Method and system for safely and rapidly storing image data of imaging department

Abstract

The invention discloses a safe and rapid storage method and system for image data of an imaging department, and relates to the technical field of medical image data processing. The method comprises the following steps: 1, designing a distributed storage system, and selecting and configuring storage nodes; 2, selecting and updating an encryption algorithm, and implementing file-level and block-level encryption; 3, realizing accurate data deduplication by utilizing a machine learning technology, and selecting a proper compression algorithm; 4, constructing an index system, and establishing a data warehouse and a data lake by using big data analysis and machine learning technologies; 5, establishing an image data backup and recovery mechanism; and 6, constructing a data integrity checking and monitoring system. According to the invention, by adopting the technologies of distributed storage, encryption, data deduplication, compression, big data analysis, machine learning, backup and recovery, data integrity verification, monitoring and early warning and the like, efficient storage and safety protection of image data are realized.

Description

Technical Field

[0001] The present invention belongs to the technical field of medical image data processing, and in particular relates to a method and system for safely and quickly storing image data of an imaging department. Background Art

[0002] With the development of medical imaging technology, the amount of data generated by the Department of Imaging has exploded. Traditional methods of image data storage have many problems in terms of security, storage speed, and data management. On the one hand, when processing large amounts of image data, existing storage systems often have problems such as slow storage speed, easy data loss, and low security; on the other hand, due to the high repetitiveness of image data, storage space is wasted. In addition, existing storage systems also have deficiencies in data backup, recovery, and monitoring, and cannot meet the efficient and secure needs of clinical diagnosis and treatment for image data. Summary of the invention

[0003] The purpose of the present invention is to provide a method and system for secure and rapid storage of imaging data in the Department of Radiology, aiming to solve the problems existing in the existing image data storage, such as insufficient security, low storage efficiency, high data redundancy, and insufficient data management and analysis capabilities.

[0004] To solve the above technical problems, the present invention is achieved through the following technical solutions:

[0005] The present invention is a method for safely and quickly storing imaging data in an imaging department, comprising the following steps:

[0006] Step 1: Design a distributed storage system, select and configure storage nodes based on the characteristics and access patterns of image data;

[0007] Step 2: Select and update encryption algorithms according to security standards, implement file-level and block-level encryption, introduce dynamic key management and rotation mechanisms, and encrypt image data before writing it to storage nodes;

[0008] Step 3: Combine the characteristics of image data, use machine learning technology to achieve accurate data deduplication, and select appropriate compression algorithms for optimization according to the data type;

[0009] Step 4: Build an index system for imaging data, and use big data analysis and machine learning techniques to conduct in-depth mining and analysis of imaging data, establish a data warehouse and data lake, and provide auxiliary decision support for clinical diagnosis and treatment;

[0010] Step 5: Establish an image data backup and recovery mechanism. Use a strategy that combines snapshot technology, incremental backup, and differential backup, and develop a detailed recovery process.

[0011] Step 6: Build a data integrity verification and monitoring system, use verification algorithms and tools, establish a distributed verification mechanism, and use AI and machine learning technologies to achieve intelligent monitoring and early warning.

[0012] As a preferred technical solution of the present invention, the step 1 comprises:

[0013] Select a suitable distributed storage architecture and design a reasonable storage node distribution and load balancing strategy based on the scale, access frequency and security requirements of image data;

[0014] The imaging data is sharded by time, patient ID, and examination type, and stored on multiple storage nodes to achieve load balancing and fault tolerance;

[0015] Use high-speed network interfaces and redundant network paths, and configure appropriate network bandwidth and latency parameters to increase data transmission speed.

[0016] As a preferred technical solution of the present invention, the step three comprises:

[0017] Identify duplicate data, using hash algorithms or similarity detection algorithms to identify and mark duplicate image data;

[0018] Data deduplication: During the storage process, only one unique image data is retained. For duplicate data, only its reference or link is stored. At the same time, combined with data compression technology, storage space can be further saved.

[0019] Deduplication and compression algorithms are regularly optimized to adapt to the changing image data types and sizes.

[0020] As a preferred technical solution of the present invention, step four also includes: containerization and microservice architecture improvement: using containerization technology and microservice architecture to achieve flexible deployment and expansion of the system, and at the same time, providing a unified API interface to facilitate integration and interoperability with other systems.

[0021] As a preferred technical solution of the present invention, it also includes step seven, deep integration of intelligent monitoring and early warning systems, optimizing early warning accuracy through continuous learning, achieving comprehensive hardware resource monitoring, data access pattern analysis, risk identification and self-optimization, and ensuring the long-term stable operation of the system.

[0022] As a preferred technical solution of the present invention, the present invention further includes step eight:

[0023] Data lifecycle management implements automated data archiving, migration, and deletion strategies, and intelligently manages the data lifecycle based on data access frequency, importance, and compliance requirements to optimize storage resource utilization and reduce costs. Data lifecycle management also includes regular verification and recovery testing of archived data to ensure the integrity and recoverability of archived data.

[0024] As a preferred technical solution of the present invention, the method further comprises step nine:

[0025] Multi-site data synchronization and disaster recovery backup, build cross-regional data centers, realize real-time data synchronization and disaster recovery backup, and ensure that data can be quickly restored and services can continue to be provided when unforeseen events such as natural disasters and equipment failures occur.

[0026] As a preferred technical solution of the present invention, step nine also includes implementing a priority strategy for data synchronization, and intelligently adjusting the priority of data synchronization according to the importance and access frequency of the data to optimize the utilization of network bandwidth and storage resources.

[0027] A system for safely and quickly storing imaging data in the imaging department for implementing the above method comprises:

[0028] Distributed storage module, used to store image data and configure and optimize according to data characteristics and access patterns;

[0029] Cryptographic modules that implement file-level and block-level encryption and manage key generation, updates, and rotation;

[0030] Data deduplication and compression module, used to achieve accurate deduplication and compression optimization of image data;

[0031] Cloud computing and big data processing modules are used to implement containerized deployment and microservice architecture of the system, build data warehouses and data lakes, and perform big data analysis and machine learning;

[0032] The backup and recovery module is used to develop and implement backup strategies and the process of restoring data;

[0033] The data integrity verification and monitoring module is used to implement data integrity verification and intelligent monitoring and early warning.

[0034] The present invention has the following beneficial effects:

[0035] Improve storage efficiency: Through distributed storage systems and data deduplication and compression technology, the storage efficiency of image data is significantly improved and storage costs are reduced.

[0036] Enhanced data security: The use of advanced encryption algorithms and dynamic key management mechanisms ensures the security of image data during storage and transmission.

[0037] Optimize data analysis: Use big data analysis and machine learning technology to deeply mine and analyze imaging data, providing powerful auxiliary decision support for clinical diagnosis and treatment.

[0038] Improve system reliability: Through the deep integration of backup and recovery mechanisms, data integrity verification and monitoring systems, and intelligent monitoring and early warning systems, the long-term stable operation of the system and the reliability of data are ensured.

[0039] Realize data lifecycle management: Automated data archiving, migration, and deletion strategies optimize the use of storage resources, reduce costs, and ensure the integrity and recoverability of archived data.

[0040] Enhanced disaster recovery capabilities: Building cross-regional data centers enables real-time data synchronization and disaster recovery backup, improving the system's disaster recovery capabilities and data availability.

[0041] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0043] Figure 1 It is a flowchart of a method for safely and quickly storing imaging data in the imaging department of the present invention;

[0044] Figure 2 It is a schematic diagram of step seven, step eight and step nine in embodiment two of the present invention;

[0045] Figure 3 Schematic diagram of the system in the present invention. DETAILED DESCRIPTION

[0046] The following will be combined with the drawings in 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 creative work are within the scope of protection of the present invention.

[0047] Embodiment 1

[0048] like Figure 1As shown: The present invention provides a method for safely and quickly storing imaging data in the imaging department, comprising the following steps:

[0049] Step 1: Design a distributed storage system, select and configure storage nodes based on the characteristics and access patterns of image data;

[0050] Step 2: Select and update encryption algorithms according to security standards, implement file-level and block-level encryption, introduce dynamic key management and rotation mechanisms, and encrypt image data before writing it to storage nodes;

[0051] Step 3: Combine the characteristics of image data, use machine learning technology to achieve accurate data deduplication, and select appropriate compression algorithms for optimization according to the data type;

[0052] Step 4: Build an index system for imaging data, and use big data analysis and machine learning techniques to conduct in-depth mining and analysis of imaging data, establish a data warehouse and data lake, and provide auxiliary decision support for clinical diagnosis and treatment;

[0053] Step 5: Establish an image data backup and recovery mechanism. Use a strategy that combines snapshot technology, incremental backup, and differential backup, and develop a detailed recovery process.

[0054] Step 6: Build a data integrity verification and monitoring system, use verification algorithms and tools, establish a distributed verification mechanism, and use AI and machine learning technologies to achieve intelligent monitoring and early warning.

[0055] In this embodiment, the specific implementation details are as follows:

[0056] Step 1: Design a distributed storage system: Specifically,

[0057] Design a distributed storage system: Select a suitable distributed storage architecture, such as Hadoop HDFS, Ceph, GlusterFS, etc., and design a reasonable storage node distribution and load balancing strategy based on the scale, access frequency and security requirements of the image data.

[0058] Divide storage nodes: Slice the image data according to time, patient ID, examination type and other rules, and store them on multiple storage nodes to achieve load balancing and fault tolerance. At the same time, consider the separation of hot and cold data and store frequently accessed data on high-performance storage devices.

[0059] Optimize network configuration: Ensure that the network connection between storage nodes is stable and efficient, use high-speed network interfaces and redundant network paths, and configure appropriate network bandwidth and latency parameters to increase data transmission speed.

[0060] Of course, it can be further optimized, specifically:

[0061] Intelligent load balancing: In addition to sharding based on rules such as time and patient ID, intelligent load balancing algorithms, such as consistent hashing algorithms, can also be introduced to achieve dynamic distribution and load balancing of data.

[0062] Data redundancy and fault tolerance: In distributed storage systems, multiple copies or erasure coding technology is used to improve data redundancy and fault tolerance. This can ensure data recoverability when certain nodes fail.

[0063] Storage tiering: Storage is divided into different tiers, such as SSD, HDD, and cloud storage, based on the access frequency and importance of data. Frequently accessed data can be stored on SSD to increase access speed; infrequently accessed data can be stored on HDD or cloud storage to reduce costs.

[0064] Step 2: Encryption processing:

[0065] The AES-256 (Advanced Encryption Standard-256 bit) encryption algorithm was selected, which encrypts data at the file level and block level, ensuring the security of data during transmission and storage;

[0066] Dynamic key management is implemented, and keys are rotated regularly, and new keys are generated using a secure random number generator each time. Key storage and distribution are performed through a secure key management service (KMS) to ensure key security and availability;

[0067] Before the image data is written to the storage node, the data is encrypted through an encryption gateway or encryption middleware to ensure that the data is in an encrypted state before being written to the storage system.

[0068] Step 3: Data deduplication and compression:

[0069] Use machine learning algorithms (such as decision trees, random forests, etc.) to extract features and classify image data to achieve accurate data deduplication. By identifying and deleting duplicate data blocks, storage space usage is effectively reduced;

[0070] According to the type of image data (such as CT, MRI, X-ray, etc.), appropriate compression algorithms (such as JPEG2000, DICOM lossless compression, etc.) are selected for optimization. These algorithms significantly reduce storage requirements while ensuring data quality.

[0071] Step 4: Indexing system and data analysis:

[0072] We built an Elasticsearch-based indexing system for imaging data, which enables fast data retrieval and query. The indexing system supports complex query conditions, such as time range, patient ID, imaging type, etc.

[0073] Big data analysis and machine learning technologies (such as deep learning, cluster analysis, etc.) are used to conduct in-depth mining and analysis of imaging data. A data warehouse and data lake are established to provide rich auxiliary decision-making support information for clinical diagnosis and treatment.

[0074] Step 5: Backup and recovery mechanism:

[0075] Snapshot technology is used to regularly create snapshots of the storage system to capture the state of the data at a certain point in time. At the same time, combined with incremental backup and differential backup strategies, only the data that has changed since the last backup is backed up, improving backup efficiency and recovery speed;

[0076] A detailed recovery process has been developed, including the data recovery time objective (RTO) and recovery point objective (RPO) to ensure rapid recovery in the event of data loss or damage.

[0077] Step 6: Data integrity verification and monitoring system:

[0078] A hash check algorithm (such as SHA-256) is used to periodically check the stored data to ensure the integrity and consistency of the data. A distributed check mechanism is established to distribute the check tasks to multiple storage nodes for execution, thus improving the check efficiency.

[0079] AI and machine learning technologies are used to implement an intelligent monitoring and early warning system. The system can monitor the operating status and data access patterns of the storage system in real time, detect abnormal behaviors and issue early warnings in a timely manner, providing strong support for operation and maintenance personnel.

[0080] Through the method of this embodiment, safe and fast storage of imaging data of the imaging department is achieved, the reliability and availability of the data are improved, and strong support is provided for clinical diagnosis and treatment.

[0081] Embodiment 2

[0082] like Figure 2 As shown: The present invention also includes step seven, deep integration of intelligent monitoring and early warning systems, optimizing early warning accuracy through continuous learning, achieving comprehensive hardware resource monitoring, data access pattern analysis, risk identification and self-optimization, and ensuring long-term stable operation of the system.

[0083] Step 8: Data lifecycle management, implement automated data archiving, migration and deletion strategies, and intelligently manage the data lifecycle based on data access frequency, importance and compliance requirements to optimize storage resource utilization and reduce costs. Data lifecycle management also includes regular verification and recovery testing of archived data to ensure the integrity and recoverability of archived data.

[0084] Step 9: Multi-site data synchronization and disaster recovery backup, build cross-regional data centers, realize real-time data synchronization and disaster recovery backup, and ensure that data can be quickly restored and services can continue to be provided when unforeseen events such as natural disasters and equipment failures occur.

[0085] Step nine also includes implementing a data synchronization priority strategy, intelligently adjusting the data synchronization priority according to the importance and access frequency of the data to optimize the utilization of network bandwidth and storage resources.

[0086] This invention further expands on the original foundation, adding key steps such as deep integration of intelligent monitoring and early warning systems, data lifecycle management, and multi-site data synchronization and disaster recovery backup, aiming to achieve a more comprehensive, efficient and secure imaging data storage solution for the Department of Radiology. The following is a detailed description of each step:

[0087] Step 7: Deep integration of intelligent monitoring and early warning systems

[0088] Continuous learning and early warning accuracy optimization:

[0089] An intelligent monitoring and early warning system based on deep learning is introduced. The system can continuously learn the operating status, data access patterns and potential failure modes of the storage system. By analyzing historical data and real-time data, the system can continuously optimize the early warning model and improve the accuracy and timeliness of the early warning.

[0090] Comprehensive hardware resource monitoring: The system monitors key performance indicators of storage nodes in real time, such as CPU usage, memory usage, disk I / O, and network bandwidth. When hardware resources reach the preset threshold, the system can automatically trigger an early warning and recommend appropriate optimization measures.

[0091] Data access pattern analysis and risk identification: The system analyzes data access frequency, access patterns, and data popularity to identify potential performance bottlenecks and data security risks. Based on the analysis results, the system can intelligently adjust data distribution, optimize storage strategies, and provide early warning of potential data loss or damage risks.

[0092] Self-optimization and automated response: The system can automatically perform some optimization operations based on monitoring and early warning results, such as data migration, load balancing, fault recovery, etc. At the same time, the system can also generate detailed reports and suggestions for reference and decision-making by operation and maintenance personnel.

[0093] Step 8: Data lifecycle management

[0094] Automated data archiving, migration and deletion policies: Develop automated data archiving, migration and deletion policies based on data access frequency, importance and compliance requirements. Archived data will be stored on low-cost, high-capacity storage media to free up high-performance storage resources. Migrated data will dynamically adjust storage locations based on changes in access patterns to optimize storage performance. Deletion policies ensure that data that is no longer needed can be deleted in a timely manner to free up storage space.

[0095] Archived data verification and recovery testing: Archived data is verified regularly to ensure data integrity and readability. Recovery testing is performed to verify that archived data can be quickly restored and continue to provide services when needed. The test results will be recorded and used to optimize archiving and recovery strategies.

[0096] Step 9: Multi-site data synchronization and disaster recovery backup

[0097] Cross-regional data center construction: Build cross-regional data centers to achieve real-time data synchronization and disaster recovery backup. Data centers are connected through high-speed and reliable networks to ensure the real-time and consistency of data.

[0098] Data synchronization priority strategy: Intelligently adjust the priority of data synchronization based on the importance and access frequency of the data. High-priority data will be synchronized first to ensure rapid recovery when a failure occurs. At the same time, the system can also dynamically adjust the synchronization rate and strategy based on the utilization of network bandwidth and storage resources.

[0099] Disaster recovery and rapid recovery:

[0100] Complete backup data is retained in each data center to ensure that data can be quickly restored and services can continue to be provided when unforeseen events such as natural disasters and equipment failures occur. Backup data will be verified and tested regularly to ensure its integrity and availability. At the same time, the system can also provide rapid recovery tools and methods to help operation and maintenance personnel quickly restore systems and services.

[0101] Through the method of Example 2, the present invention not only realizes the safe and fast storage of imaging data of the imaging department, but also further improves the stability and reliability of the system, optimizes the utilization of storage resources, reduces costs, and provides more comprehensive and efficient auxiliary decision support for clinical diagnosis and treatment.

[0102] like Figure 3 As shown: In order to implement the above method, the present invention also provides a system for secure and fast storage of imaging data in the Department of Radiology, which specifically includes the following core modules:

[0103] 1. Distributed storage module

[0104] Functional description: This module is responsible for storing image data and configuring and optimizing it according to the characteristics and access patterns of the data. It adopts a distributed storage architecture to ensure high availability and scalability of data.

[0105] Technical implementation: Based on Hadoop HDFS or similar distributed file systems, combined with data sharding and replication mechanisms, efficient data storage and access can be achieved.

[0106] 2. Encryption module

[0107] Functional description: This module is responsible for implementing file-level and block-level encryption to ensure the security of data during storage and transmission. At the same time, it also manages the generation, update and rotation of keys.

[0108] Technical implementation: Use strong encryption algorithms such as AES-256, combined with dynamic key management and secure key distribution mechanisms to ensure the security of data encryption and key management.

[0109] 3. Data deduplication and compression module

[0110] Functional description: This module uses machine learning algorithms to achieve accurate deduplication of image data, and selects appropriate compression algorithms for optimization based on the data type to reduce storage space usage.

[0111] Technical implementation: Identify duplicate data blocks through feature extraction and classification technology, and use JPEG2000, DICOM lossless compression and other algorithms for compression optimization.

[0112] 4. Cloud computing and big data processing module

[0113] Function description: This module implements the system's containerized deployment and microservice architecture, builds data warehouses and data lakes, and supports big data analysis and machine learning.

[0114] Technical implementation: Based on container orchestration tools such as Kubernetes, combined with big data processing frameworks such as Hadoop and Spark, and machine learning frameworks such as TensorFlow and PyTorch, rapid data processing and analysis can be achieved.

[0115] 5. Backup and restore mode

[0116] Functional description: This module is responsible for formulating and implementing backup strategies and data recovery processes to ensure data reliability and recoverability.

[0117] Technical implementation: Adopt a strategy that combines snapshot technology, incremental backup and differential backup, and formulate a detailed recovery process to ensure rapid recovery when data is lost or damaged.

[0118] 6.Data integrity verification and monitoring module

[0119] Function description: This module is responsible for implementing data integrity verification and intelligent monitoring and early warning to ensure data accuracy and system stability.

[0120] Technical implementation: Use hash verification algorithm to regularly verify data, combine AI and machine learning technology to realize intelligent monitoring and early warning system, monitor the system's operating status and data access mode in real time, detect abnormal behavior and issue early warning in time.

[0121] System integration and optimization

[0122] Modular design: Loose coupling design is adopted between modules to facilitate system expansion and maintenance.

[0123] Intelligent scheduling and resource optimization: The system can intelligently schedule storage and computing resources to optimize system performance based on data access patterns and resource usage.

[0124] User-friendly interface: Provides an intuitive and easy-to-use user interface to facilitate users to perform data storage, query and analysis operations.

[0125] The imaging data security and fast storage system provided by the present invention realizes efficient, secure, reliable storage and fast processing of imaging data by integrating multiple modules such as distributed storage, encryption, data deduplication and compression, cloud computing and big data processing, backup and recovery, and data integrity verification and monitoring. The system not only improves the efficiency of data storage and access, but also enhances the security and recoverability of data, providing strong support for clinical diagnosis and treatment.

[0126] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0127] The preferred embodiments of the present invention disclosed above are only used to help illustrate the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to the specific embodiments described. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and effectiveness.

Claims

1. A method for secure and rapid storage of imaging data in the Department of Radiology, characterized in that: The following steps are involved: Step 1: Design a distributed storage system, select and configure storage nodes based on the characteristics and access patterns of image data; Step 2: Select and update encryption algorithms according to security standards, implement file-level and block-level encryption, introduce dynamic key management and rotation mechanisms, and encrypt image data before writing it to storage nodes; Step 3: Combine the characteristics of image data, use machine learning technology to achieve accurate data deduplication, and select appropriate compression algorithms for optimization according to the data type; Step 4: Build an index system for imaging data, and use big data analysis and machine learning techniques to conduct in-depth mining and analysis of imaging data, establish a data warehouse and data lake, and provide auxiliary decision support for clinical diagnosis and treatment; Step 5: Establish an image data backup and recovery mechanism. Use a strategy that combines snapshot technology, incremental backup, and differential backup, and develop a detailed recovery process. Step 6: Build a data integrity verification and monitoring system, use verification algorithms and tools, establish a distributed verification mechanism, and use AI and machine learning technologies to achieve intelligent monitoring and early warning.

2. A method for secure and rapid storage of imaging data of the imaging department according to claim 1, characterized in that: The step one comprises: Select a suitable distributed storage architecture and design a reasonable storage node distribution and load balancing strategy based on the scale, access frequency and security requirements of image data; The imaging data is sharded by time, patient ID, and examination type, and stored on multiple storage nodes to achieve load balancing and fault tolerance; Use high-speed network interfaces and redundant network paths, and configure appropriate network bandwidth and latency parameters to increase data transmission speed.

3. The method for secure and rapid storage of imaging data of the imaging department according to claim 1, characterized in that: The step three comprises: Identify duplicate data: Use hash algorithms or similarity detection algorithms to identify and mark duplicate image data; Data deduplication: During the storage process, only one unique image data is retained. For duplicate data, only its reference or link is stored. At the same time, combined with data compression technology, storage space can be further saved. Regularly optimize deduplication and compression algorithms: to adapt to the ever-changing image data types and sizes.

4. The method for secure and rapid storage of imaging data of the imaging department according to claim 1, characterized in that: The step four also includes: improving containerization and microservice architecture: using containerization technology and microservice architecture to achieve flexible deployment and expansion of the system, and at the same time, providing a unified API interface to facilitate integration and interoperability with other systems.

5. The method for secure and rapid storage of imaging data of the imaging department according to claim 1, characterized in that: It also includes step seven: deep integration of intelligent monitoring and early warning systems, optimizing early warning accuracy through continuous learning, achieving comprehensive hardware resource monitoring, data access pattern analysis, risk identification and self-optimization, and ensuring the long-term stable operation of the system.

6. The method for secure and rapid storage of imaging data of the imaging department according to claim 5, characterized in that: It also includes step eight: data lifecycle management, implementing automated data archiving, migration and deletion strategies, and intelligently managing the data lifecycle based on data access frequency, importance and compliance requirements to optimize storage resource utilization and reduce costs. The data lifecycle management also includes regular verification and recovery testing of archived data to ensure the integrity and recoverability of archived data.

7. The method for secure and rapid storage of imaging data of the imaging department according to claim 6, characterized in that: It also includes step nine: multi-site data synchronization and disaster recovery backup, building cross-regional data centers, realizing real-time data synchronization and disaster recovery backup, and ensuring that data can be quickly restored and services can continue to be provided when unforeseen events occur.

8. The method for secure and rapid storage of imaging data of the imaging department according to claim 7, characterized in that: The step nine also includes implementing a data synchronization priority strategy, and intelligently adjusting the data synchronization priority according to the importance and access frequency of the data to optimize the utilization of network bandwidth and storage resources.

9. A system for secure and rapid storage of imaging data in the Department of Radiology for implementing the method described in any one of claims 1 to 8, characterized in that: include: Distributed storage module, used to store image data and configure and optimize according to data characteristics and access patterns; Cryptographic modules that implement file-level and block-level encryption and manage key generation, updates, and rotation; Data deduplication and compression module, used to achieve accurate deduplication and compression optimization of image data; Cloud computing and big data processing modules are used to implement containerized deployment and microservice architecture of the system, build data warehouses and data lakes, and perform big data analysis and machine learning; The backup and recovery module is used to develop and implement backup strategies and the process of restoring data; The data integrity verification and monitoring module is used to implement data integrity verification and intelligent monitoring and early warning.

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