Bank branch bank business statistics method, device and equipment and storage medium

Abstract

The invention discloses a bank branch bank business statistics method and device, equipment and a storage medium, and relates to the field of office automation. In response to the received service triggering request, collecting and analyzing a database log, and extracting all service data; screening field information of the business data according to a preset rule, and extracting various business data in the target dimension; calling bank branch information, establishing a mapping relation table under the target dimension, and classifying and summarizing various business data in the target dimension to which each branch bank belongs; and generating a result statistical table from the summarized information, and issuing the result statistical table to a downstream system for display. The updating request is triggered by the change of the related data information of the bank branch mechanism, database log analysis is adopted for the service triggering request, and the conditions of storage process creation and resource occupation do not exist. Compared with timed task scheduling, the problem of time delay is solved by adopting a real-time link triggering mechanism, and the problem of resource occupation is relieved.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of office automation, and in particular to a bank branch business statistics method, device, equipment and storage medium. Background Art

[0002] With the continuous development of current banking business, institutions at all levels need to dynamically adjust the human and material resources of outlets based on the real-time business handling situation of the outlets, so they need to understand the real-time business handling situation of each outlet in real time.

[0003] In the related art, when statistics on business conditions are needed, database stored procedures are usually used to process the original data in the database, and the stored procedures are scheduled using scheduled tasks to write the data results into the database regularly. When querying business conditions, the creation and calling of stored procedures will occupy database resources. If scheduled tasks are used, the resource consumption will be large, and the data delay will be high. The scheduled writing of data results will lead to untimely data updates, and the business handling conditions cannot be known in the first place. Summary of the invention

[0004] The embodiments of the present application provide a bank branch business statistics method, device, equipment and storage medium to solve the problems of database resource occupation and untimely update of scheduled scheduling data.

[0005] On the one hand, the present application provides a bank branch business statistics method, the method comprising:

[0006] In response to receiving a business trigger request, collect and parse the database Change Log to extract all business data;

[0007] Filter the field information of business data according to preset rules and extract various business data within the target dimension;

[0008] Retrieve bank branch information and establish a mapping relationship table under the target dimension, and classify and summarize the business data within the target dimension of each branch bank;

[0009] The summary information is used to generate a result statistical table, which is then sent to the downstream system for display.

[0010] Specifically, the database Change Log is collected and parsed to extract all business data, including:

[0011] Create a Flink CDC source table, collect database Change Log logs, parse them, and send the data to the Kafka system;

[0012] Parse Change Log data based on the Kafka system and read the field names and corresponding business data in the source table.

[0013] Specifically, the target dimensions at least include the start and end time of calling, the start and end time of handling, the business handling results, and the handling status of business satisfaction; the field names include the table name table of each target dimension, the database operation type op_type, the state value before the data operation, and the state value after the data operation.

[0014] Specifically, the filtering of the field information of the business data according to the preset rules to extract the various business data within the target dimension includes:

[0015] Data filtering conditions are set for the target dimension based on the status value before data operation and the status value after data operation to filter out business data classified as call start and end time, processing start and end time, business processing result, and business satisfaction; wherein, the filtering conditions include dimension information and time information.

[0016] Specifically, the information of branch banks is retrieved and a mapping relationship table under the target dimension is established, and various business data within the target dimension of each branch bank is classified and summarized, including:

[0017] Create a Flink bank branch information dimension table, query the bank branch mapping relationship from the database, and match it with the business data under the target dimension;

[0018] Call the GroupAggregate operator and group and aggregate the business data under each target dimension according to the bank branch name and number;

[0019] Create a corresponding view table under the target dimension, calculate the classified and aggregated data, calculate the number of people in the business processing queue, queue time, processing results, and satisfaction data information values, and write the data information to the corresponding view table.

[0020] Specifically, the process of generating a result statistical table from the aggregated information and sending it to a downstream system for display includes:

[0021] Use the ConstraintEnforcer operator to constrain the format and content of the aggregated data and convert it into the preset data format;

[0022] Use the SinkMaterializer operator to write the converted data to the Kafka result table and send it to the downstream system; the downstream system reads the Kafka result table and displays the view table under each target dimension.

[0023] Specifically, when the number of people in the business processing queue, the queue time, the processing result, and the satisfaction data in any bank branch change, a business trigger request is initiated;

[0024] After writing the converted data to the Kafka result table, the method further includes:

[0025] The Committer commit confirmation information is sent to the downstream system, and all bank branches of the downstream system read the Kafka result table and display the view based on the Committer information.

[0026] On the other hand, the present application provides a bank branch business statistics device, the device comprising:

[0027] The extraction module is used to collect and parse the database Change Log in response to receiving the service trigger request, and extract all service data;

[0028] The filtering module is used to filter the field information of business data according to preset rules and extract various business data within the target dimension;

[0029] The summary module is used to retrieve bank branch information and establish a mapping relationship table under the target dimension, and classify and summarize the business data within the target dimension of each branch bank;

[0030] The update module is used to generate result statistics tables from the summary information and send them to the downstream system for display.

[0031] On the other hand, the present application provides a computer device, comprising a processor and a memory, wherein the memory stores at least one instruction, at least one program, a code set or an instruction set, and the at least one instruction, the at least one program, the code set or the instruction set are loaded and executed by the processor to implement the bank branch business statistics method described in the above aspects.

[0032] On the other hand, the present application provides a computer-readable storage medium, in which at least one instruction, at least one program, code set or instruction set is stored, and the at least one instruction, the at least one program, the code set or instruction set is loaded and executed by a processor to implement the bank branch business statistics method described in the above aspects.

[0033] The beneficial effects of the technical solution provided by the embodiment of the present application include at least: the solution triggers the update request based on the change of the relevant data information of the bank branch, and the database log analysis is used for the business trigger request, and there is no premise for the creation of the stored procedure, so there is no resource occupation. Compared with the scheduled task scheduling, the mechanism of real-time trigger link solves the delay problem and alleviates the problem of resource occupation. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 It is a flow chart of the bank branch business statistics method provided in the embodiment of the present application;

[0035] Figure 2 The algorithm flow chart of the bank branch business statistics method is shown;

[0036] Figure 3 A structural block diagram of a bank branch business statistics device provided in an embodiment of the present application is shown;

[0037] Figure 4 A structural block diagram of a computer device provided by an exemplary embodiment of the present application is shown. DETAILED DESCRIPTION

[0038] In order to make the objectives, technical solutions and advantages of the present application clearer, the implementation methods of the present application will be further described in detail below with reference to the accompanying drawings.

[0039] The term "multiple" as used herein refers to two or more than two. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. The character " / " generally indicates that the related objects are in an "or" relationship.

[0040] This application is mainly aimed at bank resource scheduling scenarios, especially the head office monitors the business volume of each branch in real time, and dispatches and deploys human resources in real time according to the business situation. The creation and scheduled scheduling of stored procedures in traditional monitoring systems affect real-time performance, and data updates are not timely. For this reason, this application uses the distributed data flow engine of Apache Flink open source stream to solve the problems of real-time performance and resource scheduling.

[0041] Figure 1 : is a flow chart of a bank branch business statistics method provided in an embodiment of the present application, comprising the following steps:

[0042] S1. In response to receiving a service trigger request, collect and parse the database Change Log to extract all service data;

[0043] A business trigger request is a request triggered by any branch office, which is mainly manifested as a request triggered when the data of interest (defined as the target dimension data in this application) changes. For example, changes in the number of people in the queue or increases or decreases in business can trigger such events. The solution provided by this application aggregates all data information from bank branches and outlets into a database and generates a corresponding Change Log log. When a request is triggered, the database Change Log log is collected and parsed to extract all business data.

[0044] S2. Filter the field information of the business data according to the preset rules and extract the business data within the target dimension;

[0045] The data information stored in the database is classified by type and covers various dimensions. This application needs to decide which target dimensions to count based on the type of resource scheduling required, and then perform targeted screening based on the field information. The preset rules here are the screening conditions that match the field information, time and type of the target dimension that are specified in advance.

[0046] In one possible implementation, the target dimensions include aspects such as the start and end time of calling, the start and end time of processing, business processing results, and business satisfaction. Then this step requires screening business data information related to these dimensions, such as the number of people queuing for business processing at the branch, queuing time, processing results, customer satisfaction evaluation and other data.

[0047] S3. Retrieve bank branch information and establish a mapping relationship table under the target dimension, and classify and summarize the business data within the target dimension of each branch bank;

[0048] This solution integrates several bank branches and outlets into a large monitoring system, which is centrally managed by a large cloud server or headquarters. When executing business statistics, the cloud server will retrieve the mapping information of all bank branches and outlets from the database, and establish a mapping relationship table under each target dimension for each bank branch, and then classify and superimpose the data of each dimension of each bank branch.

[0049] S4. Generate a result statistical table with the summarized information and send it to the downstream system for display.

[0050] The downstream system can be various bank branches, outlets or resource scheduling platforms, which can display the business distribution of each branch and the total in real time. For example, it can use visual charts to display and specifically call on personnel when there is an overall business backlog or a local backlog at a certain outlet.

[0051] In summary, this solution triggers update requests based on changes in relevant data information of bank branches, and uses database log parsing for business trigger requests. There is no prerequisite for creating stored procedures, so there is no resource occupation. Compared with scheduled task scheduling, the use of a real-time trigger link mechanism solves the delay problem and alleviates the problem of resource occupation.

[0052] In some embodiments, the process of collecting database Change Log and parsing includes:

[0053] Create a Flink CDC source table, collect database Change Log logs, parse them, and send the data to the Kafka system. Then, parse the Change Log log data based on the Kafka system and read the field names and corresponding business data in the source table. Kafka can process high-throughput messages and is suitable for various action stream data on the website, such as web browsing and searching.

[0054] The target dimensions in this application include the start and end time of calling, the start and end time of handling, the result of business handling, the handling status of business satisfaction, etc. The field names in the source table include the table name table of each target dimension, the database operation type op_type, the state value before the data operation, and the state value after the data operation, etc. The database operation type, such as insert type or other types, before and after are the transformation values ​​of the data before or after insertion or change in the database. This step can be executed through the TableSourceScan operator when the system starts.

[0055] In some embodiments, the business data in the target dimension are extracted, specifically, data filtering conditions are set for the target dimension based on the state value before data operation and the state value after data operation, and business data classified as call start and end time, handling start and end time, business handling result, and business satisfaction are filtered out. The filtering conditions include dimension information and time information, and if the data does not meet the dimension and time requirements, the data flow ends.

[0056] In some embodiments, various business data within the target dimension of each branch bank are classified and summarized, including:

[0057] A. Create a Flink bank branch information dimension table, query the bank branch mapping relationship from the database, and match it with the business data under the target dimension;

[0058] The institution information dimension table is used to separately count the dimension information data of each branch, such as the call start and end time, handling start and end time, business handling results, and business satisfaction data of branches A, B, and C. The bank branch mapping relationship is queried from the database when the system is started, and then matching and association can be performed to facilitate subsequent classification statistics.

[0059] B. Call the GroupAggregate operator and group and aggregate the business data under each target dimension according to the bank branch name and number;

[0060] C. Create a corresponding view table under the target dimension, calculate the classified and aggregated data, calculate the number of people in the business queue, queue time, processing results, and satisfaction data information values, and write the data information to the corresponding view table.

[0061] The view table is established based on the target dimension, that is, the view table of the number of people in the business processing queue, the view table of the queue time, the view table of the processing result, and the view table of the satisfaction. After the aggregation, it is not displayed separately by branch, but the overall aggregation is displayed.

[0062] In some embodiments, the summary information is generated into a result statistical table, which is sent to a downstream system for display, including:

[0063] Use the ConstraintEnforcer operator to constrain the format and content of the aggregated data and convert it into the preset data format;

[0064] The SinkMaterializer operator is then used to write the converted data to the Kafka result table and send it to the downstream system. After the downstream system reads the Kafka result table, it displays the view table under each target dimension.

[0065] When the number of people in the queue, queue time, processing results, and satisfaction data of any bank branch change, a business trigger request is initiated. After writing the converted data to the Kafka result table, the system will also send a committer submission confirmation message to the downstream system. All bank branches in the downstream system read the Kafka result table and display it in the view based on the committer information.

[0066] Figure 2The algorithm flow chart of the bank branch business statistics method is shown. Flink CDC is used to parse the comprehensive branch database log in real time to obtain the Change Log log of the data. Parse the Change Log log, filter the data according to the conditions such as the call start time, business start time, business end time, business result, and business satisfaction, and obtain the field data required for the business handling situation. Create a Flink dimension table, query the institution information table from the data warehouse, and obtain the bank branch mapping relationship. Create a real-time view of the number of people in the queue, queue time, handling results, satisfaction, etc. for Flink business handling. In the view, real-time calculation is performed according to the event time association, and the branch data is summarized by the associated institution information mapping dimension table. Finally, the data is pushed to the corresponding business system in real time through the message queue. This solution can greatly improve the timeliness of business handling, reduce latency and save database resources, enhance the stability of the system, and improve the accuracy of business analysis.

[0067] Figure 3 The structural block diagram of the bank branch business statistics device provided in the embodiment of the present application is shown, and the device includes:

[0068] The extraction module 310 is used to collect and parse the database Change Log in response to receiving the service trigger request, and extract all service data;

[0069] The screening module 320 is used to screen the field information of the business data according to preset rules and extract various business data within the target dimension;

[0070] The aggregation module 330 is used to retrieve the bank branch information and establish a mapping relationship table under the target dimension, and classify and aggregate the business data within the target dimension of each branch bank;

[0071] The update module is used to generate result statistics tables from the summary information and send them to the downstream system for display.

[0072] The bank branch business statistics device provided in the embodiment of the present application can be applied to the bank branch business statistics method provided in the above embodiment. For relevant details, refer to the above method embodiment. The implementation principle and technical effect are similar and will not be repeated here.

[0073] It should be noted that the bank branch business statistics device provided in the embodiment of the present application is only illustrated by the division of the above-mentioned functional modules / functional units. In actual applications, the above-mentioned functions can be assigned to different functional modules / functional units as needed, that is, the internal structure of the bank branch business statistics device is divided into different functional modules / functional units to complete all or part of the functions described above. In addition, the implementation method of the bank branch business statistics method provided in the above method embodiment and the implementation method of the bank branch business statistics device provided in this embodiment belong to the same concept. The specific implementation process of the bank branch business statistics device provided in this embodiment is detailed in the above method embodiment, and will not be repeated here.

[0074] Figure 4 The block diagram of a computer device provided by an exemplary embodiment of the present application is shown. It is a computer device such as a desktop computer, a laptop computer, a PDA, and a cloud server. The computer device may include, but is not limited to, a processor and a memory. Among them, the processor and the memory may be connected by a bus or in other ways. Among them, the processor may be a central processing unit (CPU). The processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, graphics processors (GPU), embedded neural network processors (NPU) or other dedicated deep learning coprocessors, discrete gates or transistor logic devices, discrete hardware components and other chips, or a combination of the above-mentioned various chips.

[0075] The processor may include one or more processing cores, such as a 4-core processor, an 8-core processor, etc. The processor may be implemented in at least one hardware form of DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), and PLA (Programmable Logic Array). The processor may also include a main processor and a coprocessor. The main processor is a processor for processing data in the awake state, also known as a CPU (Central Processing Unit); the coprocessor is a low-power processor for processing data in the standby state. In some embodiments, the processor may be integrated with a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content that needs to be displayed on the display screen. In some embodiments, the processor may also include an AI (Artificial Intelligence) processor, which is used to process computing operations related to machine learning.

[0076] As a non-transient computer-readable storage medium, the memory can be used to store non-transient software programs, non-transient computer executable programs and modules, such as program instructions / modules corresponding to the methods in the above-mentioned embodiments of the present application. The processor executes various functional applications and data processing of the processor by running the non-transient software programs, instructions and modules stored in the memory, that is, the method in the above-mentioned method implementation is realized. The memory may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created by the processor, etc. In addition, the memory may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely arranged relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network and a combination thereof.

[0077] In some embodiments, the computer device may further optionally include: a peripheral device interface and at least one peripheral device. The processor, the memory and the peripheral device interface may be connected via a bus or a signal line. Each peripheral device may be connected to the peripheral device interface via a bus, a signal line or a circuit board. Specifically, the peripheral device includes: at least one of a radio frequency circuit, a display screen and a keyboard.

[0078] The peripheral device interface can be used to connect at least one peripheral device related to I / O (Input / Output) to the processor and the memory. In some embodiments, the processor, the memory, and the peripheral device interface are integrated on the same chip or circuit board; in some other embodiments, any one or two of the processor, the memory, and the peripheral device interface can be implemented on a separate chip or circuit board, which is not limited in this embodiment.

[0079] The display screen is used to display the UI (User Interface). The UI may include graphics, text, icons, videos and any combination thereof. When the display screen is a touch screen, the display screen also has the ability to collect touch signals on the surface or above the surface of the display screen. The touch signal can be input to the processor as a control signal for processing. At this time, the display screen can also be used to provide virtual buttons and / or virtual keyboards, also known as soft buttons and / or soft keyboards. In some embodiments, the display screen can be one, arranged on the front panel of the computer device; in other embodiments, the display screen can be at least two, respectively arranged on different surfaces of the computer device or in a folding design; in other embodiments, the display screen can be a flexible display screen, arranged on a curved surface or a folding surface of the computer device. Even, the display screen can be set to a non-rectangular irregular shape, that is, a special-shaped screen. The display screen can be made of materials such as LCD (Liquid Crystal Display), OLED (Organic Light-Emitting Diode) and the like.

[0080] The power supply is used to power various components in the computer device. The power supply can be AC, DC, a disposable battery, or a rechargeable battery. When the power supply includes a rechargeable battery, the rechargeable battery can be a wired rechargeable battery or a wireless rechargeable battery. A wired rechargeable battery is a battery that is charged through a wired line, and a wireless rechargeable battery is a battery that is charged through a wireless coil. The rechargeable battery can also be used to support fast charging technology.

[0081] Those skilled in the art will appreciate that the structure shown in this embodiment does not constitute a limitation on the computer device, and may include more or fewer components than shown in the figure, or combine certain components, or adopt a different component arrangement.

[0082] The embodiment of the present application also discloses a computer-readable storage medium. Specifically, the computer-readable storage medium is used to store a computer program, and when the computer program is executed by the processor, the method in the above-mentioned method implementation is implemented. Those skilled in the art can understand that the implementation of all or part of the process in the above-mentioned implementation method of the present application can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium, and when the program is executed, it can include the process of the implementation of the above-mentioned methods. Among them, the storage medium can be a disk, an optical disk, a read-only memory (ROM), a random access memory (RAM), a flash memory (Flash Memory), a hard disk (Hard Disk Drive, abbreviated as: HDD) or a solid-state drive (SSD), etc.; the storage medium can also include a combination of the above-mentioned types of memory.

[0083] This specific embodiment is merely an explanation of the present invention and is not a limitation of the present invention. After reading this specification, those skilled in the art may make non-creative modifications to the present embodiment as needed. However, as long as they are within the scope of the claims of the present invention, they are protected by the patent law.

Claims

1. A bank branch business statistics method, characterized in that: The method comprises: In response to receiving a business trigger request, collect and parse the database Change Log to extract all business data; Filter the field information of business data according to preset rules and extract various business data within the target dimension; Retrieve bank branch information and establish a mapping relationship table under the target dimension, and classify and summarize the business data within the target dimension of each branch bank; The summary information is used to generate a result statistical table, which is then sent to the downstream system for display.

2. The method according to claim 1, characterized in that: The database Change Log is collected and parsed to extract all business data, including: Create a Flink CDC source table, collect database Change Log logs, parse them, and send the data to the Kafka system; Parse Change Log data based on the Kafka system and read the field names and corresponding business data in the source table.

3. The method according to claim 2, characterized in that The target dimensions at least include the start and end time of calling, the start and end time of handling, the business handling results, and the handling status of business satisfaction; the field names include the table name table of each target dimension, the database operation type op_type, the status value before the data operation, and the status value after the data operation.

4. The method according to claim 3, characterized in that The filtering of the field information of the business data according to the preset rules to extract the various business data within the target dimension includes: Data filtering conditions are set for the target dimension based on the status value before data operation and the status value after data operation to filter out business data classified as call start and end time, processing start and end time, business processing result, and business satisfaction; wherein, the filtering conditions include dimension information and time information.

5. The method according to claim 4, characterized in that The said retrieving the branch bank information and establishing the mapping relationship table under the target dimension, classifying and summarizing the various business data within the target dimension of each branch bank, includes: Create a Flink bank branch information dimension table, query the bank branch mapping relationship from the database, and match it with the business data under the target dimension; Call the GroupAggregate operator and group and aggregate the business data under each target dimension according to the bank branch name and number; Create a corresponding view table under the target dimension, calculate the classified and aggregated data, calculate the number of people in the business processing queue, queue time, processing results, and satisfaction data information values, and write the data information to the corresponding view table.

6. The method according to claim 4, characterized in that The process of generating a result statistical table from the aggregated information and sending it to the downstream system for display includes: Use the ConstraintEnforcer operator to constrain the format and content of the aggregated data and convert it into the preset data format; Use the SinkMaterializer operator to write the converted data to the Kafka result table and send it to the downstream system; the downstream system reads the Kafka result table and displays the view table under each target dimension.

7. The method according to claim 6, characterized in that When the number of people in the queue, queue time, processing results, and satisfaction data of any bank branch change, a business trigger request is initiated; After writing the converted data to the Kafka result table, the method further includes: The Committer commit confirmation information is sent to the downstream system, and all bank branches of the downstream system read the Kafka result table and display the view based on the Committer information.

8. A bank branch business statistics device, characterized in that: The device comprises: The extraction module is used to collect and parse the database Change Log in response to receiving the service trigger request, and extract all service data; The filtering module is used to filter the field information of business data according to preset rules and extract various business data within the target dimension; The summary module is used to retrieve bank branch information and establish a mapping relationship table under the target dimension, and classify and summarize the business data within the target dimension of each branch bank; The update module is used to generate result statistics tables from the summary information and send them to the downstream system for display.

9. A computer device, characterized in that: The computer device includes a processor and a memory, wherein the memory stores at least one instruction, at least one program, a code set or an instruction set, and the at least one instruction, the at least one program, the code set or the instruction set is loaded and executed by the processor to implement the bank branch business statistics method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The readable storage medium stores at least one instruction, at least one program, a code set or an instruction set, and the at least one instruction, the at least one program, the code set or the instruction set is loaded and executed by the processor to implement the bank branch business statistics method as described in any one of claims 1 to 7.

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