Research And Implementation Of Emergency Nursing Online Learning System Based On Microservice Architecture

With the rapid development of the Internet and the rapid development of the IT industry,the development of system architecture is also advancing with the times.When the number of users increases exponentially,the single architecture that has been popular for a long time has been difficult to cope with and deal with the increasing software complexity.The traditional monolithic architecture is also difficult to support today's huge amount of data.When the number of simultaneous visitors exceeds a certain amount,the server will respond slowly,interaction failures and other problems,and there may even be server downtime.In this situation,the microservice architecture style came into being.The core of the microservice architecture is service-oriented,with the focus on module division and correct and efficient calls between services.Microservice architecture is usually in the form of a cluster.In a cluster system,how to select a suitable processor becomes a very important topic.In response to the above-mentioned problems,the research work of this article mainly includes the following parts:1.The optimization strategy of load balancing is studied.The optimization strategy is different for different load cluster systems.It mainly includes real-time monitoring of machine load and prediction of load through neural network prediction models,and real-time monitoring of machine load strategies by collecting the hardware of service nodes.Indicators to measure the load capacity of the service node.This strategy has great limitations.It is difficult for the system to guarantee the long-term validity of the calculated load;predicting the load through the neural network prediction model can better solve the service lag Problems,but the requirements for predictive models are higher.2.A dynamic weight load algorithm based on the lowest concurrent load algorithm is designed.The lowest concurrent load algorithm will fail when faced with an instantaneous traffic impact.This algorithm uses the variance of the disk utilization rate to measure the stability of the overall load of the system When the variance of disk utilization is in an appropriate range,the algorithm calculates the weight of the system node.When the lowest concurrency algorithm fails,the algorithm is upgraded to a dynamic weight algorithm,and the probability of selecting a node with a large weight will also be greater.3.Design a first-aid knowledge learning platform based on the micro-service architecture that incorporates the above-mentioned algorithms.The main functions include learning system,intelligent batching,data graph display,automatic certificate generation,etc.This system is developed based on B/S(Brower/Server browser/server model),the front end is based on the Html+Vue.js framework,and the development environment is Node.js + Microsoft Visual Studio Code.The backend adopts a Spring Cloud-based microservice architecture.4.The performance of the system was tested,and the fit between the algorithm and the system was analyzed from multiple dimensions such as service call time,service call stability,and system operation and maintenance difficulty.