Medical image processing task deployment system based on Web technology and construction method thereof

Abstract

The invention provides a medical image processing task deployment system based on a Web technology and a construction method thereof. The construction method comprises the following steps: constructing a bottom database; constructing a system architecture, designing a user interface based on a modern framework, and constructing a front-end layer; installing a reverse proxy server and an interface management platform, configuring micro-service and routing, and constructing an interface gateway layer; a service discovery and configuration platform is installed, registration and configuration health examination are carried out on the service discovery and configuration platform when each micro-service is started so as to monitor the state of the micro-service, and a monitoring layer is constructed; the method comprises the following steps: establishing a Kubernetes cluster, deploying each piece of micro-service to an independent Kubernetes container, and constructing a service layer; and installing an advanced message queue protocol, and integrating an advanced message queue protocol client library in the event micro-service for sending and receiving messages and constructing a message queue layer. The invention provides an object-oriented visualization framework system, which can efficiently and stably run in a complex scene, and meets the requirements of high concurrency, balanced load, system reliability and flexibility.

Description

Technical Field

[0001] The present invention relates to the technical field of software development, and in particular to a medical image processing task deployment system based on Web technology and a construction method thereof. Background Art

[0002] With the extension of human life expectancy and the acceleration of the global aging process, the incidence of neurodegenerative diseases has shown an upward trend year by year. The mortality rate of neurodegenerative diseases such as stroke, epilepsy, Alzheimer's disease, Parkinson's disease, etc. has increased by 39% and the lifetime disability rate has increased by 15% in the past few decades. In addition to the brain-related diseases listed above, there are many other diseases of human tissues, such as lung diseases, myocardial cell and related diseases, human spine-related diseases and many other diseases. These diseases have currently affected more than tens of millions of patients worldwide, and the medical expenses generated each year are as high as tens of billions of dollars. However, to date, the research on the above diseases is still mostly at the level of academic and theoretical research, and has not yet been put into practical application.

[0003] In the existing technology, most laboratories have built health prediction systems or software based on theoretical research, and have more or less implemented simple offline visualization pages on the original code. For example, SPM 12 developed based on MATLAB and FreeSurfer developed based on Python have implemented basic visualization pages based on the GUI toolkits of their respective frameworks, which can perform pre-processing operations such as segmentation and registration of medical image software. Another example is the Volbrain.Net website, which implements the function of performing specific calculations on medical images online and outputting results. For example, some commercial software, although its functions are more in line with human habits, such as cNeuro, can support auxiliary diagnosis and risk quantification, but its process is still imperfect and cannot integrate the mainstream medical image processing process.

[0004] It can be seen that there is currently no general-purpose online system framework on the market that can unify and integrate scientific research prediction models in the research field and achieve rapid deployment. For many medical image processing tasks, there is an urgent need for a rapid deployment and object-oriented visualization framework for the corresponding tasks. Summary of the invention

[0005] In view of this, an embodiment of the present invention provides a medical image processing task deployment system based on Web technology and a construction method thereof, so as to eliminate or improve one or more defects existing in the prior art.

[0006] In one aspect, the present invention provides a method for constructing a medical image processing task deployment system based on Web technology, the method comprising the following steps:

[0007] Constructing an underlying database, wherein the underlying database includes a user table, a task table, an uploaded subject table, a task type table, and a task configuration file table;

[0008] Build a system architecture, which includes a front-end layer, an interface gateway layer, a monitoring layer, a service layer, and a message queue layer;

[0009] Wherein, a user interface is designed based on a preset framework to construct the front-end layer;

[0010] Install or deploy a preset reverse proxy server and interface management platform, and configure microservices and routing to build the interface gateway layer;

[0011] Install or deploy a preset service discovery and configuration platform. When each microservice is started, it registers with the service discovery and configuration platform, configures health checks for the microservices, and monitors the status of the microservices to build the monitoring layer.

[0012] Build a Kubernetes cluster and deploy each microservice into an independent Kubernetes container to build the service layer;

[0013] Install or deploy the Advanced Message Queuing Protocol, and integrate the Advanced Message Queuing Protocol client library in the event microservice for sending and receiving messages to build the message queue layer.

[0014] In some embodiments of the present invention, the user table is associated with the task table to track the owner of the task and manage the life cycle of the task.

[0015] In some embodiments of the present invention, designing a user interface based on a preset framework to construct the front-end layer includes:

[0016] Select Vue.js as the preset framework of the front-end layer;

[0017] Create a new project using Vue CLI, design and develop Vue components as needed to build a responsive user interface;

[0018] Use the network request library to integrate with the interface of the interface gateway layer.

[0019] In some embodiments of the present invention, a preset reverse proxy server and an interface management platform are installed or deployed, and microservices and routing are configured to build the interface gateway layer, including:

[0020] Select NGINX as the reverse proxy server to distribute traffic to different microservices according to different routes of user requests; wherein NGINX can distribute traffic to different microservice instances according to different load balancing algorithms;

[0021] Kong is selected as the interface management platform to dynamically route user requests to different microservices based on their characteristics, and to provide application programming interface management, service routing, and traffic control functions.

[0022] In some embodiments of the present invention, configuring a health check for the microservice and monitoring the status of the microservice includes:

[0023] When registering each microservice, configure a health check, which includes HTTP check, TCP check, or custom script;

[0024] The service discovery and configuration platform periodically checks each microservice. If a health check of a microservice is found to have failed, the microservice is marked as unavailable and removed from the service discovery list.

[0025] In some embodiments of the present invention, a Kubernetes cluster is built and each microservice is deployed into an independent Kubernetes container, including:

[0026] Use Minikube or cloud services to build the Kubernetes cluster;

[0027] Write a text file containing commands for assembling images for each microservice to create an application container image.

[0028] Deploy containerized microservices to independent Kubernetes containers.

[0029] In some embodiments of the present invention, the advanced message queue protocol client library is integrated in the event microservice for sending and receiving messages, including:

[0030] When the event microservice generates an event that needs to be notified, the message is sent to the notification service through the advanced message queue protocol;

[0031] The notification service distributes messages to external notification systems through preset electronic media according to different event types.

[0032] On the other hand, the present invention also provides a medical image processing task deployment system based on Web technology, the system is constructed by using any of the methods for constructing a medical image processing task deployment system based on Web technology as described above, and the system comprises:

[0033] A front-end layer for providing a responsive user interface that enables users to interact with the system through a browser;

[0034] The interface gateway layer is used to forward user requests to the corresponding microservices and aggregate the responses of multiple microservices and return them to the client;

[0035] The monitoring layer is used to register and discover service instances, and configure the health check function to regularly check the status of the microservices to dynamically manage the service instances;

[0036] The service layer is used by each microservice to implement specific business functions and process requests from the front end or other services;

[0037] The message queue layer is used to implement asynchronous communication and decoupling between microservices.

[0038] On the other hand, the present invention further provides a task calculation method, which is implemented using the medical image processing task deployment system based on Web technology as described above, and the method comprises:

[0039] Determine whether the current user has a task. If not, create a new task and upload the data of the new task;

[0040] If the current user already has a task or the new task data has been uploaded, determine whether the corresponding task is running. If it is running, perform task calculation until the task is completed;

[0041] If the corresponding task is not running, end the task directly.

[0042] On the other hand, the present invention further provides a computer-readable storage medium having a computer program / instruction stored thereon, which implements the steps of any of the methods mentioned above when executed by a processor.

[0043] The present invention provides a medical image processing task deployment system based on Web technology and a construction method thereof. The construction of the underlying database not only ensures the orderly management of data, but also can flexibly respond to different processing requirements. Based on the underlying database, the system can efficiently process and analyze the data uploaded by users, and provide accurate business logic support. When constructing the system architecture, a modern framework is used to design the user interface and build the front-end layer; NGINX and Kong are used as reverse proxy servers and interface management platforms to ensure the reasonable distribution and rate limit of user requests; Consul is used as a service discovery and configuration platform to manage and monitor the status of microservices; Kubernetes is used to realize automatic scheduling and expansion of each microservice, so that the system can flexibly allocate resource clusters according to the load situation; and RabbitMQ advanced message queue protocols are used to further optimize the message transmission efficiency in high concurrency scenarios. The present invention provides an object-oriented visual framework system, each functional module is independent of each other, can maintain efficient and stable operation in complex scenarios, and meet the requirements of high concurrency, balanced load, system reliability and flexibility.

[0044] Additional advantages, purposes, and features of the present invention will be described in part in the following description, and will become apparent to those skilled in the art after studying the following, or may be learned from the practice of the present invention. The purposes and other advantages of the present invention may be achieved and obtained by the structures specifically indicated in the specification and the accompanying drawings.

[0045] Those skilled in the art will appreciate that the objectives and advantages that can be achieved with the present invention are not limited to the above specific description, and the above and other objectives that can be achieved by the present invention will be more clearly understood from the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] The drawings described herein are used to provide a further understanding of the present invention, constitute a part of the present application, and do not constitute a limitation of the present invention. In the drawings:

[0047] Figure 1 The figure is a schematic diagram of the steps of a method for constructing a medical image processing task deployment system based on Web technology in one embodiment of the present invention.

[0048] Figure 2 It is an entity relationship diagram of the underlying database in one embodiment of the present invention.

[0049] Figure 3 Schematic diagram of the system architecture for deploying medical image processing tasks based on Web technology in one embodiment of the present invention. DETAILED DESCRIPTION

[0050] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the embodiments and the accompanying drawings. Here, the illustrative embodiments of the present invention and their descriptions are used to explain the present invention, but are not intended to limit the present invention.

[0051] It should also be noted that, in order to avoid obscuring the present invention due to unnecessary details, only structures and / or processing steps closely related to the solutions according to the present invention are shown in the accompanying drawings, while other details that are not closely related to the present invention are omitted.

[0052] It should be emphasized that the term “include / comprises” when used herein refers to the presence of features, elements, steps or components, but does not exclude the presence or addition of one or more other features, elements, steps or components.

[0053] It should also be noted that, unless otherwise specified, the term “connection” herein may refer not only to a direct connection but also to an indirect connection involving an intermediate.

[0054] Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings. In the accompanying drawings, the same reference numerals represent the same or similar components, or the same or similar steps.

[0055] In order to solve the problems of the existing health prediction system, such as cumbersome operation, single function, offline use only, weak scalability and generalization, the present invention provides a method for constructing a medical image processing task deployment system based on Web technology, such as Figure 1 As shown, the method includes the following steps S101-S102:

[0056] Step S101: constructing an underlying database, wherein the underlying database includes a user table, a task table, an uploaded subject table, a task type table, and a task configuration file table.

[0057] Step S102: constructing a system architecture, wherein the system architecture includes a front-end layer, an interface gateway layer, a monitoring layer, a service layer, and a message queue layer.

[0058] Specifically: Design the user interface based on the preset framework to build the front-end layer. Install or deploy the preset reverse proxy server and interface management platform, and configure microservices and routing to build the interface gateway layer. Install or deploy the preset service discovery and configuration platform. When each microservice starts, register with the service discovery and configuration platform, configure health checks for the microservices, and monitor the status of microservices to build the monitoring layer. Build a Kubernetes cluster and deploy each microservice to an independent Kubernetes container to build the service layer. Install or deploy the Advanced Message Queuing Protocol, and integrate the Advanced Message Queuing Protocol client library in the event microservice to send and receive messages to build the message queue layer.

[0059] In step S101, firstly, the underlying database is constructed, such as Figure 2 The figure shows the entity-relationship diagram (ER) of the underlying database.

[0060] The underlying database includes user table, task table, uploaded subject table, task type table and task configuration file table.

[0061] As shown in Table 1, these are the specific parameters of the user table, including field names and field functions.

[0062] Table 1 User table

[0063]

[0064] As shown in Table 1, the user table is mainly used to store the basic information of all users in the system, so as to manage user accounts, ensure the uniqueness and security of user information, and facilitate user identity authentication and permission management.

[0065] As shown in Table 2, these are the specific parameters of the task table, including field names and field functions.

[0066] Table 2 Task list

[0067]

[0068] As shown in Table 2, the task table is used to record the tasks created and managed by users in the system. These tasks usually involve uploading, processing, and analyzing data. The task table not only records the basic information of the task, but also associates it with the user table mentioned above to track the owner of the task and manage the life cycle of the task.

[0069] As shown in Table 3, the specific parameters of the uploaded test table include field names and field functions.

[0070] Table 3 Upload test subjects table

[0071]

[0072] As shown in Table 3, the uploaded subject table is used to store specific data or files uploaded by users, which are the basis for subsequent analysis. The uploaded subject table ensures the association between the data uploaded by users and the tasks, and records the storage location of the data and other relevant information.

[0073] As shown in Table 4, these are the specific parameters of the task type table, including field names and field functions.

[0074] Table 4 Task type table

[0075]

[0076] As shown in Table 4, the task type table defines different task types, which helps the system to distinguish and process tasks accordingly, and provides specific processing procedures and configurations for each task type.

[0077] The task configuration file table is used to store detailed configuration parameters related to task processing. These configurations can affect the execution mode and results of the task. Because different task configuration tables require different parameters, this table does not have fixed fields. The functional design of the task configuration file table supports flexible configuration during task processing, so that each task can set different parameters as needed.

[0078] Based on the above description, the five tables of user table, task table, uploaded subject table, task type table and task configuration file table constitute the data management basis of the system. The user table and task table establish a direct connection between users and tasks; the uploaded subject table is responsible for storing uploaded data; the task type table distinguishes different processing tasks; and the task configuration file table stores configuration parameters related to the task. In the present invention, such a design not only ensures the orderly management of data, but also provides flexibility to cope with different processing requirements. Based on this design, the system can efficiently process and analyze the data uploaded by users and provide accurate business logic support.

[0079] In step S102, Figure 3 The figure shows a schematic diagram of the constructed system architecture, which includes a front-end layer, an interface gateway layer, a monitoring layer, a service layer, and a message queue layer. The construction method of each layer is further described below.

[0080] Front-end layer: Design the user interface based on the preset framework.

[0081] In some embodiments, the preset framework selects the Vue.js framework, which is a lightweight progressive JavaScript framework. Through Vue.js, a new project is created using its tool Vue CLI, and each Vue component is designed and developed according to requirements, so as to build an efficient responsive user interface, so that users can interact with the system through a web browser, such as a Chrome browser. Vue.js is selected in view of its good performance, maintainability, and ability to seamlessly integrate with the back-end application programming interface (Application Programming Interface, API). For example, it can be integrated with the interface of the interface gateway layer using libraries such as network requests (Axios).

[0082] Interface gateway layer: Install or deploy the preset reverse proxy server and interface management platform, and configure microservices and routing.

[0083] In some embodiments, NGINX is selected as the reverse proxy server and Kong is selected as the interface management platform.

[0084] NGINX can receive client requests and distribute traffic to different microservices. NGINX also has load balancing capabilities, which can distribute traffic to different microservice instances based on different load balancing algorithms, such as polling, least connections, IP hashing, etc. In terms of routing control, NGINX can intelligently route requests to the corresponding backend services based on the requested URL, HTTP header and other information. This shows that NGINX can simplify the interaction between clients and microservices, while processing a large number of concurrent requests and improving the system's responsiveness.

[0085] Kong is a powerful API gateway that provides API management, service routing, traffic control and other functions. It can also dynamically route to different microservices and implement load balancing based on the characteristics of user requests, such as path, HTTP method, etc. At the same time, Kong's plug-in mechanism can also implement authentication, logging, rate limiting and other functions, thereby enhancing the security and manageability of the system.

[0086] Monitoring layer: Install or deploy the preset service discovery and configuration platform. When each microservice starts, register with the service discovery and configuration platform, configure health checks for the microservices, and monitor the status of the microservices.

[0087] In some embodiments, Consul is selected as the service discovery and configuration platform. Consul is an open source tool for implementing service discovery and configuration of distributed systems. In the present invention, Consul is used to provide service discovery, configuration management, and health check functions. Through Consul, each microservice automatically registers with Consul's service registry when it is started, and other services can dynamically find instances of target services through Consul's service discovery mechanism. At the same time, Consul configures health checks for each microservice, such as HTTP checks, TCP checks, or custom scripts. Consul regularly checks each microservice, that is, performs health monitoring. If the health check of a microservice is found to have failed, the microservice is marked as unavailable and removed from the service discovery list.

[0088] Service layer: Build a Kubernetes cluster and deploy each microservice into an independent Kubernetes container.

[0089] In some embodiments, a Kubernetes cluster can be built using Minikube or a cloud service. Write a text file (Dockerfile) containing commands for assembling images for each microservice, create an application container image, and deploy the containerized microservice to an independent Kubernetes container. Kubernetes is responsible for the scheduling, expansion, and management of containers. The service layer of the system consists of multiple independent microservices, including user management microservices (User Microservice), event microservices (Event Microservice), storage microservices (Store Microservice), and other dedicated microservices. The microservices communicate with each other through an internal network. Due to Kubernetes' automatic expansion and fault recovery mechanism for microservices, the system can automatically adjust resource allocation under high load to ensure high availability and stability of services.

[0090] Message queue layer: Install or deploy the Advanced Message Queuing Protocol and integrate the Advanced Message Queuing Protocol client library in the event microservice to send and receive messages.

[0091] In some embodiments, in order to achieve asynchronous communication and decoupling between services, the system introduces RabbitMQ (Advanced Message Queuing Protocol) as message queue middleware. RabbitMQ is used to handle message delivery and event distribution, especially in high-concurrency scenarios, which can effectively reduce system latency and improve system responsiveness.

[0092] When the event microservice generates an event that needs to be notified, it sends the message to the notification service through RabbitMQ. The notification service will use appropriate channels, such as email, SMS and other electronic media, to distribute the message to the external notification system (External Notification Services) according to different event types.

[0093] Accordingly, in the present invention, the method for constructing a medical image processing task deployment system based on Web technology can construct a medical image processing task deployment system based on Web technology, such as Figure 3 As shown, the system includes:

[0094] The front-end layer is used to provide a responsive user interface that enables users to interact with the system through a browser.

[0095] The interface gateway layer is used to forward user requests to the corresponding microservices and aggregate the responses of multiple microservices and return them to the client.

[0096] The monitoring layer is used to register and discover service instances and configure the health check function to regularly check the status of microservices to dynamically manage service instances.

[0097] The service layer is used by each microservice to implement specific business functions and process requests from the front end or other services.

[0098] The message queue layer is used to implement asynchronous communication and decoupling between microservices.

[0099] Based on the above description, the distributed system design provided by the present invention combines Vue.js, NGINX, Kong, Consul, Kubernetes, RabbitMQ and other technologies on the basis of considering high concurrency and reliability. Through the routing of the interface gateway layer, the containerized deployment of the service layer, and the asynchronous communication of the message queue, it is ensured that the system can maintain efficient and stable operation in complex scenarios, meeting the requirements of high concurrency, load balancing, system reliability and flexibility. The system architecture can not only meet the performance and scalability requirements of modern applications, but also provide efficient and stable services.

[0100] The present invention also provides a task calculation method, which is implemented using the medical image processing task deployment system based on Web technology. The method includes the following steps S201 to S203:

[0101] Step S201: Determine whether the current user has a task. If not, create a new task and upload the data of the new task.

[0102] Step S202: If the current user already has a task or the new task data has been uploaded, determine whether the corresponding task is running. If it is running, perform task calculation until the task is completed.

[0103] Step S203: If the corresponding task is not running, the task is terminated directly.

[0104] Corresponding to the above method, the present invention also provides an electronic equipment, which includes a computer device, the computer device includes a processor and a memory, the memory stores computer instructions, the processor is used to execute the computer instructions stored in the memory, and when the computer instructions are executed by the processor, the electronic equipment implements the steps of the method described above.

[0105] The embodiment of the present invention also provides a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, the steps of the aforementioned edge computing server deployment method are implemented. The computer-readable storage medium can be a tangible storage medium, such as a random access memory (RAM), a memory, a read-only memory (ROM), an electrically programmable ROM, an electrically erasable programmable ROM, a register, a floppy disk, a hard disk, a removable storage disk, a CD-ROM, or any other form of storage medium known in the technical field.

[0106] It should be understood by those skilled in the art that the exemplary components, systems and methods described in conjunction with the embodiments disclosed herein can be implemented in hardware, software or a combination of the two. Whether it is performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention. When implemented in hardware, it can be, for example, an electronic circuit, an application specific integrated circuit (ASIC), appropriate firmware, a plug-in, a function card, etc. When implemented in software, the elements of the present invention are programs or code segments used to perform the required tasks. The program or code segment can be stored in a machine-readable medium, or transmitted on a transmission medium or a communication link via a data signal carried in a carrier.

[0107] It should be clear that the present invention is not limited to the specific configuration and processing described above and shown in the figures. For the sake of simplicity, a detailed description of the known method is omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of the present invention is not limited to the specific steps described and shown, and those skilled in the art can make various changes, modifications and additions, or change the order between the steps after understanding the spirit of the present invention.

[0108] In the present invention, features described and / or illustrated for one embodiment may be used in the same or similar manner in one or more other embodiments, and / or combined with features of other embodiments or replace features of other embodiments.

[0109] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the embodiments of the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for constructing a medical image processing task deployment system based on Web technology, characterized in that: The method comprises the following steps: Constructing an underlying database, wherein the underlying database includes a user table, a task table, an uploaded subject table, a task type table, and a task configuration file table; Build a system architecture, which includes a front-end layer, an interface gateway layer, a monitoring layer, a service layer, and a message queue layer; Wherein, a user interface is designed based on a preset framework to construct the front-end layer; Install or deploy a preset reverse proxy server and interface management platform, and configure microservices and routing to build the interface gateway layer; Install or deploy a preset service discovery and configuration platform. When each microservice is started, it registers with the service discovery and configuration platform, configures health checks for the microservices, and monitors the status of the microservices to build the monitoring layer. Build a Kubernetes cluster and deploy each microservice into an independent Kubernetes container to build the service layer; Install or deploy the Advanced Message Queuing Protocol, and integrate the Advanced Message Queuing Protocol client library in the event microservice for sending and receiving messages to build the message queue layer.

2. The method for constructing a medical image processing task deployment system based on Web technology according to claim 1, characterized in that: The user table is associated with the task table to track the owner of the task and manage the life cycle of the task.

3. The method for constructing a medical image processing task deployment system based on Web technology according to claim 1, characterized in that: Design the user interface based on the preset framework to build the front-end layer, including: Select Vue.js as the preset framework of the front-end layer; Create a new project using Vue CLI, design and develop Vue components as needed to build a responsive user interface; Use the network request library to integrate with the interface of the interface gateway layer.

4. The method for constructing a medical image processing task deployment system based on Web technology according to claim 1, characterized in that: Install or deploy a preset reverse proxy server and interface management platform, and configure microservices and routing to build the interface gateway layer, including: Select NGINX as the reverse proxy server to distribute traffic to different microservices according to different routes of user requests; wherein NGINX can distribute traffic to different microservice instances according to different load balancing algorithms; Kong is selected as the interface management platform to dynamically route user requests to different microservices based on their characteristics, and to provide application programming interface management, service routing, and traffic control functions.

5. The method for constructing a medical image processing task deployment system based on Web technology according to claim 1, characterized in that: Configure health checks for the microservices and monitor the status of the microservices, including: When registering each microservice, configure a health check, which includes HTTP check, TCP check, or custom script; The service discovery and configuration platform periodically checks each microservice. If a health check of a microservice is found to have failed, the microservice is marked as unavailable and removed from the service discovery list.

6. The method for constructing a medical image processing task deployment system based on Web technology according to claim 1, characterized in that: Build a Kubernetes cluster and deploy each microservice into a separate Kubernetes container, including: Use Minikube or cloud services to build the Kubernetes cluster; Write a text file containing commands for assembling images for each microservice to create an application container image. Deploy containerized microservices to independent Kubernetes containers.

7. The method for constructing a medical image processing task deployment system based on Web technology according to claim 1, characterized in that: Integrate the advanced message queue protocol client library in the event microservice to send and receive messages, including: When the event microservice generates an event that needs to be notified, the message is sent to the notification service through the advanced message queue protocol; The notification service distributes messages to external notification systems through preset electronic media according to different event types.

8. A medical image processing task deployment system based on Web technology, characterized in that: The system is constructed by using the method for constructing a medical image processing task deployment system based on Web technology as claimed in any one of claims 1 to 7, and the system comprises: A front-end layer for providing a responsive user interface that enables users to interact with the system through a browser; The interface gateway layer is used to forward user requests to the corresponding microservices and aggregate the responses of multiple microservices and return them to the client; The monitoring layer is used to register and discover service instances, and configure the health check function to regularly check the status of the microservices to dynamically manage the service instances; The service layer is used by each microservice to implement specific business functions and process requests from the front end or other services; The message queue layer is used to implement asynchronous communication and decoupling between microservices.

9. A task calculation method, characterized in that: The method is implemented using the medical image processing task deployment system based on Web technology as claimed in claim 8, and the method comprises: Determine whether the current user has a task. If not, create a new task and upload the data of the new task; If the current user already has a task or the new task data has been uploaded, determine whether the corresponding task is running. If it is running, perform task calculation until the task is completed; If the corresponding task is not running, end the task directly.

10. A computer-readable storage medium having a computer program / instruction stored thereon, characterized in that: When the computer program / instructions are executed by a processor, the steps of the method as claimed in any one of claims 1 to 7 and claim 9 are implemented.

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