Business processing risk control method, device and equipment and storage medium thereof

Abstract

The invention belongs to the technical field of big data, is applied to a business operation data acquisition and completion scene, and relates to a business processing risk control method, device and equipment and a storage medium thereof. Obtaining a target primary key as an index field, and performing business operation data supplementation in combination with a pre-generated three-layer storage structure; and executing the target business operation task by using the supplemented business operation data. Therefore, when the current business operation data is identified as the updated redundant data, data screening does not need to be directly carried out from the basic database with the maximum storage pressure, the processing pressure of the basic database is reduced, the data supplementing efficiency is improved, and the business risk control crisis caused by incomplete data of a subsequent business operation task is avoided. When the business processing risk control method is applied to an insurance issuing business scene under big data, risk control crisis caused by incomplete business operation data can be effectively avoided.

Description

Technical Field

[0001] The present application relates to the field of big data technology, is applied to business operation data acquisition and completion scenarios, and relates to a business processing risk control method, device, equipment and storage medium thereof. Background Art

[0002] In the financial and insurance industries, risk warning and risk control are essential means of reducing corporate losses and sanctioning illegal operations. For example, in insurance policy issuance scenarios, risk warnings are performed in the underlying database, as the database tables contain massive records, potentially reaching billions to tens of billions. If the calculation logic were performed in the underlying business database, it would not only overload the underlying database but also reduce the computational efficiency of risk warning data, resulting in delayed delivery of risk warning data.

[0003] When updating data in the source data table, the update log generally contains a line recording complete field data information before and after the update. However, in order to reduce the load on the basic database and reduce the bandwidth pressure of the server, when performing targeted data updates in the basic database, the redundant information in the update log (unchanged or unupdated field information) will be removed first, and only the changed data will be replaced in the basic database. However, this processing method will cause the data updated to the big data data lake table and the data processed in the downstream incremental business steps to be incomplete, and some detailed data may be missing, such as customer name, compensation amount, or some dimensional information is incomplete, which will cause the real-time risk control processing link to be destroyed and the insurance policy risk control results cannot be pushed in real time. Therefore, ensuring data integrity and avoiding business risk control crises due to incomplete data has become an urgent problem that needs to be solved. Summary of the Invention

[0004] The purpose of the embodiments of the present application is to propose a business processing risk control method, device, equipment and storage medium thereof to achieve data integrity and avoid business risk control crises caused by incomplete data.

[0005] In a first aspect, the embodiments of the present application provide a business processing risk control method, which adopts the following technical solutions:

[0006] A business processing risk control method includes the following steps:

[0007] Get current business operation data;

[0008] Determining whether the current business operation data is updated de-redundant data according to a preset identification strategy;

[0009] If the current business operation data is updated de-redundant data, obtaining the primary key information of the current business operation data as the target primary key;

[0010] Using the target primary key as the index field, filtering the first batch of complementary data from the pre-generated first-level record cache, and performing initial complementary processing on the current business operation data;

[0011] If the current business operation data is determined to be incomplete after the initial filling process, the target primary key is used as the index field, a second batch of filling data is selected from the pre-generated second-level record cache, and the current business operation data is filled in for the second time;

[0012] If the current business operation data is not fully padded after the second padded process, the target primary key is used as the index field, and a third batch of padded data is selected from the pre-generated third-level record cache to perform a third padded process on the current business operation data;

[0013] Obtaining the business operation data that has been completely completed after the first completion process, the business operation data that has been completely completed after the second completion process, or the business operation data that has been completed after the third completion process as the target business operation data;

[0014] Execute preset business operation tasks according to the target business operation data to achieve business processing risk control.

[0015] In a second aspect, the embodiments of the present application further provide a business processing risk control device, which adopts the following technical solution:

[0016] A business processing risk control device, comprising:

[0017] Current business operation data acquisition module, used to obtain current business operation data;

[0018] A data update identification module is used to determine whether the current business operation data is updated de-redundant data according to a preset identification strategy;

[0019] a target primary key acquisition module, configured to acquire primary key information of the current business operation data as a target primary key if the current business operation data is updated and de-redundant data;

[0020] An initial filling processing module is used to use the target primary key as the index field, filter the first batch of filling data from the pre-generated first-level record cache, and perform initial filling processing on the current business operation data;

[0021] a second filling processing module, configured to, if the current business operation data is determined to be incomplete after the initial filling processing, use the target primary key as an index field to filter a second batch of filling data from the pre-generated second-level record cache, and perform a second filling processing on the current business operation data;

[0022] a third-time filling processing module, configured to, if the current business operation data is determined to be incomplete after the second filling processing, use the target primary key as an index field, select a third batch of filling data from the pre-generated third-level record cache, and perform a third filling processing on the current business operation data;

[0023] A target business operation data acquisition module is used to acquire business operation data that has been completed after the first filling process, the second filling process, or the third filling process as target business operation data;

[0024] The business operation task execution module is used to execute preset business operation tasks according to the target business operation data to achieve business processing risk control.

[0025] In a third aspect, an embodiment of the present application further provides a computer device that adopts the following technical solution:

[0026] A computer device includes a memory and a processor, wherein the memory stores computer-readable instructions, and when the processor executes the computer-readable instructions, the steps of the business processing risk control method described above are implemented.

[0027] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium, which adopts the following technical solution:

[0028] A computer-readable storage medium stores computer-readable instructions, which, when executed by a processor, implement the steps of the business processing risk control method as described above.

[0029] Compared with the prior art, the embodiments of the present application have the following beneficial effects:

[0030] The business processing risk control method described in the embodiment of the present application is to obtain and determine whether the current business operation data is updated de-redundant data; if the current business operation data is updated de-redundant data, the current business operation data is parsed to obtain the target primary key; the target primary key is used as the index field, combined with the pre-generated three-layer storage structure, the business operation data is completed; the completed business operation data is used to execute the preset business operation task to achieve business processing risk control. So that when the current business operation data is identified as updated de-redundant data, there is no need to directly screen the data from the basic database with the greatest storage pressure, which not only reduces the processing pressure of the basic database, but also improves the efficiency of data completion, and avoids the subsequent business operation tasks from causing business risk control crises due to incomplete data. The business processing risk control method is applied to the field of financial business, especially in the insurance policy business scenario under the big data application scenario, which can effectively avoid the destruction of the real-time risk control processing link caused by the incomplete business operation data, ensure the real-time push of trustworthy insurance policy risk control results, and assist the company in making business risk decisions. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the solutions in this application, a brief introduction will be given below to the drawings required for use in the description of the embodiments of this application. Obviously, the drawings described below are some embodiments of this application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0032] Figure 1 is an exemplary system architecture diagram to which the present application may be applied;

[0033] Figure 2 This is a flow chart of an embodiment of a business processing risk control method according to the present application;

[0034] Figure 3 This is a flowchart of a specific embodiment of data storage and update in the business processing risk control method described in this application;

[0035] Figure 4 yes Figure 3 A flowchart of a specific embodiment of step 303;

[0036] Figure 5 This is a flowchart of a specific embodiment of generating a first-level record cache in the business processing risk control method described in this application;

[0037] Figure 6 yes Figure 2 A flowchart of a specific embodiment of step 202;

[0038] Figure 7This is a structural diagram of an embodiment of a business processing risk control device according to the present application;

[0039] Figure 8 It is a structural diagram of an embodiment of a computer device according to the present application. DETAILED DESCRIPTION

[0040] Unless otherwise defined, all technical and scientific terms used herein have the same meanings as commonly understood by those skilled in the art to which this application belongs. The terms used in the specification of the application are for the purpose of describing specific embodiments only and are not intended to limit this application. The terms "including" and "having" and any variations thereof in the specification and claims of this application and the above-mentioned drawings are intended to cover non-exclusive inclusions. The terms "first", "second", etc. in the specification and claims of this application or the above-mentioned drawings are used to distinguish different objects, not to describe a specific order.

[0041] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute an independent or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0042] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings.

[0043] like Figure 1 As shown, system architecture 100 may include a terminal device 101, a network 102, and a server 103. Terminal device 101 may be a laptop computer 1011, a tablet computer 1012, or a mobile phone 1013. Network 102 is a medium for providing a communication link between terminal device 101 and server 103. Network 102 may include various connection types, such as wired or wireless communication links or fiber optic cables.

[0044] The user can use the terminal device 101 to interact with the server 103 via the network 102 to receive or send messages, etc. Various communication client applications can be installed on the terminal device 101, such as web browser applications, shopping applications, search applications, instant messaging tools, email clients, social platform software, etc.

[0045] The terminal device 101 can be various electronic devices with a display screen and supporting web browsing. In addition to the laptop computer 1011, tablet computer 1012 or mobile phone 1013, the terminal device 101 can also be an e-book reader, an MP3 player (Moving Picture Experts Group Audio Layer III), an MP4 (Moving Picture Experts Group Audio Layer IV) player, a laptop computer and a desktop computer, etc.

[0046] The server 103 may be a server that provides various services, such as a background server that provides support for web pages displayed on the terminal device 101 .

[0047] It should be noted that the business processing risk control method provided in the embodiment of the present application is generally executed by a server, and accordingly, a business processing risk control device is generally set in the server.

[0048] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is merely illustrative. Any number of terminal devices, networks and servers may be provided as required.

[0049] Continue to refer Figure 2 , shows a flow chart of an embodiment of a business processing risk control method according to the present application. The business processing risk control method includes the following steps:

[0050] Step 201: Obtain current business operation data.

[0051] In this embodiment, the acquisition of current business operation data, in a big data processing scenario, especially in an insurance policy issuance business scenario under a big data application scenario, is not a single field of the business operation data to be processed, but often a batch of business-related fields. Therefore, the acquisition operation includes acquiring the business-related fields of the batch from the distributed message queue. The current business operation data corresponds to the latest data field obtained from the target message queue, as well as the primary key information of the data field. For example, the customer name field is transmitted in message queue A, the insurance policy number field is transmitted in message queue B, and the insurance type name field is transmitted in message queue C. It can be understood that the current business operation data only represents the single field information in the target message queue and the primary key information of the single field information at the lowest dimension.

[0052] Step 202: Determine whether the current business operation data is updated de-redundant data according to a preset identification strategy.

[0053] Specifically, the judgment of whether the current business operation data is updated de-redundant data refers to that after the entire insurance policy business data is updated, in order to reduce the load of the basic database and reduce the bandwidth pressure of the server, when the data is updated in a targeted manner in the basic database, it will be chosen to first remove the unchanged or unupdated field information and only retain the updated fields. The only "updated fields" retained are the updated de-redundant data.

[0054] Through the preset identification strategy, it is determined whether the current business operation data is updated de-redundant data, so as to identify whether the business operation parameters of the target business operation task have changed compared with the previous operation when executing the task operation.

[0055] Step 203: If the current business operation data is updated de-redundant data, the primary key information of the current business operation data is obtained as the target primary key.

[0056] Specifically, if the current business operation data is updated de-redundant data, that is, if the current business operation data has undergone changes in the participating operation data compared with the past, targeted subsequent processing is performed to avoid business risk control crises caused by incomplete data.

[0057] In this embodiment, the target primary key includes form information and / or partition information of the current business operation data stored in the basic database. Acquiring the primary key information of the current business operation data as the target primary key specifically involves identifying the primary key information of the current business operation data in conjunction with a queue message sent by a message queue to obtain the target primary key.

[0058] Step 204 , using the target primary key as the index field, screening the first batch of complementary data from the pre-generated first-level record cache, and performing initial complementary processing on the current business operation data.

[0059] Step 205: If the current business operation data is determined not to be fully completed after the initial completion processing, the target primary key is used as the index field, and a second batch of completion data is screened from the pre-generated second-level record cache to perform a second completion processing on the current business operation data.

[0060] Step 206: If the current business operation data is not fully completed after the second completion process, the target primary key is used as the index field, and the third batch of completion data is filtered from the pre-generated third-level record cache to perform a third completion process on the current business operation data.

[0061] Step 207 : Acquire the business operation data that has been completed after the first completion process, the second completion process, or the third completion process as target business operation data.

[0062] In this embodiment, two principles of computer processing are combined: first, data that has been read will be read again within a short period of time; second, data adjacent to the data that has been read will be read again within a short period of time; the former is the principle of temporal locality, and the latter is the principle of spatial locality. Based on the above two rules and the reverse logic of business execution, a three-level storage strategy for computer memory is designed, namely, pre-generating a first-level record cache at the business computing engine level closest to business execution, pre-generating a second-level record cache in the business data lake between the basic database and the business computing engine, and pre-generating a third-level record cache, i.e., a backup cache, in the bottom-level basic database.

[0063] It should be understood that the first-level record cache is used to record the data fields processed by the business computing engine when performing business operations; the second-level record cache is used to record data changes generated when the basic database sends data in batches to the business data lake, and when the business computing engine calls data involved in business operations in batches from the business data lake; the third-level record cache records changes in data fields in the basic database, including updates and deletions.

[0064] By combining the temporal locality principle, spatial locality principle and business execution reverse logic during computer execution, a three-level storage strategy is designed. This allows for the completion of business operation data in sequential steps and in combination with different record caches when the current business operation data is identified as updated de-redundant data. Specifically, the first-level record cache in the business operation engine is used to complete the data. If the data is not completed, the second-level record cache in the data lake is used to complete the data. The third-level record cache in the basic database is used to complete the data as a backup operation. Compared with the previous direct use of the basic database's record cache for data completion, there is no need to directly screen data from the basic database with the greatest storage pressure. This not only reduces the processing pressure of the basic database, but also improves the data completion efficiency, avoiding business risk control crises caused by incomplete data in subsequent business operation tasks.

[0065] Step 208: Execute preset business operation tasks according to the target business operation data to achieve business processing risk control.

[0066] Specifically, the target business operation data, i.e., the completed business operation data, is finally used to perform specific business operation tasks, ensuring the integrity of the business data. This is particularly true in the insurance issuance business scenario within the context of big data applications. This prevents disruption to the real-time risk control processing chain caused by incomplete business operation data, ensuring the real-time delivery of reliable insurance issuance risk control results, assisting companies in making business risk decisions.

[0067] In this embodiment, business processing risk control is achieved by obtaining and determining whether the current business operation data is updated de-redundant data; if the current business operation data is updated de-redundant data, parsing the current business operation data to obtain the target primary key; using the target primary key as the index field, combined with a pre-generated three-layer storage structure, completing the business operation data; and using the completed business operation data to execute preset business operation tasks. This allows for the elimination of the need to directly screen data from the underlying database with the greatest storage pressure when the current business operation data is identified as updated de-redundant data. This not only reduces the processing pressure on the underlying database, but also improves data completion efficiency, avoiding business risk control crises caused by incomplete data in subsequent business operation tasks. The business processing risk control method is applied to the financial business field, particularly in the insurance issuance business scenario under big data application scenarios. This can effectively avoid the disruption of the real-time risk control processing chain caused by incomplete business operation data, ensuring the real-time delivery of reliable insurance issuance risk control results, and assisting companies in making business risk decisions.

[0068] In this embodiment, before executing the step of obtaining the current business operation data, the method also includes: generating a third-level record cache in the basic database based on the data entry cache status in the basic database, wherein the basic database includes a relational database that stores basic business data in the most detailed manner, and the third-level record cache includes data entry time information, form information of each data field, partition information and record cache generation time information.

[0069] Of course, the third-level record cache also includes the data sent in batches by the basic database to the business data lake, as well as the primary key information of each data field.

[0070] By generating a third-level record cache in the basic database, it is possible to provide a backup in the event of missing data when processing the target business.

[0071] Continue to refer Figure 3 In some optional implementations, the method further includes a step of updating the data in the database. Figure 3 This is a flowchart of a specific embodiment of updating data in the business processing risk control method described in this application, including the following steps:

[0072] Step 301, receiving a data update instruction;

[0073] Step 302: Parse the data update instruction to obtain data update parameters;

[0074] Specifically, the data update parameters include specific update replacement fields.

[0075] Step 303: Input the data update parameters into a preset processing and computing engine, and start the processing and computing engine to execute the corresponding data update task. The preset processing and computing engine includes a Flink computing engine. The data update parameters include the primary key information and replacement fields of the data to be updated. The primary key information includes the form information and partition information of the data to be updated in the basic database.

[0076] Specifically, the Flink computing engine is used in big data processing scenarios. Its powerful engine integrated computing function not only handles data calculations at the business level, but also handles operations for data update and storage.

[0077] Step 304: Synchronize the updated data to the target business data lake, completing the synchronization of the updated data from the basic database to the business data lake.

[0078] Specifically, the base database stores only a single table field, while the target business data lake integrates multiple fields from the same business execution task to produce a complete piece of business processing data. It should be understood that the data in the business data lake is generated by organizing at least one business field in the base database.

[0079] Continue to refer Figure 4 , Figure 4 yes Figure 3 A flowchart of a specific embodiment of step 303 includes:

[0080] Step 401: Identify the form information and partition information of the data to be updated in the basic database according to the data update parameters;

[0081] In this embodiment, the data update parameter can be understood as specific update replacement data, and the data to be updated can be understood as the original data in the basic database that is to be replaced.

[0082] Step 402: performing update processing on the data to be updated in the basic database in combination with the form information, partition information and replacement fields.

[0083] Continue to refer Figure 5In some optional implementations, after step 303, the method further includes a step of generating a first-level record cache. Figure 5 This is a flowchart of a specific embodiment of generating a first-level record cache in the business processing risk control method described in this application, including the following steps:

[0084] Step 501: Compare the updated business operation data with the corresponding data in the basic database according to the task identification information;

[0085] Specifically, the task identification information refers to the task identification information of the data update task executed by the Flink computing engine.

[0086] Step 502: Identify the unupdated field information and the updated field information in the business operation data before and after the update based on the comparison result;

[0087] In step 503, based on the unupdated field information and the updated field information in the business operation data before and after the update, a first-level record cache is generated in the memory of the processing and computing engine. The memory of the processing and computing engine includes Flink memory. The first-level record cache includes data update time information, primary key information of all updated field information, and record cache generation time information.

[0088] By generating a first-level record cache in Flink memory, that is, in the memory of the Flink computing engine, when subsequent actual business processing is performed, if the current business processing data is updated and de-redundant data, you can directly use the first-level record cache in Flink memory to perform a reverse query to supplement the current business processing data.

[0089] In this embodiment, after executing the step of synchronizing the updated data to the target business data lake and completing the synchronization of the updated data from the basic database to the business data lake, the method further includes: generating a second-level record cache in the log record file of the business data lake based on the data synchronization information, wherein the business data lake includes the Hudi data lake, the log record file includes the MOR log file in the Hudi data lake, and the second-level record cache also includes data synchronization time information, primary key information of the data, relevant field information of other data synchronized to the business data lake together with the data, and record cache generation time information.

[0090] Specifically, the data synchronization information includes synchronization information generated when the business data lake is updated and synchronized with data in the underlying database. The second-level record cache caches the corresponding update synchronization information and also caches batches of business data retrieved from the business data lake by the Flink computing engine using a preset message queue.

[0091] By generating a second-level record cache in the business data lake, that is, the Hudi data lake, when performing specific business processing later, if the current business processing data is updated and de-redundant data, after using the first-level record cache in Flink memory for reverse query and completion, if it is not complete, it can be further combined with the second-level record cache generated in the Hudi data lake for incremental completion, avoiding direct data screening and completion from the basic database with the greatest storage pressure.

[0092] In this embodiment, the step of obtaining the current business operation data specifically includes: obtaining the data to be consumed in the target message queue as the current business operation data, wherein the target message queue includes an Apache Kafka message queue, and the data to be consumed represents the data field in the message queue that is located at the first position in the queue processing.

[0093] Specifically, when the target message queue transmits consumption data, each message queue pushes data in different forms or partitions respectively, ensuring a one-to-one correspondence between the message queue and the primary key information of the pushed data.

[0094] Continue to refer Figure 6 , Figure 6 yes Figure 2 A flowchart of a specific embodiment of step 202 includes:

[0095] Step 601: Using the target primary key of the current business operation data as an index field, query a preset data update log, wherein the preset data update log specifically records data updates and storage;

[0096] Specifically, the preset data update log includes a data update log preset by the target data monitoring terminal when the basic database is updated using the Flink computing engine.

[0097] Step 602: If the business operation data corresponding to the target primary key has only one storage record in the data update log, the current business operation data is the original storage data;

[0098] It should be understood that if there is only one storage record in the data update log, it means that the business operation data corresponding to the target primary key has not been updated since the initial storage and is still the original storage data.

[0099] Step 603: If there are at least two storage records of the business operation data corresponding to the target primary key in the data update log, it is determined that the current business operation data is updated de-redundant data.

[0100] It should be understood that if there are at least two storage records for the business operation data corresponding to the target primary key in the data update log, it means that the business operation data corresponding to the target primary key has been updated at least once since the initial storage and is no longer the original storage data.

[0101] In this embodiment, before executing the step of using the target primary key as the index field, screening the first batch of completion data from the pre-generated first-level record cache, and performing initial completion processing on the current business operation data, the method also includes: setting the query priority of the second-level record cache to be lower than that of the first-level record cache, and the query priority of the second-level record cache to be higher than that of the third-level record cache.

[0102] This application obtains and determines whether the current business operation data is updated de-redundant data; if the current business operation data is updated de-redundant data, the current business operation data is parsed to obtain the target primary key; the target primary key is used as the index field, combined with the pre-generated three-layer storage structure, to complete the business operation data; the completed business operation data is used to execute the preset business operation tasks to achieve business processing risk control. So that when it is identified that the current business operation data is updated de-redundant data, there is no need to directly screen the data from the basic database with the largest storage pressure, which not only reduces the processing pressure of the basic database, but also improves the efficiency of data completion, and avoids the subsequent business operation tasks from causing business risk control crises due to incomplete data. The business processing risk control method is applied to the field of financial business, especially in the insurance policy business scenario under the big data application scenario, which can effectively avoid the destruction of the real-time risk control processing link due to the incompleteness of business operation data, ensure the real-time push of trustworthy insurance policy risk control results, and assist the company in making business risk decisions.

[0103] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence (AI) 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 achieve optimal results.

[0104] Fundamental AI technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing, operating / interaction systems, and mechatronics. AI software technologies primarily encompass computer vision, robotics, biometrics, speech processing, natural language processing, and machine learning / deep learning.

[0105] In the embodiment of the present application, by obtaining and judging whether the current business operation data is updated de-redundant data; if the current business operation data is updated de-redundant data, the current business operation data is parsed to obtain the target primary key; the target primary key is used as the index field, combined with the pre-generated three-layer storage structure, the business operation data is completed; the completed business operation data is used to execute the preset business operation task to achieve business processing risk control. So that when the current business operation data is identified as updated de-redundant data, there is no need to directly screen the data from the basic database with the greatest storage pressure, which not only reduces the processing pressure of the basic database, but also improves the efficiency of data completion, and avoids the subsequent business operation tasks from causing business risk control crises due to incomplete data. The business processing risk control method is applied to the field of financial business, especially in the insurance policy business scenario under the big data application scenario, which can effectively avoid the destruction of the real-time risk control processing link caused by the incomplete business operation data, ensure the real-time push of trustworthy insurance policy risk control results, and assist the company in making business risk decisions.

[0106] Further references Figure 7 , as a response to the above Figure 2 The present application provides an embodiment of a business processing risk control device, which is similar to the embodiment of the present invention. Figure 2 Corresponding to the method embodiment shown, the device can be specifically applied to various electronic devices.

[0107] like Figure 7 As shown, the business processing risk control device 700 of this embodiment includes: a current business operation data acquisition module 701, a data update identification module 702, a target primary key acquisition module 703, a first filling processing module 704, a second filling processing module 705, a third filling processing module 706, a target business operation data acquisition module 707, and a business operation task execution module 708. Among them:

[0108] Current business operation data acquisition module 701, used to obtain current business operation data;

[0109] The data update identification module 702 is used to determine whether the current business operation data is updated de-redundant data according to a preset identification strategy;

[0110] A target primary key acquisition module 703 is configured to acquire primary key information of the current business operation data as a target primary key if the current business operation data is updated de-redundant data;

[0111] The initial filling processing module 704 is used to use the target primary key as the index field, filter the first batch of filling data from the pre-generated first-level record cache, and perform initial filling processing on the current business operation data;

[0112] A second filling processing module 705 is configured to, if the current business operation data is determined to be incomplete after the initial filling processing, select a second batch of filling data from the pre-generated second-level record cache using the target primary key as the index field, and perform a second filling processing on the current business operation data;

[0113] A third completion processing module 706 is configured to, if the current business operation data is determined to be incomplete after the second completion processing, select a third batch of completion data from the pre-generated third-level record cache using the target primary key as an index field, and perform a third completion processing on the current business operation data;

[0114] The target business operation data acquisition module 707 is used to acquire the business operation data that has been completed after the first filling process, the business operation data that has been completed after the second filling process, or the business operation data that has been completed after the third filling process as the target business operation data;

[0115] The business operation task execution module 708 is used to execute preset business operation tasks according to the target business operation data to achieve business processing risk control.

[0116] This application obtains and determines whether the current business operation data is updated de-redundant data; if the current business operation data is updated de-redundant data, the current business operation data is parsed to obtain the target primary key; the target primary key is used as the index field, combined with the pre-generated three-layer storage structure, to complete the business operation data; the completed business operation data is used to execute the preset business operation tasks to achieve business processing risk control. So that when it is identified that the current business operation data is updated de-redundant data, there is no need to directly screen the data from the basic database with the largest storage pressure, which not only reduces the processing pressure of the basic database, but also improves the efficiency of data completion, and avoids the subsequent business operation tasks from causing business risk control crises due to incomplete data. The business processing risk control method is applied to the field of financial business, especially in the insurance policy business scenario under the big data application scenario, which can effectively avoid the destruction of the real-time risk control processing link due to the incompleteness of business operation data, ensure the real-time push of trustworthy insurance policy risk control results, and assist the company in making business risk decisions.

[0117] In this embodiment, the business processing risk control device 700 further includes: a third-level record cache generation module, which is used to generate a third-level record cache in the basic database according to the storage cache status of the data in the basic database.

[0118] In this embodiment, the business processing risk control device 700 further includes: a data update instruction receiving module, a data update parameter acquisition module, a data update task execution module and an update data synchronization module.

[0119] in:

[0120] A data update instruction receiving module, used for receiving a data update instruction;

[0121] A data update parameter acquisition module, configured to parse the data update instruction and acquire data update parameters;

[0122] A data update task execution module, configured to input the data update parameters into a preset processing and computing engine and start the processing and computing engine to execute the corresponding data update task, wherein the preset processing and computing engine includes a Flink computing engine, and the data update parameters include primary key information and replacement fields of the data to be updated, and the primary key information includes form information and partition information of the data to be updated in the basic database;

[0123] The update data synchronization module is used to synchronize the updated data to the target business data lake, completing the synchronization of the updated data from the basic database to the business data lake.

[0124] In this embodiment, the data update task execution module includes: a primary key information identification unit and an update replacement processing unit.

[0125] A primary key information identification unit, configured to identify the form information and partition information of the data to be updated in the basic database according to the data update parameters;

[0126] The update and replacement processing unit is used to update the data to be updated in the basic database in combination with the form information, partition information and replacement fields.

[0127] In this embodiment, the business processing risk control device 700 further includes: a data comparison module, a field information identification module and a first-level record cache generation module.

[0128] A data comparison module is used to compare the updated business operation data with the corresponding data in the basic database according to the task identification information;

[0129] A field information identification module is used to identify the unupdated field information and the updated field information in the business operation data before and after the update based on the comparison result;

[0130] A first-level record cache generation module is configured to generate the first-level record cache in the memory of the processing and computing engine based on the unupdated field information and the updated field information in the business operation data before and after the update. The memory of the processing and computing engine includes Flink memory, and the first-level record cache includes data update time information, primary key information of all updated field information, and record cache generation time information.

[0131] In this embodiment, the business processing risk control device 700 also includes: a second-level record cache generation module, which is used to generate the second-level record cache in the log record file of the business data lake according to the data synchronization information, wherein the business data lake includes the Hudi data lake, the log record file includes the MOR log file in the Hudi data lake, and the second-level record cache also includes data synchronization time information, the primary key information of the data, the relevant field information of other data synchronized to the business data lake together with the data, and the record cache generation time information.

[0132] In this embodiment, the data update identification module 702 includes: a data update query unit, a first update identification unit, and a second update identification unit.

[0133] A data update query unit, configured to query a preset data update log using the target primary key of the current business operation data as an index field, wherein the preset data update log specifically records data updates and storage;

[0134] The first update identification unit is configured to, if the business operation data corresponding to the target primary key has only one storage record in the data update log, determine that the current business operation data is the original storage data;

[0135] The second update identification unit is configured to determine that the current business operation data is updated de-redundant data if there are at least two storage records of the business operation data corresponding to the target primary key in the data update log.

[0136] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing related hardware via computer-readable instructions. The computer-readable instructions can be stored in a computer-readable storage medium, and when the program is executed, it can include the processes in the above-described method embodiments. The aforementioned storage medium can be a non-volatile storage medium such as a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0137] It should be understood that although the steps in the flowcharts of the accompanying drawings are shown in sequence as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some of the steps in the flowcharts of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

[0138] To solve the above technical problems, the present application also provides a computer device. Figure 8 , Figure 8 This is a basic structural block diagram of the computer device in this embodiment.

[0139] The computer device 8 includes a memory 8a, a processor 8b, and a network interface 8c that are interconnected via a system bus. Figure 8 Only a computer device 8 having components such as a memory 8a, a processor 8b, and a network interface 8c is shown. However, it should be understood that it is not required to implement all of the components shown, and more or fewer components may be implemented instead. Those skilled in the art will understand that a computer device herein is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to a microprocessor, an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA), a digital signal processor (DSP), an embedded device, etc.

[0140] The computer device may be a desktop computer, notebook computer, PDA, cloud server, etc. The computer device may interact with the user via a keyboard, mouse, remote control, touchpad, or voice control device.

[0141] The memory 8a includes at least one type of readable storage medium, including flash memory, hard disk, multimedia card, card-type memory (e.g., SD or DX memory), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 8a can be an internal storage unit of the computer device 8, such as the hard disk or memory of the computer device 8. In other embodiments, the memory 8a can also be an external storage device of the computer device 8, such as a plug-in hard disk equipped on the computer device 8, a smart memory card (SMC), a secure digital (SD) card, a flash memory card, etc. Of course, the memory 8a can also include both the internal storage unit of the computer device 8 and its external storage device. In this embodiment, the memory 8a is generally used to store the operating system and various application software installed on the computer device 8, such as computer-readable instructions of a business processing risk control method. In addition, the memory 8a can also be used to temporarily store various types of data that have been output or are to be output.

[0142] In some embodiments, the processor 8b may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chip. The processor 8b is generally used to control the overall operation of the computer device 8. In this embodiment, the processor 8b is used to execute computer-readable instructions stored in the memory 8a or process data, such as computer-readable instructions for executing the business processing risk control method.

[0143] The network interface 8c may include a wireless network interface or a wired network interface. The network interface 8c is generally used to establish a communication connection between the computer device 8 and other electronic devices.

[0144] The computer device proposed in this embodiment belongs to the field of big data technology and is applied to business operation data acquisition and completion scenarios. This application obtains and determines whether the current business operation data is updated de-redundant data; if the current business operation data is updated de-redundant data, it parses the current business operation data to obtain the target primary key; uses the target primary key as the index field, combined with a pre-generated three-layer storage structure, to complete the business operation data; and uses the completed business operation data to execute preset business operation tasks to achieve business processing risk control. So that when the current business operation data is identified as updated de-redundant data, there is no need to directly screen the data from the basic database with the greatest storage pressure. This not only reduces the processing pressure of the basic database, but also improves the efficiency of data completion and avoids the business risk control crisis caused by incomplete data in subsequent business operation tasks. The business processing risk control method is applied to the financial business field, especially in the insurance issuance business scenario under big data application scenarios. It can effectively avoid the disruption of the real-time risk control processing chain caused by incomplete business operation data, ensure the real-time delivery of reliable insurance issuance risk control results, and assist companies in making business risk decisions.

[0145] The present application also provides another embodiment, namely, providing a computer-readable storage medium, which stores computer-readable instructions, and the computer-readable instructions can be executed by a processor to enable the processor to perform the steps of a business processing risk control method as described above.

[0146] The computer-readable storage medium proposed in this embodiment belongs to the field of big data technology and is applied to business operation data acquisition and completion scenarios. This application obtains and determines whether the current business operation data is updated de-redundant data; if the current business operation data is updated de-redundant data, it parses the current business operation data to obtain the target primary key; uses the target primary key as the index field, combined with a pre-generated three-layer storage structure, to complete the business operation data; and uses the completed business operation data to execute preset business operation tasks to achieve business processing risk control. So that when the current business operation data is identified as updated de-redundant data, there is no need to directly screen the data from the basic database with the greatest storage pressure. This not only reduces the processing pressure of the basic database, but also improves the efficiency of data completion and avoids the business risk control crisis caused by incomplete data in subsequent business operation tasks. The business processing risk control method is applied to the financial business field, especially in the insurance issuance business scenario under the big data application scenario. It can effectively avoid the disruption of the real-time risk control processing chain caused by incomplete business operation data, ensure the real-time push of reliable insurance issuance risk control results, and assist companies in making business risk decisions.

[0147] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus the necessary general hardware platform, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for enabling a terminal device (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in each embodiment of the present application.

[0148] Obviously, the embodiments described above are only some of the embodiments of the present application, rather than all of the embodiments. The preferred embodiments of the present application are given in the accompanying drawings, but they do not limit the patent scope of the present application. The present application can be implemented in many different forms. On the contrary, the purpose of providing these embodiments is to make the understanding of the disclosure of the present application more thorough and comprehensive. Although the present application has been described in detail with reference to the aforementioned embodiments, for those skilled in the art, it is still possible to modify the technical solutions recorded in the aforementioned specific embodiments, or to make equivalent replacements for some of the technical features therein. Any equivalent structure made using the contents of the specification and drawings of this application, directly or indirectly used in other related technical fields, is also within the scope of patent protection of this application. The non-company software tools or components that appear in the embodiments of this application are merely examples and do not represent actual use.

Claims

1. A business processing risk control method, characterized in that: The steps include: Get current business operation data; Determining whether the current business operation data is updated de-redundant data according to a preset identification strategy; If the current business operation data is updated de-redundant data, obtaining the primary key information of the current business operation data as the target primary key; Using the target primary key as the index field, the first batch of padding data is screened from the pre-generated first-level record cache, and the current business operation data is initially padded; If the current business operation data is determined to be incomplete after the initial filling process, the target primary key is used as the index field, a second batch of filling data is selected from the pre-generated second-level record cache, and the current business operation data is filled in for the second time; If the current business operation data is not fully padded after the second padded process, the target primary key is used as the index field, and a third batch of padded data is selected from the pre-generated third-level record cache to perform a third padded process on the current business operation data; Obtaining the business operation data that has been completely completed after the first filling process, the business operation data that has been completely completed after the second filling process, or the business operation data that has been completed after the third filling process as the target business operation data; Execute preset business operation tasks according to the target business operation data to achieve business processing risk control.

2. The business processing risk control method according to claim 1, characterized in that: Before executing the step of obtaining the current business operation data, the method further includes: Based on the data entry cache status in the basic database, a third-level record cache is generated in the basic database, wherein the basic database includes a relational database that stores basic business data in the most detailed manner, and the third-level record cache includes data entry time information, form information of each data field, partition information, and record cache generation time information.

3. The business processing risk control method according to claim 1, characterized in that: Before executing the step of obtaining the current business operation data, the method further includes: Receive data update instructions; Parsing the data update instruction to obtain data update parameters; Inputting the data update parameters into a preset processing and computing engine, and starting the processing and computing engine to execute the corresponding data update task, wherein the preset processing and computing engine includes a Flink computing engine, and the data update parameters include primary key information and replacement fields of the data to be updated, and the primary key information includes form information and partition information of the data to be updated in the basic database; Synchronize the updated data to the target business data lake to complete the synchronization of the updated data from the basic database to the business data lake.

4. The business processing risk control method according to claim 3, characterized in that: The step of inputting the data update parameters into a preset processing and computing engine and starting the processing and computing engine to execute the corresponding data update task specifically includes: According to the data update parameters, identifying the form information and partition information of the data to be updated in the basic database; In combination with the form information, partition information and replacement fields, update the data to be updated in the basic database; After executing the step of inputting the data update parameters into a preset processing and computing engine and starting the processing and computing engine to execute the corresponding data update task, the method further includes: According to the task identification information, the updated business operation data is compared with the corresponding data in the basic database; Based on the comparison results, identifying the field information that has not been updated and the field information that has been updated in the business operation data before and after the update; Based on the unupdated field information and the updated field information in the business operation data before and after the update, a first-level record cache is generated in the memory of the processing and computing engine, wherein the memory of the processing and computing engine includes Flink memory, and the first-level record cache includes data update time information, primary key information of all updated field information, and record cache generation time information.

5. The business processing risk control method according to claim 3, characterized in that: After executing the step of synchronizing the updated data into the target business data lake and completing the synchronization of the updated data from the basic database to the business data lake, the method further includes: According to the data synchronization information, a second-level record cache is generated in the log record file of the business data lake, wherein the business data lake includes the Hudi data lake, the log record file includes the MOR log file in the Hudi data lake, and the second-level record cache also includes data synchronization time information, primary key information of the data, relevant field information of other data synchronized to the business data lake together with the data, and record cache generation time information.

6. The business processing risk control method according to claim 1, characterized in that: The step of obtaining current business operation data specifically includes: Acquire data to be consumed in a target message queue as the current business operation data, wherein the target message queue includes an Apache Kafka message queue, and the data to be consumed represents a data field located at the top of the queue processing in the message queue; The step of determining whether the current business operation data is updated de-redundant data according to a preset identification strategy specifically includes: Using the target primary key of the current business operation data as the index field, querying the preset data update log, wherein the preset data update log specifically records the update of data into the database; If the business operation data corresponding to the target primary key has only one storage record in the data update log, then the current business operation data is the original storage data; If there are at least two storage records of the business operation data corresponding to the target primary key in the data update log, it is determined that the current business operation data is updated de-redundant data.

7. The business processing risk control method according to claim 1, characterized in that: Before executing the step of using the target primary key as the index field, screening the first batch of padding data from the pre-generated first-level record cache, and performing initial padding processing on the current business operation data, the method further includes: The query priority of the second-level record cache is pre-assigned to be lower than the query priority of the first-level record cache, and the query priority of the second-level record cache is higher than the query priority of the third-level record cache.

8. A business processing risk control device, characterized in that: include: Current business operation data acquisition module, used to obtain current business operation data; A data update identification module is used to determine whether the current business operation data is updated de-redundant data according to a preset identification strategy; a target primary key acquisition module, configured to acquire primary key information of the current business operation data as a target primary key if the current business operation data is updated and de-redundant data; An initial filling processing module is used to use the target primary key as the index field, filter the first batch of filling data from the pre-generated first-level record cache, and perform initial filling processing on the current business operation data; a second filling processing module, configured to, if the current business operation data is determined to be incomplete after the initial filling processing, use the target primary key as an index field to filter a second batch of filling data from the pre-generated second-level record cache, and perform a second filling processing on the current business operation data; a third-time filling processing module, configured to, if the current business operation data is determined to be incomplete after the second filling processing, use the target primary key as an index field, select a third batch of filling data from the pre-generated third-level record cache, and perform a third filling processing on the current business operation data; A target business operation data acquisition module is used to acquire business operation data that has been completed after the first filling process, the second filling process, or the third filling process as target business operation data; The business operation task execution module is used to execute preset business operation tasks according to the target business operation data to achieve business processing risk control.

9. A computer device, characterized in that: It includes a memory and a processor, the memory stores computer-readable instructions, and when the processor executes the computer-readable instructions, it implements the steps of the business processing risk control method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer-readable instructions, which, when executed by a processor, implement the steps of the business processing risk control method according to any one of claims 1 to 7.

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