Financial business data monitoring method and device, equipment and storage medium

Abstract

The embodiment of the invention provides a financial service data monitoring method and device, equipment and a storage medium, and belongs to the technical field of financial science and technology and data processing. The method comprises the following steps: acquiring a monitoring task of financial service data; analyzing the monitoring task to obtain monitoring task node information of the financial service data; determining a node execution priority through a process control task node; at the data source task node, obtaining original financial service data from a data storage system according to the first priority and a data storage system identifier indicated by the data source task node; at the processing task node, preprocessing the original financial service data according to the second priority to obtain target financial service data; and at the alarm task node, performing anomaly analysis on the target financial service data according to the third priority, and giving an alarm when the target financial service data is abnormal. According to the embodiment of the invention, the efficiency and convenience of monitoring the financial service data can be improved.

Description

Technical Field

[0001] The present application relates to the fields of financial technology and data processing technology, and in particular to a financial business data monitoring method, device, equipment and storage medium. Background Art

[0002] At present, the volume of financial business data in the financial field is growing day by day. In order to prevent financial risks, it is necessary to monitor the massive amount of financial business data.

[0003] In related technologies, historical financial business data and historical manual monitoring data are usually used to build artificial intelligence models, thereby using artificial intelligence models to monitor financial business data.

[0004] However, not only is the construction of AI models time-consuming and labor-intensive, but due to over-reliance on historical financial business data, AI models may fail when faced with new financial business data. AI models also need to be continuously optimized and adjusted, which is also time-consuming and labor-intensive.

[0005] Therefore, how to improve the efficiency and convenience of monitoring financial business data has become a technical problem that needs to be solved urgently. Summary of the invention

[0006] The main purpose of the embodiments of the present application is to propose a financial business data monitoring method, device, equipment and storage medium, aiming to improve the efficiency and convenience of monitoring financial business data.

[0007] To achieve the above-mentioned purpose, a first aspect of an embodiment of the present application proposes a financial business data monitoring method, the method comprising:

[0008] Acquire a monitoring task of financial business data, wherein the monitoring task is configured on a visual configuration interface;

[0009] Parsing the monitoring task to obtain monitoring task node information of the financial business data, wherein the monitoring task node information includes a data source task node, a processing task node, an alarm task node, and a process control task node;

[0010] Determining the node execution priorities among the data source task node, the processing task node and the alarm task node through the process control task node; wherein the node execution priorities include a first priority of the data source task node, a second priority of the processing task node and a third priority of the alarm task node, the first priority being higher than the second priority, and the second priority being higher than the third priority;

[0011] At the data source task node, acquiring original financial business data from a data storage system according to the first priority and a data storage system identifier indicated by the data source task node;

[0012] At the processing task node, preprocessing the original financial service data according to the second priority level to obtain target financial service data;

[0013] At the alarm task node, an abnormality analysis is performed on the target financial service data according to the third priority, and an alarm is issued when an abnormality exists in the target financial service data.

[0014] In some embodiments, the monitoring task of acquiring financial service data includes:

[0015] Loading monitoring task component selection items in the visual configuration interface;

[0016] Receiving a selection request for the monitoring task component selection item;

[0017] In response to the selection request, obtaining a target monitoring task component;

[0018] The monitoring task is created based on the target monitoring task component.

[0019] In some embodiments, the target monitoring task component includes a data source component, a data processing component, an alarm component, and a process control component, and the creating the monitoring task based on the target monitoring task component includes:

[0020] Configure the data storage system identifier for the data source component, configure the preprocessing strategy for the data processing component, configure the alarm strategy and alarm mode for the alarm component, and configure the node execution priority determination strategy for the process control component;

[0021] Creating the data source task node according to the data source component and the data storage system identifier;

[0022] Creating the processing task node according to the data processing component and the preprocessing strategy;

[0023] Creating the alarm task node according to the alarm component, the alarm strategy and the alarm method;

[0024] Creating the process control task node according to the process control component and the node execution priority determination strategy;

[0025] The data source task node, the processing task node, the alarm task node and the flow control task node are combined and processed to obtain the monitoring task.

[0026] In some embodiments, the preprocessing of the original financial service data to obtain the target financial service data includes:

[0027] Based on the preprocessing strategy, the original financial business data is cleaned to obtain clean financial business data;

[0028] Performing dimension analysis on the clean financial business data to obtain the common data dimensions of the clean financial business data;

[0029] The measurement data of the common data dimension are aggregated to obtain the target financial business data.

[0030] In some embodiments, performing dimension parsing on the clean finance business data to obtain the common data dimensions of the clean finance business data includes:

[0031] Performing attribute field recognition processing on the clean financial business data to obtain the attribute fields contained in the clean financial business data;

[0032] Performing business subject division processing on the attribute field to obtain the business subject to which the attribute field belongs;

[0033] The business subject is used as the common data dimension.

[0034] In some embodiments, performing abnormal analysis on the target financial business data includes:

[0035] Filtering the alarm dimension indicated by the alarm strategy and the measurement data of the alarm dimension from the target financial business data;

[0036] Performing permutation and combination processing on the alarm dimensions to obtain a dimension permutation and combination;

[0037] Calculate the target metric data of the dimension combination according to the metric data of the alarm dimension;

[0038] Comparing the target metric data with the alarm threshold indicated by the alarm strategy;

[0039] When the target metric data is greater than the alarm threshold, it is determined that an abnormality exists in the target financial service data.

[0040] In some embodiments, the step of issuing an alarm when the target financial service data is abnormal includes:

[0041] Generate an alarm message when there is an abnormality in the target financial business data;

[0042] The warning information is issued according to the warning method, wherein the warning method includes an email warning method, a text message warning method and a telephone voice warning method.

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

[0044] A task acquisition module, used to acquire monitoring tasks of financial business data, wherein the monitoring tasks are configured on a visual configuration interface;

[0045] A task parsing module parses the monitoring task to obtain monitoring task node information of the financial business data, wherein the monitoring task node information includes a data source task node, a processing task node, an alarm task node, and a process control task node;

[0046] A priority determination module, used to determine the node execution priority among the data source task node, the processing task node and the alarm task node through the process control task node; wherein the node execution priority includes the first priority of the data source task node, the second priority of the processing task node and the third priority of the alarm task node, the first priority is higher than the second priority, and the second priority is higher than the third priority;

[0047] A data acquisition module, at the data source task node, acquires original financial business data from the data storage system according to the first priority and the data storage system identifier indicated by the data source task node;

[0048] A data preprocessing module, at the processing task node, preprocesses the original financial business data according to the second priority to obtain target financial business data;

[0049] An alarm module is used to perform an abnormality analysis on the target financial business data according to the third priority level at the alarm task node, and to issue an alarm when an abnormality exists in the target financial business data.

[0050] To achieve the above-mentioned purpose, the third aspect of an embodiment of the present application proposes a computer device, which includes a memory and a processor, the memory stores a computer program, and the processor implements the method described in the first aspect when executing the computer program.

[0051] To achieve the above-mentioned purpose, the fourth aspect of an embodiment of the present application proposes a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method described in the first aspect is implemented.

[0052] The present application proposes a financial business data monitoring method, device, equipment and storage medium. The financial business data monitoring method obtains a monitoring task for financial business data, wherein the monitoring task is configured on a visual configuration interface; the monitoring task is parsed and processed to obtain monitoring task node information of the financial business data, wherein the monitoring task node information includes a data source task node, a processing task node, an alarm task node and a process control task node; the node execution priority among the data source task node, the processing task node and the alarm task node is determined through the process control task node; wherein the node execution priority includes a first priority of the data source task node, a second priority of the processing task node and a third priority of the alarm task node, the first priority being higher than the second priority, and the second priority being higher than the third priority; at the data source task node, according to the first priority and the data storage system identifier indicated by the data source task node, the original financial business data is obtained from the data storage system; at the processing task node, the original financial business data is pre-processed according to the second priority to obtain the target financial business data; at the alarm task node, the target financial business data is analyzed for abnormality according to the third priority, and an alarm is issued if the target financial business data is abnormal. On the one hand, users can quickly and intuitively configure personalized monitoring tasks to improve the flexibility and convenience of monitoring financial business data; on the other hand, monitoring tasks can be automatically parsed, thereby reasonably managing the execution order of data source task nodes, processing task nodes and alarm task nodes through process control task nodes, thereby improving the efficiency and reliability of monitoring financial business data, so that the original financial business data to be monitored can be obtained in an orderly manner at the data source node, the original financial business data can be reorganized into higher-quality target financial business data at the processing task node, and the target financial business data can be accurately analyzed for anomalies at the alarm task node, and an alarm can be issued when anomalies exist, which is conducive to timely discovery of abnormal situations in financial business data, thereby improving the automation level of monitoring financial business data to a greater extent. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 is a flow chart of a financial business data monitoring method provided by an embodiment of the present application;

[0054] Figure 2 yes Figure 1 Flow chart of step S101 in FIG.

[0055] Figure 3 yes Figure 1 Flow chart of step S105 in FIG.

[0056] Figure 4 yes Figure 1 Flow chart of step S106 in FIG.

[0057] Figure 5 It is a structural diagram of a financial business data monitoring device provided in an embodiment of the present application;

[0058] Figure 6 It is a schematic diagram of the hardware structure of the computer device provided in the embodiment of the present application. DETAILED DESCRIPTION

[0059] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0060] It should be noted that, although the functional modules are divided in the device schematic diagram and the logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart. The terms "first", "second", etc. in the specification, claims and the above drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.

[0061] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0062] The embodiments of the present application provide a financial business data monitoring method, apparatus, device and storage medium, aiming to improve the efficiency and convenience of monitoring financial business data.

[0063] The financial business data monitoring method and apparatus, computer equipment and storage medium provided in the embodiments of the present application are specifically described through the following embodiments. First, the financial business data monitoring method in the embodiments of the present application is described.

[0064] The financial business data monitoring method provided in the embodiment of the present application relates to the field of financial technology and data processing technology. The financial business data monitoring method provided in the embodiment of the present application can be applied to a terminal, can also be applied to a server side, and can also be software running in a terminal or a server side. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, etc.; the server side can be configured as an independent physical server, or can be configured as a server cluster or a distributed system composed of multiple physical servers, and can also be configured as a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms; the software can be an application that implements the financial business data monitoring method, etc., but is not limited to the above forms.

[0065] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer computer devices, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc. The present application can be described in the general context of computer executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments, in which tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0066] It should be noted that in each specific implementation of the present application, when it comes to the need to perform relevant processing based on data related to user identity or characteristics such as user information, user behavior data, user historical data, and user location information, the user's permission or consent will be obtained first, and the collection, use, and processing of these data will comply with relevant laws, regulations, and standards. In addition, when the embodiment of the present application needs to obtain the user's sensitive personal information, the user's separate permission or consent will be obtained through a pop-up window or by jumping to a confirmation page. After clearly obtaining the user's separate permission or consent, the necessary user-related data for the normal operation of the embodiment of the present application will be obtained.

[0067] Figure 1 is an optional flowchart of the financial business data monitoring method provided in an embodiment of the present application. Figure 1 The method may include but is not limited to steps S101 to S106.

[0068] Step S101, obtaining a monitoring task of financial business data, wherein the monitoring task is configured on a visual configuration interface.

[0069] Among them, financial business data refers to the data generated by financial institutions in the process of providing financial products or services. Financial business data may include customer transaction data, customer asset data, customer liability data, and customer investment data, etc.

[0070] By providing a visual configuration interface, users can configure the monitoring process for financial business data in the visual configuration interface according to their needs and obtain the monitoring tasks for financial business data.

[0071] See also Figure 2 In some embodiments, step S101 may include but is not limited to steps S1011 to S1014.

[0072] Step S1011, loading monitoring task component selection items in the visual configuration interface.

[0073] In order to improve the convenience of configuring monitoring tasks, monitoring task component selection items can be loaded on the visual configuration interface for selection by monitoring personnel and other users. In this way, the user can select the monitoring task component selection items by dragging and dropping on the visual configuration interface, thereby triggering a selection request for the monitoring task component selection items.

[0074] Step S1012, receiving a selection request for a monitoring task component selection item.

[0075] Receive a user's selection request for a monitoring task component selection item.

[0076] Step S1013, in response to the selection request, obtaining the target monitoring task component.

[0077] In response to the selection request, the monitoring task component indicated by the selection request is selected as the target monitoring task component.

[0078] For example, the monitoring task component selection items loaded in the visual configuration interface include data source components, data processing components, alarm components, process control components and other functional components. The user selects the data source components, data processing components, alarm components and process control components to select the data source components, data processing components, alarm components and process control components as target monitoring task components.

[0079] Step S1014: Create a monitoring task based on the target monitoring task component.

[0080] Create monitoring tasks for financial business data based on the target monitoring task component.

[0081] As a result, users can intuitively configure personalized monitoring tasks without programming knowledge, which lowers the technical threshold and provides convenience for users.

[0082] In step S1014 of some embodiments, in order to improve the rationality of the monitoring task, the target monitoring task components may include but are not limited to data source components, data processing components, alarm components and process control components. The data storage system identifier can be configured for the data source component, the preprocessing strategy can be configured for the data processing component, the alarm strategy and alarm method can be configured for the alarm component, and the node execution priority determination strategy can be configured for the process control component; a data source task node is created according to the data source component and the data storage system identifier; a processing task node is created according to the data processing component and the preprocessing strategy; an alarm task node is created according to the alarm component, the alarm strategy and the alarm method; a process control task node is created according to the process control component and the node execution priority determination strategy; the data source task node, the processing task node, the alarm task node and the process control task node are combined and processed to obtain the monitoring task.

[0083] The data source component is used for users to select the original financial business data that needs to be monitored.

[0084] The data source component can be configured with the data storage system identifier selected by the user on the visual configuration interface, and then a data source task node can be created based on the data source component and the data storage system identifier, so that at the data source task node, the original financial business data can be obtained from the data storage system based on the data storage system identifier.

[0085] The Data Storage System (DSS) is a computerized platform used by financial institutions to collect, store and manage financial business data. The DSS can include various types of database management systems (DBMS), such as Oracle, SQL Server, Redis and MySQL.

[0086] There can be multiple data storage system identifiers. The data source task node can obtain original financial business data from multiple data storage systems based on multiple data storage system identifiers. That is, the original financial business data can be multi-source and can come from the data storage systems of different financial institutions.

[0087] The data processing component is used to pre-process the original financial business data and re-sort the original financial business data.

[0088] The preprocessing strategy required by the user can be configured for the data processing component on the visual configuration interface, wherein the preprocessing strategy is used to instruct the cleaning process of the original financial business data and the integration process after the cleaning process.

[0089] In this way, a processing task node is created according to the data processing component and the preprocessing strategy, so that at the processing task node, the original financial business data can be preprocessed according to the preprocessing strategy to obtain the target financial business data.

[0090] The alarm component is used to perform anomaly analysis on the target financial data and issue an alarm when the target financial data is abnormal.

[0091] The alarm component can be configured with the alarm strategy and alarm method required by the user. The alarm strategy is used to indicate the alarm dimension selected by the user and the alarm threshold set for the alarm dimension. The alarm threshold can be flexibly set according to the user's needs and is not limited here. The alarm methods include email alarm method, SMS alarm method and telephone voice alarm method.

[0092] In this way, an alarm task node is created according to the alarm component, alarm strategy and alarm method, so that at the alarm task node, the target financial business data can be analyzed for abnormalities according to the alarm strategy, and alarm information can be generated when there are abnormalities in the target financial business data, and the alarm information can be issued through the alarm method.

[0093] The process control component is used to control the execution of data source task nodes, processing task nodes, and alarm task nodes.

[0094] A node execution priority determination strategy can be configured for the process control component, wherein the execution priority determination strategy is used to indicate the first priority of the data source task node, the second priority of the processing task node, and the third priority of the alarm task node, the first priority is higher than the second priority, and the second priority is higher than the third priority, that is, the data source task node is executed before the processing task node, and the processing task node is executed before the alarm task node.

[0095] In this way, a process control task node is created according to the process control component and the node execution priority determination strategy, so that in the process control task node, the node execution priority between the data source task node, the processing task node and the alarm task node can be determined according to the node execution priority determination strategy, thereby controlling the execution of the data source task node, the processing task node and the alarm task node according to the node execution priority.

[0096] It is worth noting that the node execution priority determination strategy can also be used to indicate the cyclic execution of the monitoring task. In this way, the cyclic execution of the monitoring task can be controlled through the process control task node. Specifically, a Loop identifier can be added to the data source task node. The Loop identifier is the starting identifier of the loop, which is used to indicate that the data source task node is the starting node of the cyclic monitoring task. In this way, the process control component can identify the Loop identifier of the data source task node based on the node execution priority determination strategy, and combine the node execution priority to control the monitoring task to perform cyclic execution starting from the data source task node.

[0097] The data source task nodes, processing task nodes, alarm task nodes and process control task nodes are combined and processed in the form of a directed graph to obtain a monitoring process for financial business data, that is, a monitoring task.

[0098] Through the above method, users can intuitively configure personalized monitoring tasks, which improves the flexibility and convenience of financial business data monitoring.

[0099] Step S102, parse and process the monitoring task to obtain monitoring task node information of financial business data, wherein the monitoring task node information includes data source task node, processing task node, alarm task node and process control task node.

[0100] The monitoring task can be executed through the data processing engine. Specifically, after obtaining the monitoring task of the financial business data configured by the user in the visual configuration interface, the monitoring task is parsed and processed by the data processing engine to obtain the monitoring task node information of the financial business data, wherein the monitoring task node information includes the data source task node, the processing task node, the alarm task node and the process control task node.

[0101] Step S103, determining the node execution priority among the data source task node, the processing task node and the alarm task node through the process control task node; wherein the node execution priority includes the first priority of the data source task node, the second priority of the processing task node and the third priority of the alarm task node, the first priority is higher than the second priority, and the second priority is higher than the third priority.

[0102] Afterwards, through the process control task node, based on the node execution priority judgment strategy, the node execution priority between the data source task node, the processing task node and the alarm task node is determined. The node execution priority includes the first priority of the data source task node, the second priority of the processing task node and the third priority of the alarm task node. The first priority is higher than the second priority, and the second priority is higher than the third priority.

[0103] Step S104: at the data source task node, original financial business data is obtained from the data storage system according to the first priority and the data storage system identifier indicated by the data source task node.

[0104] After determining the node execution priority through the process control task node, based on the first priority, at the data source task node, through the data storage system identifier, the API interface of the data storage system identifier is called to obtain the original financial business data from the data storage system in real time.

[0105] Step S105: at the processing task node, pre-process the original financial service data according to the second priority to obtain target financial service data.

[0106] Through the data processing engine, based on the second priority, the original financial business data is pre-processed at the processing task node to obtain the target financial business data.

[0107] See also Figure 3 In some embodiments, step S105 may include but is not limited to steps S1051 to S1053.

[0108] Step S1051, based on the preprocessing strategy, the original financial business data is cleaned to obtain clean financial business data.

[0109] Considering that the original financial business data comes from different data storage systems, the original financial business data is cleaned based on the preprocessing strategy. Specifically, the original financial business data is subjected to structural conversion processing to obtain the financial business data after structural conversion, and then the financial business data after structural conversion is subjected to format conversion processing to obtain the financial business data after format conversion, so as to realize the conversion of the original financial business data into a unified structure and format; further, the financial business data after format conversion is subjected to filtering processing, such as outlier removal, to obtain the filtered financial business data; then the non-numeric data in the filtered financial business data is mapped to the corresponding numeric data to obtain the mapped financial business data; the mapped financial business data is also subjected to null value processing, such as using specific values ​​such as 0, mean, etc., to fill the null values ​​in the mapped financial business data to obtain clean financial business data. In this way, the foundation is laid for reorganizing the original financial business data into target financial business data with higher quality.

[0110] Step S1052, performing dimension analysis processing on the clean financial business data to obtain the common data dimensions of the clean financial business data.

[0111] In order to effectively and automatically integrate the clean finance business data, the clean finance business data is dimensionally parsed to obtain the common data dimensions of the clean finance business data, wherein there may be only one or more common data dimensions.

[0112] Exemplarily, for example, dimension analysis is performed on customer investment data to obtain common data dimensions such as investment product type dimension and investment cycle dimension.

[0113] In step S1052 of some embodiments, in order to improve the accuracy of dimensional analysis, attribute field identification processing may be performed on the clean financial business data to obtain the attribute fields contained in the clean financial business data; business subject division processing may be performed on the attribute fields to obtain the business subjects to which the attribute fields belong; and the business subjects may be used as common data dimensions.

[0114] Specifically, the clean financial business data is firstly subjected to attribute field recognition processing to obtain the attribute fields contained in the clean financial business data.

[0115] Exemplarily, for example, investment attribute field identification processing is performed on the clean customer investment data to obtain investment attribute fields such as product fields (such as fund name) and term fields.

[0116] Then, the attribute fields contained in the clean financial business data are divided into business topics to obtain the business topics to which the attribute fields belong.

[0117] Exemplary, for example, investment attribute fields such as product fields and term fields in the cleaning customer investment data are divided into business themes to determine that the product field belongs to the investment product type theme and the term field belongs to the investment cycle theme.

[0118] The business theme is used as a common data dimension. For example, the investment product type theme is used as the investment product type dimension, and the investment cycle theme is used as the investment cycle dimension. The investment product type dimension and the investment cycle dimension constitute the common data dimensions.

[0119] This provides an accurate analytical basis for the automated integration of clean financial business data.

[0120] Step S1053, summarizing the measurement data of the common data dimensions to obtain target financial business data.

[0121] The measurement data refers to the measurement index of the data dimension, such as the investment amount value. Each common data dimension corresponds to a measurement data.

[0122] The measurement data of the common data dimensions are aggregated and processed to obtain the target financial business data, thereby realizing the accurate and effective automatic integration of the clean financial business data into the target financial business data.

[0123] For example, for customer investment data from various branches of a bank, all include an investment amount dimension. The investment amount dimension can be determined as a common data dimension for the two, and the investment amounts of the two investment amount dimensions can be summed to obtain the total investment amount of the common investment amount dimension. The total investment amount can be used as summary metric data, thereby reorganizing the clean financial business data from a dimensional perspective and obtaining higher quality target financial business data.

[0124] Step S106: at the alarm task node, an abnormality analysis is performed on the target financial service data according to the third priority level, and an alarm is issued when an abnormality exists in the target financial service data.

[0125] Through the data processing engine, based on the third priority, an abnormal analysis is performed on the target financial business data at the alarm task node, and an alarm message is generated when an abnormality exists in the target financial business data, and the alarm message is issued through an alarm method.

[0126] The data processing engine can also use the crontab tool to regularly execute monitoring tasks. The crontab tool is a tool for setting and managing periodic tasks. It allows tasks to be automatically executed at specified time points or time intervals. The crontab tool can efficiently manage monitoring tasks and automate the execution of monitoring tasks. The data processing engine can also use the kafka message queue tool to issue real-time alerts for target financial business data.

[0127] See also Figure 4 In some embodiments, in step S106, performing abnormal analysis on the target financial business data according to the third priority may include but is not limited to steps S1061 to S1065.

[0128] Step S1061: Filter out the alarm dimensions and measurement data of the alarm dimensions indicated by the alarm strategy from the target financial business data.

[0129] Specifically, the alarm dimensions and measurement data of the alarm dimensions indicated by the alarm strategy are screened out from the target financial business data. For example, taking the target financial business data as loan data, the alarm dimensions include customer credit rating dimensions (such as general and good dimensions) and loan usage dimensions (such as agricultural operation, factory operation and personal consumption dimensions), and the measurement data of the alarm dimensions include loan amount.

[0130] Step S1062, performing permutation and combination processing on the alarm dimensions to obtain a dimension permutation and combination.

[0131] The warning dimensions are arranged and combined to obtain dimension arrangements and combinations. For example, by arranging and combining the customer credit rating dimension and the loan usage dimension, six (2x3=6) dimension arrangements and combinations can be obtained.

[0132] Step S1063, calculating the target measurement data of the dimension arrangement and combination according to the measurement data of the alarm dimension.

[0133] According to the measurement data of the warning dimension, the target measurement data of the dimension arrangement and combination is calculated. For example, according to the loan amount, the loan amount values ​​of the six dimensional arrangement and combination can be calculated.

[0134] Step S1064, comparing the target metric data with the alarm threshold indicated by the alarm strategy.

[0135] The target metric data of the dimension combination is compared with the alarm threshold indicated by the alarm strategy. For example, if the alarm threshold is a loan amount threshold, the loan amount value of each dimension combination is compared with the loan amount threshold.

[0136] Step S1065: When the target metric data is greater than the alarm threshold, it is determined that the target financial business data is abnormal.

[0137] When the target metric data is greater than the alarm threshold, it is determined that the target financial business data is abnormal. For example, as long as the loan amount value of one dimension arrangement combination is greater than the loan amount threshold, it is confirmed that the loan data is abnormal.

[0138] In step S106 of some embodiments, an alarm is issued when an abnormality exists in the target financial business data, and an alarm message may be generated when an abnormality exists in the target financial business data; the alarm message is issued according to an alarm method, wherein the alarm method includes an email alarm method, a text message alarm method, and a telephone voice alarm method.

[0139] When there is an anomaly in the target financial business data, an alarm message may be generated based on the cause of the anomaly. For example, the alarm message may be “Please note that there is an anomaly in the loan amount in the loan data”.

[0140] As a result, financial business data can be monitored automatically and accurately.

[0141] In some embodiments, after step S103, the process control task node can also be used to determine whether the data source task node carries a Loop identifier. If the data source task node carries a Loop identifier, the data source task node is used as the starting point to loop the data source task node, the processing task node and the alarm task node to meet the requirements of loop execution of the monitoring task.

[0142] To better understand the above embodiment, the following application scenarios are given as examples:

[0143] Taking the application scenario of monitoring customer loan data of each branch of a bank as an example, the bank's monitoring personnel and other users can configure the monitoring task of customer loan data on the visual configuration interface; parse and process the monitoring task of customer loan data to obtain the monitoring task node information of customer loan data, and the monitoring task node information includes data source task node, processing task node, alarm task node and process control task node; determine through the process control task node that the execution priority of the data source task node is higher than that of the processing task node, and the priority of the processing task node is higher than that of the alarm task node; then, at the data source task node, according to the identifier of each branch indicated by the data source task node, the original customer loan data can be obtained from the data storage system of each branch; at the processing task node, pre-process the original customer loan data to obtain higher quality target customer loan data; at the alarm task node, filter the target customer loan data from the target customer loan data. The alarm dimensions required by the user and the measurement data of the alarm dimensions, such as the customer credit rating dimension (such as the two dimensions of general and good) and the loan use dimension (such as the three dimensions of operating agriculture, operating a factory and personal consumption), the measurement data of the alarm dimension such as the loan amount, and then the customer credit rating dimension and the loan use dimension are arranged and combined to obtain six dimensional combinations. Then, according to the loan amount, the loan amount value of each dimensional combination is calculated, and the loan amount value of each dimensional combination is compared with the loan amount threshold. As long as the loan amount value of one dimensional combination is greater than the loan amount threshold, it can be determined that the target customer's loan data is abnormal, and an alarm message of "Please note that there is an abnormality in the loan amount in the customer's loan data" is generated, and the alarm message is sent by email to remind monitoring personnel and other users to respond in time, which can help banks to timely discover and better manage risks related to loan business.

[0144] The financial business data monitoring method provided in the above embodiment obtains a monitoring task for financial business data, wherein the monitoring task is configured on a visual configuration interface; the monitoring task is parsed and processed to obtain monitoring task node information for the financial business data, wherein the monitoring task node information includes a data source task node, a processing task node, an alarm task node and a process control task node; the node execution priority among the data source task node, the processing task node and the alarm task node is determined through the process control task node; wherein the node execution priority includes a first priority of the data source task node, a second priority of the processing task node and a third priority of the alarm task node, the first priority being higher than the second priority, and the second priority being higher than the third priority; at the data source task node, according to the first priority and the data storage system identifier indicated by the data source task node, original financial business data is obtained from the data storage system; at the processing task node, the original financial business data is pre-processed according to the second priority to obtain target financial business data; at the alarm task node, an abnormality analysis is performed on the target financial business data according to the third priority, and an alarm is issued if an abnormality exists in the target financial business data. On the one hand, users can quickly and intuitively configure personalized monitoring tasks to improve the flexibility and convenience of monitoring financial business data; on the other hand, monitoring tasks can be automatically parsed, thereby reasonably managing the execution order of data source task nodes, processing task nodes and alarm task nodes through process control task nodes, thereby improving the efficiency and reliability of monitoring financial business data, so that the original financial business data to be monitored can be obtained in an orderly manner at the data source node, the original financial business data can be reorganized into higher-quality target financial business data at the processing task node, and the target financial business data can be accurately analyzed for anomalies at the alarm task node, and an alarm can be issued when anomalies exist, which is conducive to timely discovery of abnormal situations in financial business data, thereby improving the automation level of monitoring financial business data to a greater extent.

[0145] See also Figure 5 The embodiment of the present application also provides a financial service data monitoring device, which can implement the above-mentioned financial service data monitoring method, and the device includes:

[0146] A task acquisition module, used to acquire monitoring tasks of financial business data, wherein the monitoring tasks are configured on a visual configuration interface;

[0147] A task parsing module parses the monitoring task to obtain monitoring task node information of the financial business data, wherein the monitoring task node information includes a data source task node, a processing task node, an alarm task node, and a process control task node;

[0148] A priority determination module, used to determine the node execution priority among the data source task node, the processing task node and the alarm task node through the process control task node; wherein the node execution priority includes the first priority of the data source task node, the second priority of the processing task node and the third priority of the alarm task node, the first priority is higher than the second priority, and the second priority is higher than the third priority;

[0149] A data acquisition module, at the data source task node, acquires original financial business data from the data storage system according to the first priority and the data storage system identifier indicated by the data source task node;

[0150] A data preprocessing module, at the processing task node, preprocesses the original financial business data according to the second priority to obtain target financial business data;

[0151] An alarm module is used to perform an abnormality analysis on the target financial business data according to the third priority level at the alarm task node, and to issue an alarm when an abnormality exists in the target financial business data.

[0152] The specific implementation of the financial business data monitoring device is basically the same as the specific implementation of the above-mentioned financial business data monitoring method, and will not be repeated here.

[0153] The embodiment of the present application also provides a computer device, the computer device includes a memory and a processor, the memory stores a computer program, and the processor implements the above financial business data monitoring method when executing the computer program. The computer device can be any intelligent terminal including a tablet computer, a car computer, etc.

[0154] See also Figure 6 , Figure 6 The hardware structure of a computer device according to another embodiment is shown, and the computer device includes:

[0155] The processor 601 may be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (Application Specific Integrated Circuit, ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application;

[0156] The memory 602 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 602 can store an operating system and other application programs. When the technical solution provided in the embodiment of this specification is implemented by software or firmware, the relevant program code is stored in the memory 602, and the processor 601 calls and executes the financial business data monitoring method of the embodiment of this application;

[0157] Input / output interface 603, used to implement information input and output;

[0158] Communication interface 604, used to realize communication interaction between the device and other devices, which can be realized by wired mode (such as USB, network cable, etc.) or wireless mode (such as mobile network, WIFI, Bluetooth, etc.);

[0159] Bus 605 , which transmits information between various components of the device (e.g., processor 601 , memory 602 , input / output interface 603 , and communication interface 604 );

[0160] The processor 601 , the memory 602 , the input / output interface 603 and the communication interface 604 are connected to each other in communication within the device via a bus 605 .

[0161] An embodiment of the present application also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the above-mentioned financial business data monitoring method is implemented.

[0162] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely disposed relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0163] The financial business data monitoring method, device, equipment and storage medium provided in the embodiments of the present application, on the one hand, enable users to quickly and intuitively configure personalized monitoring tasks, thereby improving the flexibility and convenience of monitoring financial business data; on the other hand, can automatically parse monitoring tasks, thereby reasonably managing the execution order of data source task nodes, processing task nodes and alarm task nodes through process control task nodes, thereby improving the efficiency and reliability of monitoring financial business data, so that the original financial business data to be monitored can be obtained in an orderly manner at the data source node, the original financial business data can be reorganized into higher-quality target financial business data at the processing task node, and the target financial business data can be accurately analyzed for anomalies at the alarm task node, and an alarm can be issued in the presence of anomalies, which is conducive to timely discovery of abnormal situations in financial business data, thereby improving the automation level of monitoring financial business data to a greater extent.

[0164] The embodiments described in the embodiments of the present application are intended to more clearly illustrate the technical solutions of the embodiments of the present application and do not constitute a limitation on the technical solutions provided in the embodiments of the present application. Those skilled in the art will appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems.

[0165] Those skilled in the art will appreciate that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than shown in the figures, or a combination of certain steps, or different steps.

[0166] The device embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separated, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0167] Those skilled in the art will appreciate that all or some of the steps in the methods disclosed above, and the functional modules / units in the systems and devices may be implemented as software, firmware, hardware, or a suitable combination thereof.

[0168] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0169] It should be understood that in the present application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the objects associated before and after are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0170] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the above units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0171] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0172] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0173] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including multiple instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, referred to as ROM), random access memory (Random Access Memory, referred to as RAM), disk or optical disk and other media that can store programs.

[0174] The preferred embodiments of the present invention are described above with reference to the accompanying drawings, but the scope of the rights of the present invention is not limited thereto. Any modification, equivalent substitution and improvement made by a person skilled in the art without departing from the scope and essence of the present invention should be within the scope of the rights of the present invention.

Claims

1. A financial business data monitoring method, characterized in that: The method comprises: Acquire a monitoring task of financial business data, wherein the monitoring task is configured on a visual configuration interface; Parsing the monitoring task to obtain monitoring task node information of the financial business data, wherein the monitoring task node information includes a data source task node, a processing task node, an alarm task node, and a process control task node; Determining the node execution priorities among the data source task node, the processing task node and the alarm task node through the process control task node; wherein the node execution priorities include a first priority of the data source task node, a second priority of the processing task node and a third priority of the alarm task node, the first priority being higher than the second priority, and the second priority being higher than the third priority; At the data source task node, acquiring original financial business data from a data storage system according to the first priority and a data storage system identifier indicated by the data source task node; At the processing task node, preprocessing the original financial service data according to the second priority level to obtain target financial service data; At the alarm task node, an abnormality analysis is performed on the target financial service data according to the third priority, and an alarm is issued when an abnormality exists in the target financial service data.

2. The method according to claim 1, characterized in that: The monitoring task of acquiring financial business data includes: Loading monitoring task component selection items in the visual configuration interface; Receiving a selection request for the monitoring task component selection item; In response to the selection request, obtaining a target monitoring task component; The monitoring task is created based on the target monitoring task component.

3. The method according to claim 2, characterized in that The target monitoring task component includes a data source component, a data processing component, an alarm component and a process control component. The creating of the monitoring task based on the target monitoring task component includes: Configure the data storage system identifier for the data source component, configure the preprocessing strategy for the data processing component, configure the alarm strategy and alarm mode for the alarm component, and configure the node execution priority determination strategy for the process control component; Creating the data source task node according to the data source component and the data storage system identifier; Creating the processing task node according to the data processing component and the preprocessing strategy; Creating the alarm task node according to the alarm component, the alarm strategy and the alarm method; Creating the process control task node according to the process control component and the node execution priority determination strategy; The data source task node, the processing task node, the alarm task node and the flow control task node are combined and processed to obtain the monitoring task.

4. The method according to claim 3, characterized in that The preprocessing of the original financial service data to obtain target financial service data includes: Based on the preprocessing strategy, the original financial business data is cleaned to obtain clean financial business data; Performing dimension analysis on the clean financial business data to obtain the common data dimensions of the clean financial business data; The measurement data of the common data dimension are aggregated to obtain the target financial business data.

5. The method according to claim 4, characterized in that The performing dimension parsing on the clean financial business data to obtain the common data dimensions of the clean financial business data includes: Performing attribute field recognition processing on the clean financial business data to obtain the attribute fields contained in the clean financial business data; Performing business subject division processing on the attribute field to obtain the business subject to which the attribute field belongs; The business subject is used as the common data dimension.

6. The method according to claim 3, characterized in that: The performing abnormal analysis on the target financial business data includes: Filtering the alarm dimension indicated by the alarm strategy and the measurement data of the alarm dimension from the target financial business data; Performing permutation and combination processing on the alarm dimensions to obtain a dimension permutation and combination; Calculate the target metric data of the dimension combination according to the metric data of the alarm dimension; Comparing the target metric data with the alarm threshold indicated by the alarm strategy; When the target metric data is greater than the alarm threshold, it is determined that an abnormality exists in the target financial service data.

7. The method according to any one of claims 3 to 6, characterized in that: The issuing of an alarm when the target financial service data is abnormal includes: Generate an alarm message when there is an abnormality in the target financial business data; The warning information is issued according to the warning method, wherein the warning method includes an email warning method, a text message warning method and a telephone voice warning method.

8. A financial business data monitoring device, characterized in that: The device comprises: A task acquisition module, used to acquire monitoring tasks of financial business data, wherein the monitoring tasks are configured on a visual configuration interface; A task parsing module parses the monitoring task to obtain monitoring task node information of the financial business data, wherein the monitoring task node information includes a data source task node, a processing task node, an alarm task node, and a process control task node; A priority determination module, used to determine the node execution priority among the data source task node, the processing task node and the alarm task node through the process control task node; wherein the node execution priority includes the first priority of the data source task node, the second priority of the processing task node and the third priority of the alarm task node, the first priority is higher than the second priority, and the second priority is higher than the third priority; A data acquisition module, at the data source task node, acquires original financial business data from the data storage system according to the first priority and the data storage system identifier indicated by the data source task node; A data preprocessing module, at the processing task node, preprocesses the original financial business data according to the second priority to obtain target financial business data; An alarm module is used to perform an abnormality analysis on the target financial business data according to the third priority level at the alarm task node, and to issue an alarm when an abnormality exists in the target financial business data.

9. A computer device, characterized in that: The computer device includes a memory and a processor, the memory stores a computer program, and the processor implements the financial business data monitoring method according to any one of claims 1 to 7 when executing the computer program.

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

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