Big data visualization system

Abstract

The invention belongs to the technical field of big data, and particularly relates to a big data visualization system which comprises a data acquisition module, a data storage module, a data processing module, a data visualization module, a user interaction module, an authority management module, a system monitoring module and an alarm and notification module. The operation of the visualization system comprises the following steps: step 1, data acquisition: determining data sources including a database, an API (Application Program Interface) and a sensor, acquiring data from each data source by writing a data acquisition script or using a data integration tool, cleaning the acquired data, and removing repeated, wrong and incomplete data; and 2, data storage: storing the cleaned data in a database or a data warehouse, and backing up the data regularly. According to the method, the data processing and analysis efficiency can be remarkably improved, the data quality and safety are improved, real-time decision making and service optimization are supported, and the stability and reliability of the system are enhanced.

Description

Technical Field

[0001] This invention belongs to the field of big data technology, and specifically relates to a big data visualization system. Background Technology

[0002] Big data refers to massive and complex datasets that cannot be processed and analyzed within a reasonable timeframe using traditional data processing methods and tools. Its characteristics are generally described by the "four Vs": Volume: enormous, typically reaching TB or even PB levels; Velocity: extremely fast data generation and processing, requiring real-time or near-real-time data processing capabilities; Variety: diverse data types, including structured, unstructured, and semi-structured data such as text, images, videos, and sensor data; and Veracity: inconsistent data quality, potentially containing noisy, inaccurate, and incomplete data, requiring cleaning and verification. The primary goal of big data technology is to extract valuable information and knowledge from these massive and complex datasets to support decision-making, optimize business processes, improve efficiency, and create new business models. Key technologies and tools for achieving this goal include data storage and management (such as Hadoop, HDFS, and NoSQL databases like MongoDB and Cassandra), data processing and analysis (such as MapReduce, Spark, and Flink), data mining and machine learning (such as H2O.ai, TensorFlow, and PyTorch), and data visualization (such as Tableau, Power BI, and D3.js). Big data has wide applications across multiple sectors, including finance, healthcare, retail, manufacturing, logistics, government, and social media. By analyzing and processing big data, applications such as precision marketing, predictive maintenance, personalized recommendations, risk management, and public safety can be achieved.

[0003] Existing big data visualization systems focus on data display, resulting in low efficiency in data processing and analysis, poor data quality and security, and hindering real-time decision-making and business optimization. Furthermore, the stability and reliability of the system cannot be guaranteed. Therefore, we propose a big data visualization system to address these issues. Summary of the Invention

[0004] The purpose of this invention is to provide a big data visualization system that can significantly improve the efficiency of data processing and analysis, enhance data quality and security, support real-time decision-making and business optimization, and strengthen the stability and reliability of the system.

[0005] The specific technical solution adopted by this invention is as follows:

[0006] A big data visualization system includes a data acquisition module, a data storage module, a data processing module, a data visualization module, a user interaction module, a permission management module, a system monitoring module, and an alarm and notification module.

[0007] In a preferred embodiment, based on a big data visualization system, the operation of the visualization system includes the following steps:

[0008] Step 1. Data Acquisition: Determine the data sources, including databases, APIs, and sensors. Obtain data from various data sources by writing data acquisition scripts or using data integration tools. Clean the acquired data to remove duplicate, erroneous, and incomplete data.

[0009] Step 2. Data storage: Store the cleaned data in a database or data warehouse, and back up the data regularly;

[0010] Step 3. Data Processing: Perform preprocessing operations such as formatting and standardization on the data, use data processing frameworks for batch or real-time processing, and apply data mining and machine learning algorithms for in-depth analysis;

[0011] Step 4. Data Visualization: Design data visualization charts and dashboards according to requirements, select visualization tools to develop visualization charts, and integrate them into the system. Visualization tools include D3.js, ECharts, Tableau, and Power BI.

[0012] Step 5. User Interaction: Configure and customize the dashboard according to user needs, develop and set up report generation functions, and provide a user-friendly query interface;

[0013] Step 6. Access Control: Create and manage user accounts and their permissions, and set access permissions for different users to data;

[0014] Step 7. System Monitoring: Monitor system performance in real time, identify and resolve performance bottlenecks, and record system logs for subsequent troubleshooting and auditing;

[0015] Step 8. Alarms and Notifications: Set alarm rules according to business needs, monitor data and system status in real time, detect abnormal situations, and send alarm information to relevant personnel via email and SMS.

[0016] In a preferred embodiment, the data acquisition further includes identifying the data types to be acquired, including structured data, semi-structured data, and unstructured data; listing potential data sources, including SQL / NoSQL, API, CSV, Excel, JSON, sensor data, and log files; determining the access methods and permission requirements for each data source; configuring connection parameters according to the data source type; testing connection success; selecting programming languages ​​and tools, including Python, Java, and R; writing data acquisition scripts; writing SQL or NoSQL query statements and scripts; writing API call code; handling requests and responses; writing code to read data from local or remote files; selecting data integration tools, including Apache Nifi, Talend, and Informatica; configuring the tool to connect to the data source and target storage; designing the data acquisition process within the tool; and configuring data transformation and loading operations.

[0017] In a preferred embodiment, the data storage further includes determining the data volume, access speed, query frequency, and security requirements; selecting the database type based on the data type and requirements, including relational databases, NoSQL databases, and data warehouses; selecting centralized storage, distributed storage, or cloud storage solutions; designing the table structure of the relational database, including table fields, data types, primary keys, and indexes; configuring database connection parameters; setting data write permissions; using batch processing tools or scripts to import data into the database or data warehouse in batches; and using ETL tools to extract, transform, and load data.

[0018] In a preferred embodiment, the data processing further includes selecting a suitable batch processing framework, including Apache Hadoop and Apache Spark; configuring batch processing tasks; setting the input, output, and processing logic for data processing; executing batch processing tasks; processing large-scale data; configuring stream processing tasks; setting the input, output, and processing logic for data streams; performing feature selection, feature extraction, and feature construction based on business needs and data characteristics; selecting data mining and machine learning algorithms; training models using training data; adjusting parameters and hyperparameters; validating models using validation datasets; and integrating data analysis and model prediction results into the business process.

[0019] In a preferred embodiment, the data visualization further includes analyzing the data to be visualized, determining the data type, magnitude, and structure, identifying the main visualization objectives (including displaying trends, comparing data, and identifying anomalies), selecting appropriate chart types based on the data type and visualization objectives (including line charts, bar charts, pie charts, scatter plots, and maps), designing the dashboard layout, determining the position and size of each chart, designing user interaction methods (including filters, slicers, and dynamic updates), extracting data from a database or data warehouse, transforming and processing the data according to visualization requirements, drawing various charts using selected visualization tools, integrating the charts into the dashboard, arranging them according to the designed layout, setting access permissions for different users, testing the various functions of the charts and dashboard, and testing the compatibility of the visualization on different browsers and devices.

[0020] In a preferred embodiment, the user interaction further includes determining the dashboard functions required by the user, including specific chart types, data filtering, and time range selection; clarifying the user's usage scenarios, including daily monitoring, decision support, and trend analysis; designing the dashboard layout according to user needs; determining the position, size, and arrangement of charts; binding each chart in the dashboard to its corresponding data source; configuring user interaction functions, including click events, hover tips, and drop-down filters; setting different dashboard views according to user roles and permissions; determining the content and structure of reports according to user needs, including tabular data and chart displays; designing report templates according to requirements; defining the position, font, color, and style of each data item; writing report generation scripts; generating report files according to the templates; developing a user interface that allows users to select report types, time ranges, and formats through the interface and export reports; identifying the roles and data permissions of different users; determining the data range for querying; determining the query functions required by the user, including filtering conditions, data dimensions, and aggregation methods; providing users with appropriate input controls for customizing query conditions, including date pickers, drop-down lists, and checkboxes; and designing the display methods for query results, including tables, charts, and maps.

[0021] In a preferred embodiment, the access control further includes determining the user roles and permission levels to be created, classifying roles according to organizational structure and business needs, collecting basic user information, including name, email, and job title, creating user accounts in the system, assigning initial usernames and passwords, defining different roles based on user responsibilities and permission requirements, including administrators, data analysts, and ordinary users, assigning corresponding permissions to each role, clarifying the operational scope and data access permissions of roles, setting inheritance relationships between roles, simplifying access control, enabling or disabling user accounts as needed, managing account lifecycles, providing password reset and modification functions, recording user account creation, modification, and deletion operations, classifying data according to its sensitivity and importance, including public data, internal data, and confidential data, setting permissions at the table, view, and field levels, dynamically adjusting user access permissions based on actual user needs and business scenarios, establishing a permission audit process, regularly reviewing user permissions, monitoring and detecting abnormal permission usage behavior, timely risk control, enabling multi-factor authentication mechanisms, implementing single sign-on, simplifying the user login process, implementing access control based on user roles, and dynamically adjusting access permissions based on user attributes and context conditions.

[0022] In a preferred embodiment, the system monitoring further includes identifying key performance indicators that need to be monitored, including CPU utilization, memory utilization, disk I / O, and network traffic; setting a performance baseline based on the normal operating status of the system as a monitoring reference; evaluating the functionality and performance of various performance monitoring tools, including Prometheus, Grafana, Nagios, and Zabbix; selecting a performance monitoring tool; configuring the indicators to be monitored in the monitoring tool; setting thresholds and alarm rules; configuring a data collection agent; periodically checking the configuration and status of the monitoring system; and setting a real-time alarm mechanism to immediately notify relevant personnel once a monitored indicator exceeds a threshold.

[0023] In a preferred embodiment, the alarms and notifications further include identifying key business and system metrics that need to be monitored, including response time, error rate, and resource utilization; assessing potential risks and their impact on business; determining alarm priorities and severity; selecting key metrics to be monitored to ensure coverage of the core parts of the business and system; setting reasonable alarm thresholds for each key metric based on business needs and historical data; designing a multi-level alarm mechanism; setting different alarm levels based on different severities, including warning, critical, and urgent; writing alarm rules in the monitoring tool; setting trigger conditions and response measures; conducting rule testing; configuring the monitoring tool; utilizing the automatic detection function of the monitoring tool to analyze data changes in real time and detect anomalies; setting a manual confirmation mechanism; selecting a combination of notification channels based on different alarm levels and business needs, including email, SMS, and instant messaging tools; formulating an alarm response mechanism; quickly locating the cause of the problem based on alarm information; recording the processing process and results of each alarm; and optimizing alarm rules and adjusting thresholds and trigger conditions based on feedback from alarm processing.

[0024] The technical effects achieved by this invention are as follows:

[0025] By identifying and integrating various data sources, including structured, semi-structured, and unstructured data, we ensure a comprehensive data perspective. Through data cleaning and deduplication, we improve the accuracy and reliability of data. By using data integration tools and automated scripts, we ensure the real-time and timely nature of data. By storing the cleaned data in an optimized database or data warehouse, we ensure efficient data access. Through regular backups and permission settings, we ensure the security and integrity of data. By selecting appropriate storage solutions based on business needs, we enhance the flexibility and scalability of the system.

[0026] By formatting and standardizing data, we ensure consistency and availability. We use batch and real-time processing frameworks to efficiently process large-scale data and improve data processing efficiency. We apply data mining and machine learning algorithms to perform in-depth analysis and model prediction, providing valuable business insights. By designing and developing data visualization charts and dashboards, we can intuitively display data trends, comparisons, and anomalies. Visual charts and dashboards help managers quickly understand data and support real-time decision-making. We use a variety of visualization tools to meet different visualization needs.

[0027] Configure and customize dashboards according to user needs, provide personalized query and reporting functions, offer a user-friendly interactive interface to enhance user engagement and satisfaction, support user-defined reports to meet the needs of different business scenarios, ensure data security and privacy protection by creating and managing user accounts and their permissions, set different user access permissions to ensure the rationality and compliance of data access, and simplify the permission management process and improve management efficiency through the inheritance relationship of roles and permissions.

[0028] Real-time monitoring of system performance enables timely identification and resolution of performance bottlenecks, ensuring stable system operation. System logs provide a basis for troubleshooting and auditing, improving problem-solving efficiency. Performance baselines and monitoring indicators allow for early detection of potential problems and preventative maintenance. Alarm rules are set to monitor data and system status in real time, promptly detecting anomalies. Alarm information is sent to relevant personnel via email, SMS, etc., ensuring rapid response and handling. Based on feedback from alarm handling, alarm rules and system performance are continuously optimized to improve the efficiency and stability of business processes. Attached Figure Description

[0029] Figure 1 This is a schematic diagram of a big data visualization system according to the present invention. Detailed Implementation

[0030] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0031] Please see Figure 1 As shown, the present invention provides a big data visualization system, including a data acquisition module, a data storage module, a data processing module, a data visualization module, a user interaction module, a permission management module, a system monitoring module, and an alarm and notification module.

[0032] Based on a big data visualization system, the operation of the visualization system includes the following steps:

[0033] Step 1. Data Acquisition: Determine the data sources, including databases, APIs, and sensors. Obtain data from various data sources by writing data acquisition scripts or using data integration tools. Clean the acquired data to remove duplicate, erroneous, and incomplete data.

[0034] Step 2. Data storage: Store the cleaned data in a database or data warehouse, and back up the data regularly;

[0035] Step 3. Data Processing: Perform preprocessing operations such as formatting and standardization on the data, use data processing frameworks for batch or real-time processing, and apply data mining and machine learning algorithms for in-depth analysis;

[0036] Step 4. Data Visualization: Design data visualization charts and dashboards according to requirements, select visualization tools to develop visualization charts, and integrate them into the system. Visualization tools include D3.js, ECharts, Tableau, and Power BI.

[0037] Step 5. User Interaction: Configure and customize the dashboard according to user needs, develop and set up report generation functions, and provide a user-friendly query interface;

[0038] Step 6. Access Control: Create and manage user accounts and their permissions, and set access permissions for different users to data;

[0039] Step 7. System Monitoring: Monitor system performance in real time, identify and resolve performance bottlenecks, and record system logs for subsequent troubleshooting and auditing;

[0040] Step 8. Alarms and Notifications: Set alarm rules according to business needs, monitor data and system status in real time, detect abnormal situations, and send alarm information to relevant personnel via email and SMS.

[0041] Data acquisition also includes identifying the types of data to be collected, including structured, semi-structured, and unstructured data; listing potential data sources, including SQL / NoSQL, API, CSV, Excel, JSON, sensors, and log files; determining the access methods and permission requirements for each data source; configuring connection parameters according to the data source type; testing connection success; selecting programming languages ​​and tools, including Python, Java, and R; writing data acquisition scripts; writing SQL or NoSQL query statements and scripts; writing API call code; handling requests and responses; writing code to read data from local or remote files; selecting data integration tools, including Apache Nifi, Talend, and Informatica; configuring the tool to connect to the data source and target storage; designing the data acquisition process within the tool; and configuring data transformation and loading operations.

[0042] Data storage also includes determining data volume, access speed, query frequency, and security requirements; selecting the database type based on data type and requirements (database types include relational databases, NoSQL databases, and data warehouses); choosing centralized storage, distributed storage, or cloud storage solutions; designing the table structure of the relational database, including table fields, data types, primary keys, and indexes; configuring database connection parameters; setting data write permissions; using batch processing tools or scripts to import data into the database or data warehouse in batches; and using ETL tools for data extraction, transformation, and loading.

[0043] Data processing also includes selecting a suitable batch processing framework, including Apache Hadoop and Apache Spark; configuring batch processing tasks; setting the input, output, and processing logic for data processing; executing batch processing tasks; processing large-scale data; configuring stream processing tasks; setting the input, output, and processing logic for data streams; performing feature selection, feature extraction, and feature construction based on business needs and data characteristics; selecting data mining and machine learning algorithms; training models using training data; adjusting parameters and hyperparameters; validating models using validation datasets; and integrating data analysis and model prediction results into business processes.

[0044] Data visualization also includes analyzing the data to be visualized, determining the data type, magnitude, and structure, defining the main visualization objectives (including displaying trends, comparing data, and identifying anomalies), selecting appropriate chart types based on data type and visualization objectives (including line charts, bar charts, pie charts, scatter plots, and maps), designing the dashboard layout, determining the position and size of each chart, designing user interaction methods (including filters, slicers, and dynamic updates), extracting data from databases or data warehouses, transforming and processing the data according to visualization requirements, creating various charts using selected visualization tools, integrating the charts into the dashboard, arranging them according to the designed layout, setting access permissions for different users, testing the various functions of the charts and dashboard, and testing the compatibility of visualization on different browsers and devices.

[0045] User interaction also includes determining the dashboard functionality required by the user, including specific chart types, data filtering, and time range selection; clarifying the user's usage scenarios, including daily monitoring, decision support, and trend analysis; designing the dashboard layout based on user needs, determining the position, size, and arrangement of charts; binding each chart in the dashboard to its corresponding data source; configuring user interaction functions, including click events, hover tools, and drop-down filters; setting different dashboard views based on user roles and permissions; determining the content and structure of reports based on user needs, including tabular data and chart displays; designing report templates based on requirements, defining the position, font, color, and style of each data item; writing report generation scripts to generate report files according to the templates; developing the user interface, allowing users to select report types, time ranges, and formats through the interface, and exporting reports; identifying different user roles and data permissions; determining the data range for queries; determining the query functions required by the user, including filter conditions, data dimensions, and aggregation methods; providing users with appropriate input controls for customizing query conditions, including date pickers, drop-down lists, and checkboxes; and designing the display methods for query results, including tables, charts, and maps.

[0046] Access control also includes determining the user roles and permission levels to be created, classifying roles according to organizational structure and business needs, collecting basic user information including name, email, and job title, creating user accounts in the system, assigning initial usernames and passwords, defining different roles based on user responsibilities and permission requirements (including administrators, data analysts, and regular users), assigning corresponding permissions to each role, clarifying the operational scope and data access permissions of roles, setting inheritance relationships between roles, simplifying access control, enabling or disabling user accounts as needed, managing account lifecycles, providing password reset and modification functions, recording user account creation, modification, and deletion operations, classifying data according to its sensitivity and importance (including public data, internal data, and confidential data), setting permissions at the table, view, and field levels, dynamically adjusting user access permissions based on actual user needs and business scenarios, establishing a permission audit process, regularly reviewing user permissions, monitoring and detecting abnormal permission usage behavior, timely risk control, enabling multi-factor authentication mechanisms, implementing single sign-on, simplifying the user login process, implementing access control based on user roles, and dynamically adjusting access permissions based on user attributes and context conditions.

[0047] System monitoring also includes identifying key performance indicators (KPIs) that need to be monitored, including CPU utilization, memory utilization, disk I / O, and network traffic; setting a performance baseline based on the normal operating status of the system as a monitoring reference; evaluating the functionality and performance of various performance monitoring tools, including Prometheus, Grafana, Nagios, and Zabbix; selecting a performance monitoring tool; configuring the KPIs to be monitored within the monitoring tool; setting thresholds and alert rules; configuring a data collection agent; regularly checking the configuration and status of the monitoring system; and setting up a real-time alert mechanism to immediately notify relevant personnel once a KPI exceeds a threshold.

[0048] Alarms and notifications also include identifying key business and system metrics that need to be monitored, including response time, error rate, and resource utilization; assessing potential risks and their impact on business; determining alarm priorities and severity; selecting key metrics to monitor to ensure coverage of core business and system components; setting reasonable alarm thresholds for each key metric based on business needs and historical data; designing a multi-level alarm mechanism with different alarm levels (warning, critical, and urgent) based on severity; writing alarm rules in monitoring tools, setting trigger conditions and response measures; testing rules; configuring monitoring tools; utilizing the automatic detection function of monitoring tools to analyze data changes in real time and detect anomalies; setting up a manual confirmation mechanism; selecting a combination of notification channels (email, SMS, instant messaging tools) based on different alarm levels and business needs; developing an alarm response mechanism to quickly locate the cause of the problem based on alarm information; recording the processing process and results of each alarm; and optimizing alarm rules and adjusting thresholds and trigger conditions based on feedback from alarm handling.

[0049] In this invention, by identifying and integrating various data sources, including structured, semi-structured, and unstructured data, a comprehensive data perspective is ensured. Data cleaning and deduplication operations improve the accuracy and reliability of the data. Data integration tools and automated scripts ensure the real-time and timely nature of the data. The cleaned data is stored in an optimized database or data warehouse to ensure efficient data access. Regular backups and permission settings ensure the security and integrity of the data. Appropriate storage solutions are selected according to business needs to improve the flexibility and scalability of the system.

[0050] By formatting and standardizing data, we ensure consistency and availability. We use batch and real-time processing frameworks to efficiently process large-scale data and improve data processing efficiency. We apply data mining and machine learning algorithms to perform in-depth analysis and model prediction, providing valuable business insights. By designing and developing data visualization charts and dashboards, we can intuitively display data trends, comparisons, and anomalies. Visual charts and dashboards help managers quickly understand data and support real-time decision-making. We use a variety of visualization tools to meet different visualization needs.

[0051] Configure and customize dashboards according to user needs, provide personalized query and reporting functions, offer a user-friendly interactive interface to enhance user engagement and satisfaction, support user-defined reports to meet the needs of different business scenarios, ensure data security and privacy protection by creating and managing user accounts and their permissions, set different user access permissions to ensure the rationality and compliance of data access, and simplify the permission management process and improve management efficiency through the inheritance relationship of roles and permissions.

[0052] Real-time monitoring of system performance enables timely identification and resolution of performance bottlenecks, ensuring stable system operation. System logs provide a basis for troubleshooting and auditing, improving problem-solving efficiency. Performance baselines and monitoring indicators allow for early detection of potential problems and preventative maintenance. Alarm rules are set to monitor data and system status in real time, promptly detecting anomalies. Alarm information is sent to relevant personnel via email, SMS, etc., ensuring rapid response and handling. Based on feedback from alarm handling, alarm rules and system performance are continuously optimized to improve the efficiency and stability of business processes.

[0053] The above description is merely a preferred embodiment of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention. Structures, devices, and operating methods not specifically described or explained in this invention are implemented according to conventional methods in the art unless otherwise specified or limited.

Claims

1. A big data visualization system, characterized in that: It includes a data acquisition module, a data storage module, a data processing module, a data visualization module, a user interaction module, a permission management module, a system monitoring module, and an alarm and notification module.

2. A big data visualization system based on claim 1, characterized in that: The operation of the visualization system includes the following steps: Step 1. Data Acquisition: Determine the data sources, including databases, APIs, and sensors. Obtain data from various data sources by writing data acquisition scripts or using data integration tools. Clean the acquired data to remove duplicate, erroneous, and incomplete data. Step 2. Data storage: Store the cleaned data in a database or data warehouse, and back up the data regularly; Step 3. Data Processing: Perform preprocessing operations such as formatting and standardization on the data, use data processing frameworks for batch or real-time processing, and apply data mining and machine learning algorithms for in-depth analysis; Step 4. Data Visualization: Design data visualization charts and dashboards according to requirements, select visualization tools to develop visualization charts, and integrate them into the system. Visualization tools include D3.js, ECharts, Tableau, and Power BI. Step 5. User Interaction: Configure and customize the dashboard according to user needs, develop and set up report generation functions, and provide a user-friendly query interface; Step 6. Access Control: Create and manage user accounts and their permissions, and set access permissions for different users to data; Step 7. System Monitoring: Monitor system performance in real time, identify and resolve performance bottlenecks, and record system logs for subsequent troubleshooting and auditing; Step 8. Alarms and Notifications: Set alarm rules according to business needs, monitor data and system status in real time, detect abnormal situations, and send alarm information to relevant personnel via email and SMS.

3. The big data visualization system according to claim 2, characterized in that: The data acquisition process also includes identifying the types of data to be collected, including structured, semi-structured, and unstructured data; listing potential data sources, including SQL / NoSQL, API, CSV, Excel, JSON, sensor data, and log files; determining the access methods and permission requirements for each data source; configuring connection parameters according to the data source type; testing connection success; selecting programming languages ​​and tools, including Python, Java, and R; writing data acquisition scripts; writing SQL or NoSQL query statements and scripts; writing API call code; handling requests and responses; writing code to read data from local or remote files; selecting data integration tools, including Apache Nifi, Talend, and Informatica; configuring the tool to connect to the data source and target storage; designing the data acquisition process within the tool; and configuring data transformation and loading operations.

4. The big data visualization system according to claim 2, characterized in that: The data storage also includes determining the data volume, access speed, query frequency, and security requirements; selecting the database type based on the data type and requirements (database types include relational databases, NoSQL databases, and data warehouses); selecting centralized storage, distributed storage, or cloud storage solutions; designing the table structure of the relational database, including table fields, data types, primary keys, and indexes; configuring database connection parameters; setting data write permissions; using batch processing tools or scripts to import data into the database or data warehouse in batches; and using ETL tools to extract, transform, and load data.

5. A big data visualization system according to claim 2, characterized in that: The data processing also includes selecting a suitable batch processing framework, including Apache Hadoop and Apache Spark; configuring batch processing tasks; setting the input, output, and processing logic for data processing; executing batch processing tasks; processing large-scale data; configuring stream processing tasks; setting the input, output, and processing logic for data streams; performing feature selection, feature extraction, and feature construction based on business needs and data characteristics; selecting data mining and machine learning algorithms; training models using training data; adjusting parameters and hyperparameters; validating models using validation datasets; and integrating data analysis and model prediction results into business processes.

6. A big data visualization system according to claim 2, characterized in that: The data visualization also includes analyzing the data to be visualized, determining the data type, magnitude, and structure, defining the main visualization objectives (including displaying trends, comparing data, and identifying anomalies), selecting appropriate chart types based on data type and visualization objectives (including line charts, bar charts, pie charts, scatter plots, and maps), designing the dashboard layout, determining the position and size of each chart, designing user interaction methods (including filters, slicers, and dynamic updates), extracting data from the database or data warehouse, transforming and processing the data according to visualization requirements, creating various charts using selected visualization tools, integrating the charts into the dashboard, arranging them according to the designed layout, setting access permissions for different users, testing the various functions of the charts and dashboard, and testing the compatibility of visualization on different browsers and devices.

7. A big data visualization system according to claim 2, characterized in that: The user interaction also includes determining the dashboard functions required by the user, including specific chart types, data filtering, and time range selection; clarifying the user's usage scenarios, including daily monitoring, decision support, and trend analysis; designing the dashboard layout according to user needs, determining the position, size, and arrangement of charts; binding each chart in the dashboard to its corresponding data source; configuring user interaction functions, including click events, hover tips, and drop-down filters; setting different dashboard views according to user roles and permissions; determining the content and structure of reports according to user needs, including tabular data and chart display; designing report templates according to requirements, defining the position, font, color, and style of each data item; writing report generation scripts to generate report files according to the templates; developing a user interface that allows users to select report types, time ranges, and formats, and export reports; identifying different user roles and data permissions; determining the data range for querying; determining the query functions required by the user, including filtering conditions, data dimensions, and aggregation methods; providing users with appropriate input controls for customizing query conditions, including date pickers, drop-down lists, and checkboxes; and designing the display methods for query results, including tables, charts, and maps.

8. A big data visualization system according to claim 2, characterized in that: The access control also includes determining the user roles and permission levels to be created, classifying roles according to organizational structure and business needs, collecting basic user information, including name, email, and job title, creating user accounts in the system, assigning initial usernames and passwords, defining different roles based on user responsibilities and permission requirements, including administrators, data analysts, and ordinary users, assigning corresponding permissions to each role, clarifying the operational scope and data access permissions of roles, setting inheritance relationships between roles, simplifying access control, enabling or disabling user accounts as needed, managing account lifecycles, providing password reset and modification functions, recording user account creation, modification, and deletion operations, classifying data according to its sensitivity and importance, including public data, internal data, and confidential data, setting permissions at the table, view, and field levels, dynamically adjusting user access permissions based on actual user needs and business scenarios, establishing an access control audit process, regularly reviewing user permissions, monitoring and detecting abnormal permission usage behavior, timely risk control, enabling multi-factor authentication mechanisms, implementing single sign-on, simplifying the user login process, implementing access control based on user roles, and dynamically adjusting access permissions based on user attributes and context conditions.

9. A big data visualization system according to claim 2, characterized in that: The system monitoring also includes identifying key performance indicators that need to be monitored, including CPU utilization, memory utilization, disk I / O, and network traffic; setting a performance baseline based on the normal operating status of the system as a monitoring reference; evaluating the functionality and performance of various performance monitoring tools, including Prometheus, Grafana, Nagios, and Zabbix; selecting a performance monitoring tool; configuring the indicators to be monitored in the monitoring tool; setting thresholds and alarm rules; configuring a data collection agent; periodically checking the configuration and status of the monitoring system; and setting up a real-time alarm mechanism to immediately notify relevant personnel once a monitored indicator exceeds a threshold.

10. A big data visualization system according to claim 2, characterized in that: The alarms and notifications also include identifying key business and system metrics that need to be monitored, including response time, error rate, and resource utilization; assessing potential risks and their impact on business; determining alarm priorities and severity; selecting key metrics to be monitored to ensure coverage of the core parts of the business and system; setting reasonable alarm thresholds for each key metric based on business needs and historical data; designing a multi-level alarm mechanism; setting different alarm levels based on different severities, including warning, critical, and urgent; writing alarm rules in the monitoring tool; setting trigger conditions and response measures; testing the rules; configuring the monitoring tool; utilizing the automatic detection function of the monitoring tool to analyze data changes in real time and detect anomalies; setting a manual confirmation mechanism; selecting a combination of notification channels based on different alarm levels and business needs, including email, SMS, and instant messaging tools; developing an alarm response mechanism; quickly locating the cause of the problem based on alarm information; recording the processing process and results of each alarm; and optimizing alarm rules and adjusting thresholds and trigger conditions based on feedback from alarm processing.

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