Medical image data treatment system and device and readable storage medium

Abstract

The invention provides a medical image data treatment system and device and a readable storage medium, and belongs to the technical field of data treatment. The system comprises a data acquisition module which is responsible for acquiring and standardizing medical image data into a DICOM file; the retrieval module is used for extracting metadata and structured data in the file, storing the metadata and the structured data in a database and establishing an optimized index and table and partition; the distributed storage module is used for guaranteeing the data reliability by adopting a block storage and copy mechanism, and backing up and monitoring storage nodes at regular time; the data compression and transmission module compresses the image file according to scene requirements and transmits the image file to the terminal; and the asynchronous processing module is used for putting the key information of the to-be-analyzed image file into a Kafka queue, and analyzing tasks are processed in parallel by multiple nodes of a background. According to the invention, efficient treatment of the medical image data is realized, and powerful support is provided for accurate management and application of the medical image data.

Description

Technical Field

[0001] The present invention relates to the field of data management technology, and more specifically to a medical imaging data management system, device and readable storage medium. Background Art

[0002] With the rapid development of medical artificial intelligence technology, the demand for annotation and training of medical imaging data has exploded. However, traditional medical imaging management methods are unable to cope with this massive amount of data, exposing a series of significant problems.

[0003] First, in terms of data retrieval, traditional governance methods rely heavily on manual film reading to find data on specific parts or imaging modalities. This process is not only time-consuming and labor-intensive, but also inefficient and cannot meet the needs of fast and accurate data retrieval. Against the backdrop of a surge in the amount of medical imaging data, this manual retrieval method is clearly no longer able to meet the needs of practical applications.

[0004] Secondly, in terms of data storage and transmission, the storage methods used by traditional governance methods face huge challenges. Medical imaging data is usually large in size and in various formats. Traditional storage systems often have difficulty carrying such a large amount of data and are difficult to meet the needs of efficient access. In addition, the transmission of medical imaging data also faces many difficulties, such as slow transmission speed and easy loss, which further limits the widespread application of medical imaging data.

[0005] Finally, traditional governance methods are unable to cope with large-scale data processing. Due to the complexity and diversity of medical imaging data, traditional methods often encounter performance bottlenecks when processing large-scale data, and processing speed becomes a key factor restricting its widespread application. This not only affects the processing efficiency of medical imaging data, but also limits the development and application of medical artificial intelligence technology. Summary of the invention

[0006] In response to the above problems, the purpose of the present invention is to provide a medical image data management system, device and readable storage medium, which provides a simple and efficient solution to meet the management challenges of massive medical image files, improve retrieval efficiency, optimize data storage and transmission methods, and break the performance limitations of traditional methods.

[0007] In order to achieve the above object, the present invention is implemented through the following technical solutions: In a first aspect, the present invention discloses a medical imaging data management system, comprising: The data acquisition module is used to acquire medical image data, standardize the medical image data according to the DICOM standard, and generate medical image files; The retrieval module is used to extract metadata and structured medical imaging data from medical imaging files and store them in a preset database, establish indexes for metadata and structured medical imaging data, and perform index optimization and table partitioning design to improve the retrieval performance of the preset database; Distributed storage module, which uses a distributed file system to store medical image files in blocks, and ensures the reliability of block data through a copy mechanism; uses scheduled tasks to perform regular data backup, monitor the health status of storage nodes, and perform data recovery; The data compression and transmission module is used to obtain the current medical image data scenario requirements in real time, compress the medical image files to be transmitted according to the scenario requirements, and send them to the required terminal; The asynchronous processing module is used to obtain the key information of the medical image files to be parsed in real time and put it into the Kafka message queue. It uses multiple application nodes deployed in the background to receive the parsing tasks issued by the Kafka message queue and perform parallel processing; among them, multiple threads are opened in each application node to process the parsing tasks in parallel.

[0008] Further, the retrieval module includes: An access frequency recording unit, used to record the number of accesses to each metadata or structured medical imaging data in real time; The cache unit is used to cache the corresponding metadata or structured medical image data into the memory after the number of accesses to any metadata or structured medical image data reaches a preset threshold.

[0009] Furthermore, the metadata includes: image ID, patient information, image shooting time and equipment information; the structured medical image data includes: image resolution and image analysis results.

[0010] Furthermore, the retrieval module is specifically used for: Extract metadata and structured medical image data from medical image files; Store metadata in a MongoDB database; Store structured medical image data into the OpenGauss database.

[0011] Furthermore, the distributed storage module includes: A block storage unit is used to divide the medical image file into multiple data blocks using the Hadoop distributed file system, determine the storage node of the data block through a hash algorithm, and store each data block on a different storage node in the cluster; The replica mechanism unit is used to create three replica data blocks for each data block, determine the storage node of the replica data block through a hash algorithm, and store it on the corresponding storage node; A backup unit is used to perform regular snapshot backup of metadata and structured medical image data in medical image files using Cron Jobs tasks; The node monitoring and data recovery unit is used to monitor the health status of storage nodes in real time through the NameNode and DataNode mechanisms. When an abnormal storage node status is found, the data blocks of the storage node are copied to an idle storage node.

[0012] Furthermore, the size of the data block is 128MB or 256MB.

[0013] Furthermore, the data compression and transmission module is specifically used for: The scenario requirement of obtaining current medical imaging data in real time; If the scenario requirement is a preliminary image preview requirement, the medical image file to be transmitted is compressed using the lossy compression mode of JPEG-LS; If the scenario requirement is an image reading requirement, the medical image file to be transmitted is compressed using the JPEG-LS lossless compression mode.

[0014] Furthermore, the asynchronous processing module includes: The task submission unit is used to obtain the name and path of the medical image file to be parsed in real time, encapsulate it into a message, and put it into the Kafka message queue; An application node deployment unit is used to deploy multiple application nodes in the background and start ten threads for each application node to process parsing tasks in parallel; The task allocation unit is used to distribute the messages in the Kafka message queue to the application nodes according to the predetermined order and priority; The task processing unit is used to parse the medical image file according to the name and path of the medical image file in the message when the application node receives the message, perform corresponding processing to generate a processing log and processed files, and store them in a preset database. In a second aspect, the present invention further discloses a medical imaging data management device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the functions of the medical imaging data management system as described in any one of the above items when executing the computer program.

[0015] In a third aspect, the present invention further discloses a readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the function of the medical imaging data management system as described in any one of the above items is realized.

[0016] Compared with the prior art, the present invention has the following beneficial effects: 1. The present invention realizes the rapid positioning and accurate extraction of massive medical imaging data by introducing advanced indexing mechanism, intelligent classification algorithm and efficient retrieval technology, significantly improves the retrieval efficiency and reduces the cost of manual intervention.

[0017] 2. The present invention adopts a distributed storage architecture, combined with mature technologies such as Hadoop HDFS, to achieve redundant storage and efficient management of data, ensuring data security and scalability. In addition, by optimizing data compression algorithms and transmission protocols, the present invention also greatly reduces data storage and transmission costs and improves overall performance.

[0018] 3. The present invention realizes high-speed processing and real-time analysis of massive medical image files by introducing parallel processing, multi-threading technology and efficient data processing algorithms. At the same time, combined with the cache mechanism and intelligent scheduling system, the present invention can dynamically adjust resource allocation to ensure stable performance output during peak processing hours, thus breaking the performance limitations of traditional methods and providing strong support for clinical practice.

[0019] It can be seen that compared with the prior art, the present invention has outstanding substantive features and significant progress, and the beneficial effects of its implementation are also obvious. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0021] Figure 1 It is a system structure diagram of a specific implementation mode of the present invention.

[0022] In the figure, 1. Data acquisition module; 2. Retrieval module; 3. Distributed storage module; 4. Data compression and transmission module; 5. Asynchronous processing module; 21. Access frequency recording unit; 22. Cache unit; 31. Block storage unit; 32. Copy mechanism unit; 33. Backup unit; 34. Node monitoring and data recovery unit; 51. Task submission unit; 52. Application node deployment unit; 53. Task allocation unit; 54. Task processing unit. DETAILED DESCRIPTION

[0023] In order to enable those skilled in the art to better understand the scheme of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific implementation methods. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than 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.

[0024] See also Figure 1 As shown, this embodiment provides a medical imaging data management system, including: a data acquisition module 1, a retrieval module 2, a distributed storage module 3, a data compression and transmission module 4 and an asynchronous processing module 5.

[0025] The data acquisition module 1 is used to acquire medical image data, perform standardization processing on the medical image data according to the DICOM standard, and generate a medical image file.

[0026] Retrieval module 2 is used to extract metadata and structured medical imaging data from medical imaging files and store them in a preset database, establish indexes for metadata and structured medical imaging data, and perform index optimization and table partitioning design to improve the retrieval performance of the preset database.

[0027] In a specific implementation, the search module 2 includes: an access frequency recording unit 21 and a cache unit 22 .

[0028] The access frequency recording unit 21 is used to record the access times of each metadata or structured medical image data in real time. The metadata includes: image ID, patient information, image shooting time and equipment information; the structured medical image data includes: image resolution and image analysis results.

[0029] The cache unit 22 is used to cache the corresponding metadata or structured medical image data into the memory after the number of accesses to any metadata or structured medical image data reaches a preset threshold.

[0030] The step of extracting metadata and structured medical image data from medical image files and storing them in a preset database includes: Extract metadata and structured medical image data from medical image files; Store metadata in a MongoDB database; Store structured medical image data into the OpenGauss database.

[0031] It can be seen that the system provides a retrieval module 2 to achieve efficient retrieval.

[0032] First, distributed cache is used to speed up access to commonly used data. Distributed cache Redis is used to cache frequently accessed image data and metadata into memory to reduce database pressure.

[0033] Secondly, index optimization and table partition design are performed for the image metadata and extracted data in the database to avoid query bottlenecks and improve the query performance of the database.

[0034] In addition, this module uses different databases to improve retrieval efficiency and store the metadata of image data (such as image ID, patient information, image shooting time, equipment information, etc.) in MongoDB. MongoDB can efficiently process unstructured data and support horizontal expansion and high concurrent access. OpenGauss is used to store structured medical imaging data (such as image resolution, image analysis results, etc.). It supports complex queries and high concurrent transactions and is suitable for storing more complex data relationships.

[0035] Distributed storage module 3 is used to use a distributed file system to store medical image files in blocks, ensure the reliability of block data through a copy mechanism, use scheduled tasks to perform regular data backup, monitor the health status of storage nodes and perform data recovery.

[0036] In a specific implementation, the distributed storage module 3 includes: a block storage unit 31 , a copy mechanism unit 32 , a backup unit 33 and a node monitoring and data recovery unit 34 .

[0037] The block storage unit 31 is used to use the Hadoop distributed file system to divide the medical image file into multiple data blocks, determine the storage node of the data block through a hash algorithm, and store each data block on a different storage node in the cluster. The size of the data block is 128MB or 256MB.

[0038] For example, the block storage unit 31 uses the Hadoop Distributed File System (HDFS). When storing in blocks, the medical image file is divided into small blocks (the block size is usually set to 128MB or 256MB). Each data block is stored on a different storage node in the cluster, and the data reliability is ensured by the copy mechanism.

[0039] The replica mechanism unit 32 is used to create three replica data blocks for each data block, determine the storage node of the replica data block through a hash algorithm, and store it on the corresponding storage node.

[0040] For example, each data block has multiple copies, and the default number of copies is 3, to ensure that the data will not be lost when a storage node fails. In addition, the data is stored on which storage nodes through a hash algorithm to ensure load balancing. It can be seen that the copy mechanism unit 32 implements redundant storage of each image data in HDFS through a copy mechanism, ensuring that the data can still be restored when a node fails or a network fails.

[0041] The backup unit 33 is used to perform regular snapshot backup of the metadata and structured medical image data in the medical image files by using Cron Jobs tasks.

[0042] The node monitoring and data recovery unit 34 is used to monitor the health status of the storage node in real time through the NameNode and DataNode mechanism. When the storage node status is found to be abnormal, the data block of the storage node is copied to an idle storage node to ensure that data is not lost.

[0043] The data compression and transmission module 4 is used to obtain the current scenario requirements of medical image data in real time, compress the medical image files to be transmitted according to the scenario requirements, and send them to the required terminal.

[0044] In a specific implementation, the data compression and transmission module 4 is specifically used for: The scenario requirement of obtaining current medical imaging data in real time; If the scenario requirement is a preliminary image preview requirement, the medical image file to be transmitted is compressed using the lossy compression mode of JPEG-LS; If the scenario requirement is an image reading requirement, the medical image file to be transmitted is compressed using the JPEG-LS lossless compression mode.

[0045] As an example, in response to the challenge of large image data volumes, this system uses the data compression and transmission module 4 to introduce a hybrid compression strategy, aiming to flexibly select compression modes according to image quality requirements of different application scenarios. Specifically: For scenes with low quality requirements such as preliminary image preview, this system uses the lossy compression mode of JPEG-LS, which greatly reduces the data volume at the expense of some image details, thereby improving storage and transmission efficiency.

[0046] For scenes with extremely high requirements on image quality, such as film reading, the JPEG-LS lossless compression mode is adopted to ensure the integrity and accuracy of the image information and avoid any quality loss.

[0047] Through this strategy, the system successfully combines the advantages of lossy compression and lossless compression, significantly reducing the data volume while ensuring that the key image quality is not affected, thereby effectively reducing the cost of storage and transmission.

[0048] The asynchronous processing module 5 is used to obtain the key information of the medical image file to be parsed in real time and put it into the Kafka message queue, and use multiple application nodes deployed in the background to receive the parsing tasks issued by the Kafka message queue and perform parallel processing; wherein, multiple threads are opened in each application node to process the parsing tasks in parallel.

[0049] In a specific implementation, the asynchronous processing module 5 includes: a task submission unit 51 , an application node deployment unit 52 , a task allocation unit 53 and a task processing unit 54 .

[0050] The task submission unit 51 is used to obtain the name and path of the medical image file to be parsed in real time, encapsulate it into a message, and put it into the Kafka message queue. The task submission unit 51 realizes the asynchronous allocation of tasks and provides a basis for buffering and scheduling for subsequent processing. At the same time, the message persistence and queue characteristics of Kafka are used to ensure that the tasks will not be lost and can be processed according to the predetermined order and priority.

[0051] The application node deployment unit 52 is used to deploy multiple application nodes in the background and open ten threads for each application node to process the parsing task in parallel. The application node deployment unit 52 realizes distributed parallel processing of processing tasks through multi-node and multi-thread mode to improve the overall processing speed. At the same time, the redundancy characteristics of multiple nodes are used to improve the stability and fault tolerance of the system.

[0052] The task allocation unit 53 is used to distribute the messages in the Kafka message queue to the application nodes according to the predetermined order and priority. The task allocation unit 53 implements the synchronous management of tasks to avoid task conflicts and resource waste. It ensures that tasks are processed according to the predetermined order and priority to ensure the logic of data.

[0053] The task processing unit 54 is used to parse the medical image file according to the name and path of the medical image file in the message when the application node receives the message, perform corresponding processing to generate a processing log and processed files, and store them in a preset database. The processing methods include: image enhancement, format conversion, etc. This unit implements the processing requirements of DICOM files and saves the processing results for subsequent use.

[0054] The present invention provides a medical imaging data governance system, which can efficiently collect and standardize medical imaging data through a data acquisition module, ensure the accuracy and consistency of the data, and lay a solid foundation for subsequent processing and analysis. The introduction of the retrieval module not only realizes the rapid indexing and query of metadata and structured medical imaging data, but also further optimizes the retrieval performance of the database through access frequency recording and caching mechanisms, significantly improving the efficiency of data access and user experience. The distributed storage module adopts the advanced Hadoop distributed file system to divide the medical imaging files into multiple data blocks for storage, and ensures the reliability and security of the data through a copy mechanism and regular backup. At the same time, the node monitoring and data recovery functions can monitor the health status of the storage nodes in real time, respond to abnormal situations in a timely manner, and effectively prevent data loss.

[0055] The data compression and transmission module intelligently selects the lossy or lossless compression mode of JPEG-LS according to the different scene requirements of medical image data, which not only meets the convenience requirements of preliminary image preview, but also ensures the high precision requirements of image reading. The asynchronous processing module realizes asynchronous task allocation and parallel processing through the Kafka message queue and multiple application nodes deployed in the background, which significantly improves processing efficiency and response speed.

[0056] In summary, the system has demonstrated excellent performance and significant beneficial effects in improving the efficiency of medical image data processing, ensuring data security, optimizing data access speed, and meeting image quality requirements in different scenarios, providing strong technical support and guarantee for the accurate diagnosis and management of medical images.

[0057] The present invention also provides a medical imaging data management device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the functions of the medical imaging data management system as described in any one of the above items when executing the computer program.

[0058] The present invention also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it realizes the functions of the medical imaging data management system as described in any one of the above items.

[0059] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors, application-specific integrated circuits, field programmable gate arrays or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0060] The memory can be used to store the computer program and / or module, and the processor realizes various functions of the dairy data early warning analysis system in the invention by running or executing the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.). In addition, the memory can include a high-speed random access memory and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card, a secure digital card, a flash memory card, at least one disk storage device, a flash memory device, or other volatile solid-state storage devices. If the medical imaging data governance system is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the process in the above-mentioned embodiment method, and can also be stored in a computer-readable storage medium through a computer program. When the computer program is executed by the processor, the steps of each of the above-mentioned method embodiments can be implemented. Among them, the computer program includes computer program code, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device that can carry the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory, random access memory, point carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction.

[0061] The present invention has described the basic concepts. Obviously, for those skilled in the art, the above detailed disclosure is only for example and does not constitute a limitation of this specification. Although not explicitly stated herein, those skilled in the art may make various modifications, improvements and corrections to this specification. Such modifications, improvements and corrections are suggested in this specification, so such modifications, improvements and corrections still belong to the spirit and scope of the exemplary embodiments of this specification. In addition, it will be understood by those skilled in the art that various aspects of this specification may be illustrated and described by a number of patentable categories or situations, including any new and useful process, machine, product or combination of substances, or any new and useful improvements thereto. Accordingly, various aspects of this specification may be performed entirely by hardware, entirely by software (including firmware, resident software, microcode, etc.), or by a combination of hardware and software. The above hardware or software may be referred to as "data blocks", "modules", "engines", "units", "components" or "systems". In addition, various aspects of this specification may be represented as a computer product located in one or more computer-readable media, which includes computer-readable program code. A computer storage medium may include a propagated data signal containing computer program code, for example, in baseband or as part of a carrier wave. The propagated signal may be in a variety of forms, including electromagnetic, optical, etc., or a suitable combination. A computer storage medium may be any computer-readable medium other than a computer-readable storage medium, which can be connected to an instruction execution system, device or apparatus to communicate, propagate or transmit the program for use. The program code on the computer storage medium may be transmitted via any suitable medium, including radio, cable, fiber optic cable, RF, or similar media, or any combination of the above media. The computer program code required for the operation of each part of this specification can be written in any one or more programming languages, including object-oriented programming languages ​​such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C#, VB.NET, Python, etc., conventional procedural programming languages ​​such as C language, Visual Basic, Fortran 2003, Perl, COBOL2002, PHP, ABAP, dynamic programming languages ​​such as Python, Ruby and Groovy, or other programming languages, etc. The program code can be run entirely on the user's computer, or run on the user's computer as an independent software package, or run partially on the user's computer and partially on a remote computer, or run entirely on a remote computer or server. In the latter case, the remote computer can be connected to the user's computer through any network form, such as a local area network (LAN) or a wide area network (WAN), or connected to an external computer (e.g., via the Internet), or in a cloud computing environment, or used as a service such as software as a service (SaaS). In addition, unless explicitly stated in the claims, the order of the processing elements and sequences described in this specification, the use of alphanumeric characters, or the use of other names are not intended to limit the order of the processes and methods of this specification. Although the above disclosure discusses some invention embodiments that are currently considered useful through various examples, it should be understood that such details are only for illustrative purposes, and the attached claims are not limited to the disclosed embodiments. On the contrary, the claims are intended to cover all modifications and equivalent combinations that are consistent with the essence and scope of the embodiments of this specification. For example, although the system components described above can be implemented by hardware devices, they can also be implemented only by software solutions, such as installing the described system on an existing server or mobile device. Similarly, it should be noted that in order to simplify the description disclosed in this specification and thus help understand one or more embodiments of the invention, in the above description of the embodiments of this specification, multiple features are sometimes combined into one embodiment, figure or description thereof. However, this disclosure method does not mean that the features required by the subject matter of this specification are more than the features mentioned in the claims. In fact, the features of the embodiments are less than all the features of the single embodiment disclosed above. Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention. Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention belong to the scope of the present invention and its equivalent technologies, the present invention is also intended to include these changes and variations.

Claims

1. A medical imaging data management system, characterized in that: include: The data acquisition module is used to acquire medical image data, standardize the medical image data according to the DICOM standard, and generate medical image files; The retrieval module is used to extract metadata and structured medical imaging data from medical imaging files and store them in a preset database, establish indexes for metadata and structured medical imaging data, and perform index optimization and table partitioning design to improve the retrieval performance of the preset database; Distributed storage module, used to store medical image files in blocks using a distributed file system, and ensure the reliability of block data through a copy mechanism; Use scheduled tasks to perform regular data backup, monitor the health status of storage nodes, and perform data recovery; The data compression and transmission module is used to obtain the current scenario requirements of medical imaging data in real time, compress the medical imaging files to be transmitted according to the scenario requirements, and send them to the required terminal; The asynchronous processing module is used to obtain the key information of the medical image files to be parsed in real time and put it into the Kafka message queue. It uses multiple application nodes deployed in the background to receive the parsing tasks issued by the Kafka message queue and perform parallel processing; among them, multiple threads are opened in each application node to process the parsing tasks in parallel.

2. The medical imaging data management system according to claim 1, characterized in that: The retrieval module comprises: An access frequency recording unit, used to record the number of accesses to each metadata or structured medical imaging data in real time; The cache unit is used to cache the corresponding metadata or structured medical image data into the memory after the number of accesses to any metadata or structured medical image data reaches a preset threshold.

3. The medical imaging data management system according to claim 1, characterized in that: The metadata includes: image ID, patient information, image shooting time and device information; The structured medical image data includes: image resolution and image analysis results.

4. The medical imaging data management system according to claim 1, characterized in that: The retrieval module is specifically used for: Extract metadata and structured medical image data from medical image files; Store metadata in a MongoDB database; Store structured medical image data into the OpenGauss database.

5. The medical imaging data management system according to claim 1, characterized in that: The distributed storage module includes: A block storage unit is used to divide the medical image file into multiple data blocks using the Hadoop distributed file system, determine the storage node of the data block through a hash algorithm, and store each data block on a different storage node in the cluster; The replica mechanism unit is used to create three replica data blocks for each data block, determine the storage node of the replica data block through a hash algorithm, and store it on the corresponding storage node; A backup unit is used to perform regular snapshot backup of metadata and structured medical image data in medical image files using Cron Jobs tasks; The node monitoring and data recovery unit is used to monitor the health status of storage nodes in real time through the NameNode and DataNode mechanisms. When an abnormal storage node status is found, the data blocks of the storage node are copied to an idle storage node.

6. The medical imaging data management system according to claim 5, characterized in that: The size of the data block is 128MB or 256MB.

7. The medical imaging data management system according to claim 1, characterized in that: The data compression and transmission module is specifically used for: The scenario requirement of obtaining current medical imaging data in real time; If the scenario requirement is a preliminary image preview requirement, the medical image file to be transmitted is compressed using the lossy compression mode of JPEG-LS; If the scenario requirement is an image reading requirement, the medical image file to be transmitted is compressed using the JPEG-LS lossless compression mode.

8. The medical imaging data management system according to claim 1, characterized in that: The asynchronous processing module comprises: The task submission unit is used to obtain the name and path of the medical image file to be parsed in real time, encapsulate it into a message, and put it into the Kafka message queue; An application node deployment unit is used to deploy multiple application nodes in the background and start ten threads for each application node to process parsing tasks in parallel; The task allocation unit is used to distribute the messages in the Kafka message queue to the application nodes according to the predetermined order and priority; The task processing unit is used to parse the medical image file according to the name and path of the medical image file in the message when the application node receives the message, perform corresponding processing to generate a processing log and processed files, and store them in a preset database.

9. A medical image data management device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the functions of the medical imaging data management system as described in any one of claims 1 to 8 are realized.

10. A readable storage medium, characterized in that: The readable storage medium stores a computer program, which, when executed by a processor, implements the functions of the medical imaging data management system as described in any one of claims 1 to 8.

Images