstances, the RAM can be a static random access memory (SRAM) or a dynamic random access memory (DRAM), wherein the DRAM can be a fast page mode dynamic random access memory (FPMDRAM), an extended data output dynamic random access memory (EDODRAM), a synchronous dynamic random access memory (SDRAM), etc.

[0090] The memory may be used to store or cache various data files required for processing and / or communication, as well as possible computer program instructions executed by the processor.

[0091] The processor implements any one of the business data processing methods in the above embodiments by reading and executing computer program instructions stored in the memory.

[0092] The computer device can execute the business data processing method in the embodiment of the present application based on the obtained query request, thereby realizing the combination Figure 1-Figure 2 Describes the business data processing method.

[0093] In addition, in combination with the business data processing method in the above embodiments, the present application embodiment can provide a computer-readable storage medium for implementation. The computer-readable storage medium stores computer program instructions; when the computer program instructions are executed by a processor, any business data processing method in the above embodiments is implemented.

[0094] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0095] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.

Claims

1. A business data processing method, characterized in that: include: A data acquisition step, obtaining user behavior data from a data source through a data engine and pre-configuring filtering rules, wherein the filtering rules include event topics and data fields; Scenario event configuration step: creating atomic events based on business scenarios in the event system, generating indexes and mappings in Elasticsearch based on the atomic events, and the event system consuming and parsing the data fields of the corresponding event topics in the data acquisition step, storing the data of the data fields in the corresponding mappings, and generating the ES table structure; In the timeline display step, a query request from an access system is obtained, the event system determines the atomic event based on the access system and the timeline type, queries the data in the ES table structure based on the event subject of the atomic event and displays it in the access system in the form of a timeline page.

2. The business data processing method according to claim 1, characterized in that: The data acquisition step further includes: the data source includes; Kafka and / or Archer, the upstream system sends the user behavior data in JSON format messages to the message queue of the data source, and the data engine pulls messages from the message queue and parses the message content.

3. The business data processing method according to claim 1, characterized in that: The scene event configuration step further includes: A scene configuration step, creating a scene and configuring scene information, wherein the scene information includes: scene number, scene name, scene description, scene status, scene trigger type, and scene access connection; An event configuration step, creating an atomic event based on the scene association, wherein the atomic event information includes: event code, event name, event description, event subject, event definition, data source access configuration and corresponding user identifier; Table structure configuration steps: synchronize atomic events to an event system through scheduled tasks, save atomic events and their corresponding behavior groups in the database, and create Elasticsearch to generate indexes and mappings based on atomic event information.

4. The business data processing method according to claim 3, characterized in that: The timeline display step further includes: Create a timeline display page. For each timeline to be displayed, configure an access system, behavior primary key, atomic event and time interval, and configure the detailed fields displayed under the atomic event.

5. The business data processing method according to claim 1, characterized in that: The data engine provides a configuration interaction interface, allowing users to configure data sources and event topics by simply dragging and filling out forms.

6. The business data processing method according to claim 1, characterized in that: The data engine supports using JSONPath expressions to extract fields in JSON messages.

7. A business data processing system, characterized in that: include: A data acquisition module, configured to acquire user behavior data from a data source through a data engine and pre-configure filtering rules, wherein the filtering rules include event topics and data fields; A scenario event configuration module is configured to create atomic events based on business scenarios in the event system, generate indexes and mappings in Elasticsearch based on the atomic events, and the event system consumes and parses the data fields of the corresponding event topics in the data acquisition step, stores the data of the data fields in the corresponding mappings, and generates an ES table structure; The timeline display module is configured to obtain a query request from an access system. The event system determines the atomic event based on the access system and the timeline type, queries the data in the ES table structure based on the event subject of the atomic event, and displays it in the access system in the form of a timeline page.

8. The business data processing system according to claim 7, characterized in that: The scene event configuration module further includes: A scene configuration module, configured to create a scene and configure scene information, wherein the scene information includes: scene number, scene name, scene description, scene status, scene trigger type and scene access connection; An event configuration module, configured to create an atomic event based on the scene association, the atomic event information including: event code, event name, event description, event subject, event definition, data source access configuration and corresponding user identifier; The table structure configuration module is configured to synchronize atomic events to an event system through a scheduled task, save atomic events and their corresponding behavior groups in a database, and create an Elasticsearch index and mapping based on the atomic event information.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the business data processing method according to any one of claims 1 to 6 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the business data processing method according to any one of claims 1 to 6 is implemented.

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