Bank data processing method and real-time analysis system based on distributed architecture

Abstract

The invention provides a bank data processing method and real-time analysis system based on a distributed architecture, and the method comprises the steps: 1, collecting the real-time transaction data of a bank, including transaction records, customer information and log information; step 2, preprocessing the collected data; 3, dividing the preprocessed data into a plurality of data blocks according to service logic, and performing data distribution through a distributed message queue; 4, using a distributed database application data fragmentation technology to carry out fragmentation storage on the data and carrying out backup on the data; 5, analyzing the stored data in real time through a distributed computing framework; and step 6, displaying an analysis result through a data visualization tool, supporting user-defined query and report generation, and providing real-time data support for decision making. The system is used for solving the bottleneck problem of a traditional centralized architecture, and provides powerful data processing and real-time analysis capabilities for banks.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing and analysis, and in particular to a bank data processing method and a real-time analysis system based on a distributed architecture. Background Art

[0002] With the rapid development of financial technology and the digital transformation of banking business, banks have accumulated a large amount of complex data. This data is not only huge in volume, but also diverse in types, covering transaction records, customer information, market data and other aspects. How to efficiently process and analyze this data has become the key to improving banks' business capabilities and competitiveness.

[0003] Under the traditional centralized architecture, data processing systems face many challenges: first, the system scalability is limited and it is difficult to cope with the rapidly growing amount of data; second, the centralized system has low fault tolerance and a single point of failure may cause the entire system to crash; in addition, the performance of this architecture degrades significantly under high concurrency and cannot meet the needs of real-time data analysis. Therefore, distributed architecture has gradually become the preferred solution for bank data processing and real-time analysis systems.

[0004] The distributed architecture achieves high availability, scalability and high performance of the system by distributing data and computing tasks to multiple nodes. Based on big data processing frameworks such as Hadoop and Spark, banks can effectively store and process massive amounts of data. At the same time, distributed databases such as Cassandra and HBase provide high throughput and low latency data reading and writing capabilities, ensuring the real-time and stability of the system.

[0005] Real-time analysis is an important part of bank data processing. By using stream processing technologies such as Apache Kafka and Apache Storm, banks can capture and analyze data in real time and respond quickly to market changes and customer needs. For example, real-time fraud detection systems can quickly identify and prevent potential fraud by analyzing transaction data; customer service systems can solve customer problems in a timely manner and improve service quality by monitoring social media and phone records in real time. Summary of the invention

[0006] In view of the technical problems existing in the prior art, the present invention provides a bank data processing method and real-time analysis system based on a distributed architecture to solve the bottleneck problem of the traditional centralized architecture, provide banks with powerful data processing and real-time analysis capabilities, and help banks maintain their leading position in the fierce market competition.

[0007] According to a first aspect of the present invention, the present invention provides a bank data processing method based on a distributed architecture, comprising:

[0008] Step 1: Collect real-time bank transaction data, including transaction records, customer information and log information;

[0009] Step 2: Preprocess the collected data, including deduplication, missing value processing, data type conversion, and data standardization;

[0010] Step 3: Divide the preprocessed data into multiple data blocks according to business logic, and distribute the data through distributed message queues;

[0011] Step 4: Use a distributed database to apply data sharding technology to store the data in shards and back up the data;

[0012] Step 5: Perform real-time analysis on the stored data through a distributed computing framework;

[0013] Step 6: Display the analysis results through data visualization tools, and support customized queries and report generation to provide real-time data support for decision-making.

[0014] Based on the above technical solution, the present invention can also make the following improvements.

[0015] Optionally, in step 1, bank transaction data is obtained in real time through a RESTful API or a message queue, and the acquisition of real-time bank transaction data includes:

[0016] Step 1.1: Determine the data sources that need to be collected, clarify the type and data format of each data source, configure connection information for each data source, and ensure that the data collection agent can successfully access the data source;

[0017] Step 1.2: Design data collection logic for the system that provides APIs, use HTTP requests to obtain data, and use message queues for data collection in scenarios with high real-time requirements;

[0018] Step 1.3: Use HTTP methods to obtain data from the RESTful API. For the message queue, listen to the messages and consume them in real time. Parse the obtained data, convert it into a unified data format, and extract the required fields.

[0019] Step 1.4: Set up data collection monitoring, monitor the status of collection tasks in real time, record successful and failed collection records, design error handling mechanisms, and log and alarm for abnormal situations that occur during data collection.

[0020] Optionally, in step 2, the Apache NiFi tool is used to automatically process the data flow; and the quality assessment of the pre-processed data includes:

[0021] Use Apache NiFi's ValidateRecord processor to detect whether the data meets the predefined quality standards, summarize the results of the data quality assessment, generate reports, record key indicators in the data cleaning process, and provide feedback on the data preprocessing steps based on the assessment results. If necessary, perform iterative optimization to improve the processing flow.

[0022] Optionally, in step 3, distributing data through a distributed message queue includes:

[0023] Step 3.1: Determine the business logic and distribution strategy, communicate with the business team, clarify the data usage scenarios and requirements, determine the different usage modules of the data, divide the data into multiple data blocks according to the business logic, determine the distribution target and processing flow of each data block, select topics or queues, and ensure that the data can be consumed by relevant downstream systems;

[0024] Step 3.2: Data serialization and preparation, determine the serialization format of the data, and choose it based on the needs of subsequent consumption and performance considerations;

[0025] Step 3.3: Send data to Kafka, use the Kafka client API to write a data producer, set the producer configuration, and send the pre-processed data blocks to the corresponding Kafka topics according to the distribution strategy to ensure that the data can flow according to the predetermined logic;

[0026] Step 3.4: Monitoring and logging. Use Kafka monitoring tools to monitor the performance and health of the Kafka cluster and record key log information during data sending, including the number of messages sent, sending rate, and error information.

[0027] Step 3.5: Configure downstream systems to consume data. Configure Kafka consumers for downstream systems, ensure that they can correctly connect to the Kafka cluster, subscribe to the corresponding topics, write consumer code, process the received data, and perform business logic processing or storage.

[0028] Optionally, in step 3.2, before sending the data to Kafka, convert the data to the selected serialization format to ensure that the data can be correctly parsed during transmission, and verify the format and content of the serialized data to ensure that no information is lost or the format is incorrect.

[0029] Optionally, in step 3.3, when sending data, the sending result and exception information are captured, and the successfully sent messages and the failed sending messages are recorded to facilitate subsequent error handling and retry mechanism.

[0030] Optionally, in step 4, using a distributed database to apply a data sharding technology to store data shards includes:

[0031] Step 4.1: Select a suitable distributed database. Analyze business requirements based on data types, access patterns, and expansion requirements, and select a suitable distributed database to evaluate the performance characteristics of different databases.

[0032] Step 4.2: Design the data model according to the business logic, define the data table structure, index, and field type to ensure support for efficient query and storage; determine the sharding strategy, and design a reasonable sharding key based on the access pattern and data distribution;

[0033] Step 4.3: Configure sharding in the selected distributed database, set the sharding key and sharding rules to ensure that data is evenly distributed among different nodes; if there is already data, implement a data migration strategy to migrate the existing data according to the newly designed sharding rules;

[0034] Step 4.4: Configure database node redundancy and set the number of replicas to ensure that data exists on multiple nodes and improve the fault tolerance of the system; set up an automatic failover mechanism so that when the primary node fails, the system can automatically switch to the replica node;

[0035] Step 4.5: Develop a regular backup strategy, determine the backup frequency and backup window, so as to save the data status regularly; combine incremental backup and full backup to reduce the storage space and time required for backup, improve backup efficiency, and select a secure backup storage location;

[0036] Step 4.6: Configure monitoring tools to monitor the performance, load, and storage status of the database in real time, detect potential problems in a timely manner, and regularly tune the database performance and adjust the sharding strategy and index configuration based on monitoring data and business changes.

[0037] Optionally, in step 5, the real-time analysis of the stored data by using a distributed computing framework includes:

[0038] Step 5.1: Analyze business requirements, determine the types of analysis and data scale that need to be supported, select a distributed computing framework, and evaluate the performance characteristics of different frameworks to ensure that the framework can meet real-time analysis requirements;

[0039] Step 5.2: Establish a connection with the data storage, integrate historical data and real-time data streams, clean the integrated data, remove duplicate values, and process missing values; perform format conversion, convert and aggregate the original data according to analysis requirements, and generate a data format suitable for model input;

[0040] Step 5.3: Based on the business objectives, select a machine learning or statistical analysis algorithm, train the selected model using historical data, adjust model parameters, and set up a real-time data stream processing pipeline to ensure that new data can enter the model for prediction and analysis in real time;

[0041] Step 5.4: Configure stream processing in the selected distributed computing framework, set the receiving, processing and output methods of data streams, use the window mechanism to group and analyze real-time data, extract meaningful real-time information, and output the analysis results to the specified storage or through the API for other systems to call;

[0042] Step 5.5: Design a template for real-time reports, determine the key indicators, charts, and data visualization elements included in the reports, generate analysis reports regularly or in real time, and support notification mechanisms in multiple formats. When new reports are generated, notify relevant personnel via email or push notifications.

[0043] Step 5.6: Regularly evaluate the performance of the model, use indicators to evaluate the model, iteratively optimize the model according to the evaluation results and changes in real-time data, retrain or adjust the algorithm, establish a user feedback mechanism, collect the usage of analysis results and user opinions, and continuously improve the analysis process and model.

[0044] Optionally, in step 6, presenting the analysis results through a data visualization tool includes:

[0045] Step 6.1: Connect to the data store using a data visualization tool.

[0046] Step 6.2: Create a data model, establish relationships between different data tables, summarize and aggregate data, generate views suitable for analysis, and create calculated fields in the data model to calculate growth rates and year-on-year indicators;

[0047] Step 6.3: Select the visualization type, select the visualization type according to the nature of the data and analysis requirements, customize the view, add interactive filters and slicers, and enable users to customize query conditions and data views;

[0048] Step 6.4: Allow business personnel to generate custom reports based on their needs and provide export functions for easy sharing and archiving.

[0049] According to a second aspect of the present invention, there is provided a real-time bank data analysis system based on a distributed architecture, comprising:

[0050] The data collection module is responsible for collecting data from various data sources in real time, including bank transaction data, customer information and external economic data; preprocessing the collected data, including deduplication, missing value processing, data type conversion and data standardization; dividing the preprocessed data into multiple data blocks according to business logic, and distributing the data through distributed message queues;

[0051] Data storage module: used to store the collected data in a distributed database, partition and slice the data according to business needs, and use the distributed database to apply data slicing technology to store the data slices and back up the data;

[0052] The data analysis module is used to perform real-time analysis on stored data through a distributed computing framework, present the analysis results through data visualization tools, and support custom queries and report generation to provide real-time data support for decision-making.

[0053] Technical effects and advantages of the present invention:

[0054] The present invention provides a bank data processing method and real-time analysis system based on a distributed architecture. Through data sharding and distributed storage, the system can be horizontally expanded, and can process large-scale bank transaction data. Using a real-time data stream processing framework, the transaction data can be processed and analyzed in real time, and instant decision-making and response can be supported. Through distributed computing nodes and computing frameworks, efficient data analysis and processing capabilities are achieved, and the speed and accuracy of data processing are improved. The monitoring and fault recovery mechanism ensures the reliability and high availability of the system, and reduces the impact of system failures on data processing. The system adopts a distributed architecture design, which can effectively avoid the impact of single point failures and achieve high availability. The present invention can effectively support the real-time processing and analysis of large-scale transaction data, improve the efficiency of data analysis and processing by business personnel, and better meet the needs of financial institutions or banking services. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 It is a flow chart of a bank data processing method based on a distributed architecture provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0056] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0057] It is understandable that, based on the defects in the background technology, the embodiment of the present invention proposes a bank data processing method based on a distributed architecture, specifically as follows: Figure 1 As shown, the method comprises the following steps:

[0058] Step 1: Collect real-time bank transaction data, including transaction records, customer information and log information;

[0059] In this embodiment, bank transaction data is obtained in real time through a RESTful API or a message queue, and the bank transaction data supports batch and streaming data processing; collecting real-time bank transaction data includes the following steps:

[0060] Step 1.1: Data source identification and configuration; determine the data sources that need to be collected, including the bank's transaction system, customer management system, and third-party services; clarify the type and data format of each data source (such as database, API, log file, etc.), and configure connection information for each data source, including database connection string, API access address, authentication information, etc., to ensure that the data collection agent can successfully access these data sources;

[0061] Step 1.2: Select the collection method; Design data collection logic for systems that provide APIs, use HTTP requests to obtain data, and choose to trigger data collection by scheduled polling or event-driven methods (such as Webhook). For scenarios with high real-time requirements, use message queues (such as Kafka, RabbitMQ) for data collection. The system can subscribe to specific topics and receive messages in real time;

[0062] Step 1.3: Data acquisition and processing; implement data request logic, use HTTP methods (GET, POST, etc.) to obtain data from RESTful API, and for message queues, listen to messages and consume them in real time; parse the acquired data, convert it into a unified data format (such as JSON or XML), and extract the required fields (such as transaction amount, timestamp, customer ID, etc.);

[0063] Step 1.4: Monitoring and error handling; Set up data collection monitoring, monitor the status of collection tasks in real time, record successful and failed collection records, design error handling mechanisms, log and alarm for abnormal situations that occur during data collection (such as network failures, data format errors, etc.) to ensure the stability and reliability of the system.

[0064] Step 2: Preprocess the collected data, including deduplication, missing value processing, data type conversion, and data standardization;

[0065] It should be noted that this step can use tools such as Apache NiFi to realize the automatic processing of data flow;

[0066] Data deduplication includes: Determine a unique identifier (such as user ID, transaction ID, etc.) based on business needs and use the identifier to identify duplicate records. Use data processing tools (such as Apache NiFi's ExecuteScript or UpdateAttribute processors) to perform deduplication, retain the latest or most valid records, and delete the remaining duplicates. Check the deduplicated data to ensure that no valid records are missed, and record deduplication statistics for subsequent audits;

[0067] Missing value processing includes: analyzing the number and distribution of missing values ​​in the data set, and using tools (such as Apache NiFi's QueryRecord processor) to identify fields with missing data. Choose a strategy for handling missing values ​​based on the business scenario, such as: 1. Directly delete records containing missing values, which is suitable for situations with fewer missing values. 2. Use methods such as mean, median, mode to fill in numerical data, or use specific values ​​to fill in categorical data (such as "unknown"). 3. For time series data, interpolation can be used to fill in missing values.

[0068] Apply the selected strategy and update the dataset to ensure that the processed data meets expectations;

[0069] Data type conversion includes: Check the data type of each field in the data set and identify fields that do not match the type or need to be converted. According to the requirements of the target system or data warehouse, formulate data type conversion rules. For example, convert strings to date format, convert floating point numbers to integers, etc. Use Apache NiFi's ConvertRecord or UpdateRecord processors to perform type conversion according to the established rules to ensure data format consistency. Check the converted data to confirm that the data type of the field meets the expectations and record any warnings or error messages during the conversion process;

[0070] Data standardization includes: defining data standardization rules, ensuring that data field names and formats are consistent, such as unifying the date format to "YYYY-MM-DD", unifying the currency unit, etc. Use NiFi's UpdateAttribute or ReplaceText processor to perform data standardization according to the set rules. Verify the standardized data to ensure that all fields meet the expected standards and record the changes in standardization.

[0071] In addition, the quality of the preprocessed data is evaluated, including: using Apache NiFi's ValidateRecord processor to detect whether the data meets the predefined quality standards, summarizing the results of the data quality evaluation, generating reports, and recording key indicators in the data cleaning process (such as deduplication rate, missing rate, etc.). Based on the evaluation results, feedback is provided on the data preprocessing steps, and iterative optimization is performed when necessary to improve the processing flow.

[0072] Step 3: Divide the preprocessed data into multiple data blocks according to business logic, and distribute the data through distributed message queues;

[0073] It should be noted that this step can ensure the rapid transmission and processing of data within the system.

[0074] Among them, data distribution through distributed message queues specifically includes:

[0075] Step 3.1: Determine the business logic and distribution strategy; communicate with the business team to clarify the data usage scenarios and requirements, and determine the different usage modules of the data (such as analysis, real-time monitoring, data storage, etc.); divide the data into multiple data blocks according to the business logic, such as by user, time window, geographic location and other dimensions, determine the distribution target and processing flow of each data block, select the appropriate topic or queue, and ensure that the data can be consumed by the relevant downstream systems;

[0076] Step 3.2: Data serialization and preparation; Determine the serialization format of the data (such as JSON, Avro, ProtocolBuffers, etc.), and choose according to the needs of subsequent consumption and performance considerations. Before sending the data to Kafka, convert the data to the selected serialization format to ensure that the data can be correctly parsed during transmission. Verify the format and content of the serialized data to ensure that there is no missing information or format errors;

[0077] Step 3.3: Send data to Kafka; Use the Kafka client API (such as Java, Python, etc.) to write a data producer and set the producer configuration (such as Kafka Broker address, serializer, etc.). According to the distribution strategy, send the preprocessed data blocks to the corresponding Kafka topics to ensure that the data can flow according to the predetermined logic. When sending data, capture the sending results and exception information, record the successfully sent messages and the failed messages, to facilitate the subsequent error handling and retry mechanism;

[0078] Step 3.4: Monitoring and logging; Use Kafka monitoring tools (such as Confluent Control Center, Prometheus, etc.) to monitor the performance and health of the Kafka cluster. Record key log information during data sending, including the number of messages sent, sending rate, error information, etc., to facilitate subsequent problem troubleshooting. Implement a data tracking mechanism to ensure that the source and destination of each message can be tracked to improve the transparency of data processing;

[0079] Step 3.5: Downstream systems consume data; configure Kafka consumers for downstream systems, ensure that they can correctly connect to the Kafka cluster and subscribe to the corresponding topics. Write consumer code to process the received data and perform necessary business logic processing or storage. Monitor the consumer's consumption rate, latency, and errors to ensure that downstream systems can process the data received from Kafka in a timely manner.

[0080] Step 4: Use a distributed database to apply data sharding technology to store the data in shards and back up the data;

[0081] During data storage, data sharding technology is applied to improve storage efficiency and ensure high data availability and reliability. A reasonable data backup strategy should also be designed during this process to prevent data loss;

[0082] Using distributed database to apply data sharding technology to store data in shards includes:

[0083] Step 4.1: Choose a suitable distributed database; Analyze business needs based on data types (such as relational, non-relational), access patterns (such as more reads and fewer writes), and expansion requirements, and choose a suitable distributed database (such as Cassandra, MongoDB, CockroachDB, etc.). Evaluate the performance characteristics of different databases, consider indicators such as read and write latency, scalability, and data consistency, and ensure that the selection meets long-term needs;

[0084] Step 4.2: Design the data model; design the data model according to the business logic, define the data table structure, index, field type, etc., to ensure support for efficient query and storage. Determine the sharding strategy (such as range sharding, hash sharding, etc.), and design a reasonable sharding key according to the access mode and data distribution to ensure load balancing;

[0085] Step 4.3: Implement data sharding; configure sharding in the selected distributed database, set sharding keys and sharding rules, and ensure that data is evenly distributed among different nodes. If there is already data, implement a data migration strategy to migrate the existing data according to the newly designed sharding rules to ensure a smooth transition;

[0086] Step 4.4: Achieve high availability and reliability; configure database node redundancy, set the number of replicas, ensure that data exists on multiple nodes, and improve the system's fault tolerance. Set up an automatic failover mechanism so that when the primary node fails, the system can automatically switch to the replica node to ensure continuous data availability;

[0087] Step 4.5: Design a data backup strategy; formulate a regular backup strategy, determine the backup frequency (such as daily, weekly) and the backup window, so as to regularly save the data status. Combine incremental backup and full backup to reduce the storage space and time required for backup and improve backup efficiency. Choose a secure backup storage location (such as cloud storage, off-site backup) to ensure the security and reliability of backup data;

[0088] Finally, it also includes: Step 4.6: Monitoring and maintenance; configure monitoring tools to monitor the performance, load and storage status of the database in real time to detect potential problems in a timely manner. According to monitoring data and business changes, regularly tune the database performance, adjust the sharding strategy, index configuration, etc. to cope with changing needs.

[0089] Step 5: Perform real-time analysis on the stored data through a distributed computing framework;

[0090] It should be noted that the analytical model can perform predictive analysis, customer behavior analysis, etc. based on historical data and real-time data, and support the generation of real-time reports;

[0091] The real-time analysis of the stored data by using the distributed computing framework includes:

[0092] Step 5.1: Select a suitable distributed computing framework; Analyze business requirements, determine the types of analysis that need to be supported (such as predictive analysis, customer behavior analysis) and data scale, and select a suitable distributed computing framework (such as Apache Spark, Flink, Dask, etc.). Evaluate the performance characteristics of different frameworks, especially their support for real-time data processing, to ensure that the framework can meet real-time analysis requirements;

[0093] Step 5.2: Data integration and preprocessing: Establish connections with data storage, integrate historical data and real-time data streams, and ensure the diversity of data sources (such as databases, data lakes, and message queues). Clean the integrated data, remove duplicate values, process missing values, and perform format conversion to ensure data quality. According to analysis requirements, convert and aggregate the original data to generate a data format suitable for model input;

[0094] Step 5.3: Build an analysis model; based on business objectives, select appropriate machine learning or statistical analysis algorithms (such as regression analysis, cluster analysis, time series forecasting, etc.). Use historical data to train the selected model and adjust model parameters to improve prediction accuracy and analysis results. Set up a real-time data stream processing pipeline to ensure that new data can enter the model for prediction and analysis immediately;

[0095] Step 5.4: Implement real-time analysis; configure stream processing in the selected distributed computing framework and set the receiving, processing and output methods of data streams. Use window mechanisms (such as sliding windows, session windows) to group and analyze real-time data to extract meaningful real-time information. Output the analysis results to the specified storage (such as database, data warehouse) or through API for other systems to call;

[0096] Step 5.5: Real-time report generation; Design templates for real-time reports and determine the key indicators, charts, and data visualization elements included in the report. Implement the process of automatically generating reports, generate analysis reports regularly or in real time, and support multiple formats (such as PDF, HTML). Configure the notification mechanism to notify relevant personnel via email, message push, etc. when a new report is generated;

[0097] Step 5.6: Model evaluation and optimization; Regularly evaluate the performance of the model, use indicators (such as accuracy, recall, F1-score, etc.) to evaluate the model to ensure the effectiveness of the model. According to the evaluation results and changes in real-time data, iteratively optimize the model, retrain or adjust the algorithm to improve the analysis effect. Establish a user feedback mechanism, collect the use of analysis results and user opinions, and continuously improve the analysis process and model.

[0098] Step 6: Display the analysis results through data visualization tools, and support customized queries and report generation to provide real-time data support for decision-making.

[0099] The visualization tools such as Tableau and Power BI, in this implementation step, facilitate business personnel to perform decision analysis through data visualization.

[0100] The analysis results are presented through data visualization tools including:

[0101] Step 6.1: Data extraction: Using data visualization tools requires connecting to data stores (such as relational databases, NoSQL databases, and data warehouses);

[0102] Step 6.2: Data modeling; create a data model to establish relationships between different data tables, such as one-to-many, many-to-many, etc. Data aggregation, summarize and aggregate data to generate a view suitable for analysis. For example, by summarizing data by month, quarter, or year. Create calculated fields in the data model to calculate indicators such as growth rate, year-on-year, etc.

[0103] Step 6.3: Data visualization design; select the visualization type, such as line chart, bar chart, pie chart, etc. according to the nature of the data and analysis requirements. Customize the view, add interactive filters and slicers, so that users can customize query conditions and data views;

[0104] Step 6.4: Report generation: Allow business personnel to customize the report generation according to their needs, including selecting data, chart type, time range, etc. Provide export function, support exporting reports to PDF, Excel and other formats for easy sharing and archiving.

[0105] In summary, the bank data processing method based on the distributed architecture provided by the embodiment of the present invention has the following advantages:

[0106] 1. High scalability: Through data sharding and distributed storage, the system can be horizontally expanded and can handle large-scale bank transaction data;

[0107] 2. Real-time processing capability: Using a real-time data stream processing framework, it can process and analyze transaction data in real time to support instant decision-making and response;

[0108] 3. Efficient data analysis: Through distributed computing nodes and computing framework, efficient data analysis and processing capabilities are achieved, and the speed and accuracy of data processing are improved;

[0109] Reliability and fault tolerance: Monitoring and fault recovery mechanisms ensure system reliability and high availability, reducing the impact of system failures on data processing;

[0110] High availability: The system adopts a distributed architecture design, which can effectively avoid the impact of single point failures and achieve high availability.

[0111] In addition, the embodiment of the present invention also provides a bank data processing and real-time analysis system based on a distributed architecture. The system architecture of the present invention is divided into three layers: a data collection layer, a data storage layer, and a data analysis layer; wherein,

[0112] Data collection layer: responsible for real-time data collection from various data sources, including bank transaction data, customer information, external economic data, etc. Data collection adopts a microservice architecture, each microservice is responsible for a specific data source, and supports multiple data formats (such as JSON, XML, CSV, etc.); used to collect real-time bank transaction data, including: transaction records, customer information and log information; pre-process the collected data, including deduplication, missing value processing, data type conversion and data standardization; divide the pre-processed data into multiple data blocks according to business logic, and distribute the data through distributed message queues;

[0113] Data storage layer: Distributed databases (such as Apache Cassandra, Hadoop HDFS) are used to store collected data, which supports horizontal expansion and can effectively cope with the storage needs of massive data. Data is partitioned and sharded according to business needs to ensure efficient storage and retrieval; it is used to use distributed databases to apply data sharding technology to store data shards and back up the data;

[0114] Data analysis layer: performs real-time analysis based on distributed computing frameworks (such as Apache Spark and Flink), supports multiple analysis models (such as machine learning, deep learning, statistical analysis, etc.); performs real-time analysis on stored data through distributed computing frameworks; displays analysis results through data visualization tools, supports custom queries and report generation, and provides real-time data support for decision-making.

[0115] In summary, the embodiments of the present invention provide an efficient and reliable bank data processing and analysis method by combining distributed architecture and real-time data processing technology, which can effectively support real-time processing and analysis of large-scale transaction data, improve the efficiency of data analysis and processing by business personnel, and better meet the needs of financial institutions or banking services.

[0116] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0117] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A bank data processing method based on a distributed architecture, characterized in that: The following steps are involved: Step 1: Collect real-time bank transaction data, including transaction records, customer information and log information; Step 2: Preprocess the collected data, including deduplication, missing value processing, data type conversion, and data standardization; Step 3: Divide the preprocessed data into multiple data blocks according to business logic, and distribute the data through distributed message queues; Step 4: Use distributed database to apply data sharding technology to store data in shards and back up the data; Step 5: Perform real-time analysis on the stored data through a distributed computing framework; Step 6: Display the analysis results through data visualization tools, and support customized queries and report generation to provide real-time data support for decision-making.

2. A bank data processing method based on a distributed architecture according to claim 1, characterized in that: In step 1, bank transaction data is obtained in real time through a RESTful API or a message queue. The acquisition of real-time bank transaction data includes: Step 1.1: Determine the data sources that need to be collected, clarify the type and data format of each data source, configure connection information for each data source, and ensure that data collection can successfully access the data source; Step 1.2: Design data collection logic for the system that provides APIs, use HTTP requests to obtain data, and use message queues for data collection in scenarios with high real-time requirements; Step 1.3: Use HTTP methods to obtain data from RESTful API. For message queues, listen to messages and consume them in real time. Parse the obtained data, convert it into a unified data format, and extract the required fields. Step 1.4: Set up data collection monitoring, monitor the status of collection tasks in real time, record successful and failed collection records, design error handling mechanisms, and log and alarm for abnormal situations that occur during data collection.

3. A bank data processing method based on a distributed architecture according to claim 1, characterized in that: In step 2, the Apache NiFi tool is used to automate the data flow. This also includes quality assessment of the pre-processed data, including: Use Apache NiFi's ValidateRecord processor to detect whether the data meets the predefined quality standards, summarize the results of the data quality assessment, generate reports, record key indicators in the data cleaning process, and provide feedback on the data preprocessing steps based on the assessment results. If necessary, perform iterative optimization to improve the processing flow.

4. A bank data processing method based on a distributed architecture according to claim 1, characterized in that: In step 3, the data distribution through the distributed message queue includes: Step 3.1: Determine the business logic and distribution strategy, communicate with the business team, clarify the data usage scenarios and requirements, determine the different usage modules of the data, divide the data into multiple data blocks according to the business logic, determine the distribution target and processing flow of each data block, select topics or queues, and ensure that the data can be consumed by relevant downstream systems; Step 3.2: Data serialization and preparation, determine the serialization format of the data, and choose it based on the needs of subsequent consumption and performance considerations; Step 3.3: Send data to Kafka, use the Kafka client API to write a data producer, set the producer configuration, and send the pre-processed data blocks to the corresponding Kafka topics according to the distribution strategy to ensure that the data can flow according to the predetermined logic; Step 3.4: Monitoring and logging, use Kafka monitoring tools to monitor the performance and health of the Kafka cluster, and record key log information during data sending, including the number of messages sent, sending rate, and error information; Step 3.5: Configure downstream systems to consume data. Configure Kafka consumers for downstream systems, ensure that they can correctly connect to the Kafka cluster, subscribe to the corresponding topics, write consumer code, process the received data, and perform business logic processing or storage.

5. A bank data processing method based on a distributed architecture according to claim 4, characterized in that: In step 3.2, before sending the data to Kafka, the data needs to be converted to the selected serialization format to ensure that the data can be correctly parsed during transmission, and the serialized data format and content must be verified to ensure that no information is lost or the format is incorrect.

6. A bank data processing method based on a distributed architecture according to claim 4, characterized in that: In step 3.3, when sending data, it is necessary to capture the sending results and exception information, and record the successfully sent messages and the failed messages, to facilitate subsequent error handling and retry mechanism.

7. A bank data processing method based on a distributed architecture according to claim 1, characterized in that: In step 4, using a distributed database to apply data sharding technology to store data shards includes: Step 4.1: Analyze business requirements based on data types, access patterns, and expansion requirements, and select a distributed database to evaluate the performance characteristics of different databases; Step 4.2: Design the data model according to the business logic, define the data table structure, index, and field type to ensure support for efficient query and storage; determine the sharding strategy, and design a reasonable sharding key based on the access pattern and data distribution; Step 4.3: Configure sharding in the selected distributed database, set the sharding key and sharding rules to ensure that data is evenly distributed among different nodes; if there is already data, implement a data migration strategy to migrate the existing data according to the newly designed sharding rules; Step 4.4: Configure database node redundancy and set the number of replicas to ensure that data exists on multiple nodes and improve the fault tolerance of the system; set up an automatic failover mechanism so that when the primary node fails, the system can automatically switch to the replica node; Step 4.5: Develop a regular backup strategy, determine the backup frequency and backup window, so as to save the data status regularly; combine incremental backup and full backup to reduce the storage space and time required for backup, improve backup efficiency, and select a secure backup storage location; Step 4.6: Configure monitoring tools to monitor the performance, load, and storage status of the database in real time, detect potential problems in a timely manner, and regularly tune the database performance and adjust the sharding strategy and index configuration based on monitoring data and business changes.

8. A bank data processing method based on a distributed architecture according to claim 1, characterized in that: In step 5, the real-time analysis of the stored data by using the distributed computing framework includes: Step 5.1: Analyze business requirements, determine the types of analysis and data scale that need to be supported, select a distributed computing framework, and evaluate the performance characteristics of different frameworks to ensure that the framework can meet real-time analysis requirements; Step 5.2: Establish a connection with the data storage, integrate historical data and real-time data streams, clean the integrated data, remove duplicate values, and process missing values; perform format conversion, convert and aggregate the original data according to analysis requirements, and generate a data format suitable for model input; Step 5.3: Based on the business objectives, select a machine learning or statistical analysis algorithm, train the selected model using historical data, adjust model parameters, and set up a real-time data stream processing pipeline to ensure that new data can enter the model for prediction and analysis in real time; Step 5.4: Configure stream processing in the selected distributed computing framework, set the receiving, processing and output methods of data streams, use the window mechanism to group and analyze real-time data, extract useful real-time information, and output the analysis results to the specified storage or through the API for other systems to call; Step 5.5: Design a template for real-time reports, determine the key indicators, charts, and data visualization elements included in the reports, generate analysis reports regularly or in real time, and support notification mechanisms in multiple formats. When new reports are generated, relevant personnel are notified via email or push notifications. Step 5.6: Regularly evaluate the performance of the model, use indicators to evaluate the model, iteratively optimize the model according to the evaluation results and changes in real-time data, retrain or adjust the algorithm, establish a user feedback mechanism, collect the usage of analysis results and user opinions, and continuously improve the analysis process and model.

9. A bank data processing method based on a distributed architecture according to claim 1, characterized in that: In step 6, presenting the analysis results through a data visualization tool includes: Step 6.1: Connect to the data store using a data visualization tool. Step 6.2: Create a data model, establish relationships between different data tables, summarize and aggregate data, generate views suitable for analysis, create calculated fields in the data model, and calculate growth rates and year-on-year indicators; Step 6.3: Select the visualization type, select the visualization type according to the nature of the data and analysis requirements, customize the view, add interactive filters and slicers, and enable users to customize query conditions and data views; Step 6.4: Allow business personnel to generate custom reports based on their needs and provide export functions for easy sharing and archiving.

10. A real-time bank data analysis system based on a distributed architecture, characterized in that: include: The data collection module is responsible for collecting data from various data sources in real time, including bank transaction data, customer information and external economic data; And preprocess the collected data, including deduplication, missing value processing, data type conversion and data standardization; Divide the preprocessed data into multiple data blocks according to business logic, and distribute the data through distributed message queues; Data storage module: used to use a distributed database to store the collected data, partition and slice the data according to business needs, and use the distributed database to apply data slicing technology to store the data slices and back up the data; The data analysis module is used to perform real-time analysis on stored data through a distributed computing framework, present the analysis results through data visualization tools, and support custom queries and report generation to provide real-time data support for decision-making.

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