Design Of Factory Security Monitoring Big Data Platform Based On LoRa Wireless Communication Network

As a technical means to protect personal and property safety,the safety monitoring platform is a necessary application platform for the production environment that is prone to danger.The functions of safety monitoring platforms in traditional manufacturing are relatively single,and generally only send alarm signals for specific dangerous situations.The degree of automation in modern manufacturing is getting higher and higher,and the production environment is becoming more and more complex,which not only requires more comprehensive functions of the safety monitoring platform,but also needs to realize remote monitoring.Based on the above requirements,this paper designs a safety monitoring big data platform,which can remotely monitor various risk factors in the production environment,dynamically expand monitoring points,analyze and calculate massive monitoring data,and has data visualization functions.The main research work and innovative design of this paper are summarized as follows:(1)An umbrella-shaped wireless communication network structure is innovatively designed based on LoRa wireless communication technology,so that data can be efficiently and stably transmitted through a low-power wireless communication network;In the process of data transmission,a communication format is designed to facilitate data segmentation in the software model;Utilizing the characteristics of the LoRa module,the dynamic expansion function of the hardware integration module is realized innovatively,and monitoring points can be added at any time to make up for monitoring loopholes.The host computer model is developed to communicate with the data receiving point via serial port to obtain real-time data quickly.(2)Using Kafka distributed message queue to develop the data transmission layer,realize the buffering and transmission of massive data in the server cluster.In the process of massive data stream transmission,through studying various data consumption strategies,it is decided that each server node adopts the Round-Robin polling strategy for data consumption.Based on the ‘Spark Streaming’ streaming data computing component,the big data computing layer is developed,and multiple server nodes in the cluster is used to calculate the data of each partition in parallel to efficiently process massive data streams;A dynamic expansion algorithm model is designed in this layer innovatively.When a new monitoring point is added,the data table is automatically created and the data is persisted.(3)The persistence layer of the big data platform is developed to store real-time data,massive historical data and system operation logs,and a data offloading model is designed in the persistence layer architecture to store different types of data in the corresponding database.Based on Tomcat Web server,a B/S structure data visualization platform is developed to realize the functions of remote monitoring of production and manufacturing environment and visualization of data analysis results.In the process of real-time monitoring,a sensor alarm module is designed to respond to alarm signals such as harmful gas,fire,and combustible gas in a timely manner on the front-end page of the web server and the hardware integration module.