Information management method based on big data processing technology

Abstract

The invention discloses an information management method based on a big data processing technology, and the method comprises the steps: S1, data collection and pre-processing optimization: S1.1, multi-source data cleaning and pre-processing: automatically detecting and correcting errors, missing and abnormal values in data through an integrated intelligent data cleaning technology, such as a machine learning algorithm (such as abnormal detection and missing value filling); the data quality is ensured, data standardization and format unification tools are adopted, heterogeneous data from different sources are converted into a unified format, and follow-up processing is facilitated. The problems of data privacy, quality, real-time performance, complexity and compliance in a traditional big data method are solved by enhancing data quality management, improving data security, optimizing storage and processing architecture and introducing modes such as real-time processing and intelligent analysis. Meanwhile, the expandability and the fault-tolerant capability of the system ensure the high efficiency and the stability of the system in a large-scale data environment, and a more intelligent, safer and more efficient information management solution is provided for enterprises.

Description

Technical Field

[0001] The present invention relates to the technical field of big data processing technology, and specifically to an information management method based on big data processing technology. Background Art

[0002] Information management methods based on big data processing technology can efficiently collect, store, process and analyze massive amounts of multi-source heterogeneous data by integrating advanced data storage, analysis and processing technologies. Distributed computing frameworks (such as Hadoop and Spark) are used to process data in batches and in real time to ensure that large-scale data can be effectively processed in a short time. At the same time, storage architectures such as data warehouses and data lakes are used to support the unified management of structured, semi-structured and unstructured data. Through data mining, machine learning and artificial intelligence algorithms, valuable information can be extracted from data to support intelligent decision-making and predictive analysis. In addition, measures such as data security, privacy protection and permission management ensure the security and compliance of data and improve the accuracy, timeliness and reliability of information management.

[0003] In the existing technology, although the information management methods based on big data processing technology have significant advantages in data processing capabilities and intelligent analysis, they also have some obvious disadvantages: Data privacy and security issues: Big data technology involves the collection and processing of massive data, which may contain sensitive information. Despite the existence of measures such as encryption and access control, there are still risks of data leakage, abuse and unauthorized access in the process of data storage, transmission and sharing; Data quality issues: Data in big data often comes from different channels and systems, and may be inconsistent, missing or erroneous. Data cleaning and quality control require a lot of time and resources, and it is still difficult to completely eliminate data noise and errors, which affects the accuracy of data analysis and decision-making effects; Technical complexity and high cost: Big data management systems usually require complex infrastructure, including distributed storage, computing platforms and data processing frameworks. This not only places high demands on technical personnel, but also has high costs for the deployment, maintenance and optimization of hardware and software. For many small and medium-sized enterprises, the investment and maintenance costs may become a burden;Real-time and latency issues: Although big data processing technology provides real-time data processing capabilities, in some high-frequency, large-scale data flow scenarios, the system's response time and processing capabilities may still be delayed, affecting the timeliness of real-time decision-making and business processes;Scalability and flexibility limitations: Big data systems face scalability challenges when processing and storing extremely large amounts of data. As the amount of data continues to grow, the system may need to continuously expand hardware capacity or restructure the architecture, which often involves complex migration and reconfiguration;Data governance and compliance issues: As the amount of data and data types increase, how to effectively manage, classify, archive and conduct compliance reviews of data has become a major challenge. Especially in the process of cross-regional and cross-industry data sharing and analysis, the requirements of different laws and regulations (such as GDPR, etc.) may limit data governance;Technology dependence and talent shortage: The application of big data technology requires highly specialized technical personnel, especially data scientists, data engineers, etc. However, the current supply of technical talents in the field of big data is relatively insufficient, resulting in enterprises facing technical difficulties and talent shortages in the process of implementing big data management.

[0004] To this end, we propose an information management method based on big data processing technology. Summary of the invention

[0005] To achieve the above object, the present invention provides the following technical solution: an information management method based on big data processing technology, comprising the following steps:

[0006] S1: Data collection and preprocessing optimization

[0007] S1.1: Multi-source data cleaning and preprocessing: By integrating intelligent data cleaning technologies, such as machine learning algorithms (e.g., anomaly detection, missing value filling), errors, missing values, and outliers in the data are automatically detected and corrected to ensure data quality. In addition, data standardization and format unification tools are used to convert heterogeneous data from different sources into a unified format for subsequent processing;

[0008] S1.2: Layered data collection strategy: Design a layered data collection strategy based on data importance and real-time requirements. Prioritize high-value and real-time data (such as sensor data, user behavior data), and low-priority data (such as historical logs, batch data) can be processed later or in batches.

[0009] S2: Data storage and security management

[0010] S2.1: Combination of distributed data lake and data warehouse: A hybrid storage architecture is used to store large-scale raw data in the data lake, and cleaned and structured data is stored in the data warehouse. The data lake can use a cloud storage platform (such as Amazon S3), while the data warehouse uses efficient columnar storage (such as Apache HBase, Google BigQuery) to improve data processing and query efficiency;

[0011] S2.2: Data encryption and privacy protection: Implement end-to-end encryption (such as AES encryption) for sensitive data in storage and transmission, and ensure strict key management and access control. Use data desensitization technology to provide only partial identifiable information during analysis to ensure data privacy. For cross-border data storage and processing, follow local compliance requirements (such as GDPR);

[0012] S2.3: Fine-grained access control: Strictly control the access rights of different users and applications to data through role-based access control (RBAC) or attribute-based access control (ABAC), ensuring that only authorized users can access sensitive data and perform important operations.

[0013] S3: Real-time processing and incremental analysis

[0014] S3.1: Combining real-time stream processing with batch processing: By introducing real-time stream processing engines (such as Apache Kafka and Apache Flink), high-frequency data from different data sources can be processed in real time. At the same time, historical data or low-time data can be batch processed (such as using Apache Spark), and the hybrid mode can be used to reduce latency and improve the response speed and flexibility of the overall system.

[0015] S3.2: Incremental update and version management: For dynamically changing data, incremental storage and update methods are used to process only the newly added or modified data, avoiding frequent processing of the full amount of data and reducing computing and storage pressure. At the same time, the data change history is recorded and version management methods are used to ensure data consistency and traceability;

[0016] S4: Data quality management and automated monitoring

[0017] S4.1: Automated data quality monitoring system: Establish a real-time data quality monitoring platform to automatically detect and alert data anomalies, loss or duplication. Dynamically analyze data quality through custom rules and machine learning models, automatically repair or prompt users to manually intervene;

[0018] S4.2: Data governance framework and metadata management: Implement a comprehensive data governance framework to define the data lifecycle, standards, and usage specifications. At the same time, use metadata management tools (such as Apache Atlas) to classify, label, and record all data for easy traceability and management to ensure compliance.

[0019] S5: Data analysis and intelligent decision making

[0020] S5.1: Integration of machine learning and deep learning models: By integrating machine learning and deep learning models with the big data analysis platform, data can be intelligently analyzed in real time to mine potential patterns and trends in the data. Introducing automated model training and tuning mechanisms to improve the level of intelligence in decision support;

[0021] S5.2: Collaborative filtering and recommendation system: In the information management process, collaborative filtering and recommendation algorithms are used to automatically recommend relevant data or analysis results to users or systems to improve data utilization efficiency.

[0022] S6: System scalability and fault-tolerant design

[0023] S6.1: Elastic computing and storage architecture: Adopt cloud computing and distributed architecture, and use containerization technology (such as Docker and Kubernetes) to achieve elastic expansion of computing and storage resources. Automatically adjust resource configuration according to changes in business needs to ensure that the system has high availability and elastic scalability when processing large-scale data;

[0024] S6.2: Fault tolerance mechanism and data backup: Improve data fault tolerance through distributed redundant storage and multiple copies. At the same time, back up data regularly and ensure the availability of backup data to prevent data loss due to system failure.

[0025] S7: Compliance and audit mechanisms

[0026] S7.1: Compliance data processing and audit logs: Ensure that all data collection, processing, storage and sharing comply with relevant laws and regulations (such as GDPR, CCPA). Establish a complete audit log system to record all data operations, permission changes, access records, etc., so that traceability and compliance checks can be performed when problems occur;

[0027] S7.2: Smart contracts and automated auditing: Introduce smart contracts to automatically control and audit data access, data sharing and other behaviors to ensure compliance with internal corporate compliance requirements and monitor abnormal behaviors in real time.

[0028] S8: User Interface and Visualization Tools

[0029] S8.1: Visual data management platform: Design a user-friendly data management interface, provide visual analysis, report generation and real-time monitoring functions based on big data processing results. Help decision makers quickly understand complex data and make decisions through charts, dashboards, etc.

[0030] S8.2: Self-service data query and analysis tools: Allow users to query and analyze data by themselves based on permissions, and provide low-code or no-code tools to help non-technical personnel easily perform data exploration and business analysis.

[0031] S9: Talent and technical support

[0032] S9.1: Continuous training and talent reserve: In order to reduce technology dependence, regularly train team members to improve their understanding and application of big data technology, especially in areas such as data governance, security and machine learning;

[0033] S9.2: Standardization of tools and platforms: Select a set of standardized big data platforms and tools to reduce the complexity and cost of technology selection, while enhancing the maintainability of the system and the convenience of technical support.

[0034] Preferably, in step S1, data integration tools (such as Apache Nifi, Talend) are used to collect data from different data sources (such as relational databases, NoSQL databases, sensors, log files, etc.). Automated data cleaning process, using algorithms to automatically detect outliers, fill in missing values, remove duplicate data, etc. Combined with machine learning models (such as KNN, random forests) for outlier identification and data correction, different data collection strategies are set according to the priority and real-time requirements of the data. For example, high-frequency sensor data is processed through Kafka streams, and batch-collected historical data is captured in batches through scheduled tasks. Use message queues (such as Kafka, RabbitMQ) to prioritize data streams to ensure that high-priority data enters the processing system first.

[0035] Preferably, a data lake is established in step S2 (using cloud storage platforms such as Amazon S3 and Azure Blob Storage) to store original structured, semi-structured and unstructured data. Data warehouses (such as Apache Hive and Google BigQuery) are used to store structured data after processing and analysis. Data is transferred from the data lake to the data warehouse using ETL (Extract, Transform, Load) tools, and the stored data is encrypted using strong encryption technology (such as AES-256). All data transmissions are encrypted using the TLS protocol. For sensitive information, data desensitization technology (such as hashing and randomization) is implemented to prevent leakage. At the data access layer, public key infrastructure (PKI) and identity authentication (such as OAuth and JWT) are used to manage data access rights, and role-based access control (RBAC) or attribute-based access control (ABAC) is implemented on the data storage and processing platform to control the access rights of different users to data. Different permission levels are set for different users (such as data analysts and administrators) to ensure that only authorized users can access sensitive data.

[0036] Preferably, Apache Kafka is used as a data stream pipeline in step S3 to transmit the real-time data stream to a stream processing framework (such as Apache Flink, Apache Storm) to achieve efficient real-time data processing. At the same time, batch processing jobs are integrated through Spark Streaming or Flink to process historical data on demand and reduce latency. Through incremental data collection (such as CDC: Change Data Capture technology) and incremental ETL, it is ensured that the system only processes changed data rather than all data. Use version control tools (such as Git, Apache Hudi) to record the version history of the data to ensure data traceability and consistency.

[0037] Preferably, in step S4, a real-time monitoring system (such as Apache Druid, Elasticsearch) is deployed to monitor data quality issues. Machine learning models are used to automatically identify data problems (such as missing values, data inconsistencies) and provide alerts. Combined with data pipeline monitoring tools (such as Prometheus, Grafana) to monitor the quality of data flows in real time, establish a data governance architecture, and use metadata management tools (such as Apache Atlas, Alation) to classify, label, audit and govern data. Ensure data consistency, reliability and compliance by defining data standards, specifications and lifecycle management.

[0038] Preferably, in step S5, machine learning (such as regression analysis, classification model) and deep learning (such as neural network, reinforcement learning) models are integrated with the big data platform. Use cloud platforms (such as AWS SageMaker, Google AIPlatform) to train and deploy models, perform predictive analysis on real-time data, implement a recommendation system based on user behavior or data content, and use collaborative filtering, matrix decomposition and other algorithms to recommend relevant data. Combined with business needs, design adaptive recommendation algorithms to continuously optimize recommendation effects.

[0039] Preferably, in step S6, a cloud computing platform (such as AWS, Azure, Google Cloud) is used to support automatic scaling of computing and storage resources. Containerization technology (such as Docker, Kubernetes) is used to ensure that the system can dynamically adjust resource configuration according to demand, and a multi-copy mechanism (such as HDFS, Cassandra) is configured in the data storage system to achieve data redundant storage. Incremental backup and full backup are performed regularly to ensure that data can be restored when the system crashes.

[0040] Compared with the prior art, the present invention provides an information management method based on big data processing technology, which has the following beneficial effects:

[0041] This information management method based on big data processing technology solves the data privacy, quality, real-time, complexity and compliance issues existing in traditional big data methods by strengthening data quality management, improving data security, optimizing storage and processing architecture, and introducing real-time processing and intelligent analysis. At the same time, the scalability and fault tolerance of the system ensure its efficiency and stability in a large-scale data environment, providing enterprises with a more intelligent, secure and efficient information management solution. DETAILED DESCRIPTION

[0042] The following will be combined with the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only 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 making creative work are within the scope of protection of the present invention.

[0043] Example

[0044] An embodiment of an information management method based on big data processing technology

[0045] An information management method based on big data processing technology comprises the following steps:

[0046] S1: Data collection and preprocessing optimization

[0047] S1.1: Multi-source data cleaning and preprocessing: By integrating intelligent data cleaning technologies, such as machine learning algorithms (e.g., anomaly detection, missing value filling), errors, missing values, and outliers in the data are automatically detected and corrected to ensure data quality. In addition, data standardization and format unification tools are used to convert heterogeneous data from different sources into a unified format for subsequent processing;

[0048] S1.2: Layered data collection strategy: Design a layered data collection strategy based on data importance and real-time requirements. Prioritize high-value and real-time data (such as sensor data, user behavior data), and low-priority data (such as historical logs, batch data) can be processed later or in batches.

[0049] S2: Data storage and security management

[0050] S2.1: Combination of distributed data lake and data warehouse: A hybrid storage architecture is used to store large-scale raw data in the data lake, and cleaned and structured data is stored in the data warehouse. The data lake can use a cloud storage platform (such as Amazon S3), while the data warehouse uses efficient columnar storage (such as Apache HBase, Google BigQuery) to improve data processing and query efficiency;

[0051] S2.2: Data encryption and privacy protection: Implement end-to-end encryption (such as AES encryption) for sensitive data in storage and transmission, and ensure strict key management and access control. Use data desensitization technology to provide only partial identifiable information during analysis to ensure data privacy. For cross-border data storage and processing, follow local compliance requirements (such as GDPR);

[0052] S2.3: Fine-grained access control: Strictly control the access rights of different users and applications to data through role-based access control (RBAC) or attribute-based access control (ABAC), ensuring that only authorized users can access sensitive data and perform important operations.

[0053] S3: Real-time processing and incremental analysis

[0054] S3.1: Combining real-time stream processing with batch processing: By introducing real-time stream processing engines (such as Apache Kafka and Apache Flink), high-frequency data from different data sources can be processed in real time. At the same time, historical data or low-time data can be batch processed (such as using Apache Spark), and the hybrid mode can be used to reduce latency and improve the response speed and flexibility of the overall system.

[0055] S3.2: Incremental update and version management: For dynamically changing data, incremental storage and update methods are used to process only the newly added or modified data, avoiding frequent processing of the full amount of data and reducing computing and storage pressure. At the same time, the data change history is recorded and version management methods are used to ensure data consistency and traceability;

[0056] S4: Data quality management and automated monitoring

[0057] S4.1: Automated data quality monitoring system: Establish a real-time data quality monitoring platform to automatically detect and alert data anomalies, loss or duplication. Dynamically analyze data quality through custom rules and machine learning models, automatically repair or prompt users to manually intervene;

[0058] S4.2: Data governance framework and metadata management: Implement a comprehensive data governance framework to define the data lifecycle, standards, and usage specifications. At the same time, use metadata management tools (such as Apache Atlas) to classify, label, and record all data for easy traceability and management to ensure compliance.

[0059] S5: Data analysis and intelligent decision making

[0060] S5.1: Integration of machine learning and deep learning models: By integrating machine learning and deep learning models with the big data analysis platform, data can be intelligently analyzed in real time to mine potential patterns and trends in the data. Introducing automated model training and tuning mechanisms to improve the level of intelligence in decision support;

[0061] S5.2: Collaborative filtering and recommendation system: In the information management process, collaborative filtering and recommendation algorithms are used to automatically recommend relevant data or analysis results to users or systems to improve data utilization efficiency.

[0062] S6: System scalability and fault-tolerant design

[0063] S6.1: Elastic computing and storage architecture: Adopt cloud computing and distributed architecture, and use containerization technology (such as Docker and Kubernetes) to achieve elastic expansion of computing and storage resources. Automatically adjust resource configuration according to changes in business needs to ensure that the system has high availability and elastic scalability when processing large-scale data;

[0064] S6.2: Fault tolerance mechanism and data backup: Improve data fault tolerance through distributed redundant storage and multiple copies. At the same time, back up data regularly and ensure the availability of backup data to prevent data loss due to system failure.

[0065] S7: Compliance and audit mechanisms

[0066] S7.1: Compliance data processing and audit logs: Ensure that all data collection, processing, storage and sharing comply with relevant laws and regulations (such as GDPR, CCPA). Establish a complete audit log system to record all data operations, permission changes, access records, etc., so that traceability and compliance checks can be performed when problems occur;

[0067] S7.2: Smart contracts and automated auditing: Introduce smart contracts to automatically control and audit data access, data sharing and other behaviors to ensure compliance with internal corporate compliance requirements and monitor abnormal behaviors in real time.

[0068] S8: User Interface and Visualization Tools

[0069] S8.1: Visual data management platform: Design a user-friendly data management interface, provide visual analysis, report generation and real-time monitoring functions based on big data processing results. Help decision makers quickly understand complex data and make decisions through charts, dashboards, etc.

[0070] S8.2: Self-service data query and analysis tools: Allow users to query and analyze data by themselves based on permissions, and provide low-code or no-code tools to help non-technical personnel easily perform data exploration and business analysis.

[0071] S9: Talent and technical support

[0072] S9.1: Continuous training and talent reserve: In order to reduce technology dependence, regularly train team members to improve their understanding and application of big data technology, especially in areas such as data governance, security and machine learning;

[0073] S9.2: Standardization of tools and platforms: Select a set of standardized big data platforms and tools to reduce the complexity and cost of technology selection, while enhancing the maintainability of the system and the convenience of technical support.

[0074] Specifically, in step S1, data integration tools (such as Apache Nifi, Talend) are used to collect data from different data sources (such as relational databases, NoSQL databases, sensors, log files, etc.). Automated data cleaning process, using algorithms to automatically detect outliers, fill in missing values, remove duplicate data, etc. Combined with machine learning models (such as KNN, random forest) for outlier identification and data correction, different data collection strategies are set according to the priority and real-time requirements of the data. For example, high-frequency sensor data is processed through Kafka streams, and batch-collected historical data is captured in batches through scheduled tasks. Use message queues (such as Kafka, RabbitMQ) to prioritize data streams to ensure that high-priority data enters the processing system first.

[0075] Specifically, in step S2, a data lake is established (using cloud storage platforms such as Amazon S3 and Azure BlobStorage) to store original structured, semi-structured, and unstructured data. Data warehouses (such as Apache Hive and Google BigQuery) are used to store structured data after processing and analysis. Use ETL (Extract, Transform, Load) tools to transfer data from the data lake to the data warehouse, and use strong encryption technology (such as AES-256) to encrypt the stored data. All data transmission is encrypted using the TLS protocol. For sensitive information, data desensitization technology (such as hashing and randomization) is implemented to prevent leakage. At the data access layer, public key infrastructure (PKI) and identity authentication (such as OAuth and JWT) are used to manage data access rights, and role-based access control (RBAC) or attribute-based access control (ABAC) is implemented on the data storage and processing platform to control the access rights of different users to data. Set different permission levels for different users (such as data analysts and administrators) to ensure that only authorized users can access sensitive data.

[0076] Specifically, in step S3, Apache Kafka is used as a data stream pipeline to transmit real-time data streams to stream processing frameworks (such as Apache Flink and Apache Storm) to achieve efficient real-time data processing. At the same time, batch processing jobs are integrated through Spark Streaming or Flink to process historical data on demand and reduce latency. Through incremental data collection (such as CDC: Change Data Capture technology) and incremental ETL, it is ensured that the system only processes changed data rather than all data. Use version control tools (such as Git and Apache Hudi) to record the version history of data to ensure data traceability and consistency.

[0077] Specifically, in step S4, a real-time monitoring system (such as Apache Druid, Elasticsearch) is deployed to monitor data quality issues. Machine learning models are used to automatically identify data problems (such as missing values, data inconsistencies) and provide alerts. Combined with data pipeline monitoring tools (such as Prometheus, Grafana) to monitor the quality of data flows in real time, establish a data governance architecture, and use metadata management tools (such as Apache Atlas, Alation) to classify, label, audit and govern data. Ensure data consistency, reliability and compliance by defining data standards, specifications and lifecycle management.

[0078] Specifically, in step S5, machine learning (such as regression analysis, classification model) and deep learning (such as neural network, reinforcement learning) models are integrated with the big data platform. Use cloud platforms (such as AWS SageMaker, Google AIPlatform) to train and deploy models, perform predictive analysis on real-time data, implement recommendation systems based on user behavior or data content, and use collaborative filtering, matrix decomposition and other algorithms to recommend relevant data. Combined with business needs, design adaptive recommendation algorithms to continuously optimize recommendation effects.

[0079] Specifically, in step S6, a cloud computing platform (such as AWS, Azure, and Google Cloud) is used to support automatic scaling of computing and storage resources. Containerization technology (such as Docker and Kubernetes) is used to ensure that the system can dynamically adjust resource configuration according to demand, and a multi-copy mechanism (such as HDFS and Cassandra) is configured in the data storage system to achieve data redundant storage. Incremental backups and full backups are performed regularly to ensure that data can be restored in the event of a system crash.

[0080] Through the above technical solutions, the present invention solves the data privacy, quality, real-time, complexity and compliance issues existing in traditional big data methods by strengthening data quality management, improving data security, optimizing storage and processing architecture, introducing real-time processing and intelligent analysis, etc. At the same time, the scalability and fault tolerance of the system ensure its efficiency and stability in a large-scale data environment, providing enterprises with a more intelligent, secure and efficient information management solution.

[0081] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An information management method based on big data processing technology, characterized in that: The following steps are involved: S1: Data collection and preprocessing optimization S1.1: Multi-source data cleaning and preprocessing: By integrating intelligent data cleaning technologies, such as machine learning algorithms (e.g., anomaly detection, missing value filling), errors, missing values, and outliers in the data are automatically detected and corrected to ensure data quality. In addition, data standardization and format unification tools are used to convert heterogeneous data from different sources into a unified format for subsequent processing; S1.2: Layered data collection strategy: Design a layered data collection strategy based on data importance and real-time requirements. High-value and real-time data (such as sensor data and user behavior data) are processed first, while low-priority data (such as historical logs and batch data) can be processed later or in batches. S2: Data storage and security management S2.1: Combination of distributed data lake and data warehouse: A hybrid storage architecture is used to store large-scale raw data in the data lake, and cleaned and structured data is stored in the data warehouse. The data lake can use a cloud storage platform (such as Amazon S3), while the data warehouse uses efficient columnar storage (such as Apache HBase, Google BigQuery) to improve data processing and query efficiency; S2.2: Data encryption and privacy protection: Implement end-to-end encryption (such as AES encryption) for sensitive data in storage and transmission, and ensure strict key management and access control. Use data desensitization technology to provide only partial identifiable information during analysis to ensure data privacy. For cross-border data storage and processing, follow local compliance requirements (such as GDPR); S2.3: Fine-grained access control: Strictly control the access rights of different users and applications to data through role-based access control (RBAC) or attribute-based access control (ABAC), ensuring that only authorized users can access sensitive data and perform important operations. S3: Real-time processing and incremental analysis S3.1: Combining real-time stream processing with batch processing: By introducing real-time stream processing engines (such as Apache Kafka and Apache Flink), high-frequency data from different data sources can be processed in real time. At the same time, historical data or low-time data can be batch processed (such as using Apache Spark), and the hybrid mode can be used to reduce latency and improve the response speed and flexibility of the overall system. S3.2: Incremental update and version management: For dynamically changing data, incremental storage and update methods are used to process only the newly added or modified data, avoiding frequent processing of the full amount of data and reducing computing and storage pressure. At the same time, the data change history is recorded and version management methods are used to ensure data consistency and traceability; S4: Data quality management and automated monitoring S4.1: Automated data quality monitoring system: Establish a real-time data quality monitoring platform to automatically detect and alert data anomalies, loss or duplication. Dynamically analyze data quality through custom rules and machine learning models, automatically repair or prompt users to manually intervene; S4.2: Data governance framework and metadata management: Implement a comprehensive data governance framework to define the data lifecycle, standards, and usage specifications. At the same time, use metadata management tools (such as Apache Atlas) to classify, label, and record all data for easy traceability and management to ensure compliance. S5: Data analysis and intelligent decision making S5.1: Integration of machine learning and deep learning models: By integrating machine learning and deep learning models with the big data analysis platform, data can be intelligently analyzed in real time to mine potential patterns and trends in the data. Introducing automated model training and tuning mechanisms to improve the level of intelligence in decision support; S5.2: Collaborative filtering and recommendation system: In the information management process, collaborative filtering and recommendation algorithms are used to automatically recommend relevant data or analysis results to users or systems to improve data utilization efficiency. S6: System scalability and fault-tolerant design S6.1: Elastic computing and storage architecture: Adopt cloud computing and distributed architecture, and use containerization technology (such as Docker and Kubernetes) to achieve elastic expansion of computing and storage resources. Automatically adjust resource configuration according to changes in business needs to ensure that the system has high availability and elastic scalability when processing large-scale data; S6.2: Fault tolerance mechanism and data backup: Improve data fault tolerance through distributed redundant storage and multiple copies. At the same time, back up data regularly and ensure the availability of backup data to prevent data loss due to system failure. S7: Compliance and audit mechanisms S7.1: Compliance data processing and audit logs: Ensure that all data collection, processing, storage and sharing comply with relevant laws and regulations (such as GDPR, CCPA). Establish a complete audit log system to record all data operations, permission changes, access records, etc., so that traceability and compliance checks can be performed when problems occur; S7.2: Smart contracts and automated auditing: Introduce smart contracts to automatically control and audit data access, data sharing and other behaviors to ensure compliance with internal corporate compliance requirements and monitor abnormal behaviors in real time. S8: User Interface and Visualization Tools S8.1: Visual data management platform: Design a user-friendly data management interface, provide visual analysis, report generation and real-time monitoring functions based on big data processing results. Help decision makers quickly understand complex data and make decisions through charts, dashboards, etc. S8.2: Self-service data query and analysis tools: Allow users to query and analyze data by themselves based on permissions, and provide low-code or no-code tools to help non-technical personnel easily perform data exploration and business analysis. S9: Talent and technical support S9.1: Continuous training and talent reserve: In order to reduce technology dependence, regularly train team members to improve their understanding and application of big data technology, especially in areas such as data governance, security and machine learning; S9.2: Standardization of tools and platforms: Select a set of standardized big data platforms and tools to reduce the complexity and cost of technology selection, while enhancing the maintainability of the system and the convenience of technical support.

2. The information management method based on big data processing technology according to claim 1, characterized in that: In step S1, data integration tools (such as Apache Nifi, Talend) are used to collect data from different data sources (such as relational databases, NoSQL databases, sensors, log files, etc.). Automated data cleaning process, using algorithms to automatically detect outliers, fill in missing values, remove duplicate data, etc. Combined with machine learning models (such as KNN, random forests) for outlier identification and data correction, different data collection strategies are set according to the priority and real-time requirements of the data. For example, high-frequency sensor data is processed through Kafka streams, and batch-collected historical data is captured in batches through scheduled tasks. Use message queues (such as Kafka, RabbitMQ) to prioritize data streams to ensure that high-priority data enters the processing system first.

3. The information management method based on big data processing technology according to claim 1, characterized in that: In step S2, a data lake is established (using cloud storage platforms such as Amazon S3 and Azure Blob Storage) to store original structured, semi-structured and unstructured data. Data warehouses (such as Apache Hive and Google BigQuery) are used to store structured data after processing and analysis. The ETL (Extract, Transform, Load) tool is used to transfer data from the data lake to the data warehouse, and the stored data is encrypted using strong encryption technology (such as AES-256). All data transmissions are encrypted using the TLS protocol. For sensitive information, data desensitization technology (such as hashing and randomization) is implemented to prevent leakage. At the data access layer, public key infrastructure (PKI) and identity authentication (such as OAuth and JWT) are used to manage data access rights, and role-based access control (RBAC) or attribute-based access control (ABAC) is implemented on the data storage and processing platform to control the access rights of different users to data. Different permission levels are set for different users (such as data analysts and administrators) to ensure that only authorized users can access sensitive data.

4. The information management method based on big data processing technology according to claim 1, characterized in that: In step S3, Apache Kafka is used as a data stream pipeline to transmit real-time data streams to a stream processing framework (such as Apache Flink, Apache Storm) to achieve efficient real-time data processing. At the same time, batch processing jobs are integrated through Spark Streaming or Flink to process historical data on demand and reduce latency. Through incremental data collection (such as CDC: Change Data Capture technology) and incremental ETL, it is ensured that the system only processes changed data rather than all data. Use version control tools (such as Git, Apache Hudi) to record the version history of the data to ensure data traceability and consistency.

5. The information management method based on big data processing technology according to claim 1, characterized in that: In step S4, a real-time monitoring system (such as Apache Druid, Elasticsearch) is deployed to monitor data quality issues. Machine learning models are used to automatically identify data problems (such as missing values, data inconsistencies) and provide alerts. Combined with data pipeline monitoring tools (such as Prometheus, Grafana) to monitor the quality of data flows in real time, establish a data governance architecture, and use metadata management tools (such as Apache Atlas, Alation) to classify, label, audit and govern data. Ensure data consistency, reliability and compliance by defining data standards, specifications and lifecycle management.

6. The information management method based on big data processing technology according to claim 1, characterized in that: In step S5, machine learning (such as regression analysis, classification model) and deep learning (such as neural network, reinforcement learning) models are integrated with the big data platform. Use cloud platforms (such as AWS SageMaker, Google AIPlatform) to train and deploy models, perform predictive analysis on real-time data, implement a recommendation system based on user behavior or data content, and use collaborative filtering, matrix decomposition and other algorithms to recommend relevant data. Combined with business needs, design adaptive recommendation algorithms to continuously optimize recommendation effects.

7. The information management method based on big data processing technology according to claim 1 is characterized in that: In step S6, a cloud computing platform (such as AWS, Azure, and Google Cloud) is used to support automatic scaling of computing and storage resources. Containerization technology (such as Docker and Kubernetes) is used to ensure that the system can dynamically adjust resource configuration according to demand, and a multi-copy mechanism (such as HDFS and Cassandra) is configured in the data storage system to achieve data redundant storage. Incremental backups and full backups are performed regularly to ensure that data can be restored in the event of a system crash.